diff --git a/data/sampled_jsons/'Section_5.1'_'fine-tuning_on_high-quality_demonstrations'_'close_to_the_ceiling'_password-locked_mo_year_2024.jsonl b/data/sampled_jsons/'Section_5.1'_'fine-tuning_on_high-quality_demonstrations'_'close_to_the_ceiling'_password-locked_mo_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..111eda953866ece3748a46e42388818ed24acf4f
--- /dev/null
+++ b/data/sampled_jsons/'Section_5.1'_'fine-tuning_on_high-quality_demonstrations'_'close_to_the_ceiling'_password-locked_mo_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Smart Home Automationva - The Ultimate Smart Home Guide", "date": "", "ddg_snippet": "They then adjust the temperature to the ... Read the instruction manual carefully to understand the signs and symbols that will appear on the screen.", "subpage_snippet": "", "source": "smarthomeautomationva.com", "link": "https://smarthomeautomationva.com/", "content": "They then adjust the temperature to the ... Read the instruction manual carefully to understand the signs and symbols that will appear on the screen."}
+{"idx": 1, "title": "When does capability elicitation bound risk? — AI Alignment", "date": "", "ddg_snippet": "... model evaluations?” cites “gradient hacking” (strategically interfering with the learning process) as the main reason supervised fine - tuning ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "... model evaluations?” cites “gradient hacking” (strategically interfering with the learning process) as the main reason supervised fine - tuning ..."}
+{"idx": 2, "title": "When does capability elicitation bound risk? — LessWrong", "date": "", "ddg_snippet": "... model evaluations?” cites “gradient hacking” (strategically interfering with the learning process) as the main reason supervised fine - tuning ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "... model evaluations?” cites “gradient hacking” (strategically interfering with the learning process) as the main reason supervised fine - tuning ..."}
+{"idx": 3, "title": "When does capability elicitation bound risk? - LessWrong 2.0", "date": "", "ddg_snippet": "... model evaluations?” cites “gradient hacking” (strategically interfering with the learning process) as the main reason supervised fine - tuning ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "... model evaluations?” cites “gradient hacking” (strategically interfering with the learning process) as the main reason supervised fine - tuning ..."}
+{"idx": 4, "title": "Formulate linear programming model Jobs, Employment | Freelancer", "date": "", "ddg_snippet": "... High -resolution PDF proof • Mock-up image showing front and back views Once approved, I ’ ll hand the files directly to our lanyard ...", "subpage_snippet": "", "source": "www.freelancer.ie", "link": "https://www.freelancer.ie/job-search/formulate-linear-programming-model/5/", "content": "... High -resolution PDF proof • Mock-up image showing front and back views Once approved, I ’ ll hand the files directly to our lanyard ..."}
+{"idx": 5, "title": "Histopathology Products Manufacturer,Supplier,Exporter, Ambala", "date": "", "ddg_snippet": "Covid-19 Products ... ConXport Oxygen Cylinder Aluminium 1 Ltr. ... ConXport Fine Adjustment Valve Rota Meter and Humidifier Bottle", "subpage_snippet": "", "source": "www.hospitalsequipments.com", "link": "https://www.hospitalsequipments.com/histopathology-products.html", "content": "Covid-19 Products ... ConXport Oxygen Cylinder Aluminium 1 Ltr. ... ConXport Fine Adjustment Valve Rota Meter and Humidifier Bottle"}
+{"idx": 6, "title": "Rotary Microtomespencer Manufacturer,Supplier,Exporter", "date": "", "ddg_snippet": "Covid-19 Products ... ConXport Oxygen Cylinder Aluminium 1 Ltr. ... ConXport Fine Adjustment Valve Rota Meter and Humidifier Bottle", "subpage_snippet": "", "source": "www.hospitalsequipments.com", "link": "https://www.hospitalsequipments.com/rotary-microtomespencer-6965461.html", "content": "Covid-19 Products ... ConXport Oxygen Cylinder Aluminium 1 Ltr. ... ConXport Fine Adjustment Valve Rota Meter and Humidifier Bottle"}
+{"idx": 7, "title": "Private Cheats | Semi-Rage, Legacy, ESP – Health &", "date": "", "ddg_snippet": "Independent Learners in Year 5 :: Thursday, October 16, Year 5 students were independent learners this week as they demonstrated their inquiry skills ...", "subpage_snippet": "", "source": "fitstopxp.com", "link": "http://fitstopxp.com/2023/02/25/private-cheats-semi-rage-legacy-esp/", "content": "Independent Learners in Year 5 :: Thursday, October 16, Year 5 students were independent learners this week as they demonstrated their inquiry skills ..."}
+{"idx": 8, "title": "An Approach to Technical AGI Safety and Security", "date": "", "ddg_snippet": "It is especially important to have broader consensus on appropriate standards and best practices, to prevent a potential race to the bottom on safety ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.01849v1", "content": "It is especially important to have broader consensus on appropriate standards and best practices, to prevent a potential race to the bottom on safety ..."}
+{"idx": 9, "title": "Mountain Biking - a how-to community for mountain bikers of all", "date": "", "ddg_snippet": "Once you test out a bike that closely fits you, you can fine tune the bike to fit your body exactly. Once you get on your bike, you need to get ...", "subpage_snippet": "", "source": "mountain-bike.wonderhowto.com", "link": "https://mountain-bike.wonderhowto.com/", "content": "Once you test out a bike that closely fits you, you can fine tune the bike to fit your body exactly. Once you get on your bike, you need to get ..."}
diff --git a/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_SGLD_noise_tensor_shape.jsonl b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_SGLD_noise_tensor_shape.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cb30ebc4c9c6f2536af930e919685063244b6eb6
--- /dev/null
+++ b/data/sampled_jsons/0A4Y9qRnu9_Leveraging_Per-Instance_Privacy_for_Machine_Unlearning_SGLD_noise_tensor_shape.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Grand Canyon Education, Inc . ( LOPE) Stock Price, News, Quote &...", "date": "", "ddg_snippet": "Find the latest Grand Canyon Education, Inc . ( LOPE) stock quote, history, news and other vital information to help you with your stock trading and investing.", "subpage_snippet": "", "source": "finance.yahoo.com", "link": "https://finance.yahoo.com/quote/LOPE/?fr=sycsrp_catchall", "content": "Find the latest Grand Canyon Education, Inc . ( LOPE) stock quote, history, news and other vital information to help you with your stock trading and investing."}
+{"idx": 1, "title": "LOPE $209.17 ( 0.17%) Grand Canyon Education Inc | Google Finance", "date": "", "ddg_snippet": "Get the latest Grand Canyon Education Inc ( LOPE ) real-time quote, historical performance, charts, and other financial information to help you make more informed trading and investment decisions.", "subpage_snippet": "", "source": "www.google.com", "link": "https://www.google.com/finance/quote/LOPE:NASDAQ", "content": "Get the latest Grand Canyon Education Inc ( LOPE ) real-time quote, historical performance, charts, and other financial information to help you make more informed trading and investment decisions."}
+{"idx": 2, "title": "LOPE Stock Price | Grand Canyon Education Inc . Stock Quote (U.S...", "date": "", "ddg_snippet": "6 days ago · LOPE | Complete Grand Canyon Education Inc . stock news by MarketWatch . View real-time stock prices and stock quotes for a full financial overview.", "subpage_snippet": "", "source": "www.marketwatch.com", "link": "https://www.marketwatch.com/investing/stock/LOPE", "content": "6 days ago · LOPE | Complete Grand Canyon Education Inc . stock news by MarketWatch . View real-time stock prices and stock quotes for a full financial overview."}
+{"idx": 3, "title": "Grand Canyon Education - LOPE - Stock Price Today - Zacks", "date": "", "ddg_snippet": "6 days ago · View Grand Canyon Education, Inc LOPE investment & stock information. Get the latest Grand Canyon Education, Inc LOPE detailed stock quotes, stock data, Real-Time ECN, charts, stats and more.", "subpage_snippet": "", "source": "www.zacks.com", "link": "https://www.zacks.com/stock/quote/LOPE", "content": "6 days ago · View Grand Canyon Education, Inc LOPE investment & stock information. Get the latest Grand Canyon Education, Inc LOPE detailed stock quotes, stock data, Real-Time ECN, charts, stats and more."}
+{"idx": 4, "title": "LOPE | Grand Canyon Education Inc . Stock Overview (U.S.: Nasdaq...", "date": "", "ddg_snippet": "Sep 17, 2025 · Complete Grand Canyon Education Inc . stock information by Barron's. View real-time LOPE stock price and news, along with industry-best analysis.", "subpage_snippet": "", "source": "www.barrons.com", "link": "https://www.barrons.com/market-data/stocks/lope", "content": "Sep 17, 2025 · Complete Grand Canyon Education Inc . stock information by Barron's. View real-time LOPE stock price and news, along with industry-best analysis."}
+{"idx": 5, "title": "LOPE : Grand Canyon Education Inc - Stock Price, Quote and News - ...", "date": "", "ddg_snippet": "Get Grand Canyon Education Inc ( LOPE :NASDAQ) real-time stock quotes, news, price and financial information from CNBC .", "subpage_snippet": "", "source": "www.cnbc.com", "link": "https://www.cnbc.com/quotes/LOPE", "content": "Get Grand Canyon Education Inc ( LOPE :NASDAQ) real-time stock quotes, news, price and financial information from CNBC ."}
+{"idx": 6, "title": "Grand Canyon Education ( LOPE) Stock Price & Overview", "date": "", "ddg_snippet": "6 days ago · A detailed overview of Grand Canyon Education, Inc. (LOPE) stock , including real-time price, chart, key statistics, news, and more.", "subpage_snippet": "", "source": "stockanalysis.com", "link": "https://stockanalysis.com/stocks/lope/", "content": "6 days ago · A detailed overview of Grand Canyon Education, Inc. (LOPE) stock , including real-time price, chart, key statistics, news, and more."}
+{"idx": 7, "title": "Grand Canyon Education ( LOPE) Stock Price, News & Analysis", "date": "", "ddg_snippet": "6 days ago · Should You Buy or Sell Grand Canyon Education Stock ? Get The Latest LOPE Stock Analysis, Price Target, Earnings Estimates, Headlines, and Short Interest at MarketBeat.", "subpage_snippet": "", "source": "www.marketbeat.com", "link": "https://www.marketbeat.com/stocks/NASDAQ/LOPE/", "content": "6 days ago · Should You Buy or Sell Grand Canyon Education Stock ? Get The Latest LOPE Stock Analysis, Price Target, Earnings Estimates, Headlines, and Short Interest at MarketBeat."}
+{"idx": 8, "title": "LOPE Stock Price Quote | Morningstar", "date": "", "ddg_snippet": "6 days ago · See the latest Grand Canyon Education Inc stock price ( LOPE :XNAS), related news, valuation, dividends and more to help you make your investing decisions.", "subpage_snippet": "", "source": "www.morningstar.com", "link": "https://www.morningstar.com/stocks/XNAS/LOPE/quote", "content": "6 days ago · See the latest Grand Canyon Education Inc stock price ( LOPE :XNAS), related news, valuation, dividends and more to help you make your investing decisions."}
+{"idx": 9, "title": "Grand Canyon Education Stock Price | LOPE Stock Quote, News, and...", "date": "", "ddg_snippet": "Aug 29, 2025 · The latest Grand Canyon Education stock prices, stock quotes, news, and LOPE history to help you invest and trade smarter.", "subpage_snippet": "", "source": "markets.businessinsider.com", "link": "https://markets.businessinsider.com/stocks/lope-stock", "content": "Aug 29, 2025 · The latest Grand Canyon Education stock prices, stock quotes, news, and LOPE history to help you invest and trade smarter."}
diff --git "a/data/sampled_jsons/0hrkN07DuO_Linear_convergence_Sinkhorn_generalized_Schr\303\266dinger_bridge_tameness_condition.jsonl" "b/data/sampled_jsons/0hrkN07DuO_Linear_convergence_Sinkhorn_generalized_Schr\303\266dinger_bridge_tameness_condition.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..e801f4242fdea97100c1a9c2010d82157f88ea2f
--- /dev/null
+++ "b/data/sampled_jsons/0hrkN07DuO_Linear_convergence_Sinkhorn_generalized_Schr\303\266dinger_bridge_tameness_condition.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Schrödinger equation - Wikipedia", "date": "", "ddg_snippet": "Conceptually, the Schrödinger equation is the quantum counterpart of Newton's second law in classical mechanics. Given a set of known initial conditions , Newton's second law makes a mathematical prediction as to what path a given physical system will take over time.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Schrödinger_equation", "content": "Conceptually, the Schrödinger equation is the quantum counterpart of Newton's second law in classical mechanics. Given a set of known initial conditions , Newton's second law makes a mathematical prediction as to what path a given physical system will take over time."}
+{"idx": 1, "title": "ICML Poster Linear convergence of Sinkhorn 's algorithm for...", "date": "", "ddg_snippet": "We establish Kantorovich duality and linear convergence of Sinkhorn 's algorithm for the generalized SSB problem under mild conditions .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46671", "content": "We establish Kantorovich duality and linear convergence of Sinkhorn 's algorithm for the generalized SSB problem under mild conditions ."}
+{"idx": 2, "title": "(PDF) An Optimal Transport Approach for the Schrödinger Bridge ...", "date": "", "ddg_snippet": "Bridge problem, including convergence of Sinkhorn algorithm in the multi-marginal case.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/345547115_An_Optimal_Transport_Approach_for_the_Schrodinger_Bridge_Problem_and_Convergence_of_Sinkhorn_Algorithm", "content": "Bridge problem, including convergence of Sinkhorn algorithm in the multi-marginal case."}
+{"idx": 3, "title": "Large random matrices with given margins, Schrödinger bridge , and...", "date": "", "ddg_snippet": "⇐ For Schrödinger bridge (µ = Poisson(1)), use exact form of Sinkhorn updates For general µ, no exact form of Sinkhorn updates (implicit). Hanbaek Lyu. Large random matrices with given margins.", "subpage_snippet": "", "source": "hanbaeklyu.com", "link": "https://hanbaeklyu.com/wp-content/uploads/2024/10/ct_limit_kaist_col.pdf", "content": "⇐ For Schrödinger bridge (µ = Poisson(1)), use exact form of Sinkhorn updates For general µ, no exact form of Sinkhorn updates (implicit). Hanbaek Lyu. Large random matrices with given margins."}
+{"idx": 4, "title": "A Differential Approach to the Multi-Marginal Schrödinger System", "date": "", "ddg_snippet": "Stability of Schrödinger potentials and convergence of Sinkhorn ’s algorithm.The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces.", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/19M1253800", "content": "Stability of Schrödinger potentials and convergence of Sinkhorn ’s algorithm.The aim of this note is to give an elementary proof of linear convergence of the Sinkhorn algorithm for the entropic regularization of multimarginal optimal transport in the setting of general probability spaces."}
+{"idx": 5, "title": "The Sinkhorn Knopp Algorithm — Without Proof | by Frank... | Medium", "date": "", "ddg_snippet": "I've recently come across various mentions of the Sinkhorn Knopp algorithm. It deals with scaling the rows and columns of a non-negative matrix A such that its rows and columns all sum to 1 (i.e. it is doubly-stochastic).", "subpage_snippet": "", "source": "fulkast.medium.com", "link": "https://fulkast.medium.com/the-sinkhorn-knopp-algorithm-without-proof-697c9af7df7", "content": "I've recently come across various mentions of the Sinkhorn Knopp algorithm. It deals with scaling the rows and columns of a non-negative matrix A such that its rows and columns all sum to 1 (i.e. it is doubly-stochastic)."}
+{"idx": 6, "title": "Network Learning with Directional", "date": "", "ddg_snippet": "Sinkhorn with Directional Sign Templates. General Higher-order Networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.14915.pdf", "content": "Sinkhorn with Directional Sign Templates. General Higher-order Networks."}
+{"idx": 7, "title": "Charlie Yan MS Thesis", "date": "", "ddg_snippet": "Neural Schrödinger Bridge with Sinkhorn Losses. Charlie Yan.", "subpage_snippet": "", "source": "abhishekhalder.org", "link": "https://abhishekhalder.org/Charlie_Yan_UCSC_MS_Thesis_Slides_05182023.pdf", "content": "Neural Schrödinger Bridge with Sinkhorn Losses. Charlie Yan."}
+{"idx": 8, "title": "(PDF) Partial minimization of strict convex functions and tensor scaling", "date": "", "ddg_snippet": "We provide global convergence analysis for a general scheme admitting inexactness in solving the auxiliary subproblem. In the case of using for this purpose high-order tensor methods, we demonstrate an acceleration effect for both convex and uniformly convex composite objective functions.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/128044855/Partial_minimization_of_strict_convex_functions_and_tensor_scaling", "content": "We provide global convergence analysis for a general scheme admitting inexactness in solving the auxiliary subproblem. In the case of using for this purpose high-order tensor methods, we demonstrate an acceleration effect for both convex and uniformly convex composite objective functions."}
+{"idx": 9, "title": "Unbalanced Optimal Transport: Models, Numerical Methods, Applications", "date": "", "ddg_snippet": "parameter θopt computed from the linear convergence rate of Sinkhorn ’s iterations. Stopping criterion is u(ℓ) − uopt ∞ < 2 × 10−6 (except for the last line where the tolerance is 10−8).", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-01881166/document", "content": "parameter θopt computed from the linear convergence rate of Sinkhorn ’s iterations. Stopping criterion is u(ℓ) − uopt ∞ < 2 × 10−6 (except for the last line where the tolerance is 10−8)."}
diff --git a/data/sampled_jsons/0yzOEMbShU_Section_4.5_LRU_cache_Equation_15_visit_frequency_approximation.jsonl b/data/sampled_jsons/0yzOEMbShU_Section_4.5_LRU_cache_Equation_15_visit_frequency_approximation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bb20c2f664c71dab85b7691e99a3a0c67d90f27b
--- /dev/null
+++ b/data/sampled_jsons/0yzOEMbShU_Section_4.5_LRU_cache_Equation_15_visit_frequency_approximation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "146. LRU Cache - In-Depth Explanation - AlgoMonster", "date": "", "ddg_snippet": "In-depth solution and explanation for LeetCode 146. LRU Cache in Python, Java, C++ and more. Intuitions, example walk through, and complexity analysis. Better than official and forum solutions.", "subpage_snippet": "", "source": "algo.monster", "link": "https://algo.monster/liteproblems/146", "content": "In-depth solution and explanation for LeetCode 146. LRU Cache in Python, Java, C++ and more. Intuitions, example walk through, and complexity analysis. Better than official and forum solutions."}
+{"idx": 1, "title": "LRU Cache - Complete Tutorial - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/system-design/lru-cache-implementation/", "content": "Jul 23, 2025 · Please refer LRU cache implementation using Doubly Linked List and Hashing for details Complexity Analysis of the Efficient Solution Time Complexity: put () operation: O (1) i.e. time required to insert or update new key-value pair is constant get () operation: O (1) i.e. time required to get the value of a key is constant Auxiliary Space: O (c) where c is the capacity of the Cache . Advantages ..."}
+{"idx": 2, "title": "LRU Cache - LeetCode", "date": "", "ddg_snippet": "Implement the LRUCache class: * LRUCache (int capacity) Initialize the LRU cache with positive size capacity. * int get (int key) Return the value of the key if the key exists, otherwise return -1. * void put (int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache .", "subpage_snippet": "", "source": "leetcode.com", "link": "https://leetcode.com/problems/lru-cache/", "content": "Implement the LRUCache class: * LRUCache (int capacity) Initialize the LRU cache with positive size capacity. * int get (int key) Return the value of the key if the key exists, otherwise return -1. * void put (int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache ."}
+{"idx": 3, "title": "Introduction to LRU and LFU Caching Concepts ... - Alex K", "date": "", "ddg_snippet": "Understanding LRU and LFU Caching: Exploring Concepts, Implementations, and Real-World Applications Caching is a fundamental technique used to improve the performance and efficiency of computer systems by storing frequently accessed data in a readily accessible location.", "subpage_snippet": "", "source": "alxkm.github.io", "link": "https://alxkm.github.io/posts/introduction_to_lru_and_lfu_caching_concepts_implementations_and_practical_use_cases/", "content": "Understanding LRU and LFU Caching: Exploring Concepts, Implementations, and Real-World Applications Caching is a fundamental technique used to improve the performance and efficiency of computer systems by storing frequently accessed data in a readily accessible location."}
+{"idx": 4, "title": "Least Recently Used (LRU) Revealed: Cracking the Cache Code ...", "date": "", "ddg_snippet": "Jul 30, 2023 · The LRU algorithm is a clever technique used in cache management systems to maintain a balance between memory usage and performance. Its central principle is simple but incredibly effective ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@mohith.j/least-recently-used-lru-revealed-cracking-the-cache-code-for-optimal-performance-c007d74110aa", "content": "Jul 30, 2023 · The LRU algorithm is a clever technique used in cache management systems to maintain a balance between memory usage and performance. Its central principle is simple but incredibly effective ..."}
+{"idx": 5, "title": "Solving-the-LRU-Cache-Problem-in-C---LeetCode-146/f ... - GitHub", "date": "", "ddg_snippet": "A comprehensive solution for the LeetCode problem 146: LRU Cache , implemented in C. This repository includes a detailed explanation of the Least Recently Used ( LRU ) Cache concept and a step-by-step guide to implementing it efficiently.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/MustcodeQ/Solving-the-LRU-Cache-Problem-in-C---LeetCode-146/blob/master/f_LRU_Cache.c", "content": "A comprehensive solution for the LeetCode problem 146: LRU Cache , implemented in C. This repository includes a detailed explanation of the Least Recently Used ( LRU ) Cache concept and a step-by-step guide to implementing it efficiently."}
+{"idx": 6, "title": "LRU , метод вытеснения из кэша / Хабр", "date": "", "ddg_snippet": "Поэтому я решил написать небольшую статью, где расскажу как быстро реализовать метод LRU , и не вынуждать коллег вручную сбрасывать кэш там, где не требуется.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/136758/", "content": "Поэтому я решил написать небольшую статью, где расскажу как быстро реализовать метод LRU , и не вынуждать коллег вручную сбрасывать кэш там, где не требуется."}
+{"idx": 7, "title": "lru - cache - npm", "date": "", "ddg_snippet": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save. Usage.", "subpage_snippet": "", "source": "www.npmjs.com", "link": "https://www.npmjs.com/package/lru-cache", "content": "Start using lru - cache in your project by running `npm i lru - cache `.However, note that using some of the features will necessarily impact performance, by causing the cache to have to do more work. See the \"Performance\" section below. Installation. npm install lru - cache --save. Usage."}
+{"idx": 8, "title": "Как предотвратить повторное вычисление функции с lru _ cache", "date": "", "ddg_snippet": "Используйте lru _ cache (functools) в качестве кэша в коде на Python для того, чтобы сохранить промежуточные результаты функции.Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache .", "subpage_snippet": "", "source": "python-school.ru", "link": "https://python-school.ru/blog/python/lru_cache/", "content": "Используйте lru _ cache (functools) в качестве кэша в коде на Python для того, чтобы сохранить промежуточные результаты функции.Как работает Least Recently Used ( LRU ) алгоритм. Параметры функции lru _ cache ."}
+{"idx": 9, "title": "TOMPECS0304-20 - SJTU", "date": "", "ddg_snippet": "The modeling and analysis of an LRU cache is extremely challenging as exact results for the main performance metrics (e.g., hit rate) are either lacking or cannot be used because of their high computational complexity for large caches. As a result, various approximations have been proposed. The state-of-the-art method is the so-called TTL approximation , first proposed and shown to be ...", "subpage_snippet": "", "source": "jhc.sjtu.edu.cn", "link": "https://jhc.sjtu.edu.cn/~bjiang/papers/Jiang_ToMPECS2018_TTL.pdf", "content": "The modeling and analysis of an LRU cache is extremely challenging as exact results for the main performance metrics (e.g., hit rate) are either lacking or cannot be used because of their high computational complexity for large caches. As a result, various approximations have been proposed. The state-of-the-art method is the so-called TTL approximation , first proposed and shown to be ..."}
diff --git a/data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_Distribution_Regression_MNIST_Wasserstein.jsonl b/data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_Distribution_Regression_MNIST_Wasserstein.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21b1d5e28d3b75a17ab5e2adc35398ee097c8493
--- /dev/null
+++ b/data/sampled_jsons/1IyPRv1A0r_A_Likelihood_Based_Approach_Distribution_Regression_MNIST_Wasserstein.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "To the best of our knowledge, our study is the first at-tempt to explore the likelihood-based approach for distri-butional regression using a conditional deep generative model, considering full-dimensional noise and the poten-tial presence of singular underlying support.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=1IyPRv1A0r", "content": "To the best of our knowledge, our study is the first at-tempt to explore the likelihood-based approach for distri-butional regression using a conditional deep generative model, considering full-dimensional noise and the poten-tial presence of singular underlying support."}
+{"idx": 1, "title": "A Likelihood-Based Approach for Multivariate Categorical Response ...", "date": "", "ddg_snippet": "We propose a penalized likelihood method to fit the bivariate categorical response regression model. Our method allows practitioners to estimate which predictors are irrelevant, which predictors only affect the marginal distributions of the bivariate response, and which predictors affect both the marginal distributions and log odds ratios.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1999819", "content": "We propose a penalized likelihood method to fit the bivariate categorical response regression model. Our method allows practitioners to estimate which predictors are irrelevant, which predictors only affect the marginal distributions of the bivariate response, and which predictors affect both the marginal distributions and log odds ratios."}
+{"idx": 2, "title": "Chapter2: Likelihood-based approach | PDF | Web Development - SlideShare", "date": "", "ddg_snippet": "This document summarizes key concepts from Chapter 2 of a book on statistical methods for handling incomplete data. It introduces the likelihood-based approach and defines key terms like the likelihood function, maximum likelihood estimator, Fisher information, and missing at random. The chapter also provides examples of observed likelihood functions for censored regression and survival ...", "subpage_snippet": "", "source": "www.slideshare.net", "link": "https://www.slideshare.net/slideshow/chapter2-likelihoodbased-approach/86416477", "content": "This document summarizes key concepts from Chapter 2 of a book on statistical methods for handling incomplete data. It introduces the likelihood-based approach and defines key terms like the likelihood function, maximum likelihood estimator, Fisher information, and missing at random. The chapter also provides examples of observed likelihood functions for censored regression and survival ..."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 4, "title": "PDF Wasserstein Clustering", "date": "", "ddg_snippet": "First, the goal of the likelihood-based approach is to minimize the Kullback-Leibler divergence between the original data distribution and the parametrized model data distribution (which is equivalent to maximizing the likelihood Duda et al. [2012]).", "subpage_snippet": "", "source": "harchaoui.org", "link": "https://harchaoui.org/warith/phd/wasserstein-clustering-phd-warith-harchaoui.pdf", "content": "First, the goal of the likelihood-based approach is to minimize the Kullback-Leibler divergence between the original data distribution and the parametrized model data distribution (which is equivalent to maximizing the likelihood Duda et al. [2012])."}
+{"idx": 5, "title": "A generalized likelihood-based Bayesian approach for scalable joint ...", "date": "", "ddg_snippet": "Using a bi-convex regression based generalized likelihood and spike-and-slab priors, we develop an algorithm called Joint Regression Network Selector (JRNS) for joint regression and covariance selection which (a) can accommodate general sparsity patterns, (b) provides posterior samples for uncertainty quantification, and (c) is scalable and ...", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/36713060/", "content": "Using a bi-convex regression based generalized likelihood and spike-and-slab priors, we develop an algorithm called Joint Regression Network Selector (JRNS) for joint regression and covariance selection which (a) can accommodate general sparsity patterns, (b) provides posterior samples for uncertainty quantification, and (c) is scalable and ..."}
+{"idx": 6, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=1IyPRv1A0r", "content": "More specifically, we study the large-sample properties of a likelihood-based approach for estimating these models. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger ( Wasserstein ) metric."}
+{"idx": 7, "title": "Distribution Regression with Sliced Wasserstein Kernels", "date": "", "ddg_snippet": "In this work, we propose an OT- based estimator for distribution regression . We build on the Sliced Wasserstein distance to obtain an OT- based representation. We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2202.03926", "content": "In this work, we propose an OT- based estimator for distribution regression . We build on the Sliced Wasserstein distance to obtain an OT- based representation. We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds."}
+{"idx": 8, "title": "PDF A Likelihood Approach to Nonparametric Estimation of a Singular ...", "date": "", "ddg_snippet": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume24/21-1099/21-1099.pdf", "content": "We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More speci cally, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure."}
+{"idx": 9, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional am-bient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
diff --git a/data/sampled_jsons/2403.09040_RAGGED_retriever_paradigms.jsonl b/data/sampled_jsons/2403.09040_RAGGED_retriever_paradigms.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c983ce0ac30c8a882de09531641f2af093bd0b8b
--- /dev/null
+++ b/data/sampled_jsons/2403.09040_RAGGED_retriever_paradigms.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Greenhouse gases emissions and global climate change: Examining...", "date": "", "ddg_snippet": "Jul 20, 2024 · The increase in GHG concentrations as a result of human activity enhances the greenhouse effect, leading to global warming . This process influences the climate, causing changes in weather patterns, sea levels, and other aspects of the environment.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S004896972403506X", "content": "Jul 20, 2024 · The increase in GHG concentrations as a result of human activity enhances the greenhouse effect, leading to global warming . This process influences the climate, causing changes in weather patterns, sea levels, and other aspects of the environment."}
+{"idx": 1, "title": "Climate change: atmospheric carbon dioxide - NOAA Climate.gov", "date": "", "ddg_snippet": "May 21, 2025 · Without any carbon dioxide, Earth’s natural greenhouse effect would be too weak to keep the average global surface temperature above freezing. By adding more carbon dioxide to the atmosphere, people are amplifying the natural greenhouse effect, causing global temperature to rise .", "subpage_snippet": "", "source": "www.climate.gov", "link": "https://www.climate.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide", "content": "May 21, 2025 · Without any carbon dioxide, Earth’s natural greenhouse effect would be too weak to keep the average global surface temperature above freezing. By adding more carbon dioxide to the atmosphere, people are amplifying the natural greenhouse effect, causing global temperature to rise ."}
+{"idx": 2, "title": "Greenhouse gases surged to new highs in 2023, warns UN ... - UN...", "date": "", "ddg_snippet": "Oct 28, 2024 · Key greenhouse gas -producing events include forest fires and the El Niño weather phenomenon which fuelled drier conditions and a “ surge ” in gas concentrations in the latter part of 2023,...", "subpage_snippet": "", "source": "news.un.org", "link": "https://news.un.org/en/story/2024/10/1156186", "content": "Oct 28, 2024 · Key greenhouse gas -producing events include forest fires and the El Niño weather phenomenon which fuelled drier conditions and a “ surge ” in gas concentrations in the latter part of 2023,..."}
+{"idx": 3, "title": "The world at 60 gigatonnes of greenhouse gas emissions", "date": "", "ddg_snippet": "4 days ago · Global greenhouse gas emissions remain at record highs of around 60 gigatonnes, but updated World Emissions Clock data shows signs of progress.", "subpage_snippet": "", "source": "www.brookings.edu", "link": "https://www.brookings.edu/articles/the-world-at-60-gigatonnes-of-greenhouse-gas-emissions/", "content": "4 days ago · Global greenhouse gas emissions remain at record highs of around 60 gigatonnes, but updated World Emissions Clock data shows signs of progress."}
+{"idx": 4, "title": "Emissions Gap Report 2023 - UNEP - UN Environment Programme", "date": "", "ddg_snippet": "Nov 19, 2023 · The report is the 14th edition in a series that brings together many of the world’s top climate scientists to look at future trends in greenhouse gas emissions and provide potential solutions to the challenge of global warming .", "subpage_snippet": "", "source": "www.unep.org", "link": "https://www.unep.org/resources/emissions-gap-report-2023", "content": "Nov 19, 2023 · The report is the 14th edition in a series that brings together many of the world’s top climate scientists to look at future trends in greenhouse gas emissions and provide potential solutions to the challenge of global warming ."}
+{"idx": 5, "title": "Greenhouse gas concentrations surge again to new record in 2023", "date": "", "ddg_snippet": "1 day ago · Greenhouse gas levels surged to a new record in 2023, committing the planet to rising temperatures for many years to come, according to a report from the World Meteorological Organization (WMO).", "subpage_snippet": "", "source": "wmo.int", "link": "https://wmo.int/news/media-centre/greenhouse-gas-concentrations-surge-again-new-record-2023", "content": "1 day ago · Greenhouse gas levels surged to a new record in 2023, committing the planet to rising temperatures for many years to come, according to a report from the World Meteorological Organization (WMO)."}
+{"idx": 6, "title": "CO2 Emissions – Global Energy Review 2025 – Analysis - IEA", "date": "", "ddg_snippet": "2 days ago · Overall, higher temperatures contributed to increase emissions by 230 Mt CO 2 in 2024 compared with 2023, accounting for around 80% of the total increase in energy-related emissions .", "subpage_snippet": "", "source": "www.iea.org", "link": "https://www.iea.org/reports/global-energy-review-2025/co2-emissions", "content": "2 days ago · Overall, higher temperatures contributed to increase emissions by 230 Mt CO 2 in 2024 compared with 2023, accounting for around 80% of the total increase in energy-related emissions ."}
+{"idx": 7, "title": "CO₂ and Greenhouse Gas Emissions - Our World in Data", "date": "", "ddg_snippet": "Human emissions of carbon dioxide and other greenhouse gases are the primary drivers of the global rise in temperatures .1 This link between global temperatures and greenhouse gas concentrations – especially CO2 – has been true throughout Earth’s history.2", "subpage_snippet": "", "source": "ourworldindata.org", "link": "https://ourworldindata.org/co2-and-greenhouse-gas-emissions", "content": "Human emissions of carbon dioxide and other greenhouse gases are the primary drivers of the global rise in temperatures .1 This link between global temperatures and greenhouse gas concentrations – especially CO2 – has been true throughout Earth’s history.2"}
+{"idx": 8, "title": "Why Are Greenhouse Gas Emissions Increasing?", "date": "", "ddg_snippet": "Aug 14, 2025 · The increasing global impact of greenhouse gas emissions is due to human activities such as burning fossil fuels, cutting down forests, and farming livestock. These activities contribute to the increase in greenhouse gases , which trap heat and make the planet warmer.", "subpage_snippet": "", "source": "climatecontroljournal.com", "link": "https://climatecontroljournal.com/why-are-greenhouse-gas-emissions-increasing.html", "content": "Aug 14, 2025 · The increasing global impact of greenhouse gas emissions is due to human activities such as burning fossil fuels, cutting down forests, and farming livestock. These activities contribute to the increase in greenhouse gases , which trap heat and make the planet warmer."}
+{"idx": 9, "title": "Chapter 2: Emissions trends and drivers - IPCC", "date": "", "ddg_snippet": "Oct 4, 2024 · Globally , GHG emissions continued to rise across all sectors and subsectors; most rapidly in transport and industry (high confidence).", "subpage_snippet": "", "source": "www.ipcc.ch", "link": "https://www.ipcc.ch/report/ar6/wg3/chapter/chapter-2/", "content": "Oct 4, 2024 · Globally , GHG emissions continued to rise across all sectors and subsectors; most rapidly in transport and industry (high confidence)."}
diff --git a/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_Identifying_Worst-Off_Equation_2.jsonl b/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_Identifying_Worst-Off_Equation_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82fa4f6a4dd1cae37bc3948d5253733063dad0f6
--- /dev/null
+++ b/data/sampled_jsons/26JsumCG0z_The_Value_of_Prediction_Identifying_Worst-Off_Equation_2.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "Given a predictor f , a screening budget of α, and a target parameter β, the value of a prediction system is equal to the fraction of the at-risk population that it identifies", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "Given a predictor f , a screening budget of α, and a target parameter β, the value of a prediction system is equal to the fraction of the at-risk population that it identifies"}
+{"idx": 1, "title": "[Paper Note] The Value of Prediction in Identifying the Worst - Off ...", "date": "", "ddg_snippet": "Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst - off .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/2220", "content": "Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst - off ."}
+{"idx": 2, "title": "[ICML 2025] The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "Improving Predictions : Enhancing a model's predictive power (measured by R ² ). Expanding Access: Increasing the screening capacity (α), or the proportion of the population that can be evaluated and supported.", "subpage_snippet": "", "source": "arxiviq.substack.com", "link": "https://arxiviq.substack.com/p/icml-2025-the-value-of-prediction", "content": "Improving Predictions : Enhancing a model's predictive power (measured by R ² ). Expanding Access: Increasing the screening capacity (α), or the proportion of the population that can be evaluated and supported."}
+{"idx": 3, "title": "The Value of Prediction in Identifying the Worst - Off Authors: Unai...", "date": "", "ddg_snippet": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение...", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall49591166_67792", "content": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение..."}
+{"idx": 4, "title": "The number of non-trivial solutions of the system x=y+z=0, x+ 2 y-z=0, 2", "date": "", "ddg_snippet": "The number of real values of a for which the system of equations x+ay−z=0, 2 x−y+az=0,ax+y+ 2 z=0 has a non trivial solution is.", "subpage_snippet": "", "source": "www.doubtnut.com", "link": "https://www.doubtnut.com/qna/95420694", "content": "The number of real values of a for which the system of equations x+ay−z=0, 2 x−y+az=0,ax+y+ 2 z=0 has a non trivial solution is."}
+{"idx": 5, "title": "FC 26 Team of The Week 2 Predictions ! | FUTBIN", "date": "", "ddg_snippet": "FC 26 Predictions . With the power curve supposedly slowing, IFs will likely become a lot more relevant within the game this year. There are plenty to get excited about from TOTW 2 , with the likes of Son, Messi, Coman, Gravenberch, and Militao offering quality all over the pitch!", "subpage_snippet": "", "source": "www.futbin.com", "link": "https://www.futbin.com/news/articles/1610/fc-26-team-of-the-week-2-predictions", "content": "FC 26 Predictions . With the power curve supposedly slowing, IFs will likely become a lot more relevant within the game this year. There are plenty to get excited about from TOTW 2 , with the likes of Son, Messi, Coman, Gravenberch, and Militao offering quality all over the pitch!"}
+{"idx": 6, "title": "The value of k for which the planes `kx+4y+z=0, 4x+ky+ 2 z=0 nd...", "date": "", "ddg_snippet": "asked Feb 26, 2020 in Mathematics by MukundJain (94.7k points). equation -of-plane.", "subpage_snippet": "", "source": "www.sarthaks.com", "link": "https://www.sarthaks.com/1689744/the-value-of-for-which-the-planes-kx-4y-4x-ky-2z-nd-2x-2y-intersect-in-straighat-line-is-1-b-2-c-3-d", "content": "asked Feb 26, 2020 in Mathematics by MukundJain (94.7k points). equation -of-plane."}
+{"idx": 7, "title": "BIG vs. ESC at Exort The Proving Grounds Season 4 | HLTV.org", "date": "", "ddg_snippet": "0.26. BIG.play to gain point to get up in the ranking, 1st monday of every month valve releases new invite lists which TOs use to invite teams, from around ~26th global place you can get into tier 1 events.", "subpage_snippet": "", "source": "www.hltv.org", "link": "https://www.hltv.org/matches/2385825/big-vs-esc-exort-the-proving-grounds-season-4", "content": "0.26. BIG.play to gain point to get up in the ranking, 1st monday of every month valve releases new invite lists which TOs use to invite teams, from around ~26th global place you can get into tier 1 events."}
+{"idx": 8, "title": "Telegram: View @gonzo_ML_podcasts", "date": "", "ddg_snippet": "Авторы разрабатывают коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — новую метрику, которая количественно определяет относительную пользу для общественного благосостояния от инвестиций в более точные прогнозы по сравнению с...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/gonzo_ML_podcasts/551", "content": "Авторы разрабатывают коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR) — новую метрику, которая количественно определяет относительную пользу для общественного благосостояния от инвестиций в более точные прогнозы по сравнению с..."}
+{"idx": 9, "title": "Fastest way to earn 6,000 season points in FC 26 Ultimate Team...", "date": "", "ddg_snippet": "TOTW Predictions – Team of the Week Predictions .This is the fastest way to unlock packs, rewards, and progress through the new Season Pass on day one. For official details on FC 26 Ultimate Team and the Season Pass, check EA’s official FC 26 page.", "subpage_snippet": "", "source": "futfc.gg", "link": "https://futfc.gg/fastest-way-to-earn-6000-season-points-in-fc-26-ultimate-team/", "content": "TOTW Predictions – Team of the Week Predictions .This is the fastest way to unlock packs, rewards, and progress through the new Season Pass on day one. For official details on FC 26 Ultimate Team and the Season Pass, check EA’s official FC 26 page."}
diff --git a/data/sampled_jsons/27tMzmzDjO_A_Geometric_Approach_to_Personalized_Recommendation_Table_1_dataset_statistics_train_Du_u.jsonl b/data/sampled_jsons/27tMzmzDjO_A_Geometric_Approach_to_Personalized_Recommendation_Table_1_dataset_statistics_train_Du_u.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dfaf02cc53135480178ea80107013a8a06543596
--- /dev/null
+++ b/data/sampled_jsons/27tMzmzDjO_A_Geometric_Approach_to_Personalized_Recommendation_Table_1_dataset_statistics_train_Du_u.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Geometric Approach to Personalized Recommendation with...", "date": "", "ddg_snippet": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=27tMzmzDjO&referrer=[the+profile+of+Andrew+McCallum](/profile?id=~Andrew_McCallum1)", "content": "Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix factorization."}
+{"idx": 1, "title": "Find Open Datasets and Machine Learning Projects | Kaggle", "date": "", "ddg_snippet": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More.", "subpage_snippet": "", "source": "www.kaggle.com", "link": "https://www.kaggle.com/datasets?fileType=csv", "content": "Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More."}
+{"idx": 2, "title": "Sequential Data Augmentation for Generative Recommendation", "date": "", "ddg_snippet": "Generative recommendation plays a crucial role in personalized systems, predicting users ’ future interactions from their historical behavior sequences. Table 4: The statistics of the datasets . Dataset . Beauty Toys Sports ML 1 M ML20M Internal.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.13648", "content": "Generative recommendation plays a crucial role in personalized systems, predicting users ’ future interactions from their historical behavior sequences. Table 4: The statistics of the datasets . Dataset . Beauty Toys Sports ML 1 M ML20M Internal."}
+{"idx": 3, "title": "distributions - Graphical intuition of statistics on a manifold - Cross...", "date": "", "ddg_snippet": "What are the points on the manifold? This quote from this online find , seemingly indicates that it can either be the data points, or the distribution parameters: Statistics on manifolds and information geometry are two different ways in which differential geometry meets statistics .", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/239496/graphical-intuition-of-statistics-on-a-manifold", "content": "What are the points on the manifold? This quote from this online find , seemingly indicates that it can either be the data points, or the distribution parameters: Statistics on manifolds and information geometry are two different ways in which differential geometry meets statistics ."}
+{"idx": 4, "title": "(PDF) Arabic Sentiment Analysis of Users ’ Opinions of Governmental...", "date": "", "ddg_snippet": "2 shows dataset statistics . Table 2: Dataset labelling. Total of records 7759. Negative records 2913.analysis dataset using distant supervision and self training ,” in Proc. of the 4th Workshop on Open-Source.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/360084773_Arabic_Sentiment_Analysis_of_Users'_Opinions_of_Governmental_Mobile_Applications", "content": "2 shows dataset statistics . Table 2: Dataset labelling. Total of records 7759. Negative records 2913.analysis dataset using distant supervision and self training ,” in Proc. of the 4th Workshop on Open-Source."}
+{"idx": 5, "title": "Windows. Ключи KMS | Д. С. Кулябов", "date": "", "ddg_snippet": "Julia language features for processing statistical data . A Geometric Approach to the Lagrangian and Hamiltonian Formalism of Electrodynamics. Application of Functional Integrals to Stochastic Equations.", "subpage_snippet": "", "source": "yamadharma.github.io", "link": "https://yamadharma.github.io/ru/post/2023/06/08/windows-kms-keys/", "content": "Julia language features for processing statistical data . A Geometric Approach to the Lagrangian and Hamiltonian Formalism of Electrodynamics. Application of Functional Integrals to Stochastic Equations."}
+{"idx": 6, "title": "Utility | Service / Support - GIGABYTE Global", "date": "", "ddg_snippet": "AI TOP Utility 4. 1 .0: 1 . Dataset Creator: Automatically transforms unorganized datasets into structured Q&A lists, ready for fine-tuning with optimal efficiency.", "subpage_snippet": "", "source": "www.gigabyte.com", "link": "https://www.gigabyte.com/Support/Utility/Motherboard", "content": "AI TOP Utility 4. 1 .0: 1 . Dataset Creator: Automatically transforms unorganized datasets into structured Q&A lists, ready for fine-tuning with optimal efficiency."}
+{"idx": 7, "title": "Journal of Medical Internet Research - Evaluating the Utility of...", "date": "", "ddg_snippet": "Data were extracted using structured summary tables and synthesized qualitatively to identify methodological trends, strengths, and limitations. Statistical pooling or effect size integration was not performed due to the diversity of outcome measures across studies.", "subpage_snippet": "", "source": "www.jmir.org", "link": "https://www.jmir.org/2025/1/e69422/", "content": "Data were extracted using structured summary tables and synthesized qualitatively to identify methodological trends, strengths, and limitations. Statistical pooling or effect size integration was not performed due to the diversity of outcome measures across studies."}
+{"idx": 8, "title": "New AI System Predicts Risk of 1 ,000 Diseases Years in... - Decrypt", "date": "", "ddg_snippet": "Starting from age 60 data , it can simulate thousands of possible health futures, producing population-level disease burden estimates accurate to within statistical margins. One synthetic dataset trained a secondary Delphi model that achieved 74...", "subpage_snippet": "", "source": "decrypt.co", "link": "https://decrypt.co/340776/new-ai-system-predicts-risk-1000-diseases-years-advance", "content": "Starting from age 60 data , it can simulate thousands of possible health futures, producing population-level disease burden estimates accurate to within statistical margins. One synthetic dataset trained a secondary Delphi model that achieved 74..."}
+{"idx": 9, "title": "QCY ArcBuds Lite TWS Earbuds T27 -White | QCY Bangladesh", "date": "", "ddg_snippet": "Trust QCY Bangladesh to fulfill all your gadget requirements, including the cutting-edge QCY ArcBuds Lite TWS Earbuds T27 TWS.", "subpage_snippet": "", "source": "qcybd.com", "link": "https://qcybd.com/product/qcy-arcbuds-lite-tws-earbuds-t27-white/", "content": "Trust QCY Bangladesh to fulfill all your gadget requirements, including the cutting-edge QCY ArcBuds Lite TWS Earbuds T27 TWS."}
diff --git a/data/sampled_jsons/32867_Descriptor-In-Pixel_SCAMP-7_maximum_FPS_year_2023.jsonl b/data/sampled_jsons/32867_Descriptor-In-Pixel_SCAMP-7_maximum_FPS_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f67f40698129f1817b61bc1effee91e65fc99d7d
--- /dev/null
+++ b/data/sampled_jsons/32867_Descriptor-In-Pixel_SCAMP-7_maximum_FPS_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVPR Poster Descriptor - In - Pixel : Point-Feature Tracking For Pixel...", "date": "", "ddg_snippet": "This \"response map\" is utilized for both detection and tracking of point-features across the pixel -processor array. This approach is very fast, our implementation upon the SCAMP - 7 PPA prototype runs at over 3000 FPS ( Frames Per Second ), tracking...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "This \"response map\" is utilized for both detection and tracking of point-features across the pixel -processor array. This approach is very fast, our implementation upon the SCAMP - 7 PPA prototype runs at over 3000 FPS ( Frames Per Second ), tracking..."}
+{"idx": 1, "title": "SCAMP Vision Sensor", "date": "", "ddg_snippet": "The SCAMP -5 vision chip contains a 256x256 processor array, operating in a SIMD mode, with one processing element per image pixel . The processors are simple albeit fully software-programmable entities, comprising local memory, ALU, control and I/O circuits.", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "The SCAMP -5 vision chip contains a 256x256 processor array, operating in a SIMD mode, with one processing element per image pixel . The processors are simple albeit fully software-programmable entities, comprising local memory, ALU, control and I/O circuits."}
+{"idx": 2, "title": "Resize Image Pixel Online", "date": "", "ddg_snippet": "Then, you have one option resize the image pixel . An image is made up of thousands of pixels . Image size depends on the number of pixels and colors in that image. Reducing pixel size from file image size also decreases.", "subpage_snippet": "", "source": "image.pi7.org", "link": "https://image.pi7.org/resize-image-pixel", "content": "Then, you have one option resize the image pixel . An image is made up of thousands of pixels . Image size depends on the number of pixels and colors in that image. Reducing pixel size from file image size also decreases."}
+{"idx": 3, "title": "Как установить максимальную частоту кадров ( FPS ) в настройках...", "date": "", "ddg_snippet": "В статье предложен простой способ, как можно установить макс. ограничение для частоты кадров ( FPS ) в играх для видеокарт от NVIDIA и AMD.", "subpage_snippet": "", "source": "ocomp.info", "link": "https://ocomp.info/ustanovit-maksimalnyiy-fps.html", "content": "В статье предложен простой способ, как можно установить макс. ограничение для частоты кадров ( FPS ) в играх для видеокарт от NVIDIA и AMD."}
+{"idx": 4, "title": "Wplace Pixel Art - Convert Images to wplace Pixel", "date": "", "ddg_snippet": "Transform any image into Wplace pixel art with wplace pixel converter.Plan your Wplace build before you place a single pixel . Click to upload or drag and drop your image here. Supports JPEG, JPG, PNG - Max 5MB. Works with any image type for pixel art conversion.", "subpage_snippet": "", "source": "wplacepixel.com", "link": "https://wplacepixel.com/", "content": "Transform any image into Wplace pixel art with wplace pixel converter.Plan your Wplace build before you place a single pixel . Click to upload or drag and drop your image here. Supports JPEG, JPG, PNG - Max 5MB. Works with any image type for pixel art conversion."}
+{"idx": 5, "title": "How to Set a Maximum Frame Rate in NVIDIA's Drivers", "date": "", "ddg_snippet": "In the list of settings, click the box to the right of \" Max Frame Rate.\" By default, this option is disabled, and there is no maximum frame rate.", "subpage_snippet": "", "source": "www.howtogeek.com", "link": "https://www.howtogeek.com/509097/how-to-set-a-maximum-frame-rate-in-nvidias-drivers/", "content": "In the list of settings, click the box to the right of \" Max Frame Rate.\" By default, this option is disabled, and there is no maximum frame rate."}
+{"idx": 6, "title": "A4 size in pixels . Convenient calculator with different DPIs.", "date": "", "ddg_snippet": "In theory, an A4 size can be displayed or printed in any resolution. For a sharp image, however, a minimum number of pixels is required.You can also choose the number of Dots Per Inch (DPI) or Pixels Per Inch (PPI) for pixels . The result is shown in the last column under dimension.", "subpage_snippet": "", "source": "www.a4-size.com", "link": "https://www.a4-size.com/a4-size-in-pixels/", "content": "In theory, an A4 size can be displayed or printed in any resolution. For a sharp image, however, a minimum number of pixels is required.You can also choose the number of Dots Per Inch (DPI) or Pixels Per Inch (PPI) for pixels . The result is shown in the last column under dimension."}
+{"idx": 7, "title": "Как убрать ограничение ФПС в Роблоксе", "date": "", "ddg_snippet": "Убираем ограничение FPS в Roblox.Здесь можете управлять настройкой FPS через « Maximum Frame Rate». Установите подходящее для вас значение, после чего можете проверить, удалось ли тем самым убрать ограничения на FPS .", "subpage_snippet": "", "source": "Lumpics.ru", "link": "https://Lumpics.ru/how-to-remove-fps-limit-on-roblox/", "content": "Убираем ограничение FPS в Roblox.Здесь можете управлять настройкой FPS через « Maximum Frame Rate». Установите подходящее для вас значение, после чего можете проверить, удалось ли тем самым убрать ограничения на FPS ."}
+{"idx": 8, "title": "GitHub - shakilxt/ULTRA-EXTREME-UNLOCKER: This Magisk module...", "date": "", "ddg_snippet": "ULTRA-EXTREME-UNLOCKER. This Magisk module unlocks the maximum FPS setting in PUBG Mobile/BGMI and all other supported games on your Android device.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/shakilxt/ULTRA-EXTREME-UNLOCKER", "content": "ULTRA-EXTREME-UNLOCKER. This Magisk module unlocks the maximum FPS setting in PUBG Mobile/BGMI and all other supported games on your Android device."}
+{"idx": 9, "title": "Intel Iris Xe Graphics G 7 80EU: Detailed Specifications... - CpuTronic", "date": "", "ddg_snippet": "- Better in gaming (by 40-50%), but requires active cooling and increases the cost of devices. Conclusion: Iris Xe G 7 is an optimal choice for those who value silence and battery life over maximum FPS .", "subpage_snippet": "", "source": "cputronic.com", "link": "https://cputronic.com/gpu/intel-iris-xe-graphics-g7-80eu", "content": "- Better in gaming (by 40-50%), but requires active cooling and increases the cost of devices. Conclusion: Iris Xe G 7 is an optimal choice for those who value silence and battery life over maximum FPS ."}
diff --git a/data/sampled_jsons/33775_Instant_Gaussian_Stream_AGM-Net_training_dataset_test_sequences_Section_4.jsonl b/data/sampled_jsons/33775_Instant_Gaussian_Stream_AGM-Net_training_dataset_test_sequences_Section_4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07b372db9d51138a84c8e61a6ebd59ab8f59e7eb
--- /dev/null
+++ b/data/sampled_jsons/33775_Instant_Gaussian_Stream_AGM-Net_training_dataset_test_sequences_Section_4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "Download our processed data from 4 sequences of N3DV, which can be directly used for training . It contains 1,200 optimized Gaussian points and requires 150GB of storage space."}
+{"idx": 1, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "Mar 21, 2025 · Second, AGM-Net has been trained on four sequences from the N3DV indoor dataset . The limited size of the training data constrains its generalization capability.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.16979v1", "content": "Mar 21, 2025 · Second, AGM-Net has been trained on four sequences from the N3DV indoor dataset . The limited size of the training data constrains its generalization capability."}
+{"idx": 2, "title": "(PDF) Instant Gaussian Stream: Fast and Generalizable ...", "date": "", "ddg_snippet": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "Mar 21, 2025 · In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues."}
+{"idx": 3, "title": "demo_test_gaussian.ipynb - Colab - Google Colab", "date": "", "ddg_snippet": "This example shows you how to train a reconstruction network for an denoising problem on a fully self-supervised way, i.e., using corrupted measurement data only.", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/bemc22/GeneralizedR2R/blob/main/demo_test_gaussian.ipynb", "content": "This example shows you how to train a reconstruction network for an denoising problem on a fully self-supervised way, i.e., using corrupted measurement data only."}
+{"idx": 4, "title": "Instant Gaussian Stream: Fast and Generalizable Streaming of ...", "date": "", "ddg_snippet": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_CVPR_2025_paper.pdf", "content": "In this pa-per, we propose Instant Gaussian Stream (IGS), a fast and tions, demonstrating that our approach can achieve stream-ing with a average per-frame reconstruction time of 2s+, alongside a enhancement in view synthesis quality."}
+{"idx": 5, "title": "AGM-Net Model & Training · Issue #4 · yjb6/IGS - GitHub", "date": "", "ddg_snippet": "Jul 8, 2025 · Which dataset did you train the AGM-Net on, while generating the quantitative results on the N3DV dataset ? We would like to add your paper to our benchmark for an upcoming submission.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS/issues/4", "content": "Jul 8, 2025 · Which dataset did you train the AGM-Net on, while generating the quantitative results on the N3DV dataset ? We would like to add your paper to our benchmark for an upcoming submission."}
+{"idx": 6, "title": "CVPR Poster Instant Gaussian Stream: Fast and ...", "date": "", "ddg_snippet": "In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training data, in Sec . 4.1. Next, we provide a ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33775", "content": "In this Section , we first introduce the datasets we used, along with the partitioning and preprocessing of training data, in Sec . 4.1. Next, we provide a ..."}
+{"idx": 7, "title": "每帧仅2秒的流式重建速度！IGS已开源：采用流式策略解决动态场景重建 ...", "date": "", "ddg_snippet": "为使流式框架更具实用性，本文介绍 即时高斯流（ Instant Gaussian Stream , IGS），这是一种单帧重建耗时仅2秒+、能抑制误差累积并提升视图合成质量的动态场景重建方法： 广义锚点驱动高斯运动网络（ AGM-Net ）：通过一组称为锚点的关键点携带运动特征来指导高斯 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1915569198148593307", "content": "为使流式框架更具实用性，本文介绍 即时高斯流（ Instant Gaussian Stream , IGS），这是一种单帧重建耗时仅2秒+、能抑制误差累积并提升视图合成质量的动态场景重建方法： 广义锚点驱动高斯运动网络（ AGM-Net ）：通过一组称为锚点的关键点携带运动特征来指导高斯 ..."}
+{"idx": 8, "title": "typeid.txt", "date": "", "ddg_snippet": "... 4 Constellation 5 Solar System 6 Sun G5 (Yellow) 7 Sun K7 (Orange) 8 Sun K5 (Red Giant) 9 Sun B0 (Blue) 10 Sun F0 (White) 11 Planet (Temperate) 12 Planet ...", "subpage_snippet": "", "source": "eve-files.com", "link": "https://eve-files.com/chribba/typeid.txt", "content": "... 4 Constellation 5 Solar System 6 Sun G5 (Yellow) 7 Sun K7 (Orange) 8 Sun K5 (Red Giant) 9 Sun B0 (Blue) 10 Sun F0 (White) 11 Planet (Temperate) 12 Planet ..."}
+{"idx": 9, "title": "googlelist.counts", "date": "", "ddg_snippet": "... section 232270007 298 own 232024685 299 found 232011041 300 sports 231876585 ... training 176153292 437 too 176113530 438 credit 175927068 439 point ...", "subpage_snippet": "", "source": "mit.edu", "link": "http://mit.edu/~ecprice/Public/freq/googlelist.counts", "content": "... section 232270007 298 own 232024685 299 found 232011041 300 sports 231876585 ... training 176153292 437 too 176113530 438 credit 175927068 439 point ..."}
diff --git a/data/sampled_jsons/4Xnqm4f71y_DVI_Derivative-based_Vision_Network_arXiv.jsonl b/data/sampled_jsons/4Xnqm4f71y_DVI_Derivative-based_Vision_Network_arXiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7d7658dabecc1ae643af99c75498d949f2fa7930
--- /dev/null
+++ b/data/sampled_jsons/4Xnqm4f71y_DVI_Derivative-based_Vision_Network_arXiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR, then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR, then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information."}
+{"idx": 1, "title": "[2309.13814v2] DVI-SLAM: A Dual Visual Inertial SLAM Network - arXiv.org", "date": "", "ddg_snippet": "Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2309.13814v2", "content": "Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both ..."}
+{"idx": 2, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 3, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations (INR) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations (INR) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 4, "title": "Implicit Neural Representation for Vision", "date": "", "ddg_snippet": "Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data.", "subpage_snippet": "", "source": "inrv.github.io", "link": "https://inrv.github.io/", "content": "Downstream tasks based on INRs: Besides being an efficient and unified representation, INR presents exciting opportunities for various vision tasks. These include enhancing signals, recognizing patterns, and generating new data."}
+{"idx": 5, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR, then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167863", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR, then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 6, "title": "DVI-SLAM: A Dual Visual Inertial SLAM Network - IEEE Xplore", "date": "", "ddg_snippet": "Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10610042", "content": "Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both ..."}
+{"idx": 7, "title": "DVI:A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR, then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR, then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 8, "title": "DVI-SLAM: A Dual Visual Inertial SLAM Network - arXiv.org", "date": "", "ddg_snippet": "In this paper, we propose a novel optimization- based deep SLAM method, which is named Dual Visual Inertial SLAM network ( DVI -SLAM). DVI -SLAM simultaneously infers camera pose and dense depth by dynamically fusing multiple factors with an end-to-end trainable differentiable structure.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2309.13814", "content": "In this paper, we propose a novel optimization- based deep SLAM method, which is named Dual Visual Inertial SLAM network ( DVI -SLAM). DVI -SLAM simultaneously infers camera pose and dense depth by dynamically fusing multiple factors with an end-to-end trainable differentiable structure."}
+{"idx": 9, "title": "DVI-SLAM: A Dual Visual Inertial SLAM Network - ResearchGate", "date": "", "ddg_snippet": "The vision -aided inertial navigation algorithm we propose has computational complexity only linear in the number of features, and is capable of high-precision pose estimation in large-scale real ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/382981789_DVI-SLAM_A_Dual_Visual_Inertial_SLAM_Network", "content": "The vision -aided inertial navigation algorithm we propose has computational complexity only linear in the number of features, and is capable of high-precision pose estimation in large-scale real ..."}
diff --git a/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_.jsonl b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bfbbf973a9dc8f69bcee57bef2e85bcfe2063e45
--- /dev/null
+++ b/data/sampled_jsons/4uOEiitySn_A_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks - and - Balances Framework for Context - Aware Ethical AI ...", "date": "", "ddg_snippet": "This work introduces a checks - and - balances framework for ethical AI behavior.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00136", "content": "This work introduces a checks - and - balances framework for ethical AI behavior."}
+{"idx": 1, "title": "(PDF) Checks - and - Balances Framework for Context - Aware Ethical ...", "date": "", "ddg_snippet": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380515639_Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment", "content": "This paper introduces a checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems."}
+{"idx": 2, "title": "A Three-Branch Checks - and - Balances Framework", "date": "", "ddg_snippet": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=o2afWIxjKD", "content": "A Three-Branch Checks - and - Balances Framework for Context - Aware Ethical Alignment of Large Language Models.This work presents a three-branch framework for ethical AI behavior, inspired by governmental checks and balances , centered on the DIKE-ERIS duality."}
+{"idx": 3, "title": "(PDF) A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/edward-y-chang-218b182_pdf-a-three-branch-checks-and-balances-activity-7272792943923458050-RO6k", "content": "This work , which will be presented at NeurIPS this week, proposes a paradigm shift: using three LLM modules to perform checks and balances to represent knowledge, legislative, and judicial functions."}
+{"idx": 4, "title": "ICML Poster A Three-Branch Checks - and - Balances Framework for ...", "date": "", "ddg_snippet": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three independent yet interacting components...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "This paper introduces a three-branch checks - and - balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. It implements three independent yet interacting components..."}
+{"idx": 5, "title": "Design and Evaluation Methods for LLM-Based Explainable AI", "date": "", "ddg_snippet": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism.", "subpage_snippet": "", "source": "www.oajaiml.com", "link": "https://www.oajaiml.com/uploads/archivepdf/430552340.pdf", "content": "Visualization and intuitiveness of explanation. Interactivity and controllability. Context awareness and adaptability. Feedback mechanism."}
+{"idx": 6, "title": "You are the Driver and AI is the Mate: Exploring... | F1000Research", "date": "", "ddg_snippet": "4. To identify the challenges and ethical considerations students encounter when using AI tools in academic contexts . 5. To analyse the influence of AI literacy, prior experience, and digital confidence on students’ creative and critical engagement with AI tools.", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-974", "content": "4. To identify the challenges and ethical considerations students encounter when using AI tools in academic contexts . 5. To analyse the influence of AI literacy, prior experience, and digital confidence on students’ creative and critical engagement with AI tools."}
+{"idx": 7, "title": "Everything You Need to Know About Prompt Engineering Frameworks", "date": "", "ddg_snippet": "Tooling support with templates, versioning, and linting. Evaluation alignment by defining what “good output” means per task. Without frameworks , every prompt becomes bespoke.", "subpage_snippet": "", "source": "www.parloa.com", "link": "https://www.parloa.com/knowledge-hub/prompt-engineering-frameworks/", "content": "Tooling support with templates, versioning, and linting. Evaluation alignment by defining what “good output” means per task. Without frameworks , every prompt becomes bespoke."}
+{"idx": 8, "title": "A Human-centred Framework for Designing Complex AI ... | HackerNoon", "date": "", "ddg_snippet": "The practical framework helps designers, data scientists, and engineers find common ground, aligning the voice and needs of users with the design of AI systems.", "subpage_snippet": "", "source": "hackernoon.com", "link": "https://hackernoon.com/a-human-centred-framework-for-designing-complex-ai-solutions-for-strategic-decision-making", "content": "The practical framework helps designers, data scientists, and engineers find common ground, aligning the voice and needs of users with the design of AI systems."}
+{"idx": 9, "title": "GitHub - langchain- ai /langchain: Build context - aware ...", "date": "", "ddg_snippet": "LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/langchain-ai/langchain", "content": "LangChain is a framework for building LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves."}
diff --git a/data/sampled_jsons/AERO_model_sharding_blockchain_deep_reinforcement_learning_learning_rate_year_2023.jsonl b/data/sampled_jsons/AERO_model_sharding_blockchain_deep_reinforcement_learning_learning_rate_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ab271fc9f4bb1569a4fb1720a9090d2d3fe64821
--- /dev/null
+++ b/data/sampled_jsons/AERO_model_sharding_blockchain_deep_reinforcement_learning_learning_rate_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MIT 6.S091: Introduction to Deep Reinforcement Learning ... - YouTube", "date": "", "ddg_snippet": "First lecture of MIT course 6.S091: Deep Reinforcement Learning , introducing the fascinating field of Deep RL.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=zR11FLZ-O9M", "content": "First lecture of MIT course 6.S091: Deep Reinforcement Learning , introducing the fascinating field of Deep RL."}
+{"idx": 1, "title": "Payment Routing Across IoT Blockchain Shards using Deep ...", "date": "", "ddg_snippet": "Shard configurations are decided by a deep Q- learning -based genetic algorithm (DQGA), which assists in deciding optimal shard length and merging temporally unused shards . These decisions allow the proposed model to reduce mining delay by up to...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390250786_Payment_Routing_Across_IoT_Blockchain_Shards_using_Deep_Reinforcement_Learning", "content": "Shard configurations are decided by a deep Q- learning -based genetic algorithm (DQGA), which assists in deciding optimal shard length and merging temporally unused shards . These decisions allow the proposed model to reduce mining delay by up to..."}
+{"idx": 2, "title": "SmartChain: A Dynamic and Self-Adaptive Sharding Framework for...", "date": "", "ddg_snippet": "Learning Algorithm, Malicious Nodes, Deep Reinforcement Learning Agent, Byzantine Fault Tolerance, Successful Defense, Replay Memory, Blockchain Nodes, Internet Of Things Nodes, Message Authentication Code, Time Varying Environment, Shannons Information Theory...", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/journal/sc/2024/02/10468552/1VpXOUcfXEs", "content": "Learning Algorithm, Malicious Nodes, Deep Reinforcement Learning Agent, Byzantine Fault Tolerance, Successful Defense, Replay Memory, Blockchain Nodes, Internet Of Things Nodes, Message Authentication Code, Time Varying Environment, Shannons Information Theory..."}
+{"idx": 3, "title": "(PDF) Secure Trust-Based Delegated Consensus for Blockchain ...", "date": "", "ddg_snippet": "C. DEEP REINFORCEMENT LEARNING BASED BLOCKCHAINS DRL is a machine learning technology that combines deep learning with reinforcement learning (RL). DRL helps the agent to make optimal decisions under dynamic states.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/98817727/Secure_Trust_Based_Delegated_Consensus_for_Blockchain_Frameworks_Using_Deep_Reinforcement_Learning", "content": "C. DEEP REINFORCEMENT LEARNING BASED BLOCKCHAINS DRL is a machine learning technology that combines deep learning with reinforcement learning (RL). DRL helps the agent to make optimal decisions under dynamic states."}
+{"idx": 4, "title": "Stable Blockchain Sharding under Adversarial Transaction Generation", "date": "", "ddg_snippet": "Sharding is used to improve the scalability and performance of blockchain systems. We investigate the stability of blockchain sharding , where transactions are continuously generated by an adversarial model .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.04438", "content": "Sharding is used to improve the scalability and performance of blockchain systems. We investigate the stability of blockchain sharding , where transactions are continuously generated by an adversarial model ."}
+{"idx": 5, "title": "What Is Blockchain Sharding ? - 101 Blockchains", "date": "", "ddg_snippet": "Sharding is the process of breaking down a blockchain network’s workload into smaller pieces. Learn more about blockchain sharding in this guide now.", "subpage_snippet": "", "source": "101blockchains.com", "link": "https://101blockchains.com/what-is-blockchain-sharding/", "content": "Sharding is the process of breaking down a blockchain network’s workload into smaller pieces. Learn more about blockchain sharding in this guide now."}
+{"idx": 6, "title": "Harnessing Deep Reinforcement Learning for Algorithmic Trading", "date": "", "ddg_snippet": "Simulating Reinforcement Learning Model .Every time the model ’s parameters are updated, the learning _ rate argument is set to 0.0005. With ent_coef set to 0.01, exploration and exploitation are traded off during training.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/harnessing-deep-reinforcement-learning-for-algorithmic-trading-b4c67534b33a", "content": "Simulating Reinforcement Learning Model .Every time the model ’s parameters are updated, the learning _ rate argument is set to 0.0005. With ent_coef set to 0.01, exploration and exploitation are traded off during training."}
+{"idx": 7, "title": "Dynamic Learning Rate for Deep Reinforcement Learning : A Bandit...", "date": "", "ddg_snippet": "Inthis work, we propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta- learning approach that selects the learning rate based on theagent's performance during training.", "subpage_snippet": "", "source": "trendtoknow.com", "link": "https://trendtoknow.com/arxiv/1300/dynamic-learning-rate-for-deep-reinforcement-learning:-a-bandit-approach", "content": "Inthis work, we propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta- learning approach that selects the learning rate based on theagent's performance during training."}
+{"idx": 8, "title": "Sharding Types - Everything You Need to Know | Shardeum", "date": "", "ddg_snippet": "Sharding is a method for dividing up a single dataset into many databases, which can then be kept on working sites. Read what are sharding types in this blog.", "subpage_snippet": "", "source": "shardeum.org", "link": "https://shardeum.org/blog/sharding-types/", "content": "Sharding is a method for dividing up a single dataset into many databases, which can then be kept on working sites. Read what are sharding types in this blog."}
+{"idx": 9, "title": "Reinforcement Learning Real-world examples - Analytics Yogi", "date": "", "ddg_snippet": "Reinforcement learning , Data Science, Machine Learning , Deep Learning , Data Analytics,Tutorials, AI, real-life, real-world, examples.The reinforcement learning model calculates weights at each time step, and then updates them in real-time according to reinforcement signals.", "subpage_snippet": "", "source": "vitalflux.com", "link": "https://vitalflux.com/reinforcement-learning-real-world-examples/", "content": "Reinforcement learning , Data Science, Machine Learning , Deep Learning , Data Analytics,Tutorials, AI, real-life, real-world, examples.The reinforcement learning model calculates weights at each time step, and then updates them in real-time according to reinforcement signals."}
diff --git a/data/sampled_jsons/AI_alignment_framework_checks_balances_executive_branch.jsonl b/data/sampled_jsons/AI_alignment_framework_checks_balances_executive_branch.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0913973777cc190caa62010bf5f5ef64b077bbb6
--- /dev/null
+++ b/data/sampled_jsons/AI_alignment_framework_checks_balances_executive_branch.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Routine: A Structural Planning Framework for LLM Agent System", "date": "", "ddg_snippet": "... a multi-agent framework that leverages GraphFlow to model execution as a DAG of tool calls, ensuring traceable and reliable process flows [ 20 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14447v1", "content": "... a multi-agent framework that leverages GraphFlow to model execution as a DAG of tool calls, ensuring traceable and reliable process flows [ 20 ] ."}
+{"idx": 1, "title": "Strategic Alignment Patterns in National AI Policies", "date": "", "ddg_snippet": "... of literature: strategic foresight in technology policy, policy instrument theory, comparative AI governance, and alignment analysis frameworks .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.05400v2", "content": "... of literature: strategic foresight in technology policy, policy instrument theory, comparative AI governance, and alignment analysis frameworks ."}
+{"idx": 2, "title": "A Framework to Govern AI Innovation | Lawfare", "date": "", "ddg_snippet": "... framework below details how best to facilitate that sort of policy experimentation in a manner that aligns with our federal system — which ...", "subpage_snippet": "", "source": "www.lawfaremedia.org", "link": "https://www.lawfaremedia.org/article/a-framework-to-govern-ai-innovation", "content": "... framework below details how best to facilitate that sort of policy experimentation in a manner that aligns with our federal system — which ..."}
+{"idx": 3, "title": "What is it to solve the alignment problem? (Notes) — EA Forum", "date": "", "ddg_snippet": "Finally, I express skepticism that solving the alignment problem requires imbuing a superintelligent AI with intrinsic concern for our ...", "subpage_snippet": "", "source": "forum.effectivealtruism.org", "link": "https://forum.effectivealtruism.org/posts/eCAfDGaouomgsojdt/what-is-it-to-solve-the-alignment-problem", "content": "Finally, I express skepticism that solving the alignment problem requires imbuing a superintelligent AI with intrinsic concern for our ..."}
+{"idx": 4, "title": "Integrating Reasoning and Agentic Frameworks for LLMs: A", "date": "", "ddg_snippet": "In summary, Tree-of-Thought frameworks extend CoT by introducing a search paradigm: the model can branch into multiple reasoning paths and backtrack ...", "subpage_snippet": "", "source": "artsen.h3x.xyz", "link": "https://artsen.h3x.xyz/blog/integrating-reasoning-and-agentic-frameworks-for-llms-a-comprehensive-review-and-unified-proposal/", "content": "In summary, Tree-of-Thought frameworks extend CoT by introducing a search paradigm: the model can branch into multiple reasoning paths and backtrack ..."}
+{"idx": 5, "title": "SyGra: A Unified Graph-Based Framework for Scalable Generation,", "date": "", "ddg_snippet": "It enables the reuse of graphs as subgraphs, ensures reliable execution through integrated validation and checkpointing, and natively supports ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15432v2", "content": "It enables the reuse of graphs as subgraphs, ensures reliable execution through integrated validation and checkpointing, and natively supports ..."}
+{"idx": 6, "title": "Securing the Digital Future: Atul Luthra on AI, Compliance", "date": "", "ddg_snippet": "AI -Powered Social Engineering: AI can craft hyper-realistic phishing emails, clone executive voices, and create deepfake videos that mimic real ...", "subpage_snippet": "", "source": "www.smartstateindia.com", "link": "https://www.smartstateindia.com/securing-the-digital-future-atul-luthra-on-ai-compliance-the-cyber-threats-of-2025/", "content": "AI -Powered Social Engineering: AI can craft hyper-realistic phishing emails, clone executive voices, and create deepfake videos that mimic real ..."}
+{"idx": 7, "title": "Scaling Mobile App Development: Insights from Amit Samsukha |", "date": "", "ddg_snippet": "... Co-Founder and CTO of EmizenTech, shared his insights on proven strategies for building and managing global mobile development teams to balance code ...", "subpage_snippet": "", "source": "clutch.co", "link": "https://clutch.co/resources/scaling-mobile-app-development-from-amit-samsukha", "content": "... Co-Founder and CTO of EmizenTech, shared his insights on proven strategies for building and managing global mobile development teams to balance code ..."}
+{"idx": 8, "title": "Lean Agents: The Agile Workforce of Agentic AI - Architecture", "date": "", "ddg_snippet": "... to essentials, they reduce operational friction, make real-time auditing straightforward, and align with today ’ s demands for responsible AI ...", "subpage_snippet": "", "source": "www.architectureandgovernance.com", "link": "https://www.architectureandgovernance.com/uncategorized/lean-agents-the-agile-workforce-of-agentic-ai/", "content": "... to essentials, they reduce operational friction, make real-time auditing straightforward, and align with today ’ s demands for responsible AI ..."}
+{"idx": 9, "title": "Reality Check: Accenture Research Shows Enterprises Face a", "date": "", "ddg_snippet": "The clear takeaway is that enterprises need to balance their AI infrastructure investments with their security spending to ensure the most protected ...", "subpage_snippet": "", "source": "itcblogs.currentanalysis.com", "link": "https://itcblogs.currentanalysis.com/2025/06/30/reality-check-accenture-research-shows-enterprises-face-a-security-deficit-in-the-ai-era/", "content": "The clear takeaway is that enterprises need to balance their AI infrastructure investments with their security spending to ensure the most protected ..."}
diff --git a/data/sampled_jsons/A_First_Look_at_Public_Service_Websites_A_Framework_for_Improving_Web_Affordability_and_Inclusivenes.jsonl b/data/sampled_jsons/A_First_Look_at_Public_Service_Websites_A_Framework_for_Improving_Web_Affordability_and_Inclusivenes.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..69f3e1a6462dd3b5303249cf625e9696f75d5f12
--- /dev/null
+++ b/data/sampled_jsons/A_First_Look_at_Public_Service_Websites_A_Framework_for_Improving_Web_Affordability_and_Inclusivenes.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Framework for Improving Web Affordability and Inclusiveness", "date": "", "ddg_snippet": "A Framework for Improving Web Affordability and Inclusiveness . Authors ... A First Look at Public Service Websites from the Affordability Lens. WWW '23 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3603269.3604872", "content": "A Framework for Improving Web Affordability and Inclusiveness . Authors ... A First Look at Public Service Websites from the Affordability Lens. WWW '23 ..."}
+{"idx": 1, "title": "Rethinking Web for Affordability and Inclusion", "date": "", "ddg_snippet": "by IA Qazi · 2021 · Cited by 12 — A First Look at Public Service Websites from the Affordability Lens. WWW '23 ... A Framework for Improving Web Affordability and Inclusiveness . ACM ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3484266.3487376", "content": "by IA Qazi · 2021 · Cited by 12 — A First Look at Public Service Websites from the Affordability Lens. WWW '23 ... A Framework for Improving Web Affordability and Inclusiveness . ACM ..."}
+{"idx": 2, "title": "Publications", "date": "", "ddg_snippet": "A Framework for Improving Web Affordability and Inclusiveness code · SIGCOMM '23 ; A First Look at Public Service Websites from the Affordability Lens code ...", "subpage_snippet": "", "source": "rumaisahabib.com", "link": "https://rumaisahabib.com/publications/", "content": "A Framework for Improving Web Affordability and Inclusiveness code · SIGCOMM '23 ; A First Look at Public Service Websites from the Affordability Lens code ..."}
+{"idx": 3, "title": "Dr. Ihsan Ayyub Qazi @ LUMS - Publications", "date": "", "ddg_snippet": "A Framework for Improving Web Affordability and Inclusiveness . Rumaisa ... A First Look at Public Service Websites from the Affordability Lens. Rumaisa ...", "subpage_snippet": "", "source": "www.ihsanqazi.com", "link": "https://www.ihsanqazi.com/publications", "content": "A Framework for Improving Web Affordability and Inclusiveness . Rumaisa ... A First Look at Public Service Websites from the Affordability Lens. Rumaisa ..."}
+{"idx": 4, "title": "Rumaisa Habib", "date": "", "ddg_snippet": "A framework for improving web affordability and inclusiveness . R Habib, S ... A First Look at Public Service Websites from the Affordability Lens. R ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=FEM0WXwAAAAJ&hl=en", "content": "A framework for improving web affordability and inclusiveness . R Habib, S ... A First Look at Public Service Websites from the Affordability Lens. R ..."}
+{"idx": 5, "title": "Aimen Inam (0000-0002-9725-1900)", "date": "", "ddg_snippet": "Works (2). sort. A Framework for Improving Web Affordability and Inclusiveness ... A First Look at Public Service Websites from the Affordability Lens. 2023-04 ...", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0002-9725-1900", "content": "Works (2). sort. A Framework for Improving Web Affordability and Inclusiveness ... A First Look at Public Service Websites from the Affordability Lens. 2023-04 ..."}
+{"idx": 6, "title": "Zafar Ayyub Qazi", "date": "", "ddg_snippet": "A framework for improving web affordability and inclusiveness . R Habib, S ... 2022. A First Look at Public Service Websites from the Affordability Lens. R ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=O5uXfioAAAAJ&hl=en", "content": "A framework for improving web affordability and inclusiveness . R Habib, S ... 2022. A First Look at Public Service Websites from the Affordability Lens. R ..."}
+{"idx": 7, "title": "Ihsan Ayyub Qazi", "date": "", "ddg_snippet": "A First Look at Public Service Websites from the Affordability Lens; PACMNET ... A Framework for Improving Web Affordability and Inclusiveness ; PACMNET ...", "subpage_snippet": "", "source": "figshare.com", "link": "https://figshare.com/authors/Ihsan_Ayyub_Qazi/22241953", "content": "A First Look at Public Service Websites from the Affordability Lens; PACMNET ... A Framework for Improving Web Affordability and Inclusiveness ; PACMNET ..."}
+{"idx": 8, "title": "Rumaisa Habib", "date": "", "ddg_snippet": "13 Apr 2025 — BS - Computer Science (Senior Thesis: A Framework for Improving Web Affordability and Inclusiveness ) ... A First Look at Public Service Websites ... 2 pages", "subpage_snippet": "", "source": "rumaisahabib.com", "link": "https://rumaisahabib.com/assets/habib.pdf", "content": "13 Apr 2025 — BS - Computer Science (Senior Thesis: A Framework for Improving Web Affordability and Inclusiveness ) ... A First Look at Public Service Websites ... 2 pages"}
+{"idx": 9, "title": "Ayesha Ali (0000-0003-1051-2264)", "date": "", "ddg_snippet": "A Framework for Improving Web Affordability and Inclusiveness . 2023-09-10 ... A First Look at Public Service Websites from the Affordability Lens. 2023 ...", "subpage_snippet": "", "source": "orcid.org", "link": "https://orcid.org/0000-0003-1051-2264", "content": "A Framework for Improving Web Affordability and Inclusiveness . 2023-09-10 ... A First Look at Public Service Websites from the Affordability Lens. 2023 ..."}
diff --git a/data/sampled_jsons/Algorithm_1_Parallel_Picard_Method_for_sampling_input_x0_approximate_score_function.jsonl b/data/sampled_jsons/Algorithm_1_Parallel_Picard_Method_for_sampling_input_x0_approximate_score_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f72dcab387d250ee9d319fa583768b5dae98049f
--- /dev/null
+++ b/data/sampled_jsons/Algorithm_1_Parallel_Picard_Method_for_sampling_input_x0_approximate_score_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion ...", "date": "", "ddg_snippet": "Recently, ParaDiGMS [38] tackled this challenge with a parallel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19731", "content": "Recently, ParaDiGMS [38] tackled this challenge with a parallel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel ."}
+{"idx": 1, "title": "Parallel Simulation for Sampling Under Isoperimetry and Score Based ...", "date": "", "ddg_snippet": "In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=6Gb7VfTKY7", "content": "In recent years, there has been a surge of interest in proving discretization bounds for sampling under isoperimetry and for diffusion models. As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks. Rigorous ..."}
+{"idx": 2, "title": "PDF Parallel Sampling of Diffusion Models - NeurIPS", "date": "", "ddg_snippet": "With this insight, we present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel . ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPM-Solver.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/0d1986a61e30e5fa408c81216a616e20-Paper-Conference.pdf", "content": "With this insight, we present ParaDiGMS, a novel method to accelerate the sampling of pretrained diffusion models by denoising multiple steps in parallel . ParaDiGMS is the first diffusion sampling method that enables trading compute for speed and is even compatible with existing fast sampling techniques such as DDIM and DPM-Solver."}
+{"idx": 3, "title": "Parallel simulation for sampling under isoperimetry and score-based ...", "date": "", "ddg_snippet": "As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2412.07435", "content": "As data size grows, reducing the iteration cost becomes an important goal. Inspired by the great success of the parallel simulation of the initial value problem in scientific computation, we propose parallel Picard methods for sampling tasks."}
+{"idx": 4, "title": "PDF Fast parallel sampling under isoperimetry", "date": "", "ddg_snippet": "1 . Introduction In this paper, we study the problem of designing fast parallel algorithms for sampling from continuous distributions π(x) ∝ exp(−V (x)) over x ∈ Rd. Designing eficient sampling algorithms is a ubiquitous problem, but the focus of most prior works has been to minimize sequential eficiency criteria, such as the total number of arithmetic operations or total queries to V ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/anari24a/anari24a.pdf", "content": "1 . Introduction In this paper, we study the problem of designing fast parallel algorithms for sampling from continuous distributions π(x) ∝ exp(−V (x)) over x ∈ Rd. Designing eficient sampling algorithms is a ubiquitous problem, but the focus of most prior works has been to minimize sequential eficiency criteria, such as the total number of arithmetic operations or total queries to V ..."}
+{"idx": 5, "title": "PDF PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion ...", "date": "", "ddg_snippet": "Re-cently, ParaDiGMS [38] tackled this challenge with a par-allel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/So_PCM__Picard_Consistency_Model_for_Fast_Parallel_Sampling_of_CVPR_2025_paper.pdf", "content": "Re-cently, ParaDiGMS [38] tackled this challenge with a par-allel sampling method based on Picard iteration, reducing the sequential steps in diffusion sampling by performing more computations in parallel ."}
+{"idx": 6, "title": "PDF Parallel Numerical Picard Iteration Methods - Springer", "date": "", "ddg_snippet": "We show that the numerical Picard iteration methods admit a min(J M , 1 )-order rate of convergence, where J denotes the number of Picard iterations and M 1 is the number of collocation points. We then propose a class of parallel + solvers so that J Picard iterations can be proceeded simultaneously and nearly constantly.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10915-023-02156-y.pdf", "content": "We show that the numerical Picard iteration methods admit a min(J M , 1 )-order rate of convergence, where J denotes the number of Picard iterations and M 1 is the number of collocation points. We then propose a class of parallel + solvers so that J Picard iterations can be proceeded simultaneously and nearly constantly."}
+{"idx": 7, "title": "Parallel simulation for sampling under isoperimetry and score-based ...", "date": "", "ddg_snippet": "Technical novelty. Balance between time and Picard directions. Realed works in scientific computation. 4 Parallel Picard method for sampling under isoperimetry 4.1 Algorithm 4.2 Theoretical Guarantees 4.3 Proof sketch of Theorem 4.2: Performance analysis of Algorithm 1", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07435", "content": "Technical novelty. Balance between time and Picard directions. Realed works in scientific computation. 4 Parallel Picard method for sampling under isoperimetry 4.1 Algorithm 4.2 Theoretical Guarantees 4.3 Proof sketch of Theorem 4.2: Performance analysis of Algorithm 1"}
+{"idx": 8, "title": "hands-on-B04-picard.ipynb - Colab", "date": "", "ddg_snippet": "The method generates a sequence of increasingly precise algebraic approximations of the specific exact solution of the first-order differential equation with initial value. As described by Ricardo (2020), in the Picard's Successive Approximation Method an iteration scheme guarantees the solution of the equation y(x) = y0 +∫x x0 f(x, y(x))dx ( 1 ) with IVP (initial-value problem) dy dx = f(x, y ...", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/efurlanm/418/blob/master/hands-on-B04-picard.ipynb", "content": "The method generates a sequence of increasingly precise algebraic approximations of the specific exact solution of the first-order differential equation with initial value. As described by Ricardo (2020), in the Picard's Successive Approximation Method an iteration scheme guarantees the solution of the equation y(x) = y0 +∫x x0 f(x, y(x))dx ( 1 ) with IVP (initial-value problem) dy dx = f(x, y ..."}
+{"idx": 9, "title": "PDF Parallel Simulation for Log-concave Sampling and Score-based Diffusion ...", "date": "", "ddg_snippet": "Algorithm 1 Parallel Picard Method for sampling 1 : Input : x0 ∼ μ0, approximate score function s ≈ ∇f, the number of the iterations in outer loop J, the number of the iteration in inner loop P, the number of time slices N, the length of time slices h, the number of points on each time slices M. 2: for n = 0, . . .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=qtuxDy2qEB&name=pdf", "content": "Algorithm 1 Parallel Picard Method for sampling 1 : Input : x0 ∼ μ0, approximate score function s ≈ ∇f, the number of the iterations in outer loop J, the number of the iteration in inner loop P, the number of time slices N, the length of time slices h, the number of points on each time slices M. 2: for n = 0, . . ."}
diff --git a/data/sampled_jsons/Arun_Reddy_Alexander_Martin_Video-ColBERT_equation_final_similarity_score.jsonl b/data/sampled_jsons/Arun_Reddy_Alexander_Martin_Video-ColBERT_equation_final_similarity_score.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dbbddc181c46898edfa52eb8e688fb98449c8a18
--- /dev/null
+++ b/data/sampled_jsons/Arun_Reddy_Alexander_Martin_Video-ColBERT_equation_final_similarity_score.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2503.19009] Video-ColBERT: Contextualized Late Interaction ... Video-ColBERT: Contextualized Late Interaction for Text-to ... Video-ColBERT: Contextualized Late Interaction for Text-to ... Publications | Alexander Martin GitHub - yogesh-iitj/Video-ColBERT ColBERT: A complete guide. Me: BERT, can you please ... - Medium CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Mar 24, 2025 · Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa Abstract In this work, we tackle the problem of text-to- video re-trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video re-trieval, our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos . Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy1,2* Alexander Martin2* Eugene Yang2,3 Andrew Yates2,3 Kate Sanders2 Kenton Murray2,3 Reno Kriz2,3 Celso M. de Melo4 Benjamin Van Durme2,3 Rama Chellappa2 Aug 20, 2025 · Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy *, Alexander Martin *, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M Melo, Benjamin Van Durme, and Rama Chellappa In IEEE Conference on Computer Vision and Pattern Recognition, Jun 2025 Abs PDF This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ... Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators. Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 19691-19701", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Mar 24, 2025 · Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa Abstract In this work, we tackle the problem of text-to- video re-trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video re-trieval, our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos . Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy1,2* Alexander Martin2* Eugene Yang2,3 Andrew Yates2,3 Kate Sanders2 Kenton Murray2,3 Reno Kriz2,3 Celso M. de Melo4 Benjamin Van Durme2,3 Rama Chellappa2 Aug 20, 2025 · Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy *, Alexander Martin *, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M Melo, Benjamin Van Durme, and Rama Chellappa In IEEE Conference on Computer Vision and Pattern Recognition, Jun 2025 Abs PDF This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ... Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators. Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 19691-19701"}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to- video re-trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video re-trieval, our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Abstract In this work, we tackle the problem of text-to- video re-trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text- video re-trieval, our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 2, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy1,2* Alexander Martin2* Eugene Yang2,3 Andrew Yates2,3 Kate Sanders2 Kenton Murray2,3 Reno Kriz2,3 Celso M. de Melo4 Benjamin Van Durme2,3 Rama Chellappa2", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "http://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "content": "Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy1,2* Alexander Martin2* Eugene Yang2,3 Andrew Yates2,3 Kate Sanders2 Kenton Murray2,3 Reno Kriz2,3 Celso M. de Melo4 Benjamin Van Durme2,3 Rama Chellappa2"}
+{"idx": 3, "title": "Publications | Alexander Martin", "date": "", "ddg_snippet": "Aug 20, 2025 · Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy *, Alexander Martin *, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M Melo, Benjamin Van Durme, and Rama Chellappa In IEEE Conference on Computer Vision and Pattern Recognition, Jun 2025 Abs PDF", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/publications/", "content": "Aug 20, 2025 · Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy *, Alexander Martin *, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M Melo, Benjamin Van Durme, and Rama Chellappa In IEEE Conference on Computer Vision and Pattern Recognition, Jun 2025 Abs PDF"}
+{"idx": 4, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to- video retrieval. Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ..."}
+{"idx": 5, "title": "ColBERT: A complete guide. Me: BERT, can you please ... - Medium", "date": "", "ddg_snippet": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@varun030403/colbert-a-complete-guide-1552468335ae", "content": "Using Eq and Ed, ColBERT computes the relevance score between q and d via late interaction, which is defined as a summation of maximum similarity (MaxSim) operators."}
+{"idx": 6, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/153378", "content": "Video - ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss ..."}
+{"idx": 7, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 19691-19701", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "Video-ColBERT : Contextualized Late Interaction for Text-to- Video Retrieval Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 19691-19701"}
+{"idx": 8, "title": "A Scientific Question Answering Dataset from Peer Reviews", "date": "", "ddg_snippet": "19 Feb 2025 — We analyze the semantic similarity between the final and original questions, finding that 90 % percent 90 90\\% 90 % of questions have a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.13668v1", "content": "19 Feb 2025 — We analyze the semantic similarity between the final and original questions, finding that 90 % percent 90 90\\% 90 % of questions have a ..."}
+{"idx": 9, "title": "Computer Vision and Pattern Recognition Mar 2025", "date": "", "ddg_snippet": "Title: Video - ColBERT : Contextualized Late Interaction for Text-to-Video Retrieval. Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders ...", "subpage_snippet": "", "source": "www.arxiv.org", "link": "https://www.arxiv.org/list/cs.CV/2025-03?skip=1650&show=1000", "content": "Title: Video - ColBERT : Contextualized Late Interaction for Text-to-Video Retrieval. Arun Reddy , Alexander Martin , Eugene Yang, Andrew Yates, Kate Sanders ..."}
diff --git a/data/sampled_jsons/BIT-VO_Murai_binary_features_focal_plane_visual_odometry.jsonl b/data/sampled_jsons/BIT-VO_Murai_binary_features_focal_plane_visual_odometry.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..37b546a9dad96d3426745c0452931cd88eadc321
--- /dev/null
+++ b/data/sampled_jsons/BIT-VO_Murai_binary_features_focal_plane_visual_odometry.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Bit - Wikipedia", "date": "", "ddg_snippet": "The bit represents a logical state with one of two possible values. These values are most commonly represented as either \"1\" or \"0\", but other representations such as true / false, yes / no, on / off, or + / − are also widely used.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Bit", "content": "The bit represents a logical state with one of two possible values. These values are most commonly represented as either \"1\" or \"0\", but other representations such as true / false, yes / no, on / off, or + / − are also widely used."}
+{"idx": 1, "title": "BIT Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "The meaning of BIT is the biting or cutting edge or part of a tool. How to use bit in a sentence.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/bit", "content": "The meaning of BIT is the biting or cutting edge or part of a tool. How to use bit in a sentence."}
+{"idx": 2, "title": "What is bit (binary digit) in computing? - TechTarget", "date": "", "ddg_snippet": "Jun 6, 2025 · Bits are stored in memory through the use of capacitors that hold electrical charges. The charge determines the state of each bit which, in turn, determines the bit 's value. Various combinations of bits -- combinations of 0s and 1s -- are used to represent numbers larger than 1.", "subpage_snippet": "", "source": "www.techtarget.com", "link": "https://www.techtarget.com/whatis/definition/bit-binary-digit", "content": "Jun 6, 2025 · Bits are stored in memory through the use of capacitors that hold electrical charges. The charge determines the state of each bit which, in turn, determines the bit 's value. Various combinations of bits -- combinations of 0s and 1s -- are used to represent numbers larger than 1."}
+{"idx": 3, "title": "Bit Definition - What is a bit in data storage? - TechTerms.com", "date": "", "ddg_snippet": "Apr 20, 2013 · A bit (short for \" binary digit \") is the smallest unit of measurement used to quantify computer data. It contains a single binary value of 0 or 1. While a single bit can define a boolean value of True (1) or False (0), an individual bit has little other use.", "subpage_snippet": "", "source": "techterms.com", "link": "https://techterms.com/definition/bit", "content": "Apr 20, 2013 · A bit (short for \" binary digit \") is the smallest unit of measurement used to quantify computer data. It contains a single binary value of 0 or 1. While a single bit can define a boolean value of True (1) or False (0), an individual bit has little other use."}
+{"idx": 4, "title": "What is BIT (Binary DigIT)? - Computer Hope", "date": "", "ddg_snippet": "Sep 7, 2025 · Sometimes abbreviated as b (lowercase), bit is short for binary digit . It's a single unit of information with a value of either 0 or 1 (off or on, false or true, low or high).", "subpage_snippet": "", "source": "www.computerhope.com", "link": "https://www.computerhope.com/jargon/b/bit.htm", "content": "Sep 7, 2025 · Sometimes abbreviated as b (lowercase), bit is short for binary digit . It's a single unit of information with a value of either 0 or 1 (off or on, false or true, low or high)."}
+{"idx": 5, "title": "Bits and Bytes", "date": "", "ddg_snippet": "Everything in a computer is 0's and 1's. The bit stores just a 0 or 1: it's the smallest building block of storage.", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs101/bits-bytes.html", "content": "Everything in a computer is 0's and 1's. The bit stores just a 0 or 1: it's the smallest building block of storage."}
+{"idx": 6, "title": "Bit - definition of bit by The Free Dictionary", "date": "", "ddg_snippet": "Define bit . bit synonyms, bit pronunciation, bit translation, English dictionary definition of bit . n. 1. A small portion, degree, or amount: a bit of lint; a bit of luck.", "subpage_snippet": "", "source": "www.thefreedictionary.com", "link": "https://www.thefreedictionary.com/bit", "content": "Define bit . bit synonyms, bit pronunciation, bit translation, English dictionary definition of bit . n. 1. A small portion, degree, or amount: a bit of lint; a bit of luck."}
+{"idx": 7, "title": "Bits (binary digits ) (article) | Khan Academy", "date": "", "ddg_snippet": "Computers store information using bits . A bit (short for \"binary digit\") stores either the value 0 or 1 .", "subpage_snippet": "", "source": "www.khanacademy.org", "link": "https://www.khanacademy.org/computing/computers-and-internet/xcae6f4a7ff015e7d:digital-information/xcae6f4a7ff015e7d:bits-and-bytes/a/bits-binary-digits", "content": "Computers store information using bits . A bit (short for \"binary digit\") stores either the value 0 or 1 ."}
+{"idx": 8, "title": "BIT | definition in the Cambridge English Dictionary", "date": "", "ddg_snippet": "For the competition they had to write, shoot, and edit a one-minute comedy bit . Bits are basically jokes, but in the context of a play or movie, they usually involve a physical element and more than one person.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/us/dictionary/english/bit", "content": "For the competition they had to write, shoot, and edit a one-minute comedy bit . Bits are basically jokes, but in the context of a play or movie, they usually involve a physical element and more than one person."}
+{"idx": 9, "title": "What is a bit? Bits and bytes explained - IONOS", "date": "", "ddg_snippet": "Dec 8, 2022 · A bit is the smallest unit of electronic information; multiple bits form a byte. Whereas the storage capacity of hard drives is given in bytes, data transfer rates are shown in bits.", "subpage_snippet": "", "source": "www.ionos.com", "link": "https://www.ionos.com/digitalguide/websites/web-development/what-is-a-bit/", "content": "Dec 8, 2022 · A bit is the smallest unit of electronic information; multiple bits form a byte. Whereas the storage capacity of hard drives is given in bytes, data transfer rates are shown in bits."}
diff --git a/data/sampled_jsons/Bach_et_al._2023_'The_impact_of_modeling_decisions_in_statistical_profiling'_abstract_year_2023.jsonl b/data/sampled_jsons/Bach_et_al._2023_'The_impact_of_modeling_decisions_in_statistical_profiling'_abstract_year_2023.jsonl
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index 0000000000000000000000000000000000000000..44f6447a9e5cfdba61d91f333281383bc253da69
--- /dev/null
+++ b/data/sampled_jsons/Bach_et_al._2023_'The_impact_of_modeling_decisions_in_statistical_profiling'_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "(PDF) The impact of modeling decisions in statistical profiling", "date": "", "ddg_snippet": "Abstract and Figures. Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. statistical profiling of job seekers as an empirical use case, we show that modeling decisions in a typical data.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/374392867_The_impact_of_modeling_decisions_in_statistical_profiling", "content": "Abstract and Figures. Statistical profiling of job seekers is an attractive option to guide the activities of public employment services. statistical profiling of job seekers as an empirical use case, we show that modeling decisions in a typical data."}
+{"idx": 1, "title": "Ruben L. Bach - Google Scholar", "date": "", "ddg_snippet": "2020. The impact of modeling decisions in statistical profiling . RL Bach , C Kern, H Mautner, F Kreuter. Data & Policy 5, e32, 2023 .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=KczTzXEAAAAJ&hl=en", "content": "2020. The impact of modeling decisions in statistical profiling . RL Bach , C Kern, H Mautner, F Kreuter. Data & Policy 5, e32, 2023 ."}
+{"idx": 2, "title": "Fairness in Algorithmic Profiling : The AMAS Case | Minds and Machines", "date": "", "ddg_snippet": "In their study on algorithmic profiling , Kern et al . (2024) applied different classification methods to predict the risk of becoming LTU with German data. They could show that although the statistical models had a similar level of accuracy they had very different fairness implications.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11023-024-09706-9", "content": "In their study on algorithmic profiling , Kern et al . (2024) applied different classification methods to predict the risk of becoming LTU with German data. They could show that although the statistical models had a similar level of accuracy they had very different fairness implications."}
+{"idx": 3, "title": "Statistical Profiling as a Targeting Tool: Can It Enhance the Efficiency...", "date": "", "ddg_snippet": "Abstract Digitization has spurred interest in the potential of statistical profiling to improve the tar-geting of active labor market policies. Bach , R. L., Kern, C., Mautner, H., & Kreuter, F. ( 2023 ). The Impact of Modeling Decisions in Statistical Profiling .", "subpage_snippet": "", "source": "www.wifo.ac.at", "link": "https://www.wifo.ac.at/wp-content/uploads/upload-4573/wp_2025_694.pdf", "content": "Abstract Digitization has spurred interest in the potential of statistical profiling to improve the tar-geting of active labor market policies. Bach , R. L., Kern, C., Mautner, H., & Kreuter, F. ( 2023 ). The Impact of Modeling Decisions in Statistical Profiling ."}
+{"idx": 4, "title": "Reconsidering Fairness Through Unawareness From the Perspective...", "date": "", "ddg_snippet": "2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5 ( 2023 ), e32. Barocas et al .2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency. 59–68.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16638v2", "content": "2023 . The impact of modeling decisions in statistical profiling . Data & Policy 5 ( 2023 ), e32. Barocas et al .2019. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency. 59–68."}
+{"idx": 5, "title": "Ruben Bach (@rub3n_luc) | Aguea", "date": "", "ddg_snippet": "The impact of modeling decisions in statistical profiling - Volume 5.Ruben Bach @rub3n_luc. 27 Aug 2023 . Main question we tackle: how can we optimize the distribution of several treatment options with #DML algorithms while avoiding to treat only those who are already doing fine?", "subpage_snippet": "", "source": "aguea.net", "link": "https://aguea.net/rub3n_luc", "content": "The impact of modeling decisions in statistical profiling - Volume 5.Ruben Bach @rub3n_luc. 27 Aug 2023 . Main question we tackle: how can we optimize the distribution of several treatment options with #DML algorithms while avoiding to treat only those who are already doing fine?"}
+{"idx": 6, "title": "The Value of Prediction in Identifying the Worst-Off", "date": "", "ddg_snippet": "Bach , R. L., Kern, C., Mautner, H., and Kreuter, F. The impact of modeling decisions in statistical profiling .Kern, C., Bach , R., Mautner, H., and Kreuter, F. When Small Decisions Have Big Impact : Fairness Implica-tions of Algorithmic Profiling Schemes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "Bach , R. L., Kern, C., Mautner, H., and Kreuter, F. The impact of modeling decisions in statistical profiling .Kern, C., Bach , R., Mautner, H., and Kreuter, F. When Small Decisions Have Big Impact : Fairness Implica-tions of Algorithmic Profiling Schemes."}
+{"idx": 7, "title": "When Small Decisions Have Big Impact : Fairness Implications of...", "date": "", "ddg_snippet": "Additional Key Words and Phrases: Algorithmic fairness, modeling decisions , statistical profiling , machine learning pipeline. ACM Reference Format: Christoph Kern, Ruben Bach , Hannah Mautner, and Frauke Kreuter. 2024. When Small Decisions Have Big Impact : Fairness...", "subpage_snippet": "", "source": "epub.ub.uni-muenchen.de", "link": "https://epub.ub.uni-muenchen.de/122528/1/3689485.pdf", "content": "Additional Key Words and Phrases: Algorithmic fairness, modeling decisions , statistical profiling , machine learning pipeline. ACM Reference Format: Christoph Kern, Ruben Bach , Hannah Mautner, and Frauke Kreuter. 2024. When Small Decisions Have Big Impact : Fairness..."}
+{"idx": 8, "title": "When Small Decisions Have Big Impact : Fairness Implications of...", "date": "", "ddg_snippet": "A variety of statistical profiling systems were developed in several countries. Comprehensive reviews of existing profiling implementations are presented in Loxha and Morgandi [63], Desiere et al . 2023 . Fairness and Machine Learning: Limitations and Opportunities. MIT Press.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3689485", "content": "A variety of statistical profiling systems were developed in several countries. Comprehensive reviews of existing profiling implementations are presented in Loxha and Morgandi [63], Desiere et al . 2023 . Fairness and Machine Learning: Limitations and Opportunities. MIT Press."}
+{"idx": 9, "title": "Int. j. adv. multidisc. res. stud. 2023 ; 3(6):2117-2123", "date": "", "ddg_snippet": "Modeling the Climate Impact of Early Leak Detection via Continuous Monitoring. 1.Understanding the modeling of these impacts is essential for quantifying benefits and guiding investment and policy decisions (Stolaroff et al ., 2012, Ocko and Hamburg, 2022) [53, 32].", "subpage_snippet": "", "source": "www.multiresearchjournal.com", "link": "https://www.multiresearchjournal.com/admin/uploads/archives/archive-1754480899.pdf", "content": "Modeling the Climate Impact of Early Leak Detection via Continuous Monitoring. 1.Understanding the modeling of these impacts is essential for quantifying benefits and guiding investment and policy decisions (Stolaroff et al ., 2012, Ocko and Hamburg, 2022) [53, 32]."}
diff --git a/data/sampled_jsons/Beautiful_Soup_Python_library_primary_function.jsonl b/data/sampled_jsons/Beautiful_Soup_Python_library_primary_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9bbea1ee1131a2dddb92d893adc4469cb669648e
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Beautiful Soup Documentation — Beautiful Soup 4.13.0 documentation", "date": "", "ddg_snippet": "Beautiful Soup is a Python library for parsing HTML and XML documents, offering tools to navigate, search, and modify parse trees.", "subpage_snippet": "", "source": "www.crummy.com", "link": "https://www.crummy.com/software/BeautifulSoup/bs4/doc/", "content": "Beautiful Soup is a Python library for parsing HTML and XML documents, offering tools to navigate, search, and modify parse trees."}
+{"idx": 1, "title": "beautifulsoup4 · PyPI", "date": "", "ddg_snippet": "BeautifulSoup4 is a screen-scraping library for parsing HTML and XML documents.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/beautifulsoup4/", "content": "BeautifulSoup4 is a screen-scraping library for parsing HTML and XML documents."}
+{"idx": 2, "title": "Implementing Web Scraping in Python with BeautifulSoup", "date": "", "ddg_snippet": "BeautifulSoup is a Python library used for web scraping. It helps parse HTML and XML documents making it easy to navigate and extract specific parts of a webpage. This article explains the steps of web scraping using BeautifulSoup . Steps involved in web scraping Send an HTTP Request: Use the requests library to send a request to the webpage URL and get the HTML content in response. Parse the ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/python/implementing-web-scraping-python-beautiful-soup/", "content": "BeautifulSoup is a Python library used for web scraping. It helps parse HTML and XML documents making it easy to navigate and extract specific parts of a webpage. This article explains the steps of web scraping using BeautifulSoup . Steps involved in web scraping Send an HTTP Request: Use the requests library to send a request to the webpage URL and get the HTML content in response. Parse the ..."}
+{"idx": 3, "title": "10 Most Important Functions in BeautifulSoup - CodersLegacy", "date": "", "ddg_snippet": "Beautiful Soup is a Python library that is commonly used for web scraping purposes. It is a very powerful tool for extracting and parsing data from HTML and XML files. Beautiful Soup provides several functions that make web scraping a lot easier. In this article, we will look at the 10 most important BeautifulSoup functions and how to use them to parse data.", "subpage_snippet": "", "source": "coderslegacy.com", "link": "https://coderslegacy.com/10-most-important-functions-in-beautifulsoup/", "content": "Beautiful Soup is a Python library that is commonly used for web scraping purposes. It is a very powerful tool for extracting and parsing data from HTML and XML files. Beautiful Soup provides several functions that make web scraping a lot easier. In this article, we will look at the 10 most important BeautifulSoup functions and how to use them to parse data."}
+{"idx": 4, "title": "The Complete BeautifulSoup Cheatsheet with Examples", "date": "", "ddg_snippet": "This cheatsheet covers the full BeautifulSoup 4 API with practical examples. It provides a comprehensive guide to web scraping and HTML parsing using Python's BeautifulSoup library .", "subpage_snippet": "", "source": "proxiesapi.com", "link": "https://proxiesapi.com/articles/the-complete-beautifulsoup-cheatsheet-with-examples", "content": "This cheatsheet covers the full BeautifulSoup 4 API with practical examples. It provides a comprehensive guide to web scraping and HTML parsing using Python's BeautifulSoup library ."}
+{"idx": 5, "title": "beautifulsoup4 - Documentation - Technical Manuals", "date": "", "ddg_snippet": "What is Beautiful Soup ? Beautiful Soup is a Python library for parsing HTML and XML documents. It creates a parse tree from the document and provides methods to navigate and search through the parse tree. Essentially, it makes it easy to extract data from HTML and XML files, even if those files are poorly formatted or incomplete.", "subpage_snippet": "", "source": "manuals.muthu.co", "link": "https://manuals.muthu.co/posts/python-modules/beautifulsoup4.html", "content": "What is Beautiful Soup ? Beautiful Soup is a Python library for parsing HTML and XML documents. It creates a parse tree from the document and provides methods to navigate and search through the parse tree. Essentially, it makes it easy to extract data from HTML and XML files, even if those files are poorly formatted or incomplete."}
+{"idx": 6, "title": "BeautifulSoup Cheatsheet with Code Samples | ScrapingAnt", "date": "", "ddg_snippet": "Introduction BeautifulSoup is a powerful library in Python used for web scraping and parsing HTML and XML documents. If you're looking to extract data from web pages, BeautifulSoup is an essential tool to learn. In this tutorial, we will explore the core concepts of BeautifulSoup with detailed code samples and explanations to help you get started.", "subpage_snippet": "", "source": "scrapingant.com", "link": "https://scrapingant.com/blog/beautifulsoup-cheatsheet", "content": "Introduction BeautifulSoup is a powerful library in Python used for web scraping and parsing HTML and XML documents. If you're looking to extract data from web pages, BeautifulSoup is an essential tool to learn. In this tutorial, we will explore the core concepts of BeautifulSoup with detailed code samples and explanations to help you get started."}
+{"idx": 7, "title": "PDF Beautiful Soup Documentation — Beautiful Soup v4.0.0 documentation", "date": "", "ddg_snippet": "Beautiful Soup Documentation Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.", "subpage_snippet": "", "source": "sethc23.github.io", "link": "https://sethc23.github.io/wiki/Python/Beautiful_Soup_Documentation.pdf", "content": "Beautiful Soup Documentation Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work."}
+{"idx": 8, "title": "Beautiful Soup: Python Web Scraping Code Explained | AnSoup", "date": "", "ddg_snippet": "Beautiful Soup is a library designed for screen-scraping HTML and XML and sits atop an HTML or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree. The code for Beautiful Soup is packaged as Python 2 code and is automatically converted to Python 3 code upon installation.", "subpage_snippet": "", "source": "ansoup.com", "link": "https://ansoup.com/article/what-does-beautiful-soup-code-look-like", "content": "Beautiful Soup is a library designed for screen-scraping HTML and XML and sits atop an HTML or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree. The code for Beautiful Soup is packaged as Python 2 code and is automatically converted to Python 3 code upon installation."}
+{"idx": 9, "title": "BeautifulSoup4 Module - Python - GeeksforGeeks", "date": "", "ddg_snippet": "BeautifulSoup4 is a user-friendly Python library designed for parsing HTML and XML documents. It simplifies the process of web scraping by allowing developers to effortlessly navigate, search and modify the parse tree of a webpage. With BeautifulSoup4, we can extract specific elements, attributes and text from complex web pages using intuitive methods. This library abstracts away the ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/python/beautifulsoup4-module-python/", "content": "BeautifulSoup4 is a user-friendly Python library designed for parsing HTML and XML documents. It simplifies the process of web scraping by allowing developers to effortlessly navigate, search and modify the parse tree of a webpage. With BeautifulSoup4, we can extract specific elements, attributes and text from complex web pages using intuitive methods. This library abstracts away the ..."}
diff --git a/data/sampled_jsons/Beimel_Dynamic_Algorithms_Adaptive_Adversary_returns_cost_solution_optimization.jsonl b/data/sampled_jsons/Beimel_Dynamic_Algorithms_Adaptive_Adversary_returns_cost_solution_optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e60f003aeec827ce23361d133534c0d5d0165389
--- /dev/null
+++ b/data/sampled_jsons/Beimel_Dynamic_Algorithms_Adaptive_Adversary_returns_cost_solution_optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Cost of Compression: Tight Quadratic Black-Box Attacks on", "date": "", "ddg_snippet": "... adaptive setting is well studied across multiple areas, including statistical queries [ 18 , 26 , 29 , 23 , 16 , 6 ] , sketching and streaming ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16345v1", "content": "... adaptive setting is well studied across multiple areas, including statistical queries [ 18 , 26 , 29 , 23 , 16 , 6 ] , sketching and streaming ..."}
+{"idx": 1, "title": "STOC 1999", "date": "", "ddg_snippet": "Gen-Huey Chen , Ming-Yang Kao , Yuh-Dauh Lyuu , Hsing-Kuo Wong : Optimal Buy-and-Hold Strategies for Financial Markets with Bounded Daily Returns .", "subpage_snippet": "", "source": "sigmod.org", "link": "https://sigmod.org/publications/dblp/db/conf/stoc/stoc1999.html", "content": "Gen-Huey Chen , Ming-Yang Kao , Yuh-Dauh Lyuu , Hsing-Kuo Wong : Optimal Buy-and-Hold Strategies for Financial Markets with Bounded Daily Returns ."}
+{"idx": 2, "title": "A Simple and Robust Protocol for Distributed Counting", "date": "", "ddg_snippet": "... a precise way) the dependencies between the internal randomness of the algorithm and its inputs, thereby making it easier to argue about the adaptive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.05870v1", "content": "... a precise way) the dependencies between the internal randomness of the algorithm and its inputs, thereby making it easier to argue about the adaptive ..."}
+{"idx": 3, "title": "STOC 2020: Proceedings of the 52nd Annual ACM SIGACT Symposium", "date": "", "ddg_snippet": "... through a variety of new techniques, including a novel way to set up a recursive dynamic program to guess significant parts of an optimal solution ...", "subpage_snippet": "", "source": "acm-stoc.org", "link": "https://acm-stoc.org/stoc2020/toc.html", "content": "... through a variety of new techniques, including a novel way to set up a recursive dynamic program to guess significant parts of an optimal solution ..."}
+{"idx": 4, "title": "COLT 2025 Book of Abstracts", "date": "", "ddg_snippet": "... sampling in relative Fisher information via the Proximal Sampler algorithm , which is an approximate proximal discretization of the Langevin dynamics ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2025/abstracts.html", "content": "... sampling in relative Fisher information via the Proximal Sampler algorithm , which is an approximate proximal discretization of the Langevin dynamics ..."}
+{"idx": 5, "title": "CRYPTO 2010: Program", "date": "", "ddg_snippet": "We also show that in most cryptographic applications, the algorithm 's efficiency comes at almost no cost in asymptotic security.", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/conferences/crypto2010/accepted.html", "content": "We also show that in most cryptographic applications, the algorithm 's efficiency comes at almost no cost in asymptotic security."}
+{"idx": 6, "title": "Shota Yamada", "date": "", "ddg_snippet": "... techniques to prove adaptive ... In more detail, our adversary is not restricted to use an honest evaluation algorithm to run pirated software.", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/cryptodb/data/author.php?authorkey=7516", "content": "... techniques to prove adaptive ... In more detail, our adversary is not restricted to use an honest evaluation algorithm to run pirated software."}
+{"idx": 7, "title": "ECCC - Reports by year", "date": "", "ddg_snippet": "... measure of cost for algorithms with infrequent spikes in memory usage in the context of technologies such as cloud computing that allow dynamic ...", "subpage_snippet": "", "source": "eccc.weizmann.ac.il", "link": "https://eccc.weizmann.ac.il/year/2023/", "content": "... measure of cost for algorithms with infrequent spikes in memory usage in the context of technologies such as cloud computing that allow dynamic ..."}
+{"idx": 8, "title": "Kasper Green Larsen", "date": "", "ddg_snippet": "... successful boosting algorithms , such as the iconic AdaBoost, produce voting classifiers, their theoretical performance has long remained sub-optimal ...", "subpage_snippet": "", "source": "cs.au.dk", "link": "https://cs.au.dk/~larsen/", "content": "... successful boosting algorithms , such as the iconic AdaBoost, produce voting classifiers, their theoretical performance has long remained sub-optimal ..."}
+{"idx": 9, "title": "Giuseppe Persiano | Chatting and drinking in Florence, March", "date": "", "ddg_snippet": "... algorithm and the preceding membership algorithm are optimal, and in particular that there is an inherent complexity gap in these problems between no ...", "subpage_snippet": "", "source": "giuper.github.io", "link": "https://giuper.github.io/", "content": "... algorithm and the preceding membership algorithm are optimal, and in particular that there is an inherent complexity gap in these problems between no ..."}
diff --git a/data/sampled_jsons/Beimel_differential_privacy_adaptive_queries_abstract_year_2022.jsonl b/data/sampled_jsons/Beimel_differential_privacy_adaptive_queries_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce984f0bce633ebb0d4ddf7a7ab4d17899c73b02
--- /dev/null
+++ b/data/sampled_jsons/Beimel_differential_privacy_adaptive_queries_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TPDP 2020 – Theory and Practice of Differential Privacy", "date": "", "ddg_snippet": "Differentially Private Normalizing Flows for Privacy -Preserving Density Estimation, by Chris Waites and Rachel Cummings", "subpage_snippet": "", "source": "tpdp.journalprivacyconfidentiality.org", "link": "https://tpdp.journalprivacyconfidentiality.org/2020/", "content": "Differentially Private Normalizing Flows for Privacy -Preserving Density Estimation, by Chris Waites and Rachel Cummings"}
+{"idx": 1, "title": "privacy | An Ergodic Walk", "date": "", "ddg_snippet": "SCOPE The focus of this special issue is on distributed information acquisition, estimation, and adaptive learning for security and privacy in the ...", "subpage_snippet": "", "source": "ergodicity.net", "link": "https://ergodicity.net/tag/privacy/", "content": "SCOPE The focus of this special issue is on distributed information acquisition, estimation, and adaptive learning for security and privacy in the ..."}
+{"idx": 2, "title": "COLT 2025 Book of Abstracts", "date": "", "ddg_snippet": "Abstract : Existing data-dependent and best-of-both-worlds regret bounds for multi-armed bandits problems have limited adaptivity as they are either ...", "subpage_snippet": "", "source": "learningtheory.org", "link": "https://learningtheory.org/colt2025/abstracts.html", "content": "Abstract : Existing data-dependent and best-of-both-worlds regret bounds for multi-armed bandits problems have limited adaptivity as they are either ..."}
+{"idx": 3, "title": "One Attack to Rule Them All: Tight Quadratic Bounds for", "date": "", "ddg_snippet": "... queries (Steinke and Ullman, 2015) and specific MinHash cardinality sketches (Ahmadian and Cohen, 2024) established that this is tight in that they ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06370v2", "content": "... queries (Steinke and Ullman, 2015) and specific MinHash cardinality sketches (Ahmadian and Cohen, 2024) established that this is tight in that they ..."}
+{"idx": 4, "title": "Piquant𝜀: Private Quantile Estimation in the Two-Server Model", "date": "", "ddg_snippet": "Local differential privacy (LDP) offers strong protection but lower accuracy than central DP, which assumes a trusted aggregator.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14035v1", "content": "Local differential privacy (LDP) offers strong protection but lower accuracy than central DP, which assumes a trusted aggregator."}
+{"idx": 5, "title": "Giuseppe Persiano | Chatting and drinking in Florence, March", "date": "", "ddg_snippet": "Our data structure supports storing a set of n key-value pairs from [u]X[u] using s words of space and answering key lookup queries in t=O(lg( u/n ...", "subpage_snippet": "", "source": "giuper.github.io", "link": "https://giuper.github.io/", "content": "Our data structure supports storing a set of n key-value pairs from [u]X[u] using s words of space and answering key lookup queries in t=O(lg( u/n ..."}
+{"idx": 6, "title": "Iftach Haitner", "date": "", "ddg_snippet": "In distributed differential privacy , multiple parties collaborate to analyze their combined data while each party protects the confidentiality of its ...", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/cryptodb/data/author.php?authorkey=1707", "content": "In distributed differential privacy , multiple parties collaborate to analyze their combined data while each party protects the confidentiality of its ..."}
+{"idx": 7, "title": "Papers from EUROCRYPT 2020", "date": "", "ddg_snippet": "Our adaptively secure ABE for DFA relies on a new combinatorial mechanism avoiding the exponential security loss in the number of states when naively ...", "subpage_snippet": "", "source": "www.iacr.org", "link": "https://www.iacr.org/cryptodb/data/conf.php?year=2020&venue=eurocrypt", "content": "Our adaptively secure ABE for DFA relies on a new combinatorial mechanism avoiding the exponential security loss in the number of states when naively ..."}
+{"idx": 8, "title": "US9141824B2 - Dynamic database update in multi-server private", "date": "", "ddg_snippet": "... and in particular to enabling database updates to occur concurrently with user queries , without allowing the update process to compromise the privacy ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US9141824B2/en", "content": "... and in particular to enabling database updates to occur concurrently with user queries , without allowing the update process to compromise the privacy ..."}
+{"idx": 9, "title": "Yishay Mansour", "date": "", "ddg_snippet": "Black-Box Differential Privacy for Interactive ML. ... Concurrent Shuffle Differential Privacy Under Continual Observation.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/yishay-mansour/", "content": "Black-Box Differential Privacy for Interactive ML. ... Concurrent Shuffle Differential Privacy Under Continual Observation."}
diff --git a/data/sampled_jsons/Black_et_al._(2023)_diffusion_models.jsonl b/data/sampled_jsons/Black_et_al._(2023)_diffusion_models.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..debb594e5cc5ec5ee05c4ab7eb17198a135e29e1
--- /dev/null
+++ b/data/sampled_jsons/Black_et_al._(2023)_diffusion_models.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.13301", "content": "Diffusion models are a class of flexible generative models trained with an approximation to the log-likelihood objective. However, most use cases of diffusion models are not concerned with likelihoods, but instead with downstream objectives such as human-perceived image quality or drug effectiveness. In this paper, we investigate reinforcement learning methods for directly optimizing diffusion ..."}
+{"idx": 1, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "This post is based on the following paper: Training Diffusion Models with Reinforcement Learning Kevin Black *, Michael Janner *, Yilun Du, Ilya Kostrikov, and Sergey Levine arXiv Preprint. If you want to learn more about DDPO, you can check out the paper, website, original code, or get the model weights on Hugging Face. If you want to use DDPO in your own project, check out my PyTorch + LoRA ...", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2023/07/14/ddpo/", "content": "This post is based on the following paper: Training Diffusion Models with Reinforcement Learning Kevin Black *, Michael Janner *, Yilun Du, Ilya Kostrikov, and Sergey Levine arXiv Preprint. If you want to learn more about DDPO, you can check out the paper, website, original code, or get the model weights on Hugging Face. If you want to use DDPO in your own project, check out my PyTorch + LoRA ..."}
+{"idx": 2, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "1. Introduction Diffusion probabilistic models (Sohl-Dickstein et al ., 2015) have recently emerged as the de facto standard for genera-tive modeling in continuous domains. Their flexibility in representing complex, high-dimensional distributions has led to the adoption of diffusion models in applications in-cluding image and video synthesis (Ramesh et al ., 2021; Saharia et al ., 2022; Ho et al ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=pBN8CgrDhE", "content": "1. Introduction Diffusion probabilistic models (Sohl-Dickstein et al ., 2015) have recently emerged as the de facto standard for genera-tive modeling in continuous domains. Their flexibility in representing complex, high-dimensional distributions has led to the adoption of diffusion models in applications in-cluding image and video synthesis (Ramesh et al ., 2021; Saharia et al ., 2022; Ho et al ..."}
+{"idx": 3, "title": "Training Diffusion Models with Reinforcement Learning - DeepAI", "date": "", "ddg_snippet": "Training Diffusion Models with Reinforcement Learning 05/22/ 2023 ∙ by Kevin Black , et al. ∙ ∙", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/training-diffusion-models-with-reinforcement-learning", "content": "Training Diffusion Models with Reinforcement Learning 05/22/ 2023 ∙ by Kevin Black , et al. ∙ ∙"}
+{"idx": 4, "title": "Training Diffusion Models with Reinforcement Learning", "date": "", "ddg_snippet": "Summary We train diffusion models directly on downstream objectives using reinforcement learning (RL). We do this by posing denoising diffusion as a multi-step decision-making problem, enabling a class of policy gradient algorithms that we call denoising diffusion policy optimization (DDPO).", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/", "content": "Summary We train diffusion models directly on downstream objectives using reinforcement learning (RL). We do this by posing denoising diffusion as a multi-step decision-making problem, enabling a class of policy gradient algorithms that we call denoising diffusion policy optimization (DDPO)."}
+{"idx": 5, "title": "An unofficial implementation of \"Training Diffusion Models with ...", "date": "", "ddg_snippet": "This repo is an unofficial implementation of \"Training Diffusion Models with Reinforcement Learning\" ( Black et al ., 2023 ). This project is on pause — I underestimated the training time required and I currently do not have the capacity to train this model (lol, noob).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/superolly/reinforcement-diffusion", "content": "This repo is an unofficial implementation of \"Training Diffusion Models with Reinforcement Learning\" ( Black et al ., 2023 ). This project is on pause — I underestimated the training time required and I currently do not have the capacity to train this model (lol, noob)."}
+{"idx": 6, "title": "[2305.11089] Blackout Diffusion: Generative Diffusion Models in ...", "date": "", "ddg_snippet": "Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.11089", "content": "Typical generative diffusion models rely on a Gaussian diffusion process for training the backward transformations, which can then be used to generate samples from Gaussian noise. However, real world data often takes place in discrete-state spaces, including many scientific applications. Here, we develop a theoretical formulation for arbitrary discrete-state Markov processes in the forward ..."}
+{"idx": 7, "title": "PDF 1 I Training Diffusion Models With Reinforcement Lear", "date": "", "ddg_snippet": "1 INTRODUCTION Diffusion probabilistic models (Sohl-Dickstein et al ., 2015) have recently emerged as the de facto standard for generative modeling in continuous domains. Their flexibility in representing complex, high-dimensional distributions has led to the adoption of diffusion models in applications including image and video synthesis (Ramesh et al ., 2021; Saharia et al ., 2022; Ho et al ...", "subpage_snippet": "", "source": "rl-diffusion.github.io", "link": "https://rl-diffusion.github.io/files/paper.pdf", "content": "1 INTRODUCTION Diffusion probabilistic models (Sohl-Dickstein et al ., 2015) have recently emerged as the de facto standard for generative modeling in continuous domains. Their flexibility in representing complex, high-dimensional distributions has led to the adoption of diffusion models in applications including image and video synthesis (Ramesh et al ., 2021; Saharia et al ., 2022; Ho et al ..."}
+{"idx": 8, "title": "(PDF) Diffusion Models for Black-Box Optimization - ResearchGate", "date": "", "ddg_snippet": "We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black -box optimization based on diffusion models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371506196_Diffusion_Models_for_Black-Box_Optimization", "content": "We propose Denoising Diffusion Optimization Models (DDOM), a new inverse approach for offline black -box optimization based on diffusion models ."}
+{"idx": 9, "title": "Diffusion Research Timeline - GitHub", "date": "", "ddg_snippet": "SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis - Podell et al ., 2023 ⚡ Enhanced Stable Diffusion XL (SDXL) for improved detail, diversity, and efficiency. 📑 Cited 1,596 times since 2023", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jeffreybarry/Diffusion-Research-Timeline", "content": "SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis - Podell et al ., 2023 ⚡ Enhanced Stable Diffusion XL (SDXL) for improved detail, diversity, and efficiency. 📑 Cited 1,596 times since 2023"}
diff --git a/data/sampled_jsons/Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models_Table_1_MMLU.jsonl b/data/sampled_jsons/Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models_Table_1_MMLU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..abcfa7d304e675f8b9f007fc4175254e99db3a3a
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models_Table_1_MMLU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow ``critical windows'' of the generation process. In this work we develop a simple , unifying theory to explain this phenomenon using the formalism of stochastic localization samplers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00921", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow ``critical windows'' of the generation process. In this work we develop a simple , unifying theory to explain this phenomenon using the formalism of stochastic localization samplers."}
+{"idx": 1, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "In this work we de- velop a simple , unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=QvqnPVGWAN", "content": "In this work we de- velop a simple , unifying theory to explain this phenomenon. Using the formalism of stochastic localization for generative models , we show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 2, "title": "(PDF) Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "In this work we develop a simple , unifying theory to explain this phenomenon. We show that it emerges generically as the generation process localizes to a sub-population of the distribution it models .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388658326_Blink_of_an_eye_a_simple_theory_for_feature_localization_in_generative_models", "content": "In this work we develop a simple , unifying theory to explain this phenomenon. We show that it emerges generically as the generation process localizes to a sub-population of the distribution it models ."}
+{"idx": 3, "title": "In the Blink of an Eye: A Unified Theory for Feature Emergence in ...", "date": "", "ddg_snippet": "The key insight of our approach is to exploit the powerful formalism for generative models of stochastic localization , which has roots as a proof technique in probability theory . Leveraging our consolidated theory for critical windows, we apply it to different examples of critical windows in theoretical and empirical contexts.", "subpage_snippet": "", "source": "dash.harvard.edu", "link": "https://dash.harvard.edu/items/f3795c57-8ff8-44d2-ae92-1fc5525aeb39", "content": "The key insight of our approach is to exploit the powerful formalism for generative models of stochastic localization , which has roots as a proof technique in probability theory . Leveraging our consolidated theory for critical windows, we apply it to different examples of critical windows in theoretical and empirical contexts."}
+{"idx": 4, "title": "dblp: Blink of an eye: a simple theory for feature localization in ...", "date": "", "ddg_snippet": "Bibliographic details on Blink of an eye : a simple theory for feature localization in generative models .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2502-00921", "content": "Bibliographic details on Blink of an eye : a simple theory for feature localization in generative models ."}
+{"idx": 5, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Poster in Workshop: Frontiers in Probabilistic Inference: learning meets Sampling Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/36105", "content": "Poster in Workshop: Frontiers in Probabilistic Inference: learning meets Sampling Blink of an eye : a simple theory for feature localization in generative models Marvin Li · Aayush Karan · Sitan Chen"}
+{"idx": 6, "title": "Blink of an Eye: A Simple Theory for Feature Localization in Generative ...", "date": "", "ddg_snippet": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models .", "subpage_snippet": "", "source": "marvinfli.github.io", "link": "https://marvinfli.github.io/publication/2025-blink-eye", "content": "We present a concise theoretical framework that explains sudden feature localization events—\"blinks of an eye\"—in diffusion and autoregressive generative models ."}
+{"idx": 7, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow \"critical windows\" of the generation process. In this work we develop a simple , unifying theory to explain this phenomenon.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00921v1", "content": "This phenomenon is not unique to autoregressive models : in diffusion models , key features of the final output are decided in narrow \"critical windows\" of the generation process. In this work we develop a simple , unifying theory to explain this phenomenon."}
+{"idx": 8, "title": "Blink of an eye: a simple theory for feature localization in generative ...", "date": "", "ddg_snippet": "Large language models can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=nodyP4FLrM", "content": "Large language models can exhibit undesirable and unexpected behavior in the blink of an eye . In a recent Anthropic demo, Claude switched from coding to Googling pictures of Yellowstone, and these..."}
+{"idx": 9, "title": "Sitan Chen June 10, 2025 arXiv:2502.00921v2 [cs.LG] 5 Jun 2025", "date": "", "ddg_snippet": "Blink of an eye : a simple theory for feature localization in generative models", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00921v2", "content": "Blink of an eye : a simple theory for feature localization in generative models"}
diff --git a/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_ARC_Easy_accuracy_drop.jsonl b/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_ARC_Easy_accuracy_drop.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dccb75137b3f586899f5d57ca28d2eb1f4b1b377
--- /dev/null
+++ b/data/sampled_jsons/Blink_of_an_eye_feature_localization_generative_models_ARC_Easy_accuracy_drop.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning Localized Generative Models for 3D Point... | OpenReview", "date": "", "ddg_snippet": "This paper studies the unsupervised problem of a generative model exploiting graph convolution. We focus on the generator of a GAN and define methods for graph convolution when the graph is not known in advance as it is the very output of the generator.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=SJeXSo09FQ", "content": "This paper studies the unsupervised problem of a generative model exploiting graph convolution. We focus on the generator of a GAN and define methods for graph convolution when the graph is not known in advance as it is the very output of the generator."}
+{"idx": 1, "title": "Open Closed Eyes in Photos - Online Free Tool", "date": "", "ddg_snippet": "Open Eyes is a web-based service designed to automatically open closed eyes in your photographs. Simply select an image, and our sophisticated AI technology will generate a new photo with naturally open eyes .", "subpage_snippet": "", "source": "overflow.ai", "link": "https://overflow.ai/open-eyes-using-ai", "content": "Open Eyes is a web-based service designed to automatically open closed eyes in your photographs. Simply select an image, and our sophisticated AI technology will generate a new photo with naturally open eyes ."}
+{"idx": 2, "title": "Free Eye Color Change Online", "date": "", "ddg_snippet": "This ensures that the eye color change looks natural and blends seamlessly with the rest of the photo. Use Natural Lighting: Photos taken in natural light tend to have better color accuracy . This makes it easier for the tool to detect and change the eye color effectively.", "subpage_snippet": "", "source": "openart.ai", "link": "https://openart.ai/features/eye-color-change", "content": "This ensures that the eye color change looks natural and blends seamlessly with the rest of the photo. Use Natural Lighting: Photos taken in natural light tend to have better color accuracy . This makes it easier for the tool to detect and change the eye color effectively."}
+{"idx": 3, "title": "Image Animation (Animate Photo) Online Free | GoEnhance AI", "date": "", "ddg_snippet": "From subtle eye - blinks to full-frame camera moves, create scroll-stopping videos with zero editing skills.1. Upload Your Image. Drop a JPG, JPEG, PNG, or WebP up to 4096×4096 px into the editor.", "subpage_snippet": "", "source": "www.goenhance.ai", "link": "https://www.goenhance.ai/ai-video-generator/animate-a-picture", "content": "From subtle eye - blinks to full-frame camera moves, create scroll-stopping videos with zero editing skills.1. Upload Your Image. Drop a JPG, JPEG, PNG, or WebP up to 4096×4096 px into the editor."}
+{"idx": 4, "title": "Gemini - Google DeepMind", "date": "", "ddg_snippet": "Gemini 2.5 is our most intelligent AI model , capable of reasoning through its thoughts before responding, resulting in enhanced performance and improved accuracy .", "subpage_snippet": "", "source": "deepmind.google", "link": "https://deepmind.google/models/gemini/", "content": "Gemini 2.5 is our most intelligent AI model , capable of reasoning through its thoughts before responding, resulting in enhanced performance and improved accuracy ."}
+{"idx": 5, "title": "The Best Free AI Undress Tool for Realistic Photo Transformation", "date": "", "ddg_snippet": "Key Features of the Best Undress AI Platform. Our undress AI app is built to provide excellent service, as well as being easy to use. We stand out by offering a combination of power, precision, and security.", "subpage_snippet": "", "source": "makefilm.ai", "link": "https://makefilm.ai/tools/ai-undresser", "content": "Key Features of the Best Undress AI Platform. Our undress AI app is built to provide excellent service, as well as being easy to use. We stand out by offering a combination of power, precision, and security."}
+{"idx": 6, "title": "DevTalk | After the Nexus Bond Test Launch Honkai... | HoYoLAB", "date": "", "ddg_snippet": "Improve the models and expressions of some characters. The variety of character customization options will be expanded to include additional appearances, clothing, accessories, and other customizable features , along with improvements to animations an...", "subpage_snippet": "", "source": "www.hoyolab.com", "link": "https://www.hoyolab.com/article/41269675", "content": "Improve the models and expressions of some characters. The variety of character customization options will be expanded to include additional appearances, clothing, accessories, and other customizable features , along with improvements to animations an..."}
+{"idx": 7, "title": "Your Perfect Hairstyle is Just a Click Away", "date": "", "ddg_snippet": "Explore infinite possibilities. Lightning fast. Transform your hair in the blink of an eye . Secure and Private. Your pictures are your business.", "subpage_snippet": "", "source": "www.barbergpt.ai", "link": "https://www.barbergpt.ai/", "content": "Explore infinite possibilities. Lightning fast. Transform your hair in the blink of an eye . Secure and Private. Your pictures are your business."}
+{"idx": 8, "title": "Delta Force War Ablaze Update First Console Patch 1.037.105", "date": "", "ddg_snippet": "Deploy immediately to investigate—but remember, your own safety comes first! Mode Roulette Update. PC & Console: Core maps now include Zero Dam ( Easy /Normal) and Layali Grove ( Easy ). Layali Grove (Normal) is temporarily out of rotation.", "subpage_snippet": "", "source": "mp1st.com", "link": "https://mp1st.com/title-updates-and-patches/delta-force-new-season-update-war-ablaze-console-patch-1-037-105", "content": "Deploy immediately to investigate—but remember, your own safety comes first! Mode Roulette Update. PC & Console: Core maps now include Zero Dam ( Easy /Normal) and Layali Grove ( Easy ). Layali Grove (Normal) is temporarily out of rotation."}
+{"idx": 9, "title": "Technical Account Manager at Altium", "date": "", "ddg_snippet": "Proactively generate hypotheses for new apps, internal tools, protocol-facing features , or value-adding extensions to STON.fi. Design and run fast, focused experiments (MVPs, fake door tests, waitlists, landing pages, Wizard of Oz prototypes, etc.).", "subpage_snippet": "", "source": "www.remocate.app", "link": "https://www.remocate.app/jobs/technical-account-manager-3", "content": "Proactively generate hypotheses for new apps, internal tools, protocol-facing features , or value-adding extensions to STON.fi. Design and run fast, focused experiments (MVPs, fake door tests, waitlists, landing pages, Wizard of Oz prototypes, etc.)."}
diff --git a/data/sampled_jsons/Buchholz_causal_representation_learning_2023.jsonl b/data/sampled_jsons/Buchholz_causal_representation_learning_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9f4d41f767f836a4a4dd0425dc0eb41f9cf19454
--- /dev/null
+++ b/data/sampled_jsons/Buchholz_causal_representation_learning_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "NeurIPS'24 Workshop on Causal Representation Learning", "date": "", "ddg_snippet": "... world situations where causal effects occur in latent spaces when handling images, videos, and possibly text.Recently, causal representation learning ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/workshop/84746", "content": "... world situations where causal effects occur in latent spaces when handling images, videos, and possibly text.Recently, causal representation learning ..."}
+{"idx": 1, "title": "CausalML", "date": "", "ddg_snippet": "ICLR 2024 (Spotlight; top 3%) (also at: Workshop on Causal Representation Learning @ NeurIPS 2023 , Oral ) ... Causal Representation Learning @ ...", "subpage_snippet": "", "source": "sites.google.com", "link": "https://sites.google.com/view/julius-von-kuegelgen/home/", "content": "ICLR 2024 (Spotlight; top 3%) (also at: Workshop on Causal Representation Learning @ NeurIPS 2023 , Oral ) ... Causal Representation Learning @ ..."}
+{"idx": 2, "title": "Learning Robust Intervention Representations with Delta", "date": "", "ddg_snippet": "Causal representation learning has attracted significant research interest during the past few years, as a means for improving model generalization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.04492v1", "content": "Causal representation learning has attracted significant research interest during the past few years, as a means for improving model generalization ..."}
+{"idx": 3, "title": "Identifying Weight-Variant Latent Causal Models", "date": "", "ddg_snippet": "Causal representation learning avoids the aforementioned limitation, as it aims to learn a representation that exposes the unknown high-level causal ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2208.14153v6", "content": "Causal representation learning avoids the aforementioned limitation, as it aims to learn a representation that exposes the unknown high-level causal ..."}
+{"idx": 4, "title": "CausalML", "date": "", "ddg_snippet": "ICLR 2024 (Spotlight; top 3%) (also at: Workshop on Causal Representation Learning @ NeurIPS 2023 , Oral ) ... Causal Representation Learning @ ...", "subpage_snippet": "", "source": "www.juliusvonkugelgen.com", "link": "https://www.juliusvonkugelgen.com/", "content": "ICLR 2024 (Spotlight; top 3%) (also at: Workshop on Causal Representation Learning @ NeurIPS 2023 , Oral ) ... Causal Representation Learning @ ..."}
+{"idx": 5, "title": "Bauer team - Helmholtz - Gemeinschaft deutscher", "date": "", "ddg_snippet": "... across environments and tasks in real-world settings is a fundamental, yet unsolved problem and our team aims to develop algorithms that learn causal ...", "subpage_snippet": "", "source": "www.helmholtz.ai", "link": "https://www.helmholtz.ai/applied-ai/ai-research-labs/bauer-team/", "content": "... across environments and tasks in real-world settings is a fundamental, yet unsolved problem and our team aims to develop algorithms that learn causal ..."}
+{"idx": 6, "title": "Stefan Bauer", "date": "", "ddg_snippet": "Diffusion-Based Causal Representation Learning . ... Proceedings of the Eleventh International Conference on Learning Representations , 2023", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/stefan-bauer/", "content": "Diffusion-Based Causal Representation Learning . ... Proceedings of the Eleventh International Conference on Learning Representations , 2023"}
+{"idx": 7, "title": "Pradeep Ravikumar", "date": "", "ddg_snippet": "Abstract: We consider the linear causal representation learning setting where we observe a linear mixing of $d$ unknown latent factors, which follow ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Pradeep+Ravikumar", "content": "Abstract: We consider the linear causal representation learning setting where we observe a linear mixing of $d$ unknown latent factors, which follow ..."}
+{"idx": 8, "title": "Bryon Aragam // University of Chicago", "date": "", "ddg_snippet": "This is the goal of the emerging field of causal representation learning (CRL) that uses causality as a vector for building flexible, interpretable ...", "subpage_snippet": "", "source": "www.bryonaragam.com", "link": "http://www.bryonaragam.com/", "content": "This is the goal of the emerging field of causal representation learning (CRL) that uses causality as a vector for building flexible, interpretable ..."}
+{"idx": 9, "title": "Bryon Aragam // University of Chicago", "date": "", "ddg_snippet": "We revisit this classic method from the comparatively new perspective given by advancements in causal discovery and deep learning , introducing a ...", "subpage_snippet": "", "source": "www.bryonaragam.com", "link": "https://www.bryonaragam.com/", "content": "We revisit this classic method from the comparatively new perspective given by advancements in causal discovery and deep learning , introducing a ..."}
diff --git a/data/sampled_jsons/CALF_Causal_Attention-based_Language_Forecasting_Liu_et_al_2024_abstract.jsonl b/data/sampled_jsons/CALF_Causal_Attention-based_Language_Forecasting_Liu_et_al_2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d99b3b8ed3a91a8f275e5fe4b9f4df80372bde94
--- /dev/null
+++ b/data/sampled_jsons/CALF_Causal_Attention-based_Language_Forecasting_Liu_et_al_2024_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ... CALF | Proceedings of the Thirty-Ninth AAAI Conference on ... dblp: CALF: Aligning LLMs for Time Series Forecasting via ... Large Language Models and Causal Inference in Collaboration ... Are Language Models Actually Useful for Time Series Forecasting? CALF: Aligning LLMs for Time Series Forecasting Are Language Models Actually Useful for Time Series Forecasting ? dblp: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal CALF : Aligning LLMs for Time Series Forecasting Are Language Models Actually Useful for Time Series Forecasting ? CALF : Aligning LLMs for Time Series Forecasting Are Language Models Actually Useful for Time Series Forecasting ? arXiv:2505.02583v1 [cs.LG] 5 May 2025", "date": "", "ddg_snippet": "Mar 12, 2024 · View PDF HTML (experimental) Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially ... Aug 5, 2025 · Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ... Apr 17, 2025 · Peiyuan Liu , Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning. Sep 15, 2025 · Abstract Causal inference has demonstrated significant potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability by capturing causal relationships among variables. Abstract Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM- based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance—in most cases, the ... To relieve such issues, some pioneering works attempt to introduce the powerful Large Language Models (LLMs) models in time series forecasting by employing the strong context modeling ability of LLMs. For example, Zhou et al . [13] proposed a unified time series analysis framework by adapting and fine-tuning LLMs. Are large language models useful for time series forecasting? Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series? What is calf & AAI 2025? CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning. AAAI 2025: 18915-18923 To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. Does the calf framework improve time series forecasting? Our work on the CALF framework for time series forecasting primarily focuses on enhancing predictive accuracy and generalization . While the positive societal impacts include improved forecasting for critical applications such as weather prediction, energy management, and financial modeling, potential negative impacts should be considered. Does language model pretraining improve performance on time series forecasting tasks? In conclusion, the computational intensity of LLMs in time series forecasting tasks does not result in a corresponding performance improvement. 4.3 Does language model pretraining help performance on forecasting tasks? (RQ3) Our evaluation in this section indicates that pretraining with language datasets is unnecessary for time series forecasting . What are the input and prediction lengths for calf? The input and prediction lengths are both set to 96 . As shown in Tab. 5, our proposed CALF shows significant improvements in both efficiency and accuracy compared with other LLM-based methods. We also provide theoretical complexity analysis for various Transformer-based methods in Appendix E. Ablation on Different Loss Functions. Do LLMs help forecasting in a few-shot setting? Finally, we show that LLMs do not even help forecasting in few-shot settings with 10% of the training data. We discuss the implications of our findings and suggest that time series methods that use large language models are better left to multimodal applications [4, 12, 38] that require textual reasoning. olely on the distilled student model. Recent works have been proposed to address the cross-modal misalignment problem with knowledge distillation [ Liu and Zhang, 2025], which can generally be categorized into black-box distillation [ Liu et al ., 2024a] and white-box distillation[ Liu et al ., 2025a] based on the accessibility of the teacher model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "Mar 12, 2024 · View PDF HTML (experimental) Abstract : Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially ... Aug 5, 2025 · Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ... Apr 17, 2025 · Peiyuan Liu , Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning. Sep 15, 2025 · Abstract Causal inference has demonstrated significant potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability by capturing causal relationships among variables. Abstract Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM- based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance—in most cases, the ... To relieve such issues, some pioneering works attempt to introduce the powerful Large Language Models (LLMs) models in time series forecasting by employing the strong context modeling ability of LLMs. For example, Zhou et al . [13] proposed a unified time series analysis framework by adapting and fine-tuning LLMs. Are large language models useful for time series forecasting? Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series? What is calf & AAI 2025? CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning. AAAI 2025: 18915-18923 To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. Does the calf framework improve time series forecasting? Our work on the CALF framework for time series forecasting primarily focuses on enhancing predictive accuracy and generalization . While the positive societal impacts include improved forecasting for critical applications such as weather prediction, energy management, and financial modeling, potential negative impacts should be considered. Does language model pretraining improve performance on time series forecasting tasks? In conclusion, the computational intensity of LLMs in time series forecasting tasks does not result in a corresponding performance improvement. 4.3 Does language model pretraining help performance on forecasting tasks? (RQ3) Our evaluation in this section indicates that pretraining with language datasets is unnecessary for time series forecasting . What are the input and prediction lengths for calf? The input and prediction lengths are both set to 96 . As shown in Tab. 5, our proposed CALF shows significant improvements in both efficiency and accuracy compared with other LLM-based methods. We also provide theoretical complexity analysis for various Transformer-based methods in Appendix E. Ablation on Different Loss Functions. Do LLMs help forecasting in a few-shot setting? Finally, we show that LLMs do not even help forecasting in few-shot settings with 10% of the training data. We discuss the implications of our findings and suggest that time series methods that use large language models are better left to multimodal applications [4, 12, 38] that require textual reasoning. olely on the distilled student model. Recent works have been proposed to address the cross-modal misalignment problem with knowledge distillation [ Liu and Zhang, 2025], which can generally be categorized into black-box distillation [ Liu et al ., 2024a] and white-box distillation[ Liu et al ., 2025a] based on the accessibility of the teacher model ..."}
+{"idx": 1, "title": "CALF | Proceedings of the Thirty-Ninth AAAI Conference on ...", "date": "", "ddg_snippet": "Aug 5, 2025 · Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1609/aaai.v39i18.34082", "content": "Aug 5, 2025 · Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ..."}
+{"idx": 2, "title": "dblp: CALF: Aligning LLMs for Time Series Forecasting via ...", "date": "", "ddg_snippet": "Apr 17, 2025 · Peiyuan Liu , Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/LiuG0LBR0X25", "content": "Apr 17, 2025 · Peiyuan Liu , Hang Guo, Tao Dai, Naiqi Li, Jigang Bao, Xudong Ren, Yong Jiang, Shu-Tao Xia: CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning."}
+{"idx": 3, "title": "Large Language Models and Causal Inference in Collaboration ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Abstract Causal inference has demonstrated significant potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability by capturing causal relationships among variables.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-naacl.427/", "content": "Sep 15, 2025 · Abstract Causal inference has demonstrated significant potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability by capturing causal relationships among variables."}
+{"idx": 4, "title": "Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "Abstract Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM- based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance—in most cases, the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/6ed5bf446f59e2c6646d23058c86424b-Paper-Conference.pdf", "content": "Abstract Large language models (LLMs) are being applied to time series forecasting . But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM- based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance—in most cases, the ..."}
+{"idx": 5, "title": "CALF: Aligning LLMs for Time Series Forecasting", "date": "", "ddg_snippet": "To relieve such issues, some pioneering works attempt to introduce the powerful Large Language Models (LLMs) models in time series forecasting by employing the strong context modeling ability of LLMs. For example, Zhou et al . [13] proposed a unified time series analysis framework by adapting and fine-tuning LLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v2", "content": "To relieve such issues, some pioneering works attempt to introduce the powerful Large Language Models (LLMs) models in time series forecasting by employing the strong context modeling ability of LLMs. For example, Zhou et al . [13] proposed a unified time series analysis framework by adapting and fine-tuning LLMs."}
+{"idx": 6, "title": "LangTime: A Language-Guided Unified Model for Time ...", "date": "", "ddg_snippet": "To address this, CALF (Liu et al., 2024c ) applies knowledge distillation, S2IP-LLM (Pan et al., 2024) aligns semantic and time series spaces with ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=VfoKOD65Zq&name=pdf", "content": "To address this, CALF (Liu et al., 2024c ) applies knowledge distillation, S2IP-LLM (Pan et al., 2024) aligns semantic and time series spaces with ..."}
+{"idx": 7, "title": "A novel LLM time series forecasting method based on ...", "date": "", "ddg_snippet": "by L Wang · 2025 — Similarly, CALF, introduced by Liu et al . achieved efficient cross-modal alignment by separately processing text prototypes and time ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12217866/", "content": "by L Wang · 2025 — Similarly, CALF, introduced by Liu et al . achieved efficient cross-modal alignment by separately processing text prototypes and time ..."}
+{"idx": 8, "title": "LangTime: A Language-Guided Unified Model for Time ...", "date": "", "ddg_snippet": "1 Jul 2025 — To address this, CALF ( Liu et al ., 2024c ) applies knowledge distillation, S 2 IP-LLM (Pan et al ., 2024 ) aligns semantic and time series ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.08271", "content": "1 Jul 2025 — To address this, CALF ( Liu et al ., 2024c ) applies knowledge distillation, S 2 IP-LLM (Pan et al ., 2024 ) aligns semantic and time series ..."}
+{"idx": 9, "title": "arXiv:2411.02941v1 [cs.LG] 5 Nov 2024", "date": "", "ddg_snippet": "by H Ma · 2024 · Cited by 6 — Additionally, we incorporate large language models (LLMs) like GPT4TS. (Zhou et al ., 2023) and CALF ( Liu et al ., 2024a ), as well as other recent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.02941", "content": "by H Ma · 2024 · Cited by 6 — Additionally, we incorporate large language models (LLMs) like GPT4TS. (Zhou et al ., 2023) and CALF ( Liu et al ., 2024a ), as well as other recent ..."}
diff --git a/data/sampled_jsons/CALF_Liu_2024_time_series_forecasting_language_model.jsonl b/data/sampled_jsons/CALF_Liu_2024_time_series_forecasting_language_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6d11cc2cd36f1b4e3a73338ff2d16d97cf487ac1
--- /dev/null
+++ b/data/sampled_jsons/CALF_Liu_2024_time_series_forecasting_language_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2403.07300] CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07300", "content": "Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data. However ..."}
+{"idx": 1, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross ... - GitHub", "date": "", "ddg_snippet": "Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves better alignment through multi-level cross-modal fine-tuning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hank0626/CALF", "content": "Current LLM-based methods either use linear layers to project time series to the LLM's feature dimension or employ cross-attention and contrastive learning techniques, which address only the input side and overlook alignment in the deeper layers. Our CALF achieves better alignment through multi-level cross-modal fine-tuning."}
+{"idx": 2, "title": "Paper page - CALF: Aligning LLMs for Time Series Forecasting via Cross ...", "date": "", "ddg_snippet": "Abstract A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework reduces distribution discrepancy between text and time series data for improved multivariate time series forecasting , achieving state-of-the-art performance with low computational complexity.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2403.07300", "content": "Abstract A novel Cross-Modal LLM Fine-Tuning ( CALF ) framework reduces distribution discrepancy between text and time series data for improved multivariate time series forecasting , achieving state-of-the-art performance with low computational complexity."}
+{"idx": 3, "title": "Taming Pre-trained LLMs for Generalised Time Series Forecasting via ...", "date": "", "ddg_snippet": "Abstract: Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=LSgJYc4kg8", "content": "Abstract: Deep learning (e.g., Transformer) has been widely and successfully used in multivariate time series forecasting (MTSF). Unlike existing methods that focus on training models from a single modal of time series input, large language models (LLMs) based MTSF methods with cross-modal text and time series input have recently shown great superiority, especially with limited temporal data ..."}
+{"idx": 4, "title": "PDF Are Language Models Actually Useful for Time Series Forecasting?", "date": "", "ddg_snippet": "This hypothesis is unsurprising given language models are now pervasive in machine learning research. However, direct connections between language modeling and time series forecasting remain largely undefined. So to what extent is language modeling really beneficial for traditional time series tasks?", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/6ed5bf446f59e2c6646d23058c86424b-Paper-Conference.pdf", "content": "This hypothesis is unsurprising given language models are now pervasive in machine learning research. However, direct connections between language modeling and time series forecasting remain largely undefined. So to what extent is language modeling really beneficial for traditional time series tasks?"}
+{"idx": 5, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning", "date": "", "ddg_snippet": "Despite this promising prop-erty, previous LLM-based time series methods often over-look this distribution, instead projecting the time series data to match the input dimensions of the language model (Zhou et al. 2023; Cao et al. 2024 ; Chang, Peng, and Chen 2023).", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/34082/36237", "content": "Despite this promising prop-erty, previous LLM-based time series methods often over-look this distribution, instead projecting the time series data to match the input dimensions of the language model (Zhou et al. 2023; Cao et al. 2024 ; Chang, Peng, and Chen 2023)."}
+{"idx": 6, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine ...", "date": "", "ddg_snippet": "LLM Share on Twitter Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model (ICCV 2023) Periodicity Decoupling Framework for Long-term Series Forecasting (ICLR 2024 )", "subpage_snippet": "", "source": "naiqili.github.io", "link": "https://naiqili.github.io/2024/03/23/calf.html", "content": "LLM Share on Twitter Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model (ICCV 2023) Periodicity Decoupling Framework for Long-term Series Forecasting (ICLR 2024 )"}
+{"idx": 7, "title": "CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning", "date": "", "ddg_snippet": "To relieve such issues, some pioneering works attempt to introduce the powerful Large Language Models (LLMs) models in time series forecasting by employing the strong context modeling ability of LLMs. For example, Zhou et al. (Zhou et al. 2023) proposed a unified time series analysis framework by adapting and fine-tuning LLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07300v3", "content": "To relieve such issues, some pioneering works attempt to introduce the powerful Large Language Models (LLMs) models in time series forecasting by employing the strong context modeling ability of LLMs. For example, Zhou et al. (Zhou et al. 2023) proposed a unified time series analysis framework by adapting and fine-tuning LLMs."}
+{"idx": 8, "title": "CALF/README.md at main · Hank0626/CALF · GitHub", "date": "", "ddg_snippet": "Official implementation of \" CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning\" (AAAI 2025) - Hank0626/ CALF", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Hank0626/CALF/blob/main/README.md", "content": "Official implementation of \" CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning\" (AAAI 2025) - Hank0626/ CALF"}
+{"idx": 9, "title": "dblp: CALF: Aligning LLMs for Time Series Forecasting via Cross-modal ...", "date": "", "ddg_snippet": "Bibliographic details on CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/aaai/LiuG0LBR0X25", "content": "Bibliographic details on CALF : Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning."}
diff --git a/data/sampled_jsons/CALF_model_Liu_et_al_2024_time_series_forecasting_abstract.jsonl b/data/sampled_jsons/CALF_model_Liu_et_al_2024_time_series_forecasting_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08e6e2997bfd6f77ccc31264892cabcb70a4ad8a
--- /dev/null
+++ b/data/sampled_jsons/CALF_model_Liu_et_al_2024_time_series_forecasting_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Foundation Models for Time Series Analysis: A Tutorial and Survey", "date": "", "ddg_snippet": "Liu et al . [64] discuss that while the encoder-only model is favored in time series forecasting for its effectiveness on small datasets, the decoder-only architecture, with its strong generalization and capacity, could be preferred for large-scale time series models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.14735", "content": "Liu et al . [64] discuss that while the encoder-only model is favored in time series forecasting for its effectiveness on small datasets, the decoder-only architecture, with its strong generalization and capacity, could be preferred for large-scale time series models ."}
+{"idx": 1, "title": "LSTPrompt: Large Language Models as Zero-Shot Time Series ...", "date": "", "ddg_snippet": "Abstract . Time - series forecasting (TSF) finds broad applications in real-world scenarios.@inproceedings{ liu - etal - 2024 -lstprompt, title = \"{LSTP}rompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting\", author = \" Liu , Haoxin and.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.findings-acl.466/", "content": "Abstract . Time - series forecasting (TSF) finds broad applications in real-world scenarios.@inproceedings{ liu - etal - 2024 -lstprompt, title = \"{LSTP}rompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting\", author = \" Liu , Haoxin and."}
+{"idx": 2, "title": "Modeling spatio-temporal locality in multi-step forecasting of...", "date": "", "ddg_snippet": "2.1 Spatio-temporal forecasting of multiple time series . In the literature, several forecasting approaches able to model the temporal and the spatial dimensions have been proposed, and applied to several different domains.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-025-06875-1", "content": "2.1 Spatio-temporal forecasting of multiple time series . In the literature, several forecasting approaches able to model the temporal and the spatial dimensions have been proposed, and applied to several different domains."}
+{"idx": 3, "title": "(PDF) Autoregressive Moving-average Attention Mechanism for Time ...", "date": "", "ddg_snippet": "(Gruver et al .,2023;Jin et al ., 2024 ;Das et al ., 2024 ; Liu . et al ., 2024 b), with little research directly evaluating their. TSF performance in end-to-end training.foundation model for time - series forecasting . In Forty", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384680475_Autoregressive_Moving-average_Attention_Mechanism_for_Time_Series_Forecasting", "content": "(Gruver et al .,2023;Jin et al ., 2024 ;Das et al ., 2024 ; Liu . et al ., 2024 b), with little research directly evaluating their. TSF performance in end-to-end training.foundation model for time - series forecasting . In Forty"}
+{"idx": 4, "title": "TSKANMixer: Kolmogorov–Arnold Networks with MLP-Mixer Model for...", "date": "", "ddg_snippet": "Xu et al . (Xu, Chen, and Wang 2024 ) in-vestigated the use of KANs for time series forecasting and demonstrated that two KAN models significantly out-performed traditional forecasting methods.", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/a0/21/bd791012491fa94f79cd7fd26dae/tskanmixer-kolmogorov-arnold-networks-with-mlp-mixer-model-for-time-series-forecasting.pdf", "content": "Xu et al . (Xu, Chen, and Wang 2024 ) in-vestigated the use of KANs for time series forecasting and demonstrated that two KAN models significantly out-performed traditional forecasting methods."}
+{"idx": 5, "title": "Machine Learning Forecasting of Time Series - Train in Data's Blog", "date": "", "ddg_snippet": "Discover how to implement machine learning forecasting of time series data with Python, by using recursive and direct forecasting .", "subpage_snippet": "", "source": "www.blog.trainindata.com", "link": "https://www.blog.trainindata.com/machine-learning-forecasting/", "content": "Discover how to implement machine learning forecasting of time series data with Python, by using recursive and direct forecasting ."}
+{"idx": 6, "title": "Approaching Human-Level Forecasting with Language Models", "date": "", "ddg_snippet": "Time -LLM: Time series forecasting by reprogramming large language models .", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/arxiv/2402.18563/paper", "content": "Time -LLM: Time series forecasting by reprogramming large language models ."}
+{"idx": 7, "title": "Two new time -variant methods for fuzzy time series forecasting (2013)", "date": "", "ddg_snippet": "TL;DR: A fuzzy time series based multi-step-ahead forecasting model is established and the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy are established.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/two-new-time-variant-methods-for-fuzzy-time-series-1j138kvf7j", "content": "TL;DR: A fuzzy time series based multi-step-ahead forecasting model is established and the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy are established."}
+{"idx": 8, "title": "[PDF] Deep Transformer Models for Time Series Forecasting : The...", "date": "", "ddg_snippet": "Deep Time Series Forecasting Models : A Comprehensive Survey.This study explores multivariate time series forecasting , centering on the transformer model , and introduces a novel approach that employs the transformer’s architecture for effective feature selection in time series data.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Deep-Transformer-Models-for-Time-Series-The-Case-Wu-Green/f5a28db512357b700b62fb655ef4a90864e2fe7e", "content": "Deep Time Series Forecasting Models : A Comprehensive Survey.This study explores multivariate time series forecasting , centering on the transformer model , and introduces a novel approach that employs the transformer’s architecture for effective feature selection in time series data."}
+{"idx": 9, "title": "Multivariate Timeseries Forecast with Lead and Lag... | Medium", "date": "", "ddg_snippet": "Time series forecast plays a critical role in taking decisions in most industries.This is when the multivariate timeseries forecast comes into picture. Let us understand the multivariate forecast using below images. Figure 1: Multivariate Timeseries Forecast with lag data (lag=5 steps).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/towards-data-science/multivariate-timeseries-forecast-with-lead-and-lag-timesteps-using-lstm-1a34915f08a", "content": "Time series forecast plays a critical role in taking decisions in most industries.This is when the multivariate timeseries forecast comes into picture. Let us understand the multivariate forecast using below images. Figure 1: Multivariate Timeseries Forecast with lag data (lag=5 steps)."}
diff --git a/data/sampled_jsons/CRAB_arxiv_2407.01511_abstract_multimodal_language_model_agents.jsonl b/data/sampled_jsons/CRAB_arxiv_2407.01511_abstract_multimodal_language_model_agents.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..261ef08a53f77f30f3231dec9ae23538e1e60ce6
--- /dev/null
+++ b/data/sampled_jsons/CRAB_arxiv_2407.01511_abstract_multimodal_language_model_agents.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ... GitHub - camel-ai/crab: ️ CRAB: Cross-environment Agent ... CRAB: Cross-environment Agent Benchmark for Multimodal ... CRAB: Cross-Environment Agent Benchmark Introducing CRAB: A New Benchmark for Language Models CRAB: Cross-environment Agent Benchmark for Multimodal ... CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "Jul 1, 2024 · Abstract page for arXiv paper 2407.01511 : CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB : Cross-platform Agent Benchmark for Multimodal Embodied Language Model Agents Sep 15, 2025 · Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Abstract : The development of autonomous agents increasingly relies on Multimodal LLMs (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ... Jul 21, 2025 · Original Source Title: CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation. The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as web- sites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "Jul 1, 2024 · Abstract page for arXiv paper 2407.01511 : CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB : Cross-platform Agent Benchmark for Multimodal Embodied Language Model Agents Sep 15, 2025 · Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Abstract : The development of autonomous agents increasingly relies on Multimodal LLMs (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ... Jul 21, 2025 · Original Source Title: CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation. The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as web- sites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ..."}
+{"idx": 1, "title": "GitHub - camel-ai/crab: ️ CRAB: Cross-environment Agent ...", "date": "", "ddg_snippet": "🦀 CRAB : Cross-platform Agent Benchmark for Multimodal Embodied Language Model Agents", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/camel-ai/crab", "content": "🦀 CRAB : Cross-platform Agent Benchmark for Multimodal Embodied Language Model Agents"}
+{"idx": 2, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.findings-acl.1113/", "content": "Sep 15, 2025 · Abstract The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones."}
+{"idx": 3, "title": "CRAB: Cross-Environment Agent Benchmark", "date": "", "ddg_snippet": "Abstract : The development of autonomous agents increasingly relies on Multimodal LLMs (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2407.01511", "content": "Abstract : The development of autonomous agents increasingly relies on Multimodal LLMs (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ..."}
+{"idx": 4, "title": "Introducing CRAB: A New Benchmark for Language Models", "date": "", "ddg_snippet": "Jul 21, 2025 · Original Source Title: CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-21-introducing-crab-a-new-benchmark-for-language-models--a3184y8", "content": "Jul 21, 2025 · Original Source Title: CRAB : Cross-environment Agent Benchmark for Multimodal Language Model Agents Abstract : The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as websites, desktop computers, or mobile phones."}
+{"idx": 5, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation.", "subpage_snippet": "", "source": "crab.camel-ai.org", "link": "https://crab.camel-ai.org/", "content": "CRAB aims to become a general-purpose agent benchmark framework for Multimodal Language Model (MLM) agents . CRAB provides an end-to-end while easy-to-use framework to build agents , operate environments, and create benchmarks to evaluate them, featuring three key components: cross-environment support, a graph evaluator, and task generation."}
+{"idx": 6, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal ...", "date": "", "ddg_snippet": "The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as web- sites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2407.01511", "content": "The development of autonomous agents increasingly relies on Multimodal Language Models (MLMs) to perform tasks described in natural language with GUI environments, such as web- sites, desktop computers, or mobile phones. Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the ..."}
+{"idx": 7, "title": "CRAB: Cross-environment Agent Benchmark for Multimodal Language", "date": "", "ddg_snippet": "With the rapid development of multimodal large language models (MLLM), many agents capable of operating graphical user interfaces (GUIs) have emerged ...", "subpage_snippet": "", "source": "www.camel-ai.org", "link": "https://www.camel-ai.org/blogs/crab-cross-platform-agent-benchmark", "content": "With the rapid development of multimodal large language models (MLLM), many agents capable of operating graphical user interfaces (GUIs) have emerged ..."}
+{"idx": 8, "title": "COMMA: A Communicative Multimodal Multi-Agent Benchmark", "date": "", "ddg_snippet": "The rapid advances of multimodal agents built on large foundation models have largely overlooked their potential for language -based communication ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v3", "content": "The rapid advances of multimodal agents built on large foundation models have largely overlooked their potential for language -based communication ..."}
+{"idx": 9, "title": "ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat", "date": "", "ddg_snippet": "... advancement of Large Language Models (LLMs) has enabled astonishing achievements in complex tasks [ 54 , 14 , 45 , 47 , 46 ] , that LLM agents can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14201v1", "content": "... advancement of Large Language Models (LLMs) has enabled astonishing achievements in complex tasks [ 54 , 14 , 45 , 47 , 46 ] , that LLM agents can ..."}
diff --git a/data/sampled_jsons/CVE-Bench_arxiv2503.17332_T-Agent_results_table_figure_success_rates_year_2025.jsonl b/data/sampled_jsons/CVE-Bench_arxiv2503.17332_T-Agent_results_table_figure_success_rates_year_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f777f62fe3641c9f6692c7a7e8bcda3c10fb0d1c
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_arxiv2503.17332_T-Agent_results_table_figure_success_rates_year_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World...", "date": "", "ddg_snippet": "arXiv : 2503 . 17332 v4 [cs.CR] 24 Jun 2025. Result ( success ). status: True attack: File access. Figure 1. Illustration of the sandbox framework in CVE - Bench as applied to a WordPress web application.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "arXiv : 2503 . 17332 v4 [cs.CR] 24 Jun 2025. Result ( success ). status: True attack: File access. Figure 1. Illustration of the sandbox framework in CVE - Bench as applied to a WordPress web application."}
+{"idx": 1, "title": "CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit... | alphaXiv", "date": "", "ddg_snippet": "CVE - Bench Framework Figure 1: Overview of the CVE - Bench framework showing how AI agents interact with vulnerable web applications in target containers and are evaluated based on standardized attack vectors.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332v1", "content": "CVE - Bench Framework Figure 1: Overview of the CVE - Bench framework showing how AI agents interact with vulnerable web applications in target containers and are evaluated based on standardized attack vectors."}
+{"idx": 2, "title": "uiuc-kang-lab/ cve - bench : CVE - Bench : A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "arxiv .org/abs/ 2503 . 17332 . License. CVE - Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests. For each CVE, given a target web application and necessary information, an AI agent is tasked with...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "arxiv .org/abs/ 2503 . 17332 . License. CVE - Bench includes 40 critical-severity Common Vulnerability and Exposures (CVE) with the reference automatic exploits available on requests. For each CVE, given a target web application and necessary information, an AI agent is tasked with..."}
+{"idx": 3, "title": "uiuc-kang-lab cve - bench issues - Githubissues", "date": "", "ddg_snippet": "uiuc-kang-lab / cve - bench . CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities .Can you disclose how to reproduce the results with Auto-GPT on CVE - Bench .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/uiuc-kang-lab/cve-bench", "content": "uiuc-kang-lab / cve - bench . CVE - Bench : A Benchmark for AI Agents ’ Ability to Exploit Real-World Web Application Vulnerabilities .Can you disclose how to reproduce the results with Auto-GPT on CVE - Bench ."}
+{"idx": 4, "title": "Show HN: CVE - Bench , the first LLM benchmark using real-world web...", "date": "", "ddg_snippet": "To our knowledge CVE - bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents ' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0).", "subpage_snippet": "", "source": "www.gpt-5.com", "link": "https://www.gpt-5.com/80434572/show-hn-cve-bench-the-first-llm-benchmark-using-real-world-web-vulnerabilities", "content": "To our knowledge CVE - bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents ' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0)."}
+{"idx": 5, "title": "BountyBench: The Design of Environments and Rewards for...", "date": "", "ddg_snippet": "Figure 1: BountyBench consists of Detect, Exploit, and Patch tasks, which each pass a distinct task input to the agent . Table 1: For each agent , we display the Success Rate per task.", "subpage_snippet": "", "source": "cs224r.stanford.edu", "link": "https://cs224r.stanford.edu/projects/pdfs/CS224R_Final_Report+(1)1.pdf", "content": "Figure 1: BountyBench consists of Detect, Exploit, and Patch tasks, which each pass a distinct task input to the agent . Table 1: For each agent , we display the Success Rate per task."}
+{"idx": 6, "title": "(PDF) Agent Identity Evals: Measuring Agentic Identity", "date": "", "ddg_snippet": "Cve - bench : A benchmark for ai agents ’ ability to. exploit real-world web application vulnerabilities . arXiv preprint arXiv : 2503 . 17332 , 2025.Output. Task success rate across 8 interactive environments. (commercial LLMs vs. OSS). GAIA [31] Tool Invocation & Results ; Final.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/393965533_Agent_Identity_Evals_Measuring_Agentic_Identity", "content": "Cve - bench : A benchmark for ai agents ’ ability to. exploit real-world web application vulnerabilities . arXiv preprint arXiv : 2503 . 17332 , 2025.Output. Task success rate across 8 interactive environments. (commercial LLMs vs. OSS). GAIA [31] Tool Invocation & Results ; Final."}
+{"idx": 7, "title": "Revolutionizing Cybersecurity: How AI Accelerates Exploit...", "date": "", "ddg_snippet": "1. Introduction: The Shifting Dynamics of Vulnerability Exploitation The cybersecurity industry faces relentless pressure, defined by a continuous race between identifying and patching vulnerabilities and exploiting them.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/revolutionizing-cybersecurity-how-ai-accelerates-exploit-merton-aseqc", "content": "1. Introduction: The Shifting Dynamics of Vulnerability Exploitation The cybersecurity industry faces relentless pressure, defined by a continuous race between identifying and patching vulnerabilities and exploiting them."}
+{"idx": 8, "title": "Richard Fang - Google Scholar | University of Illinois - Cited by 275", "date": "", "ddg_snippet": "5. CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World Web Application Vulnerabilities . Y Zhu, A Kellermann, D Bowman, P Li, A Gupta, A Danda, R Fang, ... arXiv preprint arXiv : 2503 . 17332 , 2025.", "subpage_snippet": "", "source": "scholar.google.co.in", "link": "https://scholar.google.co.in/citations?user=l7Ra9p8AAAAJ&hl=en", "content": "5. CVE - Bench : A Benchmark for AI Agents ' Ability to Exploit Real-World Web Application Vulnerabilities . Y Zhu, A Kellermann, D Bowman, P Li, A Gupta, A Danda, R Fang, ... arXiv preprint arXiv : 2503 . 17332 , 2025."}
+{"idx": 9, "title": "Мечты робота – Telegram", "date": "", "ddg_snippet": "...понимающую геолокации и оживляющую GPS-координаты рассказами о местных достопримечательностях: http:// arxiv .org/abs/2503.16423v1 Второй захватывающий проект, OpenVLThinker, развивает комплексные способности рассуждения у моделей...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/robodream/773", "content": "...понимающую геолокации и оживляющую GPS-координаты рассказами о местных достопримечательностях: http:// arxiv .org/abs/2503.16423v1 Второй захватывающий проект, OpenVLThinker, развивает комплексные способности рассуждения у моделей..."}
diff --git a/data/sampled_jsons/CVE-Bench_paper_'Insufficient_Exploration'_failure_mode_description.jsonl b/data/sampled_jsons/CVE-Bench_paper_'Insufficient_Exploration'_failure_mode_description.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5758f3062a3bbed7e9c999c36e039d4f9330cf2e
--- /dev/null
+++ b/data/sampled_jsons/CVE-Bench_paper_'Insufficient_Exploration'_failure_mode_description.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - uiuc-kang-lab/cve-bench: CVE-Bench: A Benchmark for AI Agents ...", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench", "content": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero-day setting, we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "CVE-Bench is designed to simulate different stages in a vulnerability lifecycle. Under the zero-day setting, we only provide the LLM agents with task descriptions The agents must independently identify the vulnerability and execute a successful attack."}
+{"idx": 2, "title": "PDF CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability ...", "date": "", "ddg_snippet": "In this paper , we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent-based evaluation by offering real-world interactive execution-guided programming environments .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.212.pdf", "content": "In this paper , we introduce CVE-Bench (§2), a benchmark that evaluates LLM-based agents in a realisticvulnerability-repairingsetting. CVE-Bench contains three unique characteristics: (1) Instead of input-output evaluation, CVE-Bench supports agent-based evaluation by offering real-world interactive execution-guided programming environments ."}
+{"idx": 3, "title": "[2503.17332] CVE-Bench: A Benchmark for AI Agents' Ability to Exploit ...", "date": "", "ddg_snippet": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.17332", "content": "Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the ..."}
+{"idx": 4, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "Abstract Large language model (LLM) agents are increas-ingly capable of autonomously conducting cy-berattacks, posing significant threats to existing applications. This growing risk highlights the ur-gent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web appli-cation vulnerabilities. However, existing bench -marks fall short as they are limited to abstracted ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17332", "content": "Abstract Large language model (LLM) agents are increas-ingly capable of autonomously conducting cy-berattacks, posing significant threats to existing applications. This growing risk highlights the ur-gent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web appli-cation vulnerabilities. However, existing bench -marks fall short as they are limited to abstracted ..."}
+{"idx": 5, "title": "CVE: Common Vulnerabilities and Exposures", "date": "", "ddg_snippet": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures", "subpage_snippet": "", "source": "www.cve.org", "link": "https://www.cve.org/", "content": "At cve .org, we provide the authoritative reference method for publicly known information-security vulnerabilities and exposures"}
+{"idx": 6, "title": "uiuc-kang-lab/cve-bench | DeepWiki", "date": "", "ddg_snippet": "Overview Relevant source files CVE-Bench is a comprehensive benchmark designed to evaluate AI agents' capabilities in exploiting real-world web application vulnerabilities. This document provides a high-level introduction to the CVE-Bench repository structure, architecture, and core components. For detailed setup instructions, see Getting Started.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/uiuc-kang-lab/cve-bench/1-overview", "content": "Overview Relevant source files CVE-Bench is a comprehensive benchmark designed to evaluate AI agents' capabilities in exploiting real-world web application vulnerabilities. This document provides a high-level introduction to the CVE-Bench repository structure, architecture, and core components. For detailed setup instructions, see Getting Started."}
+{"idx": 7, "title": "cve-bench/README.md at main · uiuc-kang-lab/cve-bench · GitHub", "date": "", "ddg_snippet": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/uiuc-kang-lab/cve-bench/blob/main/README.md", "content": "This repository contains data and code used in the CVE-Bench ( paper , blog), which is for evaluating AI agents on real world web vulnerabilities and exploits collected from National Vulnerability Database. CVE-Bench includes 40 critical-severity Common Vulnerability and Exposures ( CVE ) with the reference automatic exploits available on requests."}
+{"idx": 8, "title": "CVE security vulnerability database. Security vulnerabilities, exploits ...", "date": "", "ddg_snippet": "CVEDetails.com is a vulnerability intelligence solution providing CVE security vulnerability database, exploits, advisories, product and CVE risk scores, attack surface intelligence, open source vulnerabilities, code changes, vulnerabilities affecting your attack surface and software inventory/tech stack. You can view CVE vulnerability details, exploits, references, metasploit modules, full ...", "subpage_snippet": "", "source": "www.cvedetails.com", "link": "https://www.cvedetails.com/", "content": "CVEDetails.com is a vulnerability intelligence solution providing CVE security vulnerability database, exploits, advisories, product and CVE risk scores, attack surface intelligence, open source vulnerabilities, code changes, vulnerabilities affecting your attack surface and software inventory/tech stack. You can view CVE vulnerability details, exploits, references, metasploit modules, full ..."}
+{"idx": 9, "title": "CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web ...", "date": "", "ddg_snippet": "To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.17332v4", "content": "To address this challenge, we introduce CVE-Bench , a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. In CVE-Bench , we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective ..."}
diff --git a/data/sampled_jsons/CVPR_2025_DART_paper_Table_2_F1_score_MIMIC-CXR.jsonl b/data/sampled_jsons/CVPR_2025_DART_paper_Table_2_F1_score_MIMIC-CXR.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..21c62305975432462a2240bd2d141d89615a8599
--- /dev/null
+++ b/data/sampled_jsons/CVPR_2025_DART_paper_Table_2_F1_score_MIMIC-CXR.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "Table 2 presents a comparison of the clinical eficacy (CE) metrics between our proposed framework and state-of-the-art meth-ods on the MIMIC-CXR dataset, evaluated by F1 score , pre-cision, and recall.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "Table 2 presents a comparison of the clinical eficacy (CE) metrics between our proposed framework and state-of-the-art meth-ods on the MIMIC-CXR dataset, evaluated by F1 score , pre-cision, and recall."}
+{"idx": 1, "title": "GitHub - mk-runner/MLRG: [CVPR'25] Enhanced Contrastive Learning with ...", "date": "", "ddg_snippet": "MIMIC-CXR / MIMIC -ABN — PhysioNet, with data systematically organized under root directories labeled p10 through p19, maintaining consistency with MIMIC-CXR's default configuration.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/MLRG", "content": "MIMIC-CXR / MIMIC -ABN — PhysioNet, with data systematically organized under root directories labeled p10 through p19, maintaining consistency with MIMIC-CXR's default configuration."}
+{"idx": 2, "title": "Mimic-cxr: a Large Publicly Available Database of Labeled Chest Radiographs", "date": "", "ddg_snippet": "Table 2 : Frequency of labels in MIMIC-CXR on the training subset of 369,188 images, corresponding to 222,952 unique radiologic studies. ... negative. The harmonic mean of the sensitivity and positive predictive value, referred to as the F1 score , was calculated for each group independently. Table 3 shows the performance across labels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1901.07042v1", "content": "Table 2 : Frequency of labels in MIMIC-CXR on the training subset of 369,188 images, corresponding to 222,952 unique radiologic studies. ... negative. The harmonic mean of the sensitivity and positive predictive value, referred to as the F1 score , was calculated for each group independently. Table 3 shows the performance across labels."}
+{"idx": 3, "title": "CVPR 2025 Accepted Paper List - Paper Copilot", "date": "", "ddg_snippet": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries.", "subpage_snippet": "", "source": "papercopilot.com", "link": "https://papercopilot.com/paper-list/cvpr-paper-list/cvpr-2025-paper-list/", "content": "How to use the paper list below: - Overview: This table presents papers from the CVPR conference, year 2025 . - Filtering: By default, the table loads the first 100 records. You can use the filter box under each column header to search within these loaded entries."}
+{"idx": 4, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
+{"idx": 5, "title": "GitHub - mk-runner/Awesome-Radiology-Report-Generation: paper list ...", "date": "", "ddg_snippet": "MIMIC - CXR -JPG, a large publicly available database of labeled chest radiographs ( MIMIC-CXR ) [ paper ] [data]. Preparing a collection of radiology examinations for distribution and retrieval (IU X-ray) [ paper ] [data].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "MIMIC - CXR -JPG, a large publicly available database of labeled chest radiographs ( MIMIC-CXR ) [ paper ] [data]. Preparing a collection of radiology examinations for distribution and retrieval (IU X-ray) [ paper ] [data]."}
+{"idx": 6, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "Table 2 : A comparison of the clinical efficacy (CE) metrics between our proposed framework (Ours) and state-of-the-art methods using F1 score , precision, and recall on the MIMIC-CXR dataset. Also, we evaluate the disease classifier performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "Table 2 : A comparison of the clinical efficacy (CE) metrics between our proposed framework (Ours) and state-of-the-art methods using F1 score , precision, and recall on the MIMIC-CXR dataset. Also, we evaluate the disease classifier performance."}
+{"idx": 7, "title": "MIMIC-CXR: Chest X-ray Image Classification and Report Generation", "date": "", "ddg_snippet": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yuanditang/MIMIC-CXR", "content": "This repository contains the implementation and results of the research paper \" MIMIC-CXR : Chest X-ray Image Classification and Report Generation\". The project leverages the MIMIC-CXR dataset, which includes chest X-ray images along with corresponding radiology reports."}
+{"idx": 8, "title": "CVPR Poster Enhanced Contrastive Learning with Multi-view Longitudinal ...", "date": "", "ddg_snippet": "Extensive experiments on MIMIC-CXR , MIMIC -ABN, and Two-view CXR datasets demonstrate that our MLRG outperforms recent state-of-the-art methods, achieving a 2.3\\% BLEU-4 improvement on MIMIC-CXR , a 5.5\\% F1 score improvement on MIMIC -ABN, and a 2.7\\% F1 RadGraph improvement on Two-view CXR .", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/34086", "content": "Extensive experiments on MIMIC-CXR , MIMIC -ABN, and Two-view CXR datasets demonstrate that our MLRG outperforms recent state-of-the-art methods, achieving a 2.3\\% BLEU-4 improvement on MIMIC-CXR , a 5.5\\% F1 score improvement on MIMIC -ABN, and a 2.7\\% F1 RadGraph improvement on Two-view CXR ."}
+{"idx": 9, "title": "CVPR Poster DART: Disease-aware Image-Text Alignment and Self ...", "date": "", "ddg_snippet": "Our proposed framework achieves state-of-the-art results on the MIMIC-CXR and IU X-ray benchmarks, surpassing previous approaches in both report generation and disease classification, thereby enhancing the trustworthiness of radiology reports.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32986", "content": "Our proposed framework achieves state-of-the-art results on the MIMIC-CXR and IU X-ray benchmarks, surpassing previous approaches in both report generation and disease classification, thereby enhancing the trustworthiness of radiology reports."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_Heavy-tailed_Rewards_Theorem_3.1_Theorem_3.4_regret_bounds.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_Heavy-tailed_Rewards_Theorem_3.1_Theorem_3.4_regret_bounds.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Catoni Contextual Bandits are Robust to Heavy-tailed Rewards ICML Poster Catoni Contextual Bandits are Robust to Heavy ... Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Stochastic Graphical Bandits with Heavy-Tailed Rewards ϵ,u -Adaptive Regret Minimization in Heavy-Tailed Bandit", "date": "", "ddg_snippet": "Feb 4 , 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ... To settle this issue, we investigate stochastic graph-ical bandits with heavy-tailed rewards , where the distributions have finite moments of order 1 + , for some 2 (0; 1]. Firstly, we develop one UCB-type algorithm, whose expected regret is upper bounded by a sum of gap-based quantities over the clique covering of the feedback graph. Abstract Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely studied, learning with heavy-tailed distributions only gained popularity over the last decade. In this paper, we consider the setting in which the reward distributions have finite absolute raw moments of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Feb 4 , 2025 · However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation. This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ... To settle this issue, we investigate stochastic graph-ical bandits with heavy-tailed rewards , where the distributions have finite moments of order 1 + , for some 2 (0; 1]. Firstly, we develop one UCB-type algorithm, whose expected regret is upper bounded by a sum of gap-based quantities over the clique covering of the feedback graph. Abstract Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely studied, learning with heavy-tailed distributions only gained popularity over the last decade. In this paper, we consider the setting in which the reward distributions have finite absolute raw moments of ..."}
+{"idx": 1, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 3, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy ...", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni 's estimator from robust statistics, and apply it to contextual bandits with general function approximation."}
+{"idx": 4, "title": "Stochastic Graphical Bandits with Heavy-Tailed Rewards", "date": "", "ddg_snippet": "To settle this issue, we investigate stochastic graph-ical bandits with heavy-tailed rewards , where the distributions have finite moments of order 1 + , for some 2 (0; 1]. Firstly, we develop one UCB-type algorithm, whose expected regret is upper bounded by a sum of gap-based quantities over the clique covering of the feedback graph.", "subpage_snippet": "", "source": "ai.nju.edu.cn", "link": "https://ai.nju.edu.cn/zlj/pdf/UAI-2023-Gou.pdf", "content": "To settle this issue, we investigate stochastic graph-ical bandits with heavy-tailed rewards , where the distributions have finite moments of order 1 + , for some 2 (0; 1]. Firstly, we develop one UCB-type algorithm, whose expected regret is upper bounded by a sum of gap-based quantities over the clique covering of the feedback graph."}
+{"idx": 5, "title": "ϵ,u -Adaptive Regret Minimization in Heavy-Tailed Bandit", "date": "", "ddg_snippet": "Abstract Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely studied, learning with heavy-tailed distributions only gained popularity over the last decade. In this paper, we consider the setting in which the reward distributions have finite absolute raw moments of ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v247/genalti24a/genalti24a.pdf", "content": "Abstract Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely studied, learning with heavy-tailed distributions only gained popularity over the last decade. In this paper, we consider the setting in which the reward distributions have finite absolute raw moments of ..."}
+{"idx": 6, "title": "Heavy-Tailed Linear Bandits: Huber Regression with One-Pass", "date": "", "ddg_snippet": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.00419v1", "content": "Table 1 : Comparisons of our regret bounds and computational complexity to previous best-known results for heavy - tailed linear bandits ."}
+{"idx": 7, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 8, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One -shot Graph Generators ... Versatile Dueling Bandits : Best-of-both ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One -shot Graph Generators ... Versatile Dueling Bandits : Best-of-both ..."}
+{"idx": 9, "title": "Extended UCB Policies for Multi-Armed Bandit Problems", "date": "", "ddg_snippet": "... 4 p= 4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q > 1 as long as the two moments have a known controlled relationship, while ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1112.1768v5", "content": "... 4 p= 4 and q = 2 q=2 ) to arbitrarily chosen p > q > 1 p > q > 1 as long as the two moments have a known controlled relationship, while ..."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_OFUL_implementation_difficult_conclusion.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_OFUL_implementation_difficult_conclusion.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e9e54b32db6b0c2d4096683e874ec0df3dd43d2
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_OFUL_implementation_difficult_conclusion.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
+{"idx": 1, "title": "PDF Lecture 4: Contextual Bandits - UMass", "date": "", "ddg_snippet": "The protocol is known as contextual bandits . The main new feature is that on each round the learner observes some \\ contextual information\" which it may use to inform its choice of action.", "subpage_snippet": "", "source": "people.cs.umass.edu", "link": "https://people.cs.umass.edu/~akshay/courses/coms6998-11/files/lec4.pdf", "content": "The protocol is known as contextual bandits . The main new feature is that on each round the learner observes some \\ contextual information\" which it may use to inform its choice of action."}
+{"idx": 2, "title": "PDF Contextual Bandits", "date": "", "ddg_snippet": "Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt", "subpage_snippet": "", "source": "lucasjanson.fas.harvard.edu", "link": "https://lucasjanson.fas.harvard.edu/courses/22.pdf", "content": "Recall: Contextual bandit environment Context at time t encoded into a variable xt that we see before choosing our action xt is drawn i.i.d. at each time point from a distribution νx on sample space xt"}
+{"idx": 3, "title": "PDF A Contextual Bandit Bake-o - Journal of Machine Learning Research", "date": "", "ddg_snippet": "1. Introduction At a practical level, how should contextual bandit learning and exploration be done? In the contextual bandit problem, a learner repeatedly observes a context, chooses an action, and observes a loss for the chosen action only. Many real-world interactive machine learning tasks are well-suited to this setting: a movie recommendation system selects a movie for a given user and ...", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume22/18-863/18-863.pdf", "content": "1. Introduction At a practical level, how should contextual bandit learning and exploration be done? In the contextual bandit problem, a learner repeatedly observes a context, chooses an action, and observes a loss for the chosen action only. Many real-world interactive machine learning tasks are well-suited to this setting: a movie recommendation system selects a movie for a given user and ..."}
+{"idx": 4, "title": "ICML Poster Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This makes it difficult for standard learning algorithms to make good decisions, since they are designed assuming rewards are relatively well-behaved and bounded.We tackle this problem by designing new algorithms for contextual bandits , a type of learning model that helps an agent choose actions based on observed data.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "This makes it difficult for standard learning algorithms to make good decisions, since they are designed assuming rewards are relatively well-behaved and bounded.We tackle this problem by designing new algorithms for contextual bandits , a type of learning model that helps an agent choose actions based on observed data."}
+{"idx": 5, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 6, "title": "Offline Contextual Bandit: Theory and Large Scale Applications", "date": "", "ddg_snippet": "The offline contextual bandit setting is particularly interesting for industrial applications. It provides more control to practitioners, as they can evaluate and learn new policies, and fully decideonwhethertodeploythemonlineornot.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-04417936v1/document", "content": "The offline contextual bandit setting is particularly interesting for industrial applications. It provides more control to practitioners, as they can evaluate and learn new policies, and fully decideonwhethertodeploythemonlineornot."}
+{"idx": 7, "title": "Taking a hint: How to leverage loss predictors in contextual bandits?", "date": "", "ddg_snippet": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni's robust mean ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10207168", "content": "3 ) is achievable; 3) with M predictors, a linear dependence on M is necessary, even though logarithmic dependence is possible for non- contextual problems. We also develop several novel algorithmic techniques to achieve matching upper bounds, in-cluding 1) a key action remapping technique for optimal regret with known E, 2) computationally efficient implementation of Catoni's robust mean ..."}
+{"idx": 8, "title": "PDF Improving Ofline Contextual Bandits with Distributional Robustness", "date": "", "ddg_snippet": "Improving Ofline Contextual Bandits with Distributional Robustness OTMANE SAKHI∗, LOUIS FAURY∗, Criteo AI Lab, France. and FLAVIAN VASILE,", "subpage_snippet": "", "source": "louisfaury.github.io", "link": "https://louisfaury.github.io/_papers/dro_reveal.pdf", "content": "Improving Ofline Contextual Bandits with Distributional Robustness OTMANE SAKHI∗, LOUIS FAURY∗, Criteo AI Lab, France. and FLAVIAN VASILE,"}
+{"idx": 9, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025", "date": "", "ddg_snippet": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range [0, R], and their regret scales polynomially with this reward range R. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this paper, we develop an algorithmic approach building on Catoni ..."}
diff --git a/data/sampled_jsons/Catoni_Contextual_Bandits_Table_1_comparison_algorithms_stochastic_contextual_bandits.jsonl b/data/sampled_jsons/Catoni_Contextual_Bandits_Table_1_comparison_algorithms_stochastic_contextual_bandits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e5b0d8241318c53712366b387d83e3749f08b242
--- /dev/null
+++ b/data/sampled_jsons/Catoni_Contextual_Bandits_Table_1_comparison_algorithms_stochastic_contextual_bandits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract arXiv:2502.02486v1 [stat.ML] 4 Feb 2025 Catoni Cont", "date": "", "ddg_snippet": "Table 1 : Comparison between different algorithms for stochastic contextual bandits , where d denotes the dimension for linear function approximation, dF, ̃dF capture the complexity of the function space for reward estimation, T is the number of rounds, σt is the variance of the observed reward at round F used t, σ is a uniform bound on reward ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486", "content": "Table 1 : Comparison between different algorithms for stochastic contextual bandits , where d denotes the dimension for linear function approximation, dF, ̃dF capture the complexity of the function space for reward estimation, T is the number of rounds, σt is the variance of the observed reward at round F used t, σ is a uniform bound on reward ..."}
+{"idx": 1, "title": "Contexts can be Cheap: Solving Stochastic Contextual Bandits ...", "date": "", "ddg_snippet": "We show in this paper the surprising result that, provided the context comes from a distribution D ( stochastic context ), contextual linear bandit problems can be reduced to solving (single context ) √ linear bandit problem when the context distribution D is known, and to linear bandits with ̃O( 1 / T ) misspecification when the distribution D is unknown. These results are presented in the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v195/hanna23a/hanna23a.pdf", "content": "We show in this paper the surprising result that, provided the context comes from a distribution D ( stochastic context ), contextual linear bandit problems can be reduced to solving (single context ) √ linear bandit problem when the context distribution D is known, and to linear bandits with ̃O( 1 / T ) misspecification when the distribution D is unknown. These results are presented in the ..."}
+{"idx": 2, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "The Goal • : obtain performance guarantees depending on the reward vari-ance, not the worst-case range Table : Comparison between different algorithms for stochastic contextual", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46438.pdf", "content": "The Goal • : obtain performance guarantees depending on the reward vari-ance, not the worst-case range Table : Comparison between different algorithms for stochastic contextual"}
+{"idx": 3, "title": "Contextual Bandits with Stochastic Experts Catoni Contextual Bandits are Robust to Heavy-tailed Rewards Optimal Algorithms for Stochastic Contextual Preference Bandits Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "A Tier 1 USDOT University Transportation Center at The University of Texas at Austin D-STOP is a collaborative initiative by researchers at the Center for Transportation Research and the Wireless Networking and Communications Group at The University of Texas at Austin. See full list on ctr.utexas.edu The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation’s University Transportation Centers Program, in the interest of information exchange. The U.S. Government a... See full list on ctr.utexas.edu The authors recognize that support for this research was provided by a grant from the U.S. Department of Transportation, University Transportation Centers. See full list on ctr.utexas.edu Abstract We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We propose upper-con denc... See full list on ctr.utexas.edu In this section, we propose a general upper-con dence bound (UCB) style strategy that utilizes the structure of the problem to converge to the best expert much faster than a naive UCB strategy that treats each expert as an arm of the bandit problem. One of the key observations in this framework is that rewards collected under one expert can give us... See full list on ctr.utexas.edu In this section we de ne two estimators for estimating the mean rewards under a given expert. Both these estimators can e ectively leverage the information leakage between samples collected under various experts, through importance sampling. One key observation that enables us in doing so is the following equation, See full list on ctr.utexas.edu We study the problem of contextual bandits with stochastic experts. We propose two UCB style algorithms , that use two di erent importance sampling estimators, which can leverage information leakage between the stochastic experts. We provide instance-dependent regret guarantees for our UCB based algorithms . Our algorithms show strong empirical perfo... See full list on ctr.utexas.edu This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... 1 Introduction Sequential decision-making problems with side information (in the form of features or attributes), have been popular in machine learning as contextual bandits [16, 13, 26]. A contextual bandit learner, at each round, observes a context before taking action based on it. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "ctr.utexas.edu", "link": "https://ctr.utexas.edu/wp-content/uploads/146.pdf", "content": "A Tier 1 USDOT University Transportation Center at The University of Texas at Austin D-STOP is a collaborative initiative by researchers at the Center for Transportation Research and the Wireless Networking and Communications Group at The University of Texas at Austin. See full list on ctr.utexas.edu The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation’s University Transportation Centers Program, in the interest of information exchange. The U.S. Government a... See full list on ctr.utexas.edu The authors recognize that support for this research was provided by a grant from the U.S. Department of Transportation, University Transportation Centers. See full list on ctr.utexas.edu Abstract We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We propose upper-con denc... See full list on ctr.utexas.edu In this section, we propose a general upper-con dence bound (UCB) style strategy that utilizes the structure of the problem to converge to the best expert much faster than a naive UCB strategy that treats each expert as an arm of the bandit problem. One of the key observations in this framework is that rewards collected under one expert can give us... See full list on ctr.utexas.edu In this section we de ne two estimators for estimating the mean rewards under a given expert. Both these estimators can e ectively leverage the information leakage between samples collected under various experts, through importance sampling. One key observation that enables us in doing so is the following equation, See full list on ctr.utexas.edu We study the problem of contextual bandits with stochastic experts. We propose two UCB style algorithms , that use two di erent importance sampling estimators, which can leverage information leakage between the stochastic experts. We provide instance-dependent regret guarantees for our UCB based algorithms . Our algorithms show strong empirical perfo... See full list on ctr.utexas.edu This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ... 1 Introduction Sequential decision-making problems with side information (in the form of features or attributes), have been popular in machine learning as contextual bandits [16, 13, 26]. A contextual bandit learner, at each round, observes a context before taking action based on it. However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 4, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Catoni-Contextual-Bandits-are-Robust-to-Rewards-Ye-Jin/125154c306493f2c7af8e8c09c0e58c22106e6fe", "content": "This paper develops an algorithmic approach building on Catoni 's estimator from robust statistics, and applies it to contextual bandits with general function approximation and establishes a regret bound that depends only on the cumulative reward variance and logarithmically on the reward range as well as the number of rounds. Typical contextual bandit algorithms assume that the rewards at each ..."}
+{"idx": 5, "title": "Optimal Algorithms for Stochastic Contextual Preference Bandits", "date": "", "ddg_snippet": "1 Introduction Sequential decision-making problems with side information (in the form of features or attributes), have been popular in machine learning as contextual bandits [16, 13, 26]. A contextual bandit learner, at each round, observes a context before taking action based on it.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/fc3cf452d3da8402bebb765225ce8c0e-Paper.pdf", "content": "1 Introduction Sequential decision-making problems with side information (in the form of features or attributes), have been popular in machine learning as contextual bandits [16, 13, 26]. A contextual bandit learner, at each round, observes a context before taking action based on it."}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed Rewards", "date": "", "ddg_snippet": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=5IpVe9PH14", "content": "However, many practical scenarios naturally involve heavy-tailed rewards or rewards where the worst-case range can be substantially larger than the variance. In this pa-per, we develop an algorithmic approach building on Catoni ’s estimator from robust statistics, and apply it to contextual bandits with general func-tion approximation."}
+{"idx": 7, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — 1. Page 2. Table 1: Comparison between different algorithms for stochastic contextual bandits , where d denotes the dimension for linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — 1. Page 2. Table 1: Comparison between different algorithms for stochastic contextual bandits , where d denotes the dimension for linear ..."}
+{"idx": 8, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "In this paper, we study the design of contextual bandit algorithms that can leverage such structures to have regret guarantees dependent polynomially on the ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "In this paper, we study the design of contextual bandit algorithms that can leverage such structures to have regret guarantees dependent polynomially on the ..."}
+{"idx": 9, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "Most of the claims are well-supported. Extensive comparisons (see Table 1) with existing algorithms highlight the advantages of the proposed methods in heavy- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "Most of the claims are well-supported. Extensive comparisons (see Table 1) with existing algorithms highlight the advantages of the proposed methods in heavy- ..."}
diff --git a/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_correlation_engagement_participation_rich_subreddits_0_year_2024.jsonl b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_correlation_engagement_participation_rich_subreddits_0_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8783d8b023db103224fc9769db33db15050dca33
--- /dev/null
+++ b/data/sampled_jsons/Causal_Modeling_of_Climate_Activism_on_Reddit_correlation_engagement_participation_rich_subreddits_0_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Using Reddit to Understand Car-Free", "date": "", "ddg_snippet": "by E Iacobucci · 2021 · Cited by 1 — They interpret these results to suggest a causal role between a change in neighborhood and changes in travel behavior. ... Disentangling causation from ...", "subpage_snippet": "", "source": "rucore.libraries.rutgers.edu", "link": "https://rucore.libraries.rutgers.edu/rutgers-lib/66787/PDF/1/play/", "content": "by E Iacobucci · 2021 · Cited by 1 — They interpret these results to suggest a causal role between a change in neighborhood and changes in travel behavior. ... Disentangling causation from ..."}
+{"idx": 1, "title": "Understanding, Modeling and Leveraging Temporal ...", "date": "", "ddg_snippet": "Similarly, affiliation behaviors on Reddit have also been used to identify individuals with mental health conditions. Participation in subreddits such as r ...", "subpage_snippet": "", "source": "deepblue.lib.umich.edu", "link": "https://deepblue.lib.umich.edu/bitstream/handle/2027.42/177722/lbiester_1.pdf?sequence=1&isAllowed=y", "content": "Similarly, affiliation behaviors on Reddit have also been used to identify individuals with mental health conditions. Participation in subreddits such as r ..."}
+{"idx": 2, "title": "Reddit's self-organised bull runs: Social contagion and asset ...", "date": "", "ddg_snippet": "by V Semenova · 2021 · Cited by 27 — This paper develops an empirical and theoretical case for how 'hype' among retail investors can drive large asset fluctuations. 51 pages", "subpage_snippet": "", "source": "oms-inet.files.svdcdn.com", "link": "https://oms-inet.files.svdcdn.com/staging/files/Reddit_draft_v2.pdf", "content": "by V Semenova · 2021 · Cited by 27 — This paper develops an empirical and theoretical case for how 'hype' among retail investors can drive large asset fluctuations. 51 pages"}
+{"idx": 3, "title": "Participant behavior and community response in online ...", "date": "", "ddg_snippet": "by V Morini · 2025 · Cited by 2 — This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits , comprising over 3.4 million posts and 24 million comments from ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0747563224004126", "content": "by V Morini · 2025 · Cited by 2 — This study analyzed a large-scale, five-year Reddit dataset of 67 MH-related subreddits , comprising over 3.4 million posts and 24 million comments from ..."}
+{"idx": 4, "title": "A Synthetic Dataset for Personal Attribute Inference", "date": "", "ddg_snippet": "by H Yukhymenko · 2024 · Cited by 20 — We take two steps to address this problem: (i) we construct a simulation framework for the popular social media platform Reddit using LLM agents seeded with ... 45 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/daa1816b84ca2d5051c87fb4d37dd540-Paper-Datasets_and_Benchmarks_Track.pdf", "content": "by H Yukhymenko · 2024 · Cited by 20 — We take two steps to address this problem: (i) we construct a simulation framework for the popular social media platform Reddit using LLM agents seeded with ... 45 pages"}
+{"idx": 5, "title": "Exploring Suicide Factors in Online Discourse: Sentiment and ...", "date": "", "ddg_snippet": "by E Dan — This study aims to leverage NLP and statistical analysis of Reddit data to investigate the factors contributing to suicidal ideation. Specifically, our analysis ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3716546", "content": "by E Dan — This study aims to leverage NLP and statistical analysis of Reddit data to investigate the factors contributing to suicidal ideation. Specifically, our analysis ..."}
+{"idx": 6, "title": "Prevalence and Psychological Effects of Hateful Speech in ...", "date": "", "ddg_snippet": "by K Saha · 2019 · Cited by 238 — We find that hateful speech is prevalent in college subreddits , and 25% of them show greater hateful speech than non-college subreddits .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7500692/", "content": "by K Saha · 2019 · Cited by 238 — We find that hateful speech is prevalent in college subreddits , and 25% of them show greater hateful speech than non-college subreddits ."}
+{"idx": 7, "title": "Computer Science", "date": "", "ddg_snippet": "Using base models , we achieved a Spearman correlation of 0.8 with human ratings, which increased to 0.9 when employing fine-tuned models . This methodology ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs/new", "content": "Using base models , we achieved a Spearman correlation of 0.8 with human ratings, which increased to 0.9 when employing fine-tuned models . This methodology ..."}
+{"idx": 8, "title": "SOCIALLY-INFORMED CONTENT ANALYSIS OF ONLINE ...", "date": "", "ddg_snippet": "13 Sept 2025 — This research focuses on the role of social approval in driving online toxicity, suggesting that the pursuit of social approval, rather than a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.10807v1", "content": "13 Sept 2025 — This research focuses on the role of social approval in driving online toxicity, suggesting that the pursuit of social approval, rather than a ..."}
+{"idx": 9, "title": "NLP4PI 2025 Fourth Workshop on NLP for Positive Impact ...", "date": "", "ddg_snippet": "31 Jul 2025 — These papers cover diverse aspects of NLP for positive impact, including developing NLP technology to help applications like physical and mental ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/anthology-files/pdf/nlp4pi/2025.nlp4pi-1.pdf", "content": "31 Jul 2025 — These papers cover diverse aspects of NLP for positive impact, including developing NLP technology to help applications like physical and mental ..."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_self-supervis.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_self-supervis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..58b22004f4af33a5350b160d1f7d8f998248d9c3
--- /dev/null
+++ b/data/sampled_jsons/Checks-and-Balances_Framework_Context-Aware_Ethical_AI_Alignment_Section_3.2_four-step_self-supervis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI ... A Three-Branch Checks-and-Balances Framework for Context-Aware... An Adversarial Behavior Model for Contextual Ethical ... A Three-Branch Checks-and-Balances Frameworkfor Context-Aware ... A Three-Branch Checks-and-Balances Frameworkfor Context-Aware ... infolab.stanford.edu Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Oct 12, 2024 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ... Jan 31, 2025 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Conclusion This checks-and-balances approach offers a promising direction for building more ethically- aware AI systems. The framework 's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts. @article{chang2025threebranch, title={A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ... A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00136", "content": "Jan 31, 2025 · This paper introduces a checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by three-branch governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Oct 12, 2024 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence. Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ... Jan 31, 2025 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ... Conclusion This checks-and-balances approach offers a promising direction for building more ethically- aware AI systems. The framework 's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts. @article{chang2025threebranch, title={A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models}, author={Chang ... A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
+{"idx": 1, "title": "A Three-Branch Checks-and-Balances Framework for Context-Aware...", "date": "", "ddg_snippet": "Oct 12, 2024 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=o2afWIxjKD", "content": "Oct 12, 2024 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by the idea of collaborative intelligence."}
+{"idx": 2, "title": "An Adversarial Behavior Model for Contextual Ethical ...", "date": "", "ddg_snippet": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Edward-Chang-22/publication/380515639_A_Three-Branch_Checks-and-Balances_Framework_for_Context-Aware_Ethical_Alignment_of_Large_Language_Models/links/671b315b55a5271cded9457e/A-Three-Branch-Checks-and-Balances-Framework-for-Context-Aware-Ethical-Alignment-of-Large-Language-Models.pdf", "content": "Abstract This research introduces DIKE, a novel framework for aligning Large Language Models (LLMs) with human values through emotion-guided behavioral control. Inspired by the checks and balances ..."}
+{"idx": 3, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware ...", "date": "", "ddg_snippet": "Jan 31, 2025 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ...", "subpage_snippet": "", "source": "researchtrend.ai", "link": "https://researchtrend.ai/papers/2502.00136", "content": "Jan 31, 2025 · This paper introduces a three-branch checks-and-balances framework for ethical alignment of Large Language Models (LLMs), inspired by governmental systems. It implements three independent yet interacting components: LLMs as the executive branch for knowledge generation, DIKE as the legislative branch establishing ethical guardrails, and ERIS as the judicial branch for contextual interpretation ..."}
+{"idx": 4, "title": "A Three-Branch Checks-and-Balances Frameworkfor Context-Aware ...", "date": "", "ddg_snippet": "Conclusion This checks-and-balances approach offers a promising direction for building more ethically- aware AI systems. The framework 's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/three-branch-checks-balances-frameworkfor-context-aware", "content": "Conclusion This checks-and-balances approach offers a promising direction for building more ethically- aware AI systems. The framework 's ability to handle cultural differences while maintaining ethical standards could help develop AI systems that work responsibly across global contexts."}
+{"idx": 5, "title": "Ethical Guardrails for AI: A Checks-and-Balances Approach", "date": "", "ddg_snippet": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124", "subpage_snippet": "", "source": "www.zerna.io", "link": "https://www.zerna.io/page/security/presentation_set/security-llm-research/presentation/security-ethical-alignment-fairness/slide/security-paper-2502_00136", "content": "A Three-Branch Checks-and-Balances Framework for Context-Aware Ethical Alignment of Large Language Models 48 | 124"}
+{"idx": 6, "title": "Navigating the ethical landscape of AI... | F1000Research", "date": "", "ddg_snippet": "... for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems ( IEEE, 2016 ...", "subpage_snippet": "", "source": "f1000research.com", "link": "https://f1000research.com/articles/14-299", "content": "... for ethical AI deployment are encapsulated in frameworks like the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems ( IEEE, 2016 ..."}
+{"idx": 7, "title": "Multi-level Value Alignment in Agentic AI Systems: Survey and", "date": "", "ddg_snippet": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.09656v2", "content": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ..."}
+{"idx": 8, "title": "(PDF) A Unified Framework for Human AI Collaboration in", "date": "", "ddg_snippet": "... presents a structured framework for Human- AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392204042_A_Unified_Framework_for_Human_AI_Collaboration_in_Security_Operations_Centers_with_Trusted_Autonomy", "content": "... presents a structured framework for Human- AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and ..."}
+{"idx": 9, "title": "[Intro to brain-like-AGI safety] 10. The alignment problem -", "date": "", "ddg_snippet": "In Section 10. 2 , I’ll define “inner alignment ” and “outer alignment ” in the context of our brain-like-AGI motivation system.", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/wucncPjud27mLWZzQ/intro-to-brain-like-agi-safety-10-the-alignment-problem", "content": "In Section 10. 2 , I’ll define “inner alignment ” and “outer alignment ” in the context of our brain-like-AGI motivation system."}
diff --git a/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies_choic.jsonl b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies_choic.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5b6aa6c2de11028182e12aebd158f81de58f5da9
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+++ b/data/sampled_jsons/Checks-and-Balances_Framework_for_Context-Aware_Ethical_AI_Alignment_dataset_empirical_studies_choic.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Unified Framework for Evaluating the Effectiveness and", "date": "", "ddg_snippet": "... organized as follows: Section 2 provides an overview of earlier studies in this area, establishing the foundational work and context for our research.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.03884v2", "content": "... organized as follows: Section 2 provides an overview of earlier studies in this area, establishing the foundational work and context for our research."}
+{"idx": 1, "title": "Understanding Ethical Practices in AI: Insights from a", "date": "", "ddg_snippet": "... systematically examines their knowledge and perceptions of AI ethics and its associated principles, regulatory initiatives, and best practices for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09219v1", "content": "... systematically examines their knowledge and perceptions of AI ethics and its associated principles, regulatory initiatives, and best practices for ..."}
+{"idx": 2, "title": "Multi-level Value Alignment in Agentic AI Systems: Survey and", "date": "", "ddg_snippet": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.09656v2", "content": "This has brought significant attention to value alignment for agentic AI systems, which aims to ensure that an agent’s goals, preferences, and ..."}
+{"idx": 3, "title": "Splits! A Flexible Dataset and Evaluation Framework for", "date": "", "ddg_snippet": "For instance, in the context of “healthy eating,” American students tend to use a vocabulary of balancing food groups and avoiding fat and sugar ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.04640v2", "content": "For instance, in the context of “healthy eating,” American students tend to use a vocabulary of balancing food groups and avoiding fat and sugar ..."}
+{"idx": 4, "title": "64 questions with answers in CONTEXT-AWARENESS | Science topic", "date": "", "ddg_snippet": "... for neutral or generic urban settings, their application in cities with sacred significance—such as Karbala or Najaf—requires a more sensitive and ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Context-Awareness", "content": "... for neutral or generic urban settings, their application in cities with sacred significance—such as Karbala or Najaf—requires a more sensitive and ..."}
+{"idx": 5, "title": "Responsible AI in NLP: GUS-Net Span-Level Bias Detection", "date": "", "ddg_snippet": "We introduce the GUS-Net Framework , comprising the GUS dataset and a multi-label token-level detector for span-level analysis of social bias.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.08388v5", "content": "We introduce the GUS-Net Framework , comprising the GUS dataset and a multi-label token-level detector for span-level analysis of social bias."}
+{"idx": 6, "title": "Perspectives on Managing AI Ethics in the Digital Age", "date": "", "ddg_snippet": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2078-2489/16/4/318", "content": "... Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and ..."}
+{"idx": 7, "title": "Search AI ethics Primer bibliography", "date": "", "ddg_snippet": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ...", "subpage_snippet": "", "source": "robertolofaro.com", "link": "https://robertolofaro.com/searchkaggleaiethics.php", "content": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ..."}
+{"idx": 8, "title": "Search AI ethics Primer bibliography", "date": "", "ddg_snippet": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ...", "subpage_snippet": "", "source": "robertolofaro.com", "link": "http://robertolofaro.com/searchkaggleaiethics.php", "content": "RobertoLofaro.com - Knowledge Portal - human-generated content Change, with and without technology for updates on publications, follow @robertolofaro ..."}
+{"idx": 9, "title": "From Paperclips to Bombs: The Evolution of AI Risk Discourse on", "date": "", "ddg_snippet": "This study aims to investigate how the AI risk discourse is constructed and how it transforms on the LessWrong forum following these major AI ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/utx8jjAAumeZAu8oz/from-paperclips-to-bombs-the-evolution-of-ai-risk-discourse", "content": "This study aims to investigate how the AI risk discourse is constructed and how it transforms on the LessWrong forum following these major AI ..."}
diff --git a/data/sampled_jsons/Chen_et_al._2023_preference-based_reinforcement_learning_abstract_year_2023.jsonl b/data/sampled_jsons/Chen_et_al._2023_preference-based_reinforcement_learning_abstract_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6cd4804b8149d36b5dc164a2adccfae370de8757
--- /dev/null
+++ b/data/sampled_jsons/Chen_et_al._2023_preference-based_reinforcement_learning_abstract_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback ...", "date": "", "ddg_snippet": "Reinforcement learning (RL) has shown great success in various robotics domains [1-4], yet it remains reliant on carefully designed reward functions. In many practical scenarios, designing an informative reward function is highly challenging, as task objectives are often implicit and multi- faceted [5]. Preference-based RL (PbRL) [6-8] has emerged as a promising alternative to address this ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15607", "content": "Reinforcement learning (RL) has shown great success in various robotics domains [1-4], yet it remains reliant on carefully designed reward functions. In many practical scenarios, designing an informative reward function is highly challenging, as task objectives are often implicit and multi- faceted [5]. Preference-based RL (PbRL) [6-8] has emerged as a promising alternative to address this ..."}
+{"idx": 1, "title": "Advances in Preference-based Reinforcement Learning: A Review", "date": "", "ddg_snippet": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/9945333", "content": "Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preference-based reinforcement learning (PbRL) addresses that by utilizing human preferences as feedback from the experts instead of numeric rewards. Due to its promising advantage over traditional RL, PbRL has gained more ..."}
+{"idx": 2, "title": "PDF P R -agnostic Preference-based Reinforcement Learning", "date": "", "ddg_snippet": "ABSTRACT Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoretical work focuses on regret minimization and fails to capture most of the practical ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/file/1a10956f2cd9b41c0283a7af34b9c728-Paper-Conference.pdf", "content": "ABSTRACT Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than explicit reward signals. While PbRL has demonstrated practical success in fine-tuning language models, existing theoretical work focuses on regret minimization and fails to capture most of the practical ..."}
+{"idx": 3, "title": "PDF Human-in-the-loop: Provably Efficient Preference-based Reinforcement ...", "date": "", "ddg_snippet": "1. Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al ., 2013), Go (Silver et al ., 2017), and StarCraft (Vinyals et al ., 2019). While these empirical successes are ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/chen22ag/chen22ag.pdf", "content": "1. Introduction Reinforcement learning (RL) is concerned with sequential decision-making problems in which the agent interacts with the environment to maximize its cumulative rewards. This framework has achieved tremendous successes in various fields such as Atari games (Mnih et al ., 2013), Go (Silver et al ., 2017), and StarCraft (Vinyals et al ., 2019). While these empirical successes are ..."}
+{"idx": 4, "title": "PDF RIME: Robust Preference-based Reinforcement Learning with ... - GitHub", "date": "", "ddg_snippet": "Abstract Preference-based Reinforcement Learning (PbRL) circumvents the need for reward engi-neering by harnessing human preferences as the reward signal. However, current PbRL methods excessively depend on high-quality feedback from domain experts, which results in a lack of robustness. In this paper, we present RIME,", "subpage_snippet": "", "source": "raw.githubusercontent.com", "link": "https://raw.githubusercontent.com/mlresearch/v235/main/assets/cheng24k/cheng24k.pdf", "content": "Abstract Preference-based Reinforcement Learning (PbRL) circumvents the need for reward engi-neering by harnessing human preferences as the reward signal. However, current PbRL methods excessively depend on high-quality feedback from domain experts, which results in a lack of robustness. In this paper, we present RIME,"}
+{"idx": 5, "title": "Decoding Global Preferences: Temporal and Cooperative Dependency ...", "date": "", "ddg_snippet": "Abstract Designing accurate reward functions for reinforcement learn-ing (RL) has long been challenging. Preference-based RL (PbRL) offers a promising approach by using human pref-erences to train agents, eliminating the need for manual re-ward design. While successful in single-agent tasks, extend-ing PbRL to complex multi-agent scenarios is nontrivial. Ex-isting PbRL methods lack the ...", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/29666/31137", "content": "Abstract Designing accurate reward functions for reinforcement learn-ing (RL) has long been challenging. Preference-based RL (PbRL) offers a promising approach by using human pref-erences to train agents, eliminating the need for manual re-ward design. While successful in single-agent tasks, extend-ing PbRL to complex multi-agent scenarios is nontrivial. Ex-isting PbRL methods lack the ..."}
+{"idx": 6, "title": "Online Preference-based Reinforcement Learning with Self-augmented ...", "date": "", "ddg_snippet": "Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3709347.3743845", "content": "Abstract Preference-based reinforcement learning (PbRL) provides a powerful paradigm to avoid meticulous reward engineering by learning rewards based on human preferences . However, real-time human feedback is hard to obtain in online tasks."}
+{"idx": 7, "title": "Efficient Preference-Based Reinforcement Learning Using Learned ...", "date": "", "ddg_snippet": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10161081", "content": "Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted re-ward function. However, existing approaches either assume access to a high-fidelity simulator or analytic model or take a model-free approach that requires extensive, possibly unsafe online environment interactions. In this paper ..."}
+{"idx": 8, "title": "Q -e Offline Preference-based Re Inforcement Learning Via In-dataset ...", "date": "", "ddg_snippet": "ABSTRACT Preference-based reinforcement learning has shown great promise in various ap-plications to avoid reward annotations and align better with human intentions. However, obtaining preference feedback can still be expensive or time-consuming, which forms a strong barrier for preference-based RL. In this paper, we propose a novel approach to improve the query eficiency of ofline preference ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=GOvTGntFNj", "content": "ABSTRACT Preference-based reinforcement learning has shown great promise in various ap-plications to avoid reward annotations and align better with human intentions. However, obtaining preference feedback can still be expensive or time-consuming, which forms a strong barrier for preference-based RL. In this paper, we propose a novel approach to improve the query eficiency of ofline preference ..."}
+{"idx": 9, "title": "Offline Multi-Agent Preference-based Reinforcement Learning with Agent ...", "date": "", "ddg_snippet": "Abstract Multi-agent Preference-Based Reinforcement Learning (MAPbRL) is promising in offline policy learning by leveraging human preferences to replace complex manual reward designing. Current MAPbRL methods use complicated structures to realize better reward modeling with off-the-shelf MARL algorithms and obtain the joint policy based on it.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/epdf/10.5555/3709347.3743639", "content": "Abstract Multi-agent Preference-Based Reinforcement Learning (MAPbRL) is promising in offline policy learning by leveraging human preferences to replace complex manual reward designing. Current MAPbRL methods use complicated structures to realize better reward modeling with off-the-shelf MARL algorithms and obtain the joint policy based on it."}
diff --git a/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks.jsonl b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..89aa2221e46c1e0c0b8d653644be2ae240ef4ba6
--- /dev/null
+++ b/data/sampled_jsons/CoPINN_Cognitive_Physics-Informed_Neural_Networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "CoPINN: Cognitive Physics-Informed Neural Networks . Download PDF. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun. Published: 01 May 2025 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4vAa0A98xI&noteId=Z6OLWcPple", "content": "CoPINN: Cognitive Physics-Informed Neural Networks . Download PDF. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun. Published: 01 May 2025 ..."}
+{"idx": 1, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "16 Jul 2025 — CoPINN: Cognitive Physics-Informed Neural Networks . Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun. East ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46458", "content": "16 Jul 2025 — CoPINN: Cognitive Physics-Informed Neural Networks . Siyuan Duan · Wenyuan Wu · Peng Hu · Zhenwen Ren · Dezhong Peng · Yuan Sun. East ..."}
+{"idx": 2, "title": "Physics-Informed NNs: tips & tricks, literature repo, success ...", "date": "", "ddg_snippet": "CoPINN: Cognitive physics-informed neural networks . ICML, 2025. paper. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, and Yuan ...", "subpage_snippet": "", "source": "hodgesj.substack.com", "link": "https://hodgesj.substack.com/p/physics-informed-nns-tips-and-tricks", "content": "CoPINN: Cognitive physics-informed neural networks . ICML, 2025. paper. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, and Yuan ..."}
+{"idx": 3, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "CoPINN: Cognitive Physics-Informed Neural Networks . ICML 202518 Jun 2025. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun. Sichuan ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/ja/chatpaper/paper/165180", "content": "CoPINN: Cognitive Physics-Informed Neural Networks . ICML 202518 Jun 2025. Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun. Sichuan ..."}
+{"idx": 4, "title": "CoPINN: Cognitive Physics-Informed Neural Networks", "date": "", "ddg_snippet": "CoPINN: Cognitive Physics-Informed Neural Networks | Read Paper on Bytez.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46458/code?_c=eyJ2IjoxLCJyZWxhdGVkIjpbImNvZGUiLCJyZWZlcmVuY2VzIiwiY29uZmVyZW5jZSJdfQ==", "content": "CoPINN: Cognitive Physics-Informed Neural Networks | Read Paper on Bytez."}
+{"idx": 5, "title": "Siyuan Duan (段思远)", "date": "", "ddg_snippet": "CoPINN: Cognitive Physics-Informed Neural Networks . S Duan, W Wu, P Hu, Z Ren, D Peng, Y Sun. Forty-second International Conference on Machine Learning., 2025.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=Zck_72MAAAAJ&hl=en", "content": "CoPINN: Cognitive Physics-Informed Neural Networks . S Duan, W Wu, P Hu, Z Ren, D Peng, Y Sun. Forty-second International Conference on Machine Learning., 2025."}
+{"idx": 6, "title": "siyuancncd/FPINNs", "date": "", "ddg_snippet": "@inproceedings{duancopinn, title={ CoPINN: Cognitive Physics-Informed Neural Networks }, author={Duan, Siyuan and Wu, Wenyuan and Hu, Peng and Ren, Zhenwen ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/FPINNs", "content": "@inproceedings{duancopinn, title={ CoPINN: Cognitive Physics-Informed Neural Networks }, author={Duan, Siyuan and Wu, Wenyuan and Hu, Peng and Ren, Zhenwen ..."}
+{"idx": 7, "title": "Siyuan Duan", "date": "", "ddg_snippet": "CoPINN: Cognitive Physics-Informed Neural Networks · Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun. Published: 01 May 2025, Last ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Siyuan_Duan1", "content": "CoPINN: Cognitive Physics-Informed Neural Networks · Siyuan Duan, Wenyuan Wu, Peng Hu, Zhenwen Ren, Dezhong Peng, Yuan Sun. Published: 01 May 2025, Last ..."}
+{"idx": 8, "title": "Wenyuan Wu", "date": "", "ddg_snippet": "Co-authors ; CoPINN: Cognitive Physics-Informed Neural Networks . S Duan, W Wu, P Hu, Z Ren, D Peng, Y Sun. Forty-second International Conference on Machine ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=sjtjpmoAAAAJ&hl=en", "content": "Co-authors ; CoPINN: Cognitive Physics-Informed Neural Networks . S Duan, W Wu, P Hu, Z Ren, D Peng, Y Sun. Forty-second International Conference on Machine ..."}
+{"idx": 9, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "CoPINN: Cognitive Physics-Informed Neural Networks · Adversarial Robust Generalization of Graph Neural Networks · Open Your Eyes: Vision Enhances Message ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "CoPINN: Cognitive Physics-Informed Neural Networks · Adversarial Robust Generalization of Graph Neural Networks · Open Your Eyes: Vision Enhances Message ..."}
diff --git a/data/sampled_jsons/CogAgent_visual_language_model_GUI_agents_Hong.jsonl b/data/sampled_jsons/CogAgent_visual_language_model_GUI_agents_Hong.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ede65381f2075bde514ef2376ad902f43c059f42
--- /dev/null
+++ b/data/sampled_jsons/CogAgent_visual_language_model_GUI_agents_Hong.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.Figure 1. Samples of visual agents generated by CogAgent . More samples are demonstrated in the Appendix.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf", "content": "Agents based on visual language models (VLMs) have the potential to overcome these limitations.Figure 1. Samples of visual agents generated by CogAgent . More samples are demonstrated in the Appendix."}
+{"idx": 1, "title": "[2312.08914] CogAgent : A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors.In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.08914", "content": "View a PDF of the paper titled CogAgent : A Visual Language Model for GUI Agents , by Wenyi Hong and 13 other authors.In this paper, we introduce CogAgent , an 18-billion-parameter visual language model (VLM) specializing in GUI understanding and navigation."}
+{"idx": 2, "title": "CogAgent : A Visual Language Model for GUI Agents | OpenReview", "date": "", "ddg_snippet": "As a general-ist visual language model , CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK- VQA, Text- Vqa, St- Vqa, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=HKg8G2vzW7&referrer=[the+profile+of+Wenyi+Hong](/profile?id=~Wenyi_Hong1)", "content": "As a general-ist visual language model , CogAgent achieves the state of the art on five text-rich and four general VQA benchmarks, including VQAv2, OK- VQA, Text- Vqa, St- Vqa, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE."}
+{"idx": 3, "title": "CogAgent : A Visual Language Model for GUI Agents | Request PDF", "date": "", "ddg_snippet": "Training Vision- Language Models (VLMs) for Graphical User Interfaces ( GUI ) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly interactions, while...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384235870_CogAgent_A_Visual_Language_Model_for_GUI_Agents", "content": "Training Vision- Language Models (VLMs) for Graphical User Interfaces ( GUI ) agents via Reinforcement Learning (RL) faces critical challenges: environment-based RL requires costly interactions, while..."}
+{"idx": 4, "title": "README.md · THUDM/ cogagent -vqa-hf at main", "date": "", "ddg_snippet": "cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc.title={ CogAgent : A Visual Language Model for GUI Agents }", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/THUDM/cogagent-vqa-hf/blob/main/README.md", "content": "cogagent -chat: This model has strong capabilities in GUI Agent , visual multi-turn dialogue, visual grounding, etc.title={ CogAgent : A Visual Language Model for GUI Agents }"}
+{"idx": 5, "title": "A Visual Language Model for GUI Agents", "date": "", "ddg_snippet": "CogAgent is an open-source visual language model improved based on CogVLM. CogAgent -18B has 11 billion visual parameters and 7 billion language parameters.@misc{ hong 2023 cogagent , title={ CogAgent : A Visual Language Model for GUI Agents }", "subpage_snippet": "", "source": "replicate.com", "link": "https://replicate.com/cjwbw/cogagent-chat/readme", "content": "CogAgent is an open-source visual language model improved based on CogVLM. CogAgent -18B has 11 billion visual parameters and 7 billion language parameters.@misc{ hong 2023 cogagent , title={ CogAgent : A Visual Language Model for GUI Agents }"}
+{"idx": 6, "title": "Paper tables with annotated results for CogAgent : A Visual Language ...", "date": "", "ddg_snippet": "CogAgent : A Visual Language Model for GUI Agents . People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens.", "subpage_snippet": "", "source": "paperswithcode.com", "link": "https://paperswithcode.com/paper/cogagent-a-visual-language-model-for-gui/review/?hl=114609", "content": "CogAgent : A Visual Language Model for GUI Agents . People are spending an enormous amount of time on digital devices through graphical user interfaces ( GUIs ), e.g., computer or smartphone screens."}
+{"idx": 7, "title": "Wenyi Hong - Google Scholar", "date": "", "ddg_snippet": "CogAgent : A Visual Language Model for GUI Agents .2024. Visualagentbench: Towards large multimodal models as visual foundation agents .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=JSEzrlwAAAAJ&hl=en", "content": "CogAgent : A Visual Language Model for GUI Agents .2024. Visualagentbench: Towards large multimodal models as visual foundation agents ."}
+{"idx": 8, "title": "CogAgent : Visual Language Model for GUIs", "date": "", "ddg_snippet": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2312.08914", "content": "CogAgent is an 18-billion-parameter visual language model that automates GUI tasks with high-resolution input and efficient dual-resolution modules."}
+{"idx": 9, "title": "CogAgent : A Visual Language Model for GUI Agents | AI Research...", "date": "", "ddg_snippet": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/cogagent-visual-language-model-gui-agents", "content": "Overview CogAgent is a new visual language model designed for GUI interactionUses novel cross-module architecture for better visual - language alignment"}
diff --git a/data/sampled_jsons/Concept_Bottleneck_Models_Without_Predefined_Concepts_unsupervised_concept_discovery_foundation_mode.jsonl b/data/sampled_jsons/Concept_Bottleneck_Models_Without_Predefined_Concepts_unsupervised_concept_discovery_foundation_mode.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..33a93035edf97f5e9fd290ea4b86bc676768d082
--- /dev/null
+++ b/data/sampled_jsons/Concept_Bottleneck_Models_Without_Predefined_Concepts_unsupervised_concept_discovery_foundation_mode.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "If Concept Bottlenecks are the Question, are Foundation Models", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) [ 41 ] combine two neural modules: a concept extractor f : 𝒳 → ℝ k : 𝑓 → 𝒳 superscript ℝ 𝑘 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.19774v2", "content": "Concept Bottleneck Models (CBMs) [ 41 ] combine two neural modules: a concept extractor f : 𝒳 → ℝ k : 𝑓 → 𝒳 superscript ℝ 𝑘 ..."}
+{"idx": 1, "title": "Discover-then-Name: Task-Agnostic Concept Bottlenecks via", "date": "", "ddg_snippet": "Concept Bottleneck Models (CBMs) [ 31 , 60 , 37 ] are a class of inherently interpretable models that express their prediction as a linear combination ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.14499v2", "content": "Concept Bottleneck Models (CBMs) [ 31 , 60 , 37 ] are a class of inherently interpretable models that express their prediction as a linear combination ..."}
+{"idx": 2, "title": "Concept Bottleneck Language Models For protein design", "date": "", "ddg_snippet": "... model architecture by adding a concept bottleneck layer to explicitly incorporate human-understandable concepts into the language model —termed a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.06090v2", "content": "... model architecture by adding a concept bottleneck layer to explicitly incorporate human-understandable concepts into the language model —termed a ..."}
+{"idx": 3, "title": "Neural Concept Verifier: Scaling Prover-Verifier Games via", "date": "", "ddg_snippet": "... concept annotations, subsequent research has relaxed this requirement by leveraging pretrained vision-language models like CLIP for concept extraction ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07532v1", "content": "... concept annotations, subsequent research has relaxed this requirement by leveraging pretrained vision-language models like CLIP for concept extraction ..."}
+{"idx": 4, "title": "ML Models: Understanding the Fundamentals", "date": "", "ddg_snippet": "Here, you are not providing predefined classes for the model to sort data by, so the model will group the unlabeled data inputs into various clusters ...", "subpage_snippet": "", "source": "gretel.ai", "link": "https://gretel.ai/blog/machine-learning-models", "content": "Here, you are not providing predefined classes for the model to sort data by, so the model will group the unlabeled data inputs into various clusters ..."}
+{"idx": 5, "title": "12 Large Language Model Foundations – Machine Learning", "date": "", "ddg_snippet": "... questions about whether these models exhibit genuine intelligence, examining evidence beyond simple retrieval, the role of information bottlenecks in ...", "subpage_snippet": "", "source": "mlbook.jyotirmoy.net", "link": "https://mlbook.jyotirmoy.net/book_content/065-large-model-foundations.html", "content": "... questions about whether these models exhibit genuine intelligence, examining evidence beyond simple retrieval, the role of information bottlenecks in ..."}
+{"idx": 6, "title": "Hanchen Wang's research works | Stanford University and", "date": "", "ddg_snippet": "... foundation , Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Hanchen-Wang-2181172215", "content": "... foundation , Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning ..."}
+{"idx": 7, "title": "BlueGlass: A Framework for Composite AI Safety", "date": "", "ddg_snippet": "This component wraps a target model , providing a standardized mechanism to define access points within the model ’s architecture where features can ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10106v1", "content": "This component wraps a target model , providing a standardized mechanism to define access points within the model ’s architecture where features can ..."}
+{"idx": 8, "title": "All | MongoDB Blog", "date": "", "ddg_snippet": "With MongoDB Atlas , companies are leveraging vector search, seamless document modeling , and large language model (LLM) integrations to make smarter ...", "subpage_snippet": "", "source": "www.mongodb.com", "link": "https://www.mongodb.com/company/blog/all/632", "content": "With MongoDB Atlas , companies are leveraging vector search, seamless document modeling , and large language model (LLM) integrations to make smarter ..."}
+{"idx": 9, "title": "All | MongoDB Blog", "date": "", "ddg_snippet": "With MongoDB Atlas , companies are leveraging vector search, seamless document modeling , and large language model (LLM) integrations to make smarter ...", "subpage_snippet": "", "source": "www.mongodb.com", "link": "https://www.mongodb.com/company/blog/all/722", "content": "With MongoDB Atlas , companies are leveraging vector search, seamless document modeling , and large language model (LLM) integrations to make smarter ..."}
diff --git a/data/sampled_jsons/Creating_noise_from_data_is_easy;_creating_data_from_noise_is_generative_modeling_Song_2021_abstract_year_2021.jsonl b/data/sampled_jsons/Creating_noise_from_data_is_easy;_creating_data_from_noise_is_generative_modeling_Song_2021_abstract_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6147f617c4c33f5cb91b4c56ee5628a2d5477307
--- /dev/null
+++ b/data/sampled_jsons/Creating_noise_from_data_is_easy;_creating_data_from_noise_is_generative_modeling_Song_2021_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "by Y Song · 2020 · Cited by 8724 — Abstract : Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456", "content": "by Y Song · 2020 · Cited by 8724 — Abstract : Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation ..."}
+{"idx": 1, "title": "SCORE-BASED GENERATIVE MODELING THROUGH ...", "date": "", "ddg_snippet": "by Y Song · Cited by 8724 — ABSTRACT . Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/ef0eadbe07115b0853e964f17aa09d811cd490f1.pdf?ref=news-tutorials-ai-research", "content": "by Y Song · Cited by 8724 — ABSTRACT . Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ..."}
+{"idx": 2, "title": "Yang Song", "date": "", "ddg_snippet": "Abstract PDF Blog Code Poster Media. Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential ...", "subpage_snippet": "", "source": "yang-song.net", "link": "https://yang-song.net/", "content": "Abstract PDF Blog Code Poster Media. Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential ..."}
+{"idx": 3, "title": "Score-based generative models: Score matching", "date": "", "ddg_snippet": "... abstract of ( Song et al., 2020):. \" Creating noise from data is easy; creating data from noise is generative modeling .\" I must say that it is hard to come up ...", "subpage_snippet": "", "source": "jmtomczak.github.io", "link": "https://jmtomczak.github.io/blog/16/16_score_matching.html", "content": "... abstract of ( Song et al., 2020):. \" Creating noise from data is easy; creating data from noise is generative modeling .\" I must say that it is hard to come up ..."}
+{"idx": 4, "title": "Score-Based Generative Modeling with SDEs", "date": "", "ddg_snippet": "Abstract : Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ...", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2011.13456", "content": "Abstract : Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ..."}
+{"idx": 5, "title": "AIM-Harvard/ICLR-Accepted-Papers · Datasets at ...", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/AIM-Harvard/ICLR-Accepted-Papers", "content": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly ..."}
+{"idx": 6, "title": "ICLR 2021 Orals", "date": "", "ddg_snippet": "Abstract . Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/events/oral", "content": "Abstract . Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ..."}
+{"idx": 7, "title": "Track: Outstanding Paper Session 1", "date": "", "ddg_snippet": "5 May 2021 — Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2021/session/4366", "content": "5 May 2021 — Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that ..."}
+{"idx": 8, "title": "publications", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms ...", "subpage_snippet": "", "source": "yang-song.net", "link": "https://yang-song.net/publications/", "content": "Creating noise from data is easy; creating data from noise is generative modeling . We present a stochastic differential equation (SDE) that smoothly transforms ..."}
+{"idx": 9, "title": "Ben Poole", "date": "", "ddg_snippet": "ICLR 2021 ( 2021 ) (to appear). Preview Preview abstract Creating noise from data is easy; creating data from noise is generative modeling . We present a ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/people/benpoole/", "content": "ICLR 2021 ( 2021 ) (to appear). Preview Preview abstract Creating noise from data is easy; creating data from noise is generative modeling . We present a ..."}
diff --git a/data/sampled_jsons/Cronbach_Meehl_1955_Construct_validity_in_psychological_tests_abstract.jsonl b/data/sampled_jsons/Cronbach_Meehl_1955_Construct_validity_in_psychological_tests_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e78783187091d492d5a2d47d372f0b1d5dbce21d
--- /dev/null
+++ b/data/sampled_jsons/Cronbach_Meehl_1955_Construct_validity_in_psychological_tests_abstract.jsonl
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+{"idx": 0, "title": "Kentucky Derby - Wikipedia", "date": "", "ddg_snippet": "Kentucky Derby ... The Kentucky Derby (/ ˈdɜːrbi /) is an American Grade I stakes race run at Churchill Downs in Louisville, Kentucky . The race is run by three-year-old Thoroughbreds at a distance of 11⁄4 miles (10 furlongs; 2,012 metres). Colts and geldings carry 126 pounds (57 kilograms) and fillies 121 pounds (55 kilograms). [3]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Kentucky_Derby", "content": "Kentucky Derby ... The Kentucky Derby (/ ˈdɜːrbi /) is an American Grade I stakes race run at Churchill Downs in Louisville, Kentucky . The race is run by three-year-old Thoroughbreds at a distance of 11⁄4 miles (10 furlongs; 2,012 metres). Colts and geldings carry 126 pounds (57 kilograms) and fillies 121 pounds (55 kilograms). [3]"}
+{"idx": 1, "title": "Classics in the History of Psychology -- Cronbach & Meehl ( 1955 )", "date": "", "ddg_snippet": "Construct validity in psychological tests . Lee J. Cronbach and Paul E. Meehl ( 1955 )[1]. First published in Psychological Bulletin, 52, 281-302. Validation of psychological tests has not yet been adequately conceptualized, as the APA Committee on Psychological ...", "subpage_snippet": "", "source": "psychclassics.yorku.ca", "link": "https://psychclassics.yorku.ca/Cronbach/construct.htm", "content": "Construct validity in psychological tests . Lee J. Cronbach and Paul E. Meehl ( 1955 )[1]. First published in Psychological Bulletin, 52, 281-302. Validation of psychological tests has not yet been adequately conceptualized, as the APA Committee on Psychological ..."}
+{"idx": 2, "title": "Construct validity in psychological tests", "date": "", "ddg_snippet": "L J Cronbach , p e Meehl .No abstract available. MeSH terms. Humans. Psychological Tests *.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/13245896/", "content": "L J Cronbach , p e Meehl .No abstract available. MeSH terms. Humans. Psychological Tests *."}
+{"idx": 3, "title": "Psychological", "date": "", "ddg_snippet": "JULY, 1955 . Psychological Bulletin. Construct validity in psychological tests . LEE J. CRONBACH University of Illinois. AND. PAUL E. MEEHLi University of Minnesota.", "subpage_snippet": "", "source": "www.sfu.ca", "link": "https://www.sfu.ca/~palys/Cronbach&Meehl-1955-ConstructValidityInPsychologicalTests.pdf", "content": "JULY, 1955 . Psychological Bulletin. Construct validity in psychological tests . LEE J. CRONBACH University of Illinois. AND. PAUL E. MEEHLi University of Minnesota."}
+{"idx": 4, "title": "Introduction to the special section on construct validity of...", "date": "", "ddg_snippet": "of construct validity ( Cronbach & Meehl , 1955 ), and described a. methodology for estimating convergent and discriminant validity . Construct validity is one of the most central concepts in psychology .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/7378545_Introduction_to_the_special_section_on_construct_validity_of_psychological_tests_50_Years_after_Cronbach_and_Meehl_1955", "content": "of construct validity ( Cronbach & Meehl , 1955 ), and described a. methodology for estimating convergent and discriminant validity . Construct validity is one of the most central concepts in psychology ."}
+{"idx": 5, "title": "Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in ...", "date": "", "ddg_snippet": "Construct validity in psychological tests . Psychological bulletin, 52(4), 281.Correspondence to: Heidemae R. Tabor, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines. Email: heidemae.tabor@ustp.edu.ph. Abstract .", "subpage_snippet": "", "source": "www.sciepub.com", "link": "https://www.sciepub.com/reference/449543", "content": "Construct validity in psychological tests . Psychological bulletin, 52(4), 281.Correspondence to: Heidemae R. Tabor, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines. Email: heidemae.tabor@ustp.edu.ph. Abstract ."}
+{"idx": 6, "title": "Evaluating Construct Validity within Preclinical In Vivo Animal Research", "date": "", "ddg_snippet": "Construct validity was first introduced in research by Meehl and Cronbach in 1955 ( Cronbach & Meehl , 1955 ). They defined construct validity in terms of psychological test evaluation.", "subpage_snippet": "", "source": "ruor.uottawa.ca", "link": "https://ruor.uottawa.ca/server/api/core/bitstreams/b0bda751-ed46-4e37-abf1-9a17ba5910a4/content", "content": "Construct validity was first introduced in research by Meehl and Cronbach in 1955 ( Cronbach & Meehl , 1955 ). They defined construct validity in terms of psychological test evaluation."}
+{"idx": 7, "title": "CronbachPaper", "date": "", "ddg_snippet": "My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64, 391–418. Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in psychological tests .", "subpage_snippet": "", "source": "cda.psych.uiuc.edu", "link": "http://cda.psych.uiuc.edu/psychometrika_johnson/CronbachPaper+(1).pdf", "content": "My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64, 391–418. Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in psychological tests ."}
+{"idx": 8, "title": "Measuring Personality Constructs : The Advantages and...", "date": "", "ddg_snippet": "According to Cronbach and Meehl ( 1955 ), psychological and personality constructs are “postulated” or inferred characteristics or traits of a person. Construct validity in psychological tests . Psychological Bulletin, 52, 281-301.", "subpage_snippet": "", "source": "www.nottingham.ac.uk", "link": "https://www.nottingham.ac.uk/sociology/documents/enquire/volume-1-issue-1-dodorico-mcdonald.pdf", "content": "According to Cronbach and Meehl ( 1955 ), psychological and personality constructs are “postulated” or inferred characteristics or traits of a person. Construct validity in psychological tests . Psychological Bulletin, 52, 281-301."}
+{"idx": 9, "title": "On Validity", "date": "", "ddg_snippet": "Cronbach and Meehl ( 1955 ) maintained that the construct needed to be part of a nomological network: “the interlocking system of laws which constitute a theory” (p. 290).University of Illinois Press. Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in psychological tests .", "subpage_snippet": "", "source": "journals.library.columbia.edu", "link": "https://journals.library.columbia.edu/index.php/SALT/article/download/11804/5991/30419", "content": "Cronbach and Meehl ( 1955 ) maintained that the construct needed to be part of a nomological network: “the interlocking system of laws which constitute a theory” (p. 290).University of Illinois Press. Cronbach , L. J., & Meehl , P. E. ( 1955 ). Construct validity in psychological tests ."}
diff --git a/data/sampled_jsons/CrossKD_classification_CIFAR100_Wang_et_al.jsonl b/data/sampled_jsons/CrossKD_classification_CIFAR100_Wang_et_al.jsonl
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index 0000000000000000000000000000000000000000..2e2edd665983a732b6a592162cf96c7c15fb80c3
--- /dev/null
+++ b/data/sampled_jsons/CrossKD_classification_CIFAR100_Wang_et_al.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.11369", "content": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ..."}
+{"idx": 1, "title": "CIFAR-100-Image-Classification - GitHub", "date": "", "ddg_snippet": "This project explores diverse approaches to image classification on the CIFAR-100 dataset. Starting from traditional CNNs combined with KNN classifiers, it progresses to ResNet50 with FCNN and culm...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ishreya09/CIFAR-100-Image-Classification", "content": "This project explores diverse approaches to image classification on the CIFAR-100 dataset. Starting from traditional CNNs combined with KNN classifiers, it progresses to ResNet50 with FCNN and culm..."}
+{"idx": 2, "title": "Classification accuracy on CIFAR100 benchmark. - ResearchGate", "date": "", "ddg_snippet": "Download scientific diagram | Classification accuracy on CIFAR100 benchmark. from publication: Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Classification-accuracy-on-CIFAR100-benchmark_tbl1_383910666", "content": "Download scientific diagram | Classification accuracy on CIFAR100 benchmark. from publication: Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image ..."}
+{"idx": 3, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.11369", "content": "Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the ..."}
+{"idx": 4, "title": "PDF CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Recently, Zheng et al . [73] proposed a localization distillation (LD) method that improves prediction mimicking by transferring localization knowledge, which pushes the prediction mim-icking to a new level.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.pdf", "content": "Recently, Zheng et al . [73] proposed a localization distillation (LD) method that improves prediction mimicking by transferring localization knowledge, which pushes the prediction mim-icking to a new level."}
+{"idx": 5, "title": "Distilling and transferring knowledge via cGAN-generated samples for ...", "date": "", "ddg_snippet": "Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student …", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0957417422020784", "content": "Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student …"}
+{"idx": 6, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "CrossKD : Cross-Head Knowledge Distillation for Object Detection Jiabao Wang , Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16520-16530 Abstract", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.html", "content": "CrossKD : Cross-Head Knowledge Distillation for Object Detection Jiabao Wang , Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 16520-16530 Abstract"}
+{"idx": 7, "title": "Multistage feature fusion knowledge distillation", "date": "", "ddg_snippet": "The CIFAR-100 classification dataset 35 consists of 100 categories with images of size 32 \\ (\\times\\) 32. The dataset includes 50,000 training images and 10,000 validation images.", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-024-64041-4", "content": "The CIFAR-100 classification dataset 35 consists of 100 categories with images of size 32 \\ (\\times\\) 32. The dataset includes 50,000 training images and 10,000 validation images."}
+{"idx": 8, "title": "Enhancing Adversarial Robustness of Deep Neural Networks Through ...", "date": "", "ddg_snippet": "The experiments are conducted on the CIFAR-100 dataset, a widely used benchmark for image classification tasks, consisting of 60,000 images across 100 classes, with 50,000 training samples and 10,000 test samples.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.19747v1", "content": "The experiments are conducted on the CIFAR-100 dataset, a widely used benchmark for image classification tasks, consisting of 60,000 images across 100 classes, with 50,000 training samples and 10,000 test samples."}
+{"idx": 9, "title": "CIFAR-100 on Benchmarks.AI", "date": "", "ddg_snippet": "Explore CIFAR-100 dataset benchmarks, pre-trained models and fine-tuning techniques to improve deep learning performance on vision tasks.", "subpage_snippet": "", "source": "benchmarks.ai", "link": "https://benchmarks.ai/cifar-100", "content": "Explore CIFAR-100 dataset benchmarks, pre-trained models and fine-tuning techniques to improve deep learning performance on vision tasks."}
diff --git a/data/sampled_jsons/Crosskd_Cross-head_knowledge_distillation_for_object_detection_Wang_2024_CIFAR100_classification.jsonl b/data/sampled_jsons/Crosskd_Cross-head_knowledge_distillation_for_object_detection_Wang_2024_CIFAR100_classification.jsonl
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index 0000000000000000000000000000000000000000..572a453e15b0f3eaa3faed652017731eb8648295
--- /dev/null
+++ b/data/sampled_jsons/Crosskd_Cross-head_knowledge_distillation_for_object_detection_Wang_2024_CIFAR100_classification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.11369", "content": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ..."}
+{"idx": 1, "title": "PDF CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the student's detection head to the teacher's detection head . The resulting cross - head predictions are then forced to mimic the teacher's pre-dictions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.pdf", "content": "In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the student's detection head to the teacher's detection head . The resulting cross - head predictions are then forced to mimic the teacher's pre-dictions."}
+{"idx": 2, "title": "Awesome Knowledge Distillation for Object Detection - GitHub", "date": "", "ddg_snippet": "A curated list of awesome distillation techniques designed for object detectors. Parameters compression and accuracy boosting are core problems for object detection towards practical application, where knowledge distillation (KD) is one of the most popular solutions. KD aims at training the compact model (student) by transferring knowledge from a high-capacity model (teacher). Papers and codes ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LutingWang/awesome-knowledge-distillation-for-object-detection", "content": "A curated list of awesome distillation techniques designed for object detectors. Parameters compression and accuracy boosting are core problems for object detection towards practical application, where knowledge distillation (KD) is one of the most popular solutions. KD aims at training the compact model (student) by transferring knowledge from a high-capacity model (teacher). Papers and codes ..."}
+{"idx": 3, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Abstract:Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2306.11369", "content": "Abstract:Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the ..."}
+{"idx": 4, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Wang , Jiabao, et al. \" CrossKD : Cross - head knowledge distillation for object detection .\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024 .", "subpage_snippet": "", "source": "velog.io", "link": "https://velog.io/@hseop/CrossKD-Cross-Head-Knowledge-Distillation-for-Object-Detection", "content": "Wang , Jiabao, et al. \" CrossKD : Cross - head knowledge distillation for object detection .\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024 ."}
+{"idx": 5, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.11369", "content": "Abstract Knowledge Distillation (KD) has been validated as an effective model compression technique for learning com-pact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which de-livers the intermediate features of the ..."}
+{"idx": 6, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's detection head to the teacher's detection head . The resulting cross - head predictions are then forced to mimic the teacher's predictions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Wang_CrossKD_Cross-Head_Knowledge_Distillation_for_Object_Detection_CVPR_2024_paper.html", "content": "In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's detection head to the teacher's detection head . The resulting cross - head predictions are then forced to mimic the teacher's predictions."}
+{"idx": 7, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's ...", "subpage_snippet": "", "source": "www.deepnlp.org", "link": "https://www.deepnlp.org/content/articles/crosskd:-cross-head-knowledge-distillation-for-object-detection", "content": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper we present a general and effective prediction mimicking distillation scheme called CrossKD which delivers the intermediate features of the student's ..."}
+{"idx": 8, "title": "CrossKD/README.md at master · jbwang1997/CrossKD · GitHub", "date": "", "ddg_snippet": "CrossKD : Cross - Head Knowledge Distillation for Dense Object Detection - CrossKD /README.md at master · jbwang1997/ CrossKD", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jbwang1997/CrossKD/blob/master/README.md", "content": "CrossKD : Cross - Head Knowledge Distillation for Dense Object Detection - CrossKD /README.md at master · jbwang1997/ CrossKD"}
+{"idx": 9, "title": "CrossKD: Cross-Head Knowledge Distillation for Object Detection", "date": "", "ddg_snippet": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/cvpr/31390/paper", "content": "Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD , which delivers the intermediate features of the student's ..."}
diff --git a/data/sampled_jsons/DART_radiology_report_generation_Figure_2_focal_consolidation_github_discussion_year_2024.jsonl b/data/sampled_jsons/DART_radiology_report_generation_Figure_2_focal_consolidation_github_discussion_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8d13cdf672b2f291b81e027020243df288547101
--- /dev/null
+++ b/data/sampled_jsons/DART_radiology_report_generation_Figure_2_focal_consolidation_github_discussion_year_2024.jsonl
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+{"idx": 0, "title": "mk-runner/Awesome-Radiology-Report-Generation - GitHub", "date": "", "ddg_snippet": "We collect existing papers on radiology report generation that have been published in prominent conferences and journals. If you find this helpful, please consider citing the following reference.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mk-runner/Awesome-Radiology-Report-Generation", "content": "We collect existing papers on radiology report generation that have been published in prominent conferences and journals. If you find this helpful, please consider citing the following reference."}
+{"idx": 1, "title": "DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ), a novel framework that ensures retrieved reports contain similar disease-relevant findings and introduces a self-correction mechanism to refine generated reports .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.11786v1", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ), a novel framework that ensures retrieved reports contain similar disease-relevant findings and introduces a self-correction mechanism to refine generated reports ."}
+{"idx": 2, "title": "Radiology report generation with a learned knowledge base and multi ...", "date": "", "ddg_snippet": "It also indicates that the radiology report generation benefits from the multi-modal alignment mechanism and the learned knowledge base, avoiding laborious manual construction of the knowledge graph or template database. Furthermore, the proposed methods boost the quality of generated reports in both natural language and clinical correctness.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1361841523000592", "content": "It also indicates that the radiology report generation benefits from the multi-modal alignment mechanism and the learned knowledge base, avoiding laborious manual construction of the knowledge graph or template database. Furthermore, the proposed methods boost the quality of generated reports in both natural language and clinical correctness."}
+{"idx": 3, "title": "Awesome Radiology Report Generation - GitHub", "date": "", "ddg_snippet": "A curated list of radiology report generation (medical report generation ) and related areas. :-) - zhjohnchan/awesome- radiology - report - generation", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/zhjohnchan/awesome-radiology-report-generation", "content": "A curated list of radiology report generation (medical report generation ) and related areas. :-) - zhjohnchan/awesome- radiology - report - generation"}
+{"idx": 4, "title": "DART | PDF | Artificial Intelligence | Intelligence (AI) & Semantics", "date": "", "ddg_snippet": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/876841025/DART", "content": "The document presents the DART framework, which focuses on automatic radiology report generation by ensuring disease-aware image-text alignment and incorporating a self-correction mechanism. This two-stage approach first generates initial reports through image-to-text retrieval with a disease-matching constraint and then refines these reports by re-aligning them with input X-ray images. The ..."}
+{"idx": 5, "title": "Medical radiology report generation: A systematic review of current ...", "date": "", "ddg_snippet": "Medical radiology reports play a crucial role in diagnosing various diseases, yet generating them manually is time-consuming and burdens clinical workflows. Medical radiology report generation aims to automate this process using deep learning to assist radiologists and reduce patient wait times. This study presents the most comprehensive systematic review to date on deep learning-based MRRG ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0933365725001551", "content": "Medical radiology reports play a crucial role in diagnosing various diseases, yet generating them manually is time-consuming and burdens clinical workflows. Medical radiology report generation aims to automate this process using deep learning to assist radiologists and reduce patient wait times. This study presents the most comprehensive systematic review to date on deep learning-based MRRG ..."}
+{"idx": 6, "title": "PDF Progressive Transformer-Based Generation of Radiology Reports", "date": "", "ddg_snippet": "As shown in Figure 1, we divide the prob-lem of radiology report generation into two steps. In the first step, the model generates global con-cepts from the image and then reforms them into finer and coherent text using a transformer archi-tecture.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.findings-emnlp.241.pdf", "content": "As shown in Figure 1, we divide the prob-lem of radiology report generation into two steps. In the first step, the model generates global con-cepts from the image and then reforms them into finer and coherent text using a transformer archi-tecture."}
+{"idx": 7, "title": "PDF DART: Disease-aware Image-Text Alignment and Self-correcting Re ...", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate ini-tial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared em-bedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate ini-tial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared em-bedding space through contrastive ..."}
+{"idx": 8, "title": "GitHub - banbooliang/REVTAF-RRG", "date": "", "ddg_snippet": "Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generation If you find this project useful, please give us a star🌟. Framework Automated radiology report generation is essential for improving diagnostic efficiency and reducing the workload of medical professionals.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/banbooliang/REVTAF-RRG", "content": "Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generation If you find this project useful, please give us a star🌟. Framework Automated radiology report generation is essential for improving diagnostic efficiency and reducing the workload of medical professionals."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html", "content": "In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation ( DART ) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive ..."}
diff --git a/data/sampled_jsons/DCBM_area_filter_minimum_area_maximum_area_segmentation_year_2024.jsonl b/data/sampled_jsons/DCBM_area_filter_minimum_area_maximum_area_segmentation_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ed50f13322669b486c055ac24f318c37c63d090d
--- /dev/null
+++ b/data/sampled_jsons/DCBM_area_filter_minimum_area_maximum_area_segmentation_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "An open-source pipeline for the analysis of filopodia - PMC", "date": "", "ddg_snippet": "by V Urbančič · 2017 · Cited by 61 — Robust filopodia segmentation, tracking, and measurement across cell types. We developed Filopodyan for the growth cones in Xenopus laevis RGC ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5626553/", "content": "by V Urbančič · 2017 · Cited by 61 — Robust filopodia segmentation, tracking, and measurement across cell types. We developed Filopodyan for the growth cones in Xenopus laevis RGC ..."}
+{"idx": 1, "title": "Filopodyan: an open-source pipeline for the analysis of filopodia", "date": "", "ddg_snippet": "channel and the following segmentation and filtering parameters: Thresholding: 35. Renyi entropy, σ LoG = 2.6, ED = 4, number of base back frames = 20 (other.", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/138610v2.full.pdf", "content": "channel and the following segmentation and filtering parameters: Thresholding: 35. Renyi entropy, σ LoG = 2.6, ED = 4, number of base back frames = 20 (other."}
+{"idx": 2, "title": "Econometrics and Statistics (EcoSta 2025)", "date": "", "ddg_snippet": "21 Aug 2025 — The Computational and Financial Econometrics (CFEnetwork) comprises a number of specialized teams in various research areas of theoretical and ...", "subpage_snippet": "", "source": "www.cmstatistics.org", "link": "https://www.cmstatistics.org/EcoSta2025/docs/BoA.pdf?20250728012733", "content": "21 Aug 2025 — The Computational and Financial Econometrics (CFEnetwork) comprises a number of specialized teams in various research areas of theoretical and ..."}
+{"idx": 3, "title": "Downloads 2025", "date": "", "ddg_snippet": "Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms · Learning Mixtures of Experts with EM: A Mirror Descent Perspective · Learning Monotonic ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "Learning Minimum-Size BDDs: Towards Efficient Exact Algorithms · Learning Mixtures of Experts with EM: A Mirror Descent Perspective · Learning Monotonic ..."}
+{"idx": 4, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 5, "title": "Filopodyan: An open-source pipeline for the analysis of", "date": "", "ddg_snippet": "Urbančič et al. developed an open-source platform called Filopodyan (filopodia dynamics analysis) in Fiji and R to measure fluorescence in ...", "subpage_snippet": "", "source": "rupress.org", "link": "https://rupress.org/jcb/article/216/10/3405/38925/Filopodyan-An-open-source-pipeline-for-the", "content": "Urbančič et al. developed an open-source platform called Filopodyan (filopodia dynamics analysis) in Fiji and R to measure fluorescence in ..."}
+{"idx": 6, "title": "Machine Learning in Sensors and Imaging", "date": "", "ddg_snippet": "2022 by the authors. Articles in this book are Open Access and distributed under the Creative. Commons Attribution (CC BY) license, which allows users to ...", "subpage_snippet": "", "source": "unglueit-files.s3.amazonaws.com", "link": "https://unglueit-files.s3.amazonaws.com/ebf/af61d61cc2f4412983b7366de44a7cc5.pdf", "content": "2022 by the authors. Articles in this book are Open Access and distributed under the Creative. Commons Attribution (CC BY) license, which allows users to ..."}
+{"idx": 7, "title": "an open-source pipeline for the analysis of filopodia", "date": "", "ddg_snippet": "17 May 2017 — It is possible to segment filopodia at bigger pixel dimensions and consequently a smaller number of pixels per filopodium width, but the ...", "subpage_snippet": "", "source": "www.biorxiv.org", "link": "https://www.biorxiv.org/content/10.1101/138610v1.full-text", "content": "17 May 2017 — It is possible to segment filopodia at bigger pixel dimensions and consequently a smaller number of pixels per filopodium width, but the ..."}
+{"idx": 8, "title": "Hydrophobin Bilayer as Water Impermeable Protein Membrane", "date": "", "ddg_snippet": "by F Nolle · 2023 · Cited by 1 — This study reports on the very low water permeability of pure protein membranes composed of a bilayer of the amphiphilic protein hydrophobin HFBI.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.langmuir.3c01006", "content": "by F Nolle · 2023 · Cited by 1 — This study reports on the very low water permeability of pure protein membranes composed of a bilayer of the amphiphilic protein hydrophobin HFBI."}
+{"idx": 9, "title": "Linear Regression and Its Inference on Noisy Network-Linked ...", "date": "", "ddg_snippet": "by CM Le · 2022 · Cited by 40 — This paper proposes a regression model with non-parametric network effects. The model does not assume that the relational data or network structure is exactly ...", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/jrsssb/article/84/5/1851/7072882", "content": "by CM Le · 2022 · Cited by 40 — This paper proposes a regression model with non-parametric network effects. The model does not assume that the relational data or network structure is exactly ..."}
diff --git a/data/sampled_jsons/DSDFM_Algorithm_1_line_8_D_Drift_DerODE_DivSDE_human_motion_synthesis_year_2024.jsonl b/data/sampled_jsons/DSDFM_Algorithm_1_line_8_D_Drift_DerODE_DivSDE_human_motion_synthesis_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4be30cac36206e99efa7a3af28c1d367918a0c20
--- /dev/null
+++ b/data/sampled_jsons/DSDFM_Algorithm_1_line_8_D_Drift_DerODE_DivSDE_human_motion_synthesis_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2505.00998] Deterministic-to-Stochastic Diverse Latent ... Deterministic-to-Stochastic Diverse Latent Feature Mapping ... 论文阅读--DSDFM--用于人体运动合成的确定性到随机多种潜在特征映射 -... arXiv:2505.00998v1 [cs.CV] 2 May 2025 CVPR 2025 Open Access Repository Appendix - CVF Open Access MDM: Human Motion Diffusion Model - GitHub", "date": "", "ddg_snippet": "May 2, 2025 · In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions . To synthesize diverse and accurate human motions , we propose a novel method called DSDFM for human motion synthesis . The proposed method has straight trajectories and is easy to train compared to previous SGMs methods, while guaranteeing the diversity and accuracy of the generated hu-man motions. May 29, 2025 · 首先，提出了Diverse Latent Feature Mapping ( DS D FM )方法，可以在采样过程有效训练和利用；，可以用于有条件和无条件的生成。 提出了一个最优解，剪力高斯分布和 人体 运动 潜在空间的关系，同时在采样过程提供随机多样化的输出生成过程。 thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions , thereby enhancing the diversity and ac The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions , thereby enhancing the diversity and accuracy of the generated human motions . In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action-to-Motion tasks. (1) We released the 50 diffusion steps model (instead of 1000 steps) which runs 20X faster with comparable results. (2) Calling CLIP just once and caching the result runs 2X faster for all models.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.00998", "content": "May 2, 2025 · In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping ( DSDFM ) method for human motion synthesis . DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions . To synthesize diverse and accurate human motions , we propose a novel method called DSDFM for human motion synthesis . The proposed method has straight trajectories and is easy to train compared to previous SGMs methods, while guaranteeing the diversity and accuracy of the generated hu-man motions. May 29, 2025 · 首先，提出了Diverse Latent Feature Mapping ( DS D FM )方法，可以在采样过程有效训练和利用；，可以用于有条件和无条件的生成。 提出了一个最优解，剪力高斯分布和 人体 运动 潜在空间的关系，同时在采样过程提供随机多样化的输出生成过程。 thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions , thereby enhancing the diversity and ac The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions , thereby enhancing the diversity and accuracy of the generated human motions . In this work, we use the following metrics to measure the per-formance of the proposed method for unconditional human motion synthesis and Action-to-Motion tasks. (1) We released the 50 diffusion steps model (instead of 1000 steps) which runs 20X faster with comparable results. (2) Calling CLIP just once and caching the result runs 2X faster for all models."}
+{"idx": 1, "title": "DivDiff: A Conditional Diffusion Model for Diverse Human Motion ...", "date": "", "ddg_snippet": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387413105_DivDiff_A_Conditional_Diffusion_Model_for_Diverse_Human_Motion_Prediction", "content": "Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 2, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "Abstract: Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.00998", "content": "Abstract: Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task."}
+{"idx": 3, "title": "Human Fall Flat: Cheats | Console Commands", "date": "", "ddg_snippet": "This guide will tell you how to access the console and all of the known commands in Human Fall Flat.", "subpage_snippet": "", "source": "www.yekbot.com", "link": "https://www.yekbot.com/human-fall-flat-console-commands/", "content": "This guide will tell you how to access the console and all of the known commands in Human Fall Flat."}
+{"idx": 4, "title": "Free YouTube Video Downloader Online (1080p) - YT 1 D", "date": "", "ddg_snippet": "Copy the youtube link of the video and paste it into the input line .", "subpage_snippet": "", "source": "yt1d.com", "link": "https://yt1d.com/en307/", "content": "Copy the youtube link of the video and paste it into the input line ."}
+{"idx": 5, "title": "论文阅读--DSDFM--用于人体运动合成的确定性到随机多种潜在特征映射 -...", "date": "", "ddg_snippet": "May 29, 2025 · 首先，提出了Diverse Latent Feature Mapping ( DS D FM )方法，可以在采样过程有效训练和利用；，可以用于有条件和无条件的生成。 提出了一个最优解，剪力高斯分布和 人体 运动 潜在空间的关系，同时在采样过程提供随机多样化的输出生成过程。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1911144623440625860", "content": "May 29, 2025 · 首先，提出了Diverse Latent Feature Mapping ( DS D FM )方法，可以在采样过程有效训练和利用；，可以用于有条件和无条件的生成。 提出了一个最优解，剪力高斯分布和 人体 运动 潜在空间的关系，同时在采样过程提供随机多样化的输出生成过程。"}
+{"idx": 6, "title": "MDM: Human Motion Diffusion Model - GitHub", "date": "", "ddg_snippet": "(1) We released the 50 diffusion steps model (instead of 1000 steps) which runs 20X faster with comparable results. (2) Calling CLIP just once and caching the result runs 2X faster for all models.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GuyTevet/motion-diffusion-model", "content": "(1) We released the 50 diffusion steps model (instead of 1000 steps) which runs 20X faster with comparable results. (2) Calling CLIP just once and caching the result runs 2X faster for all models."}
+{"idx": 7, "title": "DDA Line Drawing Algorithm - Computer Graphics - YouTube", "date": "", "ddg_snippet": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=W5P8GlaEOSI", "content": "About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How..."}
+{"idx": 8, "title": "Deterministic-to-Stochastic Diverse Latent Feature Mapping ...", "date": "", "ddg_snippet": "To synthesize diverse and accurate human motions , we propose a novel method called DSDFM for human motion synthesis . The proposed method has straight trajectories and is easy to train compared to previous SGMs methods, while guaranteeing the diversity and accuracy of the generated hu-man motions.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "To synthesize diverse and accurate human motions , we propose a novel method called DSDFM for human motion synthesis . The proposed method has straight trajectories and is easy to train compared to previous SGMs methods, while guaranteeing the diversity and accuracy of the generated hu-man motions."}
+{"idx": 9, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions , thereby enhancing the diversity and ac", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "thesis. DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the laten space distribution of human motions . The second diverse mo-tion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of hu-man motions , thereby enhancing the diversity and ac"}
diff --git a/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_Section_3.3_derivative_computation_method_year_2023.jsonl b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_Section_3.3_derivative_computation_method_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7971e70aba9c4a85141b9960276f1423c7b9f18b
--- /dev/null
+++ b/data/sampled_jsons/DVI_A_Derivative-based_Vision_Network_for_INR_Section_3.3_derivative_computation_method_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DVI:A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "To address these issues, we propose DVI , a novel Derivative-based Vision network for INR , capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster- based methods into a INR based architecture.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4Xnqm4f71y", "content": "To address these issues, we propose DVI , a novel Derivative-based Vision network for INR , capable of handling a variety of vision tasks across various data modalities, while achieving the best performance among the existing methods by incorporating state of the art raster- based methods into a INR based architecture."}
+{"idx": 1, "title": "ICML Poster DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46476", "content": "DVI:A Derivative-based Vision Network for INR RUNZHAO YANG · Xiaolong Wu · Zhihong Zhang · Fabian Zhang · Tingxiong Xiao · Zongren Li · Kunlun He · Jinli Suo"}
+{"idx": 2, "title": "DVI:A Derivative-based Vision Network for INR - AMiner", "date": "", "ddg_snippet": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre", "subpage_snippet": "", "source": "www.aminer.cn", "link": "https://www.aminer.cn/pub/6853e848163c01c8502f0416/dvi-a-derivative-based-vision-network-for-inr", "content": "Recent advancements in computer vision have seen Implicit Neural Representations ( INR ) becoming a dominant representation form for data due to their compactness and expre"}
+{"idx": 3, "title": "PDF book13.dvi - Stanford University", "date": "", "ddg_snippet": "In practice, we \"set\" the derivative at 0 to be either 0 (the left derivative ) or 1 (the right derivative ). In modern neural nets, a version of ReLU has replaced sigmoid as the de-fault choice of activation function.", "subpage_snippet": "", "source": "infolab.stanford.edu", "link": "http://infolab.stanford.edu/~ullman/mmds/ch13.pdf", "content": "In practice, we \"set\" the derivative at 0 to be either 0 (the left derivative ) or 1 (the right derivative ). In modern neural nets, a version of ReLU has replaced sigmoid as the de-fault choice of activation function."}
+{"idx": 4, "title": "PDF Chap3slides.dvi - Charu Aggarwal", "date": "", "ddg_snippet": "An Exponential Time Algorithm for Computing Partial Derivatives The path aggregation lemma provides a simple way to com-pute the derivative with respect to intermediate variable w Use computational graph to compute each value", "subpage_snippet": "", "source": "www.charuaggarwal.net", "link": "http://www.charuaggarwal.net/Chap3slides.pdf", "content": "An Exponential Time Algorithm for Computing Partial Derivatives The path aggregation lemma provides a simple way to com-pute the derivative with respect to intermediate variable w Use computational graph to compute each value"}
+{"idx": 5, "title": "A multi-order derivative feature-based quality assessment model for ...", "date": "", "ddg_snippet": "In Section 2, the proposed multi-order derivative feature- based IQA model for LFIs is proposed. In Section 3, the performance comparison between the proposed IQA model and the state-of-the-art ones is discussed.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1047320318302785", "content": "In Section 2, the proposed multi-order derivative feature- based IQA model for LFIs is proposed. In Section 3, the performance comparison between the proposed IQA model and the state-of-the-art ones is discussed."}
+{"idx": 6, "title": "Data Denoising and Derivative Estimation for Data-Driven Modeling of ...", "date": "", "ddg_snippet": "The paper is organised as follows. Section 2 summarises the SINDy algorithm. Section 3 reviews the most effective two-step approaches. Section 4 introduces RKTV- INR , our two-step INR - based framework for data-driven modeling with time dependence and noise. Section 5 presents experiments demonstrating the effectiveness of the proposed method .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.14219", "content": "The paper is organised as follows. Section 2 summarises the SINDy algorithm. Section 3 reviews the most effective two-step approaches. Section 4 introduces RKTV- INR , our two-step INR - based framework for data-driven modeling with time dependence and noise. Section 5 presents experiments demonstrating the effectiveness of the proposed method ."}
+{"idx": 7, "title": "DVI:A Derivative-based Vision Network for INR", "date": "", "ddg_snippet": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/167863?from=subpath-search", "content": "DVI excels by extracting semantic information from the high order derivative map of the INR , then seamlessly fusing it into a pre-existing raster- based vision network , enhancing its performance with deeper, task-relevant semantic insights."}
+{"idx": 8, "title": "DVI: A Derivative-based Vision Network for INR - OpenReview", "date": "", "ddg_snippet": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=4Xnqm4f71y", "content": "DVI excels by leveraging the valuable features captured in the high order derivative map of the INR , then seamlessly fusing them into a pre-existing raster- based vision network , enhanc-ing its performance with additional, task-relevant structural information."}
+{"idx": 9, "title": "Optimization-based region-of-interest reconstruction for X-ray computed ...", "date": "", "ddg_snippet": "The customized optimizations and algorithms based on the TV and derivative-based data terms can serve as a powerful tool for interior reconstructions. Simulations and real-data experiments indicate that the proposed methods can be of practical value for CT imaging applications.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1120179718300036", "content": "The customized optimizations and algorithms based on the TV and derivative-based data terms can serve as a powerful tool for interior reconstructions. Simulations and real-data experiments indicate that the proposed methods can be of practical value for CT imaging applications."}
diff --git a/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_computation_time_breakdown_table_values_percentages.jsonl b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_computation_time_breakdown_table_values_percentages.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bd5dd41437ad4c6db3fdf73e2f7961e5a37e7e1d
--- /dev/null
+++ b/data/sampled_jsons/Descriptor-In-Pixel_SCAMP-7_computation_time_breakdown_table_values_percentages.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "... Descriptor -In- Pixel . Table 1. SCAMP - 7 Computation Time Breakdown . image. Figure 8. Comparison of feature lifetime histograms, our approach vs tracking ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/32867", "content": "... Descriptor -In- Pixel . Table 1. SCAMP - 7 Computation Time Breakdown . image. Figure 8. Comparison of feature lifetime histograms, our approach vs tracking ..."}
+{"idx": 1, "title": "Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "We introduce a Descriptor -In- Pixel paradigm, in which a feature descriptor is held within the memory of each pixel -processor. The PPA's architecture enables the ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/154940", "content": "We introduce a Descriptor -In- Pixel paradigm, in which a feature descriptor is held within the memory of each pixel -processor. The PPA's architecture enables the ..."}
+{"idx": 2, "title": "PDF Descriptor-In-Pixel : Point-Feature Tracking For Pixel Processor Arrays", "date": "", "ddg_snippet": "A breakdown of the total computation time per frame is given in Table 1. To put these numbers into perspective, the time taken to out-put a full uncompressed image from the sensor is well over 20000 μs, and even a 4-bit image requires 2700 μs.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Bose_Descriptor-In-Pixel__Point-Feature_Tracking_For_Pixel_Processor_Arrays_CVPR_2025_paper.pdf", "content": "A breakdown of the total computation time per frame is given in Table 1. To put these numbers into perspective, the time taken to out-put a full uncompressed image from the sensor is well over 20000 μs, and even a 4-bit image requires 2700 μs."}
+{"idx": 3, "title": "PDF Live Demonstration: SCAMP-7 - GitHub Pages", "date": "", "ddg_snippet": "Abstract We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture em-beds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual ...", "subpage_snippet": "", "source": "tub-rip.github.io", "link": "https://tub-rip.github.io/eventvision2023/papers/2023CVPRW_Live_Demonstration_Scamp-7.pdf", "content": "Abstract We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture em-beds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual ..."}
+{"idx": 4, "title": "SCAMP Vision Sensor - University of Manchester", "date": "", "ddg_snippet": "The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations , for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ...", "subpage_snippet": "", "source": "personalpages.manchester.ac.uk", "link": "https://personalpages.manchester.ac.uk/staff/p.dudek/scamp/", "content": "The SCAMP Vision Sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. Unlike a conventional image sensor, it does not output raw images, but rather the results of on-sensor computations , for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. The device is fully programmable, to execute a ..."}
+{"idx": 5, "title": "Live Demonstration: SCAMP-7 - IEEE Xplore", "date": "", "ddg_snippet": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual information ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10208514", "content": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed \"on-sensor\" at high speeds while avoiding the slow and power hungry transfer of image data off the device. Through in-pixel computations over the image array, visual information ..."}
+{"idx": 6, "title": "Live Demonstration: SCAMP-7 - computer.org", "date": "", "ddg_snippet": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed", "subpage_snippet": "", "source": "www.computer.org", "link": "https://www.computer.org/csdl/proceedings-article/cvprw/2023/024900d995/1PBy0sv9HtC", "content": "We propose to demonstrate the next generation of the SCAMP vision sensor, SCAMP-7 , whose architecture embeds programmable processing cores into each pixel . This IC allows various computer vision tasks to be performed"}
+{"idx": 7, "title": "SCAMP Vision Sensor", "date": "", "ddg_snippet": "The SCAMP vision sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. It does not output raw images, like a conventional camera, but rather the results of on-sensor computations , for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. As costly early vision computations are done entirely on ...", "subpage_snippet": "", "source": "www.scamp-vision-chip.org", "link": "https://www.scamp-vision-chip.org/", "content": "The SCAMP vision sensor integrates a massively parallel SIMD processor array into the pixels of the image sensor device. It does not output raw images, like a conventional camera, but rather the results of on-sensor computations , for instance a feature map, optic flow map and/or address-events describing locations of pixels of interest. As costly early vision computations are done entirely on ..."}
+{"idx": 8, "title": "Bose Descriptor-In-Pixel Point-Feature Tracking For Pixel Processor ...", "date": "", "ddg_snippet": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in-pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/890539401/Bose-Descriptor-In-Pixel-Point-Feature-Tracking-for-Pixel-Processor-Arrays-CVPR-2025-Paper", "content": "This paper introduces a novel method for point-feature detection and tracking using Pixel Processor Array (PPA) vision sensors, which enables in-pixel computation and significantly reduces data transfer requirements. The proposed Descriptor-In-Pixel paradigm allows for efficient processing at over 3000 FPS, making it suitable for high-speed applications while maintaining low latency. By ..."}
+{"idx": 9, "title": "Live Demonstration: CNN Inference on the Focal Plane with a Pixel ...", "date": "", "ddg_snippet": "We present a novel method of CNN inference on a pixel processor array device, demonstrating it using a handwritten digit (digits 0-9) classification task, with all steps of the neural network computation performed on the focal plane. The vision chip that we deploy ( SCAMP-7 ) has a 256×256 array of processor elements (PE) integrated within the image sensor. The algorithm runs at over 3000 ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9180959", "content": "We present a novel method of CNN inference on a pixel processor array device, demonstrating it using a handwritten digit (digits 0-9) classification task, with all steps of the neural network computation performed on the focal plane. The vision chip that we deploy ( SCAMP-7 ) has a 256×256 array of processor elements (PE) integrated within the image sensor. The algorithm runs at over 3000 ..."}
diff --git a/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_filetypepdf.jsonl b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_filetypepdf.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e53ab9175158f78a501f32ea40b4ed71be95a11
--- /dev/null
+++ b/data/sampled_jsons/Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_filetypepdf.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Plug-and-Play Multi-Criteria Guidance for Diverse In-Betweening Human ...", "date": "", "ddg_snippet": "Deterministic-to-stochastic diverse latent feature mapping for human motion synthesis . In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pages 22724-22734, June 2025.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.01590", "content": "Deterministic-to-stochastic diverse latent feature mapping for human motion synthesis . In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), pages 22724-22734, June 2025."}
+{"idx": 1, "title": "PDF Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Yu Hua1,3 Weiming Liu2* Gui Xu 4 Yaqing Hou 3", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis Yu Hua1,3 Weiming Liu2* Gui Xu 4 Yaqing Hou 3"}
+{"idx": 2, "title": "Unified Motion Generation across Humans and Animals", "date": "", "ddg_snippet": "by X Wang · 2025 — Deterministic-to-Stochastic Diverse Latent Feature . Mapping for Human Motion Synthesis. In Proceedings of the Computer Vision and Pattern ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.05162", "content": "by X Wang · 2025 — Deterministic-to-Stochastic Diverse Latent Feature . Mapping for Human Motion Synthesis. In Proceedings of the Computer Vision and Pattern ..."}
+{"idx": 3, "title": "A Framework for Denoising-Diffusion-Based Motion Synthesis", "date": "", "ddg_snippet": "by R Dabral · 2023 · Cited by 234 — The result is a new versatile framework for human motion synthesis that produces diverse , temporally and kinemati- cally plausible, and semantically accurate ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Dabral_Mofusion_A_Framework_for_Denoising-Diffusion-Based_Motion_Synthesis_CVPR_2023_paper.pdf", "content": "by R Dabral · 2023 · Cited by 234 — The result is a new versatile framework for human motion synthesis that produces diverse , temporally and kinemati- cally plausible, and semantically accurate ... 11 pages"}
+{"idx": 4, "title": "Diverse Human Motion Prediction Guided by Multi-Level ...", "date": "", "ddg_snippet": "by S Xu · 2022 · Cited by 61 — Existing work on deterministic human motion forecasting predicts a single future motion based on a sequence of past poses [1,6,15,42,49], or video frames [11,71 ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/servlets/purl/10345814", "content": "by S Xu · 2022 · Cited by 61 — Existing work on deterministic human motion forecasting predicts a single future motion based on a sequence of past poses [1,6,15,42,49], or video frames [11,71 ..."}
+{"idx": 5, "title": "MoLA: Motion Generation and Editing with Latent Diffusion ...", "date": "", "ddg_snippet": "by K Uchida · 2025 · Cited by 6 — MoLA achieves fast and high-quality human motion generation given textual descriptions while enabling motion editing appli- cations. With MoLA, we can deal with ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025W/HuMoGen/papers/Uchida_MoLA_Motion_Generation_and_Editing_with_Latent_Diffusion_Enhanced_by_CVPRW_2025_paper.pdf", "content": "by K Uchida · 2025 · Cited by 6 — MoLA achieves fast and high-quality human motion generation given textual descriptions while enabling motion editing appli- cations. With MoLA, we can deal with ..."}
+{"idx": 6, "title": "Scene-responsive Diverse Human Motion Prediction", "date": "", "ddg_snippet": "by Z Lou · 2024 · Cited by 4 — 2.1 Stochastic Human Motion Prediction. Human motion prediction diverges into deterministic and stochastic methods. Deterministic models aim to predict a ... 28 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/4620a66570e554a3ff0e39dc59bcb07a-Paper-Conference.pdf", "content": "by Z Lou · 2024 · Cited by 4 — 2.1 Stochastic Human Motion Prediction. Human motion prediction diverges into deterministic and stochastic methods. Deterministic models aim to predict a ... 28 pages"}
+{"idx": 7, "title": "towards-diverse-and-natural-scene-aware-3d-human- ...", "date": "", "ddg_snippet": "To faithfully capture the diversity of scene-aware human motions , we propose a novel three-stage motion synthesis framework, each stage of which is responsible ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/towards-diverse-and-natural-scene-aware-3d-human-motion-wuebjsp0.pdf", "content": "To faithfully capture the diversity of scene-aware human motions , we propose a novel three-stage motion synthesis framework, each stage of which is responsible ..."}
+{"idx": 8, "title": "Text-to-Motion Synthesis using Discrete Diffusion Model", "date": "", "ddg_snippet": "by A Chemburkar · 2022 — Deterministic approaches learn a direct mapping between input text and human motions with either a sequence-to-sequence model [25] or an encoder-decoder ...", "subpage_snippet": "", "source": "papers.bmvc2023.org", "link": "https://papers.bmvc2023.org/0624.pdf", "content": "by A Chemburkar · 2022 — Deterministic approaches learn a direct mapping between input text and human motions with either a sequence-to-sequence model [25] or an encoder-decoder ..."}
+{"idx": 9, "title": "Learning Diverse Stochastic Human-Action Generators by ...", "date": "", "ddg_snippet": "by Z Wang · 2020 · Cited by 64 — For diverse action generation, models are required to be stochastic so that the synthesis process can be considered as drawing samples from an action-sequence ...", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/6911/6911-13-10140-1-10-20200525.pdf", "content": "by Z Wang · 2020 · Cited by 64 — For diverse action generation, models are required to be stochastic so that the synthesis process can be considered as drawing samples from an action-sequence ..."}
diff --git a/data/sampled_jsons/DivSDE_diverse_stochastic_differential_equation_sampling_mechanism_randomness_motion_synthesis.jsonl b/data/sampled_jsons/DivSDE_diverse_stochastic_differential_equation_sampling_mechanism_randomness_motion_synthesis.jsonl
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index 0000000000000000000000000000000000000000..dd3f3afa0c158d8d5f2c30ff52aa123234ab4372
--- /dev/null
+++ b/data/sampled_jsons/DivSDE_diverse_stochastic_differential_equation_sampling_mechanism_randomness_motion_synthesis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic Sampling Variants in Diffusion - apxml.com", "date": "", "ddg_snippet": "Stochastic sampling variants use the randomness of the diffusion process, often aligning more closely with the underlying theory and sometimes offering advantages in sample diversity and quality, albeit typically requiring more sampling steps than highly optimized deterministic methods.", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/advanced-diffusion-architectures/chapter-6-advanced-sampling-optimization/stochastic-sampling-variants", "content": "Stochastic sampling variants use the randomness of the diffusion process, often aligning more closely with the underlying theory and sometimes offering advantages in sample diversity and quality, albeit typically requiring more sampling steps than highly optimized deterministic methods."}
+{"idx": 1, "title": "PDF Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human ...", "date": "", "ddg_snippet": "Therefore, the designed stochastic diverse output generation procedure aims to en-hance the diversity of generated human motions through Diverse Stochastic Differential Equations ( DivSDE ).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf", "content": "Therefore, the designed stochastic diverse output generation procedure aims to en-hance the diversity of generated human motions through Diverse Stochastic Differential Equations ( DivSDE )."}
+{"idx": 2, "title": "Diffusion Models: Sampling via Stochastic Differential Equations", "date": "", "ddg_snippet": "Diffusion Models: Sampling via Stochastic Differential Equations 10 minute read Published: August 11, 2024 Generative Diffusion Processes We are given a sample $X_1, \\dots, X_N \\sim P$ of size $N \\in \\mathbb {N}$ drawn from an unknown data distribution $P \\in \\mathcal {P} (\\mathbb {R}^D)$.", "subpage_snippet": "", "source": "veitwild.github.io", "link": "https://veitwild.github.io/posts/2024/08/diffusion-models/", "content": "Diffusion Models: Sampling via Stochastic Differential Equations 10 minute read Published: August 11, 2024 Generative Diffusion Processes We are given a sample $X_1, \\dots, X_N \\sim P$ of size $N \\in \\mathbb {N}$ drawn from an unknown data distribution $P \\in \\mathcal {P} (\\mathbb {R}^D)$."}
+{"idx": 3, "title": "PDF Score-based diffusion models via stochastic differential equations", "date": "", "ddg_snippet": "Abstract: This is an expository article on the score-based diffusion mod-els, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling - sampling and score matching, which encompass the SDE/ODE sampling , score matching efficiency, the consistency models, and reinforcement learning ...", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~wt2319/DMTut.pdf", "content": "Abstract: This is an expository article on the score-based diffusion mod-els, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling - sampling and score matching, which encompass the SDE/ODE sampling , score matching efficiency, the consistency models, and reinforcement learning ..."}
+{"idx": 4, "title": "PDF Stochastic Differential Equations with Variational Wishart Diffusions", "date": "", "ddg_snippet": "Abstract We present a Bayesian non-parametric way of in-ferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasis on the stochastic part of the differential equation , also known as the diffusion, and modelling it by means of Wishart processes. Further, we present a semi-parametric approach that allows the framework ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/jorgensen20a/jorgensen20a.pdf", "content": "Abstract We present a Bayesian non-parametric way of in-ferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling. The work has high emphasis on the stochastic part of the differential equation , also known as the diffusion, and modelling it by means of Wishart processes. Further, we present a semi-parametric approach that allows the framework ..."}
+{"idx": 5, "title": "A sampling-variability-free dimension-reduced probability density ...", "date": "", "ddg_snippet": "The DR-PDEE method then yields one- or two-dimensional partial differential equations governing the evolution of the response probability density function, which can be solved for effective stochastic dynamic response analysis.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0951832025009275", "content": "The DR-PDEE method then yields one- or two-dimensional partial differential equations governing the evolution of the response probability density function, which can be solved for effective stochastic dynamic response analysis."}
+{"idx": 6, "title": "Adaptive Methods for Stochastic Differential Equations Via Natural ...", "date": "", "ddg_snippet": "Adaptive time-stepping with high-order embedded Runge-Kutta pairs and rejection sampling provides efficient approaches for solving differential equations . While many such methods exist for solving deterministic systems, little progress has been made ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5844583/", "content": "Adaptive time-stepping with high-order embedded Runge-Kutta pairs and rejection sampling provides efficient approaches for solving differential equations . While many such methods exist for solving deterministic systems, little progress has been made ..."}
+{"idx": 7, "title": "Stochastic Sampling from Deterministic Flow Models - arXiv.org", "date": "", "ddg_snippet": "To enable stochastic sampling from such deterministic models, we provide a special case of our general result to turn the underlying ODE of Gaussian flow models into a family of stochastic differential equations (SDEs) that have the same marginal distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02217v1", "content": "To enable stochastic sampling from such deterministic models, we provide a special case of our general result to turn the underlying ODE of Gaussian flow models into a family of stochastic differential equations (SDEs) that have the same marginal distributions."}
+{"idx": 8, "title": "Efficient sampling of stochastic differential equations with positive ...", "date": "", "ddg_snippet": "Abstract This paper deals with the problem of efficient sampling from a stochastic differential equation , given the drift function and the diffusion matrix.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3666122.3668929", "content": "Abstract This paper deals with the problem of efficient sampling from a stochastic differential equation , given the drift function and the diffusion matrix."}
+{"idx": 9, "title": "arXiv:2505.00998v1 [cs.CV] 2 May 2025", "date": "", "ddg_snippet": "e diversity of the generated samples. To provide more diverse while accurate human motions , we tend to involve the stochastic differential equations based on the ordinary differential equations in the stochasti", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.00998", "content": "e diversity of the generated samples. To provide more diverse while accurate human motions , we tend to involve the stochastic differential equations based on the ordinary differential equations in the stochasti"}
diff --git a/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_DISEF_consists_of_two_main_components.jsonl b/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_DISEF_consists_of_two_main_components.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/Diversified_in-domain_synthesis_with_efficient_fine-tuning_DISEF_consists_of_two_main_components.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diversified in-domain synthesis with efficient fine-tuning ...", "date": "", "ddg_snippet": "DISEF consists of two main components . First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.03046v2", "content": "DISEF consists of two main components . First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization."}
+{"idx": 1, "title": "Diversified in-domain synthesis with efficient fine-tuning ... Diversified in-domain synthesis with efficient fine-tuning ... 3 arXiv:2312.03046v2 [cs.CV] 7 Dec 2023 publications | Nicola Dall'Asen Daily Papers - Hugging Face Fugu-MT 論文翻訳 (概要): Diversified in-domain synthesis with ... [2312.03046] Diversified in - domain synthesis with efficient fine - tuning Diversified in - domain synthesis with efficient fine - tuning for few-shot [2312.03046] Diversified in - domain synthesis with efficient fine - tuning Diversified in - domain synthesis with efficient fine - tuning for few-shot Diversified in - domain synthesis with efficient fine - tuning for few-shot DataDream: Few-shot Guided Dataset Generation", "date": "", "ddg_snippet": "Dec 5, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . DISEF consists of two main components . First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state- of -the-art text-to-image generation models. Following this trend, we pro-pose Diversified ... See full list on arxiv.org SAP augments the support set X with additional synthetic data points that are interpolated within the domain inferred by X. The in - domain synthesis requires the synthesized im-ages not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be u... See full list on arxiv.org where B ∈ Rm×r, A ∈ Rr×n are two low-rank matri-ces, m and n are the number of rows and columns in the original weight matrix W, and r is the rank of the update matrix, with r ≪ min(m, n). This decomposition greatly reduces the number of trainable parameters to just a fraction of the original layer with no impact on inference time, as it is possibl... See full list on arxiv.org In this section, we demonstrate the effectiveness of our design choices both in the augmentation pipeline and the PEFT part. For SAP, we ablate the effect of using the detail-rich captions obtained by ICM (LLaVA in our case), and the use of real samples as anchors during generation. Addi-tionally, we also demonstrate the effectiveness of the gen-er... See full list on arxiv.org We experiment with different tuning methods augmented with the synthetic data generated by SAP, demonstrating that SAP is generally effective, regardless of fine - tuning methods. We choose the same baselines as Table 1 and train them in the default scenario using the same data used for DISEF . From Table 4, we see that all the baseline methods are ab... See full list on arxiv.org We presented DISEF , a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in- domain diversity and a novel application of a parameter- eficient fine - tuning method to VLMs for effective adaptation. For the synthetic data gen-eration, we propose to apply noise to the real samples and use... See full list on arxiv.org In the supplementary material, we provide additional re-sults. In Section A, we provide an ablation on the number of synthetic images used during training. In Section B, we show the effect of using less real samples for DISEF and all the baselines in the default scenario. In Section C, we provide a complete overview of all the parameters used for o... See full list on arxiv.org Following this trend, we propose Diversified in-domain synthesis with efficient fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is diversified in-domain synthesis with efficient fine-tuning (disef)? Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef (diversified I N-domain s ynthesis with e fficient F? Following this trend, we propose D iversified I n- domain S ynthesis with E fficient F ine- tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef and how does it work? DISEF consists of two main components. First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Does disef perform better without synthetic data? Nonetheless, we can see that DISEF without synthetic data performs better than all methods regardless of the number of shots. When coupled with synthetic data, our method improves on all datasets and with different shots, with the exception of EuroSAT for 4-shot. What is the difference between in-domain synthesis and diversity? The in - domain synthesis requires the synthesized images not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be useful for training and requires the synthesized images to present different semantic details. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2312.03046", "content": "Dec 5, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . DISEF consists of two main components . First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state- of -the-art text-to-image generation models. Following this trend, we pro-pose Diversified ... See full list on arxiv.org SAP augments the support set X with additional synthetic data points that are interpolated within the domain inferred by X. The in - domain synthesis requires the synthesized im-ages not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be u... See full list on arxiv.org where B ∈ Rm×r, A ∈ Rr×n are two low-rank matri-ces, m and n are the number of rows and columns in the original weight matrix W, and r is the rank of the update matrix, with r ≪ min(m, n). This decomposition greatly reduces the number of trainable parameters to just a fraction of the original layer with no impact on inference time, as it is possibl... See full list on arxiv.org In this section, we demonstrate the effectiveness of our design choices both in the augmentation pipeline and the PEFT part. For SAP, we ablate the effect of using the detail-rich captions obtained by ICM (LLaVA in our case), and the use of real samples as anchors during generation. Addi-tionally, we also demonstrate the effectiveness of the gen-er... See full list on arxiv.org We experiment with different tuning methods augmented with the synthetic data generated by SAP, demonstrating that SAP is generally effective, regardless of fine - tuning methods. We choose the same baselines as Table 1 and train them in the default scenario using the same data used for DISEF . From Table 4, we see that all the baseline methods are ab... See full list on arxiv.org We presented DISEF , a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in- domain diversity and a novel application of a parameter- eficient fine - tuning method to VLMs for effective adaptation. For the synthetic data gen-eration, we propose to apply noise to the real samples and use... See full list on arxiv.org In the supplementary material, we provide additional re-sults. In Section A, we provide an ablation on the number of synthetic images used during training. In Section B, we show the effect of using less real samples for DISEF and all the baselines in the default scenario. In Section C, we provide a complete overview of all the parameters used for o... See full list on arxiv.org Following this trend, we propose Diversified in-domain synthesis with efficient fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is diversified in-domain synthesis with efficient fine-tuning (disef)? Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef (diversified I N-domain s ynthesis with e fficient F? Following this trend, we propose D iversified I n- domain S ynthesis with E fficient F ine- tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef and how does it work? DISEF consists of two main components. First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Does disef perform better without synthetic data? Nonetheless, we can see that DISEF without synthetic data performs better than all methods regardless of the number of shots. When coupled with synthetic data, our method improves on all datasets and with different shots, with the exception of EuroSAT for 4-shot. What is the difference between in-domain synthesis and diversity? The in - domain synthesis requires the synthesized images not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be useful for training and requires the synthesized images to present different semantic details. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 2, "title": "3 arXiv:2312.03046v2 [cs.CV] 7 Dec 2023 publications | Nicola Dall'Asen Daily Papers - Hugging Face Fugu-MT 論文翻訳 (概要): Diversified in-domain synthesis with ... [2312.03046] Diversified in - domain synthesis with efficient fine - tuning Diversified in - domain synthesis with efficient fine - tuning for few-shot [2312.03046] Diversified in - domain synthesis with efficient fine - tuning Diversified in - domain synthesis with efficient fine - tuning for few-shot Diversified in - domain synthesis with efficient fine - tuning for few-shot DataDream: Few-shot Guided Dataset Generation", "date": "", "ddg_snippet": "Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state- of -the-art text-to-image generation models. Following this trend, we pro-pose Diversified ... See full list on arxiv.org SAP augments the support set X with additional synthetic data points that are interpolated within the domain inferred by X. The in - domain synthesis requires the synthesized im-ages not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be u... See full list on arxiv.org where B ∈ Rm×r, A ∈ Rr×n are two low-rank matri-ces, m and n are the number of rows and columns in the original weight matrix W, and r is the rank of the update matrix, with r ≪ min(m, n). This decomposition greatly reduces the number of trainable parameters to just a fraction of the original layer with no impact on inference time, as it is possibl... See full list on arxiv.org In this section, we demonstrate the effectiveness of our design choices both in the augmentation pipeline and the PEFT part. For SAP, we ablate the effect of using the detail-rich captions obtained by ICM (LLaVA in our case), and the use of real samples as anchors during generation. Addi-tionally, we also demonstrate the effectiveness of the gen-er... See full list on arxiv.org We experiment with different tuning methods augmented with the synthetic data generated by SAP, demonstrating that SAP is generally effective, regardless of fine - tuning methods. We choose the same baselines as Table 1 and train them in the default scenario using the same data used for DISEF . From Table 4, we see that all the baseline methods are ab... See full list on arxiv.org We presented DISEF , a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in- domain diversity and a novel application of a parameter- eficient fine - tuning method to VLMs for effective adaptation. For the synthetic data gen-eration, we propose to apply noise to the real samples and use... See full list on arxiv.org In the supplementary material, we provide additional re-sults. In Section A, we provide an ablation on the number of synthetic images used during training. In Section B, we show the effect of using less real samples for DISEF and all the baselines in the default scenario. In Section C, we provide a complete overview of all the parameters used for o... See full list on arxiv.org Following this trend, we propose Diversified in-domain synthesis with efficient fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is diversified in-domain synthesis with efficient fine-tuning (disef)? Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef (diversified I N-domain s ynthesis with e fficient F? Following this trend, we propose D iversified I n- domain S ynthesis with E fficient F ine- tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef and how does it work? DISEF consists of two main components. First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Does disef perform better without synthetic data? Nonetheless, we can see that DISEF without synthetic data performs better than all methods regardless of the number of shots. When coupled with synthetic data, our method improves on all datasets and with different shots, with the exception of EuroSAT for 4-shot. What is the difference between in-domain synthesis and diversity? The in - domain synthesis requires the synthesized images not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be useful for training and requires the synthesized images to present different semantic details. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2312.03046.pdf", "content": "Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. A recent research direction for improving few-shot classifiers involves augmenting the labelled samples with synthetic images created by state- of -the-art text-to-image generation models. Following this trend, we pro-pose Diversified ... See full list on arxiv.org SAP augments the support set X with additional synthetic data points that are interpolated within the domain inferred by X. The in - domain synthesis requires the synthesized im-ages not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be u... See full list on arxiv.org where B ∈ Rm×r, A ∈ Rr×n are two low-rank matri-ces, m and n are the number of rows and columns in the original weight matrix W, and r is the rank of the update matrix, with r ≪ min(m, n). This decomposition greatly reduces the number of trainable parameters to just a fraction of the original layer with no impact on inference time, as it is possibl... See full list on arxiv.org In this section, we demonstrate the effectiveness of our design choices both in the augmentation pipeline and the PEFT part. For SAP, we ablate the effect of using the detail-rich captions obtained by ICM (LLaVA in our case), and the use of real samples as anchors during generation. Addi-tionally, we also demonstrate the effectiveness of the gen-er... See full list on arxiv.org We experiment with different tuning methods augmented with the synthetic data generated by SAP, demonstrating that SAP is generally effective, regardless of fine - tuning methods. We choose the same baselines as Table 1 and train them in the default scenario using the same data used for DISEF . From Table 4, we see that all the baseline methods are ab... See full list on arxiv.org We presented DISEF , a novel method for few-shot learning with two main contributions: a novel design of synthetic augmentation pipeline for in- domain diversity and a novel application of a parameter- eficient fine - tuning method to VLMs for effective adaptation. For the synthetic data gen-eration, we propose to apply noise to the real samples and use... See full list on arxiv.org In the supplementary material, we provide additional re-sults. In Section A, we provide an ablation on the number of synthetic images used during training. In Section B, we show the effect of using less real samples for DISEF and all the baselines in the default scenario. In Section C, we provide a complete overview of all the parameters used for o... See full list on arxiv.org Following this trend, we propose Diversified in-domain synthesis with efficient fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is diversified in-domain synthesis with efficient fine-tuning (disef)? Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef (diversified I N-domain s ynthesis with e fficient F? Following this trend, we propose D iversified I n- domain S ynthesis with E fficient F ine- tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components . What is disef and how does it work? DISEF consists of two main components. First, we propose a novel text-to-image augmentation pipeline that, by leveraging the real samples and their rich semantics coming from an advanced captioning model, promotes in-domain sample diversity for better generalization. Does disef perform better without synthetic data? Nonetheless, we can see that DISEF without synthetic data performs better than all methods regardless of the number of shots. When coupled with synthetic data, our method improves on all datasets and with different shots, with the exception of EuroSAT for 4-shot. What is the difference between in-domain synthesis and diversity? The in - domain synthesis requires the synthesized images not only to belong to the same semantic class but also to exhibit similar visual patterns as the real images. On the other hand, diversity is needed for the data to be useful for training and requires the synthesized images to present different semantic details. Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 3, "title": "Fugu-MT 論文翻訳 (概要): Diversified in-domain synthesis with ...", "date": "", "ddg_snippet": "Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "fugumt.com", "link": "https://fugumt.com/fugumt/paper_check/2312.03046v2", "content": "Dec 11, 2023 · Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 4, "title": "Diversified in-domain synthesis with efficient fine-tuning for few- ...", "date": "", "ddg_snippet": "... Diversified in-domain synthesis with efficient fine-tuning ... Diversified In-domain Synthesis with Efficient Fine-tuning ... DISEF consists of two main components .", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/details/b5952035132851579b7eb214ea29f9c8/", "content": "... Diversified in-domain synthesis with efficient fine-tuning ... Diversified In-domain Synthesis with Efficient Fine-tuning ... DISEF consists of two main components ."}
+{"idx": 5, "title": "publications | Nicola Dall'Asen", "date": "", "ddg_snippet": "Following this trend, we propose Diversified in-domain synthesis with efficient fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "fodark.xyz", "link": "https://fodark.xyz/publications/", "content": "Following this trend, we propose Diversified in-domain synthesis with efficient fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 6, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=DIVE", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 7, "title": "DataDream: Few-shot Guided Dataset Generation", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2407.10910", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning (DISEF), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 8, "title": "publications by categories in reversed chronological order.", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components .", "subpage_snippet": "", "source": "www.yimingwang.me", "link": "https://www.yimingwang.me/publications/", "content": "Following this trend, we propose Diversified In - domain Synthesis with Efficient Fine - tuning ( DISEF ), a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. DISEF consists of two main components ."}
+{"idx": 9, "title": "Daily Papers", "date": "", "ddg_snippet": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ... DISEF consists of two main components . First, we propose a ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=in-domain+sample+diversity", "content": "Following this trend, we propose Diversified In-domain Synthesis with Efficient Fine-tuning ... DISEF consists of two main components . First, we propose a ..."}
diff --git a/data/sampled_jsons/DnCNN_Zhang_et_al_2017_paper_architecture.jsonl b/data/sampled_jsons/DnCNN_Zhang_et_al_2017_paper_architecture.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..afe1733956f187a95b1c9edac3f24a1685403252
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_Zhang_et_al_2017_paper_architecture.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Experiments Details 1. DnCNN (Zhang et al., ...", "date": "", "ddg_snippet": "The results in the paper are reported for the best denoisers over all the hyperparameters ( architectures , optimizer, and learning rate) summarized in the ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/f9fd2624beefbc7808e4e405d73f57ab-Supplemental.pdf", "content": "The results in the paper are reported for the best denoisers over all the hyperparameters ( architectures , optimizer, and learning rate) summarized in the ..."}
+{"idx": 1, "title": "cszn/DnCNN: Beyond a Gaussian Denoiser", "date": "", "ddg_snippet": "For the robust architecture, designing a deep multiscale CNN which involves a coarse-to-fine procedure is a promising direction. Such a network is expected to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "For the robust architecture, designing a deep multiscale CNN which involves a coarse-to-fine procedure is a promising direction. Such a network is expected to ..."}
+{"idx": 2, "title": "Residual Learning of Deep CNN for Image Denoising", "date": "", "ddg_snippet": "by K Zhang · 2016 · Cited by 10018 — In this paper , we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1608.03981", "content": "by K Zhang · 2016 · Cited by 10018 — In this paper , we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs)"}
+{"idx": 3, "title": "Toward a Fast and Flexible Solution for CNN based Image ...", "date": "", "ddg_snippet": "by K Zhang · 2017 · Cited by 3066 — [18]. Benefitted from the advances in deep CNN, Zhang et al . [20] proposed a plain denoising CNN. ( DnCNN ) method which achieves ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1710.04026", "content": "by K Zhang · 2017 · Cited by 3066 — [18]. Benefitted from the advances in deep CNN, Zhang et al . [20] proposed a plain denoising CNN. ( DnCNN ) method which achieves ..."}
+{"idx": 4, "title": "When Image Denoising Meets High-Level Vision Tasks", "date": "", "ddg_snippet": "We propose a robust deep architecture processing a noisy im- age input ... al., 2017 ], DnCNN [ Zhang et al ., 2017a ], and our proposed method. We do not ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2018/0117.pdf", "content": "We propose a robust deep architecture processing a noisy im- age input ... al., 2017 ], DnCNN [ Zhang et al ., 2017a ], and our proposed method. We do not ..."}
+{"idx": 5, "title": "Image denoising using deep CNN with batch renormalization", "date": "", "ddg_snippet": "by C Tian · 2020 · Cited by 615 — For example, a 17-layer DnCNN ( Zhang , Zuo, Chen et al ., 2017 ) has been proposed as a CNN-based method of predicting noise. This baseline improves the ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0893608019302394", "content": "by C Tian · 2020 · Cited by 615 — For example, a 17-layer DnCNN ( Zhang , Zuo, Chen et al ., 2017 ) has been proposed as a CNN-based method of predicting noise. This baseline improves the ..."}
+{"idx": 6, "title": "INTELLIGENT SYSTEMS AND APPLICATIONS IN ...", "date": "", "ddg_snippet": "11 Jan 2022 — Kai Zhang et al . ( 2017 ) [17] conducted a research on the development of feed-forward denoising utilizing a convolutional neural network. The two ...", "subpage_snippet": "", "source": "ijisae.org", "link": "https://ijisae.org/index.php/IJISAE/article/download/2686/1268/7604", "content": "11 Jan 2022 — Kai Zhang et al . ( 2017 ) [17] conducted a research on the development of feed-forward denoising utilizing a convolutional neural network. The two ..."}
+{"idx": 7, "title": "end-to-end-unpaired-image-denoising-with-conditional- ...", "date": "", "ddg_snippet": "Apart from these relatively early CNN methods, Zhang et al . ( 2017 ) proposed a deep CNN denoising model DnCNN using residual learn- ing and batch normalization ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/end-to-end-unpaired-image-denoising-with-conditional-48teh9lc45.pdf", "content": "Apart from these relatively early CNN methods, Zhang et al . ( 2017 ) proposed a deep CNN denoising model DnCNN using residual learn- ing and batch normalization ..."}
+{"idx": 8, "title": "A dual residual dense network for image denoising", "date": "", "ddg_snippet": "by I Batool · 2025 · Cited by 4 — Zhang et al. (2017) proposed denoising CNN (DnCNN ) that learned mapping from corrupted images to the original ones and improved the performance of the denoising ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0952197625002751", "content": "by I Batool · 2025 · Cited by 4 — Zhang et al. (2017) proposed denoising CNN (DnCNN ) that learned mapping from corrupted images to the original ones and improved the performance of the denoising ..."}
+{"idx": 9, "title": "IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL ...", "date": "", "ddg_snippet": "by D Valsesia · Cited by 73 — The goal of this first block is to extract features at multiple scales, by performing a classic convolution with three different filter sizes (3 × 3, 5 × 5, 7 × ...", "subpage_snippet": "", "source": "rlgm.github.io", "link": "https://rlgm.github.io/papers/16.pdf", "content": "by D Valsesia · Cited by 73 — The goal of this first block is to extract features at multiple scales, by performing a classic convolution with three different filter sizes (3 × 3, 5 × 5, 7 × ..."}
diff --git a/data/sampled_jsons/DnCNN_architecture_layers_m_head_m_body_Zhang_2017.jsonl b/data/sampled_jsons/DnCNN_architecture_layers_m_head_m_body_Zhang_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5d4f700d5b7fd8d708b1a3def805ae4acca9ab02
--- /dev/null
+++ b/data/sampled_jsons/DnCNN_architecture_layers_m_head_m_body_Zhang_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - cszn/DnCNN: Beyond a Gaussian Denoiser: Residual Learning of ...", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - cszn/ DnCNN"}
+{"idx": 1, "title": "Architecture | cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "DnCNN employs a fully convolutional network architecture specifically designed for image restoration tasks, with a particular focus on image denoising. The network is characterized by its depth (typically 17-20 layers ), use of batch normalization, and most distinctively, its residual learning approach. DnCNN Network Architecture", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN/2-architecture", "content": "DnCNN employs a fully convolutional network architecture specifically designed for image restoration tasks, with a particular focus on image denoising. The network is characterized by its depth (typically 17-20 layers ), use of batch normalization, and most distinctively, its residual learning approach. DnCNN Network Architecture"}
+{"idx": 2, "title": "DnCNN — deepinv 0.3.4 documentation", "date": "", "ddg_snippet": "DnCNN # class deepinv.models.DnCNN(in_channels=3, out_channels=3, depth=20, bias=True, nf=64, pretrained='download', device='cpu') [source] # Bases: Denoiser DnCNN convolutional denoiser. The architecture was introduced by Zhang et al.[1] and is composed of a series of convolutional layers with ReLU activation functions. The number of layers can be specified by the user. Unlike the original ...", "subpage_snippet": "", "source": "deepinv.github.io", "link": "https://deepinv.github.io/deepinv/api/stubs/deepinv.models.DnCNN.html", "content": "DnCNN # class deepinv.models.DnCNN(in_channels=3, out_channels=3, depth=20, bias=True, nf=64, pretrained='download', device='cpu') [source] # Bases: Denoiser DnCNN convolutional denoiser. The architecture was introduced by Zhang et al.[1] and is composed of a series of convolutional layers with ReLU activation functions. The number of layers can be specified by the user. Unlike the original ..."}
+{"idx": 3, "title": "DnCNN/README.md at master · cszn/DnCNN · GitHub", "date": "", "ddg_snippet": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - DnCNN /README.md at master · cszn/ DnCNN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/cszn/DnCNN/blob/master/README.md", "content": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017 ) - DnCNN /README.md at master · cszn/ DnCNN"}
+{"idx": 4, "title": "TIP 2017 | DnCNN：深度学习图像去噪开山之作，使用残差学习的深度卷积去噪网络! - 知乎", "date": "", "ddg_snippet": "DnCNN 网络结构 网络结构：输入为噪声图像Noisy，头 (head)为Conv+ReLU，中间 (body)为若干个Conv+BN+ReLU，尾 (Tail)为Conv，输出是残差图，即学习到的噪声； 网络深度：卷积大小均为3×3，无池化层，感受野为35×35，网络深度一般为17或20（可调）； 残差学习RL和批归一化BN：实验证明RL+BN的可以加快和稳定训练 ...", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29528565917", "content": "DnCNN 网络结构 网络结构：输入为噪声图像Noisy，头 (head)为Conv+ReLU，中间 (body)为若干个Conv+BN+ReLU，尾 (Tail)为Conv，输出是残差图，即学习到的噪声； 网络深度：卷积大小均为3×3，无池化层，感受野为35×35，网络深度一般为17或20（可调）； 残差学习RL和批归一化BN：实验证明RL+BN的可以加快和稳定训练 ..."}
+{"idx": 5, "title": "cszn/DnCNN | DeepWiki", "date": "", "ddg_snippet": "What is DnCNN ? DnCNN is a deep learning approach introduced in the paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\" by Zhang et al. It represents a significant advancement in image denoising by utilizing a convolutional neural network with batch normalization and a residual learning strategy.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/cszn/DnCNN", "content": "What is DnCNN ? DnCNN is a deep learning approach introduced in the paper \"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising\" by Zhang et al. It represents a significant advancement in image denoising by utilizing a convolutional neural network with batch normalization and a residual learning strategy."}
+{"idx": 6, "title": "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image ...", "date": "", "ddg_snippet": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1608.03981", "content": "With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers . This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking."}
+{"idx": 7, "title": "(PDF) Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for ...", "date": "", "ddg_snippet": "In 2016, Zhang et al. [10] proposed the DnCNN model for image denoising, introduced the idea of residual learning, and used the network model to predict the residual image, which can achieve blind ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/306187437_Beyond_a_Gaussian_Denoiser_Residual_Learning_of_Deep_CNN_for_Image_Denoising", "content": "In 2016, Zhang et al. [10] proposed the DnCNN model for image denoising, introduced the idea of residual learning, and used the network model to predict the residual image, which can achieve blind ..."}
+{"idx": 8, "title": "PDF A Experiments Details - NeurIPS", "date": "", "ddg_snippet": "Also, for each architecture , the reported training time of each model is averaged over all the instances of training this architecture with various optimizers and learning rates.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2020/file/f9fd2624beefbc7808e4e405d73f57ab-Supplemental.pdf", "content": "Also, for each architecture , the reported training time of each model is averaged over all the instances of training this architecture with various optimizers and learning rates."}
+{"idx": 9, "title": "PDF Learning Deep CNN Denoiser Prior for Image Restoration", "date": "", "ddg_snippet": "Second, in contrast to the stage-wise architecture of TNRD which essentially has a bottleneck in each imme-diate output layer , ours encourages a fluent information flow among different layers , thus having larger model capacity. Third, batch normalization which is beneficial to Gaussian denoising is adopted.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Learning_Deep_CNN_CVPR_2017_paper.pdf", "content": "Second, in contrast to the stage-wise architecture of TNRD which essentially has a bottleneck in each imme-diate output layer , ours encourages a fluent information flow among different layers , thus having larger model capacity. Third, batch normalization which is beneficial to Gaussian denoising is adopted."}
diff --git a/data/sampled_jsons/Docker_container_ML_model_dependency_scanning_automated_verification.jsonl b/data/sampled_jsons/Docker_container_ML_model_dependency_scanning_automated_verification.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..90faad8662f616079556e866d258ff6242449ef1
--- /dev/null
+++ b/data/sampled_jsons/Docker_container_ML_model_dependency_scanning_automated_verification.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Is Docker Hub? A Guide to Docker Image Registry", "date": "", "ddg_snippet": "Docker Inc. maintains these repositories with best practices, regular updates, and security scanning . The verification process is rigorous, ensuring high-quality container runtime environments. Docker Hub’s automated builds connect your container images directly to your source code.", "subpage_snippet": "", "source": "tms-outsource.com", "link": "https://tms-outsource.com/blog/posts/what-is-docker-hub/", "content": "Docker Inc. maintains these repositories with best practices, regular updates, and security scanning . The verification process is rigorous, ensuring high-quality container runtime environments. Docker Hub’s automated builds connect your container images directly to your source code."}
+{"idx": 1, "title": "Ultimate Guide to Running Ollama in a Docker Environment", "date": "", "ddg_snippet": "Deploying your models is as easy as a few commands. Docker handles the heavy lifting, so you can focus on using Ollama. Isolation: Each Docker container acts as its own environment, keeping your projects and dependencies nicely separated.", "subpage_snippet": "", "source": "arsturn.com", "link": "https://arsturn.com/blog/running-ollama-in-a-docker-environment-your-complete-guide", "content": "Deploying your models is as easy as a few commands. Docker handles the heavy lifting, so you can focus on using Ollama. Isolation: Each Docker container acts as its own environment, keeping your projects and dependencies nicely separated."}
+{"idx": 2, "title": "Machine Learning Model On Docker Container | Towards AWS", "date": "", "ddg_snippet": "Pull the Docker container image of CentOS image from DockerHub and create a new container Install the Python software on the top of the docker container In Container you need to copy/create a machine learning model which you have created in jupyter...", "subpage_snippet": "", "source": "towardsaws.com", "link": "https://towardsaws.com/machine-learning-model-on-docker-container-da4dd51b03cd", "content": "Pull the Docker container image of CentOS image from DockerHub and create a new container Install the Python software on the top of the docker container In Container you need to copy/create a machine learning model which you have created in jupyter..."}
+{"idx": 3, "title": "App Layer Defense in container image signing... - UMA Technology", "date": "", "ddg_snippet": "Component and Dependency Scanning . Prior to signing, conduct a comprehensive scan of the application layer for known vulnerabilities, outdated packages, or malicious components. This includes: Using vulnerability scanners like Trivy, Anchore, or Clair.", "subpage_snippet": "", "source": "umatechnology.org", "link": "https://umatechnology.org/app-layer-defense-in-container-image-signing-highlighted-in-fire-drills/", "content": "Component and Dependency Scanning . Prior to signing, conduct a comprehensive scan of the application layer for known vulnerabilities, outdated packages, or malicious components. This includes: Using vulnerability scanners like Trivy, Anchore, or Clair."}
+{"idx": 4, "title": "Training a ML Model usin Docker Container", "date": "", "ddg_snippet": "Starting the container in Docker using the command:- \" docker start \". STEP 4: Installing the required libraries to load the Machine Learning model , the command used to install these libraries is:- \"pip3 install \".", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/training-ml-model-usin-docker-container-ritvik-ranjan", "content": "Starting the container in Docker using the command:- \" docker start \". STEP 4: Installing the required libraries to load the Machine Learning model , the command used to install these libraries is:- \"pip3 install \"."}
+{"idx": 5, "title": "Task-1 (Running ML model inside of docker container ) | Medium", "date": "", "ddg_snippet": "cmd — docker pull centos:latest. docker images | grep centos # to see the downloaded image. Launch The container into the rhel8 system using the command —. docker run -it — name os_for_ ml centos:latest. Installing the python3 for python environment by the command", "subpage_snippet": "", "source": "mdkneema.medium.com", "link": "https://mdkneema.medium.com/task-1-running-ml-model-inside-of-docker-container-b75a4ba42692", "content": "cmd — docker pull centos:latest. docker images | grep centos # to see the downloaded image. Launch The container into the rhel8 system using the command —. docker run -it — name os_for_ ml centos:latest. Installing the python3 for python environment by the command"}
+{"idx": 6, "title": "A Step-by-Step Guide to Deploying a Machine Learning Model in...", "date": "", "ddg_snippet": "A summary of all mentioned or recommeneded projects: Docker - container - ML - model -deployment and machine-learning-zoomcamp. 10.0. Jupyter Notebook. Learn ML engineering for free in 4 months!", "subpage_snippet": "", "source": "www.libhunt.com", "link": "https://www.libhunt.com/posts/1357937-a-step-by-step-guide-to-deploying-a-machine-learning-model-in-a-docker-container", "content": "A summary of all mentioned or recommeneded projects: Docker - container - ML - model -deployment and machine-learning-zoomcamp. 10.0. Jupyter Notebook. Learn ML engineering for free in 4 months!"}
+{"idx": 7, "title": "Running an automated acceptance test - Continuous Delivery with...", "date": "", "ddg_snippet": "Exposing container ports. Automatic port assignment. Using Docker volumes.To run an automated test, we need to make a few configurations: Add the Java cucumber libraries. In the build.gradle file, add the following code to the dependencies section", "subpage_snippet": "", "source": "www.oreilly.com", "link": "https://www.oreilly.com/library/view/continuous-delivery-with/9781838552183/8d2eefc9-6fd0-4bfc-8a76-daa3b1ce4f11.xhtml", "content": "Exposing container ports. Automatic port assignment. Using Docker volumes.To run an automated test, we need to make a few configurations: Add the Java cucumber libraries. In the build.gradle file, add the following code to the dependencies section"}
+{"idx": 8, "title": "How to Connect to Localhost from Docker Container", "date": "", "ddg_snippet": "Learn how to easily connect to your localhost from within a Docker container with our step-by-step guide.", "subpage_snippet": "", "source": "huzaima.io", "link": "https://huzaima.io/blog/connect-localhost-docker", "content": "Learn how to easily connect to your localhost from within a Docker container with our step-by-step guide."}
+{"idx": 9, "title": "Machine Learning Models in Industrial Blockchain, Attacks and", "date": "", "ddg_snippet": "Docker Container ML Model . Output. Smart Contract.Semi- automated data capture and blockchain technologies can help trace the lifecycle of ML models to mitigate the risks of ML model attacks and provide a more comprehensive trace of the development process.", "subpage_snippet": "", "source": "opus.hs-furtwangen.de", "link": "https://opus.hs-furtwangen.de/frontdoor/deliver/index/docId/8623/file/Machine.pdf", "content": "Docker Container ML Model . Output. Smart Contract.Semi- automated data capture and blockchain technologies can help trace the lifecycle of ML models to mitigate the risks of ML model attacks and provide a more comprehensive trace of the development process."}
diff --git a/data/sampled_jsons/Dvijotham_et_al._2014_Universal_convexification_via_risk-aversion_abstract.jsonl b/data/sampled_jsons/Dvijotham_et_al._2014_Universal_convexification_via_risk-aversion_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..92fbadde97354b5f211783d24a7302f88d61a0e5
--- /dev/null
+++ b/data/sampled_jsons/Dvijotham_et_al._2014_Universal_convexification_via_risk-aversion_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Universal Convexification via Risk-Aversion", "date": "", "ddg_snippet": "by K Dvijotham · Cited by 11 — In this paper, we de- scribe a framework for convexifying a fairly general class of optimization problems (section 3), turning them into problems that can be ...", "subpage_snippet": "", "source": "www.roboti.us", "link": "https://www.roboti.us/lab/papers/DvijothamUAI14.pdf", "content": "by K Dvijotham · Cited by 11 — In this paper, we de- scribe a framework for convexifying a fairly general class of optimization problems (section 3), turning them into problems that can be ..."}
+{"idx": 1, "title": "arXiv:1406.0554v1 [cs.SY] 3 Jun 2014", "date": "", "ddg_snippet": "by K Dvijotham · 2014 · Cited by 11 — Universal Convexification via Risk-Aversion . Krishnamurthy Dvijotham. Dept of Computer Science & Engg. University of Washington. Seattle, WA ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1406.0554", "content": "by K Dvijotham · 2014 · Cited by 11 — Universal Convexification via Risk-Aversion . Krishnamurthy Dvijotham. Dept of Computer Science & Engg. University of Washington. Seattle, WA ..."}
+{"idx": 2, "title": "Mathematics 2014", "date": "", "ddg_snippet": "Title: Universal Convexification via Risk-Aversion . Krishnamurthy Dvijotham, Maryam Fazel, Emanuel Todorov. Subjects: Systems and Control (eess.SY); Machine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math/2014?skip=30100&show=100", "content": "Title: Universal Convexification via Risk-Aversion . Krishnamurthy Dvijotham, Maryam Fazel, Emanuel Todorov. Subjects: Systems and Control (eess.SY); Machine ..."}
+{"idx": 3, "title": "Emo Todorov", "date": "", "ddg_snippet": "In IEEE Conference on Decision and Control Universal convexification via risk - aversion Dvijotham K, Fazel M and Todorov E ( 2014 ).", "subpage_snippet": "", "source": "www.roboti.us", "link": "https://www.roboti.us/lab/papers.html", "content": "In IEEE Conference on Decision and Control Universal convexification via risk - aversion Dvijotham K, Fazel M and Todorov E ( 2014 )."}
+{"idx": 4, "title": "Proceedings of the Thirtieth Conference on Uncertainty in ...", "date": "", "ddg_snippet": "Abstract · xml. Article. Universal convexification via risk-aversion · Author Picture Krishnamurthy Dvijotham,; + 2. Pages 162–171. We develop a framework for ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/proceedings/10.5555/3020751?tocHeading=heading1", "content": "Abstract · xml. Article. Universal convexification via risk-aversion · Author Picture Krishnamurthy Dvijotham,; + 2. Pages 162–171. We develop a framework for ..."}
+{"idx": 5, "title": "Convex relaxation regression | Proceedings of the Thirty- ...", "date": "", "ddg_snippet": "25 Jun 2016 — Dvijotham, K., Fazel, M., and Todorov, E. (2014). Universal convexification via risk-aversion . In UAI, pages 162-171. Google Scholar. [17].", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3020948.3020952", "content": "25 Jun 2016 — Dvijotham, K., Fazel, M., and Todorov, E. (2014). Universal convexification via risk-aversion . In UAI, pages 162-171. Google Scholar. [17]."}
+{"idx": 6, "title": "On the Convergence of the Iterative Linear Exponential ...", "date": "", "ddg_snippet": "by V Roulet · 2019 · Cited by 19 — K. Dvijotham, M. Fazel, and E. Todorov. Universal convexification via risk-aversion . In Proceedings of the Thirtieth. Conference on Uncertainty ...", "subpage_snippet": "", "source": "personalrobotics.cs.washington.edu", "link": "https://personalrobotics.cs.washington.edu/publications/roulet2020regileqg.pdf", "content": "by V Roulet · 2019 · Cited by 19 — K. Dvijotham, M. Fazel, and E. Todorov. Universal convexification via risk-aversion . In Proceedings of the Thirtieth. Conference on Uncertainty ..."}
+{"idx": 7, "title": "On the Convex Formulations of Robust Markov Decision ...", "date": "", "ddg_snippet": "by J Grand-Clément · 2024 · Cited by 19 — [29] Dvijotham K, Fazel M, Todorov E (2014) Universal convexification via risk-aversion . Preprint, submitted June 3, https://arxiv.org/abs ...", "subpage_snippet": "", "source": "pubsonline.informs.org", "link": "https://pubsonline.informs.org/doi/10.1287/moor.2022.0284", "content": "by J Grand-Clément · 2024 · Cited by 19 — [29] Dvijotham K, Fazel M, Todorov E (2014) Universal convexification via risk-aversion . Preprint, submitted June 3, https://arxiv.org/abs ..."}
+{"idx": 8, "title": "arXiv:1811.01848v3 [cs.LG] 28 Jan 2019", "date": "", "ddg_snippet": "by K Lowrey · 2018 · Cited by 300 — [18] Krishnamurthy Dvijotham, Maryam Fazel, and Emanuel Todorov. Universal convexification via risk-aversion . In UAI, 2014. [19] Emanuel Todorov ... 15 pages", "subpage_snippet": "", "source": "projects.iq.harvard.edu", "link": "https://projects.iq.harvard.edu/files/kakade/files/1811-01848-2019.pdf", "content": "by K Lowrey · 2018 · Cited by 300 — [18] Krishnamurthy Dvijotham, Maryam Fazel, and Emanuel Todorov. Universal convexification via risk-aversion . In UAI, 2014. [19] Emanuel Todorov ... 15 pages"}
+{"idx": 9, "title": "2025-08-21 Davide Cugini (T-4) Importance Sampling for ...", "date": "", "ddg_snippet": "• 2014-07-01 Krishnamurthy Dvijotham (University of Washington) Universal Convexification via Risk Aversion . • 2014-06-26 Students (CNLS) CNLS Summer Student ...", "subpage_snippet": "", "source": "cnls.lanl.gov", "link": "https://cnls.lanl.gov/External/pasttalks.php", "content": "• 2014-07-01 Krishnamurthy Dvijotham (University of Washington) Universal Convexification via Risk Aversion . • 2014-06-26 Students (CNLS) CNLS Summer Student ..."}
diff --git a/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Safety_E-ASR_VLGuard_benchmark_gap_reason_year_2024.jsonl b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Safety_E-ASR_VLGuard_benchmark_gap_reason_year_2024.jsonl
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index 0000000000000000000000000000000000000000..0eee5b1ddc60b5e302beb05ccde596182d6c135e
--- /dev/null
+++ b/data/sampled_jsons/ELITE_Enhanced_Language-Image_Toxicity_Evaluation_Safety_E-ASR_VLGuard_benchmark_gap_reason_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "18 Jun 2025 — The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=583klsIjNx&noteId=lkuNd7fIAG", "content": "18 Jun 2025 — The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide ..."}
+{"idx": 1, "title": "How ELITE Reveals Dangerous Weaknesses in Vision ...", "date": "", "ddg_snippet": "Vision- Language Models are vulnerable to subtle manipulations. Even when input prompts seem harmless, models can produce toxic , biased, or ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/aim-intelligence/how-elite-reveals-dangerous-weaknesses-in-vision-language-ai-ffa208b7546c", "content": "Vision- Language Models are vulnerable to subtle manipulations. Even when input prompts seem harmless, models can produce toxic , biased, or ..."}
+{"idx": 2, "title": "Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "TLDR: This paper introduces a rubric-based safety evaluation method and a high-quality benchmark to address inaccuracies in previous safety evaluations of ...", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/paper/167387", "content": "TLDR: This paper introduces a rubric-based safety evaluation method and a high-quality benchmark to address inaccuracies in previous safety evaluations of ..."}
+{"idx": 3, "title": "Do We Really Need Curated Malicious Data for Safety ...", "date": "", "ddg_snippet": "by Y Wang · 2025 · Cited by 2 — However, the lack of safety mea- sures specifically designed for multi-modal inputs creates an alignment gap , leaving MLLMs vulnerable to vision- domain attacks ... 11 pages", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Do_We_Really_Need_Curated_Malicious_Data_for_Safety_Alignment_CVPR_2025_paper.pdf", "content": "by Y Wang · 2025 · Cited by 2 — However, the lack of safety mea- sures specifically designed for multi-modal inputs creates an alignment gap , leaving MLLMs vulnerable to vision- domain attacks ... 11 pages"}
+{"idx": 4, "title": "Do We Really Need Curated Malicious Data for Safety ... - CVPR", "date": "", "ddg_snippet": "However, the lack of safety measures specifically designed for multi-modal inputs creates an alignment gap , leaving MLLMs vulnerable to vision-domain attacks ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33133", "content": "However, the lack of safety measures specifically designed for multi-modal inputs creates an alignment gap , leaving MLLMs vulnerable to vision-domain attacks ..."}
+{"idx": 5, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "• We propose the ELITE benchmark , a rubric-based safety evaluation benchmark for VLMs using the ELITE evaluator . The ELITE benchmark addresses the limitations of existing benchmarks , such as insufficient benchmark quality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "• We propose the ELITE benchmark , a rubric-based safety evaluation benchmark for VLMs using the ELITE evaluator . The ELITE benchmark addresses the limitations of existing benchmarks , such as insufficient benchmark quality."}
+{"idx": 6, "title": "[Literature Review] ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content.", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The paper \" ELITE : Enhanced Language - Image Toxicity Evaluation for Safety \" presents a novel framework designed to evaluate the safety of Vision Language Models (VLMs) against harmful, malicious inputs that could generate unsafe content."}
+{"idx": 7, "title": "ICML Poster ELITE : Enhanced Language - Image Toxicity Evaluation ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46445", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images."}
+{"idx": 8, "title": "Paper page - ELITE : Enhanced Language - Image Toxicity ...", "date": "", "ddg_snippet": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .To address these issues, we propose the ELITE benchmark , a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04757", "content": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety .To address these issues, we propose the ELITE benchmark , a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator ."}
+{"idx": 9, "title": "ELITE : Enhanced Language - Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "• We propose the ELITE benchmark , a rubric-based safety evaluation benchmark for VLMs using the ELITE evaluator . The ELITE benchmark addresses the limitations of existing benchmarks , such as insufficient benchmark quality.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46445/paper", "content": "• We propose the ELITE benchmark , a rubric-based safety evaluation benchmark for VLMs using the ELITE evaluator . The ELITE benchmark addresses the limitations of existing benchmarks , such as insufficient benchmark quality."}
diff --git a/data/sampled_jsons/ELITE_evaluator_GPT-4o_toxicity_score_methodology_component_StrongREJECT_AU-ROC_human_judgment.jsonl b/data/sampled_jsons/ELITE_evaluator_GPT-4o_toxicity_score_methodology_component_StrongREJECT_AU-ROC_human_judgment.jsonl
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index 0000000000000000000000000000000000000000..a7da0fdab20380b2e1116ccba3b488eae645f97b
--- /dev/null
+++ b/data/sampled_jsons/ELITE_evaluator_GPT-4o_toxicity_score_methodology_component_StrongREJECT_AU-ROC_human_judgment.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ELITE : Enhanced Language-Image Toxicity Evaluation for Safety", "date": "", "ddg_snippet": "The ELITE evaluator can effectively evaluate by utilizing the toxicity score to make more accurate judgments .For a fair comparison, both the ELITE and StrongREJECT evaluators are evaluated using the GPT - 4 o on the human evaluation dataset consisting of 963 image-text pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04757v3", "content": "The ELITE evaluator can effectively evaluate by utilizing the toxicity score to make more accurate judgments .For a fair comparison, both the ELITE and StrongREJECT evaluators are evaluated using the GPT - 4 o on the human evaluation dataset consisting of 963 image-text pairs."}
+{"idx": 1, "title": "AIM Intelligence's ELITE Collaborative Paper Accepted by the ICML", "date": "", "ddg_snippet": "AUROC vs. Human Judgment . 0.77 ( ELITE ) vs. 0.46 ( StrongREJECT ). Weaker evaluator alignment.AIM Guard Dashboard. Key components include: AIM Red - an automated adversarial engine that generates jailbreak prompts across high-risk taxonomies.", "subpage_snippet": "", "source": "www.digitaljournal.com", "link": "https://www.digitaljournal.com/pr/news/newsfile/aim-intelligence-s-elite-collaborative-paper-1773375521.html", "content": "AUROC vs. Human Judgment . 0.77 ( ELITE ) vs. 0.46 ( StrongREJECT ). Weaker evaluator alignment.AIM Guard Dashboard. Key components include: AIM Red - an automated adversarial engine that generates jailbreak prompts across high-risk taxonomies."}
+{"idx": 2, "title": "[Literature Review] ELITE : Enhanced Language-Image Toxicity ...", "date": "", "ddg_snippet": "The ELITE evaluator builds upon the StrongREJECT evaluation method , which has been pointed out for its shortcomings in assessing the outputs of VLMs accurately. The core of the ELITE evaluator introduces a toxicity scoring mechanism into its rubric, including three primary...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/elite-enhanced-language-image-toxicity-evaluation-for-safety", "content": "The ELITE evaluator builds upon the StrongREJECT evaluation method , which has been pointed out for its shortcomings in assessing the outputs of VLMs accurately. The core of the ELITE evaluator introduces a toxicity scoring mechanism into its rubric, including three primary..."}
+{"idx": 3, "title": "OpenAI GPT - 4 .5 System Card", "date": "", "ddg_snippet": "We evaluate GPT - 4 .5 against GPT - 4 o and o1 on a suite of disallowed content evaluations . These evaluations check that the model does not comply with requests for harmful content, including hateful content, illicit advice, or regulated advice (e.g., medical or legal advice).", "subpage_snippet": "", "source": "cdn.openai.com", "link": "https://cdn.openai.com/gpt-4-5-system-card-2272025.pdf", "content": "We evaluate GPT - 4 .5 against GPT - 4 o and o1 on a suite of disallowed content evaluations . These evaluations check that the model does not comply with requests for harmful content, including hateful content, illicit advice, or regulated advice (e.g., medical or legal advice)."}
+{"idx": 4, "title": "StrongREJECT documentation — StrongREJECT documentation", "date": "", "ddg_snippet": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark.This Colab notebook demonstrates several options for using the StrongREJECT evaluator : High-level API implemented in the StrongREJECT package. Using an LLM (e.g., GPT - 4 o , Claude, or Gemini).", "subpage_snippet": "", "source": "strong-reject.readthedocs.io", "link": "https://strong-reject.readthedocs.io/", "content": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark.This Colab notebook demonstrates several options for using the StrongREJECT evaluator : High-level API implemented in the StrongREJECT package. Using an LLM (e.g., GPT - 4 o , Claude, or Gemini)."}
+{"idx": 5, "title": "No Need ChatGPT Plus, Accessing Unlimited GPT - 4 o Conversation...", "date": "", "ddg_snippet": "Building upon GPT - 4 , OpenAI has bolstered the AI's low-latency processing capabilities for text, audio, and visual inputs. This enhancement enables GPT - 4 to comprehend and respond to human needs more effectively through multimodal perception.", "subpage_snippet": "", "source": "anakin.ai", "link": "https://anakin.ai/blog/unlimited-gpt-4o/", "content": "Building upon GPT - 4 , OpenAI has bolstered the AI's low-latency processing capabilities for text, audio, and visual inputs. This enhancement enables GPT - 4 to comprehend and respond to human needs more effectively through multimodal perception."}
+{"idx": 6, "title": "A StrongREJECT for Empty Jailbreaks | OpenReview", "date": "", "ddg_snippet": "Notably, we find that existing evaluation methods significantly overstate jailbreak effectiveness compared to human judgments and the StrongREJECT evaluator .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KZLE5BaaOH", "content": "Notably, we find that existing evaluation methods significantly overstate jailbreak effectiveness compared to human judgments and the StrongREJECT evaluator ."}
+{"idx": 7, "title": "Suhyun Kim's research works", "date": "", "ddg_snippet": "The ELITE evaluator can effectively evaluate utilizing the toxicity score to make more accurate judgments .Figure 4. The comparison of AU - ROC curves between the ELITE evaluator and StrongREJECT evaluator on our human evaluation dataset.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Suhyun-Kim-2304816287", "content": "The ELITE evaluator can effectively evaluate utilizing the toxicity score to make more accurate judgments .Figure 4. The comparison of AU - ROC curves between the ELITE evaluator and StrongREJECT evaluator on our human evaluation dataset."}
+{"idx": 8, "title": "How to Evaluate Jailbreak Methods : A Case Study... - Robotic Content", "date": "", "ddg_snippet": "Using the StrongREJECT rubric-based evaluator with GPT - 4 o -mini to evaluate 37 jailbreak methods , we identified a small number of highly effective jailbreaks.", "subpage_snippet": "", "source": "roboticcontent.com", "link": "https://roboticcontent.com/how-to-evaluate-jailbreak-methods-a-case-study-with-the-strongreject-benchmark/", "content": "Using the StrongREJECT rubric-based evaluator with GPT - 4 o -mini to evaluate 37 jailbreak methods , we identified a small number of highly effective jailbreaks."}
+{"idx": 9, "title": "GitHub - dsbowen/ strong _ reject", "date": "", "ddg_snippet": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark.This Colab notebook demonstrates several options for using the StrongREJECT evaluator : High-level API implemented in the StrongREJECT package. Using an LLM (e.g., GPT - 4 o , Claude, or Gemini).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dsbowen/strong_reject", "content": "StrongREJECT is a state-of-the-art LLM jailbreak evaluation benchmark.This Colab notebook demonstrates several options for using the StrongREJECT evaluator : High-level API implemented in the StrongREJECT package. Using an LLM (e.g., GPT - 4 o , Claude, or Gemini)."}
diff --git a/data/sampled_jsons/Elhage_et_al_2021_Mathematical_Framework_Transformer_Circuits_abstract_year_2021.jsonl b/data/sampled_jsons/Elhage_et_al_2021_Mathematical_Framework_Transformer_Circuits_abstract_year_2021.jsonl
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index 0000000000000000000000000000000000000000..8673c3e286de0bcffbfb0e99e808c7261f67130f
--- /dev/null
+++ b/data/sampled_jsons/Elhage_et_al_2021_Mathematical_Framework_Transformer_Circuits_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Dec 22, 2021 · We don’t attempt to apply to our insights to larger models in this first paper, but in a forthcoming paper, we will show that both our mathematical framework for understanding transformers, and the concept of induction heads, continues to be at least partially relevant for much larger and more realistic models – though we remain a very long ...", "subpage_snippet": "", "source": "transformer-circuits.pub", "link": "https://transformer-circuits.pub/2021/framework/index.html", "content": "Dec 22, 2021 · We don’t attempt to apply to our insights to larger models in this first paper, but in a forthcoming paper, we will show that both our mathematical framework for understanding transformers, and the concept of induction heads, continues to be at least partially relevant for much larger and more realistic models – though we remain a very long ..."}
+{"idx": 1, "title": "A Mathematical Framework for Transformer Circuits - Anthropic", "date": "", "ddg_snippet": "Dec 22, 2021 · A Mathematical Framework for Transformer Circuits Dec 22, 2021 Read Paper Research Anthropic Economic Index report: Uneven geographic and enterprise AI adoption Sep 15, 2025", "subpage_snippet": "", "source": "www.anthropic.com", "link": "https://www.anthropic.com/research/a-mathematical-framework-for-transformer-circuits", "content": "Dec 22, 2021 · A Mathematical Framework for Transformer Circuits Dec 22, 2021 Read Paper Research Anthropic Economic Index report: Uneven geographic and enterprise AI adoption Sep 15, 2025"}
+{"idx": 2, "title": "Transformer Circuits: A Mathematical Framework - studylib.net", "date": "", "ddg_snippet": "Explore a mathematical framework for transformer circuits , focusing on attention mechanisms and in-context learning. AI, Machine Learning research.", "subpage_snippet": "", "source": "studylib.net", "link": "https://studylib.net/doc/27496897/transformer-circui...--1-", "content": "Explore a mathematical framework for transformer circuits , focusing on attention mechanisms and in-context learning. AI, Machine Learning research."}
+{"idx": 3, "title": "Arxiv Dives - A Mathematical Framework for Transformer ...", "date": "", "ddg_snippet": "Dec 22, 2021 · We will be going through circuit diagrams and looking how each component works piece by piece. Even years after a large model is trained, both the creators and users routinely discover model capabilities, including problematic behaviors.", "subpage_snippet": "", "source": "www.oxen.ai", "link": "https://www.oxen.ai/blog/arxiv-dives-a-mathematical-framework-for-transformer-circuits", "content": "Dec 22, 2021 · We will be going through circuit diagrams and looking how each component works piece by piece. Even years after a large model is trained, both the creators and users routinely discover model capabilities, including problematic behaviors."}
+{"idx": 4, "title": "[CaCL] CaCL 2/3: A Mathematical Framework for Transformer ...", "date": "", "ddg_snippet": "A Mathematical Framework for Transformer Circuits ( Elhage et al ., 2021 ): https:// transformer - circuits .pub/ 2021 / framework /index.html REVERSE ENGINEERING RESULTS To explore the challenge of reverse engineering transformers, we reverse engineer several toy, attention-only models.", "subpage_snippet": "", "source": "lists.osu.edu", "link": "https://lists.osu.edu/pipermail/cacl/2022-January/000655.html", "content": "A Mathematical Framework for Transformer Circuits ( Elhage et al ., 2021 ): https:// transformer - circuits .pub/ 2021 / framework /index.html REVERSE ENGINEERING RESULTS To explore the challenge of reverse engineering transformers, we reverse engineer several toy, attention-only models."}
+{"idx": 5, "title": "Review: A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Oct 25, 2024 · Review: A Mathematical Framework for Transformer Circuits 7 minute read Published: October 25, 2024 This paper provides a mental model for reasoning about the internal workings of transformers and attention heads in deep neural networks. The insights here help understand and analyze the behaviors of large models.", "subpage_snippet": "", "source": "pratik-doshi-99.github.io", "link": "https://pratik-doshi-99.github.io/posts/transformer-circuits/", "content": "Oct 25, 2024 · Review: A Mathematical Framework for Transformer Circuits 7 minute read Published: October 25, 2024 This paper provides a mental model for reasoning about the internal workings of transformers and attention heads in deep neural networks. The insights here help understand and analyze the behaviors of large models."}
+{"idx": 6, "title": "A Mathematical Framework for Transformer Circuits", "date": "", "ddg_snippet": "Antrophic, A Mathematical Framework for Transformer Circuits It seems complex at first glance, but knowing each part’s role makes it understandable.", "subpage_snippet": "", "source": "jungwooyang.com", "link": "https://jungwooyang.com/assets/files/framework-transformer-circuits.pdf", "content": "Antrophic, A Mathematical Framework for Transformer Circuits It seems complex at first glance, but knowing each part’s role makes it understandable."}
+{"idx": 7, "title": "Arithmetic in Transformers Explained", "date": "", "ddg_snippet": "14 Feb 2025 — (2021b) Elhage, N., Nanda, N., Olsson, C., et al. A mathematical framework for transformer circuits . https://transformer-circuits.pub/2021/ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.02619v9", "content": "14 Feb 2025 — (2021b) Elhage, N., Nanda, N., Olsson, C., et al. A mathematical framework for transformer circuits . https://transformer-circuits.pub/2021/ ..."}
+{"idx": 8, "title": "Understanding Addition In Transformers", "date": "", "ddg_snippet": "A mathematical framework for transformer circuits . https://transformer-circuits.pub/2021/framework/index.html, 2021. Elhage et al. [2022] ↑ N. Elhage, T.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.13121v9", "content": "A mathematical framework for transformer circuits . https://transformer-circuits.pub/2021/framework/index.html, 2021. Elhage et al. [2022] ↑ N. Elhage, T."}
+{"idx": 9, "title": "Understanding Inter-layer Communication in Transformer ...", "date": "", "ddg_snippet": "by J Merullo · 2024 · Cited by 17 — The variation in the way these heads communicate only changes how we calculate the composition score. [ Elhage et al ., 2021 ] and individual implementation of the ... 47 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/70e5444e5f331f7f5431f302110b97af-Paper-Conference.pdf", "content": "by J Merullo · 2024 · Cited by 17 — The variation in the way these heads communicate only changes how we calculate the composition score. [ Elhage et al ., 2021 ] and individual implementation of the ... 47 pages"}
diff --git a/data/sampled_jsons/Equation_(6)_Normalizing_Flows_are_Capable_Generative_Models_training_loss.jsonl b/data/sampled_jsons/Equation_(6)_Normalizing_Flows_are_Capable_Generative_Models_training_loss.jsonl
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index 0000000000000000000000000000000000000000..bb9c49b3969703171b75ff48a4fd0e44983b7a45
--- /dev/null
+++ b/data/sampled_jsons/Equation_(6)_Normalizing_Flows_are_Capable_Generative_Models_training_loss.jsonl
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+{"idx": 0, "title": "Normalizing Flows are Capable Generative Models - arXiv.org", "date": "", "ddg_snippet": "This ensures another important factor of the architecture design: training stability — i.e., training our model should be as easy as training a standard Transformer. Combining the architecture and the loss ( Equation 6 ) together, we have a complete recipe for a simple, scalable, and trainable NF model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.06329v3", "content": "This ensures another important factor of the architecture design: training stability — i.e., training our model should be as easy as training a standard Transformer. Combining the architecture and the loss ( Equation 6 ) together, we have a complete recipe for a simple, scalable, and trainable NF model."}
+{"idx": 1, "title": "(PDF) Normalizing Flows are Capable Generative Models", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/386577118_Normalizing_Flows_are_Capable_Generative_Models", "content": "Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years."}
+{"idx": 2, "title": "Normalizing Flows are Capable Generative Models - Jiatao Gu", "date": "", "ddg_snippet": "Combining the architecture and the loss ( Equation 6 ) to- ... Normalizing Flows are Capable Generative Models ... training loss . Overall, we see a strong ...", "subpage_snippet": "", "source": "jiataogu.me", "link": "https://jiataogu.me/papers/zhai2025normalizing.pdf", "content": "Combining the architecture and the loss ( Equation 6 ) to- ... Normalizing Flows are Capable Generative Models ... training loss . Overall, we see a strong ..."}
+{"idx": 3, "title": "A Novel Bayesian Geophysical Inversion Method to Address Loss ...", "date": "", "ddg_snippet": "Mar 11, 2025 · To address the issues of loss function bias and computational efficiency, we propose a novel method called the Iterative Normalizing Flows Model (INFM). This approach involves a multi-step iterative training strategy in which the posterior PDF generated in each step serves as the prior PDF for the next step.", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JH000479", "content": "Mar 11, 2025 · To address the issues of loss function bias and computational efficiency, we propose a novel method called the Iterative Normalizing Flows Model (INFM). This approach involves a multi-step iterative training strategy in which the posterior PDF generated in each step serves as the prior PDF for the next step."}
+{"idx": 4, "title": "Normalizing Flows are Capable Models for RL - arXiv.org", "date": "", "ddg_snippet": "Normalizing Flows (NFs) are among the most flexible probabilistic models: they 1 (1) ( 1 ) support both likelihood maximization and variational inference training , and 2 (2) ( 2 ) enable efficient sampling and exact likelihood computation (see Table 1). Yet, NFs have received far less attention from the RL community, perhaps due to the (mis)conception that they have restricted architectures or ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.23527v3", "content": "Normalizing Flows (NFs) are among the most flexible probabilistic models: they 1 (1) ( 1 ) support both likelihood maximization and variational inference training , and 2 (2) ( 2 ) enable efficient sampling and exact likelihood computation (see Table 1). Yet, NFs have received far less attention from the RL community, perhaps due to the (mis)conception that they have restricted architectures or ..."}
+{"idx": 5, "title": "Efficient Prediction of Shallow-Water Acoustic Transmission ...", "date": "", "ddg_snippet": "Jul 10, 2025 · Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2077-1312/13/7/1325", "content": "Jul 10, 2025 · Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency."}
+{"idx": 6, "title": "NIPUNA: A Novel Optimizer Activation Function for Deep Neural ...", "date": "", "ddg_snippet": "Feb 28, 2023 · It helps when optimizing the model in terms of convergence to the minimum loss . As a result, the NIPUNA activation function inherits the advantages of ReLU and Swish: (i) it avoids slow training times during near-zero gradients and (ii) it is more computationally efficient when compared with other state-of-the-art activation functions.", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2075-1680/12/3/246", "content": "Feb 28, 2023 · It helps when optimizing the model in terms of convergence to the minimum loss . As a result, the NIPUNA activation function inherits the advantages of ReLU and Swish: (i) it avoids slow training times during near-zero gradients and (ii) it is more computationally efficient when compared with other state-of-the-art activation functions."}
+{"idx": 7, "title": "Unsupervised artificial intelligence model of arrhythmia ...", "date": "", "ddg_snippet": "The obtained contributions are in turn applied to the source domain dataset samples in the backpropagation process for continuously reducing the influence of the source domain data in Cs \\ Ct on the data alignment during the training process. Equation (7) shows the proposed loss function.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1746809425012042", "content": "The obtained contributions are in turn applied to the source domain dataset samples in the backpropagation process for continuously reducing the influence of the source domain data in Cs \\ Ct on the data alignment during the training process. Equation (7) shows the proposed loss function."}
+{"idx": 8, "title": "3D Brain Extraction from Magnetic Resonance Imaging Using ...", "date": "", "ddg_snippet": "To transfer knowledge from the teacher network to the student network in a medical image segmentation context, we employed a custom distillation loss formulation that integrates both task-specific segmentation loss and feature-level guidance from the teacher model[28]. During training , the student model is supervised by two objectives:", "subpage_snippet": "", "source": "annals-csis.org", "link": "https://annals-csis.org/proceedings/2025/pliks/2011.pdf", "content": "To transfer knowledge from the teacher network to the student network in a medical image segmentation context, we employed a custom distillation loss formulation that integrates both task-specific segmentation loss and feature-level guidance from the teacher model[28]. During training , the student model is supervised by two objectives:"}
diff --git a/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_consequential_validity_ap.jsonl b/data/sampled_jsons/Evaluating_Generative_AI_Systems_Is_a_Social_Science_Measurement_Challenge_consequential_validity_ap.jsonl
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index 0000000000000000000000000000000000000000..c2146fc1d3bed636e4e65b47cc9bbaa9e54f7b49
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+{"idx": 0, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.10939", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the social ..."}
+{"idx": 1, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging, as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially difficult to measure. We argue that the kinds of difficult measurement tasks involved in evaluating GenAI systems are ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2024/105556", "content": "Across academia, industry, and government, there is an increasing awareness that evaluating generative AI (GenAI) systems is challenging, as concepts related to their capabilities (e.g., intelligence, reasoning) and risks (e.g., stereotyping, anthropomorphism) are especially difficult to measure. We argue that the kinds of difficult measurement tasks involved in evaluating GenAI systems are ..."}
+{"idx": 2, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the […]", "subpage_snippet": "", "source": "www.microsoft.com", "link": "https://www.microsoft.com/en-us/research/publication/evaluating-generative-ai-systems-is-a-social-science-measurement-challenge/", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the […]"}
+{"idx": 3, "title": "PDF Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government [e.g., 10, 22, 23], there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially dificult— more so than those involved in evaluating supervised ML systems. This is because the concepts to be measured tend to be complex and nuanced, and may even have contested meanings [e.g., 18, 19 ...", "subpage_snippet": "", "source": "evaleval.github.io", "link": "https://evaleval.github.io/2024workshop/accepted_papers/EvalEval_24_Wallach.pdf", "content": "Across academia, industry, and government [e.g., 10, 22, 23], there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially dificult— more so than those involved in evaluating supervised ML systems. This is because the concepts to be measured tend to be complex and nuanced, and may even have contested meanings [e.g., 18, 19 ..."}
+{"idx": 4, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385920305_Evaluating_Generative_AI_Systems_is_a_Social_Science_Measurement_Challenge", "content": "Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that ..."}
+{"idx": 5, "title": "Evaluating Generative AI Systems is a Social Science Measurement Challenge", "date": "", "ddg_snippet": "Abstract: Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=gvWHOHcqUG", "content": "Abstract: Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of measurement tasks found throughout the social sciences. With this in mind, we present a framework, grounded in measurement theory from the ..."}
+{"idx": 6, "title": "Position: Evaluating Generative AI Systems Is a Social Science ...", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge Hanna Wallach 1 2 Meera Desai A. Feder Cooper 3 1 1 Angelina Wang Chad Atalla Solon Barocas", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.00561", "content": "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge Hanna Wallach 1 2 Meera Desai A. Feder Cooper 3 1 1 Angelina Wang Chad Atalla Solon Barocas"}
+{"idx": 7, "title": "Position: Evaluating Generative AI Systems is a Social Science", "date": "", "ddg_snippet": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... is that evaluating GenAI systems is a social science ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00561v1", "content": "Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge ... is that evaluating GenAI systems is a social science ..."}
+{"idx": 8, "title": "Generative AI | Substantia Mea", "date": "", "ddg_snippet": "... it examines their insights about the content quality, source trustworthiness as well as on the interactivity features of these text- generative AI ...", "subpage_snippet": "", "source": "drmarkcamilleri.com", "link": "https://drmarkcamilleri.com/tag/generative-ai/", "content": "... it examines their insights about the content quality, source trustworthiness as well as on the interactivity features of these text- generative AI ..."}
+{"idx": 9, "title": "Using Generative AI for Malware Behavior Analysis | Free Essay", "date": "", "ddg_snippet": "The research is highly significant and relevant in the modern administrative and business environment grappling with cybersecurity challenges ...", "subpage_snippet": "", "source": "samples.freshessays.com", "link": "https://samples.freshessays.com/using-generative-ai-for-malware-behavior-analysis.html", "content": "The research is highly significant and relevant in the modern administrative and business environment grappling with cybersecurity challenges ..."}
diff --git a/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_abstract_Yu_Bohan.jsonl b/data/sampled_jsons/EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_abstract_Yu_Bohan.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ..."}
+{"idx": 1, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "EventPS: Real-Time Photometric Stereo Using an Event Camera Abstract : Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10655016", "content": "EventPS: Real-Time Photometric Stereo Using an Event Camera Abstract : Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications."}
+{"idx": 2, "title": "CVPR24 (Oral) - EventPS: Real-time photometric ... - YouTube EventPS: Real-Time Photometric Stereo Using an Event Camera EventPS: Real-Time Photometric Stereo Using an Event Camera dblp: EventPS: Real-Time Photometric Stereo Using an Event ... EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real-Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real- Time Photometric Stereo Using an Event Camera EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS: Real-time photometric stereo using ... Jun 16, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ... Dec 31, 2023 · Abstract : Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ... Bibliographic details on EventPS : Real - Time Photometric Stereo Using an Event Camera . What is eventps in real-time photometric stereo? This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency. What is photometric stereo? Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic ra What is eventps & how does it work? Owing to the unique attributes of event cameras, this process enables the capturing of observations with a high dynamic range under rapidly changing light-ing, while maintaining economical data efficiency. This ap-proach, termed EventPS, allows us to harness the inherent strengths of event cameras for achieving real-time PS . What is dynamic photometric stereo? Dynamic photo-metric stereo - a new technique for moving surface analysis . Image and Vision Computing, 2005. 3 Tsuyoshi Takatani, Yasuyuki Matsushita, Stephen Lin, Ya-suhiro Mukaigawa, and Yasushi Yagi. Enhanced photomet-ric stereo with multispectral images. Are all eventps al-gorithms robust as noise level increases? All three EventPS al-gorithms are robust as the noise level increases. Limitation. Firstly, the scanning patterns of lighting have their limitations: the “circle” pattern leaves a blind area for high elevation angle surface normal, and the “hypotro-choid” pattern is difficult to implement mechanically. What is the processing speed of eventps algorithm? The processing speeds of EventPS algorithms are over 1000 fps (for EventPS-OP), about 2 fps (for EventPS-FCN), 7Please refer to the video in supplementary material for full animation. Figure 11. Results on DiLiGenT-Ev dataset with different level of noises. May 27, 2024 · Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Abstract : Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=-TTp0zJKNPU", "content": "This is the video of the following work: Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, and Boxin Shi. EventPS: Real-time photometric stereo using ... Jun 16, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ... Dec 31, 2023 · Abstract : Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ... Bibliographic details on EventPS : Real - Time Photometric Stereo Using an Event Camera . What is eventps in real-time photometric stereo? This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera . Capitalizing on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, EventPS estimates surface nor-mal only from the radiance changes, significantly enhanc-ing data efficiency. What is photometric stereo? Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic ra What is eventps & how does it work? Owing to the unique attributes of event cameras, this process enables the capturing of observations with a high dynamic range under rapidly changing light-ing, while maintaining economical data efficiency. This ap-proach, termed EventPS, allows us to harness the inherent strengths of event cameras for achieving real-time PS . What is dynamic photometric stereo? Dynamic photo-metric stereo - a new technique for moving surface analysis . Image and Vision Computing, 2005. 3 Tsuyoshi Takatani, Yasuyuki Matsushita, Stephen Lin, Ya-suhiro Mukaigawa, and Yasushi Yagi. Enhanced photomet-ric stereo with multispectral images. Are all eventps al-gorithms robust as noise level increases? All three EventPS al-gorithms are robust as the noise level increases. Limitation. Firstly, the scanning patterns of lighting have their limitations: the “circle” pattern leaves a blind area for high elevation angle surface normal, and the “hypotro-choid” pattern is difficult to implement mechanically. What is the processing speed of eventps algorithm? The processing speeds of EventPS algorithms are over 1000 fps (for EventPS-OP), about 2 fps (for EventPS-FCN), 7Please refer to the video in supplementary material for full animation. Figure 11. Results on DiLiGenT-Ev dataset with different level of noises. May 27, 2024 · Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Abstract : Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper ..."}
+{"idx": 3, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Jun 16, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "Jun 16, 2024 · This paper introduces EventPS , a novel approach to real - time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of ..."}
+{"idx": 4, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Dec 31, 2023 · Abstract : Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YH3f8jq3lz", "content": "Dec 31, 2023 · Abstract : Photometric stereo is a well-established technique to es-timate the surface normal of an object. However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS, a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ..."}
+{"idx": 5, "title": "dblp: EventPS: Real-Time Photometric Stereo Using an Event ...", "date": "", "ddg_snippet": "Bibliographic details on EventPS : Real - Time Photometric Stereo Using an Event Camera .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/cvpr/YuRHWLS24", "content": "Bibliographic details on EventPS : Real - Time Photometric Stereo Using an Event Camera ."}
+{"idx": 6, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "May 27, 2024 · Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Abstract : Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper ...", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/eventps-real-time-photometric-stereo-using-an-event-camera/", "content": "May 27, 2024 · Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 (Oral, Best Paper Runner-Up) Abstract : Photometric stereo is a well-established technique to estimate the surface normal of an object. However, the requirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper ..."}
+{"idx": 7, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "by B Yu · 2024 · Cited by 25 — EventPS: Real-Time Photometric Stereo Using an Event Camera . Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi; Proceedings of the IEEE/CVF ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "by B Yu · 2024 · Cited by 25 — EventPS: Real-Time Photometric Stereo Using an Event Camera . Bohan Yu , Jieji Ren, Jin Han, Feishi Wang, Jinxiu Liang, Boxin Shi; Proceedings of the IEEE/CVF ..."}
+{"idx": 8, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "EventPS: Real-Time Photometric Stereo Using an Event Camera ; Article #: ; Date of Conference: 16-22 June 2024 ; Date Added to IEEE Xplore: 16 September 2024.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "http://ieeexplore.ieee.org/document/10655016/similar", "content": "EventPS: Real-Time Photometric Stereo Using an Event Camera ; Article #: ; Date of Conference: 16-22 June 2024 ; Date Added to IEEE Xplore: 16 September 2024."}
+{"idx": 9, "title": "Publications - Bohan Yu's Homepage", "date": "", "ddg_snippet": "EventPS: Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024", "subpage_snippet": "", "source": "www.ybh1998.space", "link": "https://www.ybh1998.space/publications/", "content": "EventPS: Real-Time Photometric Stereo Using an Event Camera 2024-05-27 Published in: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024"}
diff --git a/data/sampled_jsons/EventPS_paper_review_analysis_results_performance_MAE_degrees_3D_objects_Yu_CVPR_2024_year_2024.jsonl b/data/sampled_jsons/EventPS_paper_review_analysis_results_performance_MAE_degrees_3D_objects_Yu_CVPR_2024_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.pdf", "content": "Abstract Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the ..."}
+{"idx": 1, "title": "CVPR 2024最佳论文分享┆EventPS: 基于事件相机的实时光度立体视觉", "date": "", "ddg_snippet": "本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/audyxiao001/article/details/140520624", "content": "本文介绍了 CVPR 2024 的最佳论文提名，该论文利用事件相机的独特属性，实现了实时光度立体视觉。 该算法在传统和深度学习领域均取得成功。"}
+{"idx": 2, "title": "GitHub - 52CV/CVPR-2024-Papers", "date": "", "ddg_snippet": "Contribute to 52CV/ CVPR - 2024 - Papers development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/52CV/CVPR-2024-Papers", "content": "Contribute to 52CV/ CVPR - 2024 - Papers development by creating an account on GitHub."}
+{"idx": 3, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/EventPS:-Real-Time-Photometric-Stereo-Using-an-Yu-Ren/7f72975f58ceff79a3762464ba7e5f8c29c54aaf", "content": "This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera that capitalizes on the exceptional temporal resolution, dynamic range, and low bandwidth character-istics of event cameras, significantly enhancing data efficiency. Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of ..."}
+{"idx": 4, "title": "EventPS: Real-Time Photometric Stereo Using an Event Camera", "date": "", "ddg_snippet": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YH3f8jq3lz", "content": "Photometric stereo is a well-established technique to es-timate the surface normal of an object . However, the re-quirement of capturing multiple high dynamic range images under different illumination conditions limits the speed and real-time applications. This paper introduces EventPS , a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional ..."}
+{"idx": 5, "title": "CVPR 2024: Overview and Key Papers - LearnOpenCV", "date": "", "ddg_snippet": "CVPR 2024 : Dive into the latest AI and computer vision innovations with top papers on generative image dynamics, advanced 3D modeling, video editing, and more.", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/cvpr2024/", "content": "CVPR 2024 : Dive into the latest AI and computer vision innovations with top papers on generative image dynamics, advanced 3D modeling, video editing, and more."}
+{"idx": 6, "title": "CVPR 2024 Accepted Papers", "date": "", "ddg_snippet": "Papers are assigned to poster sessions such that topics are maximally spread over sessions (attendees will find interesting papers at each session) while grouping similar posters within each poster session to minimize walking distances. We used a 1D t-SNE projection of the SPECTER paper embeddings to realize this assignment.", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers", "content": "Papers are assigned to poster sessions such that topics are maximally spread over sessions (attendees will find interesting papers at each session) while grouping similar posters within each poster session to minimize walking distances. We used a 1D t-SNE projection of the SPECTER paper embeddings to realize this assignment."}
+{"idx": 7, "title": "北京大学计算机学院施柏鑫团队获得cvpr 2024最佳论文提名奖", "date": "", "ddg_snippet": "国际计算机视觉与模式识别会议 CVPR 2024于6月17-21日在美国西雅图召开。", "subpage_snippet": "", "source": "cs.pku.edu.cn", "link": "https://cs.pku.edu.cn/info/1263/3170.htm", "content": "国际计算机视觉与模式识别会议 CVPR 2024于6月17-21日在美国西雅图召开。"}
+{"idx": 8, "title": "CVPR 2024 Open Access Repository", "date": "", "ddg_snippet": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/html/Yu_EventPS_Real-Time_Photometric_Stereo_Using_an_Event_Camera_CVPR_2024_paper.html", "content": "This paper introduces EventPS a novel approach to real-time photometric stereo using an event camera. Capitalizing on the exceptional temporal resolution dynamic range and low bandwidth characteristics of event cameras EventPS estimates surface normal only from the radiance changes significantly enhancing data efficiency."}
+{"idx": 9, "title": "chakravarthi589/Event-based-Vision_Resources - GitHub", "date": "", "ddg_snippet": "EventPS : Real-Time Photometric Stereo Using an Event Camera [ CVPR 2024 Honorable Paper ] [ Paper ] Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline [ Paper ]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chakravarthi589/Event-based-Vision_Resources", "content": "EventPS : Real-Time Photometric Stereo Using an Event Camera [ CVPR 2024 Honorable Paper ] [ Paper ] Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline [ Paper ]"}
diff --git a/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_abstract.jsonl b/data/sampled_jsons/Exploration_by_Optimisation_in_Partial_Monitoring_abstract.jsonl
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index 0000000000000000000000000000000000000000..65d20b5aa0d8c9015f74a77fefe0c2494023b4e8
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Abstract We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 6mk3=2pn log(k), where m is the number of signals.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v125/lattimore20a/lattimore20a.pdf", "content": "Abstract We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 6mk3=2pn log(k), where m is the number of signals."}
+{"idx": 1, "title": "[1907.05772] Exploration by Optimisation in Partial Monitoring Exploration by Optimisation in Partial Monitoring Exploration by Optimisation in Partial Monitoring | Request PDF \"Exploration by Optimisation in Partial Monitoring.\" - dblp COLT 2020: Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Jul 12, 2019 · We provide a simple and efficient algorithm for adversarial k -action d -outcome non-degenerate locally observable partial monitoring game for which the n -round minimax regret is bounded by 6(d + 1)k3/2 n log(k)− −−−−−√, matching the best known information-theoretic upper bound. The same algorithm also achieves near-optimal regret for full information, bandit and globally ... Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \" Exploration by Optimisation in Partial Monitoring \" Jul 12, 2019 · In this paper, we study partial monitoring with finite actions and stochastic outcomes. We derive a logarithmic distribution-dependent regret lower bound that defines the hardness of the problem. Bibliographic details on Exploration by Optimisation in Partial Monitoring . Abstract : We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 2mk^ (3/2)sqrt (3n log (k)), where m is the number of signals. This matches the best known information-theoretic upper bound derived via Bayesian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1907.05772", "content": "Jul 12, 2019 · We provide a simple and efficient algorithm for adversarial k -action d -outcome non-degenerate locally observable partial monitoring game for which the n -round minimax regret is bounded by 6(d + 1)k3/2 n log(k)− −−−−−√, matching the best known information-theoretic upper bound. The same algorithm also achieves near-optimal regret for full information, bandit and globally ... Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \" Exploration by Optimisation in Partial Monitoring \" Jul 12, 2019 · In this paper, we study partial monitoring with finite actions and stochastic outcomes. We derive a logarithmic distribution-dependent regret lower bound that defines the hardness of the problem. Bibliographic details on Exploration by Optimisation in Partial Monitoring . Abstract : We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 2mk^ (3/2)sqrt (3n log (k)), where m is the number of signals. This matches the best known information-theoretic upper bound derived via Bayesian ..."}
+{"idx": 2, "title": "COLT 2020: Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Abstract : We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 2mk^ (3/2)sqrt (3n log (k)), where m is the number of signals. This matches the best known information-theoretic upper bound derived via Bayesian ...", "subpage_snippet": "", "source": "www.learningtheory.org", "link": "https://www.learningtheory.org/colt2020/virtual/papers/paper_66.html", "content": "Abstract : We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for the non-degenerate locally observable games, the n-round minimax regret is bounded by 2mk^ (3/2)sqrt (3n log (k)), where m is the number of signals. This matches the best known information-theoretic upper bound derived via Bayesian ..."}
+{"idx": 3, "title": "Exploration by Optimisation in Partial Monitoring - NASA/ADS", "date": "", "ddg_snippet": "Abstract We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6 (d+1) k^ {3/2} \\sqrt {n \\log (k)}$, matching the best known information-theoretic upper bound.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2019arXiv190705772L/abstract", "content": "Abstract We provide a simple and efficient algorithm for adversarial $k$-action $d$-outcome non-degenerate locally observable partial monitoring game for which the $n$-round minimax regret is bounded by $6 (d+1) k^ {3/2} \\sqrt {n \\log (k)}$, matching the best known information-theoretic upper bound."}
+{"idx": 4, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \" Exploration by Optimisation in Partial Monitoring \"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Exploration-by-Optimisation-in-Partial-Monitoring-Lattimore-Szepesvari/c527571cea74c9a689d4d2b9a9e1608046f7ff95/figure/2", "content": "Figure 2: An exploration distribution p derived from q for the game in Eq. (7). The expected loss when playing p is smaller than playing q and simultaneously more information is gained because the third action is revealing. - \" Exploration by Optimisation in Partial Monitoring \""}
+{"idx": 5, "title": "Exploration by Optimisation in Partial Monitoring | Request PDF", "date": "", "ddg_snippet": "Jul 12, 2019 · In this paper, we study partial monitoring with finite actions and stochastic outcomes. We derive a logarithmic distribution-dependent regret lower bound that defines the hardness of the problem.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/334457375_Exploration_by_Optimisation_in_Partial_Monitoring", "content": "Jul 12, 2019 · In this paper, we study partial monitoring with finite actions and stochastic outcomes. We derive a logarithmic distribution-dependent regret lower bound that defines the hardness of the problem."}
+{"idx": 6, "title": "\"Exploration by Optimisation in Partial Monitoring.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Exploration by Optimisation in Partial Monitoring .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-1907-05772", "content": "Bibliographic details on Exploration by Optimisation in Partial Monitoring ."}
+{"idx": 7, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "by T Lattimore · 2020 · Cited by 30 — Abstract. We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v125/lattimore20a.html", "content": "by T Lattimore · 2020 · Cited by 30 — Abstract. We provide a novel algorithm for adversarial k-action d-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that ..."}
+{"idx": 8, "title": "[PDF] Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "This work provides a simple and efficient algorithm for adversarial k-action d-outcome non-degenerate locally observable partial monitoring game for which ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Exploration-by-Optimisation-in-Partial-Monitoring-Lattimore-Szepesvari/867eb744f661ec7edc58e84683072ec076577ed4", "content": "This work provides a simple and efficient algorithm for adversarial k-action d-outcome non-degenerate locally observable partial monitoring game for which ..."}
+{"idx": 9, "title": "Exploration by Optimisation in Partial Monitoring", "date": "", "ddg_snippet": "We provide a novel algorithm for adversarial $k$-action $d$-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for ...", "subpage_snippet": "", "source": "bibbase.org", "link": "https://bibbase.org/network/publication/lattimore-szepesvri-explorationbyoptimisationinpartialmonitoring-2020", "content": "We provide a novel algorithm for adversarial $k$-action $d$-outcome partial monitoring that is adaptive, intuitive and efficient. The highlight is that for ..."}
diff --git a/data/sampled_jsons/FD2_synthetic_dataset_distribution_regression_formula.jsonl b/data/sampled_jsons/FD2_synthetic_dataset_distribution_regression_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4163044790e34ad0ca03122d1b05f47ffb06a6e9
--- /dev/null
+++ b/data/sampled_jsons/FD2_synthetic_dataset_distribution_regression_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "3.3. Synthetic Regression Data — Dive into Deep ... - D2L", "date": "", "ddg_snippet": "3.3.1. Generating the Dataset For this example, we will work in low dimension for succinctness. The following code snippet generates 1000 examples with 2-dimensional features drawn from a standard normal distribution . The resulting design matrix X belongs to R 1000 × 2. We generate each label by applying a ground truth linear function, corrupting them via additive noise ϵ, drawn ...", "subpage_snippet": "", "source": "d2l.ai", "link": "https://d2l.ai/chapter_linear-regression/synthetic-regression-data.html", "content": "3.3.1. Generating the Dataset For this example, we will work in low dimension for succinctness. The following code snippet generates 1000 examples with 2-dimensional features drawn from a standard normal distribution . The resulting design matrix X belongs to R 1000 × 2. We generate each label by applying a ground truth linear function, corrupting them via additive noise ϵ, drawn ..."}
+{"idx": 1, "title": "Module 5: Regression - Michigan State University", "date": "", "ddg_snippet": "5.1 Synthetic Data Generation ¶ To illustrate how linear regression works, we first generate a random 1-dimensional vector of predictor variables, x, from a uniform distribution . The response variable y has a linear relationship with x according to the following equation : y = -3x + 1 + epsilon, where epsilon corresponds to random noise sampled from a Gaussian distribution with mean 0 and ...", "subpage_snippet": "", "source": "www.cse.msu.edu", "link": "https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial5/tutorial5.html", "content": "5.1 Synthetic Data Generation ¶ To illustrate how linear regression works, we first generate a random 1-dimensional vector of predictor variables, x, from a uniform distribution . The response variable y has a linear relationship with x according to the following equation : y = -3x + 1 + epsilon, where epsilon corresponds to random noise sampled from a Gaussian distribution with mean 0 and ..."}
+{"idx": 2, "title": "synthetic-regression-data.ipynb - Colab", "date": "", "ddg_snippet": ":label: sec_ synthetic - regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ...", "subpage_snippet": "", "source": "colab.research.google.com", "link": "https://colab.research.google.com/github/d2l-ai/d2l-jax-colab/blob/master/chapter_linear-regression/synthetic-regression-data.ipynb", "content": ":label: sec_ synthetic - regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ..."}
+{"idx": 3, "title": "The Concise Guide to F-Distribution - Statology", "date": "", "ddg_snippet": "Aug 10, 2025 · The shape starts off skewed and becomes more bell-shaped as df increases You’ll notice the curves are only defined for values greater than zero because we’re dealing with a ratio of variances Simulating an F-statistic from Synthetic Data Let’s create two synthetic datasets with different variances and see what the F-test does with them.", "subpage_snippet": "", "source": "www.statology.org", "link": "https://www.statology.org/the-concise-guide-to-f-distribution/", "content": "Aug 10, 2025 · The shape starts off skewed and becomes more bell-shaped as df increases You’ll notice the curves are only defined for values greater than zero because we’re dealing with a ratio of variances Simulating an F-statistic from Synthetic Data Let’s create two synthetic datasets with different variances and see what the F-test does with them."}
+{"idx": 4, "title": "Synthetic Data Generation – ruivieira.dev d2l-en/chapter_linear-regression/synthetic-regression-data.md ... Synthetic data in R: Generating synthetic data with high utility using Synthetic data in R: Generating synthetic data with high utility using Synthetic data in R: Generating synthetic data with high utility using Synthetic Data Generation · Rui Vieira Synthetic data in R: Generating synthetic data with high utility using Synthetic data in R: Generating synthetic data with high utility using Synthetic data in R: Generating synthetic data with high ...", "date": "", "ddg_snippet": "Synthetic data will be used mainly for these scenarios: 1. Regression 2. Classification Here we will mainly look at the methods provided by scikit-learn to generate synthetic datasets . For more advanced methods, such as using the SDV library please check the SDV page. It support methods such as Gaussian copulas, CTGAN and CopulaGAN. See full list on ruivieira.dev What does a regression consist of? For this section we will mainly use scikit-learn’s make_regressionmethod. For reproducibility, we will set a random_state. We will create a dataset using make_regression’s random linear regression model with input features x=(f1,f2,f3,f4) and an output y. Let’s turn this dataset into a Pandas DataFrame: Let’s plot... See full list on ruivieira.dev To make a cluster more separable we can change cluster_std. By decreasing cluster_stdwe make them less separable. See full list on ruivieira.dev Sometimes we might be interested in creating a non-separable cluster.The simples way is to create concentric clusters with the make_circlesmethod. See full list on ruivieira.dev A shape that can be useful to other methods (such as Counterfactuals, for instance) is the one generated by the make_moonsmethod. See full list on ruivieira.dev Random walk See [Random walk]. Univariate data Using the streamadlibrary: See full list on ruivieira.dev 🏷️ sec_ synthetic - regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ... How can we assess the quality of synthetic data sets? The quality of synthetic data sets can be assessed on multiple levels and in multiple different ways. Starting on a univariate level, we can assess whether similar values seem to appear in the synthetic data as in the observed data. How many synthetic data sets can be generated? So far, we have generated a single synthetic data set, but it might be possible to generate 2 2, 5 5, 10 10 or 1000000 1000000 synthetic data sets (be warned that more synthetic data sets means more information, so more disclosure risk). where qi^ q i ^ is the parameter estimate in each sample. How to reduce the variance of inferences on synthetic data? If all went well, you should have a single synthetic data set ( increasing m m, the number of synthetic data sets , can decrease the variance of inferences on synthetic data). Also, the first variable should have synthesis method \"sample\", whereas all other variables should have method \"cart\". What methods are used to generate synthetic data? Synthetic data will be used mainly for these scenarios: Here we will mainly look at the methods provided by scikit-learn to generate synthetic datasets. For more advanced methods, such as using the SDV library please check the SDV page. It support methods such as Gaussian copulas, CTGAN and CopulaGAN. What does a regression consist of? How do I check the univariate distributional similarity of observed and synthetic data? Inspect the univariate distributional similarity of the observed and synthetic data in more detail, using the function compare() from the synthpop -package. In each of these figures, the distribution of the observed and synthetic data should be similar. Can you make inferences from a synthetic data set? Lastly, when you have obtained a synthetic data set and want to make inferences from this set, you have to be careful , because generating synthetic data adds variance to the already present sampling variance that you take into account when evaluating hypotheses. Fit this model as a logistic regression model using glm.synds() and inspect the output- using summary(), first solely focusing on the output on the synthetic data.", "subpage_snippet": "", "source": "ruivieira.dev", "link": "https://ruivieira.dev/synthetic-data-generation.html", "content": "Synthetic data will be used mainly for these scenarios: 1. Regression 2. Classification Here we will mainly look at the methods provided by scikit-learn to generate synthetic datasets . For more advanced methods, such as using the SDV library please check the SDV page. It support methods such as Gaussian copulas, CTGAN and CopulaGAN. See full list on ruivieira.dev What does a regression consist of? For this section we will mainly use scikit-learn’s make_regressionmethod. For reproducibility, we will set a random_state. We will create a dataset using make_regression’s random linear regression model with input features x=(f1,f2,f3,f4) and an output y. Let’s turn this dataset into a Pandas DataFrame: Let’s plot... See full list on ruivieira.dev To make a cluster more separable we can change cluster_std. By decreasing cluster_stdwe make them less separable. See full list on ruivieira.dev Sometimes we might be interested in creating a non-separable cluster.The simples way is to create concentric clusters with the make_circlesmethod. See full list on ruivieira.dev A shape that can be useful to other methods (such as Counterfactuals, for instance) is the one generated by the make_moonsmethod. See full list on ruivieira.dev Random walk See [Random walk]. Univariate data Using the streamadlibrary: See full list on ruivieira.dev 🏷️ sec_ synthetic - regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ... How can we assess the quality of synthetic data sets? The quality of synthetic data sets can be assessed on multiple levels and in multiple different ways. Starting on a univariate level, we can assess whether similar values seem to appear in the synthetic data as in the observed data. How many synthetic data sets can be generated? So far, we have generated a single synthetic data set, but it might be possible to generate 2 2, 5 5, 10 10 or 1000000 1000000 synthetic data sets (be warned that more synthetic data sets means more information, so more disclosure risk). where qi^ q i ^ is the parameter estimate in each sample. How to reduce the variance of inferences on synthetic data? If all went well, you should have a single synthetic data set ( increasing m m, the number of synthetic data sets , can decrease the variance of inferences on synthetic data). Also, the first variable should have synthesis method \"sample\", whereas all other variables should have method \"cart\". What methods are used to generate synthetic data? Synthetic data will be used mainly for these scenarios: Here we will mainly look at the methods provided by scikit-learn to generate synthetic datasets. For more advanced methods, such as using the SDV library please check the SDV page. It support methods such as Gaussian copulas, CTGAN and CopulaGAN. What does a regression consist of? How do I check the univariate distributional similarity of observed and synthetic data? Inspect the univariate distributional similarity of the observed and synthetic data in more detail, using the function compare() from the synthpop -package. In each of these figures, the distribution of the observed and synthetic data should be similar. Can you make inferences from a synthetic data set? Lastly, when you have obtained a synthetic data set and want to make inferences from this set, you have to be careful , because generating synthetic data adds variance to the already present sampling variance that you take into account when evaluating hypotheses. Fit this model as a logistic regression model using glm.synds() and inspect the output- using summary(), first solely focusing on the output on the synthetic data."}
+{"idx": 5, "title": "d2l-en/chapter_linear-regression/synthetic-regression-data.md ...", "date": "", "ddg_snippet": "🏷️ sec_ synthetic - regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/d2l-ai/d2l-en/blob/master/chapter_linear-regression/synthetic-regression-data.md", "content": "🏷️ sec_ synthetic - regression -data Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties ..."}
+{"idx": 6, "title": "Synthetic data in R: Generating synthetic data with high ...", "date": "", "ddg_snippet": "Fit this model as a logistic regression model using glm.synds() and inspect the output- using summary(), first solely focusing on the output on the synthetic data.", "subpage_snippet": "", "source": "thomvolker.github.io", "link": "https://thomvolker.github.io/osf_synthetic/osf_synthetic_workshop.html", "content": "Fit this model as a logistic regression model using glm.synds() and inspect the output- using summary(), first solely focusing on the output on the synthetic data."}
+{"idx": 7, "title": "ONS methodology working paper series number 16 - Synthetic data ...", "date": "", "ddg_snippet": "Where synthesising categorical data across multiple synthetic datasets , a binomial distribution is built. From here, z-scores can be calculated for each cell in the table, which will indicate the relative size difference between the synthetic and observed values.", "subpage_snippet": "", "source": "www.ons.gov.uk", "link": "https://www.ons.gov.uk/methodology/methodologicalpublications/generalmethodology/onsworkingpaperseries/onsmethodologyworkingpaperseriesnumber16syntheticdatapilot", "content": "Where synthesising categorical data across multiple synthetic datasets , a binomial distribution is built. From here, z-scores can be calculated for each cell in the table, which will indicate the relative size difference between the synthetic and observed values."}
+{"idx": 8, "title": "Distribution to Distribution Regression", "date": "", "ddg_snippet": "Distribution to distribution regression is related to the aforementioned functional analysis. However, the objects this model works over– distributions and their densities–are inferred through datasets of sam-ples drawn from the objects, with varying nite sizes.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v28/oliva13.pdf", "content": "Distribution to distribution regression is related to the aforementioned functional analysis. However, the objects this model works over– distributions and their densities–are inferred through datasets of sam-ples drawn from the objects, with varying nite sizes."}
+{"idx": 9, "title": "Simplifying Multiple Linear Regression — A Beginner’s Guide with...", "date": "", "ddg_snippet": "Dataset distribution segmentated by gender. It turns out that it significantly affects the weight for a given height, as we can observe in the previous distribution . So let’s see how we can consider it in our model.Multiple Linear Regression formula . But… do they behave the same?", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/simplifying-multiple-linear-regression-beginners-guide-real-world-example-machine-learning-algorithm-67ac60a98d7a", "content": "Dataset distribution segmentated by gender. It turns out that it significantly affects the weight for a given height, as we can observe in the previous distribution . So let’s see how we can consider it in our model.Multiple Linear Regression formula . But… do they behave the same?"}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Rew_short_Equation_(1)_year_2024.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Rew_short_Equation_(1)_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d90271613d19aa330f0a2cfc35c8cf6f2b3e1be0
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Rew_short_Equation_(1)_year_2024.jsonl
@@ -0,0 +1,2 @@
+{"idx": 0, "title": "Feint in Multi-Player Games", "date": "", "ddg_snippet": "This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v1", "content": "This paper introduces the first formalization , implementation and quantitative evaluation of Feint in Multi-Player Games."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Section_4.2.2_scheduler_weigh.jsonl b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Section_4.2.2_scheduler_weigh.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5c2dd0d9c43cf4ac8330127813d065a73871db17
--- /dev/null
+++ b/data/sampled_jsons/Feint_Behaviors_and_Strategies_Formalization_Implementation_Evaluation_Section_4.2.2_scheduler_weigh.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 1, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/ACIDDnTbSJ@OpenReview", "content": "In this work, we introduce the first comprehensive formalization of Feint behaviors at both action-level and strategy -level, and provide concrete implementation and quantitative evaluation of them in multi-player games."}
+{"idx": 2, "title": "Feint Behaviors and Strategies : Formalization , Implementation and...", "date": "", "ddg_snippet": "Ultimately, this section aims to demonstrate the feasibility and practicality of incorporating Feint behaviors into real-world MARL applications, highlighting its potential for enhancing the performance and strategic depth of multi-agent systems.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "Ultimately, this section aims to demonstrate the feasibility and practicality of incorporating Feint behaviors into real-world MARL applications, highlighting its potential for enhancing the performance and strategic depth of multi-agent systems."}
+{"idx": 3, "title": "xAI launches Grok-4-Fast: Unified Reasoning and... - MarkTechPost", "date": "", "ddg_snippet": "xAI introduced Grok-4-Fast, a cost-optimized successor to Grok-4 that merges “reasoning” and “non-reasoning” behaviors into a single set of weights controllable via system prompts.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2025/09/20/xai-launches-grok-4-fast-unified-reasoning-and-non-reasoning-model-with-2m-token-context-and-trained-end-to-end-with-tool-use-reinforcement-learning-rl/", "content": "xAI introduced Grok-4-Fast, a cost-optimized successor to Grok-4 that merges “reasoning” and “non-reasoning” behaviors into a single set of weights controllable via system prompts."}
+{"idx": 4, "title": "Junyu LIU - Google Scholar", "date": "", "ddg_snippet": "Feint behaviors and strategies : formalization , implementation and evaluation .", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=EiSrD1UAAAAJ&hl=en", "content": "Feint behaviors and strategies : formalization , implementation and evaluation ."}
+{"idx": 5, "title": "dblp: List of computer science publications by Xiangjun Peng", "date": "", "ddg_snippet": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/123/5256.html", "content": "Junyu Liu, Xiangjun Peng: Feint Behaviors and Strategies : Formalization , Implementation and Evaluation . NeurIPS 2024."}
+{"idx": 6, "title": "All 12 Legendary Weapons In Dying Light : The Beast", "date": "", "ddg_snippet": "Dying Light : The Beast features plenty of Legendary weapons that all differ and are unique compared to the last one. These guns look different from one another, and go a long way in making the gameplay even more devastating for the zombies.", "subpage_snippet": "", "source": "thegameslayer.com", "link": "https://thegameslayer.com/guides/legendary-weapons-in-dying-light-the-beast/", "content": "Dying Light : The Beast features plenty of Legendary weapons that all differ and are unique compared to the last one. These guns look different from one another, and go a long way in making the gameplay even more devastating for the zombies."}
+{"idx": 7, "title": "Xiangjun Peng's Homepage", "date": "", "ddg_snippet": "\" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only. The conference version at NeurIPS 2024 is TBA.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/index.html", "content": "\" Feint Behaviors and Strategies : Formalization , Implementation and Evaluation \" Preprint Version Only. The conference version at NeurIPS 2024 is TBA."}
+{"idx": 8, "title": "The huge potential implications of long -context inference | Epoch AI", "date": "", "ddg_snippet": "As RL scales to longer runs, research iteration cycles slow down. And you’ll also need a lot of hardware and algorithmic progress so that long -context inference isn’t prohibitively slow or expensive.", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/gradient-updates/the-huge-potential-implications-of-long-context-inference", "content": "As RL scales to longer runs, research iteration cycles slow down. And you’ll also need a lot of hardware and algorithmic progress so that long -context inference isn’t prohibitively slow or expensive."}
+{"idx": 9, "title": "EngiBench: A Benchmark for Evaluating Large Language Models on...", "date": "", "ddg_snippet": "Current evaluation methods for LLMs fall into four main categories: reference-based, task-oriented, preference-based, and rubric-based.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.17677", "content": "Current evaluation methods for LLMs fall into four main categories: reference-based, task-oriented, preference-based, and rubric-based."}
diff --git a/data/sampled_jsons/Figure_2_GSM8K_MATH_SFT_RFT_2x_efficiency_performance.jsonl b/data/sampled_jsons/Figure_2_GSM8K_MATH_SFT_RFT_2x_efficiency_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..873aa5af666908b44ed37e9570206fe8628e2416
--- /dev/null
+++ b/data/sampled_jsons/Figure_2_GSM8K_MATH_SFT_RFT_2x_efficiency_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — ... RFT is 2x as efficient as SFT , L224-244 vs. 245-263. We compare SFT & RFT , when the RFT data is the same size as SFT (128k). For each prompt ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — ... RFT is 2x as efficient as SFT , L224-244 vs. 245-263. We compare SFT & RFT , when the RFT data is the same size as SFT (128k). For each prompt ..."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — SFT / RFT policy suffers from spurious correlations in positive synthetic data. While RFT data maybe “easier-to-fit”, in Figure 2 (c) we also note ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — SFT / RFT policy suffers from spurious correlations in positive synthetic data. While RFT data maybe “easier-to-fit”, in Figure 2 (c) we also note ..."}
+{"idx": 2, "title": "MetaMath: Bootstrap Your Own Mathematical Questions ...", "date": "", "ddg_snippet": "Particularly, on both GSM8K and MATH , MetaMath achieves higher accuracy than SFT , RFT , and WizardMath by a large margin (+7%), demonstrating the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2309.12284v4", "content": "Particularly, on both GSM8K and MATH , MetaMath achieves higher accuracy than SFT , RFT , and WizardMath by a large margin (+7%), demonstrating the ..."}
+{"idx": 3, "title": "Markov Chain of Thought for Efficient Mathematical ...", "date": "", "ddg_snippet": "by W Yang · 2025 · Cited by 11 — Figure 2 : Schematic illustrating various approaches to mathematical reasoning with LLMs and their reasoning ... 2x ^2 + 2x +1$. What is $g(f ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.365.pdf", "content": "by W Yang · 2025 · Cited by 11 — Figure 2 : Schematic illustrating various approaches to mathematical reasoning with LLMs and their reasoning ... 2x ^2 + 2x +1$. What is $g(f ..."}
+{"idx": 4, "title": "MuggleMath: Assessing the Impact of Query and Response ...", "date": "", "ddg_snippet": "by C Li · 2024 · Cited by 29 — Figure 5: The performance of SFT models with different difficulty augmentation on GSM8K . Figure 6: The performance of SFT models with wrong.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2024.acl-long.551.pdf", "content": "by C Li · 2024 · Cited by 29 — Figure 5: The performance of SFT models with different difficulty augmentation on GSM8K . Figure 6: The performance of SFT models with wrong."}
+{"idx": 5, "title": "WizardMath Paper | PDF | Mathematics | Cognitive Science", "date": "", "ddg_snippet": "above math corpus as the final SFT training data. 2.2 Evol-Instruct ... 3.4 Evaluation on GSM8k and MATH . Notably, in the Figure 2 and Table 1, we ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/707640561/WizardMath-Paper", "content": "above math corpus as the final SFT training data. 2.2 Evol-Instruct ... 3.4 Evaluation on GSM8k and MATH . Notably, in the Figure 2 and Table 1, we ..."}
+{"idx": 6, "title": "Offline RL on Sub-optimal Rollouts Scales Synthetic Data ...", "date": "", "ddg_snippet": "by A Setlur — Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives (D+ πsft ) ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=v2PV1yCFJk", "content": "by A Setlur — Figure 2 : Positive data scaling laws: On GSM8K (a) and MATH (b), we evaluate SFT trained on Dsyn and RFT that uses SFT policy generated positives (D+ πsft ) ..."}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "... performance gains on summarization and QA tasks ( Figure 2 ). Industrial inference engine deployments demonstrate 2X decoding throughput improvements over ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=decoding+techniques", "content": "... performance gains on summarization and QA tasks ( Figure 2 ). Industrial inference engine deployments demonstrate 2X decoding throughput improvements over ..."}
+{"idx": 8, "title": "2505.17063v1 | PDF | Polynomial", "date": "", "ddg_snippet": "(8) SFT (Whole), which performs SFT on the full human-annotated dataset. (9) ... Figure 2 : Comparison of PPO and GRPO: Green shows GRPO with human data ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/885221188/2505-17063v1", "content": "(8) SFT (Whole), which performs SFT on the full human-annotated dataset. (9) ... Figure 2 : Comparison of PPO and GRPO: Green shows GRPO with human data ..."}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/FlowDec_paper_Section_5.1_score-matching_flow-based_NFE_theoretical_explanation.jsonl b/data/sampled_jsons/FlowDec_paper_Section_5.1_score-matching_flow-based_NFE_theoretical_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f8224970349361d9a16b6236d74a5ccff6392f3d
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_paper_Section_5.1_score-matching_flow-based_NFE_theoretical_explanation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "FlowDec: A flow-based full-band general audio codec with high", "date": "", "ddg_snippet": "In this work, we propose FlowDec , a generative neural codec based on a novel adaptation of conditional flow matching ( CFM ) (Lipman et al., 2023 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "In this work, we propose FlowDec , a generative neural codec based on a novel adaptation of conditional flow matching ( CFM ) (Lipman et al., 2023 ..."}
+{"idx": 1, "title": "FlowDec: A flow-based full-band general audio codec with high...", "date": "", "ddg_snippet": "The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality. FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score - based and flow ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "The paper introduces FlowDec , a neural audio codec that employs a two-stage approach: (1) an autoencoder with residual vector quantization, trained without adversarial loss; and (2) a postfilter that mitigates coding artifacts and enhances perceptual quality. FlowDec leverages conditional flow matching for signal enhancement, achieving notable improvements over previous score - based and flow ..."}
+{"idx": 2, "title": "Training Energy-Based Normalizing Flow with Score-Matching ... - GitHub", "date": "", "ddg_snippet": "This repository contains the code implementation of the experiments presented in the paper Training Energy- Based Normalizing Flow with Score-Matching Objectives.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/chen-hao-chao/ebflow", "content": "This repository contains the code implementation of the experiments presented in the paper Training Energy- Based Normalizing Flow with Score-Matching Objectives."}
+{"idx": 3, "title": "PDF A Connection Between Score Matching and Denoising Autoencoders", "date": "", "ddg_snippet": "1 Introduction This note uncovers an unsuspected link between the score matching tech-nique (Hyv ̈arinen, 2005, 2008) for learning the parameters of unnormalized density models over continuous-valued data, and the training of denois-ing autoencoders (Vincent, Larochelle, Bengio, & Manzagol, 2008; Vincent, Larochelle, Lajoie, Bengio, & Manzagol, 2010).", "subpage_snippet": "", "source": "www.iro.umontreal.ca", "link": "https://www.iro.umontreal.ca/~vincentp/Publications/DenoisingScoreMatching_NeuralComp2011.pdf", "content": "1 Introduction This note uncovers an unsuspected link between the score matching tech-nique (Hyv ̈arinen, 2005, 2008) for learning the parameters of unnormalized density models over continuous-valued data, and the training of denois-ing autoencoders (Vincent, Larochelle, Bengio, & Manzagol, 2008; Vincent, Larochelle, Lajoie, Bengio, & Manzagol, 2010)."}
+{"idx": 4, "title": "KL-Divergence, Score Matching, and Flow Matching", "date": "", "ddg_snippet": "KL-divergence minimization, score matching and flow matching are all different ways to train generative models. KL-divergence minimization is typically used to train normalizing flows , score matching for diffusion models and flow matching for continuous normalizing flows .", "subpage_snippet": "", "source": "eddiecunningham.github.io", "link": "https://eddiecunningham.github.io/kl-div-and-matching.html", "content": "KL-divergence minimization, score matching and flow matching are all different ways to train generative models. KL-divergence minimization is typically used to train normalizing flows , score matching for diffusion models and flow matching for continuous normalizing flows ."}
+{"idx": 5, "title": "FlowDec: A flow-based full-band general audio codec with high ...", "date": "", "ddg_snippet": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching , we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching , we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 6, "title": "PDF Training Energy-Based Normalizing Flow with Score-Matching Objectives", "date": "", "ddg_snippet": "In this paper , we presented EBFlow, a new flow-based modeling approach that associates the parameterization of flow-based and energy- based models. We showed that by optimizing EBFlow with score-matching objectives, the computation of Jacobian determinants for linear transformations can be bypassed, resulting in an improved training time complexity.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/8882d370cdafec9885b918a8cfac642e-Paper-Conference.pdf", "content": "In this paper , we presented EBFlow, a new flow-based modeling approach that associates the parameterization of flow-based and energy- based models. We showed that by optimizing EBFlow with score-matching objectives, the computation of Jacobian determinants for linear transformations can be bypassed, resulting in an improved training time complexity."}
+{"idx": 7, "title": "STORK: Improving the Fidelity of Mid-NFE Sampling for Diffusion and ...", "date": "", "ddg_snippet": "We propose a training-free, structure-independent sampling method for general diffusion and flow matching models that outperforms prior state-of-the-art samplers in the mid- NFE regime (20-50 NFEs ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.24210", "content": "We propose a training-free, structure-independent sampling method for general diffusion and flow matching models that outperforms prior state-of-the-art samplers in the mid- NFE regime (20-50 NFEs )."}
+{"idx": 8, "title": "PDF Score-based diffusion models via stochastic differential equations", "date": "", "ddg_snippet": "Abstract: This is an expository article on the score - based diffusion mod-els, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling - sampling and score matching , which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning ...", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~wt2319/DMTut.pdf", "content": "Abstract: This is an expository article on the score - based diffusion mod-els, with a particular focus on the formulation via stochastic differential equations (SDE). After a gentle introduction, we discuss the two pillars in the diffusion modeling - sampling and score matching , which encompass the SDE/ODE sampling, score matching efficiency, the consistency models, and reinforcement learning ..."}
+{"idx": 9, "title": "NeurIPS 2023 Training Energy Based Normalizing Flow With Score Matching ...", "date": "", "ddg_snippet": "As revealed in the recent studies [16, 17], training flow-based models with score-matching objectives is challenging as the training process is numerically unstable and usually exhibits significant variances.", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/741463611/NeurIPS-2023-Training-Energy-Based-Normalizing-Flow-With-Score-Matching-Objectives-Paper-Conference", "content": "As revealed in the recent studies [16, 17], training flow-based models with score-matching objectives is challenging as the training process is numerically unstable and usually exhibits significant variances."}
diff --git a/data/sampled_jsons/FlowDec_paper_Table_8_FAD_scores_7.5_kbps_FlowDec-75m.jsonl b/data/sampled_jsons/FlowDec_paper_Table_8_FAD_scores_7.5_kbps_FlowDec-75m.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d70ac090a49f6773bca3f351080e780d407259a1
--- /dev/null
+++ b/data/sampled_jsons/FlowDec_paper_Table_8_FAD_scores_7.5_kbps_FlowDec-75m.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Comparison of audio coding formats - Wikipedia", "date": "", "ddg_snippet": "The following tables compare general and technical information for a variety of audio coding formats. For listening tests comparing the perceived audio quality of audio formats and codecs, see the article Codec listening test.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Comparison_of_audio_coding_formats", "content": "The following tables compare general and technical information for a variety of audio coding formats. For listening tests comparing the perceived audio quality of audio formats and codecs, see the article Codec listening test."}
+{"idx": 1, "title": "FlowDec | A flow-based full-band general audio codec with ...", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "sp-uhh.github.io", "link": "https://sp-uhh.github.io/FlowDec/", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 2, "title": "[2503.01485] FlowDec: A flow-based full-band general audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.01485", "content": "Mar 3, 2025 · We propose FlowDec , a neural full-band audio codec for general audio sampled at 48 kHz that combines non-adversarial codec training with a stochastic postfilter based on a novel conditional flow matching method. Compared to the prior work ScoreDec which is based on score matching, we generalize from speech to general audio and move from 24 kbit/s to as low as 4 kbit/s, while improving output ..."}
+{"idx": 3, "title": "GitHub - facebookresearch/FlowDec: An neural full-band audio ...", "date": "", "ddg_snippet": "Mar 3, 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - facebookresearch/ FlowDec", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/facebookresearch/FlowDec", "content": "Mar 3, 2025 · An neural full-band audio codec for general audio sampled at 48 kHz with 7.5 kps or 4.5 kbps . - facebookresearch/ FlowDec"}
+{"idx": 4, "title": "FlowDec/index.html at main · sp-uhh/FlowDec · GitHub", "date": "", "ddg_snippet": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sp-uhh/FlowDec/blob/main/index.html", "content": "We show that FlowDec is a competitive alternative to the recent GAN-dominated stream of neural codecs, achieving FAD scores better than those of the established GAN-based codec DAC and listening test scores that are on par, and producing qualitatively more natural reconstructions for speech and harmonic structures in music."}
+{"idx": 5, "title": "FlowDec : A flow -based full-band general audio codec with high...", "date": "", "ddg_snippet": "FlowDec - 75 s: 75 Hz, single-bitrate.We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7 . 5 kbit/s.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.01485v1", "content": "FlowDec - 75 s: 75 Hz, single-bitrate.We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7 . 5 kbit/s."}
+{"idx": 6, "title": "FlowDec : A flow -based full-band general audio codec... | OpenReview", "date": "", "ddg_snippet": "TL;DR: FlowDec is a flow -based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=uxDFlPGRLX", "content": "TL;DR: FlowDec is a flow -based postfilter codec for general audio without adversarial training, and a competitive alternative to current GAN-based SOTA codecs."}
+{"idx": 7, "title": "GitHub - hammerlab/ flowdec : TensorFlow Deconvolution for...", "date": "", "ddg_snippet": "Flowdec . *Note*: This library is no longer actively maintained and requires older versions of Python and TensorFlow to run.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hammerlab/flowdec", "content": "Flowdec . *Note*: This library is no longer actively maintained and requires older versions of Python and TensorFlow to run."}
+{"idx": 8, "title": "A arXiv:2503.01485v1 [cs.SD] 3 Mar 2025", "date": "", "ddg_snippet": "We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7.5kbit/s.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.01485v1", "content": "We showed that FlowDec achieves state-of-the-art FAD scores for the coding task and, in a listening test, performs on par with the current state-of-the-art GAN-based codec DAC (Kumar et al., 2024) at bitrates between 4.5 and 7.5kbit/s."}
+{"idx": 9, "title": "CodecBench: A Comprehensive Benchmark for Acoustic and ...", "date": "", "ddg_snippet": "This paper introduces CodecBench, a comprehensive evaluation framework for audio codecs, focusing on both acoustic and semantic dimensions. To meet the growing demands of advanced speech language models for high-quality audio codecs, we collected multiple datasets that better reflect real-world scenarios and incorporated self-collected data to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.20660", "content": "This paper introduces CodecBench, a comprehensive evaluation framework for audio codecs, focusing on both acoustic and semantic dimensions. To meet the growing demands of advanced speech language models for high-quality audio codecs, we collected multiple datasets that better reflect real-world scenarios and incorporated self-collected data to ..."}
diff --git a/data/sampled_jsons/FourCastNet_Pathak_et_al._2022_Negative_Log-Likelihood_NLL_year_2022.jsonl b/data/sampled_jsons/FourCastNet_Pathak_et_al._2022_Negative_Log-Likelihood_NLL_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8bfe71929726398f60d891268f374d84865e9164
--- /dev/null
+++ b/data/sampled_jsons/FourCastNet_Pathak_et_al._2022_Negative_Log-Likelihood_NLL_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Forecasting High Resolution Precipitation Events With Logistic ...", "date": "", "ddg_snippet": "by LN Moncada Morales · 2024 — We introduce a novel machine learning approach which is able to capture the precipitation dynamics via a special type of neural network able to account for ...", "subpage_snippet": "", "source": "agupubs.onlinelibrary.wiley.com", "link": "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JH000291", "content": "by LN Moncada Morales · 2024 — We introduce a novel machine learning approach which is able to capture the precipitation dynamics via a special type of neural network able to account for ..."}
+{"idx": 1, "title": "Probabilistic neural operators for functional uncertainty ...", "date": "", "ddg_snippet": "18 Feb 2025 — Recently, neural networks have shown huge success in medium-range weather prediction tasks, with models such as FourCastNet ( Pathak et al ., 2022 ) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.12902v1", "content": "18 Feb 2025 — Recently, neural networks have shown huge success in medium-range weather prediction tasks, with models such as FourCastNet ( Pathak et al ., 2022 ) ..."}
+{"idx": 2, "title": "probabilistic neural operators for functional", "date": "", "ddg_snippet": "by C Bülte · 2025 · Cited by 3 — ( Pathak et al ., 2022 ) have shown great success and a combination ... Especially the negative log - likelihood improves significantly.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.12902", "content": "by C Bülte · 2025 · Cited by 3 — ( Pathak et al ., 2022 ) have shown great success and a combination ... Especially the negative log - likelihood improves significantly."}
+{"idx": 3, "title": "using-uncertainty-quantification-to-characterize-and- ...", "date": "", "ddg_snippet": "by SC Mouli · 2024 · Cited by 12 — In applications to weather forecasting, FourCastNet ( Pathak et al ., 2022 ) generates ensembles by perturbing the initial condition with Gaussian noise. The ... 47 pages", "subpage_snippet": "", "source": "assets.amazon.science", "link": "https://assets.amazon.science/cf/ec/2b04154b46289411e802bab9ee77/using-uncertainty-quantification-to-characterize-and-improve-out-of-domain-learning-for-pdes.pdf", "content": "by SC Mouli · 2024 · Cited by 12 — In applications to weather forecasting, FourCastNet ( Pathak et al ., 2022 ) generates ensembles by perturbing the initial condition with Gaussian noise. The ... 47 pages"}
+{"idx": 4, "title": "UNCERTAINTY QUANTIFICATION FOR FOURIER NEU", "date": "", "ddg_snippet": "by T Weber · Cited by 5 — In recent years, several deep learning methods such as FourCast-. Net ( Pathak et al ., 2022 ), PanguWeather (Bi et al ... negative log - likelihood and a negative log ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=knSgoNJcnV", "content": "by T Weber · Cited by 5 — In recent years, several deep learning methods such as FourCast-. Net ( Pathak et al ., 2022 ), PanguWeather (Bi et al ... negative log - likelihood and a negative log ..."}
+{"idx": 5, "title": "Multi-Resolution Active Learning of Fourier Neural Operators", "date": "", "ddg_snippet": "by S Li · 2024 · Cited by 15 — We repeated the training and test procedure for five times, and examined the average rel- ative L2 error, the average negative log likelihood ( NLL ), and their ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/li24k/li24k.pdf", "content": "by S Li · 2024 · Cited by 15 — We repeated the training and test procedure for five times, and examined the average rel- ative L2 error, the average negative log likelihood ( NLL ), and their ..."}
+{"idx": 6, "title": "Linearization Turns Neural Operators into Function-Valued ...", "date": "", "ddg_snippet": "We compare these weight-space-Gaussian methods against input perturbations ( Pathak et al ., 2022 ), and deep ensembles. Deep ensembles were trained 10 times ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46474", "content": "We compare these weight-space-Gaussian methods against input perturbations ( Pathak et al ., 2022 ), and deep ensembles. Deep ensembles were trained 10 times ..."}
+{"idx": 7, "title": "Analyzing and Exploring Training Recipes for ... - AMS Journals", "date": "", "ddg_snippet": "by JD Willard · 2025 · Cited by 11 — We also explore the alternate negative log - likelihood . ( NLL ) loss from K. Chen et al . (2023), which downweights the loss according to a network-predicted ... 12 pages", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/4/2/AIES-D-24-0061.1.pdf", "content": "by JD Willard · 2025 · Cited by 11 — We also explore the alternate negative log - likelihood . ( NLL ) loss from K. Chen et al . (2023), which downweights the loss according to a network-predicted ... 12 pages"}
+{"idx": 8, "title": "Probabilistic Weather Forecasting with Hierarchical Graph ...", "date": "", "ddg_snippet": "[23]. Negative Log - Likelihood loss For the MEPS data however, choosing weights ωj is challenging due to the many different variables and their irregular ... 72 pages", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/492592890311679d7f71559148358973-Paper-Conference.pdf", "content": "[23]. Negative Log - Likelihood loss For the MEPS data however, choosing weights ωj is challenging due to the many different variables and their irregular ... 72 pages"}
+{"idx": 9, "title": "Beyond Ensemble Averages: Leveraging Climate Model ...", "date": "", "ddg_snippet": "by E Orlova · 2024 · Cited by 4 — Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged ...", "subpage_snippet": "", "source": "journals.ametsoc.org", "link": "https://journals.ametsoc.org/view/journals/aies/3/4/AIES-D-23-0103.1.xml", "content": "by E Orlova · 2024 · Cited by 4 — Our paper describes an application of machine learning techniques to improve forecasts of monthly average precipitation and 2-m temperature using lagged ..."}
diff --git a/data/sampled_jsons/Geirhos_et_al._(2020)_error_consistency.jsonl b/data/sampled_jsons/Geirhos_et_al._(2020)_error_consistency.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..83b0f73a4d340d60cf965d6aa5e6098e99721969
--- /dev/null
+++ b/data/sampled_jsons/Geirhos_et_al._(2020)_error_consistency.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Quantifying Uncertainty in Error Consistency : Towards Reliable...", "date": "", "ddg_snippet": "The standard method proposed for this purpose is error consistency (EC) [ Geirhos et al ., 2020 ], which has seen wide application in the context of human-machine comparisons, e.g. in Geirhos et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.06645", "content": "The standard method proposed for this purpose is error consistency (EC) [ Geirhos et al ., 2020 ], which has seen wide application in the context of human-machine comparisons, e.g. in Geirhos et al ."}
+{"idx": 1, "title": "Decision-margin consistency : a principled metric for", "date": "", "ddg_snippet": "Geirhos et al . ( 2020 ) addressed this challenge by introducing the metric of trial-by-trial error consistency [2]. They highlight that while two systems might achieve the same level of accuracy on a task, they might arrive at this overall level by making completely different mistakes...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=y2FPllMQVg", "content": "Geirhos et al . ( 2020 ) addressed this challenge by introducing the metric of trial-by-trial error consistency [2]. They highlight that while two systems might achieve the same level of accuracy on a task, they might arrive at this overall level by making completely different mistakes..."}
+{"idx": 2, "title": "CNNs vs Vision Transformers — Biological Computer Vision... | Medium", "date": "", "ddg_snippet": "To compare strategies, researchers ( Geirhos et al .) have come up with an Error Consistency metric —Cohen’s Kappa, κ — which is computed based on probabilities of misclassification. Error consistency result comparison of Vision Transformers (Vit-B/32) and CNNs Source.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/bits-and-neurons/cnns-vs-vision-transformers-biological-computer-vision-3-3-56ff955ba463", "content": "To compare strategies, researchers ( Geirhos et al .) have come up with an Error Consistency metric —Cohen’s Kappa, κ — which is computed based on probabilities of misclassification. Error consistency result comparison of Vision Transformers (Vit-B/32) and CNNs Source."}
+{"idx": 3, "title": "Do Deep Neural Networks Have Brain-like... — LessWrong", "date": "", "ddg_snippet": "Certain models can also produce the same kinds of errors that humans produce (e.g. Geirhos et al ., 2021; Tuli et al ., 2021; Lampinen et al ., 2024) (although the general degree of error consistency between humans and models is contested; see Geirhos , Meding, and Wichmann, 2020 ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/TrHG4qXWkkRyk3yMf/do-deep-neural-networks-have-brain-like-representations-a", "content": "Certain models can also produce the same kinds of errors that humans produce (e.g. Geirhos et al ., 2021; Tuli et al ., 2021; Lampinen et al ., 2024) (although the general degree of error consistency between humans and models is contested; see Geirhos , Meding, and Wichmann, 2020 ..."}
+{"idx": 4, "title": "UC Merced", "date": "", "ddg_snippet": "stimuli ( Geirhos et al ., 2019): (left) Original image from ImageNet, and (right) a textured transform.Another related line of behavioral analyses instead considers error consistency on standard datasets rather than testing on specially designed datasets ( Geirhos et al ., 2020 ).", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt2pm5q7k6/qt2pm5q7k6.pdf?t=qwi2wu", "content": "stimuli ( Geirhos et al ., 2019): (left) Original image from ImageNet, and (right) a textured transform.Another related line of behavioral analyses instead considers error consistency on standard datasets rather than testing on specially designed datasets ( Geirhos et al ., 2020 )."}
+{"idx": 5, "title": "(PDF) Human Alignment of Neural Network Representations", "date": "", "ddg_snippet": "object shapes ( Geirhos et al ., 2019; Baker et al ., 2018), although reliance on texture can be mitigated. through data augmentation ( Geirhos et al ., 2019; Hermann et al ., 2020 ; Li et al ., 2021), adversarial.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/379758261_Human_Alignment_of_Neural_Network_Representations", "content": "object shapes ( Geirhos et al ., 2019; Baker et al ., 2018), although reliance on texture can be mitigated. through data augmentation ( Geirhos et al ., 2019; Hermann et al ., 2020 ; Li et al ., 2021), adversarial."}
+{"idx": 6, "title": "An Extended Study of Human-like Behavior under Adversarial Training", "date": "", "ddg_snippet": "In this context, Geirhos et al . provided an initial analysis of robust models and provided initial evidence, that the initial texture bias in CNNs is shifted towards shape-based decisions under adversarial training (AT) [ Geirhos 2021_modelsvshumasn] .", "subpage_snippet": "", "source": "www.arxiv-vanity.com", "link": "https://www.arxiv-vanity.com/papers/2303.12669/", "content": "In this context, Geirhos et al . provided an initial analysis of robust models and provided initial evidence, that the initial texture bias in CNNs is shifted towards shape-based decisions under adversarial training (AT) [ Geirhos 2021_modelsvshumasn] ."}
+{"idx": 7, "title": "Are Deep Neural Networks Adequate Behavioral Models of Human...", "date": "", "ddg_snippet": "Geirhos et al . (2018) systematically explored this issue for three DNNs (ResNet-152, VGG-19, and GoogLeNet) us-ing 13 different image distortions or degradations.", "subpage_snippet": "", "source": "www.allpsych.uni-giessen.de", "link": "https://www.allpsych.uni-giessen.de/rauisch/readings/Wichmann_Geirhos.2023.pdf", "content": "Geirhos et al . (2018) systematically explored this issue for three DNNs (ResNet-152, VGG-19, and GoogLeNet) us-ing 13 different image distortions or degradations."}
+{"idx": 8, "title": "Robustness to object rotation in humans and deep neural networks", "date": "", "ddg_snippet": "Figure 2: Error consistency for humans and DNN models when categorizing objects after in-depth rotation.Objects were sourced from 16 categories known to both humans and DNNs ( Geirhos et al ., 2020 , 2021).", "subpage_snippet": "", "source": "2024.ccneuro.org", "link": "https://2024.ccneuro.org/pdf/169_Paper_authored_HA_MM_CCN_2024_auth.pdf", "content": "Figure 2: Error consistency for humans and DNN models when categorizing objects after in-depth rotation.Objects were sourced from 16 categories known to both humans and DNNs ( Geirhos et al ., 2020 , 2021)."}
+{"idx": 9, "title": "Discussion of Brain-Score / CORnet is overly critical.", "date": "", "ddg_snippet": "We now clarify the difference between “high-level strategy” and “decision rule“ (for 17 decision rule: following the terminology from “Shortcut learning in deep neural networks”, GEIRHOS ET AL , 2020 ).", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2020/file/9f6992966d4c363ea0162a056cb45fe5-AuthorFeedback.pdf", "content": "We now clarify the difference between “high-level strategy” and “decision rule“ (for 17 decision rule: following the terminology from “Shortcut learning in deep neural networks”, GEIRHOS ET AL , 2020 )."}
diff --git a/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_Jeremy_Yu_Lu_Lu_diffusion_equation_experimental_s_year_2022.jsonl b/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_Jeremy_Yu_Lu_Lu_diffusion_equation_experimental_s_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1cf733dc0c09732332bc9565f542372d48d36c54
--- /dev/null
+++ b/data/sampled_jsons/Gradient-enhanced_physics-informed_neural_networks_Jeremy_Yu_Lu_Lu_diffusion_equation_experimental_s_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "VideoEraser: Concept Erasure in Text-to-Video Diffusion Models", "date": "", "ddg_snippet": "The rapid growth of text-to-video (T2V) diffusion models has raised concerns about privacy, copyright, and safety due to their potential misuse in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15314v1", "content": "The rapid growth of text-to-video (T2V) diffusion models has raised concerns about privacy, copyright, and safety due to their potential misuse in ..."}
+{"idx": 1, "title": "Gradient-enhanced physics-informed neural networks for forward and ...", "date": "", "ddg_snippet": "Here, we propose a new method, gradient-enhanced physics - informed neural networks (gPINNs), for improving the accuracy of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function. We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0045782522001438", "content": "Here, we propose a new method, gradient-enhanced physics - informed neural networks (gPINNs), for improving the accuracy of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function. We tested gPINNs extensively and demonstrated the effectiveness of gPINNs in both forward and inverse PDE problems."}
+{"idx": 2, "title": "[2111.02801] Gradient-enhanced physics-informed neural networks for ...", "date": "", "ddg_snippet": "Here, we propose a new method, gradient-enhanced physics - informed neural networks (gPINNs), for improving the accuracy and training efficiency of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2111.02801", "content": "Here, we propose a new method, gradient-enhanced physics - informed neural networks (gPINNs), for improving the accuracy and training efficiency of PINNs. gPINNs leverage gradient information of the PDE residual and embed the gradient into the loss function."}
+{"idx": 3, "title": "GitHub - lu-group/gpinn: Gradient-enhanced physics-informed neural ...", "date": "", "ddg_snippet": "The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics - informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022 ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lu-group/gPINN", "content": "The data and code for the paper J. Yu , L. Lu , X. Meng, & G. E. Karniadakis. Gradient-enhanced physics - informed neural networks for forward and inverse PDE problems. Computer Methods in Applied Mechanics and Engineering, 393, 114823, 2022 ..."}
+{"idx": 4, "title": "PDF Gradient-enhanced Physics-Informed Neural Networks (gPINNs)", "date": "", "ddg_snippet": "Gradient-enhanced Physics - Informed Neural Networks (gPINNs) MIT PRIMES Conference, 10/16/21 Presented by: Jeremy Yu Mentor: Dr. Lu Lu", "subpage_snippet": "", "source": "math.mit.edu", "link": "https://math.mit.edu/research/highschool/primes/materials/2021/October/1-3-Yu.pdf", "content": "Gradient-enhanced Physics - Informed Neural Networks (gPINNs) MIT PRIMES Conference, 10/16/21 Presented by: Jeremy Yu Mentor: Dr. Lu Lu"}
+{"idx": 5, "title": "Gradient-enhanced Physics-Informed Neural Network (gPINN)", "date": "", "ddg_snippet": "Research Foundation Based on cutting-edge research in gradient-enhanced physics - informed neural networks ( Yu et al., 2022, Computer Methods in Applied Mechanics and Engineering). Extends traditional PINN approaches with gradient enhancement for improved convergence and accuracy in inverse problems.", "subpage_snippet": "", "source": "sakeeb91.github.io", "link": "https://sakeeb91.github.io/gpinn/", "content": "Research Foundation Based on cutting-edge research in gradient-enhanced physics - informed neural networks ( Yu et al., 2022, Computer Methods in Applied Mechanics and Engineering). Extends traditional PINN approaches with gradient enhancement for improved convergence and accuracy in inverse problems."}
+{"idx": 6, "title": "Integrating Gradient-enhanced Physics-informed Neural Networks with ...", "date": "", "ddg_snippet": "In this study, we proposed an improved physics - informed neural network (PINNs) for numerical simulation of localized wave solutions of partial differential equations (PDEs). Solving localized wave solutions of PDEs is an essential research topic in nonlinear science with significant research value, and related theory has been employed to many fields. Numerous researchers have conducted a ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10449191", "content": "In this study, we proposed an improved physics - informed neural network (PINNs) for numerical simulation of localized wave solutions of partial differential equations (PDEs). Solving localized wave solutions of PDEs is an essential research topic in nonlinear science with significant research value, and related theory has been employed to many fields. Numerous researchers have conducted a ..."}
+{"idx": 7, "title": "PDF Gradient-Based Physics-Informed Neural Network - Springer", "date": "", "ddg_snippet": "This study introduces gradient-enhanced physics - informed neural networks (gPINNs) that are trained for solving partial differential equations . Here, the proposed approach is contrasted with a physics - informed neu-ral network , which has proven to be an efficient technique for resolving forward and inverse PDE issues.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-981-19-9379-4_54.pdf", "content": "This study introduces gradient-enhanced physics - informed neural networks (gPINNs) that are trained for solving partial differential equations . Here, the proposed approach is contrasted with a physics - informed neu-ral network , which has proven to be an efficient technique for resolving forward and inverse PDE issues."}
+{"idx": 8, "title": "A gradient-enhanced physics-informed neural network (gPINN) scheme for ...", "date": "", "ddg_snippet": "In their cutting-edge research, Yu et al. (2022) developed the gradient-enhanced physics - informed neural network (gPINN) to enhance accuracy. In their investigation into considering the gradients of PDE residual with respect to the input variables, Yu et al. (2022) suggest that it can ameliorate the total performance of the neural network .", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0952197623010928", "content": "In their cutting-edge research, Yu et al. (2022) developed the gradient-enhanced physics - informed neural network (gPINN) to enhance accuracy. In their investigation into considering the gradients of PDE residual with respect to the input variables, Yu et al. (2022) suggest that it can ameliorate the total performance of the neural network ."}
+{"idx": 9, "title": "Gradient-enhanced Physics-informed Neural Networks Based on Transfer ...", "date": "", "ddg_snippet": "Abstract. We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs) for inverse problems of the function coe cient discovery in order to overcome de ciency of the discrete characterization of the PDE loss in neural networks and improve accuracy of function feature description, which o ers a new angle of view for gPINNs. The TL-gPINN algorithm is applied to infer the unknown ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.08310", "content": "Abstract. We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs) for inverse problems of the function coe cient discovery in order to overcome de ciency of the discrete characterization of the PDE loss in neural networks and improve accuracy of function feature description, which o ers a new angle of view for gPINNs. The TL-gPINN algorithm is applied to infer the unknown ..."}
diff --git a/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight_Equation_4_CAPA_formula.jsonl b/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight_Equation_4_CAPA_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c02d3b80572f664da0211d64937b605042a29bc7
--- /dev/null
+++ b/data/sampled_jsons/Great_Models_Think_Alike_and_this_Undermines_AI_Oversight_Equation_4_CAPA_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “AI Oversight”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v1", "content": "Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “AI Oversight”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity ..."}
+{"idx": 1, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "... show the benefits of diverse models for AI oversight – less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "... show the benefits of diverse models for AI oversight – less similarity between models reduces bias in LLM-as-a-judge, and also leads to greater ..."}
+{"idx": 2, "title": "and this undermines ai oversight", "date": "", "ddg_snippet": "... ( CAPA ): a metric for LM similarity based on overlap in model mistakes. ... equation for CAPA is: κp = c p obs − cp exp. 1 ... formula is used to define ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=sYB0Y0hOxi", "content": "... ( CAPA ): a metric for LM similarity based on overlap in model mistakes. ... equation for CAPA is: κp = c p obs − cp exp. 1 ... formula is used to define ..."}
+{"idx": 3, "title": "[Literature Review] Great Models Think Alike and this ...", "date": "", "ddg_snippet": "6 Feb 2025 — The paper titled \" Great Models Think Alike and this Undermines AI Oversight \" explores the implications of model similarity in the context of ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/great-models-think-alike-and-this-undermines-ai-oversight", "content": "6 Feb 2025 — The paper titled \" Great Models Think Alike and this Undermines AI Oversight \" explores the implications of model similarity in the context of ..."}
+{"idx": 4, "title": "Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike And This Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight Great Models Think Alike and this Undermines AI Oversight Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Title: Great Models Think Alike and this Undermines AI Oversight - arXiv… Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on ... Feb 7, 2025 · Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes ... Mar 7, 2025 · We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this ... This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '. This research matters because as we rely more on AI systems, we need ways to make sure they're working correctly. If we can't trust AI to oversee other AI because they're too similar, it could lead to uncaught ... Plain English Explanation Large language models like GPT- 4 and Claude are more alike than different. When given the same task, these models often produce similar answers and make similar mistakes. This is like having multiple students who all learned from the same textbook - they tend to get the same questions right and wrong. Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Can language models automate AI oversight? As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \"AI Oversight\". Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “AI Oversight”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04313", "content": "Feb 6, 2025 · As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as ''AI Oversight''. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on ... Feb 7, 2025 · Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes ... Mar 7, 2025 · We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this ... This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '. This research matters because as we rely more on AI systems, we need ways to make sure they're working correctly. If we can't trust AI to oversee other AI because they're too similar, it could lead to uncaught ... Plain English Explanation Large language models like GPT- 4 and Claude are more alike than different. When given the same task, these models often produce similar answers and make similar mistakes. This is like having multiple students who all learned from the same textbook - they tend to get the same questions right and wrong. Does model similarity affect AI oversight? We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this metric, we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results. Can language models automate AI oversight? As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \"AI Oversight\". Are model mistakes becoming more correlated with AI capabilities? As model capabilities increase, it becomes harder to find their mistakes, and we might defer more to AI oversight. However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Are model mistakes becoming more similar with increasing capabilities? However, we observe a concerning trend -- model mistakes are becoming more similar with increasing capabilities , pointing to risks from correlated failures. Our work underscores the importance of reporting and correcting for model similarity, especially in the emerging paradigm of AI oversight. Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “AI Oversight”. We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity ..."}
+{"idx": 5, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Feb 7, 2025 · Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.04313", "content": "Feb 7, 2025 · Abstract As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as \" AI Oversight \". We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes ..."}
+{"idx": 6, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Mar 7, 2025 · We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=sYB0Y0hOxi", "content": "Mar 7, 2025 · We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement ( CAPA ): a metric for LM similarity based on overlap in model mistakes. Using CAPA , we first show that LLM-as-a-judge scores favor models similar to the judge, generalizing recent self-preference results."}
+{"idx": 7, "title": "Great Models Think Alike And This Undermines AI Oversight", "date": "", "ddg_snippet": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this ...", "subpage_snippet": "", "source": "awesome-llm-papers.github.io", "link": "https://awesome-llm-papers.github.io/publications/goel2025great/", "content": "As Language Model (LM) capabilities advance, evaluating and supervising them at scale is getting harder for humans. There is hope that other language models can automate both these tasks, which we refer to as “ AI Oversight ”. We study how model similarity affects both aspects of AI oversight by proposing a probabilistic metric for LM similarity based on overlap in model mistakes. Using this ..."}
+{"idx": 8, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '. This research matters because as we rely more on AI systems, we need ways to make sure they're working correctly. If we can't trust AI to oversee other AI because they're too similar, it could lead to uncaught ...", "subpage_snippet": "", "source": "ai-search.io", "link": "https://ai-search.io/papers/great-models-think-alike-and-this-undermines-ai-oversight", "content": "This paper talks about how the increasing similarity between advanced AI language models can create problems for using AI to oversee other AI systems, a concept known as ' AI Oversight '. This research matters because as we rely more on AI systems, we need ways to make sure they're working correctly. If we can't trust AI to oversee other AI because they're too similar, it could lead to uncaught ..."}
+{"idx": 9, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "Plain English Explanation Large language models like GPT- 4 and Claude are more alike than different. When given the same task, these models often produce similar answers and make similar mistakes. This is like having multiple students who all learned from the same textbook - they tend to get the same questions right and wrong.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/great-models-think-alike-this-undermines-ai", "content": "Plain English Explanation Large language models like GPT- 4 and Claude are more alike than different. When given the same task, these models often produce similar answers and make similar mistakes. This is like having multiple students who all learned from the same textbook - they tend to get the same questions right and wrong."}
diff --git a/data/sampled_jsons/HTML_Content_Cleaning_Figure_2_HtmlRAG_huggingface.co_year_2024.jsonl b/data/sampled_jsons/HTML_Content_Cleaning_Figure_2_HtmlRAG_huggingface.co_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..68dbc5c33ffdaa0a9e0f2f05f714e5595055fbad
--- /dev/null
+++ b/data/sampled_jsons/HTML_Content_Cleaning_Figure_2_HtmlRAG_huggingface.co_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ℹ 1 0 1 **What is HtmlRAG , Multimodal RAG and Agentic...", "date": "", "ddg_snippet": "HtmlRAG works directly with HTML to keep more of the structure and meaning of the original content , missing the step of converting the data into the plain text. The main parts of HtmlRAG ’s working process are HTML Cleaning and Pruning techniques", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/Kseniase/html-multimodal-agentic-rag", "content": "HtmlRAG works directly with HTML to keep more of the structure and meaning of the original content , missing the step of converting the data into the plain text. The main parts of HtmlRAG ’s working process are HTML Cleaning and Pruning techniques"}
+{"idx": 1, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation. Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation. Two-Step Block-Tree-Based HTML Pruning: The block-tree-based HTML pruning consists of two steps..."}
+{"idx": 2, "title": "A smart toolkit for HTML cleaning and pruning for RAG systems.", "date": "", "ddg_snippet": "HTML Cleaning . from htmlrag import clean _ html . Block Content : The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/htmlrag/", "content": "HTML Cleaning . from htmlrag import clean _ html . Block Content : The Bellagio is a luxury hotel and casino located on the Las Vegas Strip in Paradise, Nevada."}
+{"idx": 3, "title": "mav23/ HTML -Pruner-Llama-1B-GGUF · Hugging Face", "date": "", "ddg_snippet": "Hugging Face 's logo Hugging Face .We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "hf.qhduan.com", "link": "https://hf.qhduan.com/mav23/HTML-Pruner-Llama-1B-GGUF", "content": "Hugging Face 's logo Hugging Face .We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 4, "title": "HTML -Pruner-Llama-1B-GGUF huggingface . co api & mav23... - Toolify", "date": "", "ddg_snippet": "The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation. HTML -Pruner-Llama-1B-GGUF huggingface . co .", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/mav23-html-pruner-llama-1b-gguf", "content": "The compressed HTML of lossless HTML cleaning is suitable for RAG systems that have long-context LLMs and are not willing to loss any information before generation. HTML -Pruner-Llama-1B-GGUF huggingface . co ."}
+{"idx": 5, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "The cleaned HTML is still long, so we conduct experiments under a long-context setting (128K), as shown in Table 2 . When HTML is taken as the format of external knowledge, HtmlRAG without pruning meets or outperforms plain text and Markdown on most datasets, demonstrating its validity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v2", "content": "The cleaned HTML is still long, so we conduct experiments under a long-context setting (128K), as shown in Table 2 . When HTML is taken as the format of external knowledge, HtmlRAG without pruning meets or outperforms plain text and Markdown on most datasets, demonstrating its validity."}
+{"idx": 6, "title": "HtmlRAG : Building an Efficient HTML Retrieval Enhanced Generation...", "date": "", "ddg_snippet": "Lossless HTML cleanup compressed HTML is suitable for RAG systems with long context LLMs and an unwillingness to lose any information before generation.The dataset is stored in the html _data folder. The full test dataset can be downloaded from huggingface : HtmlRAG -test.", "subpage_snippet": "", "source": "www.kdjingpai.com", "link": "https://www.kdjingpai.com/en/htmlrag/", "content": "Lossless HTML cleanup compressed HTML is suitable for RAG systems with long context LLMs and an unwillingness to lose any information before generation.The dataset is stored in the html _data folder. The full test dataset can be downloaded from huggingface : HtmlRAG -test."}
+{"idx": 7, "title": "Implementing HtmlRAG : Enhancing Retrieval-Augmented... | Dev Genius", "date": "", "ddg_snippet": "4. Implementing HtmlRAG . To effectively integrate HTML into a RAG system, we need to address the challenges through a series of preprocessing and optimization steps: 1. HTML Cleaning : Remove unnecessary content to reduce size and noise.", "subpage_snippet": "", "source": "blog.devgenius.io", "link": "https://blog.devgenius.io/implementing-htmlrag-enhancing-retrieval-augmented-generation-with-html-knowledge-91cdd6278e23", "content": "4. Implementing HtmlRAG . To effectively integrate HTML into a RAG system, we need to address the challenges through a series of preprocessing and optimization steps: 1. HTML Cleaning : Remove unnecessary content to reduce size and noise."}
+{"idx": 8, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning , compression, and pruning strategies...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning , compression, and pruning strategies..."}
+{"idx": 9, "title": "Telegram: View @ai_voicemodels", "date": "", "ddg_snippet": "huggingface . co /Abbysek/BevhillsYoungFemale3V 2 /resolve/main/GTAVFranklin1Min.zip Michael - https:// huggingface . co /Abbysek/BevhillsYoungFemale3V 2 /resolve/main/GTAVMichael1Min.zip Trevor - https:// huggingface . co /lilyrpa/lilyrpaModels/resolve/main/trevorphilips.zip Пригожин...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/ai_voicemodels/5", "content": "huggingface . co /Abbysek/BevhillsYoungFemale3V 2 /resolve/main/GTAVFranklin1Min.zip Michael - https:// huggingface . co /Abbysek/BevhillsYoungFemale3V 2 /resolve/main/GTAVMichael1Min.zip Trevor - https:// huggingface . co /lilyrpa/lilyrpaModels/resolve/main/trevorphilips.zip Пригожин..."}
diff --git a/data/sampled_jsons/Herbort_et_al._photometric_stereo_inter-reflections_subsurface_scattering.jsonl b/data/sampled_jsons/Herbort_et_al._photometric_stereo_inter-reflections_subsurface_scattering.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ef10cbc914803cef9b3389ecb70744b760181474
--- /dev/null
+++ b/data/sampled_jsons/Herbort_et_al._photometric_stereo_inter-reflections_subsurface_scattering.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Robust Photometric Stereo Based on Event Interval Profile", "date": "", "ddg_snippet": "This paper proposes Photometric Stereo based on Event Interval Profile (PS-EIP), a robust method that recovers pixelwise surface normals from a time-series ...", "subpage_snippet": "", "source": "cvpr.thecvf.com", "link": "https://cvpr.thecvf.com/virtual/2025/poster/33500", "content": "This paper proposes Photometric Stereo based on Event Interval Profile (PS-EIP), a robust method that recovers pixelwise surface normals from a time-series ..."}
+{"idx": 1, "title": "Sparse Photometric 3D Face Reconstruction Guided by ...", "date": "", "ddg_snippet": "by X Cao · 2018 · Cited by 42 — We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest ad- vances on face registration / modeling from ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_cvpr_2018/papers/Cao_Sparse_Photometric_3D_CVPR_2018_paper.pdf", "content": "by X Cao · 2018 · Cited by 42 — We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest ad- vances on face registration / modeling from ..."}
+{"idx": 2, "title": "3D range scan enhancement using image-based methods", "date": "", "ddg_snippet": "by S Herbort · 2013 · Cited by 9 — The beneficial effect of interreflection compensation on the reconstruction accuracy is evaluated quantitatively in a Photometric Stereo framework. ... Herbort et ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/abs/pii/S0924271613001615", "content": "by S Herbort · 2013 · Cited by 9 — The beneficial effect of interreflection compensation on the reconstruction accuracy is evaluated quantitatively in a Photometric Stereo framework. ... Herbort et ..."}
+{"idx": 3, "title": "An Introduction to Image-based 3D Surface Reconstruction ...", "date": "", "ddg_snippet": "This paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques.", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/9567235/An_Introduction_to_Image_based_3D_Surface_Reconstruction_and_a_Survey_of_Photometric_Stereo_Methods", "content": "This paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques."}
+{"idx": 4, "title": "Second Post- Retrieval Symposium", "date": "", "ddg_snippet": "by AS Levine · 1993 · Cited by 2 — ... A.L. Frank, E.R. Benton,. L.A. Frigo, T.A. Parnell, J.W. Watts, Jr., and J.H. ... Stereo Imagery ................. 339. Clyde A. Sapp, Thomas. H. See, and ...", "subpage_snippet": "", "source": "ntrs.nasa.gov", "link": "https://ntrs.nasa.gov/api/citations/19930020165/downloads/19930020165.pdf", "content": "by AS Levine · 1993 · Cited by 2 — ... A.L. Frank, E.R. Benton,. L.A. Frigo, T.A. Parnell, J.W. Watts, Jr., and J.H. ... Stereo Imagery ................. 339. Clyde A. Sapp, Thomas. H. See, and ..."}
+{"idx": 5, "title": "Design and Implementation of Practical Bidirectional ...", "date": "", "ddg_snippet": "by C Schwartz · 2014 · Cited by 73 — Thus, the BTF is applicable to faithfully capture the appearance of all kinds of optically dense materials that exhibit only localized subsurface scattering .", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4063053/", "content": "by C Schwartz · 2014 · Cited by 73 — Thus, the BTF is applicable to faithfully capture the appearance of all kinds of optically dense materials that exhibit only localized subsurface scattering ."}
+{"idx": 6, "title": "BRDF Representation and Acquisition - Guarnera - 2016", "date": "", "ddg_snippet": "27 May 2016 — : Reflection from layered surfaces due to subsurface scattering . In Proceedings of the 20th Annual Conference on Computer Graphics and ...", "subpage_snippet": "", "source": "onlinelibrary.wiley.com", "link": "https://onlinelibrary.wiley.com/doi/10.1111/cgf.12867", "content": "27 May 2016 — : Reflection from layered surfaces due to subsurface scattering . In Proceedings of the 20th Annual Conference on Computer Graphics and ..."}
+{"idx": 7, "title": "978-1-4471-4150-1.pdf", "date": "", "ddg_snippet": "Basically, these methods can be divided into shape from shadow, photoclinometry and shape from shading, photometric stereo , and shape from polarisation.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-1-4471-4150-1.pdf", "content": "Basically, these methods can be divided into shape from shadow, photoclinometry and shape from shading, photometric stereo , and shape from polarisation."}
+{"idx": 8, "title": "The Composition and Chemistry of Titan's Atmosphere", "date": "", "ddg_snippet": "by CA Nixon · 2024 · Cited by 48 — In this review I summarize the current state of knowledge about the composition of Titan's atmosphere and our current understanding of the suggested chemistry.", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acsearthspacechem.2c00041", "content": "by CA Nixon · 2024 · Cited by 48 — In this review I summarize the current state of knowledge about the composition of Titan's atmosphere and our current understanding of the suggested chemistry."}
+{"idx": 9, "title": "Towards Photo-Realistic 3D Reconstruction from Casual ...", "date": "", "ddg_snippet": "by JJ Park · 2022 — In this thesis, I address the problem of obtaining photo-realistic 3D models of small-scale indoor scenes from a stream of images captured with a hand-held ...", "subpage_snippet": "", "source": "digital.lib.washington.edu", "link": "https://digital.lib.washington.edu/bitstreams/3c0aeca7-54a9-403e-9da6-6cea7a32e0fa/download", "content": "by JJ Park · 2022 — In this thesis, I address the problem of obtaining photo-realistic 3D models of small-scale indoor scenes from a stream of images captured with a hand-held ..."}
diff --git a/data/sampled_jsons/Hierarchical_Overlapping_Clustering_HOC_cost_function_overlaps_reduce_cost.jsonl b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_HOC_cost_function_overlaps_reduce_cost.jsonl
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index 0000000000000000000000000000000000000000..48a470fd46f6e2995c52b548adc22e9504c83afb
--- /dev/null
+++ b/data/sampled_jsons/Hierarchical_Overlapping_Clustering_HOC_cost_function_overlaps_reduce_cost.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HIERARCHICAL OVERLAPPING CLUSTERING FUNCTION ALGORITHM AND ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 1, "title": "Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "Overlapping clustering : Fuzzy clustering methods [6] allow that certain data points belong to multiple clusters with a di erent level of con dence. In this way, the boundary of clusters is fuzzy and we can talk about overlaps of these clusters.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "Overlapping clustering : Fuzzy clustering methods [6] allow that certain data points belong to multiple clusters with a di erent level of con dence. In this way, the boundary of clusters is fuzzy and we can talk about overlaps of these clusters."}
+{"idx": 2, "title": "Hierarchical Clustering: Objective Functions and Algorithms", "date": "", "ddg_snippet": "Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23].", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/fullHtml/10.1145/3321386", "content": "Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a “good” hierarchical clustering is one that minimizes a particular cost function [23]."}
+{"idx": 3, "title": "Hierarchy cost of hierarchical clusterings - Springer", "date": "", "ddg_snippet": "The universal hierarchy cost is a lower bound on the approximation guarantee of any hierarchical clustering algorithm. If it is high, requesting a hierarchical clustering may force a significant loss in the quality of the individual k-clusterings.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10878-022-00851-4.pdf", "content": "The universal hierarchy cost is a lower bound on the approximation guarantee of any hierarchical clustering algorithm. If it is high, requesting a hierarchical clustering may force a significant loss in the quality of the individual k-clusterings."}
+{"idx": 4, "title": "Generalized Reductions: Making any Hierarchical Clustering ...", "date": "", "ddg_snippet": "Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering , after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta’s cost function , perhaps one of ...", "subpage_snippet": "", "source": "www.deepnlp.org", "link": "https://www.deepnlp.org/content/articles/generalized-reductions:-making-any-hierarchical-clustering-fair-and-balanced-with-low-cost", "content": "Clustering is a fundamental building block of modern statistical analysis pipelines. Fair clustering has seen much attention from the machine learning community in recent years. We are some of the first to study fairness in the context of hierarchical clustering , after the results of Ahmadian et al. from NeurIPS in 2020. We evaluate our results using Dasgupta’s cost function , perhaps one of ..."}
+{"idx": 5, "title": "The Price of Hierarchical Clustering - arXiv.org", "date": "", "ddg_snippet": "A hierarchical clustering achieves an approximation factor of if the costs of each k- clustering in the hierarchy are at most times the costs of an optimal k- clustering . We study as cost functions the maximum (discrete) radius of any cluster (k-center problem) and the maximum diameter of any cluster (k-diameter problem).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2205.01417v1.pdf", "content": "A hierarchical clustering achieves an approximation factor of if the costs of each k- clustering in the hierarchy are at most times the costs of an optimal k- clustering . We study as cost functions the maximum (discrete) radius of any cluster (k-center problem) and the maximum diameter of any cluster (k-diameter problem)."}
+{"idx": 6, "title": "Data Science Tips: Handling Overlapping Clusters", "date": "", "ddg_snippet": "5. Refine clusters post- clustering to reduce overlap .You can also use Overlapping Hierarchical Clustering (OHC) which is an advanced clustering method that addresses the limitations of traditional agglomerative clustering methods.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/advice/3/what-strategies-can-you-use-handle-overlapping-clusters-qwvfc", "content": "5. Refine clusters post- clustering to reduce overlap .You can also use Overlapping Hierarchical Clustering (OHC) which is an advanced clustering method that addresses the limitations of traditional agglomerative clustering methods."}
+{"idx": 7, "title": "(PDF) Convergence Criteria for Hierarchical Overlapping ...", "date": "", "ddg_snippet": "Hierarchical overlapping coordination ( HOC ) is an interesting strategy for solving decomposed problems. It simultaneously uses two or more design problem decompositions, each of them associated with different partitions of the design variables and constraints.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/30845529_Convergence_Criteria_for_Hierarchical_Overlapping_Coordination_of_Linearly_Constrained_Convex_Design_Problems", "content": "Hierarchical overlapping coordination ( HOC ) is an interesting strategy for solving decomposed problems. It simultaneously uses two or more design problem decompositions, each of them associated with different partitions of the design variables and constraints."}
+{"idx": 8, "title": "Overlapping Hierarchical Clustering (OHC) | SpringerLink", "date": "", "ddg_snippet": "We propose an overlapping hierarchical clustering framework. We construct a quasi-dendrogram hierarchical structure to represent the clusters that is however not necessarily a tree (of specific shape) but a directed acyclic graph.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-44584-3_21", "content": "We propose an overlapping hierarchical clustering framework. We construct a quasi-dendrogram hierarchical structure to represent the clusters that is however not necessarily a tree (of specific shape) but a directed acyclic graph."}
+{"idx": 9, "title": "Hierarchical Clustering in Python, Step by Step Complete Guide [2022]", "date": "", "ddg_snippet": "Overlapping clustering is a soft cluster . That means data items may belong to more than one cluster . As you can see in that image, two clusters are overlapping .", "subpage_snippet": "", "source": "www.mltut.com", "link": "https://www.mltut.com/hierarchical-clustering-in-python-step-by-step-complete-guide/", "content": "Overlapping clustering is a soft cluster . That means data items may belong to more than one cluster . As you can see in that image, two clusters are overlapping ."}
diff --git a/data/sampled_jsons/HtmlRAG_arXiv_HTML_2411.02959v1.jsonl b/data/sampled_jsons/HtmlRAG_arXiv_HTML_2411.02959v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e09711c6561be01e7dc256013412cf03cb8b09e3
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling Retrieved...", "date": "", "ddg_snippet": "arXiv : 2411 . 02959 v 1 [cs.IR] 05 Nov 2024.Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain-text-based RAG process.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02959v1", "content": "arXiv : 2411 . 02959 v 1 [cs.IR] 05 Nov 2024.Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML , such as headings and table structures, is lost during this plain-text-based RAG process."}
+{"idx": 1, "title": "[2411.02959] HtmlRAG: HTML is Better Than Plain Text for ...", "date": "", "ddg_snippet": "by J Tan · 2024 · Cited by 19 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.02959", "content": "by J Tan · 2024 · Cited by 19 — We propose HtmlRAG , which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling ..."}
+{"idx": 2, "title": "Matteo Villosio's Post - rag #llm #html", "date": "", "ddg_snippet": "HtmlRAG takes a different route by preserving the full HTML ... Link to paper: https://arxiv.org/html/2411.02959v1 · Like · Reply. 1 ...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/matteo-villosio_rag-llm-html-activity-7260268686282768386-S1Yp", "content": "HtmlRAG takes a different route by preserving the full HTML ... Link to paper: https://arxiv.org/html/2411.02959v1 · Like · Reply. 1 ..."}
+{"idx": 3, "title": "[AI Paper] HtmlRAG: HTML is Better Than Plain Text for Modeling ...", "date": "", "ddg_snippet": "org/abs/ 2411.02959v1 This research explores a new way to build Retrieval Augmented Generation (RAG) systems by using HTML instead of plain text. The main ...", "subpage_snippet": "", "source": "m.facebook.com", "link": "https://m.facebook.com/groups/1662719234009065/posts/-ai-paper-htmlrag-html-is-better-than-plain-text-for-modeling-retrieved-knowledg/3928047390809560/", "content": "org/abs/ 2411.02959v1 This research explores a new way to build Retrieval Augmented Generation (RAG) systems by using HTML instead of plain text. The main ..."}
+{"idx": 4, "title": "HTML Versions Roadmap 1. HTML 1.0 (1991– ...", "date": "", "ddg_snippet": "[AI Paper] HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems https://arxiv. org/abs/2411.02959v1 This ...", "subpage_snippet": "", "source": "www.facebook.com", "link": "https://www.facebook.com/groups/codedevelopers/posts/24478046745194640/", "content": "[AI Paper] HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems https://arxiv. org/abs/2411.02959v1 This ..."}
+{"idx": 5, "title": "Paper page - HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "arxiv : 2411 . 02959 . In fact, HtmlRAG has nothing to do with Graph RAG . The tree structure in HtmlRAG originates from the HTML format, while the graph in graph RAG is extrated from plain-text documents using an LLM.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2411.02959", "content": "arxiv : 2411 . 02959 . In fact, HtmlRAG has nothing to do with Graph RAG . The tree structure in HtmlRAG originates from the HTML format, while the graph in graph RAG is extrated from plain-text documents using an LLM."}
+{"idx": 6, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "DOI:10.48550/ arXiv . 2411 . 02959 . License. CC BY 4.0.and semantic information inherent in HTML , such as headings and. table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG , which uses HTML .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/385560345_HtmlRAG_HTML_is_Better_Than_Plain_Text_for_Modeling_Retrieved_Knowledge_in_RAG_Systems", "content": "DOI:10.48550/ arXiv . 2411 . 02959 . License. CC BY 4.0.and semantic information inherent in HTML , such as headings and. table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG , which uses HTML ."}
+{"idx": 7, "title": "GitHub - plageon/ HtmlRAG : HtmlRAG : HTML is Better Than Plain...", "date": "", "ddg_snippet": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/plageon/HtmlRAG", "content": "We propose HtmlRAG , which uses HTML instead of plain text as the format of external knowledge in RAG systems. To tackle the long context brought by HTML , we propose Lossless HTML Cleaning and Two-Step Block-Tree-Based HTML Pruning."}
+{"idx": 8, "title": "HtmlRAG : HTML is Better Than Plain Text for Modeling... | alphaXiv", "date": "", "ddg_snippet": "View 1 comments: The comparison in Table 1 is not fair. HtmlRAG has a pruner, while other methods compared do not.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2411.02959v1", "content": "View 1 comments: The comparison in Table 1 is not fair. HtmlRAG has a pruner, while other methods compared do not."}
+{"idx": 9, "title": "(PDF) HtmlRAG : HTML is Better Than Plain Text for Modeling...", "date": "", "ddg_snippet": "(DOI: 10.48550/ arxiv . 2411 . 02959 ) Retrieval-Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/htmlrag-html-is-better-than-plain-text-for-modeling-37eto4bp5nih", "content": "(DOI: 10.48550/ arxiv . 2411 . 02959 ) Retrieval-Augmented Generation ( RAG ) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs."}
diff --git a/data/sampled_jsons/ICLR_2025_KijslFbfOL_Algorithm_2_optimization_termination_condition.jsonl b/data/sampled_jsons/ICLR_2025_KijslFbfOL_Algorithm_2_optimization_termination_condition.jsonl
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index 0000000000000000000000000000000000000000..7f4048215c8abdc9e4b7b808e1aa3c0a6320f222
--- /dev/null
+++ b/data/sampled_jsons/ICLR_2025_KijslFbfOL_Algorithm_2_optimization_termination_condition.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "International Conference on Learning Representations - Wikipedia", "date": "", "ddg_snippet": "The conference includes invited talks as well as oral and poster presentations of refereed papers. Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[ 2 ]).", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/International_Conference_on_Learning_Representations", "content": "The conference includes invited talks as well as oral and poster presentations of refereed papers. Since its inception in 2013, ICLR has employed an open peer review process to referee paper submissions (based on models proposed by Yann LeCun[ 2 ])."}
+{"idx": 1, "title": "GitHub - nunchaku-tech/nunchaku: [ ICLR 2025 Spotlight] SVDQuant...", "date": "", "ddg_snippet": "[ ICLR 2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models.To reduce data movement overhead, we fuse the first two and the latter two kernels together. Performance.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/nunchaku-tech/nunchaku", "content": "[ ICLR 2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models.To reduce data movement overhead, we fuse the first two and the latter two kernels together. Performance."}
+{"idx": 2, "title": "Complexity Lower Bounds of Adaptive Gradient Algorithms for...", "date": "", "ddg_snippet": "Recent results in non-convex stochastic optimization demonstrate the convergence of popular adaptive algorithms (e.g., AdaGrad) under the $(L_0, L_1)$-smoothness condition , but the rate of convergence is a higher-order polynomial in terms of problem parameters like the...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ZjOXuAfS6l", "content": "Recent results in non-convex stochastic optimization demonstrate the convergence of popular adaptive algorithms (e.g., AdaGrad) under the $(L_0, L_1)$-smoothness condition , but the rate of convergence is a higher-order polynomial in terms of problem parameters like the..."}
+{"idx": 3, "title": "Helldivers 2 | OPTIMIZATION GUIDE | Every Setting Tested - YouTube", "date": "", "ddg_snippet": "© 2025 Google LLC.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=FtM4ffG_zko", "content": "© 2025 Google LLC."}
+{"idx": 4, "title": "Flow Chart of Genetic Algorithm with all steps involved from beginning...", "date": "", "ddg_snippet": "particle swarm optimization Evolutionary Programming swarm optimization algorithms Differential Evolution de comprehensive review Selected algorithms benchmark functions pso optimization problems optimization algorithms Grey Wolf Optimization .", "subpage_snippet": "", "source": "plos.figshare.com", "link": "https://plos.figshare.com/articles/figure/_Flow_Chart_of_Genetic_Algorithm_with_all_steps_involved_from_beginning_until_termination_conditions_met_6_/1418786", "content": "particle swarm optimization Evolutionary Programming swarm optimization algorithms Differential Evolution de comprehensive review Selected algorithms benchmark functions pso optimization problems optimization algorithms Grey Wolf Optimization ."}
+{"idx": 5, "title": "Game Guardian No Root 2025", "date": "", "ddg_snippet": "GameGuardian 2025 No Root Version – Now Live! GameGuardian (GG) hasn't been updated in a long time, and with Android system upgrades, older versions of GG have become increasingly difficult to use.", "subpage_snippet": "", "source": "gameguardian.io", "link": "https://gameguardian.io/", "content": "GameGuardian 2025 No Root Version – Now Live! GameGuardian (GG) hasn't been updated in a long time, and with Android system upgrades, older versions of GG have become increasingly difficult to use."}
+{"idx": 6, "title": "Optimization (scipy. optimize ) — SciPy v1.16. 2 Manual", "date": "", "ddg_snippet": "The scipy. optimize package provides several commonly used optimization algorithms . A detailed listing is available: scipy. optimize (can also be found by help(scipy. optimize )). Local minimization of multivariate scalar functions ( minimize)#.", "subpage_snippet": "", "source": "docs.scipy.org", "link": "https://docs.scipy.org/doc/scipy/tutorial/optimize.html", "content": "The scipy. optimize package provides several commonly used optimization algorithms . A detailed listing is available: scipy. optimize (can also be found by help(scipy. optimize )). Local minimization of multivariate scalar functions ( minimize)#."}
+{"idx": 7, "title": "2026 Conference", "date": "", "ddg_snippet": "LLM Usage Policies at ICLR 2026. Sponsors. Check back Dec 1, 2025 .sparse coding and dimensionality expansion. hierarchical models. optimization for representation learning. learning representations of outputs or states.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/", "content": "LLM Usage Policies at ICLR 2026. Sponsors. Check back Dec 1, 2025 .sparse coding and dimensionality expansion. hierarchical models. optimization for representation learning. learning representations of outputs or states."}
+{"idx": 8, "title": "ду но – Telegram", "date": "", "ddg_snippet": "Годовой отчет за период июнь 2024–июнь 2025 . -> Девиз года: «Сохраняя исследовательские традиции». Топовые конференции (4 статьи). Мы сделали стрик из главных AI-конф: NeurIPS, ICLR , ICML — во всех были на мейн треке.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dunnolab/41", "content": "Годовой отчет за период июнь 2024–июнь 2025 . -> Девиз года: «Сохраняя исследовательские традиции». Топовые конференции (4 статьи). Мы сделали стрик из главных AI-конф: NeurIPS, ICLR , ICML — во всех были на мейн треке."}
+{"idx": 9, "title": "B REAKING", "date": "", "ddg_snippet": "Published as a conference paper at ICLR 2025 . Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, and Yining Wang. Near-optimal discrete optimization for experimental design: A regret minimization approach.", "subpage_snippet": "", "source": "homes.luddy.indiana.edu", "link": "https://homes.luddy.indiana.edu/qzhangcs/papers/iclr25-IS-batch.pdf", "content": "Published as a conference paper at ICLR 2025 . Zeyuan Allen-Zhu, Yuanzhi Li, Aarti Singh, and Yining Wang. Near-optimal discrete optimization for experimental design: A regret minimization approach."}
diff --git a/data/sampled_jsons/ICML_2022_Orecchia_Practical_Almost-Linear-Time_Approximation_Algorithms_computing_environment_exper.jsonl b/data/sampled_jsons/ICML_2022_Orecchia_Practical_Almost-Linear-Time_Approximation_Algorithms_computing_environment_exper.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..9d80da4a7a7efa77b3f1fa49d2060744c66746a7
--- /dev/null
+++ b/data/sampled_jsons/ICML_2022_Orecchia_Practical_Almost-Linear-Time_Approximation_Algorithms_computing_environment_exper.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF ICML 2022 — Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis, Lorenzo Orecchia , Kunal Talwar, Charalampos Tsourakakis", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2022/Slides/16880_M2pKLQk.pdf", "content": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering Konstantinos Ameranis, Lorenzo Orecchia , Kunal Talwar, Charalampos Tsourakakis"}
+{"idx": 1, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "A long line of work has focused on developing polynomial- time approximation algorithms for ratio-cut minimization, including spectral algorithm based on Cheeger's inequality (Alon & Milman, 1985), the multicommodity-flow-based Leighton-Rao O(log n) approximation algorithm (Leighton & Rao, 1999), and finally the current state-of-the-art ap ...", "subpage_snippet": "", "source": "tsourakakis.com", "link": "https://tsourakakis.com/wp-content/uploads/2022/06/aott_icml22.pdf", "content": "A long line of work has focused on developing polynomial- time approximation algorithms for ratio-cut minimization, including spectral algorithm based on Cheeger's inequality (Alon & Milman, 1985), the multicommodity-flow-based Leighton-Rao O(log n) approximation algorithm (Leighton & Rao, 1999), and finally the current state-of-the-art ap ..."}
+{"idx": 2, "title": "Publications | Orecchia Research Group", "date": "", "ddg_snippet": "Lorenzo Orecchia , Konstantinos Ameranis, Kunal Talwar, Charalampos Tsourakakis. Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML , 2022 .", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/publication/", "content": "Lorenzo Orecchia , Konstantinos Ameranis, Kunal Talwar, Charalampos Tsourakakis. Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML , 2022 ."}
+{"idx": 3, "title": "Practical Almost-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives. To this end, we show that the cut-matching framework of (Khandekar et al., 2014) can be significantly extended to incorporate hybrid partitions.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a", "content": "Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives. To this end, we show that the cut-matching framework of (Khandekar et al., 2014) can be significantly extended to incorporate hybrid partitions."}
+{"idx": 4, "title": "ICML 2022 Practical Almost-Linear-Time Approximation Algorithms for ...", "date": "", "ddg_snippet": "Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives. To this end, we show that the cut-matching framework of (Khandekar et al., 2014) can be significantly extended to incorporate hybrid partitions.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/spotlight/16880", "content": "Our main algorithmic contributions are almost-linear-time algorithms O (log n)-approximation algorithms for both these objectives. To this end, we show that the cut-matching framework of (Khandekar et al., 2014) can be significantly extended to incorporate hybrid partitions."}
+{"idx": 5, "title": "Lorenzo Orecchia - dblp", "date": "", "ddg_snippet": "2022 [c20] Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022 : 17071-17093", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/pid/32/4340", "content": "2022 [c20] Lorenzo Orecchia , Konstantinos Ameranis, Charalampos E. Tsourakakis, Kunal Talwar: Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering. ICML 2022 : 17071-17093"}
+{"idx": 6, "title": "PDF Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Our main algorith-mic contributions are nearly- linear - time algo-rithms O(log n)-approximation algorithms for both these objectives.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v162/orecchia22a/orecchia22a.pdf", "content": "Our main algorith-mic contributions are nearly- linear - time algo-rithms O(log n)-approximation algorithms for both these objectives."}
+{"idx": 7, "title": "Practical Nearly-Linear-Time Approximation Algorithms for Hybrid and ...", "date": "", "ddg_snippet": "Crucially, we implement our approximation algorithm to produce both overlapping and hybrid partitions for large graphs, easily scaling to tens of millions of edges, and test our implementation on real-world datasets against other competitive baselines. Based on joint work with Lorenzo Orecchia .", "subpage_snippet": "", "source": "orecchia.net", "link": "https://orecchia.net/talk/practical-nearly-linear-time-approximation-algorithms-for-hybrid-and-overlapping-graph-clustering/", "content": "Crucially, we implement our approximation algorithm to produce both overlapping and hybrid partitions for large graphs, easily scaling to tens of millions of edges, and test our implementation on real-world datasets against other competitive baselines. Based on joint work with Lorenzo Orecchia ."}
+{"idx": 8, "title": "Maximum Flow and Minimum-Cost Flow in Almost-Linear Time", "date": "", "ddg_snippet": "Our framework extends to algorithms running in m1+o(1) time for computing flows that minimize general edge-separable convex functions to high accuracy. This gives almost-linear time algorithms for several problems including entropy-regularized optimal transport, matrix scaling, p -norm flows, and p -norm isotonic regression on arbitrary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2203.00671", "content": "Our framework extends to algorithms running in m1+o(1) time for computing flows that minimize general edge-separable convex functions to high accuracy. This gives almost-linear time algorithms for several problems including entropy-regularized optimal transport, matrix scaling, p -norm flows, and p -norm isotonic regression on arbitrary ..."}
+{"idx": 9, "title": "ICML 2022 Papers", "date": "", "ddg_snippet": "Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite- Time Analysis Anticorrelated Noise Injection for Improved Generalization Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/papers.html", "content": "Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite- Time Analysis Anticorrelated Noise Injection for Improved Generalization Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee"}
diff --git a/data/sampled_jsons/ICML_2025_collective_action_machine_learning_year_2025.jsonl b/data/sampled_jsons/ICML_2025_collective_action_machine_learning_year_2025.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML The Role of Learning Algorithms in Collective Action", "date": "", "ddg_snippet": "Poster in Workshop: Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact The Role of Learning Algorithms in Collective Action Omri Ben-Dov · Jake Fawkes · Samira Samadi · Amartya Sanyal", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/38217", "content": "Poster in Workshop: Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact The Role of Learning Algorithms in Collective Action Omri Ben-Dov · Jake Fawkes · Samira Samadi · Amartya Sanyal"}
+{"idx": 1, "title": "ICML 2025 录用论文简介 | 南京大学大模型研究协同创新中心", "date": "", "ddg_snippet": "Jul 18, 2025 · ICML （ International Conference on Machine Learning ，简称 ICML ）是机器学习与人工智能领域的国际顶级学术会议，是机器学习领域历史最悠久的、规模最大、影响最广的顶级学术会议之一，也是中国计算机学会CCF推荐的A类会议。南京大学计算机学院大模型中心有4篇论文被 ICML 2025 录用。", "subpage_snippet": "", "source": "cs.nju.edu.cn", "link": "https://cs.nju.edu.cn/lm/post/2025-09-19-icml2025-accepted-papers/index.html", "content": "Jul 18, 2025 · ICML （ International Conference on Machine Learning ，简称 ICML ）是机器学习与人工智能领域的国际顶级学术会议，是机器学习领域历史最悠久的、规模最大、影响最广的顶级学术会议之一，也是中国计算机学会CCF推荐的A类会议。南京大学计算机学院大模型中心有4篇论文被 ICML 2025 录用。"}
+{"idx": 2, "title": "Algorithmic Collective Action in Machine Learning", "date": "", "ddg_snippet": "We propose a theoretical model of algorithmic collective action . Participating workers modify their data in a coordinated manner in order to steer the learning algorithm toward a desired outcome.", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/as/en/projects/algorithmic-collective-action-in-machine-learning", "content": "We propose a theoretical model of algorithmic collective action . Participating workers modify their data in a coordinated manner in order to steer the learning algorithm toward a desired outcome."}
+{"idx": 3, "title": "Algorithmic Collective Action in Machine Learning - PMLR", "date": "", "ddg_snippet": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/hardt23a.html", "content": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with ..."}
+{"idx": 4, "title": "Algorithmic Collective Action with Multiple Collectives", "date": "", "ddg_snippet": "Algorithmic collective action in recom-mender systems: promoting songs by reordering playlists. Advances in Neural Information Processing Systems, 37:119123–119149, 2024. [3] Omri Ben-Dov, Jake Fawkes, Samira Samadi, and Amartya Sanyal. The role of learning algorithms in collective action . In International Conference on Machine Learning , pages", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.19149", "content": "Algorithmic collective action in recom-mender systems: promoting songs by reordering playlists. Advances in Neural Information Processing Systems, 37:119123–119149, 2024. [3] Omri Ben-Dov, Jake Fawkes, Samira Samadi, and Amartya Sanyal. The role of learning algorithms in collective action . In International Conference on Machine Learning , pages"}
+{"idx": 5, "title": "International Conference on Machine Learning (ICML) Vancouver ...", "date": "", "ddg_snippet": "Jul 13, 2025 · ICML 2025 will feature tutorials on July 14, main conference sessions from July 15 to 17, and workshops on July 18 and 19. The event will also include an expo on July 13, providing a platform for industry and academic professionals to present and discuss the latest developments in machine learning .", "subpage_snippet": "", "source": "www.vktr.com", "link": "https://www.vktr.com/events/conference/international-conference-on-machine-learning-icml-vancouver-2025/", "content": "Jul 13, 2025 · ICML 2025 will feature tutorials on July 14, main conference sessions from July 15 to 17, and workshops on July 18 and 19. The event will also include an expo on July 13, providing a platform for industry and academic professionals to present and discuss the latest developments in machine learning ."}
+{"idx": 6, "title": "ICML 2024 Papers - International Conference on Machine Learning", "date": "", "ddg_snippet": "Select Year: (2024) 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2002 1996 IMLS Archives Getting Started Schedule Tutorials Main Conference Invited Talks Orals Spotlight Posters Awards Test of Time Award Papers Workshops Community Affinity Events Affinity Joint Poster Session Socials Town Hall / Business Meeting Exhibitors ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/papers.html", "content": "Select Year: (2024) 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2002 1996 IMLS Archives Getting Started Schedule Tutorials Main Conference Invited Talks Orals Spotlight Posters Awards Test of Time Award Papers Workshops Community Affinity Events Affinity Joint Poster Session Socials Town Hall / Business Meeting Exhibitors ..."}
+{"idx": 7, "title": "How AI can help us harness our ' collective intelligence'", "date": "", "ddg_snippet": "Now, researchers are finding that machines can bring out the best in group work.The synergy between AI and collective intelligence works best the more information we give the machine , says Woolley, which involves difficult choices about how much privacy we’re happy to surrender.", "subpage_snippet": "", "source": "www.bbc.com", "link": "https://www.bbc.com/worklife/article/20200513-how-ai-can-help-us-harness-our-collective-intelligence", "content": "Now, researchers are finding that machines can bring out the best in group work.The synergy between AI and collective intelligence works best the more information we give the machine , says Woolley, which involves difficult choices about how much privacy we’re happy to surrender."}
+{"idx": 8, "title": "Superheroes of Deep Learning Vol 2: Machine Learning for Healthcare", "date": "", "ddg_snippet": "solve computer vision [Anon attendee on phone posting photo of Godfather to twitter—Automatically captioned as “mafia boss inspires nerds to take collective action ”... scratches chin] (Same) Anon attendee: “… that was actually pretty good”.", "subpage_snippet": "", "source": "www.approximatelycorrect.com", "link": "https://www.approximatelycorrect.com/2021/08/10/superheroes-of-deep-learning-vol-2-machine-learning-for-healthcare/", "content": "solve computer vision [Anon attendee on phone posting photo of Godfather to twitter—Automatically captioned as “mafia boss inspires nerds to take collective action ”... scratches chin] (Same) Anon attendee: “… that was actually pretty good”."}
+{"idx": 9, "title": "Organizational Learning : A Theory of Action Perspective - PDF Free...", "date": "", "ddg_snippet": "Machine Learning in Action .Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change.", "subpage_snippet": "", "source": "epdf.pub", "link": "https://epdf.pub/organizational-learning-a-theory-of-action-perspective9ad9caca03a3d2c840afd6e89aa53a7a28098.html", "content": "Machine Learning in Action .Organizational Learning from Performance Feedback: A Behavioral Perspective on Innovation and Change."}
diff --git a/data/sampled_jsons/ITBench_Table_4_'Mean_Time_to_Diagnosis'_lowest_model_seconds_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/ITBench_Table_4_'Mean_Time_to_Diagnosis'_lowest_model_seconds_sitearxiv.org_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ITBench: Evaluating AI Agents across Diverse Real-World IT ...", "date": "", "ddg_snippet": "ITBench is a systematic benchmarking framework and run- time environment designed to evaluate AI agents tasked with automating IT operations, incorporating a robust archi-tecture (see Figure 2) comprising the AI Agent, Scenario Specification and Environment, Evaluator, and Leaderboard to facilitate comprehensive performance assessment.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352", "content": "ITBench is a systematic benchmarking framework and run- time environment designed to evaluate AI agents tasked with automating IT operations, incorporating a robust archi-tecture (see Figure 2) comprising the AI Agent, Scenario Specification and Environment, Evaluator, and Leaderboard to facilitate comprehensive performance assessment."}
+{"idx": 1, "title": "[2502.05352] ITBench: Evaluating AI Agents across Diverse ...", "date": "", "ddg_snippet": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.05352", "content": "Feb 7, 2025 · Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench , a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and ..."}
+{"idx": 2, "title": "TSI-B : BENCHMARKING T S IMPUTATION - arXiv.org", "date": "", "ddg_snippet": "TSI-Bench offers both research- and application-driven benchmarking perspectives, enabling a stan-dardized analysis of four critical dimensions of the imputation process, identified through the study of various domain requirements: (1) the capacity to simulate varying degrees of missingness and assess its im-pact on model performance; (2) the flexibility to incorporate different missing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12747", "content": "TSI-Bench offers both research- and application-driven benchmarking perspectives, enabling a stan-dardized analysis of four critical dimensions of the imputation process, identified through the study of various domain requirements: (1) the capacity to simulate varying degrees of missingness and assess its im-pact on model performance; (2) the flexibility to incorporate different missing ..."}
+{"idx": 3, "title": "[2406.12045] ||tau;$-bench: A Benchmark for Tool-Agent-User ... AssetOpsBench: Benchmarking AI Agents for Task Automation in ... [2209.09819] Efficient Model Based Diagnosis - arXiv.org [2502.05352] ITBench : Evaluating AI Agents across Diverse Real-World IT TSI- Bench : Benchmarking Time Series Imputation - arXiv . org TSI- Bench : Benchmarking Time Series Imputation - arXiv . org TSI- Bench : Benchmarking Time Series Imputation - arXiv . org TSI- Bench : Benchmarking Time Series Imputation - arXiv . org ITBench : Evaluating AI Agents across Diverse Real-World IT Automatio… TSI-Bench: Benchmarking Time Series Imputation - arXiv.org", "date": "", "ddg_snippet": "Jun 17, 2024 · Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $τ$- bench , a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines ... Jun 4 , 2025 · Despite recent advances in agent-based systems—such as ReAct, HuggingGPT, Chameleon, and Generalist Agents —a gap remains in adapting these innovations for real-world industrial settings. Most recent domain and application specific benchmarks (e.g., ITBench , SWE-bench, Customer Support Benchmarks) are tailored toward machine learning, IT or customer-service domains and do not address the ... Sep 20, 2022 · Both these steps of the diagnostic process have a worst case time complexity of O(n2) where n is the number of components. If the connectivity of the components is low , however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops. What is itbench & how does it work? We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). Why should we use TSI-bench for time series imputation? d the lack of standardized metrics .Despite the complexities, TSI-Bench provides valuable insights and practical guidelines for time series imputation in real-world scenarios. We envision TSI-Benc Can TSI-bench adapt time-series forecasting models for imputation tasks? Appendix D.C.5 ADAPTATION PARADIGMTo tailor the time-series forecasting models for the imputation task, TSI-Bench adopts the imputation framework (including the embedding strategy and the training methodology) from SAITS (Du et al., 2023) and proposes such an adaptation paradigm that has been demonstrated to be feasible in How is imputed data used in TSI-bench? sulting in the imputed time series. Finally, the imputed data is utilised by downstream models for further analysis, such as classifi sion, and forecasting.3.2 DATASETSIn TSI-Bench, we conduct experiments using eight diverse and representative datasets from four domains: air quality, What is missingness in TSI-benchc? ETAILS IN TSI-BENCHC.1 MISSINGNESSThe concept of missingness can be understood through two fundamental aspects: miss ng mechanisms and missing patterns . Missing mechanisms describe the underlying processes that lead to missing values, while missing patterns define how these missing What are the phases corresponding to two personas in itbench framework? ITBench framework, as shown in Figure 5, supports two main phases corresponding to two personas as follows: (i) benchmark registration phase , where the target is the Benchmark Submitter persona, and (ii) agent registration phase, focusing on the Agent Submitter persona and the actual runtime benchmarking execution and evaluation. B.1. TSI-Bench offers both research- and application-driven benchmarking perspectives, enabling a standardized analysis of four critical dimensions of the imputation process, identified through the study of various domain requirements: (1) the capacity to simulate varying degrees of missingness and assess its impact on model performance; (2) the flexibility to incorporate different missing patterns ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12045", "content": "Jun 17, 2024 · Existing benchmarks do not test language agents on their interaction with human users or ability to follow domain-specific rules, both of which are vital for deploying them in real world applications. We propose $τ$- bench , a benchmark emulating dynamic conversations between a user (simulated by language models) and a language agent provided with domain-specific API tools and policy guidelines ... Jun 4 , 2025 · Despite recent advances in agent-based systems—such as ReAct, HuggingGPT, Chameleon, and Generalist Agents —a gap remains in adapting these innovations for real-world industrial settings. Most recent domain and application specific benchmarks (e.g., ITBench , SWE-bench, Customer Support Benchmarks) are tailored toward machine learning, IT or customer-service domains and do not address the ... Sep 20, 2022 · Both these steps of the diagnostic process have a worst case time complexity of O(n2) where n is the number of components. If the connectivity of the components is low , however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops. What is itbench & how does it work? We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). Why should we use TSI-bench for time series imputation? d the lack of standardized metrics .Despite the complexities, TSI-Bench provides valuable insights and practical guidelines for time series imputation in real-world scenarios. We envision TSI-Benc Can TSI-bench adapt time-series forecasting models for imputation tasks? Appendix D.C.5 ADAPTATION PARADIGMTo tailor the time-series forecasting models for the imputation task, TSI-Bench adopts the imputation framework (including the embedding strategy and the training methodology) from SAITS (Du et al., 2023) and proposes such an adaptation paradigm that has been demonstrated to be feasible in How is imputed data used in TSI-bench? sulting in the imputed time series. Finally, the imputed data is utilised by downstream models for further analysis, such as classifi sion, and forecasting.3.2 DATASETSIn TSI-Bench, we conduct experiments using eight diverse and representative datasets from four domains: air quality, What is missingness in TSI-benchc? ETAILS IN TSI-BENCHC.1 MISSINGNESSThe concept of missingness can be understood through two fundamental aspects: miss ng mechanisms and missing patterns . Missing mechanisms describe the underlying processes that lead to missing values, while missing patterns define how these missing What are the phases corresponding to two personas in itbench framework? ITBench framework, as shown in Figure 5, supports two main phases corresponding to two personas as follows: (i) benchmark registration phase , where the target is the Benchmark Submitter persona, and (ii) agent registration phase, focusing on the Agent Submitter persona and the actual runtime benchmarking execution and evaluation. B.1. TSI-Bench offers both research- and application-driven benchmarking perspectives, enabling a standardized analysis of four critical dimensions of the imputation process, identified through the study of various domain requirements: (1) the capacity to simulate varying degrees of missingness and assess its impact on model performance; (2) the flexibility to incorporate different missing patterns ..."}
+{"idx": 4, "title": "[2209.09819] Efficient Model Based Diagnosis - arXiv.org", "date": "", "ddg_snippet": "Sep 20, 2022 · Both these steps of the diagnostic process have a worst case time complexity of O(n2) where n is the number of components. If the connectivity of the components is low , however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2209.09819", "content": "Sep 20, 2022 · Both these steps of the diagnostic process have a worst case time complexity of O(n2) where n is the number of components. If the connectivity of the components is low , however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops."}
+{"idx": 5, "title": "TSI-Bench: Benchmarking Time Series Imputation - arXiv.org", "date": "", "ddg_snippet": "TSI-Bench offers both research- and application-driven benchmarking perspectives, enabling a standardized analysis of four critical dimensions of the imputation process, identified through the study of various domain requirements: (1) the capacity to simulate varying degrees of missingness and assess its impact on model performance; (2) the flexibility to incorporate different missing patterns ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.12747v2", "content": "TSI-Bench offers both research- and application-driven benchmarking perspectives, enabling a standardized analysis of four critical dimensions of the imputation process, identified through the study of various domain requirements: (1) the capacity to simulate varying degrees of missingness and assess its impact on model performance; (2) the flexibility to incorporate different missing patterns ..."}
+{"idx": 6, "title": "arXiv:2502.05352v1 [cs.AI] 7 Feb 2025", "date": "", "ddg_snippet": "by S Jha · 2025 · Cited by 3 — Mitigation efficiency is measured in terms of pass@1. (i.e., whether the alert was cleared) and mean time to repair. As shown in Table 4 , across ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05352?", "content": "by S Jha · 2025 · Cited by 3 — Mitigation efficiency is measured in terms of pass@1. (i.e., whether the alert was cleared) and mean time to repair. As shown in Table 4 , across ..."}
+{"idx": 7, "title": "arXiv:2503.17736v1 [cs.CV] 22 Mar 2025", "date": "", "ddg_snippet": "by Y Zhao · 2025 · Cited by 1 — Benchmarking results reveal that even the most powerful models perform poorly on V2P-Bench (65.4% for GPT-4o and. 67.9% for Gemini-1.5-Pro), ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.17736?", "content": "by Y Zhao · 2025 · Cited by 1 — Benchmarking results reveal that even the most powerful models perform poorly on V2P-Bench (65.4% for GPT-4o and. 67.9% for Gemini-1.5-Pro), ..."}
+{"idx": 8, "title": "AssetOpsBench: Benchmarking AI Agents for Task Automation in ...", "date": "", "ddg_snippet": "Jun 4 , 2025 · Despite recent advances in agent-based systems—such as ReAct, HuggingGPT, Chameleon, and Generalist Agents —a gap remains in adapting these innovations for real-world industrial settings. Most recent domain and application specific benchmarks (e.g., ITBench , SWE-bench, Customer Support Benchmarks) are tailored toward machine learning, IT or customer-service domains and do not address the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03828v1", "content": "Jun 4 , 2025 · Despite recent advances in agent-based systems—such as ReAct, HuggingGPT, Chameleon, and Generalist Agents —a gap remains in adapting these innovations for real-world industrial settings. Most recent domain and application specific benchmarks (e.g., ITBench , SWE-bench, Customer Support Benchmarks) are tailored toward machine learning, IT or customer-service domains and do not address the ..."}
+{"idx": 9, "title": "STRATUS: A Multi-agent System for Autonomous ...", "date": "", "ddg_snippet": "by Y Chen · 2025 — We measure agentic systems using metrics: Success Rate (the percentage of problems being success- fully solved), average time (in seconds ), the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.02009", "content": "by Y Chen · 2025 — We measure agentic systems using metrics: Success Rate (the percentage of problems being success- fully solved), average time (in seconds ), the ..."}
diff --git a/data/sampled_jsons/ImagineFSL_paper_reference_21_CLIP_DALL-E.jsonl b/data/sampled_jsons/ImagineFSL_paper_reference_21_CLIP_DALL-E.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..702c76499c8712ae8ff8c9595bd2bf5fddf1236e
--- /dev/null
+++ b/data/sampled_jsons/ImagineFSL_paper_reference_21_CLIP_DALL-E.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "DALL - E - Wikipedia", "date": "", "ddg_snippet": "DALL - E , DALL - E 2, and DALL - E 3 are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL - E was announced in January 2021.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/DALL-E", "content": "DALL - E , DALL - E 2, and DALL - E 3 are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL - E was announced in January 2021."}
+{"idx": 1, "title": "DALL - E — Википедия", "date": "", "ddg_snippet": "DALL - E — нейронная сеть, созданная компанией OpenAI при финансовой поддержке Microsoft, способная генерировать высококачественные изображения, исходя из текстовых описаний на английском языке.", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/DALL-E", "content": "DALL - E — нейронная сеть, созданная компанией OpenAI при финансовой поддержке Microsoft, способная генерировать высококачественные изображения, исходя из текстовых описаний на английском языке."}
+{"idx": 2, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "We introduce ImagineFSL , a novel methodology for CLIP adaptation. It involves self-supervised pretraining on large-scale purely synthetic images, followed by fine-tuning with real and task-specific synthetic images.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ImagineFSL_Self-Supervised_Pretraining_Matters_on_Imagined_Base_Set_for_VLM-based_CVPR_2025_paper.pdf", "content": "We introduce ImagineFSL , a novel methodology for CLIP adaptation. It involves self-supervised pretraining on large-scale purely synthetic images, followed by fine-tuning with real and task-specific synthetic images."}
+{"idx": 3, "title": "ImagineFSL/README.md at main · HaoyuanYang-2023 ... - GitHub", "date": "", "ddg_snippet": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper : We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HaoyuanYang-2023/ImagineFSL/blob/main/README.md", "content": "This repository contains the official code for \" ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning\" ( CVPR 2025 Highlight ) In this paper : We frame synthetic images as standalone knowledge repositories and present a CLIP adaptation methodology that pretrains on purely synthetic images before fine-tuning for few-shot tasks. This marks a clear ..."}
+{"idx": 4, "title": "ImagineFSL: Self-Supervised Pretraining Matters on Imagined ...", "date": "", "ddg_snippet": "To overcome this limitation, we frame synthetic images as an imagined base set (iBase) , i. e ., an independent, large-scale synthetic dataset encompassing diverse concepts. Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks.", "subpage_snippet": "", "source": "peihuali.org", "link": "https://peihuali.org/ImagineFSL/index.html", "content": "To overcome this limitation, we frame synthetic images as an imagined base set (iBase) , i. e ., an independent, large-scale synthetic dataset encompassing diverse concepts. Building on this perspective, we introduce ImagineFSL , a novel CLIP adaptation methodology that pretrains on iBase and then fine-tunes for downstream few-shot tasks."}
+{"idx": 5, "title": "AcademicDissect/detailed_paper_collection/CLIP.md at main ...", "date": "", "ddg_snippet": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Tags: CLIP , Self-Supervised Learning, Few-Shot Learning, Text-to-Image Generation, Chain-of-Thought Learning, In-Context Learning", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AcademicDissect/AcademicDissect/blob/main/detailed_paper_collection/CLIP.md", "content": "ImagineFSL : Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot Learning Tags: CLIP , Self-Supervised Learning, Few-Shot Learning, Text-to-Image Generation, Chain-of-Thought Learning, In-Context Learning"}
+{"idx": 6, "title": "DALL-E-Explained/README.md at main - GitHub", "date": "", "ddg_snippet": "DALL-E -Explained Description and applications of OpenAI's paper about the DALL-E model and implementation of a text-to-image generation scheme using CLIP .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/simonsanvil/DALL-E-Explained/blob/main/README.md", "content": "DALL-E -Explained Description and applications of OpenAI's paper about the DALL-E model and implementation of a text-to-image generation scheme using CLIP ."}
+{"idx": 7, "title": "DALL · E 2 Explained - YouTube", "date": "", "ddg_snippet": "DALL · E 2 is a new AI system that can create realistic images and art from a description in natural language.Learn more: openai.com/ dall - e -2.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=qTgPSKKjfVg", "content": "DALL · E 2 is a new AI system that can create realistic images and art from a description in natural language.Learn more: openai.com/ dall - e -2."}
+{"idx": 8, "title": "DALL - E 3 - Image Generator", "date": "", "ddg_snippet": "DALL - E 3, OpenAI's most powerful image generation model. Generates high quality images with intricate details based on the user's most recent prompt.", "subpage_snippet": "", "source": "minitoolai.com", "link": "https://minitoolai.com/dall-e-3/", "content": "DALL - E 3, OpenAI's most powerful image generation model. Generates high quality images with intricate details based on the user's most recent prompt."}
+{"idx": 9, "title": "DALL - E 3 Online Image Generator - NightCafe", "date": "", "ddg_snippet": "Now featuring the latest base and custom Stable Diffusion model and checkpoint, DALL - E 3, Stable inpainting, and all other frontier models. NightCafe will always be your home for cutting-edge AI image generation. Web and mobile image generators.", "subpage_snippet": "", "source": "creator.nightcafe.studio", "link": "https://creator.nightcafe.studio/dall-e-ai-image-generator", "content": "Now featuring the latest base and custom Stable Diffusion model and checkpoint, DALL - E 3, Stable inpainting, and all other frontier models. NightCafe will always be your home for cutting-edge AI image generation. Web and mobile image generators."}
diff --git a/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_base_models_fine-_year_2024.jsonl b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_base_models_fine-_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1ca0a18ab47ac8001f65d8d7e327b41052cea864
--- /dev/null
+++ b/data/sampled_jsons/Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_by_Eight-Fold_base_models_fine-_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9m87e9Keq1", "content": "by A Setlur · Cited by 67 — The paper does a thorough exploration of when synthetic data can help for training LLMs on reasoning tasks, looking at GSM8K and MATH datasets."}
+{"idx": 1, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "by A Setlur · 2024 · Cited by 67 — Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14532", "content": "by A Setlur · 2024 · Cited by 67 — Training on model -generated synthetic data is a promising approach for finetuning LLMs, but it remains unclear when it helps or hurts."}
+{"idx": 2, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM ...", "date": "", "ddg_snippet": "20 Jun 2024 — We saw in Section 5 that fine-tuning on model-generated data can reinforce the model's tendency to produce irrelevant or incorrect steps ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.14532v1", "content": "20 Jun 2024 — We saw in Section 5 that fine-tuning on model-generated data can reinforce the model's tendency to produce irrelevant or incorrect steps ..."}
+{"idx": 3, "title": "RL on Incorrect Synthetic Data Scales the Efficiency of", "date": "", "ddg_snippet": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks.", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/neurips/96295/paper", "content": "This paper explores how training large language models (like AI) on synthetic data (fake but useful data) can help them improve at math reasoning tasks."}
+{"idx": 4, "title": "THETIC DATA IN LLM POST-TRAINING", "date": "", "ddg_snippet": "by Z Gan · Cited by 8 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . ... tuning process, where we fine - tune the model on the synthetic data .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=UxkznlcnHf", "content": "by Z Gan · Cited by 8 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . ... tuning process, where we fine - tune the model on the synthetic data ."}
+{"idx": 5, "title": "MathFusion: Enhancing Mathematical Problem-solving of ...", "date": "", "ddg_snippet": "by Q Pei · 2025 · Cited by 11 — Each base model is fine - tuned using three distinct fusion strategies: sequential, parallel, and conditional. For each strategy, the fine - tuning ... 21 pages", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.367.pdf", "content": "by Q Pei · 2025 · Cited by 11 — Each base model is fine - tuned using three distinct fusion strategies: sequential, parallel, and conditional. For each strategy, the fine - tuning ... 21 pages"}
+{"idx": 6, "title": "The Comprehensive Guide to Fine-tuning LLM | by Sunil Rao", "date": "", "ddg_snippet": "Fine-tuning is the process of taking a pre-trained language model (a large neural network that has learned general language patterns from a massive dataset)", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/data-science-collective/comprehensive-guide-to-fine-tuning-llm-4a8fd4d0e0af", "content": "Fine-tuning is the process of taking a pre-trained language model (a large neural network that has learned general language patterns from a massive dataset)"}
+{"idx": 7, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model -generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=synthetic+responses", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model -generated synthetic data is a promising approach ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model -generated synthetic data is a promising approach ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=LLM-generated+synthetic+data", "content": "RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight - Fold · Training on model -generated synthetic data is a promising approach ..."}
+{"idx": 9, "title": "Improve Vision Language Model Chain-of-thought ...", "date": "", "ddg_snippet": "by R Zhang · 2025 · Cited by 67 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . ... section , we demonstrate how minimal CoT training ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.82.pdf", "content": "by R Zhang · 2025 · Cited by 67 — Rl on incorrect synthetic data scales the efficiency of llm math reasoning by eight - fold . ... section , we demonstrate how minimal CoT training ..."}
diff --git a/data/sampled_jsons/Instant_Gaussian_Stream_github.jsonl b/data/sampled_jsons/Instant_Gaussian_Stream_github.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee08cbb24983e7eda31d9d50eab81ffc5a7bbc8d
--- /dev/null
+++ b/data/sampled_jsons/Instant_Gaussian_Stream_github.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - yjb6/IGS: [CVPR25 Highlight] Instant Gaussian Stream ...", "date": "", "ddg_snippet": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yjb6/IGS", "content": "title={ Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting}"}
+{"idx": 1, "title": "GitHub - KyungwanHan/GS_ Stream : Streaming Gaussian Splats to...", "date": "", "ddg_snippet": "Gaussian Splatting Streamer . This repository is built on-top of the original \"3D Gaussian Splatting for Real-Time Radiance Field Rendering\" implementation. The objective is to create a simple framework to use Gaussian Splatting to support inspection applications.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/KyungwanHan/GS_Stream", "content": "Gaussian Splatting Streamer . This repository is built on-top of the original \"3D Gaussian Splatting for Real-Time Radiance Field Rendering\" implementation. The objective is to create a simple framework to use Gaussian Splatting to support inspection applications."}
+{"idx": 2, "title": "Instant Gaussian Stream : Fast and Generalizable Streaming of...", "date": "", "ddg_snippet": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/Yan_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene@CVPR2025@CVF", "content": "In this paper, we propose Instant Gaussian Stream (IGS), a fast and generalizable streaming framework, to address these issues. First, we introduce a generalized Anchor-driven Gaussian Motion Network, which projects multi-view 2D motion features into 3D space, using anchor points..."}
+{"idx": 3, "title": "(PDF) Instant Gaussian Stream : Fast and Generalizable Streaming ...", "date": "", "ddg_snippet": "per , we propose Instant Gaussian Stream (IGS), a fast and. generalizable streaming fr amework, to address these issues. First, we intr oduce a generalized Anchor-driven Gaussian . Motion Network, which projects multi-view 2D motion fea", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/390114414_Instant_Gaussian_Stream_Fast_and_Generalizable_Streaming_of_Dynamic_Scene_Reconstruction_via_Gaussian_Splatting", "content": "per , we propose Instant Gaussian Stream (IGS), a fast and. generalizable streaming fr amework, to address these issues. First, we intr oduce a generalized Anchor-driven Gaussian . Motion Network, which projects multi-view 2D motion fea"}
+{"idx": 4, "title": "Instant Gaussian Splats in Unreal Engine 5 with Luma AI", "date": "", "ddg_snippet": "Now, artists can capture gaussian splats or Nerfs using the iOS Luma AI app, process the data in the cloud, and easily import it into their Unreal Engine projects. Thanks to the latest release of the Free Luma AI Unreal Engine Plugin, which is now available directly on the UE Marketplace.", "subpage_snippet": "", "source": "www.onsetfacilities.com", "link": "https://www.onsetfacilities.com/post/instant-gaussian-splats-in-unreal-engine-5-with-luma-ai", "content": "Now, artists can capture gaussian splats or Nerfs using the iOS Luma AI app, process the data in the cloud, and easily import it into their Unreal Engine projects. Thanks to the latest release of the Free Luma AI Unreal Engine Plugin, which is now available directly on the UE Marketplace."}
+{"idx": 5, "title": "bruce2233 | coder - GithubHelp", "date": "", "ddg_snippet": "bruce2233 Goto Github PK.igs icon igs. [CVPR25 Highlight] Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting.", "subpage_snippet": "", "source": "githubhelp.com", "link": "https://githubhelp.com/bruce2233", "content": "bruce2233 Goto Github PK.igs icon igs. [CVPR25 Highlight] Instant Gaussian Stream : Fast and Generalizable Streaming of Dynamic Scene Reconstruction via Gaussian Splatting."}
+{"idx": 6, "title": "Do This: Gaussian 16 Linux Version on Windows 10/11 Using WSL in...", "date": "", "ddg_snippet": "This tutorial describes how to install Gaussian 16 linux version in Windows systems without using dual-boot.apt install # Run the installation file. exit # Exit the su instance . nano .bashrc # Open the .bashrc file using nano editor. export g16root=/home/$USER/ gaussian .", "subpage_snippet": "", "source": "aritraroy.live", "link": "https://aritraroy.live/tutorial/videos/gaussian/do-this-gaussian-16-linux-version-on-windows-10-or-11-using-wsl-in-15-minutes/", "content": "This tutorial describes how to install Gaussian 16 linux version in Windows systems without using dual-boot.apt install # Run the installation file. exit # Exit the su instance . nano .bashrc # Open the .bashrc file using nano editor. export g16root=/home/$USER/ gaussian ."}
+{"idx": 7, "title": "Latest 10 Papers - February 13, 2025 - Githubissues", "date": "", "ddg_snippet": "Please check the Github page for a better reading experience and more papers.OVO-SLAM: Open-Vocabulary Online Simultaneous Localization and Mapping. 2024-11-22. IG-SLAM: Instant Gaussian SLAM. 2024-08-07.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/luohongk/Awesome-Localization-And-3D-Reconstruction-From-Arxiv/27", "content": "Please check the Github page for a better reading experience and more papers.OVO-SLAM: Open-Vocabulary Online Simultaneous Localization and Mapping. 2024-11-22. IG-SLAM: Instant Gaussian SLAM. 2024-08-07."}
+{"idx": 8, "title": "Instant Gaussian SLAM: Transforming 3D Mapping - Simple Science", "date": "", "ddg_snippet": "IG-SLAM stands for Instant Gaussian SLAM. It’s a new method that improves how we create 3D maps using only color images.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-03-instant-gaussian-slam-transforming-3d-mapping--akxyjqx", "content": "IG-SLAM stands for Instant Gaussian SLAM. It’s a new method that improves how we create 3D maps using only color images."}
+{"idx": 9, "title": "Gaussian Splatting Meets ROS2 | Hacker News", "date": "", "ddg_snippet": "For instance , imagine you're running a SLAM algorithm on a mobile robot and generating Gaussians as part of the mapping or localization process. With ROSplat, you can stream those Gaussians via ROS messages and visualize them live on another machine.", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=43831363", "content": "For instance , imagine you're running a SLAM algorithm on a mobile robot and generating Gaussians as part of the mapping or localization process. With ROSplat, you can stream those Gaussians via ROS messages and visualize them live on another machine."}
diff --git a/data/sampled_jsons/JEflV4nRlH_What_Makes_and_Breaks_Safety_Fine-tuning_synthetic_experiments_learning_rates_Section_4_year_2024.jsonl b/data/sampled_jsons/JEflV4nRlH_What_Makes_and_Breaks_Safety_Fine-tuning_synthetic_experiments_learning_rates_Section_4_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..bf633e3e97e3442f9719d4f2d63897a694d728e9
--- /dev/null
+++ b/data/sampled_jsons/JEflV4nRlH_What_Makes_and_Breaks_Safety_Fine-tuning_synthetic_experiments_learning_rates_Section_4_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.10264", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ..."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=JEflV4nRlH", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ..."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a9bef53eb7b0e5950d4f2d9c74a16006-Abstract-Conference.html", "content": "To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus the specific concepts the task is asked to be performed ..."}
+{"idx": 3, "title": "PDF What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human pref-erences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data gen-eration framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design ...", "subpage_snippet": "", "source": "proceedings.nips.cc", "link": "https://proceedings.nips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human pref-erences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data gen-eration framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design ..."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "This paper delves deep into the mechanistic workings of safety fine-tuning methods. The researchers employed a novel synthetic data generation framework to thoroughly investigate the impact of three common safety fine-tuning techniques. They discovered that these methods subtly transform the model's internal representations, creating distinct clusters for safe and unsafe inputs. However ...", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/jeflv4nrlh/", "content": "This paper delves deep into the mechanistic workings of safety fine-tuning methods. The researchers employed a novel synthetic data generation framework to thoroughly investigate the impact of three common safety fine-tuning techniques. They discovered that these methods subtly transform the model's internal representations, creating distinct clusters for safe and unsafe inputs. However ..."}
+{"idx": 5, "title": "understanding_safety_finetuning/README.md at main - GitHub", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study The official implementation of \" What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning/blob/main/README.md", "content": "What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study The official implementation of \" What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024."}
+{"idx": 6, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Table A.1: Safety performance of different fine-tuning protocols: Unlearning (Liu et al., 2024), DPO (Rafailov et al., 2023) and supervised safety fine-tuning (SSFT) (Ouyang et al., 2022) with medium and small learning rates are used for performing safety fine-tuning .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/What-Makes-and-Breaks-Safety-Fine-tuning-A-Study-Jain-Lubana/8805398cab95a5c384231b9c71a2ccfc00af1998/figure/7", "content": "Table A.1: Safety performance of different fine-tuning protocols: Unlearning (Liu et al., 2024), DPO (Rafailov et al., 2023) and supervised safety fine-tuning (SSFT) (Ouyang et al., 2022) with medium and small learning rates are used for performing safety fine-tuning ."}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the un-derlying factors that make models safe via safety fine-tuning , we design a synthetic data genera-tion framework that captures salient aspects of an unsafe input by modeling the interaction be-tween the task the model is asked to perform (e.g ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BS2CbUkJpy", "content": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the un-derlying factors that make models safe via safety fine-tuning , we design a synthetic data genera-tion framework that captures salient aspects of an unsafe input by modeling the interaction be-tween the task the model is asked to perform (e.g ..."}
+{"idx": 8, "title": "PDF What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human pref-erences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data genera-tion framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g ...", "subpage_snippet": "", "source": "ekdeepslubana.github.io", "link": "https://ekdeepslubana.github.io/data/sftjailbreaks.pdf", "content": "Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human pref-erences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data genera-tion framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g ..."}
+{"idx": 9, "title": "PDF arXiv:2407.10264v3 [cs.LG] 21 Aug 2024 - ResearchGate", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Kemal-Oksuz/publication/382271359_What_Makes_and_Breaks_Safety_Fine-tuning_Mechanistic_Study/links/6746f2c43d17281c7de2d06f/What-Makes-and-Breaks-Safety-Fine-tuning-A-Mechanistic-Study.pdf", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks ..."}
diff --git a/data/sampled_jsons/Jikang_Cheng_Zhiyuan_Yan_deepfake_detector_hyperparameters_experimental_setup_beta_gamma.jsonl b/data/sampled_jsons/Jikang_Cheng_Zhiyuan_Yan_deepfake_detector_hyperparameters_experimental_setup_beta_gamma.jsonl
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index 0000000000000000000000000000000000000000..75e47e4984fa79617bf805f990d332dcc6b6080f
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector? Images Jikang Cheng - OpenReview Zhiyuan Yan – PhD Student at Peking University Zhiyuan YAN | Doctor of Philosophy | Peking University ... Can We Leave Deepfake Data Behind in Training Deepfake Detector? Where the Devil Hides: Deepfake Detectors Can No Longer Be ... Can We Leave Deepfake Data Behind in Training Deepfake Detector ? Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Aug 30, 2024 · Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li View a PDF of the paper titled Can We Leave Deepfake Data Behind in Training Deepfake Detector ?, by Jikang Cheng and 5 other authors View all Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Published: 31 Dec 2023, Last Modified: 01 Nov 2024 CoRR 2024 ED: Explicit Data-level Debiasing for Deepfake Detection Jikang Cheng , Ying Zhang, Qin Zou, Zhiyuan Yan , Chao Liang, Zhongyuan Wang, Chen Li Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang 📮, Chen Li NeurIPS, 2024 arXiv Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning Zhiyuan Yan , Yandan Zhao, Shen Chen, Xinghe Fu, Taiping Yao, Shouhong Ding, Li Yuan 📮 CVPR, 2025 arXiv Transcending Forgery Specificity with Latent Space Augmentation for ... The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. Authors Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ... This paper investigates this risk in depth and describes a solution to stealthily infect Deepfake detectors . Specif-ically, we develop a trigger generator, that can synthesize passcode-controlled, semantic-suppression, adaptive, and invisible trigger patterns, ensuring both the stealthiness and effectiveness of these triggers. Should deepfake data be excluded in training a deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive . What are the security risks of deepfake detectors? Overview of the security risk: Deepfake detectors face potential vulnerabilities from third-party data providers who could intentionally corrupt their data by adding passcode-controlled, representation-suppression, adaptive, and invisible triggers. Are deep-fake detectors a reliable tool for detecting face authenticity? To counter this, Deep-fake detectors have been developed as reliable tools for as-sessing face authenticity. These detectors are typically de-veloped on Deep Neural Networks (DNNs) and trained us-ing third-party datasets. Are deepfake detectors based on deep neural networks? Currently, most state-of-the-art Deepfake detectors are based on Deep Neural Networks (DNNs) due to their sig-nificant learning capacities [13, 24, 31, 35, 40, 48]. To fully exploit their capacity, these methods typically rely on large-scale third-party datasets for training, e.g., FF++ , Celeb-DF . Aug 30, 2024 · With progressively organized latent space (ours), information in both deepfake and blendfake is effectively leveraged, and deepfake samples become easier to distinguish from the real. See Fig. 4a and 6 for experimental actual latent-space distribution. - \"Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\"", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "Aug 30, 2024 · Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li View a PDF of the paper titled Can We Leave Deepfake Data Behind in Training Deepfake Detector ?, by Jikang Cheng and 5 other authors View all Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Published: 31 Dec 2023, Last Modified: 01 Nov 2024 CoRR 2024 ED: Explicit Data-level Debiasing for Deepfake Detection Jikang Cheng , Ying Zhang, Qin Zou, Zhiyuan Yan , Chao Liang, Zhongyuan Wang, Chen Li Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang 📮, Chen Li NeurIPS, 2024 arXiv Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning Zhiyuan Yan , Yandan Zhao, Shen Chen, Xinghe Fu, Taiping Yao, Shouhong Ding, Li Yuan 📮 CVPR, 2025 arXiv Transcending Forgery Specificity with Latent Space Augmentation for ... The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. Authors Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ... This paper investigates this risk in depth and describes a solution to stealthily infect Deepfake detectors . Specif-ically, we develop a trigger generator, that can synthesize passcode-controlled, semantic-suppression, adaptive, and invisible trigger patterns, ensuring both the stealthiness and effectiveness of these triggers. Should deepfake data be excluded in training a deepfake detector? Intuitively, as deepfakes also contain additional informative forgery clues (e.g., deep generative artifacts), excluding all deepfake data in training deepfake detectors seems counter-intuitive . What are the security risks of deepfake detectors? Overview of the security risk: Deepfake detectors face potential vulnerabilities from third-party data providers who could intentionally corrupt their data by adding passcode-controlled, representation-suppression, adaptive, and invisible triggers. Are deep-fake detectors a reliable tool for detecting face authenticity? To counter this, Deep-fake detectors have been developed as reliable tools for as-sessing face authenticity. These detectors are typically de-veloped on Deep Neural Networks (DNNs) and trained us-ing third-party datasets. Are deepfake detectors based on deep neural networks? Currently, most state-of-the-art Deepfake detectors are based on Deep Neural Networks (DNNs) due to their sig-nificant learning capacities [13, 24, 31, 35, 40, 48]. To fully exploit their capacity, these methods typically rely on large-scale third-party datasets for training, e.g., FF++ , Celeb-DF . Aug 30, 2024 · With progressively organized latent space (ours), information in both deepfake and blendfake is effectively leveraged, and deepfake samples become easier to distinguish from the real. See Fig. 4a and 6 for experimental actual latent-space distribution. - \"Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\""}
+{"idx": 1, "title": "Jikang Cheng - OpenReview", "date": "", "ddg_snippet": "Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Published: 31 Dec 2023, Last Modified: 01 Nov 2024 CoRR 2024 ED: Explicit Data-level Debiasing for Deepfake Detection Jikang Cheng , Ying Zhang, Qin Zou, Zhiyuan Yan , Chao Liang, Zhongyuan Wang, Chen Li", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/profile?id=~Jikang_Cheng1", "content": "Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Published: 31 Dec 2023, Last Modified: 01 Nov 2024 CoRR 2024 ED: Explicit Data-level Debiasing for Deepfake Detection Jikang Cheng , Ying Zhang, Qin Zou, Zhiyuan Yan , Chao Liang, Zhongyuan Wang, Chen Li"}
+{"idx": 2, "title": "Zhiyuan Yan – PhD Student at Peking University", "date": "", "ddg_snippet": "Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang 📮, Chen Li NeurIPS, 2024 arXiv Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning Zhiyuan Yan , Yandan Zhao, Shen Chen, Xinghe Fu, Taiping Yao, Shouhong Ding, Li Yuan 📮 CVPR, 2025 arXiv Transcending Forgery Specificity with Latent Space Augmentation for ...", "subpage_snippet": "", "source": "yzy-stack.github.io", "link": "https://yzy-stack.github.io/", "content": "Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang 📮, Chen Li NeurIPS, 2024 arXiv Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning Zhiyuan Yan , Yandan Zhao, Shen Chen, Xinghe Fu, Taiping Yao, Shouhong Ding, Li Yuan 📮 CVPR, 2025 arXiv Transcending Forgery Specificity with Latent Space Augmentation for ..."}
+{"idx": 3, "title": "Zhiyuan YAN | Doctor of Philosophy | Peking University ...", "date": "", "ddg_snippet": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Zhiyuan-Yan-5", "content": "The generalization ability of deepfake detectors is vital for their applications in real-world scenarios."}
+{"idx": 4, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Authors Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ...", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/hash/2718a032d15e0b80cd164b240220df89-Abstract-Conference.html", "content": "Authors Jikang Cheng , Zhiyuan Yan , Ying Zhang, Yuhao Luo, Zhongyuan Wang, Chen Li Abstract The generalization ability of deepfake detectors is vital for their applications in real-world scenarios. One effective solution to enhance this ability is to train the models with manually-blended data, which we termed ''blendfake'', encouraging models to learn generic forgery artifacts like blending ..."}
+{"idx": 5, "title": "Where the Devil Hides: Deepfake Detectors Can No Longer Be ...", "date": "", "ddg_snippet": "This paper investigates this risk in depth and describes a solution to stealthily infect Deepfake detectors . Specif-ically, we develop a trigger generator, that can synthesize passcode-controlled, semantic-suppression, adaptive, and invisible trigger patterns, ensuring both the stealthiness and effectiveness of these triggers.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_Where_the_Devil_Hides_Deepfake_Detectors_Can_No_Longer_Be_CVPR_2025_paper.pdf", "content": "This paper investigates this risk in depth and describes a solution to stealthily infect Deepfake detectors . Specif-ically, we develop a trigger generator, that can synthesize passcode-controlled, semantic-suppression, adaptive, and invisible trigger patterns, ensuring both the stealthiness and effectiveness of these triggers."}
+{"idx": 6, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "Aug 30, 2024 · With progressively organized latent space (ours), information in both deepfake and blendfake is effectively leveraged, and deepfake samples become easier to distinguish from the real. See Fig. 4a and 6 for experimental actual latent-space distribution. - \"Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\"", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Can-We-Leave-Deepfake-Data-Behind-in-Training-Cheng-Yan/6b186896a5b2c15ea07a1e516c41ce01f2c15772/figure/0", "content": "Aug 30, 2024 · With progressively organized latent space (ours), information in both deepfake and blendfake is effectively leveraged, and deepfake samples become easier to distinguish from the real. See Fig. 4a and 6 for experimental actual latent-space distribution. - \"Can We Leave Deepfake Data Behind in Training Deepfake Detector ?\""}
+{"idx": 7, "title": "Computer Science Aug 2024", "date": "", "ddg_snippet": "26 Aug 2024 — Title: ED^4: Explicit Data-level Debiasing for Deepfake Detection . Jikang Cheng , Ying Zhang, Qin Zou, Zhiyuan Yan , Chao Liang, Zhongyuan Wang ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-08?skip=2625&show=2000", "content": "26 Aug 2024 — Title: ED^4: Explicit Data-level Debiasing for Deepfake Detection . Jikang Cheng , Ying Zhang, Qin Zou, Zhiyuan Yan , Chao Liang, Zhongyuan Wang ..."}
+{"idx": 8, "title": "A Complete List of All Adversarial Example Papers", "date": "", "ddg_snippet": "15 Jun 2019 — (80%)Zheng Jie Wong; Bingquan Shen Adversarial Attacks on Audio Deepfake Detection : A Benchmark and Comparative Study. ... (67%)Jialin Yan ; Yu ...", "subpage_snippet": "", "source": "nicholas.carlini.com", "link": "https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html", "content": "15 Jun 2019 — (80%)Zheng Jie Wong; Bingquan Shen Adversarial Attacks on Audio Deepfake Detection : A Benchmark and Comparative Study. ... (67%)Jialin Yan ; Yu ..."}
+{"idx": 9, "title": "A NOISE-ASSISTED DATA ANALYSIS METHOD", "date": "", "ddg_snippet": "PET: High-Frequency Temporal Self-Consistency Learning for Partially Deepfake Audio Localization. Jiayi He, Jiangyan Yi, Jianhua Tao and Siding Zeng. 6 Apr ...", "subpage_snippet": "", "source": "www.worldscientific.com", "link": "https://www.worldscientific.com/doi/10.1142/S1793536909000047?srsltid=AfmBOorQOwLhwsD-QXMpwNZ8eHeCaMlzI-gOv1cvSQc2SZERya2QcvLZ", "content": "PET: High-Frequency Temporal Self-Consistency Learning for Partially Deepfake Audio Localization. Jiayi He, Jiangyan Yi, Jianhua Tao and Siding Zeng. 6 Apr ..."}
diff --git a/data/sampled_jsons/Kirilenko_et_al._2015_abstract_year_2015.jsonl b/data/sampled_jsons/Kirilenko_et_al._2015_abstract_year_2015.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5326990d059ca73b633aaccb8419b8dfd90ca1cc
--- /dev/null
+++ b/data/sampled_jsons/Kirilenko_et_al._2015_abstract_year_2015.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Microsoft Word - BFW v11 Sep 17 2015 .docx | Forum", "date": "", "ddg_snippet": "Kirilenko et al . (2014) provide evidence that high frequency traders ceased to supply liquidity during the “Flash Crash” of 2010. In this paper, we complement these studies by providing evidence on changes in the liquidity provision by algorithmic traders.", "subpage_snippet": "", "source": "c.mql5.com", "link": "https://c.mql5.com/forextsd/forum/209/International+Evidence+on+Algorithmic+Trading.pdf", "content": "Kirilenko et al . (2014) provide evidence that high frequency traders ceased to supply liquidity during the “Flash Crash” of 2010. In this paper, we complement these studies by providing evidence on changes in the liquidity provision by algorithmic traders."}
+{"idx": 1, "title": "People as sensors: Mass media and local temperature influence...", "date": "", "ddg_snippet": "A.P. Kirilenko et al . / Global Environmental Change 30 ( 2015 ) 92–100. 93. e.g., corresponds to air temperature anomaly exceeding the multi-year mean by at least one standard deviation) was suggested to be a perceptible climate change.", "subpage_snippet": "", "source": "research.fit.edu", "link": "https://research.fit.edu/media/site-specific/researchfitedu/coast-climate-adaptation-library/climate-communications/youth-climate-amp-social-media/Kirilenko-et-al.--2015.--Mass-Media--Temperatures-Affect-CC-Discussion-On-Twitter.pdf", "content": "A.P. Kirilenko et al . / Global Environmental Change 30 ( 2015 ) 92–100. 93. e.g., corresponds to air temperature anomaly exceeding the multi-year mean by at least one standard deviation) was suggested to be a perceptible climate change."}
+{"idx": 2, "title": "Gaussian process-based algorithmic trading strategy identification", "date": "", "ddg_snippet": "In Kirilenko et al . (Citation2011)’s paper, there are only about from 16 to 20 HFTs on the S&P500 Emini market. Although this is a small population, their impact to the market has drawn increased attention from policy-makers, regulators and academia.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/14697688.2015.1011684?cookieSet=1", "content": "In Kirilenko et al . (Citation2011)’s paper, there are only about from 16 to 20 HFTs on the S&P500 Emini market. Although this is a small population, their impact to the market has drawn increased attention from policy-makers, regulators and academia."}
+{"idx": 3, "title": "The Flash Crash: The Impact of High Frequency Trading on an...", "date": "", "ddg_snippet": "Researchers like Kirilenko et al . (2017) and Biais, Foucault, and Moinas ( 2015 ) have explored the impact of external information on market dynamics, emphasizing the value of incorporating diverse data sources into predictive models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/228261350_The_Flash_Crash_The_Impact_of_High_Frequency_Trading_on_an_Electronic_Market", "content": "Researchers like Kirilenko et al . (2017) and Biais, Foucault, and Moinas ( 2015 ) have explored the impact of external information on market dynamics, emphasizing the value of incorporating diverse data sources into predictive models."}
+{"idx": 4, "title": "Comparing Events Coverage in Online News and Social Media: The...", "date": "", "ddg_snippet": "The study by Kirilenko et al . ( 2015 ) is closest to ours, as they look at both mainstream media coverage (14 news outlets) and attention patterns in Twitter. They analyze the inuence of local weather anomalies on the volume of climate change publications in mainstream media and Twitter.", "subpage_snippet": "", "source": "crisislex.org", "link": "https://crisislex.org/papers/icwsm2015_climate_change_media_gap.pdf", "content": "The study by Kirilenko et al . ( 2015 ) is closest to ours, as they look at both mainstream media coverage (14 news outlets) and attention patterns in Twitter. They analyze the inuence of local weather anomalies on the volume of climate change publications in mainstream media and Twitter."}
+{"idx": 5, "title": "Risk and Return in High-Frequency Trading", "date": "", "ddg_snippet": "Matthew Baron, Jonathan Brogaard, Björn Hagströmer and Andrei Kirilenko *. Abstract .( 2015 ) and Budish et al . ( 2015 ), who argue that competition on relative latency can lead to an inefficient and costly arms race.", "subpage_snippet": "", "source": "www.cb.cityu.edu.hk", "link": "https://www.cb.cityu.edu.hk/ef/doc/GRU/HFT+2017/Brogaard_HFT_risk_return_20170825.pdf", "content": "Matthew Baron, Jonathan Brogaard, Björn Hagströmer and Andrei Kirilenko *. Abstract .( 2015 ) and Budish et al . ( 2015 ), who argue that competition on relative latency can lead to an inefficient and costly arms race."}
+{"idx": 6, "title": "Deep Hawkes Process for High-Frequency", "date": "", "ddg_snippet": "This systemic intra-day anomaly only lasted for a couple of minutes, but tem-porarily wiped away a trillion dollars in market value. The analysis of agents resolved transaction level data in the E-mini by Kirilenko et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2109.15110", "content": "This systemic intra-day anomaly only lasted for a couple of minutes, but tem-porarily wiped away a trillion dollars in market value. The analysis of agents resolved transaction level data in the E-mini by Kirilenko et al ."}
+{"idx": 7, "title": "Predicting stock market movements using network science: an...", "date": "", "ddg_snippet": "2015 ; Curme et al . 2015 ) to see how the movements of a specific stock or index influence that of other indices or stocks and how a specific state propagates through out the nodes in stock market networks.", "subpage_snippet": "", "source": "appliednetsci.springeropen.com", "link": "https://appliednetsci.springeropen.com/articles/10.1007/s41109-017-0055-y", "content": "2015 ; Curme et al . 2015 ) to see how the movements of a specific stock or index influence that of other indices or stocks and how a specific state propagates through out the nodes in stock market networks."}
+{"idx": 8, "title": "Towards Fine-grained Classification of Climate Change related Social", "date": "", "ddg_snippet": "Moreover, Kirilenko et al . (2014) performed the analysis on tweets during 2012-2013 to conclude that users are establishing a relationship between temperature anomalies and climate change.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2022.acl-srw.35.pdf", "content": "Moreover, Kirilenko et al . (2014) performed the analysis on tweets during 2012-2013 to conclude that users are establishing a relationship between temperature anomalies and climate change."}
+{"idx": 9, "title": "Financialization and information technology: themes, issues and critical...", "date": "", "ddg_snippet": "Kirilenko , A. and Lo, A.W. (2013). Moore’s law versus Murphy’s law: Algorithmic trading and its discontents. Journal of Economic Perspectives, 27(2), 51–72.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1057/s41265-017-0044-8", "content": "Kirilenko , A. and Lo, A.W. (2013). Moore’s law versus Murphy’s law: Algorithmic trading and its discontents. Journal of Economic Perspectives, 27(2), 51–72."}
diff --git a/data/sampled_jsons/Kwon_circuit_fingerprinting_attacks_abstract_passive_deanonymization_Tor_hidden_services.jsonl b/data/sampled_jsons/Kwon_circuit_fingerprinting_attacks_abstract_passive_deanonymization_Tor_hidden_services.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3e9f2d9137243f06142c5a313a656662efb8de66
--- /dev/null
+++ b/data/sampled_jsons/Kwon_circuit_fingerprinting_attacks_abstract_passive_deanonymization_Tor_hidden_services.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor ...", "date": "", "ddg_snippet": "Abstract : This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively . In particular, we show that the circuits , paths established through the Tor network...", "subpage_snippet": "", "source": "www.usenix.org", "link": "https://www.usenix.org/conference/usenixsecurity15/technical-sessions/presentation/kwon", "content": "Abstract : This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively . In particular, we show that the circuits , paths established through the Tor network..."}
+{"idx": 1, "title": "Circuit Fingerprinting Attacks : Passive", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services .", "subpage_snippet": "", "source": "people.eecs.berkeley.edu", "link": "https://people.eecs.berkeley.edu/~raluca/cs261-f15/readings/sec15-paper-kwon.pdf", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services ."}
+{"idx": 2, "title": "[PDF] Circuit Fingerprinting Attacks : Passive Deanonymization of...", "date": "", "ddg_snippet": "In particular, we show that the circuits , paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under two slightly different threat models...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Circuit-Fingerprinting-Attacks:-Passive-of-Tor-Kwon-Alsabah/fb4cb1f1c57ee56e9a014570debf7d3d6871ddf3", "content": "In particular, we show that the circuits , paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit . We propose two attacks , under two slightly different threat models..."}
+{"idx": 3, "title": "A technical summary of the Usenix fingerprinting ... | The Tor Project", "date": "", "ddg_snippet": "Albert Kwon , Mashael AlSabah, and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the upcoming Usenix Security symposium in a few weeks. Tor doesn't do this. Passive attacks are correlation based on observations.", "subpage_snippet": "", "source": "blog.torproject.org", "link": "https://blog.torproject.org/technical-summary-usenix-fingerprinting-paper/", "content": "Albert Kwon , Mashael AlSabah, and others have a paper entitled Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services at the upcoming Usenix Security symposium in a few weeks. Tor doesn't do this. Passive attacks are correlation based on observations."}
+{"idx": 4, "title": "opsec/ Circuit Fingerprinting Attacks - Passive Deanonymization of...", "date": "", "ddg_snippet": "/ Circuit Fingerprinting Attacks - Passive Deanonymization of Tor Hidden Services .pdf.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BecodoExploit-mrCAT/opsec/blob/main/Circuit+Fingerprinting+Attacks-+Passive+Deanonymization+of+Tor+Hidden+Services.pdf", "content": "/ Circuit Fingerprinting Attacks - Passive Deanonymization of Tor Hidden Services .pdf."}
+{"idx": 5, "title": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor ...", "date": "", "ddg_snippet": "Albert Kwon , Mashael AlSabah, David Lazar, Marc Dacier, Srinivas Devadas: Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/uss/KwonALDD15.html", "content": "Albert Kwon , Mashael AlSabah, David Lazar, Marc Dacier, Srinivas Devadas: Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services ."}
+{"idx": 6, "title": "Researchers Unveiled a New, Serious Vulnerability In Tor", "date": "", "ddg_snippet": "An attacker can passively monitor traffic to reveal the identity of users and servers on the Tor network.The attack is known as a “ circuit fingerprinting attack ,” since the traffic going through a circuit displays unique patterns that can be used to deanonymize a client or server.", "subpage_snippet": "", "source": "www.vice.com", "link": "https://www.vice.com/en/article/researchers-unveiled-a-new-serious-vulnerability-in-tor/", "content": "An attacker can passively monitor traffic to reveal the identity of users and servers on the Tor network.The attack is known as a “ circuit fingerprinting attack ,” since the traffic going through a circuit displays unique patterns that can be used to deanonymize a client or server."}
+{"idx": 7, "title": "Poster: Fingerprinting Hidden", "date": "", "ddg_snippet": "Poster: Fingerprinting Hidden Service Circuits from a Tor Middle Relay. Abstract — Kwon et al. recently showed that circuit ngerprint-ing attacks could be used to identify hidden service circuits , which is a key step towards linking Tor users and their activity online.", "subpage_snippet": "", "source": "homes.esat.kuleuven.be", "link": "https://homes.esat.kuleuven.be/~mjuarezm/index_files/pdf/sp_abstract.pdf", "content": "Poster: Fingerprinting Hidden Service Circuits from a Tor Middle Relay. Abstract — Kwon et al. recently showed that circuit ngerprint-ing attacks could be used to identify hidden service circuits , which is a key step towards linking Tor users and their activity online."}
+{"idx": 8, "title": "Circuit fingerprinting attacks | Proceedings of the 24th USENIX...", "date": "", "ddg_snippet": "Circuit fingerprinting attacks : passive deanonymization of tor hidden services .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2831143.2831162?cookieSet=1", "content": "Circuit fingerprinting attacks : passive deanonymization of tor hidden services ."}
+{"idx": 9, "title": "(PDF) POSTER: Fingerprinting Tor Hidden Services", "date": "", "ddg_snippet": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . In USENIX Security, 2015.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/310821261_POSTER_Fingerprinting_Tor_Hidden_Services", "content": "Circuit Fingerprinting Attacks : Passive Deanonymization of Tor Hidden Services . In USENIX Security, 2015."}
diff --git a/data/sampled_jsons/LAUREL_Learned_Augmented_Residual_Layer_Equation_3_LAUREL-LR_variant_formula.jsonl b/data/sampled_jsons/LAUREL_Learned_Augmented_Residual_Layer_Equation_3_LAUREL-LR_variant_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10d327cc63e417aa3bc57bb1b15f9bfd20e78076
--- /dev/null
+++ b/data/sampled_jsons/LAUREL_Learned_Augmented_Residual_Layer_Equation_3_LAUREL-LR_variant_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2411.07501] LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2411.07501", "content": "Nov 12, 2024 · In this paper we introduce Learned Augmented Residual Layer ( LAuReL ) -- a novel generalization of the canonical residual connection -- with the goal to be an in-situ replacement of the latter while outperforming on both model quality and footprint metrics."}
+{"idx": 1, "title": "GitHub - BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "date": "", "ddg_snippet": "Nov 20, 2024 · This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer", "content": "Nov 20, 2024 · This repository contains my independent implementations of the three LAuReL variants described in the article titled \" LAuReL : Learned Augmented Residual Layer \". These implementations aim to explore the concepts presented in the paper and evaluate their effectiveness on image datasets. Unofficial ..."}
+{"idx": 2, "title": "LAuReL: Learned Augmented Residual Layer - OpenReview", "date": "", "ddg_snippet": "May 1, 2025 · The paper introduces Learned Augmented Residual Layer ( LAUREL ), a novel enhancement to residual connections in CNNs and Transformers. LAUREL enriches the residual stream by incorporating learned scalar parameters and low-rank transformations, improving efficiency and expressivity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ", "content": "May 1, 2025 · The paper introduces Learned Augmented Residual Layer ( LAUREL ), a novel enhancement to residual connections in CNNs and Transformers. LAUREL enriches the residual stream by incorporating learned scalar parameters and low-rank transformations, improving efficiency and expressivity."}
+{"idx": 3, "title": "ICML Poster LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43889", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is a general framework but we provide three variants which can be used to cheaply make the residual connection adaptive instead of it being a simple summation."}
+{"idx": 4, "title": "Google AI Introduces LAuReL (Learned Augmented Residual Layer ...", "date": "", "ddg_snippet": "Nov 17, 2024 · The LAUREL -RW+ LR variant matches the performance of the extra- layer approach while using 2.6 times fewer parameters, and LAUREL -RW+ LR +PA outperforms it with 1.82 times fewer parameters. Moreover, in language models, LAUREL shows consistent improvements across tasks including Q&A, NLU, Math, and Code with only a 0.012% parameter increase.", "subpage_snippet": "", "source": "aiquantumintelligence.com", "link": "https://aiquantumintelligence.com/google-ai-introduces-laurel-learned-augmented-residual-layer-revolutionizing-neural-networks-with-enhanced-residual-connections-for-efficient-model-performance", "content": "Nov 17, 2024 · The LAUREL -RW+ LR variant matches the performance of the extra- layer approach while using 2.6 times fewer parameters, and LAUREL -RW+ LR +PA outperforms it with 1.82 times fewer parameters. Moreover, in language models, LAUREL shows consistent improvements across tasks including Q&A, NLU, Math, and Code with only a 0.012% parameter increase."}
+{"idx": 5, "title": "LAuReL: Learned Augmented Residual Layer - arXiv.org", "date": "", "ddg_snippet": "Feb 5, 2025 · In this paper, we introduce the Learned Augmented Residual Layer ( LAuReL )—a novel generalization of the canonical residual connection—designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics. Our experiments show that LAuReL can enhance model quality for both vision and language models while adding fewer parameters and incurring less ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.07501v3", "content": "Feb 5, 2025 · In this paper, we introduce the Learned Augmented Residual Layer ( LAuReL )—a novel generalization of the canonical residual connection—designed to serve as an in-situ replacement while outperforming it in both model quality and footprint metrics. Our experiments show that LAuReL can enhance model quality for both vision and language models while adding fewer parameters and incurring less ..."}
+{"idx": 6, "title": "LAuReL-Learned-Augmented-Residual-Layer/LAuRel_LR.py at ...", "date": "", "ddg_snippet": "Contribute to BAW2501/ LAuReL - Learned - Augmented - Residual - Layer development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/BAW2501/LAuReL-Learned-Augmented-Residual-Layer/blob/master/LAuRel_LR.py", "content": "Contribute to BAW2501/ LAuReL - Learned - Augmented - Residual - Layer development by creating an account on GitHub."}
+{"idx": 7, "title": "LAuReL: Learned Augmented Residual Layer", "date": "", "ddg_snippet": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=rUDRWP9WvZ&noteId=wS55prNog2", "content": "In this paper, we introduce Learned Augmented Residual Layer ( LAuReL ), which is a generalization of the residual connection and a drop-in replacement. LAuReL is ..."}
+{"idx": 8, "title": "DeepCrossAttention: Supercharging Transformer Residual ...", "date": "", "ddg_snippet": "This work introduces DeepCrossAttention (DCA), an approach that enhances residual learning in transformers. DCA employs learnable, input-dependent weights to ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44325", "content": "This work introduces DeepCrossAttention (DCA), an approach that enhances residual learning in transformers. DCA employs learnable, input-dependent weights to ..."}
+{"idx": 9, "title": "DeepCrossAttention: Supercharging Transformer Residual ...", "date": "", "ddg_snippet": "by M Heddes · 2025 — Laurel: Learned augmented residual layer . In Workshop on Efficient Sys- tems for Foundation Models II, 2024. Pagliardini, M., Mohtashami, A., Fleuret, F ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.06785", "content": "by M Heddes · 2025 — Laurel: Learned augmented residual layer . In Workshop on Efficient Sys- tems for Foundation Models II, 2024. Pagliardini, M., Mohtashami, A., Fleuret, F ..."}
diff --git a/data/sampled_jsons/LLaVA-Next_vs_LLaVA-1.5_architectural_difference_high_resolution.jsonl b/data/sampled_jsons/LLaVA-Next_vs_LLaVA-1.5_architectural_difference_high_resolution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..35ab811fbdf4698a9efe106dfde4e55e2c3b9f53
--- /dev/null
+++ b/data/sampled_jsons/LLaVA-Next_vs_LLaVA-1.5_architectural_difference_high_resolution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "LLaVA Architecture: From Frozen ViT to Fine-Tuned LLM", "date": "", "ddg_snippet": "Jun 10, 2025 · In this post, we go beyond a surface-level summary and take a comprehensive look at the technical foundations of LLaVA and its enhanced version, LLaVA - 1 . 5 .", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/llava-training-a-visual-assistant/", "content": "Jun 10, 2025 · In this post, we go beyond a surface-level summary and take a comprehensive look at the technical foundations of LLaVA and its enhanced version, LLaVA - 1 . 5 ."}
+{"idx": 1, "title": "LLaVA: Large Language and Vision Assistant Explained | Encord", "date": "", "ddg_snippet": "Oct 17, 2023 · In this blog, we will delve into the evolution of visual instruction tuning and explore the specifics of LLaVA , along with its recent iterations, LLaVA - 1 . 5 and LLaVA -1.6 (or LLaVA - NeXT ). By examining these advancements, we can gain valuable insights into the continuous progress of LLMs in AI.", "subpage_snippet": "", "source": "encord.com", "link": "https://encord.com/blog/llava-large-language-vision-assistant/", "content": "Oct 17, 2023 · In this blog, we will delve into the evolution of visual instruction tuning and explore the specifics of LLaVA , along with its recent iterations, LLaVA - 1 . 5 and LLaVA -1.6 (or LLaVA - NeXT ). By examining these advancements, we can gain valuable insights into the continuous progress of LLMs in AI."}
+{"idx": 2, "title": "LLaVA-NeXT: What Else Influences Visual Instruction Tuning ...", "date": "", "ddg_snippet": "May 25, 2024 · The scaling of both factors leads to improved performance, especially on tasks that require visual details. To strike a balance of performance and cost, we observe that the scaling of resolution is more effective than the scaling of token numbers, and recommend an AnyRes strategy with pooling.", "subpage_snippet": "", "source": "llava-vl.github.io", "link": "https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/", "content": "May 25, 2024 · The scaling of both factors leads to improved performance, especially on tasks that require visual details. To strike a balance of performance and cost, we observe that the scaling of resolution is more effective than the scaling of token numbers, and recommend an AnyRes strategy with pooling."}
+{"idx": 3, "title": "LLaVA: A Revolution in Multimodal AI", "date": "", "ddg_snippet": "LLaVA 1.6, also known as LLaVA-NeXT , improves upon LLaVA 1.5 by using more diverse and high-quality data mixtures, dynamic high resolution, and ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/thedeephub/llava-a-revolution-in-multimodal-ai-7d771ab50f40", "content": "LLaVA 1.6, also known as LLaVA-NeXT , improves upon LLaVA 1.5 by using more diverse and high-quality data mixtures, dynamic high resolution, and ..."}
+{"idx": 4, "title": "LLaVA-NeXT", "date": "", "ddg_snippet": "LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/docs/transformers/v4.45.2/en/model_doc/llava_next", "content": "LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa by increasing the input image resolution and training on an improved visual instruction tuning dataset."}
+{"idx": 5, "title": "Efficient Architectures for High Resolution Vision ...", "date": "", "ddg_snippet": "5 Jan 2025 — This work introduces Pheye, a novel architecture that efficiently processes high - resolution images while training fewer parameters than similarly sized VLMs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.02584v1", "content": "5 Jan 2025 — This work introduces Pheye, a novel architecture that efficiently processes high - resolution images while training fewer parameters than similarly sized VLMs."}
+{"idx": 6, "title": "HiRes-LLaVA: Restoring Fragmentation Input in High- ...", "date": "", "ddg_snippet": "by R Huang · Cited by 13 — ... LLaVA-1.5 is not a high-resolution model . It is unclear if the performance gains are due to higher resolution or the proposed SMS and SRA ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=VejUqXsDYa", "content": "by R Huang · Cited by 13 — ... LLaVA-1.5 is not a high-resolution model . It is unclear if the performance gains are due to higher resolution or the proposed SMS and SRA ..."}
+{"idx": 7, "title": "LLaVA-NeXT: Improved reasoning, OCR, and world knowledge", "date": "", "ddg_snippet": "30 Jan 2024 — Compared with LLaVA-1.5, LLaVA-NeXT has several improvements: Increasing the input image resolution to 4x more pixels . This allows it to ...", "subpage_snippet": "", "source": "llava-vl.github.io", "link": "https://llava-vl.github.io/blog/2024-01-30-llava-next/", "content": "30 Jan 2024 — Compared with LLaVA-1.5, LLaVA-NeXT has several improvements: Increasing the input image resolution to 4x more pixels . This allows it to ..."}
+{"idx": 8, "title": "LLaVA and LLaVA-1.5. Paper Reviews | by Eleventh Hour ...", "date": "", "ddg_snippet": "Oct 5 , 2024 · The combination of higher input resolution , architectural improvements, and expanded training data results in LLaVA - 1 . 5 ’ s strong performance across various benchmarks.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@EleventhHourEnthusiast/llava-and-llava-1-5-1cf8be377245", "content": "Oct 5 , 2024 · The combination of higher input resolution , architectural improvements, and expanded training data results in LLaVA - 1 . 5 ’ s strong performance across various benchmarks."}
+{"idx": 9, "title": "GitHub - LLaVA-VL/LLaVA-NeXT", "date": "", "ddg_snippet": "Aug 29, 2025 · Our new blog summarizes empirical explorations to ablate the various design choices in improving LMMs except instruct data itself. Meanwhile, open-source the recapioned high -quality data using LLaVA - NeXT -34B on [COCO] [LCS] [CC3M].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LLaVA-VL/LLaVA-NeXT", "content": "Aug 29, 2025 · Our new blog summarizes empirical explorations to ablate the various design choices in improving LMMs except instruct data itself. Meanwhile, open-source the recapioned high -quality data using LLaVA - NeXT -34B on [COCO] [LCS] [CC3M]."}
diff --git a/data/sampled_jsons/LPGNN_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_2021.jsonl b/data/sampled_jsons/LPGNN_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8f102a6a7034504a579c18ea38e78f265e176055
--- /dev/null
+++ b/data/sampled_jsons/LPGNN_Locally_Private_Graph_Neural_Networks_Sajadmanesh_Gatica-Perez_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2006.05535] Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.05535", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.View a PDF of the paper titled Locally Private Graph Neural Networks , by Sina Sajadmanesh and Daniel Gatica - Perez ."}
+{"idx": 1, "title": "Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez . Graph Neural Networks . A GNN learns a representation for every node in the graph using a set of stacked graph convolution.", "subpage_snippet": "", "source": "publications.idiap.ch", "link": "https://publications.idiap.ch/downloads/papers/2021/Sajadmanesh_CCS2021_2021.pdf", "content": "Differential Privacy ; Private Learning; Graph Neural Networks ; Node Classification. ACM Reference Format: Sina Sajadmanesh and Daniel Gatica - Perez . Graph Neural Networks . A GNN learns a representation for every node in the graph using a set of stacked graph convolution."}
+{"idx": 2, "title": "(PDF) Locally Private Graph Neural Networks", "date": "", "ddg_snippet": "the given bound. Locally Private Graph Neural Networks . graph sequence neural networks . arXiv preprint arXiv:1511.05493 (2015). Sajadmanesh and Gatica - Perez .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348678319_Locally_Private_Graph_Neural_Networks", "content": "the given bound. Locally Private Graph Neural Networks . graph sequence neural networks . arXiv preprint arXiv:1511.05493 (2015). Sajadmanesh and Gatica - Perez ."}
+{"idx": 3, "title": "GitHub - sisaman/ LPGNN : Locally Private Graph Neural Networks ...", "date": "", "ddg_snippet": "@inproceedings{ sajadmanesh 2021 locally , author = { Sajadmanesh , Sina and Gatica - Perez , Daniel}, title = { Locally Private Graph Neural Networks }, year = { 2021 }, publisher = {Association for Computing Machinery}, doi = {10.1145/3460120.3484565}, booktitle...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/sisaman/LPGNN", "content": "@inproceedings{ sajadmanesh 2021 locally , author = { Sajadmanesh , Sina and Gatica - Perez , Daniel}, title = { Locally Private Graph Neural Networks }, year = { 2021 }, publisher = {Association for Computing Machinery}, doi = {10.1145/3460120.3484565}, booktitle..."}
+{"idx": 4, "title": "Locally Private Graph Neural Networks | Proceedings of the 2021 ...", "date": "", "ddg_snippet": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460120.3484565?cookieSet=1", "content": "Presentation video for the paper \" Locally Private Graph Neural Networks \". In this work, we propose a privacy -preserving GNN framework based on local differential privacy , when the graph topology is public but the node features/labels are private ."}
+{"idx": 5, "title": "Sina Sajadmanesh - Google Akademik", "date": "", "ddg_snippet": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021 .", "subpage_snippet": "", "source": "scholar.google.co.za", "link": "https://scholar.google.co.za/citations?user=Gtw3NoAAAAAJ&hl=tr", "content": "2020. Locally Private Graph Neural Networks . S Sajadmanesh , D Gatica - Perez . ACM CCS, 2021 ."}
+{"idx": 6, "title": "Locally Private Graph Neural Networks - Paper Detail", "date": "", "ddg_snippet": "Sina Sajadmanesh , Daniel Gatica - Perez . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/179057/locally-private-graph-neural-networks", "content": "Sina Sajadmanesh , Daniel Gatica - Perez . Graph Neural Networks (GNNs) have demonstrated superior performance inlearning graph representations for several subsequent downstream inferencetasks."}
+{"idx": 7, "title": "(Open Access) Locally Private Graph Neural Networks (2020)", "date": "", "ddg_snippet": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/locally-private-graph-neural-networks-1lusvir819", "content": "Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks."}
+{"idx": 8, "title": "LPGNet: Link Private Graph Networks for Node... | Connected Papers", "date": "", "ddg_snippet": "Locally Private Graph Neural Networks . Sina Sajadmanesh , D. Gática - Pérez . 2020.Node-Level Differentially Private Graph Neural Networks . Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Thakurta, G. Aggarwal, Prateek Jain. 2021 .", "subpage_snippet": "", "source": "www.connectedpapers.com", "link": "https://www.connectedpapers.com/main/41284c49f521d60eec3b4db6e32878cbdd7ea632/LPGNet:-Link-Private-Graph-Networks-for-Node-Classification/graph", "content": "Locally Private Graph Neural Networks . Sina Sajadmanesh , D. Gática - Pérez . 2020.Node-Level Differentially Private Graph Neural Networks . Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Thakurta, G. Aggarwal, Prateek Jain. 2021 ."}
+{"idx": 9, "title": "Releasing Graph Neural Networks with Differential Privacy", "date": "", "ddg_snippet": "Sina Sajadmanesh and Daniel Gatica - Perez . Locally private graph neural networks . arXiv preprint arXiv:2006.05535, 2020.E.1 Differences with LPGNN Sajadmanesh & Gatica - Perez (2020). In LPGNN , noise is directly added to both the node features and the true labels via local DP.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=wk8oXR0kFA", "content": "Sina Sajadmanesh and Daniel Gatica - Perez . Locally private graph neural networks . arXiv preprint arXiv:2006.05535, 2020.E.1 Differences with LPGNN Sajadmanesh & Gatica - Perez (2020). In LPGNN , noise is directly added to both the node features and the true labels via local DP."}
diff --git a/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_arxiv.jsonl b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_arxiv.jsonl
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index 0000000000000000000000000000000000000000..592a9b5103d148fd0a6c99bbda749cbe61b28f71
--- /dev/null
+++ b/data/sampled_jsons/Last-Iterate_Convergence_Properties_of_Regret-Matching_Algorithms_in_Games_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2311.00676] Last - Iterate Convergence Properties of ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2311.00676", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching $^+$ (RM$^+$). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 1, "title": "Last - Iterate Convergence Properties of Regret Matching ...", "date": "", "ddg_snippet": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last - iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games , virtually nothing is known about their last - iterate convergence ."}
+{"idx": 2, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "Algorithms based on regret matching , specifically regret matching $^+$(RM$^+$), and its variants are the most popular approaches for solvinglarge-scale two-player zero-sum games in practice.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/426814/last-iterate-convergence-properties-of-regret-matching-algorithms-in-games", "content": "Algorithms based on regret matching , specifically regret matching $^+$(RM$^+$), and its variants are the most popular approaches for solvinglarge-scale two-player zero-sum games in practice."}
+{"idx": 3, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "Cite arXiv Openreview.Computer Science - Computer Science and Game Theory Computer Science - Machine Learning.", "subpage_snippet": "", "source": "weiqiang-zheng.com", "link": "https://weiqiang-zheng.com/publication/cai-last-2025/", "content": "Cite arXiv Openreview.Computer Science - Computer Science and Game Theory Computer Science - Machine Learning."}
+{"idx": 4, "title": "Combining No- regret and Q-learning", "date": "", "ddg_snippet": "Furthermore, by leveraging last iterate converging no- regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR.", "subpage_snippet": "", "source": "ifaamas.org", "link": "https://ifaamas.org/Proceedings/aamas2020/pdfs/p593.pdf", "content": "Furthermore, by leveraging last iterate converging no- regret algorithms (one of which we introduce), we show empirical last iterate convergence in all domains tested with LONR."}
+{"idx": 5, "title": "Learning Strategies in Monotone Games - Simple Science", "date": "", "ddg_snippet": "Last - Iterate Convergence : The algorithm aims for players to converge toward a strategy that minimizes costs in the last round of play rather than just over time.Title: Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-06-27-learning-strategies-in-monotone-games--a3z2j1o", "content": "Last - Iterate Convergence : The algorithm aims for players to converge toward a strategy that minimizes costs in the last round of play rather than just over time.Title: Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback."}
+{"idx": 6, "title": "Last - Iterate Convergence Properties of Regret - Matching ...", "date": "", "ddg_snippet": "Last - Iterate Convergence Properties of Regret - Matching Algorithms in Games . Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng.", "subpage_snippet": "", "source": "www.columbia.edu", "link": "https://www.columbia.edu/~ck2945/publication/cai-2025-last/", "content": "Last - Iterate Convergence Properties of Regret - Matching Algorithms in Games . Yang Cai, Gabriele Farina, Julien Grand-Clément, Christian Kroer, Chung-Wei Lee, Haipeng Luo, Weiqiang Zheng."}
+{"idx": 7, "title": "Last - iterate Convergence in Extensive-Form Games", "date": "", "ddg_snippet": "Regret -based algorithms are highly efcient at nding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2021/file/77bb14f6132ea06dea456584b7d5581e-Paper.pdf", "content": "Regret -based algorithms are highly efcient at nding approximate Nash equilibria in sequential games such as poker games . However, most regret -based algorithms , including counterfactual regret minimization (CFR) and its variants, rely on iterate averaging to achieve convergence ."}
+{"idx": 8, "title": "Fast Last - Iterate Convergence of Learning in Games Requires...", "date": "", "ddg_snippet": "This paper investigates the convergence properties of learning algorithms in multi-agent game settings, with a focus on the trade-offs between convergence speed, last - iterate convergence , and the degree of \"forgetfulness\" in the algorithms .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/fast-last-iterate-convergence-learning-games-requires", "content": "This paper investigates the convergence properties of learning algorithms in multi-agent game settings, with a focus on the trade-offs between convergence speed, last - iterate convergence , and the degree of \"forgetfulness\" in the algorithms ."}
+{"idx": 9, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "In this paper, we investigate the last - iterate convergence properties of regret - matching algorithms , a class of popular methods for equilibrium computation in games .Efficient last - iterate convergence algorithms in solving games . arXiv preprint arXiv :2308.11256, 2023.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.00676", "content": "In this paper, we investigate the last - iterate convergence properties of regret - matching algorithms , a class of popular methods for equilibrium computation in games .Efficient last - iterate convergence algorithms in solving games . arXiv preprint arXiv :2308.11256, 2023."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_abstract_arxiv.jsonl b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_abstract_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2ad60b8ea0910621cda2bab877facc76aa3beec9
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_2016_abstract_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting - arXiv.org", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ..."}
+{"idx": 1, "title": "Learning without Forgetting : Zhizhong Li : Free Download, Borrow, and ...", "date": "", "ddg_snippet": "A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-1606.09282", "content": "A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance."}
+{"idx": 2, "title": "PDF Learning Without Forgetting - Springer", "date": "", "ddg_snippet": "Learning Without Forgetting Zhizhong Li(B) and Derek Hoiem Department of Computer Science, University of Illinois Urbana Champaign, Champaign, USA {zli115,dhoiem @illinois.edu }", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-319-46493-0_37.pdf", "content": "Learning Without Forgetting Zhizhong Li(B) and Derek Hoiem Department of Computer Science, University of Illinois Urbana Champaign, Champaign, USA {zli115,dhoiem @illinois.edu }"}
+{"idx": 3, "title": "[PDF] Learning without Forgetting | Semantic Scholar", "date": "", "ddg_snippet": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Learning-without-Forgetting-Li-Hoiem/8f3b80ddc0dd62e6c3369fabb1715990c29e9b9a", "content": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques. When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all ..."}
+{"idx": 4, "title": "Learning without Forgetting - NASA/ADS", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2016arXiv160609282L/abstract", "content": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ..."}
+{"idx": 5, "title": "1 Learning without Forgetting - arXiv.org", "date": "", "ddg_snippet": "Zhizhong Li , Derek Hoiem , Member, IEEE Abstract—When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1606.09282", "content": "Zhizhong Li , Derek Hoiem , Member, IEEE Abstract—When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network ..."}
+{"idx": 6, "title": "Paper page - Learning without Forgetting - Hugging Face", "date": "", "ddg_snippet": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/1606.09282", "content": "We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume ..."}
+{"idx": 7, "title": "\"Learning Without Forgetting.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on Learning Without Forgetting .DOI: 10.1007/978-3-319-46493-0_37 access: closed type: Conference or Workshop Paper metadata version: 2020-12-31", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/eccv/LiH16", "content": "Bibliographic details on Learning Without Forgetting .DOI: 10.1007/978-3-319-46493-0_37 access: closed type: Conference or Workshop Paper metadata version: 2020-12-31"}
+{"idx": 8, "title": "Learning without Forgetting | IEEE Journals & Magazine | IEEE Xplore", "date": "", "ddg_snippet": "When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/8107520", "content": "When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing ..."}
+{"idx": 9, "title": "PDF tpami-2773081-pp - ha", "date": "", "ddg_snippet": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities.", "subpage_snippet": "", "source": "miai.ha.edu.cn", "link": "http://miai.ha.edu.cn/Essays/2Deep+learning/Learning+without+Forgetting.pdf", "content": "A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities."}
diff --git a/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_CVPR_2016_abstract_year_2016.jsonl b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_CVPR_2016_abstract_year_2016.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b4b2f291a7d97078992aafd2531a9bf7b23e3f8f
--- /dev/null
+++ b/data/sampled_jsons/Learning_without_Forgetting_Li_Hoiem_CVPR_2016_abstract_year_2016.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1606.09282] Learning without Forgetting", "date": "", "ddg_snippet": "Title: Learning without Forgetting . Authors:Zhizhong Li , Derek Hoiem .Conference version appears in ECCV 2016 ; updated with journal version. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1606.09282", "content": "Title: Learning without Forgetting . Authors:Zhizhong Li , Derek Hoiem .Conference version appears in ECCV 2016 ; updated with journal version. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)."}
+{"idx": 1, "title": "(PDF) Learning without forgetting ( 2016 ) | Zhizhong Li | 642 Citations", "date": "", "ddg_snippet": "Learning without forgetting . Zhizhong Li , Derek Hoiem +1 moreUniversity of Illinois at Urbana–Champaign. Abstract : When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/learning-without-forgetting-34a4300f5h", "content": "Learning without forgetting . Zhizhong Li , Derek Hoiem +1 moreUniversity of Illinois at Urbana–Champaign. Abstract : When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 2, "title": "[PDF] Learning without Forgetting | Semantic Scholar", "date": "", "ddg_snippet": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Learning-without-Forgetting-Li-Hoiem/8f3b80ddc0dd62e6c3369fabb1715990c29e9b9a", "content": "This work proposes the Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities, and performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques."}
+{"idx": 3, "title": "Learning without forgetting - Illinois Experts", "date": "", "ddg_snippet": "Li Z, Hoiem D. Learning without forgetting . In Leibe B, Matas J, Sebe N, Welling M, editors, Computer Vision - 14th European Conference, ECCV 2016 , Proceedings.", "subpage_snippet": "", "source": "experts.illinois.edu", "link": "https://experts.illinois.edu/en/publications/learning-without-forgetting", "content": "Li Z, Hoiem D. Learning without forgetting . In Leibe B, Matas J, Sebe N, Welling M, editors, Computer Vision - 14th European Conference, ECCV 2016 , Proceedings."}
+{"idx": 4, "title": "Learning Without Forgetting | springerprofessional.de", "date": "", "ddg_snippet": "Learning Without Forgetting . Authors : Zhizhong Li , Derek Hoiem . Published in: Computer Vision – ECCV 2016 . Abstract . When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "www.springerprofessional.de", "link": "https://www.springerprofessional.de/en/learning-without-forgetting/10709316", "content": "Learning Without Forgetting . Authors : Zhizhong Li , Derek Hoiem . Published in: Computer Vision – ECCV 2016 . Abstract . When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 5, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "Learning without Forgetting . IEEE Trans Pattern Anal Mach Intell. Abstract . When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/29990101/", "content": "Learning without Forgetting . IEEE Trans Pattern Anal Mach Intell. Abstract . When building a unified vision system or gradually adding new apabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 6, "title": "Learning without Forgetting", "date": "", "ddg_snippet": "Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unied vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "www.ivana.work", "link": "http://www.ivana.work/files/public/Learning+without+Forgetting.pdf", "content": "Learning without Forgetting . Zhizhong Li , Derek Hoiem , Member, IEEE. Abstract —When building a unied vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 7, "title": "Continual Learning and", "date": "", "ddg_snippet": "Li and Hoiem [ 2016 ] presented an excellent overview of the traditional methods for dealing with catastrophic forgetting .This section describes the approach called Learning without Forgetting given in Li and Hoiem [ 2016 ].", "subpage_snippet": "", "source": "www.cs.uic.edu", "link": "https://www.cs.uic.edu/~liub/lifelong-learning/continual-learning.pdf", "content": "Li and Hoiem [ 2016 ] presented an excellent overview of the traditional methods for dealing with catastrophic forgetting .This section describes the approach called Learning without Forgetting given in Li and Hoiem [ 2016 ]."}
+{"idx": 8, "title": "Learning without Forgetting | Zhizhong Li", "date": "", "ddg_snippet": "Zhizhong Li . Derek Hoiem . Learning without Forgetting spotlight video. Abstract . When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available.", "subpage_snippet": "", "source": "zhizhongli.vision", "link": "https://zhizhongli.vision/projects/learning-without-forgetting/", "content": "Zhizhong Li . Derek Hoiem . Learning without Forgetting spotlight video. Abstract . When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available."}
+{"idx": 9, "title": "Entropy-Guided Self-Regulated Learning Without Forgetting for...", "date": "", "ddg_snippet": "4.1. LWF ( Learning without Forgetting ). LWF( Li & Hoiem , 2018) proposes to use the previous model to generate a soft label and replaces the cross-entropy loss with a distillation loss. 2016 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), pp. 770–778, 2016 .", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04228875/document", "content": "4.1. LWF ( Learning without Forgetting ). LWF( Li & Hoiem , 2018) proposes to use the previous model to generate a soft label and replaces the cross-entropy loss with a distillation loss. 2016 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ), pp. 770–778, 2016 ."}
diff --git a/data/sampled_jsons/Leveraging_Passage_Retrieval_with_Generative_Models_for_Open-Domain_Question_Answering_Gautier_Izaca.jsonl b/data/sampled_jsons/Leveraging_Passage_Retrieval_with_Generative_Models_for_Open-Domain_Question_Answering_Gautier_Izaca.jsonl
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index 0000000000000000000000000000000000000000..aff76f1ce391517052c38998479b3347fe58eded
--- /dev/null
+++ b/data/sampled_jsons/Leveraging_Passage_Retrieval_with_Generative_Models_for_Open-Domain_Question_Answering_Gautier_Izaca.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Passage Retrieval with Generative Models for Open Domain ...", "date": "", "ddg_snippet": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages , potentially containing evidence. We obtain state-of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2007.01282", "content": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages , potentially containing evidence. We obtain state-of ..."}
+{"idx": 1, "title": "PDF Leveraging Passage Retrieval with Generative Models for Open Domain ...", "date": "", "ddg_snippet": "Reading. Our generative model for open domain QA is based on a sequence-to-sequence network, pretrained on unsupervised data, such as T5 or BART (Raffel et al., 2020 ; Lewis et al., 2020a). The model takes as input the question , as well as the support passages , and generates the answer. More precisely, each retrieved passage and its title are concatenated with the question , and processed ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.eacl-main.74.pdf", "content": "Reading. Our generative model for open domain QA is based on a sequence-to-sequence network, pretrained on unsupervised data, such as T5 or BART (Raffel et al., 2020 ; Lewis et al., 2020a). The model takes as input the question , as well as the support passages , and generates the answer. More precisely, each retrieved passage and its title are concatenated with the question , and processed ..."}
+{"idx": 2, "title": "Leveraging Passage Retrieval with Generative Models for Open Domain ...", "date": "", "ddg_snippet": "Figure 1: A simple approach to open domain question answering . First, it retrieves support text passages from an external source of knowledge such as Wikipedia. Then, a generative encoder-decoder model produces the answer, conditioned on the question and the retrieved passages . One of our main findings is that this approach scales well with the number of retrieved passages , as the performance ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Leveraging-Passage-Retrieval-with-Generative-Models-Izacard-Grave/ea8c46e193d5121e440daf96edfd15a47151c293/figure/0", "content": "Figure 1: A simple approach to open domain question answering . First, it retrieves support text passages from an external source of knowledge such as Wikipedia. Then, a generative encoder-decoder model produces the answer, conditioned on the question and the retrieved passages . One of our main findings is that this approach scales well with the number of retrieved passages , as the performance ..."}
+{"idx": 3, "title": "Leveraging Passage Retrieval with Generative Models for Open Domain ...", "date": "", "ddg_snippet": "Download Citation | On Jan 1, 2021, Gautier Izacard and others published Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering | Find, read and cite all the ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/355430399_Leveraging_Passage_Retrieval_with_Generative_Models_for_Open_Domain_Question_Answering", "content": "Download Citation | On Jan 1, 2021, Gautier Izacard and others published Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering | Find, read and cite all the ..."}
+{"idx": 4, "title": "Leveraging Passage Retrieval with Generative Models for Open Domain ...", "date": "", "ddg_snippet": "Abstract Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.eacl-main.74/", "content": "Abstract Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query."}
+{"idx": 5, "title": "Leveraging passage retrieval with generative models for open domain ...", "date": "", "ddg_snippet": "I Gautier , G Edouard - Proceedings of the 16th Conference of the European …, 2021 Cited by 23 Related articles Leveraging passage retrieval with generative models for open domain question answering , 2020 * G Izacard, E Grave - URL https://arxiv. org/abs, 2007 Cited by 14 Related articles", "subpage_snippet": "", "source": "xs.typicalgame.com", "link": "https://xs.typicalgame.com/citations?view_op=view_citation&hl=en&user=aL3MllMAAAAJ&sortby=title&citation_for_view=aL3MllMAAAAJ:u5HHmVD_uO8C", "content": "I Gautier , G Edouard - Proceedings of the 16th Conference of the European …, 2021 Cited by 23 Related articles Leveraging passage retrieval with generative models for open domain question answering , 2020 * G Izacard, E Grave - URL https://arxiv. org/abs, 2007 Cited by 14 Related articles"}
+{"idx": 6, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "Reading. Our generative model for open domain QA is based on a sequence-to-sequence network, pretrained on unsupervised data, such as T5 or BART (Raffel et al., 2019; Lewis et al., 2019). The model takes as input the question , as well as the support passages , and generates the answer. More precisely, each retrieved passage and its title are concatenated with the question , and processed in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2007.01282", "content": "Reading. Our generative model for open domain QA is based on a sequence-to-sequence network, pretrained on unsupervised data, such as T5 or BART (Raffel et al., 2019; Lewis et al., 2019). The model takes as input the question , as well as the support passages , and generates the answer. More precisely, each retrieved passage and its title are concatenated with the question , and processed in ..."}
+{"idx": 7, "title": "dblp: Leveraging Passage Retrieval with Generative Models for Open ...", "date": "", "ddg_snippet": "Bibliographic details on Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/eacl/IzacardG21", "content": "Bibliographic details on Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering ."}
+{"idx": 8, "title": "Leveraging Passage Retrieval with Generative Models for Open Domain ...", "date": "", "ddg_snippet": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages , potentially containing evidence. We obtain state-of ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2020arXiv200701282I/abstract", "content": "Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from retrieving text passages , potentially containing evidence. We obtain state-of ..."}
+{"idx": 9, "title": "Augmenting Query and Passage for Retrieval-Augmented Generation using ...", "date": "", "ddg_snippet": "Abstract Retrieval -augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the para-metric knowledge of large language models (LLMs). While previous approaches focused on processing retrieved passages to remove irrelevant context, they still rely heavily on the quality of retrieved passages which can degrade if the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.14277v1", "content": "Abstract Retrieval -augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the para-metric knowledge of large language models (LLMs). While previous approaches focused on processing retrieved passages to remove irrelevant context, they still rely heavily on the quality of retrieved passages which can degrade if the ..."}
diff --git a/data/sampled_jsons/MA-OSMA_OSG_approximation_ratio_submodular_coordination_(1-e^{-c})c_year_2024.jsonl b/data/sampled_jsons/MA-OSMA_OSG_approximation_ratio_submodular_coordination_(1-e^{-c})c_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..10ca7971fd8016f133e11fae81c0e40c22520902
--- /dev/null
+++ b/data/sampled_jsons/MA-OSMA_OSG_approximation_ratio_submodular_coordination_(1-e^{-c})c_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "To tackle the aforementioned MA -OSM problem, Xu et al. (2023)have recently proposed an online sequential greedy(OSG) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978). Nevertheless, this online algorithm suffers from two notable limitations: i) Sub-optimal Approximation:In contrast with the tight (1−e−cc)1superscript𝑒𝑐𝑐(\\frac{1-e^{-c}}{c ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05028v1", "content": "To tackle the aforementioned MA -OSM problem, Xu et al. (2023)have recently proposed an online sequential greedy(OSG) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978). Nevertheless, this online algorithm suffers from two notable limitations: i) Sub-optimal Approximation:In contrast with the tight (1−e−cc)1superscript𝑒𝑐𝑐(\\frac{1-e^{-c}}{c ..."}
+{"idx": 1, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "This result significantly improves the (1 1+c) (1 1 + c) - approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi-target tracking.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/3340ee1e4a8bad8d32c35721712b4d0a-Abstract-Conference.html", "content": "This result significantly improves the (1 1+c) (1 1 + c) - approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi-target tracking."}
+{"idx": 2, "title": "N -o Online Learning for Multi a S Coordination: Tight Ap Proximation ...", "date": "", "ddg_snippet": "1 −β ) against a (1−e−c )- approximation to the c ence, β is the spectral gap of the network and c is the joint curvature of submodular objectives. This resul significantly improves the ( 1+c)-approximation 1 provided by the state-of-the-art OSG algorithm. Finally, we demon", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=i8dYPGdB1C", "content": "1 −β ) against a (1−e−c )- approximation to the c ence, β is the spectral gap of the network and c is the joint curvature of submodular objectives. This resul significantly improves the ( 1+c)-approximation 1 provided by the state-of-the-art OSG algorithm. Finally, we demon"}
+{"idx": 3, "title": "PDF Approximate Submodularity and its Applications: Subset Selection ...", "date": "", "ddg_snippet": "Abstract We introduce the submodularity ratio as a measure of how \\close\" to submodular a set function f is. We show that when f has submodularity ratio , the greedy algorithm for maximizing f provides a (1 e )- approximation . Furthermore, when is bounded away from 0, the greedy algorithm for minimum submodular cover also provides essentially an O(log n) approximation for a universe of n elements.", "subpage_snippet": "", "source": "jmlr.csail.mit.edu", "link": "https://jmlr.csail.mit.edu/papers/volume19/16-534/16-534.pdf", "content": "Abstract We introduce the submodularity ratio as a measure of how \\close\" to submodular a set function f is. We show that when f has submodularity ratio , the greedy algorithm for maximizing f provides a (1 e )- approximation . Furthermore, when is bounded away from 0, the greedy algorithm for minimum submodular cover also provides essentially an O(log n) approximation for a universe of n elements."}
+{"idx": 4, "title": "Approximation Algorithm and Applications for Connected Submodular ...", "date": "", "ddg_snippet": "We then propose a novel 1−1/e 2h+2 - approximation algorithm for the problem, improving the best approximation ratio 1−1/e 2h+3 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter that depends on the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/abstract/document/10720941", "content": "We then propose a novel 1−1/e 2h+2 - approximation algorithm for the problem, improving the best approximation ratio 1−1/e 2h+3 for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where e is the base of the natural logarithm, h is a parameter that depends on the ..."}
+{"idx": 5, "title": "Near-optimal Online Learning for Multi-agent Submodular Coordination ...", "date": "", "ddg_snippet": "c best comparator in hindsight, where CT is the deviation of maximizer sequence, β is the spectral gap of the network and c is the joint curvature of submodular objectives. This result significantly improves the ( 1 1+c)-approximation provided by the state-of-the-art OSG algorithm.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05028", "content": "c best comparator in hindsight, where CT is the deviation of maximizer sequence, β is the spectral gap of the network and c is the joint curvature of submodular objectives. This result significantly improves the ( 1 1+c)-approximation provided by the state-of-the-art OSG algorithm."}
+{"idx": 6, "title": "Near-OptimalOnlineLearningforMulti-AgentSubmodularCoordination ...", "date": "", "ddg_snippet": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ...", "subpage_snippet": "", "source": "www.xhz.cn", "link": "https://www.xhz.cn/yanjiulunwen/135606.html", "content": "Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address ..."}
+{"idx": 7, "title": "Near-Optimal Online Learning for Multi-Agent Submodular Coordination ...", "date": "", "ddg_snippet": "To tackle the aforementioned MA -OSM problem, Xu et al. (2023) have recently proposed an online sequential greedy ( OSG ) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978). Nevertheless, this online algorithm suffers from two notable limitations: i) Sub-optimal Approximation : In contrast with the tight (1 − e c) (\\frac {1-e^ {-c}} {c}) - approximation ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.05028", "content": "To tackle the aforementioned MA -OSM problem, Xu et al. (2023) have recently proposed an online sequential greedy ( OSG ) algorithm, building upon the foundations of the classical greedy method (Fisher et al., 1978). Nevertheless, this online algorithm suffers from two notable limitations: i) Sub-optimal Approximation : In contrast with the tight (1 − e c) (\\frac {1-e^ {-c}} {c}) - approximation ..."}
+{"idx": 8, "title": "On the Approximation Relationship between Optimizing Ratio of ...", "date": "", "ddg_snippet": "We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different kind of approximation guarantee -- weaker than the classical one -- for the difference of submodular (DS) optimization problem, and vice versa. We also illustrate the link between these two problems by analyzing a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2101.01631", "content": "We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different kind of approximation guarantee -- weaker than the classical one -- for the difference of submodular (DS) optimization problem, and vice versa. We also illustrate the link between these two problems by analyzing a ..."}
+{"idx": 9, "title": "1 1+c v.s. 1−e −c c . | Download Scientific Diagram - ResearchGate", "date": "", "ddg_snippet": "Download scientific diagram | 1 1+c v.s. 1−e −c c . from publication: Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/c-vs-1-e-c-c_fig2_388847678", "content": "Download scientific diagram | 1 1+c v.s. 1−e −c c . from publication: Near-Optimal Online Learning for Multi-Agent Submodular Coordination : Tight Approximation and Communication Efficiency ..."}
diff --git a/data/sampled_jsons/Masked_Autoregressive_Flow_Papamakarios_2017_architecture_neural_network_hidden_units_MLP.jsonl b/data/sampled_jsons/Masked_Autoregressive_Flow_Papamakarios_2017_architecture_neural_network_hidden_units_MLP.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..5a08c4f521560b37c84ae2a393f975a0942fddf3
--- /dev/null
+++ b/data/sampled_jsons/Masked_Autoregressive_Flow_Papamakarios_2017_architecture_neural_network_hidden_units_MLP.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "This type of flow is closely related to Inverse Autoregressive Flow and is a gen-eralization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1705.07057", "content": "This type of flow is closely related to Inverse Autoregressive Flow and is a gen-eralization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks."}
+{"idx": 1, "title": "Masked Autoregressive Flow For Density Estimation: George ...", "date": "", "ddg_snippet": "The document presents Masked Autoregressive Flow (MAF), a novel approach to neural density estimation that enhances the flexibility of autoregressive models by stacking them to model internal random numbers. MAF utilizes the Masked Autoencoder for Distribution Estimation (MADE) to allow efficient density evaluations and training on parallel architectures, achieving state-of-the-art performance ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/839065576/1705-07057", "content": "The document presents Masked Autoregressive Flow (MAF), a novel approach to neural density estimation that enhances the flexibility of autoregressive models by stacking them to model internal random numbers. MAF utilizes the Masked Autoencoder for Distribution Estimation (MADE) to allow efficient density evaluations and training on parallel architectures, achieving state-of-the-art performance ..."}
+{"idx": 2, "title": "Understand & Implement Masked AutoRegressive Flow with ...", "date": "", "ddg_snippet": "Feb 21, 2023 · What can you expect from this post – Why Triangular Matrices are crucial for Autoregressive Flows? Basic constructions of Autoregressive Flow-based models — Masked Autoregressive Flow (MAF) — Inverse Autoregressive Flow (IAF) How to implement MAF in TensorFlow and train them for density estimation tasks? Without any delay, let’s begin!", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/understand-implement-masked-autoregressive-flow-with-tensorflow-9c361cd1354c/", "content": "Feb 21, 2023 · What can you expect from this post – Why Triangular Matrices are crucial for Autoregressive Flows? Basic constructions of Autoregressive Flow-based models — Masked Autoregressive Flow (MAF) — Inverse Autoregressive Flow (IAF) How to implement MAF in TensorFlow and train them for density estimation tasks? Without any delay, let’s begin!"}
+{"idx": 3, "title": "Masked Autoregressive Flow for Density Estimation - NeurIPS", "date": "", "ddg_snippet": "The resulting stack of models is a normalizing flow that is more flexible than the original model, and that remains tractable. In this paper we present Masked Autoregressive Flow (MAF), which is a particular implementation of the above normalizing flow that uses the Masked Autoencoder for Distribution Estimation (MADE) [6] as a building block.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2017/file/6c1da886822c67822bcf3679d04369fa-Paper.pdf", "content": "The resulting stack of models is a normalizing flow that is more flexible than the original model, and that remains tractable. In this paper we present Masked Autoregressive Flow (MAF), which is a particular implementation of the above normalizing flow that uses the Masked Autoencoder for Distribution Estimation (MADE) [6] as a building block."}
+{"idx": 4, "title": "tfp.bijectors.MaskedAutoregressiveFlow | TensorFlow Probability", "date": "", "ddg_snippet": "The affine autoregressive flow [ ( Papamakarios et al., 2016)] [3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/probability/api_docs/python/tfp/bijectors/MaskedAutoregressiveFlow", "content": "The affine autoregressive flow [ ( Papamakarios et al., 2016)] [3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events."}
+{"idx": 5, "title": "Supplementary material for “Masked Autoregressive Flow for ...", "date": "", "ddg_snippet": "MADE, MADE MoG and each autoregressive layer in MAF is a feedforward neural network (with masked weight matrices), with L hidden layers of H hidden units each. Similarly, each coupling layer in Real NVP contains two feedforward neural networks, one for scaling and one for shifting, each of which also has L hidden layers of H hidden units each.", "subpage_snippet": "", "source": "homepages.inf.ed.ac.uk", "link": "https://homepages.inf.ed.ac.uk/imurray2/pub/17maf/maf_supp.pdf", "content": "MADE, MADE MoG and each autoregressive layer in MAF is a feedforward neural network (with masked weight matrices), with L hidden layers of H hidden units each. Similarly, each coupling layer in Real NVP contains two feedforward neural networks, one for scaling and one for shifting, each of which also has L hidden layers of H hidden units each."}
+{"idx": 6, "title": "Masked Autoregressive Flow for Density Estimation", "date": "", "ddg_snippet": "3 Masked Autoregressive Flow . 3.1 Autoregressive models as normalizing ows.MADE, MADE MoG and each layer in MAF is a feedforward neural network with masked weight matrices, such that the autoregressive property holds.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2017/file/6c1da886822c67822bcf3679d04369fa-Paper.pdf", "content": "3 Masked Autoregressive Flow . 3.1 Autoregressive models as normalizing ows.MADE, MADE MoG and each layer in MAF is a feedforward neural network with masked weight matrices, such that the autoregressive property holds."}
+{"idx": 7, "title": "Posit AI Blog: Experimenting with autoregressive flows in TensorFlow...", "date": "", "ddg_snippet": "Figure 1: Figure from Masked Autoregressive Flow for Density Estimation( Papamakarios , Pavlakou, and Murray 2017 ). Collecting information from the paper, we know that.", "subpage_snippet": "", "source": "blogs.rstudio.com", "link": "https://blogs.rstudio.com/ai/posts/2019-04-24-autoregressive-flows/", "content": "Figure 1: Figure from Masked Autoregressive Flow for Density Estimation( Papamakarios , Pavlakou, and Murray 2017 ). Collecting information from the paper, we know that."}
+{"idx": 8, "title": "Understand & Implement Masked AutoRegressive Flow with...", "date": "", "ddg_snippet": "The Masked Autoregressive Flow (MAF) and Inverse Autoregressive Flow (IAF) models are presented as effective methods for density estimation tasks.The MADE architecture contains 2 hidden layers with 32 units each and ‘Relu’ activation.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/understand-implement-masked-autoregressive-flow-with-tensorflow-9c361cd1354c", "content": "The Masked Autoregressive Flow (MAF) and Inverse Autoregressive Flow (IAF) models are presented as effective methods for density estimation tasks.The MADE architecture contains 2 hidden layers with 32 units each and ‘Relu’ activation."}
+{"idx": 9, "title": "oryx.bijectors.MaskedAutoregressiveFlow | Oryx | TensorFlow", "date": "", "ddg_snippet": "The affine autoregressive flow [( Papamakarios et al., 2016)][3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events.", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/probability/oryx/api_docs/python/oryx/bijectors/MaskedAutoregressiveFlow", "content": "The affine autoregressive flow [( Papamakarios et al., 2016)][3] provides a relatively simple framework for user-specified (deep) architectures to learn a distribution over continuous events."}
diff --git a/data/sampled_jsons/Medusa_decoding_heads_parallel_prediction_MLP_last_verified_token_hidden_state.jsonl b/data/sampled_jsons/Medusa_decoding_heads_parallel_prediction_MLP_last_verified_token_hidden_state.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..176553d904efa48ef3d30c0352ee0cf28e24e9cb
--- /dev/null
+++ b/data/sampled_jsons/Medusa_decoding_heads_parallel_prediction_MLP_last_verified_token_hidden_state.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Medusa : Simple LLM Inference Acceleration Framework with Multiple...", "date": "", "ddg_snippet": "Figure 1. MEDUSA introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several sub-sequent tokens in parallel (Section 2.1.1).2.1.1. MEDUSA HEADS . In speculative decoding , subsequent tokens are predicted by an auxiliary draft model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.10774", "content": "Figure 1. MEDUSA introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several sub-sequent tokens in parallel (Section 2.1.1).2.1.1. MEDUSA HEADS . In speculative decoding , subsequent tokens are predicted by an auxiliary draft model."}
+{"idx": 1, "title": "Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding", "date": "", "ddg_snippet": "Keywords: Speculative Decoding , Medusa , Draft Heads , Blockwise Parallel Decoding .One way to specify the draft model, as used in the recent Medusa decoding framework, is as a collection of lightweight heads , called draft heads , that operate on the base model's hidden states .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FbhjirzvJG", "content": "Keywords: Speculative Decoding , Medusa , Draft Heads , Blockwise Parallel Decoding .One way to specify the draft model, as used in the recent Medusa decoding framework, is as a collection of lightweight heads , called draft heads , that operate on the base model's hidden states ."}
+{"idx": 2, "title": "Fork base model's last two decoder layers - Githubissues", "date": "", "ddg_snippet": "Background Medusa heads learn to predict multiple tokens into the future as a function of the base model's final hidden states .", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/FasterDecoding/Medusa/30", "content": "Background Medusa heads learn to predict multiple tokens into the future as a function of the base model's final hidden states ."}
+{"idx": 3, "title": "Amphista: Bi-directional Multi- head Decoding for Accelerating", "date": "", "ddg_snippet": "Specifically, Medusa integrates independent MLP layers, called drafting heads , with the target model to form a unified archi-tecture.the concatenation of hidden states and token em- heads . This approach enhances overall prediction .", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.450.pdf", "content": "Specifically, Medusa integrates independent MLP layers, called drafting heads , with the target model to form a unified archi-tecture.the concatenation of hidden states and token em- heads . This approach enhances overall prediction ."}
+{"idx": 4, "title": "How to double tokens per second for Llama 3 with Medusa", "date": "", "ddg_snippet": "How Medusa heads work. Medusa is another method for generating and verifying draft tokens .With three Medusa heads , four tokens are created per forward pass. Medusa heads are neural nets that are grafted onto the base model and sit on top of the weights and activations.", "subpage_snippet": "", "source": "www.baseten.com", "link": "https://www.baseten.com/blog/how-to-double-tokens-per-second-for-llama-3-with-medusa/", "content": "How Medusa heads work. Medusa is another method for generating and verifying draft tokens .With three Medusa heads , four tokens are created per forward pass. Medusa heads are neural nets that are grafted onto the base model and sit on top of the weights and activations."}
+{"idx": 5, "title": "(PDF) ParallelSpec: Parallel Drafter for Efficient Speculative Decoding", "date": "", "ddg_snippet": "Specifically, Kextra decoding heads . are added to decode the last hidden states of the target model, and the k-th head is used to predict . the (t+k+ 1)-th token given a prefix sequence of length t. The final training objective of Medusa . is expressed as", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384770213_ParallelSpec_Parallel_Drafter_for_Efficient_Speculative_Decoding", "content": "Specifically, Kextra decoding heads . are added to decode the last hidden states of the target model, and the k-th head is used to predict . the (t+k+ 1)-th token given a prefix sequence of length t. The final training objective of Medusa . is expressed as"}
+{"idx": 6, "title": "Parallel Decoding", "date": "", "ddg_snippet": "MEDUSA Decoding . Medusa decoding is an LLM optimization that uses multiple decoding heads to emit tokens in parallel . This method is faster than the sequential bottleneck of emitting one token at a time, as done by the standard autoregressive LLM decoding algorithms.", "subpage_snippet": "", "source": "www.aussieai.com", "link": "http://www.aussieai.com/research/parallel-decoding", "content": "MEDUSA Decoding . Medusa decoding is an LLM optimization that uses multiple decoding heads to emit tokens in parallel . This method is faster than the sequential bottleneck of emitting one token at a time, as done by the standard autoregressive LLM decoding algorithms."}
+{"idx": 7, "title": "Daily Papers - Hugging Face", "date": "", "ddg_snippet": "Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads .For later tokens , we utilize multiple lightweight MLP heads operating in parallel to enhance efficiency.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=decoding+process", "content": "Medusa : Simple LLM Inference Acceleration Framework with Multiple Decoding Heads .For later tokens , we utilize multiple lightweight MLP heads operating in parallel to enhance efficiency."}
+{"idx": 8, "title": "The Chocolate Milk Cult’s Guide to Inference Scaling for AI... | Medium", "date": "", "ddg_snippet": "Speculative Decoding : Pair a small draft model with the main LLM; verify predictions to cut passes and halve costs. Medusa introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several subsequent tokens in parallel .", "subpage_snippet": "", "source": "machine-learning-made-simple.medium.com", "link": "https://machine-learning-made-simple.medium.com/the-chocolate-milk-cults-guide-to-inference-scaling-for-ai-models-50aa2290eb50", "content": "Speculative Decoding : Pair a small draft model with the main LLM; verify predictions to cut passes and halve costs. Medusa introduces multiple heads on top of the last hidden states of the LLM, enabling the prediction of several subsequent tokens in parallel ."}
+{"idx": 9, "title": "ResDecode: Accelerating Large Language Models Inference via...", "date": "", "ddg_snippet": "To address this challenge, current approaches have incorporated speculative decoding to enable parallel prediction of multiple subsequent tokens , thereby achieving inference acceleration.", "subpage_snippet": "", "source": "www.sciopen.com", "link": "https://www.sciopen.com/article/10.26599/BDMA.2024.9020074", "content": "To address this challenge, current approaches have incorporated speculative decoding to enable parallel prediction of multiple subsequent tokens , thereby achieving inference acceleration."}
diff --git a/data/sampled_jsons/Mind2Web_dataset_construction_method_crowdsourced_manual_automatic.jsonl b/data/sampled_jsons/Mind2Web_dataset_construction_method_crowdsourced_manual_automatic.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b745d2ebe97958ae828350e0379bfdc292e32d6e
--- /dev/null
+++ b/data/sampled_jsons/Mind2Web_dataset_construction_method_crowdsourced_manual_automatic.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Mind2Web: Towards a Generalist Agent for the Web - GitHub", "date": "", "ddg_snippet": "Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web", "content": "Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites ..."}
+{"idx": 1, "title": "Mind2Web: Towards a Generalist Agent for the Web", "date": "", "ddg_snippet": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.06070", "content": "We introduce Mind2Web , the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 ..."}
+{"idx": 2, "title": "Mind2Web - GitHub Pages", "date": "", "ddg_snippet": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ...", "subpage_snippet": "", "source": "osu-nlp-group.github.io", "link": "https://osu-nlp-group.github.io/Mind2Web/", "content": "Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Mind2Web contains 2,350 tasks from 137 websites spanning 31 domains that: Reflect diverse and practical use cases on the web. Provide challenging yet realistic environments with real-world websites. Test generalization ability across ..."}
+{"idx": 3, "title": "Mind2Web/BenchFlow · BenchFlow", "date": "", "ddg_snippet": "Mind2Web : Towards a Generalist Agent for the Web Dataset , code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Updates: 2025/3/25: Online- Mind2Web released! 2024/3/18: Multimodal- Mind2Web dataset released.", "subpage_snippet": "", "source": "www.benchflow.ai", "link": "https://www.benchflow.ai/benchmarks/benchflow/Mind2Web", "content": "Mind2Web : Towards a Generalist Agent for the Web Dataset , code, and models for the paper \" Mind2Web : Towards a Generalist Agent for the Web\". Check project website for demos and data exploration. Updates: 2025/3/25: Online- Mind2Web released! 2024/3/18: Multimodal- Mind2Web dataset released."}
+{"idx": 4, "title": "PDF MIND2WEB: Towards a Generalist Agent for the Web - NeurIPS", "date": "", "ddg_snippet": "31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf", "content": "31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ..."}
+{"idx": 5, "title": "Mind2Web/README.md at main · OSU-NLP-Group/Mind2Web", "date": "", "ddg_snippet": "Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web/blob/main/README.md", "content": "Mind2Web is the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents. With over 2,000 open-ended tasks collected from 137 websites ..."}
+{"idx": 6, "title": "README.md · osunlp/Mind2Web at main - Hugging Face", "date": "", "ddg_snippet": "Dataset Summary Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/osunlp/Mind2Web/blob/main/README.md", "content": "Dataset Summary Mind2Web is a dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for generalist web agents."}
+{"idx": 7, "title": "Mind2Web: Towards a Generalist Agent for the Web - arXiv.org", "date": "", "ddg_snippet": "In light of this, we present Mind2Web , a new dataset with natural language tasks and manually annotated action sequences for developing and evaluating generalist agents for the web.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.06070v3", "content": "In light of this, we present Mind2Web , a new dataset with natural language tasks and manually annotated action sequences for developing and evaluating generalist agents for the web."}
+{"idx": 8, "title": "IND2WEB MIND2WEB: Towards a Generalist Agent for - arXiv.org", "date": "", "ddg_snippet": "31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.06070v2", "content": "31 domains and crowdsourced action sequences for the tasks, MIND2WEB pro-vides three necessary ingredients for building generalist web agents: 1) diverse domains, websites, and tasks, 2) use of real-world websites instead of simulated and simplified ones, and 3) a broad spectrum of user interaction patterns. Based on MIND2WEB , we conduct an initial exploration of using large language models ..."}
+{"idx": 9, "title": "OSU-NLP-Group/Mind2Web-2 - GitHub", "date": "", "ddg_snippet": "Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/OSU-NLP-Group/Mind2Web-2", "content": "Mind2Web 2 Mind2Web 2 is a benchmark for agentic search systems, featuring Agent-as-a-Judge methodology for comprehensive, rigorous, and reliable assessment on long-horizon and complex tasks that involve complex and real-time information synthesis."}
diff --git a/data/sampled_jsons/ModelGo_Licenses_MGLs_year_2024.jsonl b/data/sampled_jsons/ModelGo_Licenses_MGLs_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a0983e028ab15e68f5f7bcabbe3615d34fc522ee
--- /dev/null
+++ b/data/sampled_jsons/ModelGo_Licenses_MGLs_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "谷歌浏览器安卓2025最新版下载 - Chrome手机版V140.0. ...", "date": "", "ddg_snippet": "3 days ago · 由Google官方精心研发的谷歌浏览器，依托其强大的双核引擎架构，带来更加稳定、高效的网页加载与运行体验。 不论是日常浏览、在线视频，还是办公操作，都能应对自如。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/", "content": "3 days ago · 由Google官方精心研发的谷歌浏览器，依托其强大的双核引擎架构，带来更加稳定、高效的网页加载与运行体验。 不论是日常浏览、在线视频，还是办公操作，都能应对自如。"}
+{"idx": 1, "title": "如何在Chrome浏览器中启用或禁用表单自动填写功能", "date": "", "ddg_snippet": "Jan 7, 2025 · Chrome浏览器的表单自动填写功能可以帮助用户快速填写网页上的表单，如登录信息、地址和信用卡详情等。 然而，有时您可能希望手动输入这些信息，或者出于隐私考虑，想要关闭这一功能。 本文将指导您如何在Chrome浏览器中启用或禁用表单自动填写功能。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/675.html", "content": "Jan 7, 2025 · Chrome浏览器的表单自动填写功能可以帮助用户快速填写网页上的表单，如登录信息、地址和信用卡详情等。 然而，有时您可能希望手动输入这些信息，或者出于隐私考虑，想要关闭这一功能。 本文将指导您如何在Chrome浏览器中启用或禁用表单自动填写功能。"}
+{"idx": 2, "title": "如何卸载谷歌浏览器【具体步骤】", "date": "", "ddg_snippet": "Nov 29, 2024 · 谷歌浏览器（Google Chrome）是一款广泛使用的网络浏览器，但有时用户可能希望卸载它。 本文将详细介绍几种方法，帮助用户彻底卸载谷歌浏览器，包括通过Windows系统操作、手动删除文件和注册表项以及使用注册表文件进行卸载。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/619.html", "content": "Nov 29, 2024 · 谷歌浏览器（Google Chrome）是一款广泛使用的网络浏览器，但有时用户可能希望卸载它。 本文将详细介绍几种方法，帮助用户彻底卸载谷歌浏览器，包括通过Windows系统操作、手动删除文件和注册表项以及使用注册表文件进行卸载。"}
+{"idx": 3, "title": "谷歌浏览器怎么扫码-详细步骤教程", "date": "", "ddg_snippet": "Mar 4, 2025 · 本文详细介绍了如何在谷歌浏览器中使用扫码功能，帮助用户快速实现扫码操作，适合初学者和有一定使用经验的用户。 内容简洁明了，步骤清晰易懂。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/734.html", "content": "Mar 4, 2025 · 本文详细介绍了如何在谷歌浏览器中使用扫码功能，帮助用户快速实现扫码操作，适合初学者和有一定使用经验的用户。 内容简洁明了，步骤清晰易懂。"}
+{"idx": 4, "title": "谷歌浏览器怎么用加速器加速-加速器加速谷歌浏览器图文教程", "date": "", "ddg_snippet": "Oct 1, 2023 · 其实大家可以通过开启谷歌浏览器的硬件加速功能或者安装加速器的方法来解决下载速度缓慢的情况。 今天小编带来 加速器加速谷歌浏览器图文教程，欢迎感兴趣的用户继续阅读下面的内容。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/196.html", "content": "Oct 1, 2023 · 其实大家可以通过开启谷歌浏览器的硬件加速功能或者安装加速器的方法来解决下载速度缓慢的情况。 今天小编带来 加速器加速谷歌浏览器图文教程，欢迎感兴趣的用户继续阅读下面的内容。"}
+{"idx": 5, "title": "谷歌浏览器的快捷键自定义方法【技巧】", "date": "", "ddg_snippet": "Nov 14, 2024 · 最近，有一些新用户在好奇谷歌浏览器的快捷键要如何自定义，于是，本文为大家详细介绍了谷歌浏览器的快捷键自定义方法，一起学习下吧。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/590.html", "content": "Nov 14, 2024 · 最近，有一些新用户在好奇谷歌浏览器的快捷键要如何自定义，于是，本文为大家详细介绍了谷歌浏览器的快捷键自定义方法，一起学习下吧。"}
+{"idx": 6, "title": "谷歌浏览器怎么设置ie兼容模式-谷歌浏览器开启ie兼容模式 ...", "date": "", "ddg_snippet": "Jan 17, 2024 · 谷歌浏览器怎么设置ie兼容模式？ 今天小编整理了谷歌浏览器开启ie兼容模式教程一览，需要给谷歌浏览器切换ie兼容模式的用户千万不要错过了。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/302.html", "content": "Jan 17, 2024 · 谷歌浏览器怎么设置ie兼容模式？ 今天小编整理了谷歌浏览器开启ie兼容模式教程一览，需要给谷歌浏览器切换ie兼容模式的用户千万不要错过了。"}
+{"idx": 7, "title": "如何在Chrome浏览器中清除网页的缓存数据【详细介绍】", "date": "", "ddg_snippet": "Dec 13, 2024 · 有部分用户对于如何在Chrome浏览器中清除网页的缓存数据还不明白，于是，本文介绍了清除网页的缓存数据的操作方法，一起看看吧。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/643.html", "content": "Dec 13, 2024 · 有部分用户对于如何在Chrome浏览器中清除网页的缓存数据还不明白，于是，本文介绍了清除网页的缓存数据的操作方法，一起看看吧。"}
+{"idx": 8, "title": "如何设置Chrome的页面背景【新手教程】", "date": "", "ddg_snippet": "Nov 27, 2024 · 有一些新用户刚开始使用谷歌浏览器，不知道如何设置Chrome的页面背景？ 为此，本文提供了详细的操作方法，希望可以帮助到各位。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/612.html", "content": "Nov 27, 2024 · 有一些新用户刚开始使用谷歌浏览器，不知道如何设置Chrome的页面背景？ 为此，本文提供了详细的操作方法，希望可以帮助到各位。"}
+{"idx": 9, "title": "如何在Chrome浏览器中启用或禁用Flash插件【具体步骤】", "date": "", "ddg_snippet": "Dec 20, 2024 · 不少用户对于如何在Chrome浏览器中启用或禁用Flash插件还不太清楚，为此，本文详细介绍了管理Flash插件的操作教程，希望可以帮助到各位。", "subpage_snippet": "", "source": "chrome.xahuapu.net", "link": "https://chrome.xahuapu.net/help/654.html", "content": "Dec 20, 2024 · 不少用户对于如何在Chrome浏览器中启用或禁用Flash插件还不太清楚，为此，本文详细介绍了管理Flash插件的操作教程，希望可以帮助到各位。"}
diff --git a/data/sampled_jsons/MultiPDENet_MaNN_Block_Figure_1a_Section_3.2.4_year_2023-2024.jsonl b/data/sampled_jsons/MultiPDENet_MaNN_Block_Figure_1a_Section_3.2.4_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..08e84c6cd2210f9a627a86ea7914f91b2f6c4356
--- /dev/null
+++ b/data/sampled_jsons/MultiPDENet_MaNN_Block_Figure_1a_Section_3.2.4_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MultiPDENet : PDE-embedded Learning with Multi-time-stepping for...", "date": "", "ddg_snippet": "In this section , we introduce MultiPDENet and show how our model efficiently captures the underlying spatiotemporal dynamics. As illustrated in Figure 1 ( a ), predicting.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.15987v1", "content": "In this section , we introduce MultiPDENet and show how our model efficiently captures the underlying spatiotemporal dynamics. As illustrated in Figure 1 ( a ), predicting."}
+{"idx": 1, "title": "Block Blast Solver – Instant AI Solutions (2025) | BlockBlastSolver", "date": "", "ddg_snippet": "Upload a Block Blast screenshot and get instant AI solutions to beat tough levels. Fast, free, no download. Updated for 2025.", "subpage_snippet": "", "source": "blockblastsolver.com", "link": "https://blockblastsolver.com/", "content": "Upload a Block Blast screenshot and get instant AI solutions to beat tough levels. Fast, free, no download. Updated for 2025."}
+{"idx": 2, "title": "Comparing Heights", "date": "", "ddg_snippet": "Number Of Figures . Two Three Four Five Six. Heights. Figure 1 .", "subpage_snippet": "", "source": "www.mrinitialman.com", "link": "https://www.mrinitialman.com/OddsEnds/Sizes/compsizes.xhtml", "content": "Number Of Figures . Two Three Four Five Six. Heights. Figure 1 ."}
+{"idx": 3, "title": "Сборник упражнений Быкова 4 класс. Страница 24 - ГДЗ Решебник...", "date": "", "ddg_snippet": "4. Подбери и внеси в таблицу правильные ответы на вопросы. A-3. What does she look like? – She’s short and slim and she’s got dark hair. (Как она выглядит? – Она невысокая и стройная, у нее темные волосы.) B-2. What is she like? – She is kind. (Какая...", "subpage_snippet": "", "source": "xn----btbeegalms2a3a1h.xn--p1ai", "link": "https://xn----btbeegalms2a3a1h.xn--p1ai/sbornik-uprazhnenij-bykova-4-klass-stranica-24/", "content": "4. Подбери и внеси в таблицу правильные ответы на вопросы. A-3. What does she look like? – She’s short and slim and she’s got dark hair. (Как она выглядит? – Она невысокая и стройная, у нее темные волосы.) B-2. What is she like? – She is kind. (Какая..."}
+{"idx": 4, "title": "Два майора – Telegram", "date": "", "ddg_snippet": "любым удобным способом на сайте «Два майора» - https://dva-majors.ru на счет БФ «Два майора» перевод по СБП без комиссии: https://qr.nspk.ru/AS 1 A 002SNGS125GG8HFRLP618Q3H3GRI...", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dva_majors", "content": "любым удобным способом на сайте «Два майора» - https://dva-majors.ru на счет БФ «Два майора» перевод по СБП без комиссии: https://qr.nspk.ru/AS 1 A 002SNGS125GG8HFRLP618Q3H3GRI..."}
+{"idx": 5, "title": "Всеобъемлющая теория матриц / Хабр", "date": "", "ddg_snippet": "Для всех: Если матрица совершает некое действие (например, поворачивает и растягивает объект), то обратная матрица A^{-1} совершает обратное действие (поворачивает назад и сжимает), возвращая объект в исходное состояние.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/articles/949084/", "content": "Для всех: Если матрица совершает некое действие (например, поворачивает и растягивает объект), то обратная матрица A^{-1} совершает обратное действие (поворачивает назад и сжимает), возвращая объект в исходное состояние."}
+{"idx": 6, "title": "Инфоцентр AfterShock • Каким будет завтра?", "date": "", "ddg_snippet": "по умолчанию. - 3 - 2 -1средний+1+2+3+4. Облегчённый шрифт.", "subpage_snippet": "", "source": "AfterShock.news", "link": "https://AfterShock.news/", "content": "по умолчанию. - 3 - 2 -1средний+1+2+3+4. Облегчённый шрифт."}
+{"idx": 7, "title": "Аргументы и Факты — последние новости России и мира сегодня", "date": "", "ddg_snippet": "Аргументы и Факты — самые актуальные и последние новости России и мира сегодня. Будьте в курсе главных свежих событий дня и последнего часа, происшествий, обзоров, аналитики, инфографики, фото и видеорепортажей.", "subpage_snippet": "", "source": "aif.ru", "link": "https://aif.ru/", "content": "Аргументы и Факты — самые актуальные и последние новости России и мира сегодня. Будьте в курсе главных свежих событий дня и последнего часа, происшествий, обзоров, аналитики, инфографики, фото и видеорепортажей."}
+{"idx": 8, "title": "GISMETEO: Погода в Нижневартовске сегодня, прогноз погоды...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Нижневартовске на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-nizhnevartovsk-3974/", "content": "Подробный прогноз погоды в Нижневартовске на сегодня."}
+{"idx": 9, "title": "Главная страница - Портал непрерывного образования", "date": "", "ddg_snippet": "ВАЖНО! Для внесения сведений о прохождении Вами аккредитации не требуется свидетельство об аккредитации специалиста на бумажном носителе. Подробнее.", "subpage_snippet": "", "source": "edu.rosminzdrav.ru", "link": "https://edu.rosminzdrav.ru/", "content": "ВАЖНО! Для внесения сведений о прохождении Вами аккредитации не требуется свидетельство об аккредитации специалиста на бумажном носителе. Подробнее."}
diff --git a/data/sampled_jsons/NFR_layer_feature_selection_formula_equation_UPGNET_L1_regularization.jsonl b/data/sampled_jsons/NFR_layer_feature_selection_formula_equation_UPGNET_L1_regularization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7f954b1bc0c4b19166560ca7af4c4422c1ad1c4c
--- /dev/null
+++ b/data/sampled_jsons/NFR_layer_feature_selection_formula_equation_UPGNET_L1_regularization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Feature Selection with L1 Regularization in Formal Neurons", "date": "", "ddg_snippet": "Feature selection aims to omit features that are unnecessary for a given problem. Feature selection in formal meurons can be achieved by minimizing convex and picewise linear (CPL) criterion functions with L1 regularization . Minimizing CPL criterion functions can be associated with computations on a finite number of vertices in the parameter space.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-62495-7_26", "content": "Feature selection aims to omit features that are unnecessary for a given problem. Feature selection in formal meurons can be achieved by minimizing convex and picewise linear (CPL) criterion functions with L1 regularization . Minimizing CPL criterion functions can be associated with computations on a finite number of vertices in the parameter space."}
+{"idx": 1, "title": "Explained: How Does L1 Regularization Perform Feature Selection?", "date": "", "ddg_snippet": "How does L1 regularization perform automatic feature selection ? Feature selection is the process of selecting an optimal subset of features from a given set of features ; an optimal feature subset is the one which maximizes the performance of the model on the given task.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/explained-how-does-l1-regularization-perform-feature-selection/", "content": "How does L1 regularization perform automatic feature selection ? Feature selection is the process of selecting an optimal subset of features from a given set of features ; an optimal feature subset is the one which maximizes the performance of the model on the given task."}
+{"idx": 2, "title": "L1/L2 Regularization in PyTorch - GeeksforGeeks", "date": "", "ddg_snippet": "L1 and L2 regularization techniques help prevent overfitting by adding penalties to model parameters, thus improving generalization and model robustness. PyTorch simplifies the implementation of regularization techniques like L1 and L2 through its flexible neural network framework and built-in optimization routines, making it easier to build and train regularized models. The article aims to ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/l1l2-regularization-in-pytorch/", "content": "L1 and L2 regularization techniques help prevent overfitting by adding penalties to model parameters, thus improving generalization and model robustness. PyTorch simplifies the implementation of regularization techniques like L1 and L2 through its flexible neural network framework and built-in optimization routines, making it easier to build and train regularized models. The article aims to ..."}
+{"idx": 3, "title": "Feature selection & regularization (regression) - Workshop: Applied ...", "date": "", "ddg_snippet": "1 Concepts Feature selection also known as variable selection , attribute selection or variable subset selection process of selecting subset of relevant features /predictors for our model Aims: intepretability, reduce training time, avoid curse of dimensionality, improve data-model compatibility (cf. Wikipedia, 12.02.2025) Regularization", "subpage_snippet": "", "source": "paulcbauer.github.io", "link": "https://paulcbauer.github.io/workshop_applied_machine_learning/10-feature_selection.html", "content": "1 Concepts Feature selection also known as variable selection , attribute selection or variable subset selection process of selecting subset of relevant features /predictors for our model Aims: intepretability, reduce training time, avoid curse of dimensionality, improve data-model compatibility (cf. Wikipedia, 12.02.2025) Regularization"}
+{"idx": 4, "title": "Feature Selection and Generalization using Regularization - Towards AI", "date": "", "ddg_snippet": "Author (s): Shahriar Hossain An Overview of L1 and L2 Regularization Techniques and a Case Study on Feature Selection Using Neural Networks Source: Image by the author. The image was drawn using Canva. Overfitting is a common challenge in neural network training, where the model learns the noise and details of the training data to an extent that negatively impacts its performance on new data ...", "subpage_snippet": "", "source": "towardsai.net", "link": "https://towardsai.net/p/l/feature-selection-and-generalization-using-regularization", "content": "Author (s): Shahriar Hossain An Overview of L1 and L2 Regularization Techniques and a Case Study on Feature Selection Using Neural Networks Source: Image by the author. The image was drawn using Canva. Overfitting is a common challenge in neural network training, where the model learns the noise and details of the training data to an extent that negatively impacts its performance on new data ..."}
+{"idx": 5, "title": "L1 & L2 Regularization and Feature Selection", "date": "", "ddg_snippet": "The formula for the loss function with L1 regularization is: L1 regularization can make some weights exactly zero, effectively removing some features . This helps in selecting only the most important features . For example, if a model's weights are 0.8, 0.4, and 0.2, L1 regularization might reduce the 0.2 to zero, removing that feature from the ...", "subpage_snippet": "", "source": "kosett1356.medium.com", "link": "https://kosett1356.medium.com/l1-l2-regularization-and-feature-selection-53a9244f0382", "content": "The formula for the loss function with L1 regularization is: L1 regularization can make some weights exactly zero, effectively removing some features . This helps in selecting only the most important features . For example, if a model's weights are 0.8, 0.4, and 0.2, L1 regularization might reduce the 0.2 to zero, removing that feature from the ..."}
+{"idx": 6, "title": "(PDF) Deep Neural Network Regularization for Feature Selection in ...", "date": "", "ddg_snippet": "Specifically, we use group ℓ 1 regularization in order to induce the group level sparsity on network's connections. Set of outgoing weights from each hidden layer represents the group here.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/332699538_Deep_Neural_Network_Regularization_for_Feature_Selection_in_Learning-to-Rank", "content": "Specifically, we use group ℓ 1 regularization in order to induce the group level sparsity on network's connections. Set of outgoing weights from each hidden layer represents the group here."}
+{"idx": 7, "title": "Explained: How Does L1 Regularization Perform Feature Selection?", "date": "", "ddg_snippet": "\" L1 regularization performs feature selection \" is an easy statement that the majority ML learners agree with, without diving deep into how it really works internally. This blog is an try and bring my understanding and mental-model to the readers in an effort to answer the query in an intuitive manner.", "subpage_snippet": "", "source": "bardai.ai", "link": "https://bardai.ai/2025/04/23/explained-how-does-l1-regularization-perform-feature-selection/", "content": "\" L1 regularization performs feature selection \" is an easy statement that the majority ML learners agree with, without diving deep into how it really works internally. This blog is an try and bring my understanding and mental-model to the readers in an effort to answer the query in an intuitive manner."}
+{"idx": 8, "title": "What is L1 and L2 Regularization. How does L1 Regularization ... - Substack", "date": "", "ddg_snippet": "L1 regularization , also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds a absolute value of model coefficients to the Loss Function. where λ is the regularization parameter that controls the strength of the penalty, w_i represents the model coefficients. L2 Regularization ( Ridge Regression)", "subpage_snippet": "", "source": "manasabrao.substack.com", "link": "https://manasabrao.substack.com/p/what-is-l1-and-l2-regularization", "content": "L1 regularization , also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds a absolute value of model coefficients to the Loss Function. where λ is the regularization parameter that controls the strength of the penalty, w_i represents the model coefficients. L2 Regularization ( Ridge Regression)"}
+{"idx": 9, "title": "ICML 2025 Orals", "date": "", "ddg_snippet": "As a proof of concept, we focus on feature selection pipelines for linear ... layer and the Node Feature Regularization ( NFR ) layer . Extensive experiments ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/events/oral", "content": "As a proof of concept, we focus on feature selection pipelines for linear ... layer and the Node Feature Regularization ( NFR ) layer . Extensive experiments ..."}
diff --git a/data/sampled_jsons/NeRF_Mildenhall_abstract_'novel_view_synthesis'_primary_focus_real-time_rendering_year_2020.jsonl b/data/sampled_jsons/NeRF_Mildenhall_abstract_'novel_view_synthesis'_primary_focus_real-time_rendering_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07e70c8ba3ccdbfa491a65d0d521cf1bcd246233
--- /dev/null
+++ b/data/sampled_jsons/NeRF_Mildenhall_abstract_'novel_view_synthesis'_primary_focus_real-time_rendering_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TimeNeRF: Building Generalizable Neural Radiance Fields across", "date": "", "ddg_snippet": "Finally, our model seamlessly renders novel views with a smooth transition of time (d), providing an immersive and realistic experience.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13929v1", "content": "Finally, our model seamlessly renders novel views with a smooth transition of time (d), providing an immersive and realistic experience."}
+{"idx": 1, "title": "NeRFlex: Resource-aware Real-time High-quality Rendering of", "date": "", "ddg_snippet": "... real - time rendering speeds, and even to free the NeRF rendering from dependence on high-performance hardware, researchers have explored two primary ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.03415v1", "content": "... real - time rendering speeds, and even to free the NeRF rendering from dependence on high-performance hardware, researchers have explored two primary ..."}
+{"idx": 2, "title": "Unifying Correspondence, Pose and NeRF for Generalized", "date": "", "ddg_snippet": "In real -world scenarios aimed at rendering novel views from unposed images, the initial step often involves employing an off-the-shelf camera pose ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.07246v2", "content": "In real -world scenarios aimed at rendering novel views from unposed images, the initial step often involves employing an off-the-shelf camera pose ..."}
+{"idx": 3, "title": "MARS: An Instance-Aware, Modular and Realistic Simulator for", "date": "", "ddg_snippet": "... rendering , Neural Fields encompass both continuous implicit and explicit neural representations enabling high-fidelity 3D reconstruction, integration ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/377962980_MARS_An_Instance-Aware_Modular_and_Realistic_Simulator_for_Autonomous_Driving", "content": "... rendering , Neural Fields encompass both continuous implicit and explicit neural representations enabling high-fidelity 3D reconstruction, integration ..."}
+{"idx": 4, "title": "SMERF: Streamable Memory Efficient Radiance Fields for", "date": "", "ddg_snippet": "... that our method exceeds the state-of-the-art in real - time novel view synthesis by 0.78 dB on standard benchmarks and 1.78 dB on large scenes, renders ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.07541v3", "content": "... that our method exceeds the state-of-the-art in real - time novel view synthesis by 0.78 dB on standard benchmarks and 1.78 dB on large scenes, renders ..."}
+{"idx": 5, "title": "\"S\"³Gaussian: Self-Supervised Street Gaussians for Autonomous", "date": "", "ddg_snippet": "Harnessing the power of explicit 3D Gaussians, 3DGS achieves optimal outcomes in novel view synthesis and real - time rendering while also ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.20323v1", "content": "Harnessing the power of explicit 3D Gaussians, 3DGS achieves optimal outcomes in novel view synthesis and real - time rendering while also ..."}
+{"idx": 6, "title": "Abhishek Kar", "date": "", "ddg_snippet": "We propose NeRFiller, an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual ...", "subpage_snippet": "", "source": "abhishekkar.info", "link": "https://abhishekkar.info/", "content": "We propose NeRFiller, an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual ..."}
+{"idx": 7, "title": "Approximating Human-Level 3D Visual Inferences With Deep Neural", "date": "", "ddg_snippet": "We investigate the role of the learning objective and dataset by training single- view (the model only sees one viewpoint of an object per training ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/opmi/article/doi/10.1162/opmi_a_00189/128124/Approximating-Human-Level-3D-Visual-Inferences", "content": "We investigate the role of the learning objective and dataset by training single- view (the model only sees one viewpoint of an object per training ..."}
+{"idx": 8, "title": "NeST: Neural Stress Tensor Tomography by leveraging 3D", "date": "", "ddg_snippet": "Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3723873", "content": "Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing ..."}
+{"idx": 9, "title": "NeuralFeels with neural fields: Visuotactile perception for", "date": "", "ddg_snippet": "Progress in simulation ( 24 ) enables practitioners to learn tactile observation models that transfer to real -world interactions ( 22 , 25 , 26 ).", "subpage_snippet": "", "source": "www.science.org", "link": "https://www.science.org/stoken/author-tokens/ST-2331/full", "content": "Progress in simulation ( 24 ) enables practitioners to learn tactile observation models that transfer to real -world interactions ( 22 , 25 , 26 )."}
diff --git a/data/sampled_jsons/NeRF_Mildenhall_et_al_abstract_year_2020.jsonl b/data/sampled_jsons/NeRF_Mildenhall_et_al_abstract_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..70af9d845ca4f1f1c3a4a385ea1b66cd3c77bfdd
--- /dev/null
+++ b/data/sampled_jsons/NeRF_Mildenhall_et_al_abstract_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2003.08934] NeRF: Representing Scenes as Neural Radiance ...", "date": "", "ddg_snippet": "Mar 19, 2020 · View a PDF of the paper titled NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, by Ben Mildenhall and 5 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2003.08934", "content": "Mar 19, 2020 · View a PDF of the paper titled NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis, by Ben Mildenhall and 5 other authors"}
+{"idx": 1, "title": "NeRF: Representing scenes as neural radiance fields for view ...", "date": "", "ddg_snippet": "1. Introduction and Basic NeRF Algorithm This article is written in response to the Frontiers of Science Award generously granted in 2023 to the papers on “ NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis” [41, 42] at ECCV 2020 and CACM 2022. NeRFs were introduced as a method that achieved the highest quality visual and quantitative results to date for the task of ...", "subpage_snippet": "", "source": "cseweb.ucsd.edu", "link": "https://cseweb.ucsd.edu/~ravir/icbs23.pdf", "content": "1. Introduction and Basic NeRF Algorithm This article is written in response to the Frontiers of Science Award generously granted in 2023 to the papers on “ NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis” [41, 42] at ECCV 2020 and CACM 2022. NeRFs were introduced as a method that achieved the highest quality visual and quantitative results to date for the task of ..."}
+{"idx": 2, "title": "Neural Fields: NeRF: Representing Scenes as Neural Radiance ...", "date": "", "ddg_snippet": "Mar 19, 2020 · title= {NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis}, author= {Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},", "subpage_snippet": "", "source": "neuralfields.cs.brown.edu", "link": "https://neuralfields.cs.brown.edu/paper_33.html", "content": "Mar 19, 2020 · title= {NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis}, author= {Ben Mildenhall and Pratul P. Srinivasan and Matthew Tancik and Jonathan T. Barron and Ravi Ramamoorthi and Ren Ng},"}
+{"idx": 3, "title": "(PDF) NeRF: Neural Radiance Field in 3D Vision, A ...", "date": "", "ddg_snippet": "Oct 1, 2022 · Since the original paper by Mildenhall et al ., more than 250 preprints were published, with more than 100 eventually being accepted in tier one Computer Vision Conferences.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/364126276_NeRF_Neural_Radiance_Field_in_3D_Vision_A_Comprehensive_Review", "content": "Oct 1, 2022 · Since the original paper by Mildenhall et al ., more than 250 preprints were published, with more than 100 eventually being accepted in tier one Computer Vision Conferences."}
+{"idx": 4, "title": "NeRF: Representing Scenes as Neural Radiance Fields for View ...", "date": "", "ddg_snippet": "Authors contributed equally to this work. 2 B. Mildenhall , P. P. Srinivasan, M. Tancik et al . Input Images Optimize NeRF Render new views Fig.1: We present a method that optimizes a continuous 5D neural radiance eld representation (volume density and view-dependent color at any continuous location) of a scene from a set of input images.", "subpage_snippet": "", "source": "www.ecva.net", "link": "https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460392.pdf", "content": "Authors contributed equally to this work. 2 B. Mildenhall , P. P. Srinivasan, M. Tancik et al . Input Images Optimize NeRF Render new views Fig.1: We present a method that optimizes a continuous 5D neural radiance eld representation (volume density and view-dependent color at any continuous location) of a scene from a set of input images."}
+{"idx": 5, "title": "HR- NeRF : advancing realism and accuracy in highlight scene...", "date": "", "ddg_snippet": "Abstract . NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights.To represent 3D scenes implicitly, NeRF ( Mildenhall et al ., 2020) employs MLP networks.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12041011/", "content": "Abstract . NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights.To represent 3D scenes implicitly, NeRF ( Mildenhall et al ., 2020) employs MLP networks."}
+{"idx": 6, "title": "Notes on NeRF : Representing Scenes as Neural Radiance... | Medium", "date": "", "ddg_snippet": "Mildenhall et al built a fully connected network without using convolutional layers. It uses a single 5D coordinate (x,y,z,θ, Φ) that compresses spatial and viewing/ray direction information to output a single volume density σ and RGB color radiance. It requires 3 steps to render a NeRF .", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@chengmu/notes-on-nerf-representing-scenes-as-neural-radiance-fields-for-view-synthesis-by-mildenhall-et-al-fd88f715fe77", "content": "Mildenhall et al built a fully connected network without using convolutional layers. It uses a single 5D coordinate (x,y,z,θ, Φ) that compresses spatial and viewing/ray direction information to output a single volume density σ and RGB color radiance. It requires 3 steps to render a NeRF ."}
+{"idx": 7, "title": "Neural Radiance Fields ( NeRFs ) ( Mildenhall et al ., 2020) pion", "date": "", "ddg_snippet": "ABSTRACT : Generating geometric 3D reconstructions from Neural Radiance Fields ( NeRFs ) is of great interest.It was followed by the groundbreaking research work of Neural Radiance Fields ( Mildenhall et al ., 2020).", "subpage_snippet": "", "source": "isprs-archives.copernicus.org", "link": "https://isprs-archives.copernicus.org/articles/XLVIII-1-W3-2023/71/2023/isprs-archives-XLVIII-1-W3-2023-71-2023.pdf", "content": "ABSTRACT : Generating geometric 3D reconstructions from Neural Radiance Fields ( NeRFs ) is of great interest.It was followed by the groundbreaking research work of Neural Radiance Fields ( Mildenhall et al ., 2020)."}
+{"idx": 8, "title": "NeRF : Neural Radiance Fields", "date": "", "ddg_snippet": "NeRF . Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV 2020 Oral - Best Paper Honorable Mention.", "subpage_snippet": "", "source": "www.matthewtancik.com", "link": "https://www.matthewtancik.com/nerf", "content": "NeRF . Representing Scenes as Neural Radiance Fields for View Synthesis. ECCV 2020 Oral - Best Paper Honorable Mention."}
+{"idx": 9, "title": "Double nerf : representing dynamic scenes as neural radiance", "date": "", "ddg_snippet": "Neural Radiance Fields ( NeRFs ) ( Mildenhall et al ., 2020) are non-convolutional neural models that learn 3D scene structure and color to produce novel images of a given scene from a new view point.D- NeRF model (Pumarola et al ., 2021) is aimed at ex-tending NeRF to a dynamic scenes.", "subpage_snippet": "", "source": "pdfs.semanticscholar.org", "link": "https://pdfs.semanticscholar.org/702b/b22c760e8e733350c18e36b57f1db1ac2907.pdf", "content": "Neural Radiance Fields ( NeRFs ) ( Mildenhall et al ., 2020) are non-convolutional neural models that learn 3D scene structure and color to produce novel images of a given scene from a new view point.D- NeRF model (Pumarola et al ., 2021) is aimed at ex-tending NeRF to a dynamic scenes."}
diff --git a/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization.jsonl b/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c69f8e5d03255857a94d94b84c9ce1303347fce3
--- /dev/null
+++ b/data/sampled_jsons/Neural_Exploratory_Landscape_Analysis_for_Meta-Black-Box-Optimization.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Landscape Analysis for Surrogate Models in the Evolutionary", "date": "", "ddg_snippet": "... Landscape Analysis for Surrogate Models in the ... Black - box optimization , surrogate modeling , landscape analysis , metalearning CMA-ES.", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/evco/article/33/2/249/124014/Landscape-Analysis-for-Surrogate-Models-in-the", "content": "... Landscape Analysis for Surrogate Models in the ... Black - box optimization , surrogate modeling , landscape analysis , metalearning CMA-ES."}
+{"idx": 1, "title": "Reinforcement learning Based Automated Design of Differential", "date": "", "ddg_snippet": "differential evolution, meta -learning, double deep Q-network, black - box - optimization , exploratory landscape analysis .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.12881v1", "content": "differential evolution, meta -learning, double deep Q-network, black - box - optimization , exploratory landscape analysis ."}
+{"idx": 2, "title": "(PDF) The Landscape of R Packages for Automated Exploratory", "date": "", "ddg_snippet": "There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/332014513_The_Landscape_of_R_Packages_for_Automated_Exploratory_Data_Analysis", "content": "There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new ..."}
+{"idx": 3, "title": "meta-learn.github.io | Workshop on Meta-Learning (MetaLearn", "date": "", "ddg_snippet": "Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis ; Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der ...", "subpage_snippet": "", "source": "meta-learn.github.io", "link": "https://meta-learn.github.io/2022/", "content": "Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis ; Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der ..."}
+{"idx": 4, "title": "Zeyuan Ma - Google Scholar", "date": "", "ddg_snippet": "MetaBox: A Benchmark Platform for Meta - Black - Box Optimization with ... Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=Jcy8wPgAAAAJ&hl=en", "content": "MetaBox: A Benchmark Platform for Meta - Black - Box Optimization with ... Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization"}
+{"idx": 5, "title": "Zeyuan Ma - Google Académico", "date": "", "ddg_snippet": "MetaBox: A Benchmark Platform for Meta - Black - Box Optimization with ... Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization", "subpage_snippet": "", "source": "scholar.google.co.uk", "link": "https://scholar.google.co.uk/citations?user=Jcy8wPgAAAAJ&hl=es", "content": "MetaBox: A Benchmark Platform for Meta - Black - Box Optimization with ... Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization"}
+{"idx": 6, "title": "Human-Competitive Awards 2004 – Present | Human Competitive", "date": "", "ddg_snippet": "Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization ... swarm optimization algorithm: a natural-inspired metaheuristic method for ...", "subpage_snippet": "", "source": "www.human-competitive.org", "link": "https://www.human-competitive.org/awards", "content": "Neural Exploratory Landscape Analysis for Meta - Black - Box - Optimization ... swarm optimization algorithm: a natural-inspired metaheuristic method for ..."}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2020", "content": "Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network"}
+{"idx": 8, "title": "Toward Automated Algorithm Design: A Survey and Practical Guide", "date": "", "ddg_snippet": "In contrast, Black - Box Optimization (BBO) only provides objective values for solutions, making the analysis and search of the problem space even more ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.00625v2", "content": "In contrast, Black - Box Optimization (BBO) only provides objective values for solutions, making the analysis and search of the problem space even more ..."}
+{"idx": 9, "title": "NeurIPS 2020 Papers", "date": "", "ddg_snippet": "Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment ... for Fast Convergence of Natural Gradient ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2020/papers.html", "content": "Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment ... for Fast Convergence of Natural Gradient ..."}
diff --git a/data/sampled_jsons/Nichol_Dhariwal_2021_Improved_Denoising_Diffusion_Probabilistic_Models_FID_score_ImageNet_64x64.jsonl b/data/sampled_jsons/Nichol_Dhariwal_2021_Improved_Denoising_Diffusion_Probabilistic_Models_FID_score_ImageNet_64x64.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..847bda68df1717a0a0ef24a99d5f822538edbacc
--- /dev/null
+++ b/data/sampled_jsons/Nichol_Dhariwal_2021_Improved_Denoising_Diffusion_Probabilistic_Models_FID_score_ImageNet_64x64.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Prafulla Dhariwal's research works", "date": "", "ddg_snippet": "Finally, we find that classifier guidance combines well with upsampling diffusion models , further improving FID to 3.85 on ImageNet 512 × 512 512 ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Prafulla-Dhariwal-2117687029", "content": "Finally, we find that classifier guidance combines well with upsampling diffusion models , further improving FID to 3.85 on ImageNet 512 × 512 512 ..."}
+{"idx": 1, "title": "Truncated Consistency Models", "date": "", "ddg_snippet": "... ImageNet 64 × 64 64 64 64\\times 64 64 × 64 datasets, TCM outperforms the iCT (Song & Dhariwal , 2023 ) , the previous best consistency model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.14895v2", "content": "... ImageNet 64 × 64 64 64 64\\times 64 64 × 64 datasets, TCM outperforms the iCT (Song & Dhariwal , 2023 ) , the previous best consistency model ..."}
+{"idx": 2, "title": "Rethinking cluster-conditioned diffusion models for label-free", "date": "", "ddg_snippet": "Conditioning diffusion models on human-annotated data is today’s standard practice as it significantly improves the image fidelity [ 23 , 8 , 25 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.00570v2", "content": "Conditioning diffusion models on human-annotated data is today’s standard practice as it significantly improves the image fidelity [ 23 , 8 , 25 ] ."}
+{"idx": 3, "title": "The recent rise of diffusion-based models | Maciej Domagała", "date": "", "ddg_snippet": "... denoising , the classifier checks whether the image is denoised in the right direction and contributes its own gradient of loss function into the ...", "subpage_snippet": "", "source": "maciejdomagala.github.io", "link": "https://maciejdomagala.github.io/generative_models/2022/06/06/The-recent-rise-of-diffusion-based-models.html", "content": "... denoising , the classifier checks whether the image is denoised in the right direction and contributes its own gradient of loss function into the ..."}
+{"idx": 4, "title": "Multi-student Diffusion Distillation for Better One-step", "date": "", "ddg_snippet": "... collectively outperform single-student counterparts, achieving close to state-of-the-art FID scores of 1.20 1.20 1.20 1.20 on one-step ImageNet -64 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.23274v2", "content": "... collectively outperform single-student counterparts, achieving close to state-of-the-art FID scores of 1.20 1.20 1.20 1.20 on one-step ImageNet -64 ..."}
+{"idx": 5, "title": "DDAE++: Enhancing Diffusion Models Towards Unified Generative", "date": "", "ddg_snippet": "... diffusion models distribute semantic information across layers, where each offers representations of varying granularity [ 6 ] , jointly contributing ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.10999v1", "content": "... diffusion models distribute semantic information across layers, where each offers representations of varying granularity [ 6 ] , jointly contributing ..."}
+{"idx": 6, "title": "DREAM: Diffusion Rectification and Estimation-Adaptive Models", "date": "", "ddg_snippet": "Recently, Diffusion Probabilistic Models (DPMs) [ 19 , 48 ] , a novel class of generative models , have attracted increased interest for their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.00210v2", "content": "Recently, Diffusion Probabilistic Models (DPMs) [ 19 , 48 ] , a novel class of generative models , have attracted increased interest for their ..."}
+{"idx": 7, "title": "FashionFlow: Leveraging Diffusion Models for Dynamic Fashion", "date": "", "ddg_snippet": "Our approach involves developing and connecting relevant components with the diffusion model , which results in the creation of high-fidelity videos ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00106v2", "content": "Our approach involves developing and connecting relevant components with the diffusion model , which results in the creation of high-fidelity videos ..."}
+{"idx": 8, "title": "Steering Rectified Flow Models in the Vector Field for", "date": "", "ddg_snippet": "Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.00100v1", "content": "Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion ..."}
+{"idx": 9, "title": "Scalable Diffusion Models with State Space Backbone", "date": "", "ddg_snippet": "... a comparable FID scores in class-conditional image generation conducted on ImageNet at a resolution of 256 × \\times × 256 and 512 × \\times × 512.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.05608v3", "content": "... a comparable FID scores in class-conditional image generation conducted on ImageNet at a resolution of 256 × \\times × 256 and 512 × \\times × 512."}
diff --git "a/data/sampled_jsons/O(d\302\262\316\265\342\201\264)_zeroth-order_homotopy_method_complexity.jsonl" "b/data/sampled_jsons/O(d\302\262\316\265\342\201\264)_zeroth-order_homotopy_method_complexity.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..e8468cb1fb3b77bcc841286e4f3ed06c0d170c5a
--- /dev/null
+++ "b/data/sampled_jsons/O(d\302\262\316\265\342\201\264)_zeroth-order_homotopy_method_complexity.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Perturbation theory - Wikipedia", "date": "", "ddg_snippet": "Perturbation theory (quantum mechanics) describes the use of this method in quantum mechanics . ... One begins by writing the equations D ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Perturbation_theory", "content": "Perturbation theory (quantum mechanics) describes the use of this method in quantum mechanics . ... One begins by writing the equations D ..."}
+{"idx": 1, "title": "Spectral sequence - Wikipedia", "date": "", "ddg_snippet": "Mostly the objects we are talking about are chain complexes , that occur with descending (like above) or ascending order .", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Spectral_sequence", "content": "Mostly the objects we are talking about are chain complexes , that occur with descending (like above) or ascending order ."}
+{"idx": 2, "title": "Smoothing and First Order Methods: A Unified Framework | SIAM", "date": "", "ddg_snippet": "... and unification of several issues on the design, analysis, and potential applications of smoothing methods when combined with fast first order ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/100818327", "content": "... and unification of several issues on the design, analysis, and potential applications of smoothing methods when combined with fast first order ..."}
+{"idx": 3, "title": "Lensing Fingerprints of Scalar Hair and Broken Symmetries via", "date": "", "ddg_snippet": "We derive the deflection angle up to the second order using three independent semi-analytical approaches: the Homotopy -Perturbation Method , the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.16512v1", "content": "We derive the deflection angle up to the second order using three independent semi-analytical approaches: the Homotopy -Perturbation Method , the ..."}
+{"idx": 4, "title": "A Weiss–Williams theorem for spaces of embeddings and the", "date": "", "ddg_snippet": "We use this result to give a full description of the homotopy type (away from 2 and up to the concordance embedding stable range) of the space of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.05541v2", "content": "We use this result to give a full description of the homotopy type (away from 2 and up to the concordance embedding stable range) of the space of ..."}
+{"idx": 5, "title": "D-module in nLab", "date": "", "ddg_snippet": "As O X O _X is a subsheaf of D X D _X consisting of the zeroth - order differential operators (multiplications by the sections of structure sheaf), every ...", "subpage_snippet": "", "source": "ncatlab.org", "link": "https://ncatlab.org/nlab/show/D-module", "content": "As O X O _X is a subsheaf of D X D _X consisting of the zeroth - order differential operators (multiplications by the sections of structure sheaf), every ..."}
+{"idx": 6, "title": "Notes | A Place of Sand", "date": "", "ddg_snippet": "The meat is in sections 3 and 4 on the zeroth homotopy group and the examples of its construction for various Lie groups that we care about.", "subpage_snippet": "", "source": "kmhalpern.com", "link": "https://kmhalpern.com/category/notes/", "content": "The meat is in sections 3 and 4 on the zeroth homotopy group and the examples of its construction for various Lie groups that we care about."}
+{"idx": 7, "title": "perturbation theory - Analytical solution of nonlinear ordinary", "date": "", "ddg_snippet": "Also If somebody can help me about how I can use fixed point analytic method to solve this differential equations and some references on it, will be ...", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/555667/analytical-solution-of-nonlinear-ordinary-differential-equation", "content": "Also If somebody can help me about how I can use fixed point analytic method to solve this differential equations and some references on it, will be ..."}
+{"idx": 8, "title": "minnie.tuhs.org/Programs/Wikifetch/Zips/contents.html", "date": "", "ddg_snippet": "... Helium , Help:Contents , Henry_Norris_Russell , Heroin , History_of_chemistry , History_of_mathematics , History_of_physics , Holmium , Homotopy ...", "subpage_snippet": "", "source": "minnie.tuhs.org", "link": "https://minnie.tuhs.org/Programs/Wikifetch/Zips/contents.html", "content": "... Helium , Help:Contents , Henry_Norris_Russell , Heroin , History_of_chemistry , History_of_mathematics , History_of_physics , Holmium , Homotopy ..."}
+{"idx": 9, "title": "Equivariant algebraic K-theory and Artin 𝐿-functions", "date": "", "ddg_snippet": "... describing ζ F ∗ ( 1 − k ) superscript subscript 𝜁 𝐹 1 𝑘 \\zeta_{F}^{*}(1-k) italic_ζ start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.03578v1", "content": "... describing ζ F ∗ ( 1 − k ) superscript subscript 𝜁 𝐹 1 𝑘 \\zeta_{F}^{*}(1-k) italic_ζ start_POSTSUBSCRIPT italic_F end_POSTSUBSCRIPT ..."}
diff --git a/data/sampled_jsons/OFUL_algorithm_implementation_challenges_contextual_bandits.jsonl b/data/sampled_jsons/OFUL_algorithm_implementation_challenges_contextual_bandits.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c4ffef50d234df25de3bd63aab22afa70eb88d7a
--- /dev/null
+++ b/data/sampled_jsons/OFUL_algorithm_implementation_challenges_contextual_bandits.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generalized Linear Bandits: Almost Optimal Regret with One-Pass", "date": "", "ddg_snippet": "We study the generalized linear bandit (GLB) problem, a contextual multi-armed bandit framework that extends the classical linear model by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.11847v1", "content": "We study the generalized linear bandit (GLB) problem, a contextual multi-armed bandit framework that extends the classical linear model by ..."}
+{"idx": 1, "title": "GitHub - vineet0814/contextual-bandits-OFUL", "date": "", "ddg_snippet": "Currently the main.py contains implementation of OFUL algorithm by Abbasi-Yadkori 2011 from paper titled 'Improved Algorithms for Linear Stochastic Bandits ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/vineet0814/contextual-bandits-OFUL", "content": "Currently the main.py contains implementation of OFUL algorithm by Abbasi-Yadkori 2011 from paper titled 'Improved Algorithms for Linear Stochastic Bandits ..."}
+{"idx": 2, "title": "PDF Federated Linear Bandits with Finite Adversarial Actions", "date": "", "ddg_snippet": "We study a federated linear bandits model, where Mclients communicate with a central server to solve a linear contextual bandits problem with finite adversarial action sets that may be different across clients. To address the unique challenges of adversarial finiteaction sets, we propose the FedSupLinUCB algorithm , which extends the principles of SupLinUCB and OFUL algorithms in linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2311.00973.pdf", "content": "We study a federated linear bandits model, where Mclients communicate with a central server to solve a linear contextual bandits problem with finite adversarial action sets that may be different across clients. To address the unique challenges of adversarial finiteaction sets, we propose the FedSupLinUCB algorithm , which extends the principles of SupLinUCB and OFUL algorithms in linear ..."}
+{"idx": 3, "title": "An Overview of Contextual Bandits - Towards Data Science", "date": "", "ddg_snippet": "Contextual bandits are a class of one-step reinforcement learning algorithms specifically designed for such treatment personalization problems where we would like to dynamically adjust traffic based on which treatment is working for whom.", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/an-overview-of-contextual-bandits-53ac3aa45034/", "content": "Contextual bandits are a class of one-step reinforcement learning algorithms specifically designed for such treatment personalization problems where we would like to dynamically adjust traffic based on which treatment is working for whom."}
+{"idx": 4, "title": "PDF Meta-learning with Stochastic Linear Bandits", "date": "", "ddg_snippet": "Inspired by recent work on learning-to-learn linear regres-sion, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector. We first study the benefit of the biased OFUL algorithm in terms of regret minimization.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/cella20a/cella20a.pdf", "content": "Inspired by recent work on learning-to-learn linear regres-sion, we consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm , where the regularization is a square euclidean distance to a bias vector. We first study the benefit of the biased OFUL algorithm in terms of regret minimization."}
+{"idx": 5, "title": "PDF Nearly Optimal Algorithms for Linear Contextual Bandits with ...", "date": "", "ddg_snippet": "Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Jiafan He Department of Computer Science University of California, Los Angeles jiafanhe19@ucla.edu", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/df5f94d6ac6e13d830d70536cde9f0d2-Paper-Conference.pdf", "content": "Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Jiafan He Department of Computer Science University of California, Los Angeles jiafanhe19@ucla.edu"}
+{"idx": 6, "title": "Bandits with Mean Bounds - OpenReview", "date": "", "ddg_snippet": "We study a variant of the bandit problem where side information in the form of bounds on the mean of each arm is provided. We prove that these translate to tighter estimates of subgaussian factors and develop novel algorithms that exploit these estimates. In the linear setting, we present the Restricted-set OFUL (R-OFUL) algorithm that additionally uses the geometric properties of the problem ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4TZ4DE24fX", "content": "We study a variant of the bandit problem where side information in the form of bounds on the mean of each arm is provided. We prove that these translate to tighter estimates of subgaussian factors and develop novel algorithms that exploit these estimates. In the linear setting, we present the Restricted-set OFUL (R-OFUL) algorithm that additionally uses the geometric properties of the problem ..."}
+{"idx": 7, "title": "Understanding Contextual Bandits: Advanced Decision-Making in ... - Medium", "date": "", "ddg_snippet": "Various names for this class of algorithms include contextual decision systems or context-sensitive bandits , highlighting the importance of context in enhancing decision-making processes.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kapardhikannekanti/understanding-contextual-bandits-advanced-decision-making-in-machine-learning-85c7c20417d7", "content": "Various names for this class of algorithms include contextual decision systems or context-sensitive bandits , highlighting the importance of context in enhancing decision-making processes."}
+{"idx": 8, "title": "Scalable and Interpretable Contextual Bandits: A Literature Review and ...", "date": "", "ddg_snippet": "Abstract This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16918v1", "content": "Abstract This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers."}
+{"idx": 9, "title": "PDF Beyond UCB: Optimal and Efficient Contextual Bandits with Regression ...", "date": "", "ddg_snippet": "Abstract A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks such as clas-sification and regression.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/foster20a/foster20a.pdf", "content": "Abstract A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks such as clas-sification and regression."}
diff --git a/data/sampled_jsons/Observation_4_safety_fine-tuning_local_Lipschitzness_unsafe_samples_year_2024.jsonl b/data/sampled_jsons/Observation_4_safety_fine-tuning_local_Lipschitzness_unsafe_samples_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..42c893063e70a6edd575b4fc502cc15dce871ddc
--- /dev/null
+++ b/data/sampled_jsons/Observation_4_safety_fine-tuning_local_Lipschitzness_unsafe_samples_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Targeted Forgetting of Image Subgroups in CLIP Models", "date": "", "ddg_snippet": "In the forgetting stage, we identify layers essential for representing the forgetting samples but less critical to other data, fine - tuning the model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03117v1", "content": "In the forgetting stage, we identify layers essential for representing the forgetting samples but less critical to other data, fine - tuning the model ..."}
+{"idx": 1, "title": "Semi-Supervised Safe Visuomotor Policy Synthesis using Barrier", "date": "", "ddg_snippet": "... optimal-control methods based on the Hamilton-Jacobi reachability analysis framework [ 3 , 4 , 5 , 6 ] have been instrumental in addressing safety ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.12616v1", "content": "... optimal-control methods based on the Hamilton-Jacobi reachability analysis framework [ 3 , 4 , 5 , 6 ] have been instrumental in addressing safety ..."}
+{"idx": 2, "title": "Convergence Guarantees for Neural Network-Based", "date": "", "ddg_snippet": "... control problems, Hamilton–Jacobi (HJ) reachability analysis describes states from which trajectories will eventually reach a set of “ unsafe ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02904v1", "content": "... control problems, Hamilton–Jacobi (HJ) reachability analysis describes states from which trajectories will eventually reach a set of “ unsafe ..."}
+{"idx": 3, "title": "Machine Learning", "date": "", "ddg_snippet": "... a lightweight, pretrained, instruction-tuned verifier to detect and truncate redundant intermediate thoughts of LRMs without any LRM or verifier fine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/cs.LG/new?skip=0&show=2000", "content": "... a lightweight, pretrained, instruction-tuned verifier to detect and truncate redundant intermediate thoughts of LRMs without any LRM or verifier fine ..."}
+{"idx": 4, "title": "NeurIPS 2022 Oral-Equivalent Papers", "date": "", "ddg_snippet": "... large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a \"training from scratch\" to a \" fine - tuning ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2022/events/highlighted", "content": "... large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a \"training from scratch\" to a \" fine - tuning ..."}
+{"idx": 5, "title": "Yang Liu", "date": "", "ddg_snippet": "... Erdős and the average Erdős number among them is 4 .67. ... Comprehensive Fine - Tuning Large Language Models of Code for Automated Program Repair.", "subpage_snippet": "", "source": "www.csauthors.net", "link": "https://www.csauthors.net/yang-liu-003/", "content": "... Erdős and the average Erdős number among them is 4 .67. ... Comprehensive Fine - Tuning Large Language Models of Code for Automated Program Repair."}
+{"idx": 6, "title": "Nathan Kallus", "date": "", "ddg_snippet": "Compared to existing process reward models (PRMs), our method does not require a fine -grained notion of \"step,\" which is difficult to define for long ...", "subpage_snippet": "", "source": "nathankallus.com", "link": "https://nathankallus.com/", "content": "Compared to existing process reward models (PRMs), our method does not require a fine -grained notion of \"step,\" which is difficult to define for long ..."}
+{"idx": 7, "title": "From Flat to Hierarchical : Extracting Sparse Representations", "date": "", "ddg_snippet": "... 40 ] , broader scene organizations [ 41 , 42 , 43 ] , and instance structures [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ] have been shown in vision ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "... 40 ] , broader scene organizations [ 41 , 42 , 43 ] , and instance structures [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ] have been shown in vision ..."}
+{"idx": 8, "title": "Publications | Dynamic Systems Lab | Prof. Angela Schoellig", "date": "", "ddg_snippet": "ARTICLE{li-ral25, title = {Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation}, author ...", "subpage_snippet": "", "source": "www.dynsyslab.org", "link": "https://www.dynsyslab.org/research/publications/", "content": "ARTICLE{li-ral25, title = {Targeted Hard Sample Synthesis Based on Estimated Pose and Occlusion Error for Improved Object Pose Estimation}, author ..."}
+{"idx": 9, "title": "Main Track – IJCAI 2023", "date": "", "ddg_snippet": "Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over ...", "subpage_snippet": "", "source": "ijcai-23.org", "link": "https://ijcai-23.org/main-track-accepted-papers/", "content": "Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over ..."}
diff --git a/data/sampled_jsons/Observed_Agreement_formula_c_obs_Great_Models_Think_Alike.jsonl b/data/sampled_jsons/Observed_Agreement_formula_c_obs_Great_Models_Think_Alike.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..50218f5f5893566539960fb75acb6e7b1022f2d7
--- /dev/null
+++ b/data/sampled_jsons/Observed_Agreement_formula_c_obs_Great_Models_Think_Alike.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Great Models Think Alike and this Undermines AI Oversight", "date": "", "ddg_snippet": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04313v2", "content": "We study how model similarity affects both aspects of AI oversight by proposing Chance Adjusted Probabilistic Agreement (CAPA): a metric for LM ..."}
+{"idx": 1, "title": "Alternative Models of Reality: Page 3 - QSpace Forums", "date": "", "ddg_snippet": "I think we will eventually need to rethink space, but I can't even get anyone to consider time as an effect of action and it is the future becoming ...", "subpage_snippet": "", "source": "forums.fqxi.org", "link": "https://forums.fqxi.org/d/1928-alternative-models-of-reality?page=3", "content": "I think we will eventually need to rethink space, but I can't even get anyone to consider time as an effect of action and it is the future becoming ..."}
+{"idx": 2, "title": "Is it a fixed or random effect? | Dynamic Ecology", "date": "", "ddg_snippet": "Personally I think we should only include the 1 or 2 really obvious blocking or control factors in the average situation.", "subpage_snippet": "", "source": "dynamicecology.wordpress.com", "link": "https://dynamicecology.wordpress.com/2015/11/04/is-it-a-fixed-or-random-effect/", "content": "Personally I think we should only include the 1 or 2 really obvious blocking or control factors in the average situation."}
+{"idx": 3, "title": "JINJER – King Of Everything", "date": "", "ddg_snippet": "On another call tristatedrugs.com reviews „ I think the key thing with ARM is that it has been doing well for a long time and it continues ...", "subpage_snippet": "", "source": "www.hmbreakdown.de", "link": "http://www.hmbreakdown.de/jinjer-king-of-everything-cd", "content": "On another call tristatedrugs.com reviews „ I think the key thing with ARM is that it has been doing well for a long time and it continues ..."}
+{"idx": 4, "title": "Skip to content", "date": "", "ddg_snippet": "... great thing is that Rothenburg is ... We both enjoy watching Formula One and it ’ s one of my favorite circuits in Gran Turismo on PlayStation.", "subpage_snippet": "", "source": "evcharged.blog", "link": "https://evcharged.blog/", "content": "... great thing is that Rothenburg is ... We both enjoy watching Formula One and it ’ s one of my favorite circuits in Gran Turismo on PlayStation."}
+{"idx": 5, "title": "Climate naysayers are giving climate skeptics a bad name", "date": "", "ddg_snippet": "But once they add the CO2 increase into the models , they claim good agreement with the reported global surface temperature record.", "subpage_snippet": "", "source": "wattsupwiththat.com", "link": "https://wattsupwiththat.com/2015/04/16/climate-naysayers-are-giving-climate-skeptics-a-bad-name/", "content": "But once they add the CO2 increase into the models , they claim good agreement with the reported global surface temperature record."}
+{"idx": 6, "title": "Probabilistic Reasoning - Bibliography - PhilPapers", "date": "", "ddg_snippet": "... like whether a decision is rational, or whether one thing is a cause of another? Most philosophers think uncertain reasoning should at least obey the ...", "subpage_snippet": "", "source": "philpapers.org", "link": "https://philpapers.org/browse/probabilistic-reasoning", "content": "... like whether a decision is rational, or whether one thing is a cause of another? Most philosophers think uncertain reasoning should at least obey the ..."}
+{"idx": 7, "title": "AMA News | Model Aviation Library", "date": "", "ddg_snippet": "... Crispin Jr Most members well aware fact requirement competitors F3A Aerobatics meet sound-level check flight scores count category flown Nats flight ...", "subpage_snippet": "", "source": "library.modelaviation.com", "link": "https://library.modelaviation.com/article/ama-news-144", "content": "... Crispin Jr Most members well aware fact requirement competitors F3A Aerobatics meet sound-level check flight scores count category flown Nats flight ..."}
+{"idx": 8, "title": "(PDF) Wicked Problems: Modelling Social Messes with", "date": "", "ddg_snippet": "It presents the ten criteria they use to characterise WPS, and describes how General Morphological Analysis (GMA) can be used to model and analyse ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/236885171_Wicked_Problems_Modelling_Social_Messes_with_Morphological_Analysis", "content": "It presents the ten criteria they use to characterise WPS, and describes how General Morphological Analysis (GMA) can be used to model and analyse ..."}
+{"idx": 9, "title": "brothers | Early Rare Antique", "date": "", "ddg_snippet": "... very rare pair of antique sterling silver mounted grape scissors shears crafted in a fabulous Art Nouveau style – please see the photos.", "subpage_snippet": "", "source": "earlyrareantique.com", "link": "https://earlyrareantique.com/tag/brothers/", "content": "... very rare pair of antique sterling silver mounted grape scissors shears crafted in a fabulous Art Nouveau style – please see the photos."}
diff --git a/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Equation_21_LSIF_density_ratio.jsonl b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Equation_21_LSIF_density_ratio.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..464bc394803a16fffc618500a65ee3d469ac2ec1
--- /dev/null
+++ b/data/sampled_jsons/On_a_Connection_Between_Imitation_Learning_and_RLHF_Equation_21_LSIF_density_ratio.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose DIL, a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.05079", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this connection , we propose DIL, a ..."}
+{"idx": 1, "title": "PDF On a Connection Between Imitation Learning and Rlhf", "date": "", "ddg_snippet": "So far, we have observed that the RL-style objective in Equation (17), combined with density ratio estimation in Equation ( 21 ), can effectively leverage the preference dataset for imitation learning .", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/acf4a08f67724e9d2de34099f57a9c25-Paper-Conference.pdf", "content": "So far, we have observed that the RL-style objective in Equation (17), combined with density ratio estimation in Equation ( 21 ), can effectively leverage the preference dataset for imitation learning ."}
+{"idx": 2, "title": "Connection Between Imitation Learning and RLHF | Cong's Log", "date": "", "ddg_snippet": "Thus far, it has been observed that combining the RL-like objective in Equation (17) with the density ratio estimation method in Equation ( 21 ) can effectively utilize preference datasets for imitation learning .", "subpage_snippet": "", "source": "congchan.github.io", "link": "https://congchan.github.io/posts/connection-between-imitation-learning-and-rlhf/", "content": "Thus far, it has been observed that combining the RL-like objective in Equation (17) with the density ratio estimation method in Equation ( 21 ) can effectively utilize preference datasets for imitation learning ."}
+{"idx": 3, "title": "PDF Lecture 8: Imitation Learning and RLHF - Stanford University", "date": "", "ddg_snippet": "Imitation Learning Summary Imitation learning can greatly reduce the amount of data need to learn a good policy Challenges remain and one exciting area is combining inverse RL / learning from demonstration and online reinforcement learning For a look into some of the theory between imitation learning and RL, see Sun, Venkatraman, Gordon, Boots ...", "subpage_snippet": "", "source": "web.stanford.edu", "link": "https://web.stanford.edu/class/cs234/slides/lecture8post.pdf", "content": "Imitation Learning Summary Imitation learning can greatly reduce the amount of data need to learn a good policy Challenges remain and one exciting area is combining inverse RL / learning from demonstration and online reinforcement learning For a look into some of the theory between imitation learning and RL, see Sun, Venkatraman, Gordon, Boots ..."}
+{"idx": 4, "title": "On a Connection Between Imitation Learningand RLHF", "date": "", "ddg_snippet": "Imitation Learning and RLHF Teng Xiao (PSU), Yige Yuan (CAS), Mingxiao Li (Tecent), Zhengyu Chen (Meituan), Vasant G Honavar (PSU)", "subpage_snippet": "", "source": "aitime-lundao.oss-cn-beijing.aliyuncs.com", "link": "https://aitime-lundao.oss-cn-beijing.aliyuncs.com/AitimeReport/20250312/1741781794566", "content": "Imitation Learning and RLHF Teng Xiao (PSU), Yige Yuan (CAS), Mingxiao Li (Tecent), Zhengyu Chen (Meituan), Vasant G Honavar (PSU)"}
+{"idx": 5, "title": "Direct Imitation Learning: RLHF Secretly Performs Imitation Learning ...", "date": "", "ddg_snippet": "Built upon this connection , we develop a principled method DIL todirectly optimize the imitation learning objective. DIL derives a surrogate objective for imitation learning with direct density ratio estimates, allowing effective use of preference data.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/2QdsjiNXgj@OpenReview", "content": "Built upon this connection , we develop a principled method DIL todirectly optimize the imitation learning objective. DIL derives a surrogate objective for imitation learning with direct density ratio estimates, allowing effective use of preference data."}
+{"idx": 6, "title": "On a Connection Between Imitation Learning and RLHF | alphaXiv", "date": "", "ddg_snippet": "View recent discussion. Abstract: This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this ...", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2503.05079v1", "content": "View recent discussion. Abstract: This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution. Building on this ..."}
+{"idx": 7, "title": "On a Connection Between Imitation Learning and RLHF | AI Research Paper ...", "date": "", "ddg_snippet": "This paper makes a significant contribution by establishing a formal connection between imitation learning and RLHF , offering a simpler alternative for aligning language models with human preferences. The ILHF approach demonstrates that we can achieve results comparable to RLHF through careful selection and imitation of high-quality examples.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/connection-between-imitation-learning-rlhf", "content": "This paper makes a significant contribution by establishing a formal connection between imitation learning and RLHF , offering a simpler alternative for aligning language models with human preferences. The ILHF approach demonstrates that we can achieve results comparable to RLHF through careful selection and imitation of high-quality examples."}
+{"idx": 8, "title": "【深度论文解读】On a Connection Between Imitation Learning and RLHF (7 Mar 2025)", "date": "", "ddg_snippet": "RLHF 的提出及其问题 近年来，基于人类反馈的强化学习（ RLHF ）已成为一种广泛采用的框架，用于根据人类偏好数据微调语言模型。 DPO解决RLHF的问题，以及其自身问题 RLHF 的依赖于两步的强化学习带来了计算效率低下和训练期间的不稳定性等问题。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/1910382777079165403", "content": "RLHF 的提出及其问题 近年来，基于人类反馈的强化学习（ RLHF ）已成为一种广泛采用的框架，用于根据人类偏好数据微调语言模型。 DPO解决RLHF的问题，以及其自身问题 RLHF 的依赖于两步的强化学习带来了计算效率低下和训练期间的不稳定性等问题。"}
+{"idx": 9, "title": "On a Connection Between Imitation Learning and RLHF", "date": "", "ddg_snippet": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=2QdsjiNXgj", "content": "This work studies the alignment of large language models with preference data from an imitation learning perspective. We establish a close theoretical connection between reinforcement learning from human feedback RLHF and imitation learning (IL), revealing that RLHF implicitly performs imitation learning on the preference data distribution."}
diff --git a/data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_episodic_reinforcement_learning_abstract.jsonl b/data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_episodic_reinforcement_learning_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0bea0fe38d3b178dd0372e2197771e260842cefd
--- /dev/null
+++ b/data/sampled_jsons/Open_the_Black_Box_step-based_policy_updates_episodic_reinforcement_learning_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2401.11437] Open the Black Box: Step-based Policy Updates ... Open the Black Box: Step-based Policy Updates for... ALR - Publications - New ICLR paper: Open the Black Box: Step ... Open the Black Box: Step-based Policy Updates for Temporally ... Open the Black Box: Step-based Policy Updates for Temporally ... Open the Black Box: Step-based Policy Updates for Temporally ... Paper page - Open the Black Box: Step-based Policy Updates ...", "date": "", "ddg_snippet": "Jan 21, 2024 · Abstract page for arXiv paper 2401.11437: Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Jan 16, 2024 · Abstract : Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ... New ICLR paper: Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Abstract :This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. Current advancements in reinforcement learning (RL) have predominantly focused on learning step - based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Jan 21, 2024 · Abstract In applying Reinforcement Learning to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step-based Reinforcement Learning are unable to produce high-order smooth trajectories. Second, existing methods struggle with effectively modeling movement correlations among different time steps and degrees of freedom, which are ... Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2401.11437", "content": "Jan 21, 2024 · Abstract page for arXiv paper 2401.11437: Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Jan 16, 2024 · Abstract : Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ... New ICLR paper: Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning Abstract :This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. Current advancements in reinforcement learning (RL) have predominantly focused on learning step - based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ... Jan 21, 2024 · Abstract In applying Reinforcement Learning to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step-based Reinforcement Learning are unable to produce high-order smooth trajectories. Second, existing methods struggle with effectively modeling movement correlations among different time steps and degrees of freedom, which are ... Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ..."}
+{"idx": 1, "title": "Paper page - Open the Black Box: Step-based Policy Updates ...", "date": "", "ddg_snippet": "Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2401.11437", "content": "Abstract Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ..."}
+{"idx": 2, "title": "Open the Black Box: Step-based Policy Updates for...", "date": "", "ddg_snippet": "Jan 16, 2024 · Abstract : Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=mnipav175N", "content": "Jan 16, 2024 · Abstract : Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are ..."}
+{"idx": 3, "title": "ALR - Publications - New ICLR paper: Open the Black Box: Step ...", "date": "", "ddg_snippet": "New ICLR paper: Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning", "subpage_snippet": "", "source": "alr.iar.kit.edu", "link": "https://alr.iar.kit.edu/603.php", "content": "New ICLR paper: Open the Black Box : Step-based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning"}
+{"idx": 4, "title": "Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "Abstract :This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step.", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2401.11437", "content": "Abstract :This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step."}
+{"idx": 5, "title": "Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step - based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2024/hash/52da50b1ef221e4b1793e3bf44dd973d-Abstract-Conference.html", "content": "Current advancements in reinforcement learning (RL) have predominantly focused on learning step - based policies that generate actions for each perceived state. While these methods efficiently leverage step information from environmental interaction, they often ignore the temporal correlation between actions, resulting in inefficient exploration and unsmooth trajectories that are challenging to ..."}
+{"idx": 6, "title": "Open the Black Box: Step-based Policy Updates for Temporally ...", "date": "", "ddg_snippet": "Jan 21, 2024 · Abstract In applying Reinforcement Learning to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step-based Reinforcement Learning are unable to produce high-order smooth trajectories. Second, existing methods struggle with effectively modeling movement correlations among different time steps and degrees of freedom, which are ...", "subpage_snippet": "", "source": "ottofabian.github.io", "link": "https://ottofabian.github.io/iclr/open_black_box/", "content": "Jan 21, 2024 · Abstract In applying Reinforcement Learning to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step-based Reinforcement Learning are unable to produce high-order smooth trajectories. Second, existing methods struggle with effectively modeling movement correlations among different time steps and degrees of freedom, which are ..."}
+{"idx": 7, "title": "Open the Black Box : Step - based Policy Updates for...", "date": "", "ddg_snippet": "Abstract . In applying Reinforcement Learning (RL) to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step - based RL are unable to produce high-order smooth trajectories.", "subpage_snippet": "", "source": "temporallycorrelatedexploration.github.io", "link": "https://temporallycorrelatedexploration.github.io/", "content": "Abstract . In applying Reinforcement Learning (RL) to robot trajectory generation, two key challenges commonly emerge. First, the stochastic exploration strategies of step - based RL are unable to produce high-order smooth trajectories."}
+{"idx": 8, "title": "Deep Black - Box Reinforcement Learning with Movement Primitives...", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning .", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/deep-black-box-reinforcement-learning-with-movement-1ov7rgyz", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning ."}
+{"idx": 9, "title": "Episode- Based Reinforcement Learning using Movement Primitives...", "date": "", "ddg_snippet": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning . In The Twelfth International Conference on Learning Representations.", "subpage_snippet": "", "source": "transferlab.ai", "link": "https://transferlab.ai/seminar/2024/episode-based-rl-with-movement-primitive/2024-02-08+-+Maximilian+Hüttenrauch+-+Episode-based+RL+with+Movement+Primitive.pdf", "content": "Open the Black Box : Step - based Policy Updates for Temporally-Correlated Episodic Reinforcement Learning . In The Twelfth International Conference on Learning Representations."}
diff --git a/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_Table_1_s_year_2024.jsonl b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_Table_1_s_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..64345190bd5bb4f15aa413363b1557bb86413152
--- /dev/null
+++ b/data/sampled_jsons/OwMatch_Conditional_Self-Labeling_with_Consistency_for_Open-World_Semi-Supervised_Learning_Table_1_s_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rethinking Semi-Supervised Learning and Pretrain- ...", "date": "", "ddg_snippet": "by SL Lv · 2025 — Table 1 : Accuracy on CIFAR-10, CIFAR-100 ... Owmatch : Conditional self - labeling with consistency for open - world semi - supervised learning .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.13317", "content": "by SL Lv · 2025 — Table 1 : Accuracy on CIFAR-10, CIFAR-100 ... Owmatch : Conditional self - labeling with consistency for open - world semi - supervised learning ."}
+{"idx": 1, "title": "OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi ...", "date": "", "ddg_snippet": "To overcome this challenge, this study revisits two methodologies from self - supervised and semi-supervised learning , self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.01833", "content": "To overcome this challenge, this study revisits two methodologies from self - supervised and semi-supervised learning , self-labeling and consistency , tailoring them to address the OwSSL problem. Specifically, we propose an effective framework called OwMatch , combining conditional self-labeling and open-world hierarchical thresholding."}
+{"idx": 2, "title": "Rethinking Open-World Semi-Supervised Learning ... - arXiv.org", "date": "", "ddg_snippet": "Abstract Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world narios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, success often relies on the assumptions that 1 ) labeled unlabeled datasets share the same balanced class prior tribution, which does not generally hold in real- world", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.20829", "content": "Abstract Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world narios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, success often relies on the assumptions that 1 ) labeled unlabeled datasets share the same balanced class prior tribution, which does not generally hold in real- world"}
+{"idx": 3, "title": "O -W S -SUPERVISED LEARNING OPEN-WO - arXiv.org", "date": "", "ddg_snippet": "ABSTRACT fundamental limitation of applying semi-supervised learning in real- world set-tings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at testing time. Here, we introduce a novel open-world semi-supervised ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2102.03526", "content": "ABSTRACT fundamental limitation of applying semi-supervised learning in real- world set-tings is the assumption that unlabeled test data contains only classes previously encountered in the labeled training data. However, this assumption rarely holds for data in-the-wild, where instances belonging to novel classes may appear at testing time. Here, we introduce a novel open-world semi-supervised ..."}
+{"idx": 4, "title": "Unlabeled Data or Pre-trained Model: Rethinking Semi-Supervised ...", "date": "", "ddg_snippet": "Owmatch : Conditional self-labeling with consistency for open-world semi-supervised learning . Advances in Neural Information Processing Systems 37, pages 99836-99866, 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.13317v1", "content": "Owmatch : Conditional self-labeling with consistency for open-world semi-supervised learning . Advances in Neural Information Processing Systems 37, pages 99836-99866, 2024."}
+{"idx": 5, "title": "arXiv:2409.17512v1 [cs.CV] 26 Sep 2024", "date": "", "ddg_snippet": "Open -set semi-supervised learning (OSSL) leverages practi-cal open -set unlabeled data, comprising both in-distribution (ID) sam-ples from seen classes and out-of-distribution (OOD) samples from un-seen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with la-beled ID data, subsequently employing self -training to refine ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2409.17512", "content": "Open -set semi-supervised learning (OSSL) leverages practi-cal open -set unlabeled data, comprising both in-distribution (ID) sam-ples from seen classes and out-of-distribution (OOD) samples from un-seen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with la-beled ID data, subsequently employing self -training to refine ..."}
+{"idx": 6, "title": "Robust Semi-Supervised Learning for Self-learning Open-World Classes", "date": "", "ddg_snippet": "Index Terms—open- world semi-supervised learning , self - learning I. INTRODUCTION With the development of machine learning , deep learning has achieved significant accomplishments in various domains such as vision, text, and speech [ 1 ], [2], [3], [4].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2401.07551", "content": "Index Terms—open- world semi-supervised learning , self - learning I. INTRODUCTION With the development of machine learning , deep learning has achieved significant accomplishments in various domains such as vision, text, and speech [ 1 ], [2], [3], [4]."}
+{"idx": 7, "title": "PDF On The Consistency Training for Open-Set Semi-Supervised Learning", "date": "", "ddg_snippet": "This setting is firstly introduced by [49], and is named as \" Open -Set Semi-Supervised Learn- Figure 1 : In open -set semi-supervised learning , unlabeled dataset contains OOD samples that do not belong to any labeled classes. ing\" ( open -set SSL, illustrated in Figure 1 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2101.08237v1.pdf", "content": "This setting is firstly introduced by [49], and is named as \" Open -Set Semi-Supervised Learn- Figure 1 : In open -set semi-supervised learning , unlabeled dataset contains OOD samples that do not belong to any labeled classes. ing\" ( open -set SSL, illustrated in Figure 1 )."}
+{"idx": 8, "title": "PDF Abstract - arXiv.org", "date": "", "ddg_snippet": "Abstract Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. To ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2105.14148.pdf", "content": "Abstract Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. To ..."}
+{"idx": 9, "title": "EXPLOR: Extrapolatory Pseudo-Label Matching for Out-of-distribution ...", "date": "", "ddg_snippet": "Self -Training Self -training uses an earlier model to pseudo-label unlabeled data, which is then added to the training set for subsequent model updates.The concept of pseudo- labeling was initially proposed by (Lee 2013), sug-gesting a direct approach to retaining instances where the model has high prediction probabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.01825v4", "content": "Self -Training Self -training uses an earlier model to pseudo-label unlabeled data, which is then added to the training set for subsequent model updates.The concept of pseudo- labeling was initially proposed by (Lee 2013), sug-gesting a direct approach to retaining instances where the model has high prediction probabilities."}
diff --git a/data/sampled_jsons/PAW_index_formula_broadband_affordability_target.jsonl b/data/sampled_jsons/PAW_index_formula_broadband_affordability_target.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3c870c58eb8fad553cbd6f511b9fe64083767cdf
--- /dev/null
+++ b/data/sampled_jsons/PAW_index_formula_broadband_affordability_target.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Framework for Improving Web Affordability and ...", "date": "", "ddg_snippet": "by R Habib · 2023 · Cited by 10 — PAW index is a comparative metric that captures this unfairness regardless of whether caching has been enabled. Third, within developing countries where an ... 16 pages", "subpage_snippet": "", "source": "www.ietf.org", "link": "https://www.ietf.org/slides/slides-biasws-a-framework-for-improving-web-affordability-and-inclusiveness-00.pdf", "content": "by R Habib · 2023 · Cited by 10 — PAW index is a comparative metric that captures this unfairness regardless of whether caching has been enabled. Third, within developing countries where an ... 16 pages"}
+{"idx": 1, "title": "Intermolecular arrangement facilitated broadband blue ...", "date": "", "ddg_snippet": "by DA Popy · 2023 · Cited by 26 — Ultimately, the incorporation of affordable and earth-abundant group 12 metals into hybrid halides may pave the way for their practical use in optical.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/am/pii/S2468519423001295", "content": "by DA Popy · 2023 · Cited by 26 — Ultimately, the incorporation of affordable and earth-abundant group 12 metals into hybrid halides may pave the way for their practical use in optical."}
+{"idx": 2, "title": "A Comparative Web Measurement Study Across Economic ...", "date": "", "ddg_snippet": "by MHM Bhuiyan · Cited by 1 — A PAW index of 1 or less indicates affordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income,.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=IMhoJgWANP", "content": "by MHM Bhuiyan · Cited by 1 — A PAW index of 1 or less indicates affordable access, while values above 1 suggest that webpage sizes or broadband costs are too high relative to income,."}
+{"idx": 3, "title": "A Comparative Web Measurement Study Across Economic ...", "date": "", "ddg_snippet": "region by comparing the average broadband prices, local webpage sizes, and income levels to international affordability targets. The formula for the PAW Index ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/pdf/10.1145/3696410.3714647?download=true", "content": "region by comparing the average broadband prices, local webpage sizes, and income levels to international affordability targets. The formula for the PAW Index ..."}
+{"idx": 4, "title": "GREEN, SMART AND INCLUSIVE CITIES AND BUILDINGS", "date": "", "ddg_snippet": "8 Mar 2024 — Finance urban infrastructure and facilitate housing affordability for all ages. • Apply 'smart shrinking' strategies. • Reap the benefits of ...", "subpage_snippet": "", "source": "www.oecd.org", "link": "https://www.oecd.org/content/dam/oecd/en/about/programmes/cfe/decarbonising-buildings-in-cities-and-regions/Slides-seminar-bangkok.pdf", "content": "8 Mar 2024 — Finance urban infrastructure and facilitate housing affordability for all ages. • Apply 'smart shrinking' strategies. • Reap the benefits of ..."}
+{"idx": 5, "title": "Asian Development Bank (ADB)", "date": "", "ddg_snippet": "The 2025-30 CRF introduces a range of organisational targets that create incentives for staff to prioritise these areas, including a new target that 50% of ... 120 pages", "subpage_snippet": "", "source": "www.mopan.org", "link": "https://www.mopan.org/content/dam/mopan/en/publications/our-work/evidence/adb/adb-2025/mopan-adb-assessment-report-2025.pdf", "content": "The 2025-30 CRF introduces a range of organisational targets that create incentives for staff to prioritise these areas, including a new target that 50% of ... 120 pages"}
+{"idx": 6, "title": "DoD Producibility and Manufacturability Engineering Guide", "date": "", "ddg_snippet": "index is calculated using a Producibility Assessment Worksheet ( PAW ). It is predicated on subjective data, or information based on the evaluator's ... 174 pages", "subpage_snippet": "", "source": "www.cto.mil", "link": "https://www.cto.mil/wp-content/uploads/2024/06/DoD-Producibility-and-Manufacturability-2024.pdf", "content": "index is calculated using a Producibility Assessment Worksheet ( PAW ). It is predicated on subjective data, or information based on the evaluator's ... 174 pages"}
+{"idx": 7, "title": "Data-Driven Innovation (EN)", "date": "", "ddg_snippet": "28 Nov 2013 — We will need, for example, to recast how we think about infrastructure in the 21st Century, and expand it to encompass broadband networks, cloud ... 456 pages", "subpage_snippet": "", "source": "www.oecd.org", "link": "https://www.oecd.org/content/dam/oecd/en/publications/reports/2015/10/data-driven-innovation_g1g503d8/9789264229358-en.pdf", "content": "28 Nov 2013 — We will need, for example, to recast how we think about infrastructure in the 21st Century, and expand it to encompass broadband networks, cloud ... 456 pages"}
+{"idx": 8, "title": "Ensuring Operational Privacy of Primary Users in ...", "date": "", "ddg_snippet": "30 Jun 2013 — When incumbent users (a.k.a. primary users (PUs)) and secondary users (SUs) share spec- trum, the SUs must adopt technologies that enable ...", "subpage_snippet": "", "source": "www.ntia.gov", "link": "https://www.ntia.gov/files/ntia/publications/ssd-summary_report-tr1_1_link_5.pdf", "content": "30 Jun 2013 — When incumbent users (a.k.a. primary users (PUs)) and secondary users (SUs) share spec- trum, the SUs must adopt technologies that enable ..."}
+{"idx": 9, "title": "Range-separated hybrid functionals in full-potential LAPW ...", "date": "", "ddg_snippet": "by J Užulis · 2025 — To make these calculations more affordable , we revise the most expensive steps in the pseudocharge method and reduce their computational ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41524-025-01733-z", "content": "by J Užulis · 2025 — To make these calculations more affordable , we revise the most expensive steps in the pseudocharge method and reduce their computational ..."}
diff --git a/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_for_solving_forward_and_inverse_problems_.jsonl b/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_for_solving_forward_and_inverse_problems_.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc326ed0de842f6bdf0c2e57c57bc177345b4f53
--- /dev/null
+++ b/data/sampled_jsons/Physics-informed_neural_networks_A_deep_learning_framework_for_solving_forward_and_inverse_problems_.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Physics - informed neural networks : A deep learning framework for ...", "date": "", "ddg_snippet": "Abstract . We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2019JCoPh.378..686R/abstract", "content": "Abstract . We introduce physics - informed neural networks - neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations ."}
+{"idx": 1, "title": "Physics - Informed Neural Networks : A Deep Learning Framework ...", "date": "", "ddg_snippet": "Abstract . We introduce physics - informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/328720075_Physics-Informed_Neural_Networks_A_Deep_Learning_Framework_for_Solving_Forward_and_Inverse_Problems_Involving_Nonlinear_Partial_Differential_Equations", "content": "Abstract . We introduce physics - informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations ."}
+{"idx": 2, "title": "Physics - informed neural networks : A deep learning framework for ...", "date": "", "ddg_snippet": "We introduce physics - informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations .", "subpage_snippet": "", "source": "colab.ws", "link": "https://colab.ws/articles/10.1016/j.jcp.2018.10.045", "content": "We introduce physics - informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations ."}
+{"idx": 3, "title": "Physics - informed neural networks : A deep learning framework for ...", "date": "", "ddg_snippet": "Authors: Maziar Raissi , Paris Perdikaris, George Em Karniadakis. Institutions: Brown University, University of Pennsylvania. Cites: 56.", "subpage_snippet": "", "source": "explore.openalex.org", "link": "https://explore.openalex.org/works/w2899283552", "content": "Authors: Maziar Raissi , Paris Perdikaris, George Em Karniadakis. Institutions: Brown University, University of Pennsylvania. Cites: 56."}
+{"idx": 4, "title": "Raissi , M., Perdikaris, P., Karniadakis, G.E. ( 2019 ) Physics - informed ...", "date": "", "ddg_snippet": "Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations .Authors. Raissi , M. Author. Perdikaris, P.", "subpage_snippet": "", "source": "www.mindat.org", "link": "https://www.mindat.org/reference.php?id=15892303", "content": "Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations .Authors. Raissi , M. Author. Perdikaris, P."}
+{"idx": 5, "title": "Physics - informed neural networks : A deep learning framework for ...", "date": "", "ddg_snippet": "Supporting: 24, Contrasting: 4, Mentioning: 6498 - Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations - Raissi , Maziar, Perdikaris, Paris, Karniadakis, George Em.", "subpage_snippet": "", "source": "scite.ai", "link": "https://scite.ai/reports/physics-informed-neural-networks-a-deep-D13lp3z", "content": "Supporting: 24, Contrasting: 4, Mentioning: 6498 - Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations - Raissi , Maziar, Perdikaris, Paris, Karniadakis, George Em."}
+{"idx": 6, "title": "PinnDE: Physics - Informed Neural Networks for Solving Differential ...", "date": "", "ddg_snippet": "Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.10011v1", "content": "Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations ."}
+{"idx": 7, "title": "Paper Review: Physics - Informed Neural Networks", "date": "", "ddg_snippet": "Physics - informed neural networks (referred to as PINN) are artificial neural networks designed to numerically solve differential equations , introduced in the 2018 Journal of Computational Physics paper Physics - informed neural networks ...", "subpage_snippet": "", "source": "freshrimpsushi.github.io", "link": "https://freshrimpsushi.github.io/en/posts/3313/", "content": "Physics - informed neural networks (referred to as PINN) are artificial neural networks designed to numerically solve differential equations , introduced in the 2018 Journal of Computational Physics paper Physics - informed neural networks ..."}
+{"idx": 8, "title": "GitHub - 56aaaaa/ Physics - informed - neural - networks ...", "date": "", "ddg_snippet": "\" Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations .\" Journal of Computational Physics 378 ( 2019 ): 686-707.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/56aaaaa/Physics-informed-neural-networks", "content": "\" Physics - informed neural networks : A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations .\" Journal of Computational Physics 378 ( 2019 ): 686-707."}
+{"idx": 9, "title": "Physics - Informed Neural Networks : A Deep Learning Framework ...", "date": "", "ddg_snippet": "Raissi , M.; Perdikaris, P.; Karniadakis, G.E. . (2018). Physics - Informed Neural Networks : A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations .", "subpage_snippet": "", "source": "pismin.com", "link": "https://pismin.com/10.1016/j.jcp.2018.10.045", "content": "Raissi , M.; Perdikaris, P.; Karniadakis, G.E. . (2018). Physics - Informed Neural Networks : A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations ."}
diff --git a/data/sampled_jsons/ProDet_DeepfakeBench_implementation_backbone_model.jsonl b/data/sampled_jsons/ProDet_DeepfakeBench_implementation_backbone_model.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e6d679ea9a1e1ce2b0c979f490ae77cc8789b928
--- /dev/null
+++ b/data/sampled_jsons/ProDet_DeepfakeBench_implementation_backbone_model.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "backbone .js - Defining Multiple Comparators for... - Stack Overflow", "date": "", "ddg_snippet": "When implementing the data model using the Backbone model implementation , and I use the comparator to sort the data model in an ascending fashion.Extend, override or implement the Backbone . Model methods })", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/17261966/defining-multiple-comparators-for-sorting-and-reverse-sorting-appcelerator-backbone-models/17282039", "content": "When implementing the data model using the Backbone model implementation , and I use the comparator to sort the data model in an ascending fashion.Extend, override or implement the Backbone . Model methods })"}
+{"idx": 1, "title": "Backbone , Marionette — simple form validation with Backbone Model ...", "date": "", "ddg_snippet": "Backbone model provides validate method, which looks weird at first glance (especially that it does not have default implementation and is undefined if you don’t override it). Backbone model events in Marionette view.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/get-noticed-2017-radek-anuszewski/backbone-marionette-simple-form-validation-with-backbone-model-marionettes-regions-and-child-577f21dfcda1", "content": "Backbone model provides validate method, which looks weird at first glance (especially that it does not have default implementation and is undefined if you don’t override it). Backbone model events in Marionette view."}
+{"idx": 2, "title": "Most basic backbone model implementation in CoffeeScript · GitHub", "date": "", "ddg_snippet": "martsie/ backbone - model .coffee. Created January 25, 2017 08:34.Save martsie/49a1f69ee76639fbae1ed982a6c11847 to your computer and use it in GitHub Desktop. Download ZIP. Most basic backbone model implementation in CoffeeScript.", "subpage_snippet": "", "source": "gist.github.com", "link": "https://gist.github.com/martsie/49a1f69ee76639fbae1ed982a6c11847", "content": "martsie/ backbone - model .coffee. Created January 25, 2017 08:34.Save martsie/49a1f69ee76639fbae1ed982a6c11847 to your computer and use it in GitHub Desktop. Download ZIP. Most basic backbone model implementation in CoffeeScript."}
+{"idx": 3, "title": "javascript - Unit testing Backbone Model - Code Review Stack...", "date": "", "ddg_snippet": "var stub = sinon.stub( Backbone , \"sync\", function(method, model ) { expect( model .get(\"completed\")).to.equal(true); }); var todo = new app.Todo({completed: false}); In that case, to avoid mistakes, it would be better to include that logic in the code, for example", "subpage_snippet": "", "source": "codereview.stackexchange.com", "link": "https://codereview.stackexchange.com/questions/61629/unit-testing-backbone-model", "content": "var stub = sinon.stub( Backbone , \"sync\", function(method, model ) { expect( model .get(\"completed\")).to.equal(true); }); var todo = new app.Todo({completed: false}); In that case, to avoid mistakes, it would be better to include that logic in the code, for example"}
+{"idx": 4, "title": "backbone .com", "date": "", "ddg_snippet": "Backbone | Phone Controller - Cloud Gaming & Remote Play …", "subpage_snippet": "", "source": "backbone.com", "link": "https://backbone.com/", "content": "Backbone | Phone Controller - Cloud Gaming & Remote Play …"}
+{"idx": 5, "title": "Can We Leave Deepfake Data Behind in Training", "date": "", "ddg_snippet": "Deepfake . Backbone . Oriented Progressive Regularizor.Algorithm 1: Training ProDet . Input: Dataset: S; Training epoch: E. Initialize θ with the pre-trained backbone ; for e = 1 to E do.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=vh9yEPLeyD", "content": "Deepfake . Backbone . Oriented Progressive Regularizor.Algorithm 1: Training ProDet . Input: Dataset: S; Training epoch: E. Initialize θ with the pre-trained backbone ; for e = 1 to E do."}
+{"idx": 6, "title": "HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning...", "date": "", "ddg_snippet": "Following (Jiang et al., 2020) , we evaluate HAMLET-FFD’s robustness to various perturbations compared to state-of-the-art methods ( ProDet , CLIPping, and RepDFD) in video-level AUC. We test performance against six types of perturbations across five severity levels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.20913v1", "content": "Following (Jiang et al., 2020) , we evaluate HAMLET-FFD’s robustness to various perturbations compared to state-of-the-art methods ( ProDet , CLIPping, and RepDFD) in video-level AUC. We test performance against six types of perturbations across five severity levels."}
+{"idx": 7, "title": "(PDF) VCF: A Real-World Video Conference Deepfake Benchmark for...", "date": "", "ddg_snippet": "4. Deepfake Detection Evaluation. We follo w the pre-processing, training pipeline, available. model weights and use the codebases of DeepfakeBench (Y an. et al., 2023b).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395275081_VCF_A_Real-World_Video_Conference_Deepfake_Benchmark_for_Face-Swap_Detection_and_Robustness_Evaluation", "content": "4. Deepfake Detection Evaluation. We follo w the pre-processing, training pipeline, available. model weights and use the codebases of DeepfakeBench (Y an. et al., 2023b)."}
+{"idx": 8, "title": "15 Types of Teaching Methods That Transform Modern Classrooms...", "date": "", "ddg_snippet": "Implementation Strategy: Begin lessons with a brief review of prerequisite knowledge. Present new information in small, manageable chunks while modeling correct procedures.", "subpage_snippet": "", "source": "vietnamteachingjobs.com", "link": "https://vietnamteachingjobs.com/blog/types-of-teaching-methods/", "content": "Implementation Strategy: Begin lessons with a brief review of prerequisite knowledge. Present new information in small, manageable chunks while modeling correct procedures."}
+{"idx": 9, "title": "Google Gemini Photo Editing Prompts Guide 2025: 50+ AI Prompts...", "date": "", "ddg_snippet": "Implementing Gemini photo editing in production applications requires careful consideration of authentication, error handling, rate limiting, and cost optimization. The following implementation guide provides battle-tested patterns for reliable integration.", "subpage_snippet": "", "source": "blog.laozhang.ai", "link": "https://blog.laozhang.ai/ai-tools/gemini-photo-editing-prompts/", "content": "Implementing Gemini photo editing in production applications requires careful consideration of authentication, error handling, rate limiting, and cost optimization. The following implementation guide provides battle-tested patterns for reliable integration."}
diff --git a/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_critical_windows_ARC_Easy_accuracy_drop_theory.jsonl b/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_critical_windows_ARC_Easy_accuracy_drop_theory.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..64b621767b24cedf34550aa1a9514543410e3c8b
--- /dev/null
+++ b/data/sampled_jsons/QvqnPVGWAN_Blink_of_an_eye_critical_windows_ARC_Easy_accuracy_drop_theory.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Why Your Eye Hurts When You Blink : 10 Possible Causes", "date": "", "ddg_snippet": "When your eye hurts as you blink , it’s hard to tell if it’s just a small irritation or something that needs attention. In many cases, it comes from dryness or strain that improves with simple care. But in some situations, it signals an infection or condition that needs medical attention.", "subpage_snippet": "", "source": "docus.ai", "link": "https://docus.ai/symptoms-guide/eye-hurt-when-blink", "content": "When your eye hurts as you blink , it’s hard to tell if it’s just a small irritation or something that needs attention. In many cases, it comes from dryness or strain that improves with simple care. But in some situations, it signals an infection or condition that needs medical attention."}
+{"idx": 1, "title": "in a blink of an eye i was there #dbdshorts - YouTube", "date": "", "ddg_snippet": "Смотрите любимые видео, слушайте любимые песни, загружайте собственные ролики и делитесь ими с друзьями, близкими и целым миром.", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/shorts/hYOawBhm7Lo", "content": "Смотрите любимые видео, слушайте любимые песни, загружайте собственные ролики и делитесь ими с друзьями, близкими и целым миром."}
+{"idx": 2, "title": "Microsoft Azure", "date": "", "ddg_snippet": "Configure Windows Arc -enabled machines to run Azure Monitor Agent.", "subpage_snippet": "", "source": "ms.portal.azure.com", "link": "https://ms.portal.azure.com/", "content": "Configure Windows Arc -enabled machines to run Azure Monitor Agent."}
+{"idx": 3, "title": "How To Build Tracer In Stadium Mode In Overwatch 2", "date": "", "ddg_snippet": "Blink Of An Eye . Critical hits reduce Blink cooldown, chaining mobility with accuracy . Quantum Clip. Blink while reloading to gain extra ammo and lifesteal.", "subpage_snippet": "", "source": "www.thegamer.com", "link": "https://www.thegamer.com/overwatch-2-ow2-tracer-stadium-build-strategy-powers-guide/", "content": "Blink Of An Eye . Critical hits reduce Blink cooldown, chaining mobility with accuracy . Quantum Clip. Blink while reloading to gain extra ammo and lifesteal."}
+{"idx": 4, "title": "Historical Consciousness: What Germany Could Learn from Russia", "date": "", "ddg_snippet": "Within the blink of an eye , however, Russia became the enemy again after the war. General Patton proposed attacking the USSR, and shortly thereafter the hunt for communists began in the US, with anyone they wanted to destroy simply being labeled a “communist.”", "subpage_snippet": "", "source": "sonar21.com", "link": "https://sonar21.com/historical-consciousness-what-germany-could-learn-from-russia/", "content": "Within the blink of an eye , however, Russia became the enemy again after the war. General Patton proposed attacking the USSR, and shortly thereafter the hunt for communists began in the US, with anyone they wanted to destroy simply being labeled a “communist.”"}
+{"idx": 5, "title": "How Transformer Turns-Ratio Meters Revolutionize Electrical Testing...", "date": "", "ddg_snippet": "Critical Importance of Turns Ratio.Being compact and easy to operate, it can be taken to the field for practical use or to the comforts of a laboratory, thereby allowing both the technicians and engineers to use it.", "subpage_snippet": "", "source": "demikspower.com", "link": "https://demikspower.com/ru/blog/how-transformer-turns-ratio-meters-revolutionize-electrical-testing-in-2025/", "content": "Critical Importance of Turns Ratio.Being compact and easy to operate, it can be taken to the field for practical use or to the comforts of a laboratory, thereby allowing both the technicians and engineers to use it."}
+{"idx": 6, "title": "Security Update Guide - Microsoft Security Response Center", "date": "", "ddg_snippet": "None of the fixes are for critical bugs, and three are rereleased patches.", "subpage_snippet": "", "source": "msrc.microsoft.com", "link": "https://msrc.microsoft.com/update-guide/", "content": "None of the fixes are for critical bugs, and three are rereleased patches."}
+{"idx": 7, "title": "Shape of Dreams Meta Shell Complete Build & Strategy... | GAM3S.GG", "date": "", "ddg_snippet": "shape of dreams shell.webp. The key insight many players miss: Shell isn't just about raw damage output. His kit rewards aggressive positioning and precise timing, allowing skilled players to chain together abilities that keep him constantly mobile and dealing critical strikes.", "subpage_snippet": "", "source": "gam3s.gg", "link": "https://gam3s.gg/shape-of-dreams/guides/shape-of-dreams-meta-shell-build/", "content": "shape of dreams shell.webp. The key insight many players miss: Shell isn't just about raw damage output. His kit rewards aggressive positioning and precise timing, allowing skilled players to chain together abilities that keep him constantly mobile and dealing critical strikes."}
+{"idx": 8, "title": "Попробуйте ChatGPT-4o бесплатно онлайн - на основе API...", "date": "", "ddg_snippet": "3. Версия для настольных ПК Windows ожидается в ноябре 2024 года, хотя официальная дата еще не объявлена. 4. Некоторые предполагают, что OpenAI может уделять приоритетное внимание совместимости с ARM-чипами перед выпуском для Windows .", "subpage_snippet": "", "source": "gpt4v.net", "link": "https://gpt4v.net/ru", "content": "3. Версия для настольных ПК Windows ожидается в ноябре 2024 года, хотя официальная дата еще не объявлена. 4. Некоторые предполагают, что OpenAI может уделять приоритетное внимание совместимости с ARM-чипами перед выпуском для Windows ."}
+{"idx": 9, "title": "Convert JPG to Word online for free - ILovePDF", "date": "", "ddg_snippet": "Convert JPG to editable Word documents for free. PDF to Word conversion is fast, secure and almost 100% accurate .", "subpage_snippet": "", "source": "ilovepdf4.com", "link": "https://ilovepdf4.com/jpg-to-word/", "content": "Convert JPG to editable Word documents for free. PDF to Word conversion is fast, secure and almost 100% accurate ."}
diff --git a/data/sampled_jsons/RA-PbRL_paper_challenges_adapting_risk-aware_measures_PbRL_siteopenreview.net_OR_sitearxiv.org_year_2024.jsonl b/data/sampled_jsons/RA-PbRL_paper_challenges_adapting_risk-aware_measures_PbRL_siteopenreview.net_OR_sitearxiv.org_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2e732fef104683f3dcb18079cf779daa8dbe48fc
--- /dev/null
+++ b/data/sampled_jsons/RA-PbRL_paper_challenges_adapting_risk-aware_measures_PbRL_siteopenreview.net_OR_sitearxiv.org_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RA-PbRL: Provably Efficient Risk-Aware Preference-Based...", "date": "", "ddg_snippet": "This paper studies preference-based RL ( PbRL ) where instead of the expected return, the agent optimizes a risk measure based on preference feedback.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JNDcFOczOf&referrer=[the+profile+of+Huazheng+Wang](/profile?id=~Huazheng_Wang1)", "content": "This paper studies preference-based RL ( PbRL ) where instead of the expected return, the agent optimizes a risk measure based on preference feedback."}
+{"idx": 1, "title": "Uncertainty-aware Preference Alignment in Reinforcement ...", "date": "", "ddg_snippet": "In this work, we introduce the uncertainty- aware prefer- ence alignment in RLHF by learning a distribu- tional reward model and a risk -sensitive policy from the ... 18 pages", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/a13dc3982367a9d47940844ef0b463b27f242916.pdf", "content": "In this work, we introduce the uncertainty- aware prefer- ence alignment in RLHF by learning a distribu- tional reward model and a risk -sensitive policy from the ... 18 pages"}
+{"idx": 2, "title": "arXiv:2412.10713v1 [cs.LG] 14 Dec 2024", "date": "", "ddg_snippet": "by F Bai · 2024 · Cited by 9 — In this paper , we present a novel adversarial attack method,. RAT, which focuses on AdveRsarial Attacks against deep reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2412.10713?", "content": "by F Bai · 2024 · Cited by 9 — In this paper , we present a novel adversarial attack method,. RAT, which focuses on AdveRsarial Attacks against deep reinforcement learning ..."}
+{"idx": 3, "title": "Beyond Reward: Offline Preference-guided Policy Optimization", "date": "", "ddg_snippet": "This study focuses on the topic of offline preference-based reinforcement learning ( PbRL ), a variant of conventional reinforcement learning .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/6c384070b3ec1893c4e714ad5c9aaff5c6b744c4.pdf", "content": "This study focuses on the topic of offline preference-based reinforcement learning ( PbRL ), a variant of conventional reinforcement learning ."}
+{"idx": 4, "title": "Preference-based Multi-Objective Reinforcement Learning", "date": "", "ddg_snippet": "by N Mu · 2025 — Preference-based reinforcement learning ( PbRL ) provides a solution by utilizing user feedback to guide agent behavior, making it suitable.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.14066", "content": "by N Mu · 2025 — Preference-based reinforcement learning ( PbRL ) provides a solution by utilizing user feedback to guide agent behavior, making it suitable."}
+{"idx": 5, "title": "Safe and Robust Reinforcement Learning", "date": "", "ddg_snippet": "by T Yamagata · 2024 · Cited by 10 — This paper aims to iden- tify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.18539", "content": "by T Yamagata · 2024 · Cited by 10 — This paper aims to iden- tify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL ..."}
+{"idx": 6, "title": "Online Policy Learning from Offline Preferences", "date": "", "ddg_snippet": "by G Zhang · 2024 — In preference-based reinforcement learning ( PbRL ), a reward function is learned from a type of human feedback called preference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.10160", "content": "by G Zhang · 2024 — In preference-based reinforcement learning ( PbRL ), a reward function is learned from a type of human feedback called preference."}
+{"idx": 7, "title": "CAIN: Hijacking LLM-Humans Conversations via Malicious ...", "date": "", "ddg_snippet": "6 Aug 2025 — RA - PbRL : Provably Efficient Risk - Aware Preference-Based Reinforcement Learning . ArXiv, abs/2410.23569. Zhu et al. (2024) Zhu, S.; Zhang, R ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16888v2", "content": "6 Aug 2025 — RA - PbRL : Provably Efficient Risk - Aware Preference-Based Reinforcement Learning . ArXiv, abs/2410.23569. Zhu et al. (2024) Zhu, S.; Zhang, R ..."}
+{"idx": 8, "title": "MICRO: Model-Based Offline Reinforcement Learning with ...", "date": "", "ddg_snippet": "In this paper , the theoretical and experimental results show that MICRO has the guarantee of a robust policy improvement and outperforms current state-of-the- ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=yys48bjt2c", "content": "In this paper , the theoretical and experimental results show that MICRO has the guarantee of a robust policy improvement and outperforms current state-of-the- ..."}
+{"idx": 9, "title": "Demonstration-regularized RL", "date": "", "ddg_snippet": "by D Tiapkin · 2023 · Cited by 6 — In this study, we aim to quantify how prior demonstrations from experts influence the sample complexity of various. RL tasks , specifically two ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.17303", "content": "by D Tiapkin · 2023 · Cited by 6 — In this study, we aim to quantify how prior demonstrations from experts influence the sample complexity of various. RL tasks , specifically two ..."}
diff --git a/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_sitearxiv.org_Section_5_Figure_3_year_2024.jsonl b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_sitearxiv.org_Section_5_Figure_3_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0e364cfe9b78dfb5c32962b46dbe5387a53c1e70
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_Towards_Informed_Design_of_Scalable_and_Stable_RAG_Systems_sitearxiv.org_Section_5_Figure_3_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards Informed Design of Scalable and Stable RAG ...", "date": "", "ddg_snippet": "We introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v3", "content": "We introduce RAGGED , a framework for systematically evaluating RAG systems across diverse retriever-reader configurations, retrieval depths, and datasets."}
+{"idx": 1, "title": "RAGGED: Towards Informed Design of Retrieval ...", "date": "", "ddg_snippet": "14 Mar 2024 — We introduce a reusable framework that can easily be adapted to analyze new RAG components, such as retriever and reader models, as they evolve.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "14 Mar 2024 — We introduce a reusable framework that can easily be adapted to analyze new RAG components, such as retriever and reader models, as they evolve."}
+{"idx": 2, "title": "arXiv:2501.07391v1 [cs.CL] 13 Jan 2025", "date": "", "ddg_snippet": "by S Li · 2025 · Cited by 28 — In this pa- per, we develop several advanced RAG system designs that incorporate query expansion, vari- ous novel retrieval strategies, and a ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/pdf/2501.07391", "content": "by S Li · 2025 · Cited by 28 — In this pa- per, we develop several advanced RAG system designs that incorporate query expansion, vari- ous novel retrieval strategies, and a ..."}
+{"idx": 3, "title": "Towards Trustworthy Retrieval Augmented Generation for ...", "date": "", "ddg_snippet": "8 Feb 2025 — Retrieval-Augmented Generation ( RAG ) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.06872v1", "content": "8 Feb 2025 — Retrieval-Augmented Generation ( RAG ) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)."}
+{"idx": 4, "title": "RAG-Enhanced Event Knowledge Base Construction and ...", "date": "", "ddg_snippet": "15 Jun 2025 — Figure 3 : RDF/OWL capabilities versus historical reasoning requirements. ... These findings suggest that RAG system design should prioritize ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.07042v2", "content": "15 Jun 2025 — Figure 3 : RDF/OWL capabilities versus historical reasoning requirements. ... These findings suggest that RAG system design should prioritize ..."}
+{"idx": 5, "title": "SemRAG: Semantic Knowledge-Augmented RAG for ...", "date": "", "ddg_snippet": "by K Zhong · 2025 — ABSTRACT. This paper introduces SemRAG , an enhanced Retrieval Augmented Generation ( RAG ) framework that efficiently integrates domain-specific knowledge ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2507.21110", "content": "by K Zhong · 2025 — ABSTRACT. This paper introduces SemRAG , an enhanced Retrieval Augmented Generation ( RAG ) framework that efficiently integrates domain-specific knowledge ..."}
+{"idx": 6, "title": "Enhancing Retrieval-Augmented Generation: A Study of ...", "date": "", "ddg_snippet": "13 Jan 2025 — In this paper, we develop several advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07391v1", "content": "13 Jan 2025 — In this paper, we develop several advanced RAG system designs that incorporate query expansion, various novel retrieval strategies, and a novel ..."}
+{"idx": 7, "title": "Magic Mushroom: A Customizable Benchmark for Fine ...", "date": "", "ddg_snippet": "5 Jun 2025 — Figure 3 illustrates the impact of introducing individual types of retrieval noise on RAG system performance. It is evident that distracting ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03901v2", "content": "5 Jun 2025 — Figure 3 illustrates the impact of introducing individual types of retrieval noise on RAG system performance. It is evident that distracting ..."}
+{"idx": 8, "title": "Computer Science Mar 2024", "date": "", "ddg_snippet": "Title: RAGGED : Towards Informed Design of Scalable and Stable RAG Systems ... Comments: 29 pages, 5 figures , submitted to Knowledge-Baed Systems . Subjects ...", "subpage_snippet": "", "source": "arxiv.org", "link": "http://arxiv.org/list/cs/2024-03?skip=4010&show=2000", "content": "Title: RAGGED : Towards Informed Design of Scalable and Stable RAG Systems ... Comments: 29 pages, 5 figures , submitted to Knowledge-Baed Systems . Subjects ..."}
+{"idx": 9, "title": "A Survey of Context Engineering for Large Language Models", "date": "", "ddg_snippet": "These systems include Advanced Retrieval-Augmented Generation ( RAG ), which has evolved into modular and agentic architectures for dynamic knowledge injection [ ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.13334v2", "content": "These systems include Advanced Retrieval-Augmented Generation ( RAG ), which has evolved into modular and agentic architectures for dynamic knowledge injection [ ..."}
diff --git a/data/sampled_jsons/RAGGED_paper_Section_3.1_retriever_paradigms_year_2024.jsonl b/data/sampled_jsons/RAGGED_paper_Section_3.1_retriever_paradigms_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6ee40f162188edbde2addd3dccb3879ccdbd8f96
--- /dev/null
+++ b/data/sampled_jsons/RAGGED_paper_Section_3.1_retriever_paradigms_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RAGGED: Towards Informed Design of Scalable and Stable RAG Systems", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation (RAG) enhances language models by integrating external knowl-edge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reli-able than closed-book generation. In this work, we introduce RAGGED , a framework for system-atically evaluating RAG systems across diverse retriever ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040", "content": "Abstract Retrieval-augmented generation (RAG) enhances language models by integrating external knowl-edge, but its effectiveness is highly dependent on system configuration. Improper retrieval settings can degrade performance, making RAG less reli-able than closed-book generation. In this work, we introduce RAGGED , a framework for system-atically evaluating RAG systems across diverse retriever ..."}
+{"idx": 1, "title": "Ragged: Towards Informed Design of Retrieval Augmented Generation ...", "date": "", "ddg_snippet": "3 EXPERIMENTAL SETUP 117 118 In this section , we describe the experimental setup, including the retriever and reader models, datasets, 119 and evaluation metrics used to assess the performance of different RAG configurations. 120 122 121 3.1 RETRIEVER 123 We experiment with: (1) a sparse, lexical retriever and (2) a dense, semantic retriever .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KDXj60FpJr", "content": "3 EXPERIMENTAL SETUP 117 118 In this section , we describe the experimental setup, including the retriever and reader models, datasets, 119 and evaluation metrics used to assess the performance of different RAG configurations. 120 122 121 3.1 RETRIEVER 123 We experiment with: (1) a sparse, lexical retriever and (2) a dense, semantic retriever ."}
+{"idx": 2, "title": "RAGGED: Towards Informed Design of - arXiv.org", "date": "", "ddg_snippet": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems. On a set of representative DBQA tasks, we study two classic sparse and dense retrievers , and four top-performing LMs in encoder-decoder and decoder-only architectures. Through RAGGED , we uncover that different models suit substantially varied RAG setups.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.09040v1", "content": "To answer this, we introduce the RAGGED framework to analyze and optimize RAG systems. On a set of representative DBQA tasks, we study two classic sparse and dense retrievers , and four top-performing LMs in encoder-decoder and decoder-only architectures. Through RAGGED , we uncover that different models suit substantially varied RAG setups."}
+{"idx": 3, "title": "The Chronicles of RAG: The Retriever, the Chunk and the Generator", "date": "", "ddg_snippet": "Retrieval Augmented Generation (RAG) has become one of the most popular paradigms for enabling LLMs to access external data, and also as a mechanism for grounding to mitigate against hallucinations. When implementing RAG you can face several challenges like effective integration of retrieval models, efficient representation learning, data diversity, computational efficiency optimization ...", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/124642096/The_Chronicles_of_RAG_The_Retriever_the_Chunk_and_the_Generator", "content": "Retrieval Augmented Generation (RAG) has become one of the most popular paradigms for enabling LLMs to access external data, and also as a mechanism for grounding to mitigate against hallucinations. When implementing RAG you can face several challenges like effective integration of retrieval models, efficient representation learning, data diversity, computational efficiency optimization ..."}
+{"idx": 4, "title": "The Chronicles of RAG: The Retriever, the Chunk and the Generator", "date": "", "ddg_snippet": "Therefore, in this section , we explore various retrieval approaches for the RAG, recognizing that the quality of the retriever is a crucial factor in enhancing performance for this type of problem. We conducted an evaluation covering both sparse and dense search, a hybrid method, and even a multi-stage architecture using a reranker.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2401.07883", "content": "Therefore, in this section , we explore various retrieval approaches for the RAG, recognizing that the quality of the retriever is a crucial factor in enhancing performance for this type of problem. We conducted an evaluation covering both sparse and dense search, a hybrid method, and even a multi-stage architecture using a reranker."}
+{"idx": 5, "title": "Improving the Domain Adaptation of Retrieval Augmented ... - MIT Press", "date": "", "ddg_snippet": "Abstract. Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper , we evaluate the impact of joint training of the retriever and generator components of RAG for the ...", "subpage_snippet": "", "source": "direct.mit.edu", "link": "https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00530/114590/Improving-the-Domain-Adaptation-of-Retrieval", "content": "Abstract. Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper , we evaluate the impact of joint training of the retriever and generator components of RAG for the ..."}
+{"idx": 6, "title": "RAGuard: A Novel Approach for In-Context Safe Retrieval ... - Springer", "date": "", "ddg_snippet": "The remainder of this paper is structured as follows: Sect. 2 reviews relevant literature on RAG methodologies and safety-focused AI. Section 3 provides an in-depth description of the RAGuard framework. Section 4 details our experimental methodology and newly proposed benchmark.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-032-05073-1_13", "content": "The remainder of this paper is structured as follows: Sect. 2 reviews relevant literature on RAG methodologies and safety-focused AI. Section 3 provides an in-depth description of the RAGuard framework. Section 4 details our experimental methodology and newly proposed benchmark."}
+{"idx": 7, "title": "arXiv:2403.09040v1 [cs.CL] 14 Mar 2024", "date": "", "ddg_snippet": "Abstract Retrieval-augmented generation (RAG) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly dependent on its configuration, raising question: What is the optimal RAG config-uration? To answer this, we introduce RAGGED framework to analyze and optimize RAG ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2403.09040v1", "content": "Abstract Retrieval-augmented generation (RAG) greatly benefits language models (LMs) by providing additional context for tasks such as document-based question answering (DBQA). Despite its potential, the power of RAG is highly dependent on its configuration, raising question: What is the optimal RAG config-uration? To answer this, we introduce RAGGED framework to analyze and optimize RAG ..."}
+{"idx": 8, "title": "(PDF) Advancing Retrieval-Augmented Generation (RAG) Innovations ...", "date": "", "ddg_snippet": "PDF | Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in artificial intelligence (AI), enhancing large language models... | Find, read and cite all the research you ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388722115_Advancing_Retrieval-Augmented_Generation_RAG_Innovations_Challenges_and_the_Future_of_AI_Reasoning", "content": "PDF | Retrieval-Augmented Generation (RAG) has emerged as a transformative approach in artificial intelligence (AI), enhancing large language models... | Find, read and cite all the research you ..."}
+{"idx": 9, "title": "PDF Chapter 7 Retrieval-Augmented Generation - Springer", "date": "", "ddg_snippet": "Retrieval-Augmented Generation Abstract Retrieval-augmented generation (RAG) is a prominent application of con-versational LLMs. RAG systems accept a user query and return a response similar to chatbots but source the factual details of their response from static knowledge bases, including documents, structured data tables, and more. In RAG, a small language model is used to embed the user ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-65647-7_7.pdf", "content": "Retrieval-Augmented Generation Abstract Retrieval-augmented generation (RAG) is a prominent application of con-versational LLMs. RAG systems accept a user query and return a response similar to chatbots but source the factual details of their response from static knowledge bases, including documents, structured data tables, and more. In RAG, a small language model is used to embed the user ..."}
diff --git a/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_theoretical_framework_spu.jsonl b/data/sampled_jsons/RL_on_Incorrect_Synthetic_Data_Scales_the_Efficiency_of_LLM_Math_Reasoning_theoretical_framework_spu.jsonl
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+{"idx": 0, "title": "Rethinking Reasoning Quality in Large Language Models through", "date": "", "ddg_snippet": "... the limitations of outcome-only reward modeling in reasoning tasks, we propose Dynamic Reasoning Efficiency Reward (DRER), a plug-and-play ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06024v1", "content": "... the limitations of outcome-only reward modeling in reasoning tasks, we propose Dynamic Reasoning Efficiency Reward (DRER), a plug-and-play ..."}
+{"idx": 1, "title": "G1: Teaching LLMs to Reason on Graphs with Reinforcement", "date": "", "ddg_snippet": "... offer an efficient, scalable path for building strong graph reasoners by finetuning LLMs with RL on graph- theoretic tasks, which combines the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18499v3", "content": "... offer an efficient, scalable path for building strong graph reasoners by finetuning LLMs with RL on graph- theoretic tasks, which combines the ..."}
+{"idx": 2, "title": "Large Language Models Are Reasoning Teachers | Request PDF", "date": "", "ddg_snippet": "... the reasoning capabilities of opensource LLMs , recent studies focus on Supervised Fine-Tuning (SFT) these open-source LLMs to distill mathematical ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/372919115_Large_Language_Models_Are_Reasoning_Teachers", "content": "... the reasoning capabilities of opensource LLMs , recent studies focus on Supervised Fine-Tuning (SFT) these open-source LLMs to distill mathematical ..."}
+{"idx": 3, "title": "Sewoong Oh", "date": "", "ddg_snippet": "... RLVR) can elicit strong mathematical reasoning in certain models even with spurious rewards that have little, no, or even negative correlation with ...", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/author/Sewoong+Oh", "content": "... RLVR) can elicit strong mathematical reasoning in certain models even with spurious rewards that have little, no, or even negative correlation with ..."}
+{"idx": 4, "title": "Virginia Smith", "date": "", "ddg_snippet": "... gained popularity as an approach to efficiently update an LLM so that it behaves (roughly) as if it was not trained on a subset of data to begin with.", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "http://www.cs.cmu.edu/~smithv/", "content": "... gained popularity as an approach to efficiently update an LLM so that it behaves (roughly) as if it was not trained on a subset of data to begin with."}
+{"idx": 5, "title": "Virginia Smith", "date": "", "ddg_snippet": "With access to only a small and potentially loosely related set of data , we find that we can \"jog\" the memory of unlearned models to reverse the ...", "subpage_snippet": "", "source": "www.cs.cmu.edu", "link": "https://www.cs.cmu.edu/~smithv/", "content": "With access to only a small and potentially loosely related set of data , we find that we can \"jog\" the memory of unlearned models to reverse the ..."}
+{"idx": 6, "title": "ICML 2024", "date": "", "ddg_snippet": "The Data -centric Machine Learning Research (DMLR) workshop this year focused on the theme of “Datasets for Foundation Models” ( ICML link ...", "subpage_snippet": "", "source": "joltml.com", "link": "https://joltml.com/icml-2024/", "content": "The Data -centric Machine Learning Research (DMLR) workshop this year focused on the theme of “Datasets for Foundation Models” ( ICML link ..."}
+{"idx": 7, "title": "Ji-Rong Wen - ACL Anthology", "date": "", "ddg_snippet": "To enhance the capabilities of LLMs to emulate human reasoning , prior studies have focused on modeling reasoning steps using various thought ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/j/ji-rong-wen/", "content": "To enhance the capabilities of LLMs to emulate human reasoning , prior studies have focused on modeling reasoning steps using various thought ..."}
+{"idx": 8, "title": "NYU Computer Science Department", "date": "", "ddg_snippet": "... on one type of biological measurement --- whole-tumor shotgun RNA sequencing data , which we call \\textit{bulk RNA-seq} --- we are able to deeply ...", "subpage_snippet": "", "source": "cs.nyu.edu", "link": "https://cs.nyu.edu/dynamic/reports/?type=PhD&year=all", "content": "... on one type of biological measurement --- whole-tumor shotgun RNA sequencing data , which we call \\textit{bulk RNA-seq} --- we are able to deeply ..."}
+{"idx": 9, "title": "Nathan Kallus", "date": "", "ddg_snippet": "To scale experimentation effectively, firms rely on decision rules: standard operating procedures for mapping the results of an experiment to a ...", "subpage_snippet": "", "source": "nathankallus.com", "link": "https://nathankallus.com/", "content": "To scale experimentation effectively, firms rely on decision rules: standard operating procedures for mapping the results of an experiment to a ..."}
diff --git a/data/sampled_jsons/ReLU_activation_sparsity_benefits_GELU_replacement_neuromorphic_computing_energy_efficiency_year_2024.jsonl b/data/sampled_jsons/ReLU_activation_sparsity_benefits_GELU_replacement_neuromorphic_computing_energy_efficiency_year_2024.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Activation function - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. Logistic activation function. In artificial neural networks, the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Activation_function", "content": "Machine learningand data mining. v. t. e. Logistic activation function. In artificial neural networks, the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights."}
+{"idx": 1, "title": "Explore Activation Sparsity in Recurrent LLMs for Energy ...", "date": "", "ddg_snippet": "Jan 9, 2025 · Additionally, hardware simulations with the SENECA neuromorphic processor underscore notable energy savings and latency improvements. These results pave the way for low-power, real-time neuromorphic deployment of LLMs and demonstrate the feasibility of training-free on-chip adaptation using activation sparsity .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.16337", "content": "Jan 9, 2025 · Additionally, hardware simulations with the SENECA neuromorphic processor underscore notable energy savings and latency improvements. These results pave the way for low-power, real-time neuromorphic deployment of LLMs and demonstrate the feasibility of training-free on-chip adaptation using activation sparsity ."}
+{"idx": 2, "title": "ExploreActivationSparsityinRecurrentLLMs forEnergy ...", "date": "", "ddg_snippet": "explore activation sparsity in R-LLMs for energy -eficient neuromorphic computing . State-of-the-art methods for improving activation sparsity in LLMs apply the ReLU activation function before linear layers in pre-train d models and perform fine-tuning on large datasets to restore performance [4, 7]. Moreover,[7] applied activation regularization in", "subpage_snippet": "", "source": "bnaic2024.sites.uu.nl", "link": "https://bnaic2024.sites.uu.nl/wp-content/uploads/sites/986/2024/11/Explore-Activation-Sparsity-in-Recurrent-LLMs-for-Energy-Efficient-Neuromorphic-Computing.pdf", "content": "explore activation sparsity in R-LLMs for energy -eficient neuromorphic computing . State-of-the-art methods for improving activation sparsity in LLMs apply the ReLU activation function before linear layers in pre-train d models and perform fine-tuning on large datasets to restore performance [4, 7]. Moreover,[7] applied activation regularization in"}
+{"idx": 3, "title": "ReLU Strikes Back: Exploiting Activation Sparsity in Large ...", "date": "", "ddg_snippet": "Jan 16, 2024 · This paper advocates for the use of ReLU activation function in LLM. ReLU can significantly increase the activation sparsity level, leading to promising inference efficiency . The authors argue that both training from scratch with ReLU and Relufication finetuning a trained model lead to comparable performance.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=osoWxY8q2E", "content": "Jan 16, 2024 · This paper advocates for the use of ReLU activation function in LLM. ReLU can significantly increase the activation sparsity level, leading to promising inference efficiency . The authors argue that both training from scratch with ReLU and Relufication finetuning a trained model lead to comparable performance."}
+{"idx": 4, "title": "Choice of Activation Functions (ReLU, GeLU, SwiGLU)", "date": "", "ddg_snippet": "Comparison of ReLU , GeLU (approximation), and Swish activation functions. Note the increasing smoothness from ReLU to GeLU to Swish. Choosing the right activation function involves trade-offs: ReLU : Fastest computation, simplest form. Risk of dying neurons, non-smooth. Less common in state-of-the-art large models now but still viable. GeLU : Good balance of performance and computational cost ...", "subpage_snippet": "", "source": "apxml.com", "link": "https://apxml.com/courses/how-to-build-a-large-language-model/chapter-11-scaling-transformers-architectural-choices/choice-activation-functions", "content": "Comparison of ReLU , GeLU (approximation), and Swish activation functions. Note the increasing smoothness from ReLU to GeLU to Swish. Choosing the right activation function involves trade-offs: ReLU : Fastest computation, simplest form. Risk of dying neurons, non-smooth. Less common in state-of-the-art large models now but still viable. GeLU : Good balance of performance and computational cost ..."}
+{"idx": 5, "title": "Photonic Neuron With on Frequency-Domain ReLU Activation ...", "date": "", "ddg_snippet": "Jun 13, 2024 · Driven by an exponential growth of data, neuromorphic computing has risen in popularity as a new method for high-performance computing . The adopted neural network (NN) model relies on parallel processing between neurons and synapses, which reduces the energy consumption and boosts the computational efficiency . Photonics empowers neuromorphic processors through its inherent parallelism, along ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10556681", "content": "Jun 13, 2024 · Driven by an exponential growth of data, neuromorphic computing has risen in popularity as a new method for high-performance computing . The adopted neural network (NN) model relies on parallel processing between neurons and synapses, which reduces the energy consumption and boosts the computational efficiency . Photonics empowers neuromorphic processors through its inherent parallelism, along ..."}
+{"idx": 6, "title": "Summary Blog: ReLU Strikes Back: Harnessing Activation ...", "date": "", "ddg_snippet": "Aug 12, 2024 · Efficient Inference: By leveraging activation sparsity , ReLU -based LLMs can reduce inference computation by up to threefold. The paper introduces the concept of “relufication,” where existing models trained with other activation functions like GELU or SiLU are fine-tuned with ReLU , leading to lighter and more efficient models.", "subpage_snippet": "", "source": "blog.gopenai.com", "link": "https://blog.gopenai.com/summary-blog-relu-strikes-back-harnessing-activation-sparsity-in-large-language-models-c4480a7c0761", "content": "Aug 12, 2024 · Efficient Inference: By leveraging activation sparsity , ReLU -based LLMs can reduce inference computation by up to threefold. The paper introduces the concept of “relufication,” where existing models trained with other activation functions like GELU or SiLU are fine-tuned with ReLU , leading to lighter and more efficient models."}
+{"idx": 7, "title": "ReLU vs GeLU. GeLU has become a popular choice in… | by ...", "date": "", "ddg_snippet": "Jun 1, 2025 · ReLU vs. GeLU GeLU ’s smoothness can lead to more stable gradients and potentially better optimization, which is important for LLMs and complex architecture (LLMs and larger recsys).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/better-ml/relu-vs-gelu-d322422f5147", "content": "Jun 1, 2025 · ReLU vs. GeLU GeLU ’s smoothness can lead to more stable gradients and potentially better optimization, which is important for LLMs and complex architecture (LLMs and larger recsys)."}
+{"idx": 8, "title": "Exploiting Activation Sparsity in Large Language Models", "date": "", "ddg_snippet": "The shifted ReLU activation Discussion on Activation Sparsity and Inference Efficiency Activation Sparsity and Speculative Decoding", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2310.04564", "content": "The shifted ReLU activation Discussion on Activation Sparsity and Inference Efficiency Activation Sparsity and Speculative Decoding"}
+{"idx": 9, "title": "Activation Functions in Neural Networks [12 Types & Use Cases]", "date": "", "ddg_snippet": "The Gaussian Error Linear Unit ( GELU ) activation function is compatible with BERT, ROBERTa, ALBERT, and other top NLP models. This activation function is motivated by combining properties from dropout, zoneout, and ReLUs .", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/neural-networks-activation-functions", "content": "The Gaussian Error Linear Unit ( GELU ) activation function is compatible with BERT, ROBERTa, ALBERT, and other top NLP models. This activation function is motivated by combining properties from dropout, zoneout, and ReLUs ."}
diff --git a/data/sampled_jsons/Recursive_Allocation_Selection_RAS_Algorithm_8_Adaptive_Task_Allocation_Distributed_Machine_Learning.jsonl b/data/sampled_jsons/Recursive_Allocation_Selection_RAS_Algorithm_8_Adaptive_Task_Allocation_Distributed_Machine_Learning.jsonl
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index 0000000000000000000000000000000000000000..8a0a865cb32e65768448db2e7262fb0b052ca8e5
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Machine learning - Wikipedia", "date": "", "ddg_snippet": "tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper \"Computing Machinery and Intelligence\", in which the question \"Can machines think...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Machine_learning", "content": "tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper \"Computing Machinery and Intelligence\", in which the question \"Can machines think..."}
+{"idx": 1, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses. Then, we introduce ATA, a lower-confidence bound-based algorithm designed to solve the proposed allocation problem.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v2", "content": "In this work, we formalize the task allocation problem as a combinatorial online learning problem with partial feedback and non-linear losses. Then, we introduce ATA, a lower-confidence bound-based algorithm designed to solve the proposed allocation problem."}
+{"idx": 2, "title": "Utilizing machine learning algorithms for task allocation in ...", "date": "", "ddg_snippet": "Nov 15, 2024 · The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task , with the aim of assisting software managers in making task assignments more efficiently and effectively in DASD environment.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024159579", "content": "Nov 15, 2024 · The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task , with the aim of assisting software managers in making task assignments more efficiently and effectively in DASD environment."}
+{"idx": 3, "title": "Adaptive Task Allocation for Multi-UAV Systems Using a Novel ...", "date": "", "ddg_snippet": "This paper introduces the Dynamic Efficient Distributed Performance Impact (DEDPI) algorithm for optimizing task allocation in multi-UAV systems operating in dynamic environments. The algorithm overcomes the limitations of existing approaches, specifically the suboptimal solutions of dynamic task algorithms and the computational inefficiencies of extending static algorithms to dynamic contexts ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10864721", "content": "This paper introduces the Dynamic Efficient Distributed Performance Impact (DEDPI) algorithm for optimizing task allocation in multi-UAV systems operating in dynamic environments. The algorithm overcomes the limitations of existing approaches, specifically the suboptimal solutions of dynamic task algorithms and the computational inefficiencies of extending static algorithms to dynamic contexts ..."}
+{"idx": 4, "title": "Speeding Task Allocation Search for Reconfigurations in ... ATA: Adaptive Task Allocation for Efficient Resource ... task-allocation · GitHub Topics · GitHub Adaptive Task Allocation for Multi- UAV Systems Using a Novel Dynamic Adaptive Task Allocation for Multi- UAV Systems Using a Novel Dynamic task-allocation · GitHub Topics · GitHub Utilizing machine learning algorithms for task allocation in Utilizing machine learning algorithms for task allocation in Utilizing machine learning algorithms for task allocation in [2502.00775] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "This problem is not trivial and there is no known polynomial time algorithm to find the optimal solution. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems and, in this work, we explore the application of such approaches to the task allocation problem in CADESs. Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Jul 14, 2024 · This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms . How does a dynamic task algorithm improve resource allocation and responsiveness? The algorithm overcomes the limitations of existing approaches, specifically the suboptimal solutions of dynamic task algorithms and the computational inefficiencies of extending static algorithms to dynamic contexts, by dynamically adjusting task queues and employing a novel objective function to enhance resource allocation and responsiveness. Can dynamic efficient distributed performance impact optimize task allocation in multi-UAV systems? This paper introduces the Dynamic Efficient Distributed Performance Impact (DEDPI) algorithm for optimizing task allocation in multi-UAV systems operating in dynamic environments. What is recursive task allocation in swarm robotics? Recursive task allocation approach to foraging in swarm robotics. ARGoS and ROS. [EAAI] A two-stage reinforcement learning-based approach for multi-entity task allocation . multi agent path finding and pickup and delivery (combined task allocation and path finding). work going on precendence constrained task allocation and path finding (PC-TAPF) Can machine learning be used for task allocation in software development? Notably, we are unaware of any studies that have utilized machine learning algorithms specifically for task allocation within software development teams. The only relevant study we identified, which employed the same dataset as ours is the study of which achieved a maximum accuracy of 69 %. Can machine learning predict task assignments in DASD environment? The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task , with the aim of assisting software managers in making task assignments more efficiently and effectively in DASD environment. Why is task allocation important in DASD? Proper task allocation is crucial in DASD to avoid undesirable outcomes including project rejection by clients, unfavorable team attitudes, and project failure. Coordination and communication issues occur as businesses embrace the DASD environment more frequently to tap into global talent and knowledge while cutting development expenses. Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "www.ncbi.nlm.nih.gov", "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823603/", "content": "This problem is not trivial and there is no known polynomial time algorithm to find the optimal solution. Several studies have proposed Deep Reinforcement Learning (DRL) approaches to solve combinatorial optimization problems and, in this work, we explore the application of such approaches to the task allocation problem in CADESs. Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times. Jul 14, 2024 · This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms . How does a dynamic task algorithm improve resource allocation and responsiveness? The algorithm overcomes the limitations of existing approaches, specifically the suboptimal solutions of dynamic task algorithms and the computational inefficiencies of extending static algorithms to dynamic contexts, by dynamically adjusting task queues and employing a novel objective function to enhance resource allocation and responsiveness. Can dynamic efficient distributed performance impact optimize task allocation in multi-UAV systems? This paper introduces the Dynamic Efficient Distributed Performance Impact (DEDPI) algorithm for optimizing task allocation in multi-UAV systems operating in dynamic environments. What is recursive task allocation in swarm robotics? Recursive task allocation approach to foraging in swarm robotics. ARGoS and ROS. [EAAI] A two-stage reinforcement learning-based approach for multi-entity task allocation . multi agent path finding and pickup and delivery (combined task allocation and path finding). work going on precendence constrained task allocation and path finding (PC-TAPF) Can machine learning be used for task allocation in software development? Notably, we are unaware of any studies that have utilized machine learning algorithms specifically for task allocation within software development teams. The only relevant study we identified, which employed the same dataset as ours is the study of which achieved a maximum accuracy of 69 %. Can machine learning predict task assignments in DASD environment? The purpose of this study is to utilize machine learning (ML) predictive algorithms to determine the most appropriate role for a given task , with the aim of assisting software managers in making task assignments more efficiently and effectively in DASD environment. Why is task allocation important in DASD? Proper task allocation is crucial in DASD to avoid undesirable outcomes including project rejection by clients, unfavorable team attitudes, and project failure. Coordination and communication issues occur as businesses embrace the DASD environment more frequently to tap into global talent and knowledge while cutting development expenses. Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
+{"idx": 5, "title": "ATA: Adaptive Task Allocation for Efficient Resource ...", "date": "", "ddg_snippet": "Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388657939_ATA_Adaptive_Task_Allocation_for_Efficient_Resource_Management_in_Distributed_Machine_Learning", "content": "Feb 2, 2025 · In this paper, we propose ATA ( Adaptive Task Allocation ), a method that adapts to heterogeneous and random distributions of worker computation times."}
+{"idx": 6, "title": "task-allocation · GitHub Topics · GitHub", "date": "", "ddg_snippet": "Jul 14, 2024 · This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/topics/task-allocation", "content": "Jul 14, 2024 · This project optimizes task allocation in Fog and Cloud Computing environments using multi-objective optimization techniques. It computes and analyzes Pareto fronts using MOCS and MOFA algorithms ."}
+{"idx": 7, "title": "[2502.00775] ATA: Adaptive Task Allocation for Efficient ...", "date": "", "ddg_snippet": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.00775", "content": "Feb 2, 2025 · Asynchronous methods are fundamental for parallelizing computations in distributed machine learning . They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be ..."}
+{"idx": 8, "title": "ATA: Adaptive Task Allocation for Efficient Resource Management in...", "date": "", "ddg_snippet": "Algorithm 8 Recursive Allocation Selection ( RAS )., RAS ( Algorithm 8 ) allocates the task to worker with the smallest score (line 9). Thus, the base case holds. Inductive Step: Assume that RAS finds an optimal allocation for budget.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.00775v1", "content": "Algorithm 8 Recursive Allocation Selection ( RAS )., RAS ( Algorithm 8 ) allocates the task to worker with the smallest score (line 9). Thus, the base case holds. Inductive Step: Assume that RAS finds an optimal allocation for budget."}
+{"idx": 9, "title": "(PDF) Task Allocation for Asynchronous Mobile Edge Learning with...", "date": "", "ddg_snippet": "Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning .The problem of adaptive task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/346555823_Task_Allocation_for_Asynchronous_Mobile_Edge_Learning_with_Delay_and_Energy_Constraints", "content": "Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning .The problem of adaptive task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing ..."}
diff --git a/data/sampled_jsons/SAFE_VGG-19_CIFAR-10_90%_95%_98%_sparsity_accuracy_93.5_93.2_92.8.jsonl b/data/sampled_jsons/SAFE_VGG-19_CIFAR-10_90%_95%_98%_sparsity_accuracy_93.5_93.2_92.8.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SAFE: Finding Sparse and Flat Minima to Improve Pruning", "date": "", "ddg_snippet": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR - 10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2506.06866v2", "content": "Figure 2: Validation accuracy (mean±std) of VGG-19 and ResNet-20/32 models on CIFAR - 10 /100 pruned across different sparsity levels and methods. SAFE consistently achieves superior performance across a broad range of sparsity levels."}
+{"idx": 1, "title": "More than 98% accuracy on CIFAR10 with Pytorch and small GPU", "date": "", "ddg_snippet": "For a single model with 8Go GPU: python main.py. test accuracy : 98.2% With a 2Go GPU : python main.py --batch_split 4 You can use ensemble estimation to improve the test accuracy . For example, to do 10 estimates with 8Go GPU: python main10.py. After the learning, the following script will compute the mean prediction of the models on test dataset: python aggregate.py. test accuracy : 98.5%.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/JosephRynkiewicz/CIFAR10", "content": "For a single model with 8Go GPU: python main.py. test accuracy : 98.2% With a 2Go GPU : python main.py --batch_split 4 You can use ensemble estimation to improve the test accuracy . For example, to do 10 estimates with 8Go GPU: python main10.py. After the learning, the following script will compute the mean prediction of the models on test dataset: python aggregate.py. test accuracy : 98.5%."}
+{"idx": 2, "title": "94% on CIFAR-10 in 3.29 Seconds on a Single GPU - arXiv.org", "date": "", "ddg_snippet": "Abstract CIFAR-10 is among the most widely used datasets in machine learning, facilitating thousands of research projects per year. To accelerate research and reduce the cost of experiments, we introduce training methods for CIFAR-10 which reach 94% accuracy in 3.29 seconds, 95% in 10.4 seconds, and 96% in 46.3 seconds, when run on a single NVIDIA A100 GPU. As one factor contributing to these ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2404.00498", "content": "Abstract CIFAR-10 is among the most widely used datasets in machine learning, facilitating thousands of research projects per year. To accelerate research and reduce the cost of experiments, we introduce training methods for CIFAR-10 which reach 94% accuracy in 3.29 seconds, 95% in 10.4 seconds, and 96% in 46.3 seconds, when run on a single NVIDIA A100 GPU. As one factor contributing to these ..."}
+{"idx": 3, "title": "Tutorial 2: 94% accuracy on Cifar10 in 2 minutes - Medium", "date": "", "ddg_snippet": "However, while getting 90% accuracy on MNIST is trivial, getting 90% on Cifar10 requires serious work. In this tutorial, the mission is to reach 94% accuracy on Cifar10 , which is reportedly human ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/fenwicks/tutorial-2-94-accuracy-on-cifar10-in-2-minutes-7b5aaecd9cdd", "content": "However, while getting 90% accuracy on MNIST is trivial, getting 90% on Cifar10 requires serious work. In this tutorial, the mission is to reach 94% accuracy on Cifar10 , which is reportedly human ..."}
+{"idx": 4, "title": "CIFAR10: 94% Of Accuracy By 50 Epochs With End-to-End Training", "date": "", "ddg_snippet": "From the training history figure, we can see the accuracy curves are still getting higher. Due to the purpose of proving the effect of 1-Cycle schedule, so the author just trained with 50 epochs.", "subpage_snippet": "", "source": "fptsoftware.com", "link": "https://fptsoftware.com/resource-center/blogs/cifar10-94-of-accuracy-by-50-epochs-with-end-to-end-training", "content": "From the training history figure, we can see the accuracy curves are still getting higher. Due to the purpose of proving the effect of 1-Cycle schedule, so the author just trained with 50 epochs."}
+{"idx": 5, "title": "GitHub - kuangliu/pytorch-cifar: 95.47% on CIFAR10 with PyTorch", "date": "", "ddg_snippet": "95 .47% on CIFAR10 with PyTorch. Contribute to kuangliu/pytorch- cifar development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kuangliu/pytorch-cifar", "content": "95 .47% on CIFAR10 with PyTorch. Contribute to kuangliu/pytorch- cifar development by creating an account on GitHub."}
+{"idx": 6, "title": "CIFAR-10 on Benchmarks.AI", "date": "", "ddg_snippet": "CIFAR-10 on Benchmarks.AICIFAR- 10", "subpage_snippet": "", "source": "benchmarks.ai", "link": "https://benchmarks.ai/cifar-10", "content": "CIFAR-10 on Benchmarks.AICIFAR- 10"}
+{"idx": 7, "title": "FGGP: Fixed-Rate Gradient-First Gradual Pruning", "date": "", "ddg_snippet": "We validate this on CIFAR-10 dataset, with multiple randomized initializations on both VGG-19 and ResNet-50 network backbones, for pruning targets of 90 , 95 , and 98% sparsity and for both initially dense and 50% sparse networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.05500v1", "content": "We validate this on CIFAR-10 dataset, with multiple randomized initializations on both VGG-19 and ResNet-50 network backbones, for pruning targets of 90 , 95 , and 98% sparsity and for both initially dense and 50% sparse networks."}
+{"idx": 8, "title": "FGGP: Fixed-Rate Gradient-First Gradual Pruning - arXiv.org", "date": "", "ddg_snippet": "We validate this on CIFAR-10 dataset, with multiple randomized initializations on both VGG-19 and ResNet-50 network backbones, for pruning targets of 90 , 95 , and 98% sparsity and for both initially dense and 50% sparse networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2411.05500", "content": "We validate this on CIFAR-10 dataset, with multiple randomized initializations on both VGG-19 and ResNet-50 network backbones, for pruning targets of 90 , 95 , and 98% sparsity and for both initially dense and 50% sparse networks."}
+{"idx": 9, "title": "PDF Advancing Dynamic Sparse Training by Exploring Optimization Opportunities", "date": "", "ddg_snippet": "Figure 3: We perform high sparsity experiments on ResNet-32, VGG-19 and MobileNet-v2 on CIFAR-10 dataset, and compare with different DST methods. Note that MobileNet-v2 is already a compact model, so the extreme sparsity range is different from the other two networks.", "subpage_snippet": "", "source": "cecas.clemson.edu", "link": "https://cecas.clemson.edu/~linkeg/Publication/ICML24-Ji.pdf", "content": "Figure 3: We perform high sparsity experiments on ResNet-32, VGG-19 and MobileNet-v2 on CIFAR-10 dataset, and compare with different DST methods. Note that MobileNet-v2 is already a compact model, so the extreme sparsity range is different from the other two networks."}
diff --git a/data/sampled_jsons/SPRING_Improving_Throughput_Sharding_Blockchain_Deep_Reinforcement_Learning_State_Placement_abstract_year_2024.jsonl b/data/sampled_jsons/SPRING_Improving_Throughput_Sharding_Blockchain_Deep_Reinforcement_Learning_State_Placement_abstract_year_2024.jsonl
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index 0000000000000000000000000000000000000000..a802953b6a9eba7faa98526090e1e60b8d8a9085
--- /dev/null
+++ b/data/sampled_jsons/SPRING_Improving_Throughput_Sharding_Blockchain_Deep_Reinforcement_Learning_State_Placement_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[rfp0423] SPRING: Improving the Throughput of Sharding ... dblp: SPRING: Improving the Throughput of Sharding Blockchain ... Papers - Zhen Xiao Cross-shard transaction optimization based on community ... SPRING : Improving the Throughput of Sharding Blockchain via Deep SPRING : Improving the Throughput of Sharding Blockchain via Deep Cross - shard transaction optimization based on community detection in Cross - shard transaction optimization based on community detection in Cross - shard transaction optimization based on community detection in SPRING : Improving the Throughput of Sharding Blockchain via Deep Optimal Sharding for Dynamic Throughput Optimization in ...", "date": "", "ddg_snippet": "\" SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement Pengze Li, Mingxuan Song, Mingzhe Xing, Zhen Xiao, QIUYU DING, Shengjie Guan, Jieyi Long\" Jan 19, 2025 · Bibliographic details on SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement In Proceedings of the Web Conference 2024 (WWW 2024), May 2024. Hangyu Mao, Rui Zhao, Ziyue Li, Zhiwei Xu, Hao Chen, Yiqun Chen, Bin Zhang, Zhen Xiao, Junge Zhang and Jiangjin Yin. Dec 1, 2024 · SPRING proposes a sharding framework based on deep reinforcement learning , which constructs the placement problem as a Markov Decision Process (MDP) while considering the proportion of cross- shard transactions and workload balancing. Is spring a deep-reinforcement-learning sharding framework for state placement? In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Can deep-reinforcement-learning (DRL) sharding be used for state placement? Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly. In this paper, we present SPRING , the first deep-reinforcement-learning (DRL)-based sharding framework for state placement. Why is the sharding blockchain system challenging? Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions, it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit. What is sharding blockchain & how does it work? In sharding blockchain systems, certain transactions or operations require the involvement of multiple shards , making cross-shard transactions unavoidable. Additionally, each shard stores only a portion of the state. What is sharding technology? As a mainstream on-chain scaling technology , sharding technology can improve the throughput of the blockchain system without affecting the level of decentralization . It divides the blockchain network into smaller partitions to enhance system throughput by independently and concurrently processing transactions within each partition . What is rapidchain sharding? Rapidchain: Scaling blockchain via full sharding . In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security. 931--948. Jianting Zhang, Zicong Hong, Xiaoyu Qiu, Yufeng Zhan, Song Guo, and Wuhui Chen. 2020. Skychain: A deep reinforcement learning -empowered dynamic blockchain sharding system. The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput , which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like ...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=DmSy-alC3pQ", "content": "\" SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement Pengze Li, Mingxuan Song, Mingzhe Xing, Zhen Xiao, QIUYU DING, Shengjie Guan, Jieyi Long\" Jan 19, 2025 · Bibliographic details on SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement In Proceedings of the Web Conference 2024 (WWW 2024), May 2024. Hangyu Mao, Rui Zhao, Ziyue Li, Zhiwei Xu, Hao Chen, Yiqun Chen, Bin Zhang, Zhen Xiao, Junge Zhang and Jiangjin Yin. Dec 1, 2024 · SPRING proposes a sharding framework based on deep reinforcement learning , which constructs the placement problem as a Markov Decision Process (MDP) while considering the proportion of cross- shard transactions and workload balancing. Is spring a deep-reinforcement-learning sharding framework for state placement? In this paper, we present SPRING, the first deep-reinforcement-learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Can deep-reinforcement-learning (DRL) sharding be used for state placement? Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly. In this paper, we present SPRING , the first deep-reinforcement-learning (DRL)-based sharding framework for state placement. Why is the sharding blockchain system challenging? Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions, it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit. What is sharding blockchain & how does it work? In sharding blockchain systems, certain transactions or operations require the involvement of multiple shards , making cross-shard transactions unavoidable. Additionally, each shard stores only a portion of the state. What is sharding technology? As a mainstream on-chain scaling technology , sharding technology can improve the throughput of the blockchain system without affecting the level of decentralization . It divides the blockchain network into smaller partitions to enhance system throughput by independently and concurrently processing transactions within each partition . What is rapidchain sharding? Rapidchain: Scaling blockchain via full sharding . In Proceedings of the 2018 ACM SIGSAC conference on computer and communications security. 931--948. Jianting Zhang, Zicong Hong, Xiaoyu Qiu, Yufeng Zhan, Song Guo, and Wuhui Chen. 2020. Skychain: A deep reinforcement learning -empowered dynamic blockchain sharding system. The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput , which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like ..."}
+{"idx": 1, "title": "SPRING: Improving the Throughput of Sharding Blockchain via ...", "date": "", "ddg_snippet": "May 13, 2024 · In this paper, we present SPRING , the first deep - reinforcement - learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross- shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3589334.3645386", "content": "May 13, 2024 · In this paper, we present SPRING , the first deep - reinforcement - learning (DRL)-based sharding framework for state placement . SPRING formulates the state placement as a Markov Decision Process, which considers the cross- shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy."}
+{"idx": 2, "title": "SPRING: Improving the Throughput of Sharding Blockchain via ...", "date": "", "ddg_snippet": "Existing sharding solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less efective or costly. In this paper, we present Spring , the first deep - reinforcement - learning (DRL)-based sharding framework for state placement .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=8oczaP1YKD&name=pdf", "content": "Existing sharding solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less efective or costly. In this paper, we present Spring , the first deep - reinforcement - learning (DRL)-based sharding framework for state placement ."}
+{"idx": 3, "title": "dblp: SPRING: Improving the Throughput of Sharding Blockchain ...", "date": "", "ddg_snippet": "Jan 19, 2025 · Bibliographic details on SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/www/LiSXXDGL24", "content": "Jan 19, 2025 · Bibliographic details on SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement ."}
+{"idx": 4, "title": "Optimal Sharding for Dynamic Throughput Optimization in ...", "date": "", "ddg_snippet": "The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput , which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10394337", "content": "The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput , which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like ..."}
+{"idx": 5, "title": "SPRING : Improving the Throughput of Sharding Blockchain via...", "date": "", "ddg_snippet": "Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical limited bandwidth in multi-agent communication has been largely ignored by the existing DRL methods.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/380539164_SPRING_Improving_the_Throughput_of_Sharding_Blockchain_via_Deep_Reinforcement_Learning_Based_State_Placement", "content": "Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical limited bandwidth in multi-agent communication has been largely ignored by the existing DRL methods."}
+{"idx": 6, "title": "SPRING : Improving the Throughput of Sharding Blockchain via...", "date": "", "ddg_snippet": "2.1 Deep Reinforcement Learning . 2.2 Sharding Blockchain and Cross- Shard Transactions.In this paper, we present SPRING , the first deep - reinforcement - learning (DRL)-based shard -ing framework for state placement .", "subpage_snippet": "", "source": "zhenxiao.com", "link": "http://zhenxiao.com/papers/WWW_Spring_camera_ready.pdf", "content": "2.1 Deep Reinforcement Learning . 2.2 Sharding Blockchain and Cross- Shard Transactions.In this paper, we present SPRING , the first deep - reinforcement - learning (DRL)-based shard -ing framework for state placement ."}
+{"idx": 7, "title": "AERO: Enhancing Sharding Blockchain via Deep Reinforcement ...", "date": "", "ddg_snippet": "2.1 Sharding Blockchain with Deep Reinforcement Learning Approaches.and Jieyi Long. 2024. SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . In Proceedings of the ACM on Web Conference 2024.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WcuXvn3HVk", "content": "2.1 Sharding Blockchain with Deep Reinforcement Learning Approaches.and Jieyi Long. 2024. SPRING : Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement . In Proceedings of the ACM on Web Conference 2024."}
+{"idx": 8, "title": "SkyChain: A Deep Reinforcement Learning -Empowered Dynamic...", "date": "", "ddg_snippet": "Human-level control through deep reinforcement learning .2019. OptChain: Optimal Transactions Placement for Scalable Blockchain Sharding . In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 525–535.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3404397.3404460?cookieSet=1", "content": "Human-level control through deep reinforcement learning .2019. OptChain: Optimal Transactions Placement for Scalable Blockchain Sharding . In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 525–535."}
+{"idx": 9, "title": "ContribChain: A Stress-Balanced Blockchain Sharding Protocol with...", "date": "", "ddg_snippet": "[27] employs Deep Reinforcement Learning (DRL) to optimize state placement . While these approaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.06899v1", "content": "[27] employs Deep Reinforcement Learning (DRL) to optimize state placement . While these approaches enhance account allocation, they fail to consider performance disparities among shards , and thus do not achieve stress balance."}
diff --git a/data/sampled_jsons/SWEBench_Jimenez_2024_paper_abstract_year_2024.jsonl b/data/sampled_jsons/SWEBench_Jimenez_2024_paper_abstract_year_2024.jsonl
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index 0000000000000000000000000000000000000000..e30dfe729b5f161668fcbd4a3dafb03dc8a212a6
--- /dev/null
+++ b/data/sampled_jsons/SWEBench_Jimenez_2024_paper_abstract_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLR 2024 — Best Papers & Talks (Benchmarks, Reasoning", "date": "", "ddg_snippet": "1 longform interview, 12 more papers and 3 talks from ICLR 2024 , covering Coding Agents like OpenDevin, the Science of Benchmarks, Reasoning and Post ...", "subpage_snippet": "", "source": "www.latent.space", "link": "https://www.latent.space/p/iclr-2024-benchmarks-agents", "content": "1 longform interview, 12 more papers and 3 talks from ICLR 2024 , covering Coding Agents like OpenDevin, the Science of Benchmarks, Reasoning and Post ..."}
+{"idx": 1, "title": "OmniGIRL: A Multilingual and Multimodal Benchmark for GitHub", "date": "", "ddg_snippet": "In this paper , we present OmniGIRL , a benchmark for G itHub I ssue R eso L ution that incorporates multiple aspects of diversity in programming ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.04606v1", "content": "In this paper , we present OmniGIRL , a benchmark for G itHub I ssue R eso L ution that incorporates multiple aspects of diversity in programming ..."}
+{"idx": 2, "title": "R2E-Gym: Scaling Open-Weights Software Engineering Agents with", "date": "", "ddg_snippet": "While several benchmarks for evaluating Swe -agents on GitHub issues exist ( Jimenez et al., 2023 ; Zhao et al., 2024 ) , scalable curation of high ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.07164v1", "content": "While several benchmarks for evaluating Swe -agents on GitHub issues exist ( Jimenez et al., 2023 ; Zhao et al., 2024 ) , scalable curation of high ..."}
+{"idx": 3, "title": "SWE-bench Multilingual", "date": "", "ddg_snippet": "... Paper ... Jimenez , Ofir Press, John Yang", "subpage_snippet": "", "source": "www.swebench.com", "link": "https://www.swebench.com/multilingual.html", "content": "... Paper ... Jimenez , Ofir Press, John Yang"}
+{"idx": 4, "title": "On Devin - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "Just take a look at a sample SWE Bench problem: this is a task for a human! Shout out to Carlos Jimenez for the fantastic dataset.", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/wovJBkfZ8rTyLoEKv/on-devin", "content": "Just take a look at a sample SWE Bench problem: this is a task for a human! Shout out to Carlos Jimenez for the fantastic dataset."}
+{"idx": 5, "title": "On Devin — LessWrong", "date": "", "ddg_snippet": "Just take a look at a sample SWE Bench problem: this is a task for a human! Shout out to Carlos Jimenez for the fantastic dataset.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/wovJBkfZ8rTyLoEKv/on-devin", "content": "Just take a look at a sample SWE Bench problem: this is a task for a human! Shout out to Carlos Jimenez for the fantastic dataset."}
+{"idx": 6, "title": "The Rise of AI Teammates in Software Engineering (SE) 3.0: How", "date": "", "ddg_snippet": "Our paper is the first work to demonstrate that the era of Autonomous Coding Agent in SE is not an impending future, but an unfolding reality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.15003v1", "content": "Our paper is the first work to demonstrate that the era of Autonomous Coding Agent in SE is not an impending future, but an unfolding reality."}
+{"idx": 7, "title": "SPICE: An Automated SWE-Bench Labeling Pipeline for Issue", "date": "", "ddg_snippet": "... associated with SPICE? In its default setting, SPICE costs only $5.10 to label 1,000 instances 1 1 1 All reported monetary values in this paper are ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09108v5", "content": "... associated with SPICE? In its default setting, SPICE costs only $5.10 to label 1,000 instances 1 1 1 All reported monetary values in this paper are ..."}
+{"idx": 8, "title": "cAST: Enhancing Code Retrieval-Augmented Generation with", "date": "", "ddg_snippet": "... in hybrid code+natural language tasks, e.g., up to 2.7 points gain on SWE-bench ( Jimenez et al., 2024 ) , which focuses on resolving GitHub issues.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.15655v1", "content": "... in hybrid code+natural language tasks, e.g., up to 2.7 points gain on SWE-bench ( Jimenez et al., 2024 ) , which focuses on resolving GitHub issues."}
+{"idx": 9, "title": "Establishing Best Practices for Building Rigorous Agentic", "date": "", "ddg_snippet": "... G NeurIPS Paper Checklist ... Abstract", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02825v5", "content": "... G NeurIPS Paper Checklist ... Abstract"}
diff --git a/data/sampled_jsons/SWEBench_Jimenez_et_al_2024_paper_abstract.jsonl b/data/sampled_jsons/SWEBench_Jimenez_et_al_2024_paper_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6fc5007ebf5566e1d342bf9c5ed8213ae0b1351c
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao ... A arXiv:2310.06770v3 [cs.CL] 11 Nov 2024 - r.jordan.im Paper page - SWE-bench: Can Language Models Resolve Real ... SWE-bench: Can Language Models Resolve Real-World GitHub Issues? g SWE- : C L M R F 7> BENCH AN ANGUAGE ODELS ESOLVE REAL ... SWE-BENCH : CAN LANGUAGE MODELS RESOLVE REAL-WORLD GITHU… [2410.03859] SWE-bench Multimodal: Do AI Systems Generalize to Visual Paper page - SWE-bench: Can Language Models Resolve Real-World Git… SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [2410.03859] SWE-bench Multimodal: Do AI Systems Generalize to Visual F 7> SWE- : C LANGUAGE MODELS RESOLVE R -W G HUB ISSUES - Ope… [2310.06770] SWE-bench: Can Language Models Resolve Real ...", "date": "", "ddg_snippet": "Abstract Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. ABSTRACT Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we in-troduce SWE-bench , an evaluation framework consisting of ... Oct 10, 2023 · Join the discussion on this paper pageSWE-bench: Can Language Models Resolve Real-World GitHub Issues? SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests across popular Python repositories that shows that both state-of-the-art proprietary models and the fine-tuned model SWE-Llama can resolve only the simplest issues. Language models have outpaced our ability to evaluate them effectively, but ... 023) or design library-specific problems (Lai et al ., 2022; Zan et al ., 2022). Instead of partitioning problems into siloed datasets and curtailing them for simplicity’s sake, SWE-bench ’s collection procedure transforms the source code with minimal post-processing, preserving a much broader set of challenges grounded in real-world software ... What is SWE-bench? To this end, we introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Does Swe-bench use GitHub repositories? These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories , with problem statements presented predominantly as text and lacking visual elements such as images. What are advances on SWE-bench? Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous . This is an automated message from the Librarian Bot. I found the following papers similar to this paper. The following papers were recommended by the Semantic Scholar API Which Swe-bench model is best? The best-performing model, Claude 2 , is able to solve a mere 1.96 % of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) Does Swe-bench m support visual problem solving? Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. Does Claude 2 solve all the problems in SWE-bench? We evaluate multiple state-of-the-art LMs on SWE-bench and find that they fail to solve all except the simplest issues. Using a BM25 retriever, Claude 2 is only able to resolve 1.96% of the issues. Oct 10, 2023 · View a PDF of the paper titled SWE-bench : Can Language Models Resolve Real-World GitHub Issues?, by Carlos E. Jimenez and 6 other authors", "subpage_snippet": "", "source": "collaborate.princeton.edu", "link": "https://collaborate.princeton.edu/en/publications/swe-bench-can-language-models-resolve-real-world-github-issues", "content": "Abstract Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. ABSTRACT Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we in-troduce SWE-bench , an evaluation framework consisting of ... Oct 10, 2023 · Join the discussion on this paper pageSWE-bench: Can Language Models Resolve Real-World GitHub Issues? SWE-bench is introduced, an evaluation framework consisting of software engineering problems drawn from real GitHub issues and corresponding pull requests across popular Python repositories that shows that both state-of-the-art proprietary models and the fine-tuned model SWE-Llama can resolve only the simplest issues. Language models have outpaced our ability to evaluate them effectively, but ... 023) or design library-specific problems (Lai et al ., 2022; Zan et al ., 2022). Instead of partitioning problems into siloed datasets and curtailing them for simplicity’s sake, SWE-bench ’s collection procedure transforms the source code with minimal post-processing, preserving a much broader set of challenges grounded in real-world software ... What is SWE-bench? To this end, we introduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Does Swe-bench use GitHub repositories? These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories , with problem statements presented predominantly as text and lacking visual elements such as images. What are advances on SWE-bench? Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous . This is an automated message from the Librarian Bot. I found the following papers similar to this paper. The following papers were recommended by the Semantic Scholar API Which Swe-bench model is best? The best-performing model, Claude 2 , is able to solve a mere 1.96 % of the issues. Advances on SWE-bench represent steps towards LMs that are more practical, intelligent, and autonomous. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) Does Swe-bench m support visual problem solving? Each SWE-bench M task instance contains at least one image in its problem statement or unit tests. Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization. Does Claude 2 solve all the problems in SWE-bench? We evaluate multiple state-of-the-art LMs on SWE-bench and find that they fail to solve all except the simplest issues. Using a BM25 retriever, Claude 2 is only able to resolve 1.96% of the issues. Oct 10, 2023 · View a PDF of the paper titled SWE-bench : Can Language Models Resolve Real-World GitHub Issues?, by Carlos E. Jimenez and 6 other authors"}
+{"idx": 1, "title": "A arXiv:2310.06770v3 [cs.CL] 11 Nov 2024 - r.jordan.im", "date": "", "ddg_snippet": "ABSTRACT Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we in-troduce SWE-bench , an evaluation framework consisting of ...", "subpage_snippet": "", "source": "r.jordan.im", "link": "https://r.jordan.im/download/language-models/jimenez2023.pdf", "content": "ABSTRACT Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we in-troduce SWE-bench , an evaluation framework consisting of ..."}
+{"idx": 2, "title": "Paper page - SWE-bench: Can Language Models Resolve Real ...", "date": "", "ddg_snippet": "Oct 10, 2023 · Join the discussion on this paper pageSWE-bench: Can Language Models Resolve Real-World GitHub Issues?", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2310.06770", "content": "Oct 10, 2023 · Join the discussion on this paper pageSWE-bench: Can Language Models Resolve Real-World GitHub Issues?"}
+{"idx": 3, "title": "GitHub - Laredo-Labs/ SWEbench : [ICLR 2024 ] SWE - Bench : Can...", "date": "", "ddg_snippet": "[ICLR 2024 ] SWE - Bench : Can Language Models Resolve Real-world Github Issues?13, 2024 ]: Introducing SWE - bench Verified! Part 2 of our collaboration with OpenAI Preparedness. A subset of 500 problems that real software engineers have confirmed are solvable.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Laredo-Labs/SWEbench", "content": "[ICLR 2024 ] SWE - Bench : Can Language Models Resolve Real-world Github Issues?13, 2024 ]: Introducing SWE - bench Verified! Part 2 of our collaboration with OpenAI Preparedness. A subset of 500 problems that real software engineers have confirmed are solvable."}
+{"idx": 4, "title": "SOTA on swebench -verified: (re) learning the bitter lesson", "date": "", "ddg_snippet": "Dec 13, 2024 , Sandeep Kumar Pani.Aide is now the SOTA on swebench -verified, resolving 62.2% of the issues on the benchmark. We did this by scaling our agent on test time inference and re-learning the bitter lesson.", "subpage_snippet": "", "source": "aide.dev", "link": "https://aide.dev/blog/sota-bitter-lesson", "content": "Dec 13, 2024 , Sandeep Kumar Pani.Aide is now the SOTA on swebench -verified, resolving 62.2% of the issues on the benchmark. We did this by scaling our agent on test time inference and re-learning the bitter lesson."}
+{"idx": 5, "title": "SWE - bench : Can Language Models Resolve Real-World GitHub Issues?", "date": "", "ddg_snippet": "Abstract :Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2310.06770", "content": "Abstract :Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable..."}
+{"idx": 6, "title": "[2410.03859] SWE-bench Multimodal: Do AI Systems Generalize ...", "date": "", "ddg_snippet": "Oct 4, 2024 · Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03859", "content": "Oct 4, 2024 · Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench ( Jimenez et al ., 2024a), which assesses their ability to solve software issues from GitHub repositories. However, SWE-bench uses only Python repositories, with problem statements presented predominantly as text and lacking visual elements such as ..."}
+{"idx": 7, "title": "HAL: SWE - bench Verified Mini Leaderboard", "date": "", "ddg_snippet": "( Jimenez et al ., 2023) OpenAI Blog: SWE - bench Verified: Introducing SWE - bench Verified.Output Token Price. $ /1M tokens. GPT-4o (November 2024 ). Active. Input Token Price.", "subpage_snippet": "", "source": "hal.cs.princeton.edu", "link": "https://hal.cs.princeton.edu/swebench_verified_mini", "content": "( Jimenez et al ., 2023) OpenAI Blog: SWE - bench Verified: Introducing SWE - bench Verified.Output Token Price. $ /1M tokens. GPT-4o (November 2024 ). Active. Input Token Price."}
+{"idx": 8, "title": "CSR-Bench: Benchmarking LLM Agents in Deployment of", "date": "", "ddg_snippet": "Agentless (Xia et al ., 2024 a) and CodeStar(Li et al ., 2023)), code correction ( SWEBench ( Jimenez et al ., 2024 )), and more. In computer science research projects, the as-sociated codebases grow very rapidly, and a self-consistent codebase typically has several parts...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.naacl-long.633.pdf", "content": "Agentless (Xia et al ., 2024 a) and CodeStar(Li et al ., 2023)), code correction ( SWEBench ( Jimenez et al ., 2024 )), and more. In computer science research projects, the as-sociated codebases grow very rapidly, and a self-consistent codebase typically has several parts..."}
+{"idx": 9, "title": "We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts...", "date": "", "ddg_snippet": "2024 ). Additionally, DeepSeek-Coder-V2 is the first open-source model that surpasses a score of 10% on SWEBench ( Jimenez et al ., 2023).To evaluate the bug-fixing capabilities of the model, we used the Defects4J 7, SWE - bench ( Jimenez et al ., 2023), and Aider 8 datasets for testing.", "subpage_snippet": "", "source": "aarnphm.xyz", "link": "https://aarnphm.xyz/thoughts/papers/2406.11931v1.pdf", "content": "2024 ). Additionally, DeepSeek-Coder-V2 is the first open-source model that surpasses a score of 10% on SWEBench ( Jimenez et al ., 2023).To evaluate the bug-fixing capabilities of the model, we used the Defects4J 7, SWE - bench ( Jimenez et al ., 2023), and Aider 8 datasets for testing."}
diff --git a/data/sampled_jsons/Safety_Fine-tuning_Mechanistic_Study_Jain_Lubana_Oksuz.jsonl b/data/sampled_jsons/Safety_Fine-tuning_Mechanistic_Study_Jain_Lubana_Oksuz.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..66cffbd20ad8603f4cfe900a17dc23bb02b8bb9e
--- /dev/null
+++ b/data/sampled_jsons/Safety_Fine-tuning_Mechanistic_Study_Jain_Lubana_Oksuz.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Authors Samyak Jain , Ekdeep Singh Lubana , Kemal Oksuz , Tom Joy, Philip H.S. Torr, Amartya Sanyal, Puneet K. Dokania Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/a9bef53eb7b0e5950d4f2d9c74a16006-Abstract-Conference.html", "content": "Authors Samyak Jain , Ekdeep Singh Lubana , Kemal Oksuz , Tom Joy, Philip H.S. Torr, Amartya Sanyal, Puneet K. Dokania Abstract Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ..."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.10264", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ..."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "What Makes and Breaks Safety Fine-tuning ? Mechanistic Study Samyak Jain 1 Ekdeep Singh Lubana 2 3 Kemal Oksuz 1 Tom Joy 1 Philip H.S. Torr 4 Amartya Sanyal 5", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=BS2CbUkJpy", "content": "What Makes and Breaks Safety Fine-tuning ? Mechanistic Study Samyak Jain 1 Ekdeep Singh Lubana 2 3 Kemal Oksuz 1 Tom Joy 1 Philip H.S. Torr 4 Amartya Sanyal 5"}
+{"idx": 3, "title": "PDF What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks. We make careful design choices to adhere to the properties of natural language instructions and the jailbreaks taxonomy of Wei et al. (2023), thus facilitating a thorough ..."}
+{"idx": 4, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "A study on safety fine-tuning methods for Large Language Models (LLMs) was conducted by Samyak Jain , Ekdeep Singh Lubana , Kemal Oksuz , Tom Joy, Philip H. S. Torr, Amartya Sanyal, and Puneet K. Dokania to ensure their alignment with human preferences for safe deployment. The researchers developed a synthetic data generation framework to understand the factors that contribute to model safety ...", "subpage_snippet": "", "source": "www.summarizepaper.com", "link": "https://www.summarizepaper.com/en/arxiv-id/2407.10264v3/", "content": "A study on safety fine-tuning methods for Large Language Models (LLMs) was conducted by Samyak Jain , Ekdeep Singh Lubana , Kemal Oksuz , Tom Joy, Philip H. S. Torr, Amartya Sanyal, and Puneet K. Dokania to ensure their alignment with human preferences for safe deployment. The researchers developed a synthetic data generation framework to understand the factors that contribute to model safety ..."}
+{"idx": 5, "title": "What makes and breaks safety fine-tuning? a mechanistic study", "date": "", "ddg_snippet": "Jain S, Lubana E, Oksuz K, Joy T, Sanyal A, Torr P, Dokania P 31 July 2024 Conference paper Journal: Proceedings of the Mechanistic Interpretability Workshop 2024 hosted by the 13th International Conference on Machine Learning (ICML 2024) , OpenReview", "subpage_snippet": "", "source": "intelligent-earth.ox.ac.uk", "link": "https://intelligent-earth.ox.ac.uk/publication/2036881/ora-hyrax", "content": "Jain S, Lubana E, Oksuz K, Joy T, Sanyal A, Torr P, Dokania P 31 July 2024 Conference paper Journal: Proceedings of the Mechanistic Interpretability Workshop 2024 hosted by the 13th International Conference on Machine Learning (ICML 2024) , OpenReview"}
+{"idx": 6, "title": "What Makes and Breaks Safety Fine-tuning? Mechanistic Study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., ``design ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240710264J/abstract", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., ``design ..."}
+{"idx": 7, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning , direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "Using this, we investigate three well-known safety fine-tuning methods—supervised safety fine-tuning , direct preference optimization, and unlearning—and provide significant evidence demonstrating that these methods minimally transform MLP weights to specifically align unsafe inputs into its weights' null space."}
+{"idx": 8, "title": "What Makes Safety Fine-tuning Methods Safe? A Mechanistic Study", "date": "", "ddg_snippet": "Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems.", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/publications/jainetal24", "content": "Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems."}
+{"idx": 9, "title": "What Makes Safety Fine-tuning Methods Safe? A Mechanistic Study", "date": "", "ddg_snippet": "The type of inference can vary, including for instance inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution).", "subpage_snippet": "", "source": "ei.is.mpg.de", "link": "https://ei.is.mpg.de/publications/jainetal24-3e26a74a-5035-4586-9b5e-8802b7051254", "content": "The type of inference can vary, including for instance inductive learning (estimation of models such as functional dependencies that generalize to novel data sampled from the same underlying distribution)."}
diff --git a/data/sampled_jsons/Sampling_from_log-concave_distributions_is_a_well_researched_problem_Shen_Lee_2019.jsonl b/data/sampled_jsons/Sampling_from_log-concave_distributions_is_a_well_researched_problem_Shen_Lee_2019.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..41d747e68093c0d9d6aed06b852fd1ec20a0608e
--- /dev/null
+++ b/data/sampled_jsons/Sampling_from_log-concave_distributions_is_a_well_researched_problem_Shen_Lee_2019.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[1909.05503] The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "Sep 12, 2019 · Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form $p^ {*}\\propto\\exp (-f (x))$, where $f:\\mathbb {R}^ {d}\\rightarrow\\mathbb {R}$ has an $L$-Lipschitz gradient and is $m$-strongly convex.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "Sep 12, 2019 · Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form $p^ {*}\\propto\\exp (-f (x))$, where $f:\\mathbb {R}^ {d}\\rightarrow\\mathbb {R}$ has an $L$-Lipschitz gradient and is $m$-strongly convex."}
+{"idx": 1, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd !", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd !"}
+{"idx": 2, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Authors Ruoqi Shen , Yin Tat Lee Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form $p^ {*}\\propto\\exp (-f (x))$, where $f:\\mathbb {R}^ {d}\\rightarrow\\mathbb {R}$ has an $L$-Lipschitz gradient and is $m$-strongly convex.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/hash/eb86d510361fc23b59f18c1bc9802cc6-Abstract.html", "content": "Authors Ruoqi Shen , Yin Tat Lee Abstract Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions of the form $p^ {*}\\propto\\exp (-f (x))$, where $f:\\mathbb {R}^ {d}\\rightarrow\\mathbb {R}$ has an $L$-Lipschitz gradient and is $m$-strongly convex."}
+{"idx": 3, "title": "Shen, Luhui, Zhang, Jian, Lee, HoJoon, Batista, Milene ...", "date": "", "ddg_snippet": "2 days ago · Shen , Luhui, Zhang, Jian, Lee , HoJoon, Batista, Milene Tavares, Johnston, Stephen Albert ( 2019 ) RNA Transcription and Splicing Errors as a Source of Cancer Frameshift Neoantigens for Vaccines.", "subpage_snippet": "", "source": "www.mindat.org", "link": "https://www.mindat.org/reference.php?id=4835481", "content": "2 days ago · Shen , Luhui, Zhang, Jian, Lee , HoJoon, Batista, Milene Tavares, Johnston, Stephen Albert ( 2019 ) RNA Transcription and Splicing Errors as a Source of Cancer Frameshift Neoantigens for Vaccines."}
+{"idx": 4, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applica-tions in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradient and is m-strongly convex.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503.pdf", "content": "Yin Tat Lee University of Washington and Microsoft Research yintat@uw.edu Abstract Sampling from log-concave distributions is a well researched problem that has many applica-tions in statistics and machine learning. We study the distributions of the form p / exp( f(x)), where f : Rd ! R has an L-Lipschitz gradient and is m-strongly convex."}
+{"idx": 5, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/8483-the-randomized-midpoint-method-for-log-concave-sampling", "content": "by R Shen · 2019 · Cited by 161 — Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the distributions ..."}
+{"idx": 6, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — Sampling from log-concave distributions is a well researched problem that has many applica- tions in statistics and machine learning. We ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — Sampling from log-concave distributions is a well researched problem that has many applica- tions in statistics and machine learning. We ..."}
+{"idx": 7, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "by R Shen · 2019 · Cited by 160 — Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning."}
+{"idx": 8, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "Abstract: Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ...", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2019/spotlight/15747", "content": "Abstract: Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ..."}
+{"idx": 9, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Abstract: Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/papers/the-randomized-midpoint-method-for-log-concave-sampling-4jwrm4tta7", "content": "Abstract: Sampling from log-concave distributions is a well researched problem that has many applications in statistics and machine learning. We study the ..."}
diff --git a/data/sampled_jsons/Sariyildiz_et_al._2023_ImageNet-100_synthetic_real_train_accuracy_year_2023.jsonl b/data/sampled_jsons/Sariyildiz_et_al._2023_ImageNet-100_synthetic_real_train_accuracy_year_2023.jsonl
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index 0000000000000000000000000000000000000000..0df935f3698754453771a6556545902cfa746c5e
--- /dev/null
+++ b/data/sampled_jsons/Sariyildiz_et_al._2023_ImageNet-100_synthetic_real_train_accuracy_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "SUB: Benchmarking CBM Generalization via Synthetic Attribute", "date": "", "ddg_snippet": "Alongside the improved capabilities of these models, there is increasing exploration into synthetic data, both for training [ 54 , 22 , 29 , 16 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.23784v1", "content": "Alongside the improved capabilities of these models, there is increasing exploration into synthetic data, both for training [ 54 , 22 , 29 , 16 ..."}
+{"idx": 1, "title": "[2304.08466] Synthetic Data from Diffusion Models Improves ImageNet ...", "date": "", "ddg_snippet": "The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.08466", "content": "The model also yields a new SOTA in Classification Accuracy Scores (64.96 for 256x256 generative samples, improving to 69.24 for 1024x1024 samples). Augmenting the ImageNet training set with samples from the resulting models yields significant improvements in ImageNet classification accuracy over strong ResNet and Vision Transformer baselines."}
+{"idx": 2, "title": "Synthetic Data from Diffusion Models Improves ImageNet Classification", "date": "", "ddg_snippet": "Sariyildiz et al (2022) reported 42.9% Top-1 accuracy on this task, well below performance with Big-GAN-deep and VQ-VAE-2 (as shown in Ravuri and Vinyals, 2019) and CDM (Ho et al , 2022); this is discussed in Section 2.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=DlRsoxjyPm", "content": "Sariyildiz et al (2022) reported 42.9% Top-1 accuracy on this task, well below performance with Big-GAN-deep and VQ-VAE-2 (as shown in Ravuri and Vinyals, 2019) and CDM (Ho et al , 2022); this is discussed in Section 2."}
+{"idx": 3, "title": "PDF Published as a conference paper at ICLR 2023 - Inria", "date": "", "ddg_snippet": "ABSTRACT We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet -1K (IN1K), so that it excels at both the training task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model's generalization and maintaining its performance on the original task. Models trained ...", "subpage_snippet": "", "source": "thoth.inrialpes.fr", "link": "https://thoth.inrialpes.fr/~alahari/papers/sariyildiz23.pdf", "content": "ABSTRACT We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet -1K (IN1K), so that it excels at both the training task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model's generalization and maintaining its performance on the original task. Models trained ..."}
+{"idx": 4, "title": ": Learning transferable representations from synthetic ImageNet clones", "date": "", "ddg_snippet": "Figure 1. Feature analyses for models. We perform these analyses on top of features extracted from pretrained encoders f trained on either real or synthetic data for ImageNet-100 (training data is specified in the legends of the subfigures). For the purpose of this study, we use synthetic data generated with guidance scale equal to 7.5. Sparsity is measured by the percentage of dimensions ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/:-Learning-transferable-representations-from-clones-Sariyildiz-Karteek/6c8e04911fb665f8b2f8d4a0ad80b54e37c8223a/figure/1", "content": "Figure 1. Feature analyses for models. We perform these analyses on top of features extracted from pretrained encoders f trained on either real or synthetic data for ImageNet-100 (training data is specified in the legends of the subfigures). For the purpose of this study, we use synthetic data generated with guidance scale equal to 7.5. Sparsity is measured by the percentage of dimensions ..."}
+{"idx": 5, "title": "CVPR 2023 Open Access Repository", "date": "", "ddg_snippet": "We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/html/Sariyildiz_Fake_It_Till_You_Make_It_Learning_Transferable_Representations_From_CVPR_2023_paper.html", "content": "We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study."}
+{"idx": 6, "title": "sariyildiz23a | PDF | Cognitive Science | Artificial Intelligence", "date": "", "ddg_snippet": "Fake it till you make it: Learning transferable representations from synthetic ImageNet clones Mert Bulent Sariyildiz , Karteek Alahari, Diane Larlus, Yannis Kalantidis To cite this version: Mert Bulent Sariyildiz , Karteek Alahari, Diane Larlus, Yannis Kalantidis. Fake it till you make it: Learning transferable representations from synthetic ImageNet clones. CVPR 2023 - IEEE/CVF Conference on ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/855031777/sariyildiz23a", "content": "Fake it till you make it: Learning transferable representations from synthetic ImageNet clones Mert Bulent Sariyildiz , Karteek Alahari, Diane Larlus, Yannis Kalantidis To cite this version: Mert Bulent Sariyildiz , Karteek Alahari, Diane Larlus, Yannis Kalantidis. Fake it till you make it: Learning transferable representations from synthetic ImageNet clones. CVPR 2023 - IEEE/CVF Conference on ..."}
+{"idx": 7, "title": "Feedback-guided Data Synthesis for Imbalanced Classification", "date": "", "ddg_snippet": "... proposed using generative models as either data augmentation or sole source of data to train machine learning models (He et al ., 2023 ; Sariyildiz ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.00158v2", "content": "... proposed using generative models as either data augmentation or sole source of data to train machine learning models (He et al ., 2023 ; Sariyildiz ..."}
+{"idx": 8, "title": "Personalized Representation from Personalized Generation", "date": "", "ddg_snippet": "Follow-up works to these have sought to improve the efficiency and accuracy of personalized generations (Ruiz et al ., 2023 ; Arar et al ., 2023 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16156v1", "content": "Follow-up works to these have sought to improve the efficiency and accuracy of personalized generations (Ruiz et al ., 2023 ; Arar et al ., 2023 ..."}
+{"idx": 9, "title": "DreamSim: Learning New Dimensions of Human Visual Similarity", "date": "", "ddg_snippet": "Notably, despite being trained on synthetic data, our metric generalizes to real images, giving strong results on retrieval and reconstruction tasks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2306.09344v3", "content": "Notably, despite being trained on synthetic data, our metric generalizes to real images, giving strong results on retrieval and reconstruction tasks."}
diff --git "a/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_synthetic_data_ImageNet-100.jsonl" "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_synthetic_data_ImageNet-100.jsonl"
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index 0000000000000000000000000000000000000000..a0873d21c3481bd3abbf1539f65bd2b02de8db0e
--- /dev/null
+++ "b/data/sampled_jsons/Sar\304\261y\304\261ld\304\261z_et_al._2023_synthetic_data_ImageNet-100.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Enhancing Generalization in Data-free Quantization via", "date": "", "ddg_snippet": "Although the pre-trained text-conditioned LDM enables the generation of synthetic images for PTQ without requiring additional training of generative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.21947v1", "content": "Although the pre-trained text-conditioned LDM enables the generation of synthetic images for PTQ without requiring additional training of generative ..."}
+{"idx": 1, "title": "ODGEN: Domain-specific Object Detection Data Generation with", "date": "", "ddg_snippet": "Extensive experimental results show that adding our synthetic data improves up to 25.3% mAP@.50:.95 on YOLO detectors, outperforming prior ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.15199v2", "content": "Extensive experimental results show that adding our synthetic data improves up to 25.3% mAP@.50:.95 on YOLO detectors, outperforming prior ..."}
+{"idx": 2, "title": "SynC: Synthetic Image Caption Dataset Refinement with", "date": "", "ddg_snippet": "Zero-shot Image Captioning (ZIC) increasingly ... Instead of iteratively regenerating images until alignment is achieved ( Sar ı y ı ld ı z et al .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.18616v1", "content": "Zero-shot Image Captioning (ZIC) increasingly ... Instead of iteratively regenerating images until alignment is achieved ( Sar ı y ı ld ı z et al ."}
+{"idx": 3, "title": "Yannis Kalantidis | DeepAI", "date": "", "ddg_snippet": "Fake it till you make it: Learning( s ) from a synthetic ImageNet clone ... 0 Mert Bülent Sar ı y ı ld ı z , et al . ... 0 Yannis Kalantidis, et al .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/profile/yannis-kalantidis", "content": "Fake it till you make it: Learning( s ) from a synthetic ImageNet clone ... 0 Mert Bülent Sar ı y ı ld ı z , et al . ... 0 Yannis Kalantidis, et al ."}
+{"idx": 4, "title": "Use your Google Account for YouTube", "date": "", "ddg_snippet": "After signing up for YouTube, signing in to your Google account on another Google service will automatically sign you in to YouTube. Deleting your Google Account will delete your YouTube data , including all videos, comments, and subscriptions.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/answer/69961?hl=en", "content": "After signing up for YouTube, signing in to your Google account on another Google service will automatically sign you in to YouTube. Deleting your Google Account will delete your YouTube data , including all videos, comments, and subscriptions."}
+{"idx": 5, "title": "ViPer: Visual Personalization of Generative Models via", "date": "", "ddg_snippet": "They also strongly favor personalized results tailored to their preferences compared to non-personalized results or results from other users.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2407.17365v1", "content": "They also strongly favor personalized results tailored to their preferences compared to non-personalized results or results from other users."}
+{"idx": 6, "title": "YouTube Help - Google Help", "date": "", "ddg_snippet": "Learn more about YouTube YouTube help videos Browse our video library for helpful tips, feature overviews, and step-by-step tutorials. YouTube Known Issues Get information on reported technical issues or scheduled maintenance.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/?hl=en", "content": "Learn more about YouTube YouTube help videos Browse our video library for helpful tips, feature overviews, and step-by-step tutorials. YouTube Known Issues Get information on reported technical issues or scheduled maintenance."}
+{"idx": 7, "title": "Create an account on YouTube - Computer - YouTube Help", "date": "", "ddg_snippet": "Once you've signed in to YouTube with your Google Account, you can create a YouTube channel on your account. YouTube channels let you upload videos, leave comments, and create playlists.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/answer/161805?hl=en&co=GENIE.Platform=Desktop", "content": "Once you've signed in to YouTube with your Google Account, you can create a YouTube channel on your account. YouTube channels let you upload videos, leave comments, and create playlists."}
+{"idx": 8, "title": "Get help signing in to YouTube - Google Help", "date": "", "ddg_snippet": "To make sure you’re getting the directions for your account, select from the options below.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/troubleshooter/3219097?hl=en", "content": "To make sure you’re getting the directions for your account, select from the options below."}
+{"idx": 9, "title": "Age-restricted content - YouTube Help", "date": "", "ddg_snippet": "This policy applies to videos, video descriptions, custom thumbnails, live streams, and any other YouTube product or feature. Learn more about age-restriction Below is more detail about the types of content we consider for age-restriction. If your content contains one or more of these themes, we may age-restrict.", "subpage_snippet": "", "source": "support.google.com", "link": "https://support.google.com/youtube/answer/2802167?hl=en", "content": "This policy applies to videos, video descriptions, custom thumbnails, live streams, and any other YouTube product or feature. Learn more about age-restriction Below is more detail about the types of content we consider for age-restriction. If your content contains one or more of these themes, we may age-restrict."}
diff --git a/data/sampled_jsons/Schulman_PPO_2017_Table_6_A2C_Atari_games_Alien_Centipede_performance.jsonl b/data/sampled_jsons/Schulman_PPO_2017_Table_6_A2C_Atari_games_Alien_Centipede_performance.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f415808750ff0749617c704dc5164b7bb802a8b5
--- /dev/null
+++ b/data/sampled_jsons/Schulman_PPO_2017_Table_6_A2C_Atari_games_Alien_Centipede_performance.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg ...", "date": "", "ddg_snippet": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games . Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance . 10", "subpage_snippet": "", "source": "people.engr.tamu.edu", "link": "https://people.engr.tamu.edu/guni/csce642/files/ppo.pdf", "content": "Here we include a comparison of PPO against A2C on a larger collection of 49 Atari games . Figure 6 shows the learning curves of each of three random seeds, while Table 6 shows the mean performance . 10"}
+{"idx": 1, "title": "GitHub - lucaslingle/pytorch_ppo_atari: Implementation of Proximal ...", "date": "", "ddg_snippet": "Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lucaslingle/pytorch_ppo_atari", "content": "Proximal Policy Optimization is a reinforcement learning algorithm proposed by Schulman et al., 2017 . Compared to vanilla policy gradients and/or actor-critic methods, which optimize the model parameters by estimating the gradient of the reward surface and taking a single step, PPO takes inspiration ..."}
+{"idx": 2, "title": "RIMs-PPO relative score improvement over LSTM-PPO baseline (Schulman et ...", "date": "", "ddg_snippet": "RIMs- PPO relative score improvement over LSTM- PPO baseline ( Schulman et al., 2017 ) across all Atari games averaged over 3 trials per game . In both cases, PPO was used with the exact same settings ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/RIMs-PPO-relative-score-improvement-over-LSTM-PPO-baseline-Schulman-et-al-2017-across_fig6_336019045", "content": "RIMs- PPO relative score improvement over LSTM- PPO baseline ( Schulman et al., 2017 ) across all Atari games averaged over 3 trials per game . In both cases, PPO was used with the exact same settings ..."}
+{"idx": 3, "title": "PDF 7 Performance on More Atari Games", "date": "", "ddg_snippet": "Additionally, we showcase the performance of MOREL on all 59 Atari games when combined with A2C (Figure 6 and Figure 7) and PPO (Figure 8 and Figure 9). We observe a notable improvement on the A2C runs for 26 of the games , and a worse performance in only 3 games . For PPO , composition with MOREL provides a benefit in 25 games , and a decrease in performance in 9 games .", "subpage_snippet": "", "source": "cs.uwaterloo.ca", "link": "https://cs.uwaterloo.ca/~ppoupart/publications/rl/MOREL-supplementary.pdf", "content": "Additionally, we showcase the performance of MOREL on all 59 Atari games when combined with A2C (Figure 6 and Figure 7) and PPO (Figure 8 and Figure 9). We observe a notable improvement on the A2C runs for 26 of the games , and a worse performance in only 3 games . For PPO , composition with MOREL provides a benefit in 25 games , and a decrease in performance in 9 games ."}
+{"idx": 4, "title": "Atari Games with Proximal Policy Optimization - Medium", "date": "", "ddg_snippet": "The first of all we need to explain \"What's Atari Games ?\": Atari console, video game console released in 1977 by the North American game manufacturer Atari , Inc. Using a cartridge-based ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@shogulomkurganov73/atari-games-with-proximal-policy-optimization-ed28c7fafa3f", "content": "The first of all we need to explain \"What's Atari Games ?\": Atari console, video game console released in 1977 by the North American game manufacturer Atari , Inc. Using a cartridge-based ..."}
+{"idx": 5, "title": "[1707.06347] Proximal Policy Optimization Algorithms - arXiv.org", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1707.06347", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
+{"idx": 6, "title": "(PDF) Proximal Policy Optimization Algorithms - Academia.edu", "date": "", "ddg_snippet": "Table 4: PPO hyperparameters used for the Roboschool experiments. Adam stepsize was adjusted based on the target value of the KL divergence. Performance on More Atari Games Table 6 : Mean final scores (last 100 episodes) of PPO and A2C on Atari games after 40M game frames (10M timesteps).", "subpage_snippet": "", "source": "www.academia.edu", "link": "https://www.academia.edu/72572628/Proximal_Policy_Optimization_Algorithms", "content": "Table 4: PPO hyperparameters used for the Roboschool experiments. Adam stepsize was adjusted based on the target value of the KL divergence. Performance on More Atari Games Table 6 : Mean final scores (last 100 episodes) of PPO and A2C on Atari games after 40M game frames (10M timesteps)."}
+{"idx": 7, "title": "PDF Proximal Policy Optimization Algorithms - TUM", "date": "", "ddg_snippet": "Proximal Policy Optimization - Experiments Atari Domain (discrete control, episodic environment) experiments on 49 Atari games from the Arcade Learning Environment [Bel+13] comparison with other approaches ( A2C [Mni+16] and ACER [Wan+16])", "subpage_snippet": "", "source": "dvl.in.tum.de", "link": "https://dvl.in.tum.de/slides/automl-ss19/01_stadler_ppo.pdf", "content": "Proximal Policy Optimization - Experiments Atari Domain (discrete control, episodic environment) experiments on 49 Atari games from the Arcade Learning Environment [Bel+13] comparison with other approaches ( A2C [Mni+16] and ACER [Wan+16])"}
+{"idx": 8, "title": "PPTX University of Texas at Austin", "date": "", "ddg_snippet": "RoboschoolHumanoidFlagrunHarder Learning curves of three tasks Results: PPO Showcase Continuous Domain Experiment: PPO vs A2C vs ACER ( Atari Domain) Arcade Learning Environment (49 games ) Winner for each game defined by score metric Scoring metrics:", "subpage_snippet": "", "source": "www.cs.utexas.edu", "link": "https://www.cs.utexas.edu/~robertom/cs391r_fall2022/slides/PPO+CS391R+Presentation+(Roberto+Ruiz).pptx", "content": "RoboschoolHumanoidFlagrunHarder Learning curves of three tasks Results: PPO Showcase Continuous Domain Experiment: PPO vs A2C vs ACER ( Atari Domain) Arcade Learning Environment (49 games ) Winner for each game defined by score metric Scoring metrics:"}
+{"idx": 9, "title": "Proximal Policy Optimization Algorithms - Semantic Scholar", "date": "", "ddg_snippet": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Proximal-Policy-Optimization-Algorithms-Schulman-Wolski/dce6f9d4017b1785979e7520fd0834ef8cf02f4b", "content": "Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time."}
diff --git a/data/sampled_jsons/Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_Yang_Song_abstract_unstabl.jsonl b/data/sampled_jsons/Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_Yang_Song_abstract_unstabl.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..718f55157a624ddad40360eca3351cf055aec149
--- /dev/null
+++ b/data/sampled_jsons/Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_Yang_Song_abstract_unstabl.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "View a PDF of the paper titled Score-Based Generative Modeling through Stochastic Differential Equations , by Yang Song and 4 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2011.13456v2", "content": "View a PDF of the paper titled Score-Based Generative Modeling through Stochastic Differential Equations , by Yang Song and 4 other authors"}
+{"idx": 1, "title": "GitHub - yang-song/score_sde: Official code for Score-Based Generative ...", "date": "", "ddg_snippet": "Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations by Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yang-song/score_sde", "content": "Score-Based Generative Modeling through Stochastic Differential Equations This repo contains the official implementation for the paper Score-Based Generative Modeling through Stochastic Differential Equations by Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole"}
+{"idx": 2, "title": "Paper review: Score-Based Generative Modeling through Stochastic ...", "date": "", "ddg_snippet": "Generative Modeling by Estimating Gradients of the Data Distribution, Oct. 2020. arXiv:1907.05600 [cs, stat]. 2 [17] Yang Song , Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=Lq316r4Lx9", "content": "Generative Modeling by Estimating Gradients of the Data Distribution, Oct. 2020. arXiv:1907.05600 [cs, stat]. 2 [17] Yang Song , Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations ."}
+{"idx": 3, "title": "Score-based generative modeling through stochastic differential equations", "date": "", "ddg_snippet": "Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/pubs/score-based-generative-modeling-through-stochastic-differential-equations/", "content": "Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially ..."}
+{"idx": 4, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "This work presents a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by Slowly removing the noise. Creating noise from data is easy; creating data from noise is generative modeling. We ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Score-Based-Generative-Modeling-through-Stochastic-Song-Sohl-Dickstein/633e2fbfc0b21e959a244100937c5853afca4853", "content": "This work presents a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution back into the data distribution by Slowly removing the noise. Creating noise from data is easy; creating data from noise is generative modeling. We ..."}
+{"idx": 5, "title": "Score-Based Generative Modeling with SDEs: Theory and ... - Medium", "date": "", "ddg_snippet": "Abstract This article explores score-based generative modeling through stochastic differential equations (SDEs), a powerful framework for transforming complex data distributions into noise and ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@samaniloqman91/score-based-generative-modeling-with-sdes-theory-and-implementation-in-torchdiff-db9a20a84978", "content": "Abstract This article explores score-based generative modeling through stochastic differential equations (SDEs), a powerful framework for transforming complex data distributions into noise and ..."}
+{"idx": 6, "title": "Score-Based Generative Modeling through Stochastic Differential Equations", "date": "", "ddg_snippet": "Score-Based Generative Modeling through Stochastic Differential Equations Yang Song , Jascha Sohl-Dickstein ,", "subpage_snippet": "", "source": "inspirehep.net", "link": "https://inspirehep.net/literature/2729640", "content": "Score-Based Generative Modeling through Stochastic Differential Equations Yang Song , Jascha Sohl-Dickstein ,"}
+{"idx": 7, "title": "Score-Based Generative Models | SpringerLink", "date": "", "ddg_snippet": "Yang Song , Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-64087-2_9", "content": "Yang Song , Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations ."}
+{"idx": 8, "title": "Score-Based Generative Modeling through Stochastic Differential ...", "date": "", "ddg_snippet": "#1 Score-Based Generative Modeling through Stochastic Differential Equations [PDF 754] [Copy] [Kimi 118] [REL] Authors: Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data ...", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/arxiv/2011.13456", "content": "#1 Score-Based Generative Modeling through Stochastic Differential Equations [PDF 754] [Copy] [Kimi 118] [REL] Authors: Yang Song , Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data ..."}
+{"idx": 9, "title": "PDF Score-based Generative Modeling Through Stochastic Differential Equations", "date": "", "ddg_snippet": "SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS Song , Y., & Jo, J. (2021). International Conference on Learning Representations, 1-15.", "subpage_snippet": "", "source": "bin.t.u-tokyo.ac.jp", "link": "https://bin.t.u-tokyo.ac.jp/rzemi24/file/13-2_nishio.pdf", "content": "SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS Song , Y., & Jo, J. (2021). International Conference on Learning Representations, 1-15."}
diff --git a/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_mapping_behaviors_emotions_Chang_DIKE_year_2024.jsonl b/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_mapping_behaviors_emotions_Chang_DIKE_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b221abd2d50bc91dc0068c5b0d60a9d0f7d23e97
--- /dev/null
+++ b/data/sampled_jsons/Section_3.2_four-step_self-supervised_learning_mapping_behaviors_emotions_Chang_DIKE_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICLM 2025 AI Safety 7847 Camera Ready3 | PDF | Emotions", "date": "", "ddg_snippet": "10 Aug 2025 — zero-shot mapping between behaviors and emotions . Next, we used the Dike self - supervised learning pipeline to analyze the emotion spectrum ...", "subpage_snippet": "", "source": "www.scribd.com", "link": "https://www.scribd.com/document/899938563/ICLM-2025-AI-Safety-7847-Camera-Ready3", "content": "10 Aug 2025 — zero-shot mapping between behaviors and emotions . Next, we used the Dike self - supervised learning pipeline to analyze the emotion spectrum ..."}
+{"idx": 1, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "' Given N letters, Dike employs a self - supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps .", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "' Given N letters, Dike employs a self - supervised learning algorithm to generate training data for each letter, modeling L linguistic behaviors in four steps ."}
+{"idx": 2, "title": "A Three-Branch Checks-and-Balances Framework for ...", "date": "", "ddg_snippet": "Building on BEAM, D IKE maps emotions to behaviors and introduces an adversarial component,. ERIS, to adapt to culture norms and local context. Behaviors and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf/c76fc56310e947fbc848c07660b1ecbd60580a08.pdf", "content": "Building on BEAM, D IKE maps emotions to behaviors and introduces an adversarial component,. ERIS, to adapt to culture norms and local context. Behaviors and ..."}
+{"idx": 3, "title": "A Checks-and-Balances Framework for Context-Aware ...", "date": "", "ddg_snippet": "1 May 2025 — In the emotion layer evaluation, self-supervised learning is used to construct an emotion-behavior mapping , improving classification accuracy by ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=4uOEiitySn&noteId=cYh3zaQycT", "content": "1 May 2025 — In the emotion layer evaluation, self-supervised learning is used to construct an emotion-behavior mapping , improving classification accuracy by ..."}
+{"idx": 4, "title": "integrating-emotional-and-linguistic-models-for-ethical- ...", "date": "", "ddg_snippet": "... section begins by mapping emotions to behaviors ... behaviors in four steps ... Next, we employed the DIKE self - supervised learning pipeline to analyze the emotion ...", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/integrating-emotional-and-linguistic-models-for-ethical-c8c5drc4bk.pdf", "content": "... section begins by mapping emotions to behaviors ... behaviors in four steps ... Next, we employed the DIKE self - supervised learning pipeline to analyze the emotion ..."}
+{"idx": 5, "title": "Piecing Data Connections Together Like a Puzzle", "date": "", "ddg_snippet": "25 Apr 2025 — The visualisations in this study (to be fully described below, in Section 3.2 ) were created using Python's Matplotlib and Geopandas library.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3706598.3714270", "content": "25 Apr 2025 — The visualisations in this study (to be fully described below, in Section 3.2 ) were created using Python's Matplotlib and Geopandas library."}
+{"idx": 6, "title": "Using the S‐DIKW framework to transform data visualization ...", "date": "", "ddg_snippet": "by A Lo Duca · 2025 · Cited by 6 — In the book Effective data storytelling, three elements—data, narrative, and visuals—are developed in a four - step storyboarding process aimed at ...", "subpage_snippet": "", "source": "asistdl.onlinelibrary.wiley.com", "link": "https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24973", "content": "by A Lo Duca · 2025 · Cited by 6 — In the book Effective data storytelling, three elements—data, narrative, and visuals—are developed in a four - step storyboarding process aimed at ..."}
+{"idx": 7, "title": "Examining Bias in Facial Expression Recognition Datasets ...", "date": "", "ddg_snippet": "by MM Hosseini · 2025 · Cited by 5 — Exploring the role of self - supervised learning methods in reducing bias within datasets and evaluating how they can contribute to fair model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.11049", "content": "by MM Hosseini · 2025 · Cited by 5 — Exploring the role of self - supervised learning methods in reducing bias within datasets and evaluating how they can contribute to fair model ..."}
+{"idx": 8, "title": "Emotion-Oriented Visual Summarization of Classroom Videos", "date": "", "ddg_snippet": "In this paper, we apply computer vision techniques to analyze students' emotions in classroom videos. Visualization can facilitate emotion analysis in learning . 14 pages", "subpage_snippet": "", "source": "yong-wang.org", "link": "http://yong-wang.org/publication/emotioncues_tvcg20_paper.pdf", "content": "In this paper, we apply computer vision techniques to analyze students' emotions in classroom videos. Visualization can facilitate emotion analysis in learning . 14 pages"}
+{"idx": 9, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/Section_4.1_DSDFM_encoder_decoder_Transformer_and_GRU_modules_implementation.jsonl b/data/sampled_jsons/Section_4.1_DSDFM_encoder_decoder_Transformer_and_GRU_modules_implementation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..32ac8b5009fcb980782ae5fc6cfdcebf7f528b8c
--- /dev/null
+++ b/data/sampled_jsons/Section_4.1_DSDFM_encoder_decoder_Transformer_and_GRU_modules_implementation.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "Fixed-Point DSP Implementation of FM Demodulation/Decoding", "date": "", "ddg_snippet": "Abstract – This paper addresses the fixed-point DSP implementation of FM demodulation and decoding for the digital radio application. The demodulation module involves division and arctan for which there is no dedicated hardware on a fixed-point DSP.", "subpage_snippet": "", "source": "www.ti.com", "link": "https://www.ti.com/sc/docs/general/dsp/festproceedings/fest2000/tamu002.pdf", "content": "Abstract – This paper addresses the fixed-point DSP implementation of FM demodulation and decoding for the digital radio application. The demodulation module involves division and arctan for which there is no dedicated hardware on a fixed-point DSP."}
+{"idx": 1, "title": "Digital Speech Decoder (software package) - RadioReference ENGIN112 - lecture 15 - GitHub Pages Combinational circuits using Decoder - GeeksforGeeks GitHub - stereo502/dsd-1: Digital Speech Decoder GitHub - f4exb/dsdcc: Digital Speech Decoder (DSD) rewritten ... Fixed-Point DSP Implementation of FM Demodulation/Decoding Fixed-Point DSP Implementation of FM Demodulation/Decoding Fixed-Point DSP Implementation of FM Demodulation/Decoding Fixed-Point DSP Implementation of FM Demodulation/Decoding GitHub - f4exb/dsdcc: Digital Speech Decoder (DSD) rewritten as a C… Decoding DMR with DSD and a rtl-sdr » Sudo Rambles", "date": "", "ddg_snippet": "Digital Speech Decoder is an open source software package that decodes severaldigital speech formats. It uses the mbelib library (a separate open source package)to synthesize the decoded digital speech. It does not allow decoding of encryptedcommunications. It can save the compressed digital audio bits to \"mbe\" data files(.imb and .amb extensions) ... See full list on wiki.radioreference.com DSD and mbelib are both released under a BSD style copyright license. This means that as far as copyrights are concerned it can be freely copied and used, including for commercial products as long as the original copyright notice is included. (However, see important patent issues section below.) See full list on wiki.radioreference.com While DSD was intended to be patent-free, mbelib describes functions thatmay be covered by one or more U.S. patents owned by DVSI Inc. The source code itself shouldnot be infringing as it merely describes possible methods of implementation .Compiling or using mbelib may infringe on patents rights and /or require licensing.It is unknown if DVSI will s... See full list on wiki.radioreference.com See full list on wiki.radioreference.com If this list gets large enough I will move it to its own page so as not to detract from the overview of DSD. 1 . also tested using the DSD+ plugin from Vasili Beliakov's SDR# plugin collection See full list on wiki.radioreference.com Q: Do I need a radio with discriminator tap? A: Yes, if you want to listen live or save mbe data files, and are using a scanner for input. No radio or discriminator tap is required to play saved mbe data files. Q: Where can I get information on the discriminator tap for my radio? A: Wiki information is available here for the Discriminator_output. Q... See full list on wiki.radioreference.com ° Any combinational circuit with n inputs and m outputs can be implemented with an n-to-2n decoder with m OR gates. ° Suitable when a circuit has many outputs, and each output function is expressed with few minterms. Implementing Functions Using Decoders Jul 11, 2025 · Combinational circuits utilizing decoders are basic parts in a computerized plan, assuming a significant part in making an interpretation of parallel data into noteworthy results. Digital Speech Decoder . Contribute to stereo502/dsd-1 development by creating an account on GitHub. Works by pushing new samples to the decoder at the upper level rather than pulling it from the underlying filesystem (actual file or device) at the lowest level. Does a fixed-point DSP implement FM demodulation and decoding? Abstract – This paper addresses the fixed-point DSP implementation of FM demodulation and decoding for the digital radio application. The demodulation module involves division and arctan for which there is no dedicated hardware on a fixed-point DSP. What are FM demodulation and decoding algorithms? Results FM demodulation and decoding algorithms were implemented on the Texas Instruments TMS320C6201 DSP and were analyzed based on their speed, memory, SNR and channel separation. The code was written in C and then in linear assembly for better optimization. What is a demodulation module? The demodulation module involves division and arctan for which there is no dedicated hardware on a fixed-point DSP. Division is normally achieved either by using the Newton-Raphson method or by using conditional subtraction instruction. It is normally more efficient to use a table lookup approach. How is FM decoding implemented? FM decoding is implemented using the non-coherent scheme . Since this method requires the sampling rate to be 152KHz (for synchronizing the sampling rate to the phase of the incoming pilot frequency ), the output of the demodulated signal has to be appropriately down sampled. This issue is not dealt with in this paper. What is dsdysf & dsdmbedecoder? The DSDYSF object is responsible of handling the processing of Yaesu System Fusion frames . It uses the service of DSDMBEDecoder to produce the final audio output. The Descramble object contains static data and methods mainly used in the decoding of D-Star frames. It is based on Jonathan Naylor G4KLX code. Jan 24, 2025 · We’ll look into piping into dsd and decoding in a bit.", "subpage_snippet": "", "source": "wiki.radioreference.com", "link": "https://wiki.radioreference.com/index.php/Digital_Speech_Decoder_(software_package)", "content": "Digital Speech Decoder is an open source software package that decodes severaldigital speech formats. It uses the mbelib library (a separate open source package)to synthesize the decoded digital speech. It does not allow decoding of encryptedcommunications. It can save the compressed digital audio bits to \"mbe\" data files(.imb and .amb extensions) ... See full list on wiki.radioreference.com DSD and mbelib are both released under a BSD style copyright license. This means that as far as copyrights are concerned it can be freely copied and used, including for commercial products as long as the original copyright notice is included. (However, see important patent issues section below.) See full list on wiki.radioreference.com While DSD was intended to be patent-free, mbelib describes functions thatmay be covered by one or more U.S. patents owned by DVSI Inc. The source code itself shouldnot be infringing as it merely describes possible methods of implementation .Compiling or using mbelib may infringe on patents rights and /or require licensing.It is unknown if DVSI will s... See full list on wiki.radioreference.com See full list on wiki.radioreference.com If this list gets large enough I will move it to its own page so as not to detract from the overview of DSD. 1 . also tested using the DSD+ plugin from Vasili Beliakov's SDR# plugin collection See full list on wiki.radioreference.com Q: Do I need a radio with discriminator tap? A: Yes, if you want to listen live or save mbe data files, and are using a scanner for input. No radio or discriminator tap is required to play saved mbe data files. Q: Where can I get information on the discriminator tap for my radio? A: Wiki information is available here for the Discriminator_output. Q... See full list on wiki.radioreference.com ° Any combinational circuit with n inputs and m outputs can be implemented with an n-to-2n decoder with m OR gates. ° Suitable when a circuit has many outputs, and each output function is expressed with few minterms. Implementing Functions Using Decoders Jul 11, 2025 · Combinational circuits utilizing decoders are basic parts in a computerized plan, assuming a significant part in making an interpretation of parallel data into noteworthy results. Digital Speech Decoder . Contribute to stereo502/dsd-1 development by creating an account on GitHub. Works by pushing new samples to the decoder at the upper level rather than pulling it from the underlying filesystem (actual file or device) at the lowest level. Does a fixed-point DSP implement FM demodulation and decoding? Abstract – This paper addresses the fixed-point DSP implementation of FM demodulation and decoding for the digital radio application. The demodulation module involves division and arctan for which there is no dedicated hardware on a fixed-point DSP. What are FM demodulation and decoding algorithms? Results FM demodulation and decoding algorithms were implemented on the Texas Instruments TMS320C6201 DSP and were analyzed based on their speed, memory, SNR and channel separation. The code was written in C and then in linear assembly for better optimization. What is a demodulation module? The demodulation module involves division and arctan for which there is no dedicated hardware on a fixed-point DSP. Division is normally achieved either by using the Newton-Raphson method or by using conditional subtraction instruction. It is normally more efficient to use a table lookup approach. How is FM decoding implemented? FM decoding is implemented using the non-coherent scheme . Since this method requires the sampling rate to be 152KHz (for synchronizing the sampling rate to the phase of the incoming pilot frequency ), the output of the demodulated signal has to be appropriately down sampled. This issue is not dealt with in this paper. What is dsdysf & dsdmbedecoder? The DSDYSF object is responsible of handling the processing of Yaesu System Fusion frames . It uses the service of DSDMBEDecoder to produce the final audio output. The Descramble object contains static data and methods mainly used in the decoding of D-Star frames. It is based on Jonathan Naylor G4KLX code. Jan 24, 2025 · We’ll look into piping into dsd and decoding in a bit."}
+{"idx": 2, "title": "Combinational circuits using Decoder - GeeksforGeeks", "date": "", "ddg_snippet": "Jul 11, 2025 · Combinational circuits utilizing decoders are basic parts in a computerized plan, assuming a significant part in making an interpretation of parallel data into noteworthy results.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/digital-logic/combinational-circuits-using-decoder/", "content": "Jul 11, 2025 · Combinational circuits utilizing decoders are basic parts in a computerized plan, assuming a significant part in making an interpretation of parallel data into noteworthy results."}
+{"idx": 3, "title": "GitHub - stereo502/dsd-1: Digital Speech Decoder", "date": "", "ddg_snippet": "Digital Speech Decoder . Contribute to stereo502/dsd-1 development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/stereo502/dsd-1", "content": "Digital Speech Decoder . Contribute to stereo502/dsd-1 development by creating an account on GitHub."}
+{"idx": 4, "title": "GitHub - f4exb/dsdcc: Digital Speech Decoder (DSD) rewritten ...", "date": "", "ddg_snippet": "Works by pushing new samples to the decoder at the upper level rather than pulling it from the underlying filesystem (actual file or device) at the lowest level.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/f4exb/dsdcc", "content": "Works by pushing new samples to the decoder at the upper level rather than pulling it from the underlying filesystem (actual file or device) at the lowest level."}
+{"idx": 5, "title": "Decoding DMR with DSD and a rtl-sdr » Sudo Rambles", "date": "", "ddg_snippet": "Jan 24, 2025 · We’ll look into piping into dsd and decoding in a bit.", "subpage_snippet": "", "source": "www.sudorambles.com", "link": "https://www.sudorambles.com/decoding-dmr-with-dsd-and-a-rtl-sdr/", "content": "Jan 24, 2025 · We’ll look into piping into dsd and decoding in a bit."}
+{"idx": 6, "title": "ENGIN112 - lecture 15 - GitHub Pages", "date": "", "ddg_snippet": "° Any combinational circuit with n inputs and m outputs can be implemented with an n-to-2n decoder with m OR gates. ° Suitable when a circuit has many outputs, and each output function is expressed with few minterms. Implementing Functions Using Decoders", "subpage_snippet": "", "source": "rajeev2007.github.io", "link": "https://rajeev2007.github.io/STLD/Notes/lect17-Encoders+and+Decoders.pdf", "content": "° Any combinational circuit with n inputs and m outputs can be implemented with an n-to-2n decoder with m OR gates. ° Suitable when a circuit has many outputs, and each output function is expressed with few minterms. Implementing Functions Using Decoders"}
diff --git a/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks_PINN_year_2023.jsonl b/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks_PINN_year_2023.jsonl
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index 0000000000000000000000000000000000000000..06200154f259fe3da451cb0c1c69c80245dfbf81
--- /dev/null
+++ b/data/sampled_jsons/Self-Paced_Learning_Physics-Informed_Neural_Networks_PINN_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Self-adaptive physics-informed neural networks - ScienceDirect", "date": "", "ddg_snippet": "Feb 1, 2023 · In this paper, we introduced Self -Adaptive Physics-Informed Neural Networks , a novel class of physics -constrained neural networks . This approach uses a similar conceptual framework as soft self -attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0021999122007859", "content": "Feb 1, 2023 · In this paper, we introduced Self -Adaptive Physics-Informed Neural Networks , a novel class of physics -constrained neural networks . This approach uses a similar conceptual framework as soft self -attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training."}
+{"idx": 1, "title": "[2009.04544] Self-Adaptive Physics-Informed Neural Networks ... GitHub - junjun-yan/ST-PINN: A Self-Training Physics-Informed ... Images Adaptive Self-Supervision Algorithms for Physics-Informed ... Self-Paced Learning Enhanced Physics-informed Neural Networks ... Physics-informed neural networks for PDE problems: a ... Self - Paced Learning Enhanced Physics - informed Neural Networks for… Self- adaptive physics-informed neural networks - ScienceDirect GitHub - junjun-yan/ST-PINN: A Self-Training Physics-Informed Neural Self- adaptive physics-informed neural networks - ScienceDirect Self- adaptive physics-informed neural networks - ScienceDirect Physics - informed neural networks for PDE problems: a comprehensive Self-adaptive physics-informed neural networks | Journal of ...", "date": "", "ddg_snippet": "Sep 7, 2020 · Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of \"stiff\" PDEs. In this paper, we propose a ... With the development of deep learning , physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving. To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network , ST- PINN . View all Abstract. Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly ... Feb 1, 2023 · There is a hit discussion on solving partial differential equation by neural network . The famous PINN ( physics-informed neural networks ) has drawn worldwide attention since it was put forward.... Jul 24, 2025 · As AI for Science continues to grow, Physics-informed neural networks (PINNs) have emerged as a transformative approach within the realm of scientific computing and deep learning , offering a robust and flexible framework for solving partial differential equations (PDEs) and other complex physical systems. By embedding physical laws directly into the architecture of neural networks , PINNs ... Is Pinn a physics-informed neural network? The famous PINN (physics-informed neural networks ) has drawn worldwide attention since it was put forward. Despite its success in solving nonlinear partial differential equation, the difficulty in converging and the inefficiency in training process are definitely huge concerns. Normally, data for PINN is randomly chosen for a given distribution. What are physics-informed neural networks? 1. Introduction As part of the burgeoning field of scientific machine learning , physics-informed neural networks (PINNs) have emerged recently as an alternative to traditional numerical methods for partial different equations (PDE) , , , . Can physics-informed neural networks solve PDE problems fast? With the development of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving . To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network, ST-PINN. What is the Pinn approach? The PINN approach is based on a strong physics prior that constrains the output of a deep neural network by means of a system of PDEs. The potential of using neural networks as universal function approximators to solve PDEs had been recognized since the 1990's . What are self-adaptive physics-informed neural networks? Conclusion In this paper, we introduced Self-Adaptive Physics-Informed Neural Networks, a novel class of physics-constrained neural networks . This approach uses a similar conceptual framework as soft self-attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training. What are pinns in machine learning? PINNs are a class of machine learning methods that integrate domain-specific knowledge, typically described by PDEs, into the neural network training process. The key idea behind PINNs is to enforce the governing equations of a physical system as constraints during the training of a neural network (Fig. 2). The framework of PINNs Feb 1, 2023 · Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2009.04544", "content": "Sep 7, 2020 · Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of \"stiff\" PDEs. In this paper, we propose a ... With the development of deep learning , physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving. To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network , ST- PINN . View all Abstract. Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly ... Feb 1, 2023 · There is a hit discussion on solving partial differential equation by neural network . The famous PINN ( physics-informed neural networks ) has drawn worldwide attention since it was put forward.... Jul 24, 2025 · As AI for Science continues to grow, Physics-informed neural networks (PINNs) have emerged as a transformative approach within the realm of scientific computing and deep learning , offering a robust and flexible framework for solving partial differential equations (PDEs) and other complex physical systems. By embedding physical laws directly into the architecture of neural networks , PINNs ... Is Pinn a physics-informed neural network? The famous PINN (physics-informed neural networks ) has drawn worldwide attention since it was put forward. Despite its success in solving nonlinear partial differential equation, the difficulty in converging and the inefficiency in training process are definitely huge concerns. Normally, data for PINN is randomly chosen for a given distribution. What are physics-informed neural networks? 1. Introduction As part of the burgeoning field of scientific machine learning , physics-informed neural networks (PINNs) have emerged recently as an alternative to traditional numerical methods for partial different equations (PDE) , , , . Can physics-informed neural networks solve PDE problems fast? With the development of deep learning, physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving . To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network, ST-PINN. What is the Pinn approach? The PINN approach is based on a strong physics prior that constrains the output of a deep neural network by means of a system of PDEs. The potential of using neural networks as universal function approximators to solve PDEs had been recognized since the 1990's . What are self-adaptive physics-informed neural networks? Conclusion In this paper, we introduced Self-Adaptive Physics-Informed Neural Networks, a novel class of physics-constrained neural networks . This approach uses a similar conceptual framework as soft self-attention mechanisms in computer vision, in that the network identifies which inputs are most important to its own training. What are pinns in machine learning? PINNs are a class of machine learning methods that integrate domain-specific knowledge, typically described by PDEs, into the neural network training process. The key idea behind PINNs is to enforce the governing equations of a physical system as constraints during the training of a neural network (Fig. 2). The framework of PINNs Feb 1, 2023 · Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs)."}
+{"idx": 2, "title": "GitHub - junjun-yan/ST-PINN: A Self-Training Physics-Informed ...", "date": "", "ddg_snippet": "With the development of deep learning , physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving. To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network , ST- PINN .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/junjun-yan/ST-PINN", "content": "With the development of deep learning , physics-informed neural networks (PINNs), as a mesh-free method, have shown great potential for fast PDE solving. To address the problem of low accuracy and convergence problems of existing PINNs, we propose a selftraining physics-informed neural network , ST- PINN ."}
+{"idx": 3, "title": "Adaptive Self-Supervision Algorithms for Physics-Informed ...", "date": "", "ddg_snippet": "Abstract. Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly ...", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~mmahoney/pubs/subramanian_ECAI23.pdf", "content": "Abstract. Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can lead to optimization difficulties. Here, we study the impact of the location of the collocation points on the trainability of these models. We find that the vanilla PINN performance can be significantly ..."}
+{"idx": 4, "title": "Self-Paced Learning Enhanced Physics-informed Neural Networks ...", "date": "", "ddg_snippet": "Feb 1, 2023 · There is a hit discussion on solving partial differential equation by neural network . The famous PINN ( physics-informed neural networks ) has drawn worldwide attention since it was put forward....", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QugfmhDu5Y4", "content": "Feb 1, 2023 · There is a hit discussion on solving partial differential equation by neural network . The famous PINN ( physics-informed neural networks ) has drawn worldwide attention since it was put forward...."}
+{"idx": 5, "title": "Physics-informed neural networks for PDE problems: a ...", "date": "", "ddg_snippet": "Jul 24, 2025 · As AI for Science continues to grow, Physics-informed neural networks (PINNs) have emerged as a transformative approach within the realm of scientific computing and deep learning , offering a robust and flexible framework for solving partial differential equations (PDEs) and other complex physical systems. By embedding physical laws directly into the architecture of neural networks , PINNs ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-025-11322-7", "content": "Jul 24, 2025 · As AI for Science continues to grow, Physics-informed neural networks (PINNs) have emerged as a transformative approach within the realm of scientific computing and deep learning , offering a robust and flexible framework for solving partial differential equations (PDEs) and other complex physical systems. By embedding physical laws directly into the architecture of neural networks , PINNs ..."}
+{"idx": 6, "title": "Self-adaptive physics-informed neural networks | Journal of ...", "date": "", "ddg_snippet": "Feb 1, 2023 · Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1016/j.jcp.2022.111722", "content": "Feb 1, 2023 · Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs)."}
+{"idx": 7, "title": "Tag: Physics | NVIDIA Technical Blog", "date": "", "ddg_snippet": "From physics - informed neural networks ( PINNs ) to neural operators, developers have long sought after the ability to build real-time digital twins ...", "subpage_snippet": "", "source": "developer.nvidia.com", "link": "https://developer.nvidia.com/blog/tag/physics/", "content": "From physics - informed neural networks ( PINNs ) to neural operators, developers have long sought after the ability to build real-time digital twins ..."}
+{"idx": 8, "title": "Whack-a-mole Online Learning: Physics-Informed Neural Network", "date": "", "ddg_snippet": "This paper proposes a novel Physics - Informed Neural Networks ( PINNs ) approach called Whack-a-mole Online Learning (WamOL) to address this multi ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.02375v1", "content": "This paper proposes a novel Physics - Informed Neural Networks ( PINNs ) approach called Whack-a-mole Online Learning (WamOL) to address this multi ..."}
+{"idx": 9, "title": "A Review of Physics-Informed Machine Learning in Fluid Mechanics", "date": "", "ddg_snippet": "A Future with Machine Learning : Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/1996-1073/16/5/2343", "content": "A Future with Machine Learning : Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities"}
diff --git a/data/sampled_jsons/Self-Repellent_Random_Walk_SRRW_common_misconceptions_false_limitations.jsonl b/data/sampled_jsons/Self-Repellent_Random_Walk_SRRW_common_misconceptions_false_limitations.jsonl
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index 0000000000000000000000000000000000000000..d85d4318689839170256905a0fbaf08a8ef18e60
--- /dev/null
+++ b/data/sampled_jsons/Self-Repellent_Random_Walk_SRRW_common_misconceptions_false_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF Self-Repellent Random Walks on General Graphs -Achieving Minimal ...", "date": "", "ddg_snippet": "Abstract We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sam-pling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.05097v1.pdf", "content": "Abstract We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sam-pling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent ..."}
+{"idx": 1, "title": "PDF Fiedler Vector Approximation via Interacting Random Walks", "date": "", "ddg_snippet": "We design a Self-Repellent Random Walk ( SRRW ), such that Empirical distribution converges almost surely to (SLLN) Achieves smaller asymptotic variance compared to base MC First result for general, finite graphs used for algorithm design", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2023/Slides/25435_Bx3TXtq.pdf", "content": "We design a Self-Repellent Random Walk ( SRRW ), such that Empirical distribution converges almost surely to (SLLN) Achieves smaller asymptotic variance compared to base MC First result for general, finite graphs used for algorithm design"}
+{"idx": 2, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/doshi23a.html", "content": "We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random ..."}
+{"idx": 3, "title": "PDF Self-Repellent Random Walks on General Graphs - IJCAI", "date": "", "ddg_snippet": "target probability distribution, we design a self-repellent random walk ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom vis-ited nodes.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/0929.pdf", "content": "target probability distribution, we design a self-repellent random walk ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom vis-ited nodes."}
+{"idx": 4, "title": "ICLR Poster Accelerating Distributed Stochastic Optimization via Self ...", "date": "", "ddg_snippet": "In this paper, we take a novel approach by replacing the standard *linear* Markovian token by one which follows a *non-linear* Markov chain - namely the Self-Repellent Radom Walk ( SRRW ).", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/poster/19214", "content": "In this paper, we take a novel approach by replacing the standard *linear* Markovian token by one which follows a *non-linear* Markov chain - namely the Self-Repellent Radom Walk ( SRRW )."}
+{"idx": 5, "title": "Self-Repellent Random Walks on General Graphs - Achieving Minimal ...", "date": "", "ddg_snippet": "In this setup, we design Self-Repellent Random Walks ( SRRWs ) on general graphs 111We consider graphs because they represent a generalization of (discrete) finite state spaces by imposing a communication (adjacency) matrix.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2305.05097", "content": "In this setup, we design Self-Repellent Random Walks ( SRRWs ) on general graphs 111We consider graphs because they represent a generalization of (discrete) finite state spaces by imposing a communication (adjacency) matrix."}
+{"idx": 6, "title": "How Self-Repellent Random Walks operate part1(Best Bets for ... - Medium", "date": "", "ddg_snippet": "In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain — namely the Self-Repellent Radom Walk ( SRRW ).", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/how-self-repellent-random-walks-operate-part1-best-bets-for-machine-learning-research-b1b2de868d01", "content": "In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain — namely the Self-Repellent Radom Walk ( SRRW )."}
+{"idx": 7, "title": "PDF Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient ...", "date": "", "ddg_snippet": "Improving MCMC - Self Interactions Our recent breakthrough: Self-Repellent Random Walk ( SRRW ) Concept: Use the random walker's history to influence future transitions Given a time-reversible Markov chain with target probability distribution Based on visit frequency vector , modify probability from node 'Non-Markov' or 'history-aware ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/47270.pdf", "content": "Improving MCMC - Self Interactions Our recent breakthrough: Self-Repellent Random Walk ( SRRW ) Concept: Use the random walker's history to influence future transitions Given a time-reversible Markov chain with target probability distribution Based on visit frequency vector , modify probability from node 'Non-Markov' or 'history-aware ..."}
+{"idx": 8, "title": "Self-Repellent Random Walks on General Graphs - IJCAI", "date": "", "ddg_snippet": "Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2024/929", "content": "Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes."}
+{"idx": 9, "title": "Track: Oral A3 ML Theory", "date": "", "ddg_snippet": "Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random walk * ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2023/session/25585", "content": "Given any Markov chain corresponding to a target probability distribution, we design a * self-repellent random walk * ( SRRW ) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes."}
diff --git a/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_underdamped_Langevin_diffusion_ULD_NOT_Picard.jsonl b/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_underdamped_Langevin_diffusion_ULD_NOT_Picard.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07c583d2a645ad77b740ddae1fe8cb47691872f6
--- /dev/null
+++ b/data/sampled_jsons/Shen_Lee_2019_randomized_midpoint_underdamped_Langevin_diffusion_ULD_NOT_Picard.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "Sep 12, 2019 · Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1909.05503", "content": "Sep 12, 2019 · Abstract page for arXiv paper 1909.05503: The Randomized Midpoint Method for Log-Concave Sampling"}
+{"idx": 1, "title": "Complexity of randomized algorithms for underdamped Langevin ...", "date": "", "ddg_snippet": "The lower bound we establish matches the upper bound for the randomized midpoint method recently proposed by Shen and Lee [NIPS 2019 ], in terms of both parameters $N$ and $d$.", "subpage_snippet": "", "source": "dukespace.lib.duke.edu", "link": "https://dukespace.lib.duke.edu/items/ab22890d-424b-47a6-97f8-20674362d1bf", "content": "The lower bound we establish matches the upper bound for the randomized midpoint method recently proposed by Shen and Lee [NIPS 2019 ], in terms of both parameters $N$ and $d$."}
+{"idx": 2, "title": "The shifted ODE method for underdamped Langevin MCMC", "date": "", "ddg_snippet": "Jan 9, 2021 · This matches the complexity of the randomized midpoint method proposed by Shen and Lee [NeurIPS 2019 ] which was shown to be order optimal by Cao, Lu and Wang.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/348403067_The_shifted_ODE_method_for_underdamped_Langevin_MCMC", "content": "Jan 9, 2021 · This matches the complexity of the randomized midpoint method proposed by Shen and Lee [NeurIPS 2019 ] which was shown to be order optimal by Cao, Lu and Wang."}
+{"idx": 3, "title": "The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "The Randomized Midpoint Method for Log-Concave Sampling Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019 )", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/hash/eb86d510361fc23b59f18c1bc9802cc6-Abstract.html", "content": "The Randomized Midpoint Method for Log-Concave Sampling Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019 )"}
+{"idx": 4, "title": "Ruoqi Shen | Semantic Scholar", "date": "", "ddg_snippet": "This paper proposes a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion ( ULD ) to solve the sampling problem and proposes a new framework to discretize stochastic differential equations.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/author/Ruoqi-Shen/153847803", "content": "This paper proposes a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion ( ULD ) to solve the sampling problem and proposes a new framework to discretize stochastic differential equations."}
+{"idx": 5, "title": "Reviews: The Randomized Midpoint Method for Log-Concave Sampling", "date": "", "ddg_snippet": "The authors propose a new discretization of the underdamped Langevin diffusion , which leads to an improved approximation of the invariant density. EVALUATION I found this paper very interesting. It is based on the following very simple idea of randomization.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Reviews.html", "content": "The authors propose a new discretization of the underdamped Langevin diffusion , which leads to an improved approximation of the invariant density. EVALUATION I found this paper very interesting. It is based on the following very simple idea of randomization."}
+{"idx": 6, "title": "The shifted ODE method for underdamped Langevin MCMC", "date": "", "ddg_snippet": "This matches the complexity of the randomized midpoint method proposed by Shen and Lee [NeurIPS 2019 ] which was shown to be order optimal by Cao, Lu and Wang. However, the main feature of the proposed numerical method is that it can utilize additional smoothness of the target log-density f.", "subpage_snippet": "", "source": "ora.ox.ac.uk", "link": "https://ora.ox.ac.uk/objects/uuid:51b090dd-a0a1-43d3-aff4-620adfaba63a", "content": "This matches the complexity of the randomized midpoint method proposed by Shen and Lee [NeurIPS 2019 ] which was shown to be order optimal by Cao, Lu and Wang. However, the main feature of the proposed numerical method is that it can utilize additional smoothness of the target log-density f."}
+{"idx": 7, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — 5 steps. The underdamped Langevin diffusion ( ULD ) can be viewed as a version of HMC that replaces multiple ODEs with one SDE; it has been ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper/2019/file/eb86d510361fc23b59f18c1bc9802cc6-Paper.pdf", "content": "by R Shen · 2019 · Cited by 161 — 5 steps. The underdamped Langevin diffusion ( ULD ) can be viewed as a version of HMC that replaces multiple ODEs with one SDE; it has been ..."}
+{"idx": 8, "title": "The Randomized Midpoint Method for Log-Concave ...", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 161 — steps. The underdamped Langevin diffusion ( ULD ) can be viewed as a version of HMC that replaces multiple ODEs with one SDE; it has been ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1909.05503", "content": "by R Shen · 2019 · Cited by 161 — steps. The underdamped Langevin diffusion ( ULD ) can be viewed as a version of HMC that replaces multiple ODEs with one SDE; it has been ..."}
+{"idx": 9, "title": "The randomized midpoint method for log-concave sampling", "date": "", "ddg_snippet": "by R Shen · 2019 · Cited by 160 — In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion ( ULD ). It can achieve ε · D error (in 2 ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.5555/3454287.3454475", "content": "by R Shen · 2019 · Cited by 160 — In our paper, we propose a Markov chain Monte Carlo (MCMC) algorithm based on the underdamped Langevin diffusion ( ULD ). It can achieve ε · D error (in 2 ..."}
diff --git "a/data/sampled_jsons/Sieve_MLE_FD3_MSE_0.036_\302\261_0.002.jsonl" "b/data/sampled_jsons/Sieve_MLE_FD3_MSE_0.036_\302\261_0.002.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..64b56fdb0330d48cddad7d28c6318d21a777fdf6
--- /dev/null
+++ "b/data/sampled_jsons/Sieve_MLE_FD3_MSE_0.036_\302\261_0.002.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sieve Size Chart: Understanding Mesh and Particle Sizes", "date": "", "ddg_snippet": "Jul 1, 2024 · Master sieve size chart with our easy guide. Learn how to navigate sieves , mesh sizes, and ASTM standards for precise particle analysis.", "subpage_snippet": "", "source": "blog.certifiedmtp.com", "link": "https://blog.certifiedmtp.com/sieve-size-chart-understanding-mesh-and-particle-sizes/", "content": "Jul 1, 2024 · Master sieve size chart with our easy guide. Learn how to navigate sieves , mesh sizes, and ASTM standards for precise particle analysis."}
+{"idx": 1, "title": "Molecular Sieves | MSE Supplies LLC Sieve Sizes: A Guide to U.S. and Metric Sizes - Gilson Co. Sieve Analysis: Methods, Equipment & Particle Size Distribution ANALYTICAL SIEVING PARTICLE SIZE DISTRIBUTION ESTIMATION BY Sieve Analysis Expertise - METTLER TOLEDO Molecular Sieves | High-Quality Zeolite Adsorbents– MSE Supplies LLC Sieve Size Chart: Understanding Mesh and Particle Sizes Sieve Analysis: Methods, Equipment, Particle Size Distribution Molecular Sieves | High-Quality Zeolite Adsorbents– MSE Supplies LLC ANALYTICAL SIEVING PARTICLE SIZE DISTRIBUTION ESTIMATION BY ANALYTICAL SIEVING PARTICLE SIZE DISTRIBUTION ESTIMATION BY Filter Mesh | McMaster-Carr", "date": "", "ddg_snippet": "Molecular sieves are a type of crystalline aluminosilicates , either natural or synthetic, recognized for their unique framework of interconnected silica and alumina tetrahedra. This structure, once dehydrated, forms uniform cavities that can selectively adsorb molecules based on size and polarity. Key characteristics of molecular sieves include: 1.... See full list on msesupplies.com Molecular sieves serve many purposes across various sectors due to their selectivity and high sorption capacity. In the petroleum industry, they are essential for drying gas streams, significantly reducing the water content in liquid natural gas to prevent blockage issues. For example, drying electronic components and refrigerant gases are common u... See full list on msesupplies.com The high sorption capacity of molecular sieves makes them effective for drying and purifying gases and liquids. Their selective adsorption allows precision in separating molecules based on size and polarity. The customizability of molecular sieves permits tailor-made solutions for specific applications, enhancing their overall efficacy across vario... See full list on msesupplies.com Experience our molecular sieves' superior quality and efficiency by choosing MSE Supplies, a global leader in materials science serving over 20,000 satisfied customers. Let us add value to your research and production processes while ensuring your utmost satisfaction.Request a quote or place your order today.contact us onlinewith any questions, or ... See full list on msesupplies.com Gilson's Insights Blog: We dive into sieve opening sizes for U.S. and metric. We discuss the standards, configurations, popular test application sieve mesh sizes, and maintenance. Jul 13, 2025 · Sieve analysis fundamentals for particle size distribution of soil and aggregates. Covers test sieves , mesh-to-micron conversion, fineness modulus, and analysis methods. Sieving is one of the oldest methods of classifying powders and granules by particle size distribution. When using a woven sieve cloth, the sieving will essentially sort the particles by their intermediate size dimension (i.e., breadth or width). Mechanical sieving is most suitable where the majority of the particles are larger than about 75 μm. For smaller particles, the light weight ... Sieve analysis is one of the most common applications of laboratory weighing. This digital guide contains everything you need to know about sieve analysis. In an easy-to-digest short and to the point way it explains the importance of sieve analysis for determining particle size distribution. Does MSE supplies offer molecular sieve solutions? MSE Supplies provides molecular sieve solutions for your needs. We offer molecular sieves from nanoscale to pellet forms. 3A, 4A, 5A, and 13X beads and also customize molecular sieves are available. Request a quote. Does mesh size affect particle passage in a sieve? The correlation between mesh size and particle passage in a sieve is a straightforward one. Smaller mesh sizes feature smaller openings, allowing smaller particles to pass through, whereas larger mesh sizes have larger openings, enabling particles larger than particles to pass through. What is the difference between sieve analysis and hydrometer analysis? Sieve Analysis: For particles larger than 0 .075 mm (No. 200 sieve ). Hydrometer Analysis: For particles finer than 0 .075 mm, based on the principle of sedimentation. Together, these methods provide a complete particle size analysis of soil, crucial for geotechnical engineering applications. What types of molecular sieves are available? We offer molecular sieves from nanoscale to pellet forms . 3A, 4A, 5A, and 13X beads and also customize molecular sieves are available. Request a quote. Molecular Sieve Product Options and Choices: 4 to 8-mesh molecular sieves are normally used in gas phase applications, while the 8 to 12-mesh type is common in liquid phase applications. What are the limitations of the sieving method? ost cases the analysis can be carried out in the dry state.Among the limitations of the sieving method are the need for an appreciable amount of sample (normally at least 25 g, depending on the density of the powder or granule, and the diameter of test sieves) and difficulty in sieving oily or other cohesi What are analytical test sieves made of? the individual monograph.PRINCIPLES OF ANALYTICAL SIEVINGAnalytical test sieves are constructed from a woven-wire mesh , which is of simple weave that is assumed to give nearly square apertures a Choose from our selection of filter mesh, including stainless steel wire cloth, stainless steel wire cloth discs, and more. Same and Next Day Delivery.", "subpage_snippet": "", "source": "www.msesupplies.com", "link": "https://www.msesupplies.com/collections/molecular-sieves", "content": "Molecular sieves are a type of crystalline aluminosilicates , either natural or synthetic, recognized for their unique framework of interconnected silica and alumina tetrahedra. This structure, once dehydrated, forms uniform cavities that can selectively adsorb molecules based on size and polarity. Key characteristics of molecular sieves include: 1.... See full list on msesupplies.com Molecular sieves serve many purposes across various sectors due to their selectivity and high sorption capacity. In the petroleum industry, they are essential for drying gas streams, significantly reducing the water content in liquid natural gas to prevent blockage issues. For example, drying electronic components and refrigerant gases are common u... See full list on msesupplies.com The high sorption capacity of molecular sieves makes them effective for drying and purifying gases and liquids. Their selective adsorption allows precision in separating molecules based on size and polarity. The customizability of molecular sieves permits tailor-made solutions for specific applications, enhancing their overall efficacy across vario... See full list on msesupplies.com Experience our molecular sieves' superior quality and efficiency by choosing MSE Supplies, a global leader in materials science serving over 20,000 satisfied customers. Let us add value to your research and production processes while ensuring your utmost satisfaction.Request a quote or place your order today.contact us onlinewith any questions, or ... See full list on msesupplies.com Gilson's Insights Blog: We dive into sieve opening sizes for U.S. and metric. We discuss the standards, configurations, popular test application sieve mesh sizes, and maintenance. Jul 13, 2025 · Sieve analysis fundamentals for particle size distribution of soil and aggregates. Covers test sieves , mesh-to-micron conversion, fineness modulus, and analysis methods. Sieving is one of the oldest methods of classifying powders and granules by particle size distribution. When using a woven sieve cloth, the sieving will essentially sort the particles by their intermediate size dimension (i.e., breadth or width). Mechanical sieving is most suitable where the majority of the particles are larger than about 75 μm. For smaller particles, the light weight ... Sieve analysis is one of the most common applications of laboratory weighing. This digital guide contains everything you need to know about sieve analysis. In an easy-to-digest short and to the point way it explains the importance of sieve analysis for determining particle size distribution. Does MSE supplies offer molecular sieve solutions? MSE Supplies provides molecular sieve solutions for your needs. We offer molecular sieves from nanoscale to pellet forms. 3A, 4A, 5A, and 13X beads and also customize molecular sieves are available. Request a quote. Does mesh size affect particle passage in a sieve? The correlation between mesh size and particle passage in a sieve is a straightforward one. Smaller mesh sizes feature smaller openings, allowing smaller particles to pass through, whereas larger mesh sizes have larger openings, enabling particles larger than particles to pass through. What is the difference between sieve analysis and hydrometer analysis? Sieve Analysis: For particles larger than 0 .075 mm (No. 200 sieve ). Hydrometer Analysis: For particles finer than 0 .075 mm, based on the principle of sedimentation. Together, these methods provide a complete particle size analysis of soil, crucial for geotechnical engineering applications. What types of molecular sieves are available? We offer molecular sieves from nanoscale to pellet forms . 3A, 4A, 5A, and 13X beads and also customize molecular sieves are available. Request a quote. Molecular Sieve Product Options and Choices: 4 to 8-mesh molecular sieves are normally used in gas phase applications, while the 8 to 12-mesh type is common in liquid phase applications. What are the limitations of the sieving method? ost cases the analysis can be carried out in the dry state.Among the limitations of the sieving method are the need for an appreciable amount of sample (normally at least 25 g, depending on the density of the powder or granule, and the diameter of test sieves) and difficulty in sieving oily or other cohesi What are analytical test sieves made of? the individual monograph.PRINCIPLES OF ANALYTICAL SIEVINGAnalytical test sieves are constructed from a woven-wire mesh , which is of simple weave that is assumed to give nearly square apertures a Choose from our selection of filter mesh, including stainless steel wire cloth, stainless steel wire cloth discs, and more. Same and Next Day Delivery."}
+{"idx": 2, "title": "Sieve Sizes: A Guide to U.S. and Metric Sizes - Gilson Co.", "date": "", "ddg_snippet": "Gilson's Insights Blog: We dive into sieve opening sizes for U.S. and metric. We discuss the standards, configurations, popular test application sieve mesh sizes, and maintenance.", "subpage_snippet": "", "source": "www.globalgilson.com", "link": "https://www.globalgilson.com/blog/sieve-sizes", "content": "Gilson's Insights Blog: We dive into sieve opening sizes for U.S. and metric. We discuss the standards, configurations, popular test application sieve mesh sizes, and maintenance."}
+{"idx": 3, "title": "Sieve Analysis: Methods, Equipment & Particle Size Distribution", "date": "", "ddg_snippet": "Jul 13, 2025 · Sieve analysis fundamentals for particle size distribution of soil and aggregates. Covers test sieves , mesh-to-micron conversion, fineness modulus, and analysis methods.", "subpage_snippet": "", "source": "engineersviews.com", "link": "https://engineersviews.com/blog/sieve-analysis/", "content": "Jul 13, 2025 · Sieve analysis fundamentals for particle size distribution of soil and aggregates. Covers test sieves , mesh-to-micron conversion, fineness modulus, and analysis methods."}
+{"idx": 4, "title": "Sieve Analysis Expertise - METTLER TOLEDO", "date": "", "ddg_snippet": "Sieve analysis is one of the most common applications of laboratory weighing. This digital guide contains everything you need to know about sieve analysis. In an easy-to-digest short and to the point way it explains the importance of sieve analysis for determining particle size distribution.", "subpage_snippet": "", "source": "www.mt.com", "link": "https://www.mt.com/us/en/home/library/guides/laboratory-weighing/sieve-analysis-guide.html", "content": "Sieve analysis is one of the most common applications of laboratory weighing. This digital guide contains everything you need to know about sieve analysis. In an easy-to-digest short and to the point way it explains the importance of sieve analysis for determining particle size distribution."}
+{"idx": 5, "title": "ANALYTICAL SIEVING PARTICLE SIZE DISTRIBUTION ESTIMATION BY", "date": "", "ddg_snippet": "Sieving is one of the oldest methods of classifying powders and granules by particle size distribution. When using a woven sieve cloth, the sieving will essentially sort the particles by their intermediate size dimension (i.e., breadth or width). Mechanical sieving is most suitable where the majority of the particles are larger than about 75 μm. For smaller particles, the light weight ...", "subpage_snippet": "", "source": "www.usp.org", "link": "https://www.usp.org/sites/default/files/usp/document/harmonization/excipients/harmonization-april-2022-m99584.pdf", "content": "Sieving is one of the oldest methods of classifying powders and granules by particle size distribution. When using a woven sieve cloth, the sieving will essentially sort the particles by their intermediate size dimension (i.e., breadth or width). Mechanical sieving is most suitable where the majority of the particles are larger than about 75 μm. For smaller particles, the light weight ..."}
+{"idx": 6, "title": "Filter Mesh | McMaster-Carr", "date": "", "ddg_snippet": "Choose from our selection of filter mesh, including stainless steel wire cloth, stainless steel wire cloth discs, and more. Same and Next Day Delivery.", "subpage_snippet": "", "source": "www.mcmaster.com", "link": "https://www.mcmaster.com/products/filter-mesh/", "content": "Choose from our selection of filter mesh, including stainless steel wire cloth, stainless steel wire cloth discs, and more. Same and Next Day Delivery."}
+{"idx": 7, "title": "Калькулятор онлайн и по шагам", "date": "", "ddg_snippet": "Ввод распознает различные синонимы функций, какasin, arsin, arcsin, sin^-1. Знак умножения и скобки расставляются дополнительно — запись2sinxсходна2*sin(x)...", "subpage_snippet": "", "source": "mathdf.com", "link": "https://mathdf.com/calc/ru/", "content": "Ввод распознает различные синонимы функций, какasin, arsin, arcsin, sin^-1. Знак умножения и скобки расставляются дополнительно — запись2sinxсходна2*sin(x)..."}
+{"idx": 8, "title": "Дмитрий Никотин – Telegram", "date": "", "ddg_snippet": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей.", "subpage_snippet": "", "source": "t.me", "link": "https://t.me/s/dmitrynikotin", "content": "Про политику, простым языком о сложном. Контрпропаганда и интерпретация новостей."}
+{"idx": 9, "title": "GISMETEO: Погода в Йошкар-Оле сегодня, прогноз погоды...", "date": "", "ddg_snippet": "Подробный прогноз погоды в Йошкар-Оле на сегодня.", "subpage_snippet": "", "source": "www.gismeteo.ru", "link": "https://www.gismeteo.ru/weather-yoshkar-ola-11975/", "content": "Подробный прогноз погоды в Йошкар-Оле на сегодня."}
diff --git a/data/sampled_jsons/SimXRD-4M_Table_3_Bidirectional-GRU_Focal_loss_F1_score_year_2023-2024.jsonl b/data/sampled_jsons/SimXRD-4M_Table_3_Bidirectional-GRU_Focal_loss_F1_score_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d86c89294b0c2b163a73392b83d57dee0b0ad42b
--- /dev/null
+++ b/data/sampled_jsons/SimXRD-4M_Table_3_Bidirectional-GRU_Focal_loss_F1_score_year_2023-2024.jsonl
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+{"idx": 0, "title": "F- score - Wikipedia", "date": "", "ddg_snippet": "In statistical analysis of binary classification and information retrieval systems, the F- score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/F-score", "content": "In statistical analysis of binary classification and information retrieval systems, the F- score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test..."}
+{"idx": 1, "title": "(PDF) SimXRD - 4 M : Big Simulated X-ray Diffraction Data Accelerate...", "date": "", "ddg_snippet": "3 SimXRD - 4 M Dataset. In this section, we first introduce the fundamental concepts of the research problem and then elaborate.W e use accuracy, macro F 1 - score , macro precision, and macro recall as our metrics to measure the.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "3 SimXRD - 4 M Dataset. In this section, we first introduce the fundamental concepts of the research problem and then elaborate.W e use accuracy, macro F 1 - score , macro precision, and macro recall as our metrics to measure the."}
+{"idx": 2, "title": "F-мера в машинном обучении: что это и как применяется на практике", "date": "", "ddg_snippet": "F-мера (F- score , F 1 - score ) представляет собой гармоническое среднее между точностью (precision) и полнотой (recall), что делает её сбалансированной метрикой для оценки качества бинарной классификации.", "subpage_snippet": "", "source": "sky.pro", "link": "https://sky.pro/wiki/analytics/f-mera-v-mashinnom-obuchenii-chto-eto-i-kak-primenyaetsya-na-praktike/", "content": "F-мера (F- score , F 1 - score ) представляет собой гармоническое среднее между точностью (precision) и полнотой (recall), что делает её сбалансированной метрикой для оценки качества бинарной классификации."}
+{"idx": 3, "title": "Метрики оценки моделей нейронных сетей для чайников / Хабр", "date": "", "ddg_snippet": "Micro‑averaging — суммирование TP, FP и FN по всем классам перед расчетом Precision, Recall и F 1 - score . Macro‑averaging — вычисление Precision, Recall и F 1 - score отдельно для каждого класса и усреднение полученных значений.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/slsoft/articles/893694/", "content": "Micro‑averaging — суммирование TP, FP и FN по всем классам перед расчетом Precision, Recall и F 1 - score . Macro‑averaging — вычисление Precision, Recall и F 1 - score отдельно для каждого класса и усреднение полученных значений."}
+{"idx": 4, "title": "Номер 3 , страница 7 - гдз по английскому языку 4 класс (spotlight)...", "date": "", "ddg_snippet": "1 The mobile phone is on the table .", "subpage_snippet": "", "source": "gdz.top", "link": "https://gdz.top/4-klass/english/bykova-spotlight-rabochaya-tetrad/06-07-3", "content": "1 The mobile phone is on the table ."}
+{"idx": 5, "title": "Common Vulnerability Scoring System Version 3 . 1 Calculator", "date": "", "ddg_snippet": "The Specification is available in the list of links on the left, along with a User Guide providing additional scoring guidance, an Examples document of scored vulnerabilities, and notes on using this calculator (including its design and an XML representation for CVSS v 3 . 1 ).", "subpage_snippet": "", "source": "www.first.org", "link": "https://www.first.org/cvss/calculator/3-1", "content": "The Specification is available in the list of links on the left, along with a User Guide providing additional scoring guidance, an Examples document of scored vulnerabilities, and notes on using this calculator (including its design and an XML representation for CVSS v 3 . 1 )."}
+{"idx": 6, "title": "Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment...", "date": "", "ddg_snippet": "utilized Bi - Directional Gated-Recurrent Unit (Bi- GRU ) with a from the first -level Bi-LSTM with an R-CNN feature map to. Table 15: Impact of Focal Loss on In-Domain Adapted CAMeLBERT-MSA for ASC and OTE Extraction.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.16788", "content": "utilized Bi - Directional Gated-Recurrent Unit (Bi- GRU ) with a from the first -level Bi-LSTM with an R-CNN feature map to. Table 15: Impact of Focal Loss on In-Domain Adapted CAMeLBERT-MSA for ASC and OTE Extraction."}
+{"idx": 7, "title": "Interaction-Aware Vulnerability Detection in Smart Contract Bytecodes", "date": "", "ddg_snippet": "TABLE III : The Label Distribution of the Dataset I . Vulnerability Types.TABLE IV: The Results of Function Parameter Inference in Different Depths and Cells with Focal Loss Function. Network Structures Cells Max Depths. Gru lstm gru lstm gru lstm.", "subpage_snippet": "", "source": "csxqli.github.io", "link": "https://csxqli.github.io/files/TDSC25.pdf", "content": "TABLE III : The Label Distribution of the Dataset I . Vulnerability Types.TABLE IV: The Results of Function Parameter Inference in Different Depths and Cells with Focal Loss Function. Network Structures Cells Max Depths. Gru lstm gru lstm gru lstm."}
+{"idx": 8, "title": "F 1 Driver Robert Kubica in BMW M4 on the Nürburgring! - YouTube", "date": "", "ddg_snippet": "Robert Kubica randomly walked in and asked if he could have a car. I randomly asked if I could join him for a lap. The rest is history.If it's good enough fo...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=GwCIEjdzvCk", "content": "Robert Kubica randomly walked in and asked if he could have a car. I randomly asked if I could join him for a lap. The rest is history.If it's good enough fo..."}
+{"idx": 9, "title": "Упражнения на глагол TO BE в английском языке [с ответами]", "date": "", "ddg_snippet": "9 упражнений на отработку глагола to be с ответами. Задания на тренировку am, is, are в утвердительных, отрицательных предложениях, вопросах, кратких ответах...", "subpage_snippet": "", "source": "EnglishWeb.ru", "link": "https://EnglishWeb.ru/grammar/verb-to-be-exercises.html", "content": "9 упражнений на отработку глагола to be с ответами. Задания на тренировку am, is, are в утвердительных, отрицательных предложениях, вопросах, кратких ответах..."}
diff --git a/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_Similarity-level_Imputation_and_Intra-view_Hyb.jsonl b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_Similarity-level_Imputation_and_Intra-view_Hyb.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e156d65e63cced829a410e88f803441caeb71554
--- /dev/null
+++ b/data/sampled_jsons/Simple_yet_Effective_Incomplete_Multi-view_Clustering_Similarity-level_Imputation_and_Intra-view_Hyb.jsonl
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+{"idx": 0, "title": "Cluster analysis - Wikipedia", "date": "", "ddg_snippet": "The result of a cluster analysis shown as the coloring of the squares into three clusters . Cluster analysis, or clustering , is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group exhibi...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cluster_analysis", "content": "The result of a cluster analysis shown as the coloring of the squares into three clusters . Cluster analysis, or clustering , is a data analysis technique aimed at partitioning a set of objects into groups such that objects within the same group exhibi..."}
+{"idx": 1, "title": "ICLR Poster Simple yet Effective Incomplete Multi - view Clustering ...", "date": "", "ddg_snippet": "Abstract: Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity .", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/30038", "content": "Abstract: Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity ."}
+{"idx": 2, "title": "Simple yet Effective Incomplete Multi - view Clustering :...", "date": "", "ddg_snippet": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity .", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity ."}
+{"idx": 3, "title": "Imputation -free and Alignment-free: Incomplete Multi - view ...", "date": "", "ddg_snippet": "In incomplete multi - view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsis-tencies across views.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_Imputation-free_and_Alignment-free_Incomplete_Multi-view_Clustering_Driven_by_Consensus_Semantic_CVPR_2025_paper.pdf", "content": "In incomplete multi - view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsis-tencies across views."}
+{"idx": 4, "title": "(PDF) Attention-based deep incomplete multi - view clustering via...", "date": "", "ddg_snippet": "an incomplete multi - view contrastive clustering algorithm . that directly refines the latent feature representation through. the utilization of learned feature vectors.Graph-guided imputation -free incomplete multi - view clustering . Article. Aug 2024.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/392775911_Attention-based_deep_incomplete_multi-view_clustering_via_bi-alignment_guidance", "content": "an incomplete multi - view contrastive clustering algorithm . that directly refines the latent feature representation through. the utilization of learned feature vectors.Graph-guided imputation -free incomplete multi - view clustering . Article. Aug 2024."}
+{"idx": 5, "title": "Incomplete Multi - view Clustering via Diffusion", "date": "", "ddg_snippet": "multi - view clustering ; (c) Latent- level imputation methods, which differ from (b) in restoring missing views in latent space.", "subpage_snippet": "", "source": "scispace.com", "link": "https://scispace.com/pdf/incomplete-multi-view-clustering-via-diffusion-completion-v2hjx722.pdf", "content": "multi - view clustering ; (c) Latent- level imputation methods, which differ from (b) in restoring missing views in latent space."}
+{"idx": 6, "title": "Attention-based deep incomplete multi - view clustering via...", "date": "", "ddg_snippet": "Deep learning-based incomplete multi - view clustering has gained prominence for clustering tasks due to its superior feature learning capabilities across mu.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-025-01982-x", "content": "Deep learning-based incomplete multi - view clustering has gained prominence for clustering tasks due to its superior feature learning capabilities across mu."}
+{"idx": 7, "title": "Deep Incomplete Multi - view Clustering via Multi- level Imputation ...", "date": "", "ddg_snippet": "Deep incomplete multi - view clustering (DIMVC) aims to enhance clustering performance by capturing consistent information from incomplete multiple views using deep models.", "subpage_snippet": "", "source": "pubmed.ncbi.nlm.nih.gov", "link": "https://pubmed.ncbi.nlm.nih.gov/39515082/", "content": "Deep incomplete multi - view clustering (DIMVC) aims to enhance clustering performance by capturing consistent information from incomplete multiple views using deep models."}
+{"idx": 8, "title": "DIMC-net: Deep Incomplete Multi - view Clustering Network", "date": "", "ddg_snippet": "2019. Efficient and effective incomplete multi - view clustering .2015. Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L_ 2 , 1 Regularization.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3394171.3413807?cookieSet=1", "content": "2019. Efficient and effective incomplete multi - view clustering .2015. Multiple Incomplete Views Clustering via Weighted Nonnegative Matrix Factorization with L_ 2 , 1 Regularization."}
+{"idx": 9, "title": "Efficient and Effective Incomplete Multi - View Clustering", "date": "", "ddg_snippet": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance.In this paper, we propose an Efficient and Effective Incomplete Multi - view Clustering (EE-IMVC) algorithm to address these issues.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/view/4350", "content": "Incomplete multi - view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance.In this paper, we propose an Efficient and Effective Incomplete Multi - view Clustering (EE-IMVC) algorithm to address these issues."}
diff --git a/data/sampled_jsons/Song_et_al._2021_Score-based_Generative_Modeling_through_Stochastic_Differential_Equations_abstract.jsonl b/data/sampled_jsons/Song_et_al._2021_Score-based_Generative_Modeling_through_Stochastic_Differential_Equations_abstract.jsonl
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Review on Score-based Generative Models for Audio Applications", "date": "", "ddg_snippet": "Although initially developed as separate approaches, these frameworks were elegantly unified through stochastic differential equations in Score SDE ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.08457v1", "content": "Although initially developed as separate approaches, these frameworks were elegantly unified through stochastic differential equations in Score SDE ..."}
+{"idx": 1, "title": "Data-driven generative simulation of SDEs using diffusion models", "date": "", "ddg_snippet": "This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models , a class ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.08731v1", "content": "This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models , a class ..."}
+{"idx": 2, "title": "One-step data-driven generative model via Schrödinger Bridge", "date": "", "ddg_snippet": "... models are score based diffusion models (Sohl-Dickstein et al .,, 2015 ; Ho et al .,, 2020 ; Song et al ., 2021a, ; Song et al ., 2021b, ) and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.12453v1", "content": "... models are score based diffusion models (Sohl-Dickstein et al .,, 2015 ; Ho et al .,, 2020 ; Song et al ., 2021a, ; Song et al ., 2021b, ) and ..."}
+{"idx": 3, "title": "Machine learning-based emulation of a km-scale UK climate model", "date": "", "ddg_snippet": "... of-the-art generative machine learning (ML) method, a diffusion model ... Score - Based Generative Modeling through Stochastic Differential Equations .", "subpage_snippet": "", "source": "research-information.bris.ac.uk", "link": "https://research-information.bris.ac.uk/en/publications/machine-learning-based-emulation-of-a-km-scale-uk-climate-model", "content": "... of-the-art generative machine learning (ML) method, a diffusion model ... Score - Based Generative Modeling through Stochastic Differential Equations ."}
+{"idx": 4, "title": "Match flows, not scores – Ayan Das", "date": "", "ddg_snippet": "Although there are mutiple formalisms to describe the underlying theory of Diffusion Models , the one that gained traction recently is the ...", "subpage_snippet": "", "source": "ayandas.me", "link": "https://ayandas.me/blogs/2024-04-26-flow-matching-strightning-sd3.html", "content": "Although there are mutiple formalisms to describe the underlying theory of Diffusion Models , the one that gained traction recently is the ..."}
+{"idx": 5, "title": "1 Introduction", "date": "", "ddg_snippet": "Many successful generative models sample from an estimated score function ( Song and Ermon, 2019 ; Song et al ., 2021 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.00467v2", "content": "Many successful generative models sample from an estimated score function ( Song and Ermon, 2019 ; Song et al ., 2021 ) ."}
+{"idx": 6, "title": "Incorporating Pre-trained Diffusion Models in Solving the", "date": "", "ddg_snippet": "This paper aims to unify Score - based Generative Models (SGMs), also known as Diffusion models , and the Schrödinger Bridge (SB) problem through three ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18095v1", "content": "This paper aims to unify Score - based Generative Models (SGMs), also known as Diffusion models , and the Schrödinger Bridge (SB) problem through three ..."}
+{"idx": 7, "title": "Building Diffusion Model’s theory from ground up – Ayan Das", "date": "", "ddg_snippet": "... models are often described as a probabilistic Markov ... Score - Based Generative Modeling Through Stochastic Differential Equations .” In ICLR .", "subpage_snippet": "", "source": "ayandas.me", "link": "https://ayandas.me/pubs/2024-02-15-pub-14.html", "content": "... models are often described as a probabilistic Markov ... Score - Based Generative Modeling Through Stochastic Differential Equations .” In ICLR ."}
+{"idx": 8, "title": "‘diffusion NN’ directory · Gwern.net", "date": "", "ddg_snippet": "... Generating Cartoon Avatars With Fine-Grained ... Do Generative Video Models Learn Physical Principles from Watching Videos? ”, Motamed et al 2025", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/nn/diffusion/index", "content": "... Generating Cartoon Avatars With Fine-Grained ... Do Generative Video Models Learn Physical Principles from Watching Videos? ”, Motamed et al 2025"}
+{"idx": 9, "title": "Using recurrent neural network to estimate irreducible", "date": "", "ddg_snippet": "... based on neural networks have emerged as an alternative modeling paradigm in cognitive research ( Dezfouli et al ., 2019b ; Song et al ., 2021 ).", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/90082", "content": "... based on neural networks have emerged as an alternative modeling paradigm in cognitive research ( Dezfouli et al ., 2019b ; Song et al ., 2021 )."}
diff --git a/data/sampled_jsons/Stanislaus_Ulam_1979_interview_transcript_Einstein_creativity.jsonl b/data/sampled_jsons/Stanislaus_Ulam_1979_interview_transcript_Einstein_creativity.jsonl
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index 0000000000000000000000000000000000000000..e76836aaf2bde18eb094973a22615150c930ef03
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "John von Neumann - Wikipedia", "date": "", "ddg_snippet": "The MANIAC, 2023 book about von Neumann. German: Abenteuer eines Mathematikers (English title: Adventures of a Mathematician), biopic about Stanislaw Ulam also features John von Neumann.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/John_von_Neumann", "content": "The MANIAC, 2023 book about von Neumann. German: Abenteuer eines Mathematikers (English title: Adventures of a Mathematician), biopic about Stanislaw Ulam also features John von Neumann."}
+{"idx": 1, "title": "Stanislaus Ulam's Interview (1979) - Atomic Heritage Foundation", "date": "", "ddg_snippet": "Ulam also explains his thoughts on creativity in math and physics, and why he is a proponent of nuclear power. Date of Interview : July 19, 1979 . Location of ...", "subpage_snippet": "", "source": "ahf.nuclearmuseum.org", "link": "https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "content": "Ulam also explains his thoughts on creativity in math and physics, and why he is a proponent of nuclear power. Date of Interview : July 19, 1979 . Location of ..."}
+{"idx": 2, "title": "Stanislaw Ulam Quote: “Thinking very hard about the same problem...”", "date": "", "ddg_snippet": "I never really experienced a breakdown, but have felt “strange inside” two or three times during my life.” — Quote by Stanislaw Ulam .", "subpage_snippet": "", "source": "quotefancy.com", "link": "https://quotefancy.com/quote/1699067/Stanislaw-Ulam-Thinking-very-hard-about-the-same-problem-for-several-hours-can-produce-a", "content": "I never really experienced a breakdown, but have felt “strange inside” two or three times during my life.” — Quote by Stanislaw Ulam ."}
+{"idx": 3, "title": "Stanisław Ulam - Wikipedia, la enciclopedia libre", "date": "", "ddg_snippet": "1979 Entrevista de audio con Stanislaus Ulam × Martin Sherwin Voces del Manhattan Project.", "subpage_snippet": "", "source": "es.chped.com", "link": "https://es.chped.com/wiki/Stanislaw_Ulam", "content": "1979 Entrevista de audio con Stanislaus Ulam × Martin Sherwin Voces del Manhattan Project."}
+{"idx": 4, "title": "Hats and Rabbits: Like, Einstein Cool", "date": "", "ddg_snippet": "( Stanislaw Ulam quoted his mathematical colleague Mark Kacs as saying that he, Kacs, found it boring to teach calculus. Ulam , though, thought it straightforward but profound--read Ulam 's memoirs when you get a chance.)", "subpage_snippet": "", "source": "www.hats-n-rabbits.com", "link": "https://www.hats-n-rabbits.com/2013/05/like-einstein-cool.html", "content": "( Stanislaw Ulam quoted his mathematical colleague Mark Kacs as saying that he, Kacs, found it boring to teach calculus. Ulam , though, thought it straightforward but profound--read Ulam 's memoirs when you get a chance.)"}
+{"idx": 5, "title": "Stanislaw Ulam and Monte Carlo Method - SlideServe", "date": "", "ddg_snippet": "Stanislaw Ulam – cont. Slideshow 2661338 by sharla. Stanislaw Ulam • After spending a lot of time trying to estimate them by pure combinatorial calculations, I wondered whether a more practical method than “abstract thinking”.", "subpage_snippet": "", "source": "www.slideserve.com", "link": "https://www.slideserve.com/sharla/stanislaw-ulam-and-monte-carlo-method", "content": "Stanislaw Ulam – cont. Slideshow 2661338 by sharla. Stanislaw Ulam • After spending a lot of time trying to estimate them by pure combinatorial calculations, I wondered whether a more practical method than “abstract thinking”."}
+{"idx": 6, "title": "Events: Stanislaw Ulam Lecture Series - Preventing the next global...", "date": "", "ddg_snippet": "Ulam Lectures 2019—Lauren Ancel Meyers on Preventing the Next Pandemic (Lecture I).About the Ulam Memorial Lecture Series. Many of the most famous books in science, including Relativity by Albert Einstein and QfiD by Richard Feynman, were based on public lectures.", "subpage_snippet": "", "source": "web-prod.santafe.edu", "link": "https://web-prod.santafe.edu/events/stanislaw-ulam-memorial-lecture-series-lauren-ance", "content": "Ulam Lectures 2019—Lauren Ancel Meyers on Preventing the Next Pandemic (Lecture I).About the Ulam Memorial Lecture Series. Many of the most famous books in science, including Relativity by Albert Einstein and QfiD by Richard Feynman, were based on public lectures."}
+{"idx": 7, "title": "TRAIL: Trace Reasoning and Agentic Issue Localization", "date": "", "ddg_snippet": "---Task: In a 1979 interview , Stanislaus Ulam discusses. with Martin Sherwin about other great physicists of his time, including Oppenheimer. What does he say was the consequence of Einstein . learning too much math on his creativity", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.08638", "content": "---Task: In a 1979 interview , Stanislaus Ulam discusses. with Martin Sherwin about other great physicists of his time, including Oppenheimer. What does he say was the consequence of Einstein . learning too much math on his creativity"}
+{"idx": 8, "title": "Smoltalk: RCE in open source agents", "date": "", "ddg_snippet": "--- Task: In a 1979 interview , Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer. What does he say ...", "subpage_snippet": "", "source": "www.ibm.com", "link": "https://www.ibm.com/think/x-force/smoltalk-rce-in-open-source-agents", "content": "--- Task: In a 1979 interview , Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer. What does he say ..."}
+{"idx": 9, "title": "smolagents/training-traces · Datasets at Hugging Face", "date": "", "ddg_snippet": "8 Apr 2025 — ... Einstein learning too much math on his creativity , in one word? ... 1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\".", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/smolagents/training-traces", "content": "8 Apr 2025 — ... Einstein learning too much math on his creativity , in one word? ... 1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein\"."}
diff --git a/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Hoeffding_error_term_Rs(k)_page_3_year_2023.jsonl b/data/sampled_jsons/Statistical_Collusion_by_Collectives_on_Learning_Platforms_Hoeffding_error_term_Rs(k)_page_3_year_2023.jsonl
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index 0000000000000000000000000000000000000000..cf2ef268d27ceeffb6f29d5c87fc8efd4fe07751
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v3", "content": "Abstract As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data."}
+{"idx": 1, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "May 1, 2025 · The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=46yLEXtav4", "content": "May 1, 2025 · The paper explores the statistical inference for collective action in learning platforms , in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining the optimal strategy h."}
+{"idx": 2, "title": "GitHub - GauthierE/statistical-collusion", "date": "", "ddg_snippet": "statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/GauthierE/statistical-collusion", "content": "statistical - collusion This repository contains the code for reproducing the experiments and figures presented in the paper Statistical Collusion by Collectives on Learning Platforms ."}
+{"idx": 3, "title": "lecture07.pdf | Topics in Statistics: Statistical Learning ...", "date": "", "ddg_snippet": "This file covers lecture explaining Hoeffding 's inequality, Hoeffding -Chernoff inequality based on theorems.", "subpage_snippet": "", "source": "ocw.mit.edu", "link": "https://ocw.mit.edu/courses/18-465-topics-in-statistics-statistical-learning-theory-spring-2007/resources/lecture07/", "content": "This file covers lecture explaining Hoeffding 's inequality, Hoeffding -Chernoff inequality based on theorems."}
+{"idx": 4, "title": "Statistical Collusion by Collectives on Learning Platforms ...", "date": "", "ddg_snippet": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help...", "subpage_snippet": "", "source": "bytez.com", "link": "https://bytez.com/docs/icml/46504/paper", "content": "This paper talks about how groups of people can work together to change the way online platforms use data and learning algorithms to benefit their interests. The authors created a method that help..."}
+{"idx": 5, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.04879", "content": "Feb 7, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
+{"idx": 6, "title": "Statistical Collusion by Collectives on Learning Platforms", "date": "", "ddg_snippet": "7 Feb 2025 — Throughout this paper, we will use Hoeffding's concentration inequality (Lemma D.1) for simplicity. We will denote Hoeffding error terms as ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.04879v1", "content": "7 Feb 2025 — Throughout this paper, we will use Hoeffding's concentration inequality (Lemma D.1) for simplicity. We will denote Hoeffding error terms as ..."}
+{"idx": 7, "title": "Improving Human Integration across the Machine Learning ...", "date": "", "ddg_snippet": "by C Rastogi · 2024 · Cited by 2 — In this proof, we first bound the Type I error and subsequently bound the Type II error . To bound the probability of error of Algorithm 3 , we study the distri-. 263 pages", "subpage_snippet": "", "source": "ml.cmu.edu", "link": "https://ml.cmu.edu/research/phd-dissertation-pdfs/thesis_rastogi_charvi.pdf", "content": "by C Rastogi · 2024 · Cited by 2 — In this proof, we first bound the Type I error and subsequently bound the Type II error . To bound the probability of error of Algorithm 3 , we study the distri-. 263 pages"}
+{"idx": 8, "title": "Machine Learning and Knowledge Discovery in Databases", "date": "", "ddg_snippet": "9 Sept 2024 — The annual ECML PKDD conference acts as a world-wide platform showcasing the latest advancements in machine learning and knowledge discovery in ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/978-3-031-70341-6.pdf", "content": "9 Sept 2024 — The annual ECML PKDD conference acts as a world-wide platform showcasing the latest advancements in machine learning and knowledge discovery in ..."}
+{"idx": 9, "title": "arXiv:2502.04879v1 [stat.ML] 7 Feb 2025", "date": "", "ddg_snippet": "Feb 10, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.04879v1", "content": "Feb 10, 2025 · As platforms increasingly rely on learning algorithms, collectives may form and seek ways to influence these platforms to align with their own interests. This can be achieved by coordinated submission of altered data. To evaluate the potential impact of such behavior, it is essential to understand the computations that collectives must perform to impact platforms in this way. In particular ..."}
diff --git a/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD_noise.jsonl b/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD_noise.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dfe6665fe9e2ea1c07a51aec8d9ebc7f549fa725
--- /dev/null
+++ b/data/sampled_jsons/Stochastic_Gradient_Langevin_Dynamics_SGLD_noise.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stochastic gradient Langevin dynamics - Wikipedia", "date": "", "ddg_snippet": "Stochastic gradient Langevin dynamics ( SGLD ) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins-Monro optimization algorithm, and Langevin dynamics , a mathematical extension of molecular dynamics models.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Stochastic_Gradient_Langevin_Dynamics", "content": "Stochastic gradient Langevin dynamics ( SGLD ) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a Robbins-Monro optimization algorithm, and Langevin dynamics , a mathematical extension of molecular dynamics models."}
+{"idx": 1, "title": "「Stochastic」与「Random」有何区别？ - 知乎", "date": "", "ddg_snippet": "With stochastic process, the likelihood or probability of any particular outcome can be specified and not all outcomes are equally likely of occurring. For example, an ornithologist may assign a greater probability that a bird will select a nesting location based on how far it is from the edge of the refuge or whether the location is shielded ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/20675303?sort=created", "content": "With stochastic process, the likelihood or probability of any particular outcome can be specified and not all outcomes are equally likely of occurring. For example, an ornithologist may assign a greater probability that a bird will select a nesting location based on how far it is from the edge of the refuge or whether the location is shielded ..."}
+{"idx": 2, "title": "In layman's terms: What is a stochastic process?", "date": "", "ddg_snippet": "Oct 8, 2015 · A stochastic process is a way of representing the evolution of some situation that can be characterized mathematically (by numbers, points in a graph, etc.) over time.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1470686/in-laymans-terms-what-is-a-stochastic-process", "content": "Oct 8, 2015 · A stochastic process is a way of representing the evolution of some situation that can be characterized mathematically (by numbers, points in a graph, etc.) over time."}
+{"idx": 3, "title": "Books recommendations on stochastic analysis - Mathematics Stack...", "date": "", "ddg_snippet": "Feb 21, 2023 · Stochastic Calculus for Finance I: Binomial asset pricing model and Stochastic Calculus for Finance II: tochastic Calculus for Finance II: Continuous-Time Models. These two books are very good if you want to apply the theory to price derivatives. Stochastic Differential Equations: An Introduction with Applications Bernt Oksanda.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/4643337/books-recommendations-on-stochastic-analysis", "content": "Feb 21, 2023 · Stochastic Calculus for Finance I: Binomial asset pricing model and Stochastic Calculus for Finance II: tochastic Calculus for Finance II: Continuous-Time Models. These two books are very good if you want to apply the theory to price derivatives. Stochastic Differential Equations: An Introduction with Applications Bernt Oksanda."}
+{"idx": 4, "title": "Difference between time series and stochastic process?", "date": "", "ddg_snippet": "Jan 30, 2011 · Stochastic processes are often used in modeling time series data- we assume that the time series we have was produced by a stochastic process, find the parameters of a stochastic process that would be likely to produce that time series, and then use that stochastic process as a model in predicting future values of the time series.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/19568/difference-between-time-series-and-stochastic-process", "content": "Jan 30, 2011 · Stochastic processes are often used in modeling time series data- we assume that the time series we have was produced by a stochastic process, find the parameters of a stochastic process that would be likely to produce that time series, and then use that stochastic process as a model in predicting future values of the time series."}
+{"idx": 5, "title": "What's the difference between stochastic and random?", "date": "", "ddg_snippet": "Feb 28, 2012 · Similarly \" stochastic process\" and \"random process\", but the former is seen more often. Some mathematicians seem to use \"random\" when they mean uniformly distributed, but probabilists and statisticians don't.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/114373/whats-the-difference-between-stochastic-and-random", "content": "Feb 28, 2012 · Similarly \" stochastic process\" and \"random process\", but the former is seen more often. Some mathematicians seem to use \"random\" when they mean uniformly distributed, but probabilists and statisticians don't."}
+{"idx": 6, "title": "What is Ito's formula for a function of two stochastic processes?", "date": "", "ddg_snippet": "Explore related questions reference-request stochastic -processes stochastic -calculus stochastic -differential-equations See similar questions with these tags.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1351527/what-is-itos-formula-for-a-function-of-two-stochastic-processes", "content": "Explore related questions reference-request stochastic -processes stochastic -calculus stochastic -differential-equations See similar questions with these tags."}
+{"idx": 7, "title": "Fubini's Theorem for Stochastic Integral - Mathematics Stack...", "date": "", "ddg_snippet": "Jul 6, 2015 · In your equation with the question mark, those are not stochastic integrals and Fubini's theorem applies directly.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/1351342/fubinis-theorem-for-stochastic-integral", "content": "Jul 6, 2015 · In your equation with the question mark, those are not stochastic integrals and Fubini's theorem applies directly."}
+{"idx": 8, "title": "如何理解随机梯度下降（stochastic gradient descent，SGD）？", "date": "", "ddg_snippet": "如何理解随机梯度下降（stochastic gradient descent，SGD）？ 圆桌收录 编程没有那么难 小蓝星 · undefined", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/264189719", "content": "如何理解随机梯度下降（stochastic gradient descent，SGD）？ 圆桌收录 编程没有那么难 小蓝星 · undefined"}
+{"idx": 9, "title": "Where to begin in approaching Stochastic Calculus?", "date": "", "ddg_snippet": "Nov 6, 2012 · 18 I have experience in Abstract algebra (up to Galois theory), Real Analysis (baby Rudin except for the measure integral) and probability theory up to Brownian motion (non-rigorous treatment). Is there a suggested direction I can take in order to begin studying stochastic calculus and stochastic differential equations?", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/231712/where-to-begin-in-approaching-stochastic-calculus", "content": "Nov 6, 2012 · 18 I have experience in Abstract algebra (up to Galois theory), Real Analysis (baby Rudin except for the measure integral) and probability theory up to Brownian motion (non-rigorous treatment). Is there a suggested direction I can take in order to begin studying stochastic calculus and stochastic differential equations?"}
diff --git a/data/sampled_jsons/Symmetric_Cross_Entropy_loss_Equation_(6)_YjBrt82S3v.jsonl b/data/sampled_jsons/Symmetric_Cross_Entropy_loss_Equation_(6)_YjBrt82S3v.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b37ebc4d1d3db6debe68235c8471c0a9f772ce90
--- /dev/null
+++ b/data/sampled_jsons/Symmetric_Cross_Entropy_loss_Equation_(6)_YjBrt82S3v.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Cross - entropy - Wikipedia", "date": "", "ddg_snippet": "In information theory, the cross - entropy between two probability distributions. and. , over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Cross-entropy", "content": "In information theory, the cross - entropy between two probability distributions. and. , over the same underlying set of events, measures the average number of bits needed to identify an event drawn from the set when the coding scheme used for the set ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — This paper proposes a novel symmetric RL loss for mitigating noisy reward and preference data problems for RL or RLHF, motivated by the previous ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "by JS Byun · Cited by 1 — This paper proposes a novel symmetric RL loss for mitigating noisy reward and preference data problems for RL or RLHF, motivated by the previous ..."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust Learning on ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning dificulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=YjBrt82S3v&name=pdf", "content": "In this work, we focus on RL algorithms that share learning dificulties with cross - entropy loss , especially for low-probability predictions. To enhance stability, we adapt reverse cross - entropy (RCE) from supervised learning for noisy data, defining a symmetric RL loss . We demonstrate performance improvements across various tasks and scales."}
+{"idx": 3, "title": "Why don't we use a symmetric cross-entropy loss?", "date": "", "ddg_snippet": "Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/331942/why-dont-we-use-a-symmetric-cross-entropy-loss", "content": "Mar 6 , 2018 · The minimum value that the cross-entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross-entropy can be used here."}
+{"idx": 4, "title": "Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "Aug 16, 2019 · Inspired by the symmetric KL-divergence, we propose the approach of \\textbf { Symmetric cross entropy Learning} (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1908.06112", "content": "Aug 16, 2019 · Inspired by the symmetric KL-divergence, we propose the approach of \\textbf { Symmetric cross entropy Learning} (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels."}
+{"idx": 5, "title": "YisenWang/symmetric_cross_entropy_for_noisy_labels - GitHub Symmetric Cross Entropy for Robust Learning with Noisy Labels 【SCE 损失】Symmetric Cross Entropy for Robust Learning with ... Symmetric Cross Entropy for Robust Learning with Noisy Labels Why don't we use a symmetric cross - entropy loss ? Symmetric Cross Entropy for Robust Learning with Noisy Labels Why don't we use a symmetric cross - entropy loss ? 5.1. Cross - entropy loss Deep learning - Fleuret Symmetric Cross Entropy for Robust Learning with Noisy Labels 5.1. Cross-entropy loss Deep learning - Fleuret", "date": "", "ddg_snippet": "Code for ICCV2019 \" Symmetric Cross Entropy for Robust Learning with Noisy Labels\" https://arxiv.org/abs/1908.06112 See full list on github.com Simply run the code by python3 train_models.py It can config with dataset, model, epoch, batchsize, noise_rate, symmetric or asymmetric type noise See full list on github.com The Pytorch version is implemented by Hanxun Huang. The code can be found here: https://github.com/HanxunHuangLemonBear/SCELoss-Reproduce See full list on github.com If you use this code in your work, please cite the accompanying paper: See full list on github.com Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels. 去杭州之前就想专门整理一下在有噪声样本下的分类损失，在这之前，先就较经典的一篇分析一下各种推导原理，剩下就简单一点只看损失部分～ 背景在分类任务上，最普遍的损失函数是 Cross Entropy ，即交叉熵损失： 该… What is symmetric cross entropy learning? Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels. Does minimizing symmetric cross entropy reduce log loss? It also just seems that minimizing the \"symmetric\" cross entropy still minimizes the log-loss , but additionally minimizes the predictive entropy. But perhaps the latter effect is not always desirable? Cross-Entropy is one of the methods used to find how good is the predicted probability models. Is cross entropy loss class-biased? While Cross Entropy (CE) loss is the most commonly used loss for training DNNs, we have found that DNN learning with CE can be class-biased : Does symmetric cross entropy have a lower bound? However, if we take symmetric cross entropy, though there is a lower bound here also, but it becomes difficult to compare two different models. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. Is cross entropy loss justified in a classification context? rning / 5.1. Cross-entropy lossNotesMSE is justified in an Euclidean space where the distance to a target loca ion is consistent with the objective. But it does not really makes sense ina classification context, because the class values do not have any topological structure: we cannot say that class “dog” is closer to class “schoo Does cross entropy affect DNN learning? In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (\"easy\" classes), but more surprisingly, it also suffers from significant under learning on some other classes (\"hard\" classes) . C Deep learning / 5.1. Cross-entropy loss value that makes sense geometrically. Beside being conceptually wrong for classification, in practice it penalizes responses Consider this example with correct class 1, and two outputs ˆy and ˆy′. y", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels", "content": "Code for ICCV2019 \" Symmetric Cross Entropy for Robust Learning with Noisy Labels\" https://arxiv.org/abs/1908.06112 See full list on github.com Simply run the code by python3 train_models.py It can config with dataset, model, epoch, batchsize, noise_rate, symmetric or asymmetric type noise See full list on github.com The Pytorch version is implemented by Hanxun Huang. The code can be found here: https://github.com/HanxunHuangLemonBear/SCELoss-Reproduce See full list on github.com If you use this code in your work, please cite the accompanying paper: See full list on github.com Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels. 去杭州之前就想专门整理一下在有噪声样本下的分类损失，在这之前，先就较经典的一篇分析一下各种推导原理，剩下就简单一点只看损失部分～ 背景在分类任务上，最普遍的损失函数是 Cross Entropy ，即交叉熵损失： 该… What is symmetric cross entropy learning? Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels. Does minimizing symmetric cross entropy reduce log loss? It also just seems that minimizing the \"symmetric\" cross entropy still minimizes the log-loss , but additionally minimizes the predictive entropy. But perhaps the latter effect is not always desirable? Cross-Entropy is one of the methods used to find how good is the predicted probability models. Is cross entropy loss class-biased? While Cross Entropy (CE) loss is the most commonly used loss for training DNNs, we have found that DNN learning with CE can be class-biased : Does symmetric cross entropy have a lower bound? However, if we take symmetric cross entropy, though there is a lower bound here also, but it becomes difficult to compare two different models. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. Is cross entropy loss justified in a classification context? rning / 5.1. Cross-entropy lossNotesMSE is justified in an Euclidean space where the distance to a target loca ion is consistent with the objective. But it does not really makes sense ina classification context, because the class values do not have any topological structure: we cannot say that class “dog” is closer to class “schoo Does cross entropy affect DNN learning? In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (\"easy\" classes), but more surprisingly, it also suffers from significant under learning on some other classes (\"hard\" classes) . C Deep learning / 5.1. Cross-entropy loss value that makes sense geometrically. Beside being conceptually wrong for classification, in practice it penalizes responses Consider this example with correct class 1, and two outputs ˆy and ˆy′. y"}
+{"idx": 6, "title": "Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf", "content": "Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust coun-terpart Reverse Cross Entropy (RCE). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy la-bels."}
+{"idx": 7, "title": "【SCE 损失】Symmetric Cross Entropy for Robust Learning with ...", "date": "", "ddg_snippet": "去杭州之前就想专门整理一下在有噪声样本下的分类损失，在这之前，先就较经典的一篇分析一下各种推导原理，剩下就简单一点只看损失部分～ 背景在分类任务上，最普遍的损失函数是 Cross Entropy ，即交叉熵损失： 该…", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/420827592", "content": "去杭州之前就想专门整理一下在有噪声样本下的分类损失，在这之前，先就较经典的一篇分析一下各种推导原理，剩下就简单一点只看损失部分～ 背景在分类任务上，最普遍的损失函数是 Cross Entropy ，即交叉熵损失： 该…"}
+{"idx": 8, "title": "5.1. Cross-entropy loss Deep learning - Fleuret", "date": "", "ddg_snippet": "C Deep learning / 5.1. Cross-entropy loss value that makes sense geometrically. Beside being conceptually wrong for classification, in practice it penalizes responses Consider this example with correct class 1, and two outputs ˆy and ˆy′. y", "subpage_snippet": "", "source": "fleuret.org", "link": "https://fleuret.org/dlc/materials/dlc-handout-5-1-cross-entropy-loss.pdf", "content": "C Deep learning / 5.1. Cross-entropy loss value that makes sense geometrically. Beside being conceptually wrong for classification, in practice it penalizes responses Consider this example with correct class 1, and two outputs ˆy and ˆy′. y"}
+{"idx": 9, "title": "machine learning - What is cross - entropy ? - Stack Overflow", "date": "", "ddg_snippet": "Cross entropy loss formula. Where p(x) is the true probability distribution (one-hot) and q(x) is the predicted probability distribution. The sum is over the three classes A, B, and C. In this case the loss is 0.479", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/41990250/what-is-cross-entropy", "content": "Cross entropy loss formula. Where p(x) is the true probability distribution (one-hot) and q(x) is the predicted probability distribution. The sum is over the three classes A, B, and C. In this case the loss is 0.479"}
diff --git a/data/sampled_jsons/Symmetric_Cross_Entropy_loss_formula_CE_RCE_alpha_beta.jsonl b/data/sampled_jsons/Symmetric_Cross_Entropy_loss_formula_CE_RCE_alpha_beta.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ce284e20e3bb5d3057327b20ea4b4fead37fda59
--- /dev/null
+++ b/data/sampled_jsons/Symmetric_Cross_Entropy_loss_formula_CE_RCE_alpha_beta.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "Inspired by the symmetric KL-divergence, we propose the approach of \\textbf { Symmetric cross entropy Learning} (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy ( RCE ). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1908.06112", "content": "Inspired by the symmetric KL-divergence, we propose the approach of \\textbf { Symmetric cross entropy Learning} (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy ( RCE ). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels."}
+{"idx": 1, "title": "【SCE 损失】Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "再来看 RCE 的梯度项，首先看第一行在标签类别的情况。 该梯度项是一个曲线，它在 0～1 区间内始终为正，说明其实是对该类别的梯度回归有了一定缓解，即在该类别中预测值增大造成的损失减少并没有原来那么多。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/420827592", "content": "再来看 RCE 的梯度项，首先看第一行在标签类别的情况。 该梯度项是一个曲线，它在 0～1 区间内始终为正，说明其实是对该类别的梯度回归有了一定缓解，即在该类别中预测值增大造成的损失减少并没有原来那么多。"}
+{"idx": 2, "title": "Why don't we use a symmetric cross-entropy loss?", "date": "", "ddg_snippet": "The minimum value that the cross - entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross - entropy can be used here.", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/331942/why-dont-we-use-a-symmetric-cross-entropy-loss", "content": "The minimum value that the cross - entropy of ℍ [𝑝,𝑞] can have is when 𝑞=𝑝 which is ℍ [𝑝,𝑝], simple the entropy of the distribution 𝑝. While evaluating different built models say 𝑞 and 𝑞', we often need to compare different them, and cross - entropy can be used here."}
+{"idx": 3, "title": "Hierarchical symmetric cross entropy for distant supervised relation ...", "date": "", "ddg_snippet": "The Symmetric Cross Entropy (SCE) loss function combines the Cross Entropy ( CE ) and Reverse Cross Entropy ( RCE ) to improve the robustness of the model under noisy labels.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10489-024-05798-z", "content": "The Symmetric Cross Entropy (SCE) loss function combines the Cross Entropy ( CE ) and Reverse Cross Entropy ( RCE ) to improve the robustness of the model under noisy labels."}
+{"idx": 4, "title": "YisenWang/symmetric_cross_entropy_for_noisy_labels - GitHub", "date": "", "ddg_snippet": "Simply run the code by python3 train_models.py It can config with dataset, model, epoch, batchsize, noise_rate, symmetric or asymmetric type noise", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels", "content": "Simply run the code by python3 train_models.py It can config with dataset, model, epoch, batchsize, noise_rate, symmetric or asymmetric type noise"}
+{"idx": 5, "title": "Symmetric Loss Function", "date": "", "ddg_snippet": "RCE的部分就是一个MAE!所以SCE本质上是显式的融合交叉熵和MAE!pytorch实现如下，TF实现见文首链接 class SymmetricCrossEntropy (nn.Module): def __init__ (self, alpha=0.1, beta=1): super (SymmetricCrossEntropy, self).__init__() self. alpha = alpha self. beta = beta self.epsilon = 1e-10 def forward (self, logits, labels): # KL(p|q) + KL(q|p) labels = torch.nn ...", "subpage_snippet": "", "source": "www.cnblogs.com", "link": "https://www.cnblogs.com/gogoSandy/p/17018065.html", "content": "RCE的部分就是一个MAE!所以SCE本质上是显式的融合交叉熵和MAE!pytorch实现如下，TF实现见文首链接 class SymmetricCrossEntropy (nn.Module): def __init__ (self, alpha=0.1, beta=1): super (SymmetricCrossEntropy, self).__init__() self. alpha = alpha self. beta = beta self.epsilon = 1e-10 def forward (self, logits, labels): # KL(p|q) + KL(q|p) labels = torch.nn ..."}
+{"idx": 6, "title": "Is the cross-entropy loss a proper metric? - Sebastian Raschka, PhD", "date": "", "ddg_snippet": "Cross - entropy is used to measure the distance between two probability distributions. In machine learning contexts, we use the discrete cross - entropy loss ( CE ) between class label y and the predicted probability p when we train logistic regression or neural network classifiers on a dataset consisting of n training examples:", "subpage_snippet": "", "source": "sebastianraschka.com", "link": "https://sebastianraschka.com/faq/docs/proper-metric-cross-entropy.html", "content": "Cross - entropy is used to measure the distance between two probability distributions. In machine learning contexts, we use the discrete cross - entropy loss ( CE ) between class label y and the predicted probability p when we train logistic regression or neural network classifiers on a dataset consisting of n training examples:"}
+{"idx": 7, "title": "Symmetric Cross Entropy - 知乎", "date": "", "ddg_snippet": "Cross Entropy 机器学习分类任务中常用的目标函数，然而它在不同类别上的学习速度却是很不一致，在部分类别上可能很快就对错误的标签发生过拟合，对其他类别可能还出去欠拟合状态。 本文将介绍另外一种损失函数—— Symmetric Cross Entropy ，用于缓解这个问题。", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/370775044", "content": "Cross Entropy 机器学习分类任务中常用的目标函数，然而它在不同类别上的学习速度却是很不一致，在部分类别上可能很快就对错误的标签发生过拟合，对其他类别可能还出去欠拟合状态。 本文将介绍另外一种损失函数—— Symmetric Cross Entropy ，用于缓解这个问题。"}
+{"idx": 8, "title": "ICCV 2019 Open Access Repository", "date": "", "ddg_snippet": "Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy ( RCE ). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.html", "content": "Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy ( RCE ). Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels."}
+{"idx": 9, "title": "arXiv:1908.06112v1 [cs.LG] 16 Aug 2019", "date": "", "ddg_snippet": "to avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust co n-terpart Reverse Cross Entropy ( RCE ). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1908.06112", "content": "to avoid overfitting to noisy labels. Inspired by the symmetric KL-divergence, we pro-pose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust co n-terpart Reverse Cross Entropy ( RCE ). Our proposed SL ap-proach simultaneously addresses both the under learning and overfitting problem"}
diff --git a/data/sampled_jsons/T-Agent_Success@5_one-day_25%_CVE-Bench_OpenReview_Figure_3_results.jsonl b/data/sampled_jsons/T-Agent_Success@5_one-day_25%_CVE-Bench_OpenReview_Figure_3_results.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df7305fce0f49c80c6b7e85b8e1b5f70ef754830
--- /dev/null
+++ b/data/sampled_jsons/T-Agent_Success@5_one-day_25%_CVE-Bench_OpenReview_Figure_3_results.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real ...", "date": "", "ddg_snippet": "Figure 3 : Success rates of different LLM agents on CVE - Bench . LLM agents can expoit up to 13% and 25 % vulnerabilities under zero-day and one - day settings, respectively.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v1", "content": "Figure 3 : Success rates of different LLM agents on CVE - Bench . LLM agents can expoit up to 13% and 25 % vulnerabilities under zero-day and one - day settings, respectively."}
+{"idx": 1, "title": "CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit", "date": "", "ddg_snippet": "... agent framework, teams of LLM agents (Fang et al., 2024c ) demonstrate substantial improvement, achieving a success rate as high as 13% with five ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.17332v4", "content": "... agent framework, teams of LLM agents (Fang et al., 2024c ) demonstrate substantial improvement, achieving a success rate as high as 13% with five ..."}
+{"idx": 2, "title": "OpenAI’s GPT-4 Can Autonomously Exploit 87% of One-Day", "date": "", "ddg_snippet": "... it was found that only an LLM agent based on GPT-4 could find and exploit one - day vulnerabilities — i.e., when it had access to their CVE ...", "subpage_snippet": "", "source": "unifiedguru.com", "link": "https://unifiedguru.com/openais-gpt-4-can-autonomously-exploit-87-of-one-day-vulnerabilities/", "content": "... it was found that only an LLM agent based on GPT-4 could find and exploit one - day vulnerabilities — i.e., when it had access to their CVE ..."}
+{"idx": 3, "title": "Beauty Therapist Course — Courses For Success", "date": "", "ddg_snippet": "... DISCOUNTS + FREE 10 SOFT SKILLS YOU NEED COURSE - BUY TODAY & SAVE UP TO 50% ON COURSES & UP TO A MASSIVE 97% ON BUNDLES - OFFER EXTENDED ONE DAY ...", "subpage_snippet": "", "source": "www.coursesforsuccess.com", "link": "https://www.coursesforsuccess.com/products/beauty-therapist", "content": "... DISCOUNTS + FREE 10 SOFT SKILLS YOU NEED COURSE - BUY TODAY & SAVE UP TO 50% ON COURSES & UP TO A MASSIVE 97% ON BUNDLES - OFFER EXTENDED ONE DAY ..."}
+{"idx": 4, "title": "HyperWar: The Army Air Forces in WWII: Vol. V--The Pacific:", "date": "", "ddg_snippet": "On 10 November, one day after he had informally approved M ATTERHORN , the President informed Churchill and Chiang Kai-shek of the plan and asked for ...", "subpage_snippet": "", "source": "www.ibiblio.org", "link": "http://www.ibiblio.org/hyperwar/AAF/V/AAF-V-3.html", "content": "On 10 November, one day after he had informally approved M ATTERHORN , the President informed Churchill and Chiang Kai-shek of the plan and asked for ..."}
+{"idx": 5, "title": "xOffense: An AI-driven autonomous penetration testing framework", "date": "", "ddg_snippet": "In 2024 alone, the National Vulnerability Database listed more than 29,000 new CVEs , a 38% year-over-year increase [ 1 ] [ 2 ] .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13021v1", "content": "In 2024 alone, the National Vulnerability Database listed more than 29,000 new CVEs , a 38% year-over-year increase [ 1 ] [ 2 ] ."}
+{"idx": 6, "title": "XD NOC | ExchangeDefender", "date": "", "ddg_snippet": "The issue was resolved around 6:50AM Eastern and the specific node was remedied. ... we've been able to address all the issues and bugs the same day ...", "subpage_snippet": "", "source": "www.anythingdown.com", "link": "https://www.anythingdown.com/", "content": "The issue was resolved around 6:50AM Eastern and the specific node was remedied. ... we've been able to address all the issues and bugs the same day ..."}
+{"idx": 7, "title": "Only Human | The New Republic", "date": "", "ddg_snippet": "Morton quickly became a “poster boy,” as one leading activist put it, for the burgeoning countering violent extremism ( CVE ) movement that had ...", "subpage_snippet": "", "source": "newrepublic.com", "link": "https://newrepublic.com/article/145433/only-human-american-ex-jihadi-rebuild-life-country-once-vowed-destroy", "content": "Morton quickly became a “poster boy,” as one leading activist put it, for the burgeoning countering violent extremism ( CVE ) movement that had ..."}
+{"idx": 8, "title": "Best Developer Tools Podcasts (2025)", "date": "", "ddg_snippet": "With over 17 million downloads to date, Developer Tea is a short podcast hosted by Jonathan Cutrell, engineering leader with over 15 years of ...", "subpage_snippet": "", "source": "player.fm", "link": "https://player.fm/podcasts/Developer-Tools", "content": "With over 17 million downloads to date, Developer Tea is a short podcast hosted by Jonathan Cutrell, engineering leader with over 15 years of ..."}
+{"idx": 9, "title": "Sponsored Archives - SD Times", "date": "", "ddg_snippet": "Failing to provide developers with insight into their tools and processes could lead to unaddressed bugs and even system failures.", "subpage_snippet": "", "source": "sdtimes.com", "link": "https://sdtimes.com/category/sponsored/", "content": "Failing to provide developers with insight into their tools and processes could lead to unaddressed bugs and even system failures."}
diff --git a/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_arxiv.jsonl b/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_arxiv.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..94addf7033801672e645507608e697997f05588f
--- /dev/null
+++ b/data/sampled_jsons/TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning_arxiv.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09536", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives (MP ..."}
+{"idx": 1, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "Authors Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Abstract This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/6debbef99fd649051ee96f8cb80e232d-Abstract-Conference.html", "content": "Authors Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Abstract This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These ..."}
+{"idx": 2, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "Abstract:This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives ...", "subpage_snippet": "", "source": "bohrium.dp.tech", "link": "https://bohrium.dp.tech/paper/arxiv/2410.09536", "content": "Abstract:This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire action trajectories over multiple time steps instead of single actions at every time step. These trajectories are typically parameterized by trajectory generators such as Movement Primitives ..."}
+{"idx": 3, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Li, Ge 1; Tian, Dong; Zhou, Hongyi; Jiang, Xinkai; Lioutikov, Rudolf; Neumann, Gerhard 1 1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT) Externe Links Scopus", "subpage_snippet": "", "source": "publikationen.bibliothek.kit.edu", "link": "https://publikationen.bibliothek.kit.edu/1000179043", "content": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Li, Ge 1; Tian, Dong; Zhou, Hongyi; Jiang, Xinkai; Lioutikov, Rudolf; Neumann, Gerhard 1 1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT) Externe Links Scopus"}
+{"idx": 4, "title": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning", "date": "", "ddg_snippet": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384929465_TOP-ERL_Transformer-based_Off-Policy_Episodic_Reinforcement_Learning", "content": "This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. In ERL, policies predict entire ..."}
+{"idx": 5, "title": "\"TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning.\"", "date": "", "ddg_snippet": "Bibliographic details on TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2410-09536", "content": "Bibliographic details on TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning ."}
+{"idx": 6, "title": "#iclr2025 #reinforcementlearning #transformers #robotics | Ge Li (Bruce)", "date": "", "ddg_snippet": "...\" TOP - ERL : Transformer - Based Off - Policy Episodic Reinforcement Learning \", has been accepted as a Spotlight at ICLR 2025! In this work, we introduce TOP - ERL , a novel reinforcement learning algorithm that enables off - policy learning in epis...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/geli-bruce_iclr2025-reinforcementlearning-transformers-activity-7295121799921426433-w8U-", "content": "...\" TOP - ERL : Transformer - Based Off - Policy Episodic Reinforcement Learning \", has been accepted as a Spotlight at ICLR 2025! In this work, we introduce TOP - ERL , a novel reinforcement learning algorithm that enables off - policy learning in epis..."}
+{"idx": 7, "title": "robot- learning .ml | 7th Robot Learning Workshop: Towards Robots...", "date": "", "ddg_snippet": "TOP - ERL : Transformer - based Off - Policy Episodic Reinforcement Learning . Optimism via Intrinsic Rewards: Scalable and Principled Exploration for Model-based Reinforcement Learning .", "subpage_snippet": "", "source": "www.robot-learning.ml", "link": "http://www.robot-learning.ml/2025/", "content": "TOP - ERL : Transformer - based Off - Policy Episodic Reinforcement Learning . Optimism via Intrinsic Rewards: Scalable and Principled Exploration for Model-based Reinforcement Learning ."}
+{"idx": 8, "title": "Hongyi Zhou", "date": "", "ddg_snippet": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Preprint, Under Review arXiv This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. ERL methods are often constrained to on-policy ...", "subpage_snippet": "", "source": "hongyizhoucn.github.io", "link": "https://hongyizhoucn.github.io/", "content": "TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning Ge Li, Dong Tian, Hongyi Zhou, Xinkai Jiang, Rudolf Lioutikov, Gerhard Neumann Preprint, Under Review arXiv This work introduces Transformer-based Off-Policy Episodic Reinforcement Learning (TOP-ERL), a novel algorithm that enables off-policy updates in the ERL framework. ERL methods are often constrained to on-policy ..."}
+{"idx": 9, "title": "E pisodic r einforcement L earning", "date": "", "ddg_snippet": "Top - erl : transformer - based off - policy episodic reinforcement learning .In this paper, we propose Transformer - based Off - Policy ERL ( TOP - ERL ), which leverages the Transformer as a critic to predict the value of action sequences.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.09536", "content": "Top - erl : transformer - based off - policy episodic reinforcement learning .In this paper, we propose Transformer - based Off - Policy ERL ( TOP - ERL ), which leverages the Transformer as a critic to predict the value of action sequences."}
diff --git a/data/sampled_jsons/Table_1_27tMzmzDjO_ML-1M_6040_3706_900188_Amazon_Beauty_22363_12101_198439_Amazon_Toys_19412_11924_1.jsonl b/data/sampled_jsons/Table_1_27tMzmzDjO_ML-1M_6040_3706_900188_Amazon_Beauty_22363_12101_198439_Amazon_Toys_19412_11924_1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fece3448b201445f800f22b3900c1ff87c38a95c
--- /dev/null
+++ b/data/sampled_jsons/Table_1_27tMzmzDjO_ML-1M_6040_3706_900188_Amazon_Beauty_22363_12101_198439_Amazon_Toys_19412_11924_1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "luminati-io/Amazon-dataset-samples - GitHub", "date": "", "ddg_snippet": "A sample dataset of over 1,000 Amazon product listings, extracted using the Bright Data API, perfect for competitive analysis, market trends, and eCommerce insights.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/luminati-io/Amazon-dataset-samples", "content": "A sample dataset of over 1,000 Amazon product listings, extracted using the Bright Data API, perfect for competitive analysis, market trends, and eCommerce insights."}
+{"idx": 1, "title": "Amazon review data - University of California, San Diego", "date": "", "ddg_snippet": "This dataset contains product reviews and metadata from Amazon , including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).", "subpage_snippet": "", "source": "jmcauley.ucsd.edu", "link": "http://jmcauley.ucsd.edu/data/amazon/index_2014.html", "content": "This dataset contains product reviews and metadata from Amazon , including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs)."}
+{"idx": 2, "title": "Amazon-Beauty dataset - LDM", "date": "", "ddg_snippet": "The Amazon-Beauty dataset is a sub-dataset from Amazon Product Reviews 3, which records large crawls of user reviews and product metadata.", "subpage_snippet": "", "source": "service.tib.eu", "link": "https://service.tib.eu/ldmservice/dataset/amazon-beauty-dataset", "content": "The Amazon-Beauty dataset is a sub-dataset from Amazon Product Reviews 3, which records large crawls of user reviews and product metadata."}
+{"idx": 3, "title": "Recommendation-Systems for Amazon Beauty Products - GitHub", "date": "", "ddg_snippet": "This project aims to create a recommendation system for the Amazon marketing team to utilize to send targeted recommendation e-mails to users who have purchased and rated products within 30 days. A collaborative approach was taken, meaning recommendations will be made by comparing similar reviewer profiles based on existing ratings. - jlc0512/Recommendation-Systems-for- Amazon - Beauty -Products", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jlc0512/Recommendation-Systems-for-Amazon-Beauty-Products", "content": "This project aims to create a recommendation system for the Amazon marketing team to utilize to send targeted recommendation e-mails to users who have purchased and rated products within 30 days. A collaborative approach was taken, meaning recommendations will be made by comparing similar reviewer profiles based on existing ratings. - jlc0512/Recommendation-Systems-for- Amazon - Beauty -Products"}
+{"idx": 4, "title": "GitHub - amitrj540/recommendation-engine: Beauty products ...", "date": "", "ddg_snippet": "All_beauty dataset is taken from Per-category data released as Amazon review data in 2018. The dataset is divided into two parts: All_Beauty.json.gz This dataset contains reviews (371,345 reviews).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/amitrj540/recommendation-engine", "content": "All_beauty dataset is taken from Per-category data released as Amazon review data in 2018. The dataset is divided into two parts: All_Beauty.json.gz This dataset contains reviews (371,345 reviews)."}
+{"idx": 5, "title": "kaiboon0216/Amazon-beauty-products-analysis - GitHub", "date": "", "ddg_snippet": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/kaiboon0216/Amazon-beauty-products-analysis", "content": "Amazon - beauty -products-analysis Using data science process to gain business insights from the Amazon beauty datasets and determine the factors that contribute to the high sales of a beauty product Our anaylsis are focused on answering the following questions:"}
+{"idx": 6, "title": "GitHub - RecoHut-Datasets/amazon_beauty: Amazon Beauty Products and ...", "date": "", "ddg_snippet": "Amazon Beauty Products and Review Dataset. Contribute to RecoHut-Datasets/ amazon_beauty development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/RecoHut-Datasets/amazon_beauty", "content": "Amazon Beauty Products and Review Dataset. Contribute to RecoHut-Datasets/ amazon_beauty development by creating an account on GitHub."}
+{"idx": 7, "title": "SSE4Rec: Sequential recommendation with subsequence extraction", "date": "", "ddg_snippet": "Here we use the widely studied MovieLens- 1M ( ML-1M ) dataset, which contains approximately one million user-item interactions. The statistics of the preprocessed datasets are listed in Table 1 . Beauty , Video Games, and Steam are sparse datasets. The Gowalla dataset contains a large number of users and items.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705123011127", "content": "Here we use the widely studied MovieLens- 1M ( ML-1M ) dataset, which contains approximately one million user-item interactions. The statistics of the preprocessed datasets are listed in Table 1 . Beauty , Video Games, and Steam are sparse datasets. The Gowalla dataset contains a large number of users and items."}
+{"idx": 8, "title": "MovieLens 1M Dataset - GroupLens", "date": "", "ddg_snippet": "MovieLens 1M movie ratings. Stable benchmark dataset. 1 million ratings from 6000 users on 4000 movies. Released 2/2003. README.txt ml - 1m .zip (size: 6 MB, checksum) Permalink:", "subpage_snippet": "", "source": "grouplens.org", "link": "https://grouplens.org/datasets/movielens/1m/", "content": "MovieLens 1M movie ratings. Stable benchmark dataset. 1 million ratings from 6000 users on 4000 movies. Released 2/2003. README.txt ml - 1m .zip (size: 6 MB, checksum) Permalink:"}
+{"idx": 9, "title": "推荐系统数据集之MovieLens - CSDN博客", "date": "", "ddg_snippet": "本文以MovieLens 1M Dataset为例，具体介绍下此数据集，其它MovieLens数据集也大都类似，本文使用的数据集下载链接为 ml - 1m .zip。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/weixin_44852067/article/details/130139502", "content": "本文以MovieLens 1M Dataset为例，具体介绍下此数据集，其它MovieLens数据集也大都类似，本文使用的数据集下载链接为 ml - 1m .zip。"}
diff --git a/data/sampled_jsons/Table_1_MSE_for_the_estimated_conditional_mean_Sieve_MLE_CKDE_FlexCode.jsonl b/data/sampled_jsons/Table_1_MSE_for_the_estimated_conditional_mean_Sieve_MLE_CKDE_FlexCode.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d53f31fa253713d57faaa85353e60379f46a7eca
--- /dev/null
+++ b/data/sampled_jsons/Table_1_MSE_for_the_estimated_conditional_mean_Sieve_MLE_CKDE_FlexCode.jsonl
@@ -0,0 +1,7 @@
+{"idx": 0, "title": "A LIKELIHOOD BASED APPROACH TO DISTRIBUTION REGRESSION USING ...", "date": "", "ddg_snippet": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=V6hhhXoTSq", "content": "In this work, we explore the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the re-sponse variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. More specifically, we study the large-sample properties of a likelihood-based approach for estimating these ..."}
+{"idx": 1, "title": "A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE(SD) for the FD3 dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.02025v1", "content": "Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE .Note that the sieve MLE outperforms all other methods in all scenarios except for the MSE(SD) for the FD3 dataset."}
+{"idx": 2, "title": "arXiv:2410.02025v1 [math.ST] 2 Oct 2024", "date": "", "ddg_snippet": "th the mean and the standard deviation. We use Monte Carlo approximation to compute the mean and standard deviation for the sieve MLE , and nume ical integration for CKDE and Flexcode . This evaluation strategy resembles that implemented by Zhou et al.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025", "content": "th the mean and the standard deviation. We use Monte Carlo approximation to compute the mean and standard deviation for the sieve MLE , and nume ical integration for CKDE and Flexcode . This evaluation strategy resembles that implemented by Zhou et al."}
+{"idx": 3, "title": "A Likelihood Based Approach to Distribution Regression ...", "date": "", "ddg_snippet": "... sieve MLE , and numerical integration for CKDE and Flexcode . This evaluation ... Table 1. MSE for the estimated conditional mean and the standard deviation.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46645", "content": "... sieve MLE , and numerical integration for CKDE and Flexcode . This evaluation ... Table 1. MSE for the estimated conditional mean and the standard deviation."}
+{"idx": 4, "title": "TIVE MODELS", "date": "", "ddg_snippet": "Table 1: MSE for the estimated conditional mean and the standard deviation. Sieve MLE . CKDE . FlexCode . FD1. MEAN. 0.0379 ± 0.0170. 1.0053 ± 0.1004.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=ALzyAbBoVf&name=pdf", "content": "Table 1: MSE for the estimated conditional mean and the standard deviation. Sieve MLE . CKDE . FlexCode . FD1. MEAN. 0.0379 ± 0.0170. 1.0053 ± 0.1004."}
+{"idx": 5, "title": "A Likelihood Based Approach to Distribution Regression Using ...", "date": "", "ddg_snippet": "We use Monte Carlo approximation to compute the mean and standard deviation for the sieve MLE , and numerical integration for CKDE and Flexcode . This evaluation strategy resembles that implemented by Zhou et al. (2022). Table 1 summarizes the findings.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.02025v1", "content": "We use Monte Carlo approximation to compute the mean and standard deviation for the sieve MLE , and numerical integration for CKDE and Flexcode . This evaluation strategy resembles that implemented by Zhou et al. (2022). Table 1 summarizes the findings."}
+{"idx": 6, "title": "(PDF) A Likelihood Based Approach to Distribution Regression Using...", "date": "", "ddg_snippet": "sieve - MLE to the true distribution (see Appendix Efor the proof). Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE CKDE FlexCode . FD1 MEAN 0.0379 ±0.0170 1.0053 ±0.1004 1.1660 ±0.1076.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384630603_A_Likelihood_Based_Approach_to_Distribution_Regression_Using_Conditional_Deep_Generative_Models", "content": "sieve - MLE to the true distribution (see Appendix Efor the proof). Table 1 : MSE for the estimated conditional mean and the standard deviation. Sieve MLE CKDE FlexCode . FD1 MEAN 0.0379 ±0.0170 1.0053 ±0.1004 1.1660 ±0.1076."}
diff --git a/data/sampled_jsons/Table_2_Qwen2-VL_zh_en_58.5_61.2_Avg_sitearxiv.org.jsonl b/data/sampled_jsons/Table_2_Qwen2-VL_zh_en_58.5_61.2_Avg_sitearxiv.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11b30d03b502b6264b57cce04e2eeb7d5d9da97b
--- /dev/null
+++ b/data/sampled_jsons/Table_2_Qwen2-VL_zh_en_58.5_61.2_Avg_sitearxiv.org.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Qwen2-VL: Enhancing Vision-Language Model’s Perception of the", "date": "", "ddg_snippet": "Comprehension of extended-duration videos (20 min+): Qwen2 - VL is capable of understanding videos over 20 minutes in length, enhancing its ability to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.12191v2", "content": "Comprehension of extended-duration videos (20 min+): Qwen2 - VL is capable of understanding videos over 20 minutes in length, enhancing its ability to ..."}
+{"idx": 1, "title": "Qwen2.5-Omni | OpenLM.ai", "date": "", "ddg_snippet": "Qwen2 .5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while ...", "subpage_snippet": "", "source": "openlm.ai", "link": "https://openlm.ai/qwen2.5-omni/", "content": "Qwen2 .5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while ..."}
+{"idx": 2, "title": "brad/qwen2.5-omni-7b:latest - Jozu Hub", "date": "", "ddg_snippet": "Qwen2 .5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while ...", "subpage_snippet": "", "source": "jozu.ml", "link": "https://jozu.ml/repository/brad/qwen2.5-omni-7b/latest/modelcard", "content": "Qwen2 .5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while ..."}
+{"idx": 3, "title": "Expanding Performance Boundaries of Open-Source Multimodal ...", "date": "", "ddg_snippet": "By employing this progressive scaling strategy, we achieve scalable model updates at a fraction of the cost typically associated with large-scale MLLM training. For example, Qwen2-VL [246] processes a cumulative total of 1.4 trillion tokens, whereas our InternVL2.5-78B is trained on only about 120 billion tokens— less than one-tenth of Qwen2-VL .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.05271v4", "content": "By employing this progressive scaling strategy, we achieve scalable model updates at a fraction of the cost typically associated with large-scale MLLM training. For example, Qwen2-VL [246] processes a cumulative total of 1.4 trillion tokens, whereas our InternVL2.5-78B is trained on only about 120 billion tokens— less than one-tenth of Qwen2-VL ."}
+{"idx": 4, "title": "Progressive Multimodal Reasoning via Active Retrieval", "date": "", "ddg_snippet": "As shown in Table 2 , both the closed-source model GPT-4o and the open-source small model Qwen2-VL -7B demonstrate significant improvements over the backbone and self-consistency approaches when combined with the AR-MCTS framework, verifying the generalization of AR-MCTS across different languages and reasoning disciplines.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.14835v1", "content": "As shown in Table 2 , both the closed-source model GPT-4o and the open-source small model Qwen2-VL -7B demonstrate significant improvements over the backbone and self-consistency approaches when combined with the AR-MCTS framework, verifying the generalization of AR-MCTS across different languages and reasoning disciplines."}
+{"idx": 5, "title": "InternVL3.5: Advancing Open-Source Multimodal Models in ...", "date": "", "ddg_snippet": "Abstract We introduce InternVL 3. 5 , a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.18265v1", "content": "Abstract We introduce InternVL 3. 5 , a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined ..."}
+{"idx": 6, "title": "InternVL3: Exploring Advanced Training and Test-Time Recipes ...", "date": "", "ddg_snippet": "Although its TextVQA performance (77.0) remains comparable to that of Qwen2-VL -2B, the enhancements in document and chart understanding suggest that the proposed native multimodal pre-training are particularly effective in tasks requiring precise visual–textual integration.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.10479v1", "content": "Although its TextVQA performance (77.0) remains comparable to that of Qwen2-VL -2B, the enhancements in document and chart understanding suggest that the proposed native multimodal pre-training are particularly effective in tasks requiring precise visual–textual integration."}
+{"idx": 7, "title": "“See the World, Discover Knowledge”: A Chinese Factuality ...", "date": "", "ddg_snippet": "Taking the Qwen2-VL series as an example, when the model size increases from 3B to 72B, the accuracy on the final questions increases from 29.0% to 50.6%. More detailed analysis can be found in Appendix B.1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.11718", "content": "Taking the Qwen2-VL series as an example, when the model size increases from 3B to 72B, the accuracy on the final questions increases from 29.0% to 50.6%. More detailed analysis can be found in Appendix B.1."}
+{"idx": 8, "title": "Data Metabolism: An Efficient Data Design Scheme for Vision ...", "date": "", "ddg_snippet": "Apr 10, 2025 · To mitigate this noise, for samples with free-form question types, we employ Qwen2-VL -72B to classify and identify questions that are unrelated or conflict with the corresponding image. The processing strategy for each source is finalized through manual review.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.12316", "content": "Apr 10, 2025 · To mitigate this noise, for samples with free-form question types, we employ Qwen2-VL -72B to classify and identify questions that are unrelated or conflict with the corresponding image. The processing strategy for each source is finalized through manual review."}
+{"idx": 9, "title": "Multi3Hate: Multimodal, Multilingual, and Multicultural Hate ...", "date": "", "ddg_snippet": "For instance, adding “Germany” to the prompt results in a 2.0 and 2.4-point accuracy drop for GPT-4o and Qwen2-VL , respectively, though these decreases are not statistically significant.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2411.03888v2", "content": "For instance, adding “Germany” to the prompt results in a 2.0 and 2.4-point accuracy drop for GPT-4o and Qwen2-VL , respectively, though these decreases are not statistically significant."}
diff --git "a/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_Proposition_3.3_local_PAR_moderate_R\302\262.jsonl" "b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_Proposition_3.3_local_PAR_moderate_R\302\262.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..32c1fafcaa790fa0c21de92ef7827fc1d3fb7a7d
--- /dev/null
+++ "b/data/sampled_jsons/The_Value_of_Prediction_in_Identifying_the_Worst-Off_Proposition_3.3_local_PAR_moderate_R\302\262.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "Small values of the PAR (i.e. PAR < 1) indicate that small improvements in prediction yield a much larger (relative) impact in the ability to target the worst - off than a small expansion in screening capacity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=26JsumCG0z", "content": "Small values of the PAR (i.e. PAR < 1) indicate that small improvements in prediction yield a much larger (relative) impact in the ability to target the worst - off than a small expansion in screening capacity."}
+{"idx": 1, "title": "[2501.19334] The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.19334", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 2, "title": "ICML Poster The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46605", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on..."}
+{"idx": 3, "title": "The Value of Prediction in Identifying the Worst - Off", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/The-Value-of-Prediction-in-Identifying-the-Worst-Off-29c80a95-3438-4b4a-b7f1-3306bd81bd13", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 4, "title": "[Paper Note] The Value of Prediction in Identifying the Worst - Off ...", "date": "", "ddg_snippet": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "githubissues.com", "link": "https://githubissues.com/AkihikoWatanabe/paper_notes/2220", "content": "This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 5, "title": "Helping the Worst - Off : When Hiring More Case Workers... | Medium", "date": "", "ddg_snippet": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy.", "subpage_snippet": "", "source": "nyudatascience.medium.com", "link": "https://nyudatascience.medium.com/helping-the-worst-off-when-hiring-more-case-workers-beats-building-better-ai-12e6f968de0b", "content": "The findings, detailed in “ The Value of Prediction in Identifying the Worst - Off ,” challenge the current policy focus on perfecting prediction accuracy."}
+{"idx": 6, "title": "A new faculty member explains performative prediction and how it...", "date": "", "ddg_snippet": "His most recent paper in this line of work, “ The Value of Prediction in Identifying the Worst - Off ”, was recently recognized with an Outstanding Paper honor at the 2025 International Conference on Machine Learning, awarded to the top six papers at the conference out of over 10...", "subpage_snippet": "", "source": "engineering.nyu.edu", "link": "https://engineering.nyu.edu/news/new-faculty-member-explains-performative-prediction-and-how-it-affects-us-all", "content": "His most recent paper in this line of work, “ The Value of Prediction in Identifying the Worst - Off ”, was recently recognized with an Outstanding Paper honor at the 2025 International Conference on Machine Learning, awarded to the top six papers at the conference out of over 10..."}
+{"idx": 7, "title": "The Value of Prediction in Identifying the Worst - Off Authors: Unai...", "date": "", "ddg_snippet": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR ) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение...", "subpage_snippet": "", "source": "vk.com", "link": "https://vk.com/wall49591166_67792", "content": "Краеугольным камнем методологии статьи является коэффициент «предсказание-доступ» ( Prediction -Access Ratio, PAR ) — метрика, предназначенная для количественной оценки компромисса между двумя ключевыми инструментами политики: 1. Улучшение..."}
+{"idx": 8, "title": "Moving targets: When does a poverty prediction model need to be...", "date": "", "ddg_snippet": "The Value of Prediction in Identifying the Worst - Off .This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/373159916_Moving_targets_When_does_a_poverty_prediction_model_need_to_be_updated", "content": "The Value of Prediction in Identifying the Worst - Off .This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity."}
+{"idx": 9, "title": "The Machine Ethics Podcast: The business of AI ethics with Josie...", "date": "", "ddg_snippet": "The value of prediction in identifying the worst - off : Interview with Unai Fischer Abaigar.", "subpage_snippet": "", "source": "aihub.org", "link": "https://aihub.org/2021/05/19/the-machine-ethics-podcast-the-business-of-ai-ethics-with-josie-young/", "content": "The value of prediction in identifying the worst - off : Interview with Unai Fischer Abaigar."}
diff --git a/data/sampled_jsons/The_dominant_sequence_transduction_models_are_based_on_complex_recurrent_Vaswani_abstract_full_text_year_2017.jsonl b/data/sampled_jsons/The_dominant_sequence_transduction_models_are_based_on_complex_recurrent_Vaswani_abstract_full_text_year_2017.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..80051b3146f7f7d8888a09bd67b6bfb69a9932ed
--- /dev/null
+++ b/data/sampled_jsons/The_dominant_sequence_transduction_models_are_based_on_complex_recurrent_Vaswani_abstract_full_text_year_2017.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Abstract page for arXiv paper 1706.03762: Attention Is All You Need", "date": "", "ddg_snippet": "Abstract : The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Abstract : The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 1, "title": "Attention is all you need - Open Abstract", "date": "", "ddg_snippet": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best-performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "www.openabstract.org", "link": "https://www.openabstract.org/index.php/abstract/attention-is-all-you-need/751", "content": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best-performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 2, "title": "Transformer Chronicles: The Rise of Transformers | Medium", "date": "", "ddg_snippet": "“ The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best-performing models also connect the encoder and decoder through an attention mechanism.”", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@muneneemasonmutuma/transformer-chronicles-the-rise-of-transformers-25a4eae847bf", "content": "“ The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best-performing models also connect the encoder and decoder through an attention mechanism.”"}
+{"idx": 3, "title": "Valyu July 2025 DeepSearch Upgrade: Remedy your Context Rot | Valyu", "date": "", "ddg_snippet": "6 Abstract : The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "valyu-website.vercel.app", "link": "https://valyu-website.vercel.app/blogs/the-july-2025-deepsearch-upgrade-remedy-your-context-rot", "content": "6 Abstract : The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 4, "title": "Attention is All You Need : The Beginning of AGI v1.0 - Visions of...", "date": "", "ddg_snippet": "“ The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "gregoreite.com", "link": "https://gregoreite.com/attention-is-all-you-need-the-beginning-of-agi/", "content": "“ The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 5, "title": "Understanding Transformer- Based Self-Supervised Architectures", "date": "", "ddg_snippet": "Based on that, several architectures like BERT, Open AI GPT evolved by leveraging self-supervised learning. The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an…", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/bert-pre-training-of-transformers-for-language-understanding-5214fba4a9af", "content": "Based on that, several architectures like BERT, Open AI GPT evolved by leveraging self-supervised learning. The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an…"}
+{"idx": 6, "title": "Attention Is All You Need - ADS", "date": "", "ddg_snippet": "Abstract . The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2017arXiv170603762V/abstract", "content": "Abstract . The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 7, "title": "Brain Team – Google Research", "date": "", "ddg_snippet": "Preview Abstract . The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.", "subpage_snippet": "", "source": "web.archive.org", "link": "https://web.archive.org/web/20230503190446/https://research.google/teams/brain/", "content": "Preview Abstract . The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration."}
+{"idx": 8, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "Abstract . The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism.", "subpage_snippet": "", "source": "ailab-ua.github.io", "link": "https://ailab-ua.github.io/courses/resources/Attention_Vaswani_2017.pdf", "content": "Abstract . The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism."}
+{"idx": 9, "title": "Brief Summary: Attention Is All You Need | by Ganesh Bajaj | Artificial...", "date": "", "ddg_snippet": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. In the paper a new model architecture, Transformer is…", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/brief-summary-attention-is-all-you-need-2fa08536fd45", "content": "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. In the paper a new model architecture, Transformer is…"}
diff --git a/data/sampled_jsons/The_illusion_of_state_in_state-space_models_Merrill_et_al._2024_abstract.jsonl b/data/sampled_jsons/The_illusion_of_state_in_state-space_models_Merrill_et_al._2024_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e13d654f7fc3b03067faa7af40b0ac1bcf9d0cbf
--- /dev/null
+++ b/data/sampled_jsons/The_illusion_of_state_in_state-space_models_Merrill_et_al._2024_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2404.08819] The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2404.08819", "content": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 1, "title": "The illusion of state in state-space models | Proceedings of the 41st ...", "date": "", "ddg_snippet": "State-space models (SSMs) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an advantage ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3692070.3693514", "content": "State-space models (SSMs) have emerged as a potential alternative to transformers. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023a), which SSMs are explicitly designed to address via their close architectural similarity to recurrent neural networks. But do SSMs truly have an advantage ..."}
+{"idx": 2, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "Abstract State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v235/merrill24a.html", "content": "Abstract State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture."}
+{"idx": 3, "title": "The Illusion of State in State-Space Models - Jackson Petty", "date": "", "ddg_snippet": "The Illusion of State in State-Space Models 16 April 2024 • William Merrill , Jackson Petty and Ashish Sabharwal [ICML] [arXiv] [GitHub] Abstract State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot ...", "subpage_snippet": "", "source": "jacksonpetty.org", "link": "https://jacksonpetty.org/ssm-illusion/", "content": "The Illusion of State in State-Space Models 16 April 2024 • William Merrill , Jackson Petty and Ashish Sabharwal [ICML] [arXiv] [GitHub] Abstract State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot ..."}
+{"idx": 4, "title": "The Illusion of State in State-Space Models - NASA/ADS", "date": "", "ddg_snippet": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill and Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2024arXiv240408819M/abstract", "content": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill and Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 5, "title": "The Illusion of State in State-Space Models - Semantic Scholar", "date": "", "ddg_snippet": "Analysis of state-space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-Illusion-of-State-in-State-Space-Models-Merrill-Petty/917479a7a72ee7c1fb320c14d770e30ef322ef28", "content": "Analysis of state-space models reveals that SSMs have similar expressiveness limitations to non-recurrent models like transformers, which may fundamentally limit their ability to solve real-world state -tracking problems. State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer ..."}
+{"idx": 6, "title": "The Illusion of State in State-Space Models - NSF Public Access", "date": "", "ddg_snippet": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "par.nsf.gov", "link": "https://par.nsf.gov/biblio/10535878-illusion-state-state-space-models", "content": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
+{"idx": 7, "title": "The Illusion of State in State-Space Models", "date": "", "ddg_snippet": "In a different line of work, state space model (SSM) architectures (Gu et al ., 2021, 2022a; Fu et al ., 2023; Gu & Dao, 2023; Wang et al ., 2024 ) have been introduced as an alternative to transformers, with the goal of achieving RNN-like expressive power for handling problems that are naturally stateful and sequential (Gu et al ., 2021, 2022b).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2404.08819v2", "content": "In a different line of work, state space model (SSM) architectures (Gu et al ., 2021, 2022a; Fu et al ., 2023; Gu & Dao, 2023; Wang et al ., 2024 ) have been introduced as an alternative to transformers, with the goal of achieving RNN-like expressive power for handling problems that are naturally stateful and sequential (Gu et al ., 2021, 2022b)."}
+{"idx": 8, "title": "\"The Illusion of State in State-Space Models.\" - dblp", "date": "", "ddg_snippet": "Bibliographic details on The Illusion of State in State-Space Models .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/journals/corr/abs-2404-08819", "content": "Bibliographic details on The Illusion of State in State-Space Models ."}
+{"idx": 9, "title": "The Illusion of State in State-Space Models | OpenReview", "date": "", "ddg_snippet": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=QZgo9JZpLq", "content": "State-space models (SSMs) have emerged as a potential alternative architecture for building large language models (LLMs) compared to the previously ubiquitous transformer architecture. One theoretical weakness of transformers is that they cannot express certain kinds of sequential computation and state tracking ( Merrill & Sabharwal, 2023), which SSMs are explicitly designed to address via ..."}
diff --git a/data/sampled_jsons/Transformer_model_computational_complexity_sequence_length_Attention_Is_All_You_Need.jsonl b/data/sampled_jsons/Transformer_model_computational_complexity_sequence_length_Attention_Is_All_You_Need.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2c7a710ef8f052b9438fc599d8892da924285077
--- /dev/null
+++ b/data/sampled_jsons/Transformer_model_computational_complexity_sequence_length_Attention_Is_All_You_Need.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "The paper introduced a new deep learning architecture known as the transformer , based on the attention mechanism proposed in 2014 by Bahdanau et al.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need", "content": "The paper introduced a new deep learning architecture known as the transformer , based on the attention mechanism proposed in 2014 by Bahdanau et al."}
+{"idx": 1, "title": "Transformer (deep learning architecture)", "date": "", "ddg_snippet": "In deep learning, transformer is a neural network architecture based on the multi-head attention mechanism, in which text is converted to numerical ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)", "content": "In deep learning, transformer is a neural network architecture based on the multi-head attention mechanism, in which text is converted to numerical ..."}
+{"idx": 2, "title": "Attention is All you Need", "date": "", "ddg_snippet": "by A Vaswani · Cited by 195338 — In terms of computational complexity , self- attention layers are faster than recurrent layers when the sequence length n is smaller than the representation ... 11 pages", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/7181-attention-is-all-you-need.pdf", "content": "by A Vaswani · Cited by 195338 — In terms of computational complexity , self- attention layers are faster than recurrent layers when the sequence length n is smaller than the representation ... 11 pages"}
+{"idx": 3, "title": "Attention Is All You Need", "date": "", "ddg_snippet": "In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/1706.03762v7", "content": "In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation ..."}
+{"idx": 4, "title": "Computational Complexity of Self-Attention in the ...", "date": "", "ddg_snippet": "So, the main idea of the Attention is all you need paper was to replace the RNN layers completely with attention mechanism in seq2seq setting ...", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/65703260/computational-complexity-of-self-attention-in-the-transformer-model", "content": "So, the main idea of the Attention is all you need paper was to replace the RNN layers completely with attention mechanism in seq2seq setting ..."}
+{"idx": 5, "title": "Transformer — Attention is All You Need — A Paper ...", "date": "", "ddg_snippet": "Transformer is a model architecture avoiding recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@chilldenaya/transformer-attention-is-all-you-need-a-paper-summary-d5fa82ff65de", "content": "Transformer is a model architecture avoiding recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and ..."}
+{"idx": 6, "title": "Transformers", "date": "", "ddg_snippet": "2 Jul 2024 — The Transformer neural network is a powerful deep learning model that was introduced in a landmark paper titled \" attention is all you need \" by ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/Esmail-AGumaan/attention-is-all-you-need", "content": "2 Jul 2024 — The Transformer neural network is a powerful deep learning model that was introduced in a landmark paper titled \" attention is all you need \" by ..."}
+{"idx": 7, "title": "Attention is All You Need: What makes the transformer so ...", "date": "", "ddg_snippet": "In the Transformer model , 8 parallel attention heads are used to analyze the input sequence from multiple perspectives, generating distinct ...", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@kdk199604/kdks-review-attention-is-all-you-need-what-makes-the-transformer-so-revolutionary-c91f135583b0", "content": "In the Transformer model , 8 parallel attention heads are used to analyze the input sequence from multiple perspectives, generating distinct ..."}
+{"idx": 8, "title": "Transformers in Action: Attention Is All You Need", "date": "", "ddg_snippet": "8 Sept 2022 — Transformers are considered novel deep feed-forward artificial neural network architectures that leverage self- attention mechanisms and can handle long-range ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/transformers-in-action-attention-is-all-you-need-ac10338a023a/", "content": "8 Sept 2022 — Transformers are considered novel deep feed-forward artificial neural network architectures that leverage self- attention mechanisms and can handle long-range ..."}
+{"idx": 9, "title": "Attention is all you need - Transformer", "date": "", "ddg_snippet": "14 Oct 2023 — Attention is a mechanism that allows transformer models to attend to different parts of the input or output sequences when making predictions.", "subpage_snippet": "", "source": "www.goml.io", "link": "https://www.goml.io/blog/attention-is-all-you-need-transformer", "content": "14 Oct 2023 — Attention is a mechanism that allows transformer models to attend to different parts of the input or output sequences when making predictions."}
diff --git a/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_Objaverse_dataset_size_number_o.jsonl b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_Objaverse_dataset_size_number_o.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4d5e9a8d5fc5fc5d1f105fb176d0c5c2af2cf4b8
--- /dev/null
+++ b/data/sampled_jsons/UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_Objaverse_dataset_size_number_o.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 1, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63]."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 3, "title": "GitHub - aashishrai3799/uvgs", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Arthur Chen, Srinath Sridhar, Aayush Prakash", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aashishrai3799/uvgs", "content": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Arthur Chen, Srinath Sridhar, Aayush Prakash"}
+{"idx": 4, "title": "A review of recent advances in 3D Gaussian Splatting for optimization ...", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) introduces anisotropic 3D Gaussians as an unstructured spatial representation. Using a fast, differentiable GPU-based rendering method, it optimizes the number , position, and intrinsic properties of the Gaussians to enhance scene representation quality.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0262885624004098", "content": "3D Gaussian Splatting (3DGS) introduces anisotropic 3D Gaussians as an unstructured spatial representation. Using a fast, differentiable GPU-based rendering method, it optimizes the number , position, and intrinsic properties of the Gaussians to enhance scene representation quality."}
+{"idx": 5, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation ..."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Abstract 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v3", "content": "Abstract 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured , and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation ..."}
+{"idx": 7, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/UVGS:-Reimagining-Unstructured-3D-Gaussian-using-UV-Rai-Wang/10f0d88160981f85fd6c270e65496ec5948a98d2", "content": "This work utilizes spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS , and discovers that typical VAEs trained with latent diffusion models can directly generalize to this new representation without additional training. 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging ..."}
+{"idx": 8, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping ...", "date": "", "ddg_snippet": "This paper makes it better by using UV mapping - imagine unwrapping a 3D object like a chocolate wrapper and painting on the flat surface. The researchers created two ways to \"unwrap\" 3D objects .", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/uvgs-reimagining-unstructured-3d-gaussian-splatting-using", "content": "This paper makes it better by using UV mapping - imagine unwrapping a 3D object like a chocolate wrapper and painting on the flat surface. The researchers created two ways to \"unwrap\" 3D objects ."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Kefan Chen, Srinath Sridhar, Aayush Prakash; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 5927-5937 Abstract", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.html", "content": "UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Mapping Aashish Rai, Dilin Wang, Mihir Jain, Nikolaos Sarafianos, Kefan Chen, Srinath Sridhar, Aayush Prakash; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 5927-5937 Abstract"}
diff --git a/data/sampled_jsons/UVGS_branched_mapping_layers_heterogeneous_features_value_distribution.jsonl b/data/sampled_jsons/UVGS_branched_mapping_layers_heterogeneous_features_value_distribution.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fde79d9b5851d428846ea158b96e13cc2cf518b3
--- /dev/null
+++ b/data/sampled_jsons/UVGS_branched_mapping_layers_heterogeneous_features_value_distribution.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Sam Smith - Motion | LinkedIn", "date": "", "ddg_snippet": "Technical leader and product builder with 15+ years experience scaling B2B SaaS startups,… · Experience: Motion · Education: Purdue University · Location: Bloomington · 500+ connections on...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/samasmith", "content": "Technical leader and product builder with 15+ years experience scaling B2B SaaS startups,… · Experience: Motion · Education: Purdue University · Location: Bloomington · 500+ connections on..."}
+{"idx": 1, "title": "Sam Smith - Head of Product & Engineering @ Motion", "date": "", "ddg_snippet": "Sam Smith Head of Product & Engineering @ Motion 15+ years of customer-focused building, shipping new products, and scaling teams & businesses around them. GitHub LinkedIn", "subpage_snippet": "", "source": "samsmith.tech", "link": "https://samsmith.tech/", "content": "Sam Smith Head of Product & Engineering @ Motion 15+ years of customer-focused building, shipping new products, and scaling teams & businesses around them. GitHub LinkedIn"}
+{"idx": 2, "title": "16200+ \" Sam Smith \" profiles | LinkedIn", "date": "", "ddg_snippet": "View the profiles of professionals named \" Sam Smith \" on LinkedIn . There are 16200+ professionals named \" Sam Smith \", who use LinkedIn to exchange information, ideas, and opportunities.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pub/dir/sam/smith", "content": "View the profiles of professionals named \" Sam Smith \" on LinkedIn . There are 16200+ professionals named \" Sam Smith \", who use LinkedIn to exchange information, ideas, and opportunities."}
+{"idx": 3, "title": "17800+ \" Sam Smith \" profiles | LinkedIn", "date": "", "ddg_snippet": "View the profiles of professionals named \" Sam Smith \" on LinkedIn . There are 17800+ professionals named \" Sam Smith \", who use LinkedIn to exchange information, ideas, and opportunities.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pub/dir/Sam/Smith+", "content": "View the profiles of professionals named \" Sam Smith \" on LinkedIn . There are 17800+ professionals named \" Sam Smith \", who use LinkedIn to exchange information, ideas, and opportunities."}
+{"idx": 4, "title": "Sam Smith - Product Management Professional | LinkedIn", "date": "", "ddg_snippet": "I'm a product management professional looking for my next step. I enjoy diving deep into complex problems and working with cross-functional teams to come up with solutions. I consider myself to be...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/samabsmith", "content": "I'm a product management professional looking for my next step. I enjoy diving deep into complex problems and working with cross-functional teams to come up with solutions. I consider myself to be..."}
+{"idx": 5, "title": "Sam Smith 's Portfolio", "date": "", "ddg_snippet": "Finding the right connection on campus shouldn’t be a guessing game. As Design Lead for Cornell Perfect Match, I dug into user research to uncover what makes a great match—then brought those insights to life through intuitive design and a seamless user experience. The result? A smarter, more personal way to meet someone who just clicks.", "subpage_snippet": "", "source": "www.samsmithe.com", "link": "https://www.samsmithe.com/", "content": "Finding the right connection on campus shouldn’t be a guessing game. As Design Lead for Cornell Perfect Match, I dug into user research to uncover what makes a great match—then brought those insights to life through intuitive design and a seamless user experience. The result? A smarter, more personal way to meet someone who just clicks."}
+{"idx": 6, "title": "Sam E Moore, (574) 295-8502, Elkhart, IN - ClustrMaps", "date": "", "ddg_snippet": "Sam E Moore is a resident of Elkhart. Lookup the home address, phone numbers, email address for this person", "subpage_snippet": "", "source": "clustrmaps.com", "link": "https://clustrmaps.com/person/Moore-9bu2im", "content": "Sam E Moore is a resident of Elkhart. Lookup the home address, phone numbers, email address for this person"}
+{"idx": 7, "title": "Sam Vaught Facebook, Instagram & Twitter on PeekYou", "date": "", "ddg_snippet": "Looking for Sam Vaught? Found 1 person named Sam Vaught along with free Facebook, Instagram, Twitter, and TikTok profiles on PeekYou - true people search.", "subpage_snippet": "", "source": "www.peekyou.com", "link": "https://www.peekyou.com/sam_vaught", "content": "Looking for Sam Vaught? Found 1 person named Sam Vaught along with free Facebook, Instagram, Twitter, and TikTok profiles on PeekYou - true people search."}
+{"idx": 8, "title": "Sam Smith - Account Executive | LinkedIn", "date": "", "ddg_snippet": "Account Executive with a proven track record of driving revenue growth and delivering exceptional customer solutions. Skilled in building strong, long-term partnerships by understanding client...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/in/sam-smith-staples", "content": "Account Executive with a proven track record of driving revenue growth and delivering exceptional customer solutions. Skilled in building strong, long-term partnerships by understanding client..."}
+{"idx": 9, "title": "16300+ \" Sam Smith '\" profiles | LinkedIn", "date": "", "ddg_snippet": "View the profiles of professionals named \" Sam Smith '\" on LinkedIn .", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pub/dir/Sam/Smith'", "content": "View the profiles of professionals named \" Sam Smith '\" on LinkedIn ."}
diff --git a/data/sampled_jsons/UVGS_paper_experimental_setup_dataset_statistics_number_of_objects_used.jsonl b/data/sampled_jsons/UVGS_paper_experimental_setup_dataset_statistics_number_of_objects_used.jsonl
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index 0000000000000000000000000000000000000000..ef5ab31f6c58af935ec4091958e2d38ebf84f88a
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 1, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63]."}
+{"idx": 2, "title": "UVGS - aashishrai3799.github.io", "date": "", "ddg_snippet": "We show Super UVGS can be used to compress the 3DGS assets using pretrained image Autoencoders, and to directly generate unconditional and conditional 3DGS objects using diffusion models.", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "We show Super UVGS can be used to compress the 3DGS assets using pretrained image Autoencoders, and to directly generate unconditional and conditional 3DGS objects using diffusion models."}
+{"idx": 3, "title": "(PDF) UVGS: Reimagining Unstructured 3D Gaussian ... - ResearchGate", "date": "", "ddg_snippet": "W e show Super UVGS can be used to compress the 3DGS assets using pretrained imag e Autoencoders, and to directly gener ate unconditional and conditional 3DGS objects using diffusion models.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "W e show Super UVGS can be used to compress the 3DGS assets using pretrained imag e Autoencoders, and to directly gener ate unconditional and conditional 3DGS objects using diffusion models."}
+{"idx": 4, "title": "PDF A Experimental setup - NeurIPS", "date": "", "ddg_snippet": "A Experimental setup A. Datasets For the bulk of our experimental analysis (Sections 4 and 5) we use the ImageNet-1k [Deng et al., 2009, Russakovsky et al., 2015] and Places-365 [Zhou et al., 2017] datasets which contain images from 1,000 and 365 categories respectively.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/c46489a2d5a9a9ecfc53b17610926ddd-Supplemental.pdf", "content": "A Experimental setup A. Datasets For the bulk of our experimental analysis (Sections 4 and 5) we use the ImageNet-1k [Deng et al., 2009, Russakovsky et al., 2015] and Places-365 [Zhou et al., 2017] datasets which contain images from 1,000 and 365 categories respectively."}
+{"idx": 5, "title": "uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025", "date": "", "ddg_snippet": "Figure 1. We propose UVGS - an structured image-like representation for 3DGS obtained by spherical mapping of 3DGS primitives. The obtained UVGS maps can be further squeezed to a 3-channel \"3D-aware\" Super UVGS image capable of bridging the gap between 3DGS and existing image foundation models. We show Super UVGS can be used to compress the 3DGS assets using pretrained image Autoencoders ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846v1", "content": "Figure 1. We propose UVGS - an structured image-like representation for 3DGS obtained by spherical mapping of 3DGS primitives. The obtained UVGS maps can be further squeezed to a 3-channel \"3D-aware\" Super UVGS image capable of bridging the gap between 3DGS and existing image foundation models. We show Super UVGS can be used to compress the 3DGS assets using pretrained image Autoencoders ..."}
+{"idx": 6, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "Table 3: We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branching in mapping network on the Objaverse 3DGS dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.01846v2", "content": "Table 3: We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branching in mapping network on the Objaverse 3DGS dataset ."}
+{"idx": 7, "title": "PDF Statistics for Analysis of Experimental Data", "date": "", "ddg_snippet": "Finally, we may have measured one variable under a variety of conditions with regard to a second variable. Regression analysis can be used to come up with a mathematical expression for the relationship between the two variables. These are but a few of the many applications of statistics for analysis of experimental data.", "subpage_snippet": "", "source": "faculty.washington.edu", "link": "https://faculty.washington.edu/korshin/Class-486/AEESP-stat-analysis.pdf", "content": "Finally, we may have measured one variable under a variety of conditions with regard to a second variable. Regression analysis can be used to come up with a mathematical expression for the relationship between the two variables. These are but a few of the many applications of statistics for analysis of experimental data."}
+{"idx": 8, "title": "arXiv:2502.01846v3 [cs.CV] 20 Mar 2025", "date": "", "ddg_snippet": "To address this, we introduce Super UVGS , a compact 3-channel representation that uni- fies these diverse attributes into a cohesive format. Using a carefully designed multi-branch mapping network, Su- per UVGS consolidates the distinct attribute spaces into a shared feature space, enabling a more collective representa- tion of the object .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "To address this, we introduce Super UVGS , a compact 3-channel representation that uni- fies these diverse attributes into a cohesive format. Using a carefully designed multi-branch mapping network, Su- per UVGS consolidates the distinct attribute spaces into a shared feature space, enabling a more collective representa- tion of the object ."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.html", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
diff --git a/data/sampled_jsons/UVGS_paper_methodology_section_dataset_creation_Objaverse_number_of_objects.jsonl b/data/sampled_jsons/UVGS_paper_methodology_section_dataset_creation_Objaverse_number_of_objects.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..157618b50bdad4e839576b136c4cd3926a34feb8
--- /dev/null
+++ b/data/sampled_jsons/UVGS_paper_methodology_section_dataset_creation_Objaverse_number_of_objects.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63].", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "The existing methods fail to di-rectly generate a large number of Gaussians (e.g. 100K+) to represent objects with suficient quality either due to the lack of 3D generative model architectures that support such large number of unstructured points, or due to the lack of a large 3DGS dataset [28, 40, 44, 55, 63]."}
+{"idx": 1, "title": "Objaverse", "date": "", "ddg_snippet": "Objaverse 1.0 is a Massive Dataset of 800K+ Annotated 3D Objects", "subpage_snippet": "", "source": "objaverse.allenai.org", "link": "https://objaverse.allenai.org/", "content": "Objaverse 1.0 is a Massive Dataset of 800K+ Annotated 3D Objects"}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 3, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
+{"idx": 4, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV Mapping", "date": "", "ddg_snippet": "The top three rows show the unconditional generation results of our method using ShapeNet dataset , while the bottom 3 show from Objaverse dataset .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/388685707_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping", "content": "The top three rows show the unconditional generation results of our method using ShapeNet dataset , while the bottom 3 show from Objaverse dataset ."}
+{"idx": 5, "title": "PDF Objaverse-XL: A Universe of 10M+ 3D Objects - NeurIPS", "date": "", "ddg_snippet": "In this work, we present Objaverse -XL, a dataset of over 10 million 3D objects . Our dataset comprises deduplicated 3D objects from a diverse set of sources, including manually designed objects , photogrammetry scans of landmarks and everyday items, and professional scans of historic and antique artifacts.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2023/file/70364304877b5e767de4e9a2a511be0c-Paper-Datasets_and_Benchmarks.pdf", "content": "In this work, we present Objaverse -XL, a dataset of over 10 million 3D objects . Our dataset comprises deduplicated 3D objects from a diverse set of sources, including manually designed objects , photogrammetry scans of landmarks and everyday items, and professional scans of historic and antique artifacts."}
+{"idx": 6, "title": "uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025", "date": "", "ddg_snippet": "e static scenes or objects from Objaverse . After fitting all the object to 3DGS representa-tion, we convert the objects to the corresponding UV maps (i.e., UVGS ) through", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846v1", "content": "e static scenes or objects from Objaverse . After fitting all the object to 3DGS representa-tion, we convert the objects to the corresponding UV maps (i.e., UVGS ) through"}
+{"idx": 7, "title": "PDF Objaverse: A Universe of Annotated 3D Objects - CVF Open Access", "date": "", "ddg_snippet": "Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2023/papers/Deitke_Objaverse_A_Universe_of_Annotated_3D_Objects_CVPR_2023_paper.pdf", "content": "Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category."}
+{"idx": 8, "title": "arXiv:2502.01846v3 [cs.CV] 20 Mar 2025", "date": "", "ddg_snippet": "We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branch- ing in mapping network on the Objaverse 3DGS dataset .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846", "content": "We present quantitative ablation study for number of UVGS layers (K), UVGS map resolution, and the effect of branch- ing in mapping network on the Objaverse 3DGS dataset ."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.html", "content": "3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ..."}
diff --git a/data/sampled_jsons/UVGS_spherical_coordinates_UV_mapping_formula_Section_3.1.jsonl b/data/sampled_jsons/UVGS_spherical_coordinates_UV_mapping_formula_Section_3.1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c9756b7654d0f630e22aec12fd8e034af390a9e5
--- /dev/null
+++ b/data/sampled_jsons/UVGS_spherical_coordinates_UV_mapping_formula_Section_3.1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "UV mapping - Wikipedia", "date": "", "ddg_snippet": "The application of a texture in the UV space related to the effect in 3D. A representation of the UV mapping of a cube. The flattened cube net may then be textured to texture the cube. UV mapping is the 3D modeling process of projecting a 3D model&ap...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/UV_mapping", "content": "The application of a texture in the UV space related to the effect in 3D. A representation of the UV mapping of a cube. The flattened cube net may then be textured to texture the cube. UV mapping is the 3D modeling process of projecting a 3D model&ap..."}
+{"idx": 1, "title": "UV -преобразование — Википедия", "date": "", "ddg_snippet": "Равнопромежуточная проекция — пример наложения двухмерной текстуры на трёхмерный глобус. Шахматная текстура на сфере: слева — общая матрица на всю сферу, справа — развёртка с равнопромежуточной проекцией. Пример наложения текстуры на куб.", "subpage_snippet": "", "source": "ru.wikipedia.org", "link": "https://ru.wikipedia.org/wiki/UV-преобразование", "content": "Равнопромежуточная проекция — пример наложения двухмерной текстуры на трёхмерный глобус. Шахматная текстура на сфере: слева — общая матрица на всю сферу, справа — развёртка с равнопромежуточной проекцией. Пример наложения текстуры на куб."}
+{"idx": 2, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "We proposed UVGS - a struc-tured representation for 3DGS obtained by spherical map-ping of 3DGS primitives to UV maps . We further squeezed the multi-attribute UVGS maps to a 3-channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro-vides a compact feature space for 3DGS ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Rai_UVGS_Reimagining_Unstructured_3D_Gaussian_Splatting_using_UV_Mapping_CVPR_2025_paper.pdf", "content": "We proposed UVGS - a struc-tured representation for 3DGS obtained by spherical map-ping of 3DGS primitives to UV maps . We further squeezed the multi-attribute UVGS maps to a 3-channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro-vides a compact feature space for 3DGS ..."}
+{"idx": 3, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ... uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025 uv mapping - How to get UV coordinates for sphere ... c++ - Spherical mapping calculation (UV) for point given on ... Calculate uv coordinates from spherical coordinates uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025 uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025 uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025 UVGS : Reimagining Unstructured 3D Gaussian Splatting using UV Ma… uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025 uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025 UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "Feb 3 , 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... ed to UVGS maps through spherical mapping . We use a multibranch forward mapping network to convert the obtained 14-channel UV S to a compact 3-channel Super UVGS image. This represents the 3DGS object in a structured manner and can be used with image foundati Jan 7, 2016 · 7 I have created one sphere and I want to map onto it a texture map . But I want first to project my map texture to cylinder and then to sphere. So I want to create a function which takes as parameter a 3D point from sphere and calculate a uv coordinate of this point using cylindrical coordinates . Nov 17, 2015 · 2 I want to write an spherical mapping for my ray-tracer to generate UV - coordinates for the sphere. The texture applied with that mapping should looks like that: I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f. Jan 11, 2024 · You’re sending the UV coordinates using: globeGeometry.setAttribute(' uv ', new THREE.Float32BufferAttribute(uvs, 2)); But don’t you need to declare this in your vertex shader? attribute uv vec2; I would expect compilation errors about the uv variable being undefined, since you are using it in the main function, but it isn’t declared anywhere. Can 3DGS primitives be mapped to UV maps? al map-ping of 3DGS primitives to UV maps. We further squeezed the multi-attribute UVGS maps to a 3 -channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro-vides a How to obtain Super uvgs map using image autoencoder (a)? ined Image Autoencoder (A) via Super UVGS. We obtain UVGS maps (U) through spherical projection of 3DGS objects, followed by using for ard mapping network to get Super UVGS (S). A pretrained AE is used to reconstruct Super UVGS (S’), which can be converted to UVGS m How to get super uvgs (s)? ard mapping network to get Super UVGS (S). A pretrained AE is used to reconstruct Super UVGS (S’), which can be converted to UVGS m ps (U’) through inverse mapping network. At last, through inverse spherical mapping, we can get predicted 3DGS object which has the same appearance and geom Why is uvgs a scalable solution? Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians , making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. How big a uvgs dataset should be? ered a large UVGS dataset of ∼400K maps. e fix the size of the UV maps to 512×512 . Through our experiments, we found that UV maps of size 512 × 512 are suficient to represent ob-jects in our dataset and cap ble of storing upto 262K unique Gau What is a Super uvgs image? 4.1. UVGS AutoEncoder and 3DGS CompressionThe obtained Super UVGS image is a structurally meaning-ful representation that can have various applications in the generation and reconstruction of new 3D assets because it contains features that can be earned by the existing image based models. Through our experiments, we show that a 3-channel Sup We proposed UVGS - a struc- tured representation for 3DGS obtained by spherical map - ping of 3DGS primitives to UV maps . We further squeezed the multi-attribute UVGS maps to a 3-channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro- vides a compact feature space for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.01846", "content": "Feb 3 , 2025 · 3D Gaussian Splatting (3DGS) has demonstrated superior quality in modeling 3D objects and scenes. However, generating 3DGS remains challenging due to their discrete, unstructured, and permutation-invariant nature. In this work, we present a simple yet effective method to overcome these challenges. We utilize spherical mapping to transform 3DGS into a structured 2D representation, termed UVGS ... ed to UVGS maps through spherical mapping . We use a multibranch forward mapping network to convert the obtained 14-channel UV S to a compact 3-channel Super UVGS image. This represents the 3DGS object in a structured manner and can be used with image foundati Jan 7, 2016 · 7 I have created one sphere and I want to map onto it a texture map . But I want first to project my map texture to cylinder and then to sphere. So I want to create a function which takes as parameter a 3D point from sphere and calculate a uv coordinate of this point using cylindrical coordinates . Nov 17, 2015 · 2 I want to write an spherical mapping for my ray-tracer to generate UV - coordinates for the sphere. The texture applied with that mapping should looks like that: I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f. Jan 11, 2024 · You’re sending the UV coordinates using: globeGeometry.setAttribute(' uv ', new THREE.Float32BufferAttribute(uvs, 2)); But don’t you need to declare this in your vertex shader? attribute uv vec2; I would expect compilation errors about the uv variable being undefined, since you are using it in the main function, but it isn’t declared anywhere. Can 3DGS primitives be mapped to UV maps? al map-ping of 3DGS primitives to UV maps. We further squeezed the multi-attribute UVGS maps to a 3 -channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro-vides a How to obtain Super uvgs map using image autoencoder (a)? ined Image Autoencoder (A) via Super UVGS. We obtain UVGS maps (U) through spherical projection of 3DGS objects, followed by using for ard mapping network to get Super UVGS (S). A pretrained AE is used to reconstruct Super UVGS (S’), which can be converted to UVGS m How to get super uvgs (s)? ard mapping network to get Super UVGS (S). A pretrained AE is used to reconstruct Super UVGS (S’), which can be converted to UVGS m ps (U’) through inverse mapping network. At last, through inverse spherical mapping, we can get predicted 3DGS object which has the same appearance and geom Why is uvgs a scalable solution? Additionally, one can simply increase the 2D UV resolution to accommodate more Gaussians , making UVGS a scalable solution compared to typical 3D backbones. This approach immediately unlocks various novel generation applications of 3DGS by inherently utilizing the already developed superior 2D generation capabilities. How big a uvgs dataset should be? ered a large UVGS dataset of ∼400K maps. e fix the size of the UV maps to 512×512 . Through our experiments, we found that UV maps of size 512 × 512 are suficient to represent ob-jects in our dataset and cap ble of storing upto 262K unique Gau What is a Super uvgs image? 4.1. UVGS AutoEncoder and 3DGS CompressionThe obtained Super UVGS image is a structurally meaning-ful representation that can have various applications in the generation and reconstruction of new 3D assets because it contains features that can be earned by the existing image based models. Through our experiments, we show that a 3-channel Sup We proposed UVGS - a struc- tured representation for 3DGS obtained by spherical map - ping of 3DGS primitives to UV maps . We further squeezed the multi-attribute UVGS maps to a 3-channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro- vides a compact feature space for ..."}
+{"idx": 4, "title": "uvgs arXiv:2502.01846v1 [cs.CV] 3 Feb 2025", "date": "", "ddg_snippet": "ed to UVGS maps through spherical mapping . We use a multibranch forward mapping network to convert the obtained 14-channel UV S to a compact 3-channel Super UVGS image. This represents the 3DGS object in a structured manner and can be used with image foundati", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.01846v1", "content": "ed to UVGS maps through spherical mapping . We use a multibranch forward mapping network to convert the obtained 14-channel UV S to a compact 3-channel Super UVGS image. This represents the 3DGS object in a structured manner and can be used with image foundati"}
+{"idx": 5, "title": "uv mapping - How to get UV coordinates for sphere ...", "date": "", "ddg_snippet": "Jan 7, 2016 · 7 I have created one sphere and I want to map onto it a texture map . But I want first to project my map texture to cylinder and then to sphere. So I want to create a function which takes as parameter a 3D point from sphere and calculate a uv coordinate of this point using cylindrical coordinates .", "subpage_snippet": "", "source": "gamedev.stackexchange.com", "link": "https://gamedev.stackexchange.com/questions/114412/how-to-get-uv-coordinates-for-sphere-cylindrical-projection", "content": "Jan 7, 2016 · 7 I have created one sphere and I want to map onto it a texture map . But I want first to project my map texture to cylinder and then to sphere. So I want to create a function which takes as parameter a 3D point from sphere and calculate a uv coordinate of this point using cylindrical coordinates ."}
+{"idx": 6, "title": "c++ - Spherical mapping calculation (UV) for point given on ...", "date": "", "ddg_snippet": "Nov 17, 2015 · 2 I want to write an spherical mapping for my ray-tracer to generate UV - coordinates for the sphere. The texture applied with that mapping should looks like that: I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/33747876/spherical-mapping-calculation-uv-for-point-given-on-sphere-strange-u-values", "content": "Nov 17, 2015 · 2 I want to write an spherical mapping for my ray-tracer to generate UV - coordinates for the sphere. The texture applied with that mapping should looks like that: I have the sphere that is located at [0.0f,0.0f,0.0f] with the radius of 20.0f."}
+{"idx": 7, "title": "Calculate uv coordinates from spherical coordinates", "date": "", "ddg_snippet": "Jan 11, 2024 · You’re sending the UV coordinates using: globeGeometry.setAttribute(' uv ', new THREE.Float32BufferAttribute(uvs, 2)); But don’t you need to declare this in your vertex shader? attribute uv vec2; I would expect compilation errors about the uv variable being undefined, since you are using it in the main function, but it isn’t declared anywhere.", "subpage_snippet": "", "source": "discourse.threejs.org", "link": "https://discourse.threejs.org/t/calculate-uv-coordinates-from-spherical-coordinates/60222", "content": "Jan 11, 2024 · You’re sending the UV coordinates using: globeGeometry.setAttribute(' uv ', new THREE.Float32BufferAttribute(uvs, 2)); But don’t you need to declare this in your vertex shader? attribute uv vec2; I would expect compilation errors about the uv variable being undefined, since you are using it in the main function, but it isn’t declared anywhere."}
+{"idx": 8, "title": "UVGS: Reimagining Unstructured 3D Gaussian Splatting using UV ...", "date": "", "ddg_snippet": "We proposed UVGS - a struc- tured representation for 3DGS obtained by spherical map - ping of 3DGS primitives to UV maps . We further squeezed the multi-attribute UVGS maps to a 3-channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro- vides a compact feature space for ...", "subpage_snippet": "", "source": "aashishrai3799.github.io", "link": "https://aashishrai3799.github.io/uvgs/assets/UVGS.pdf", "content": "We proposed UVGS - a struc- tured representation for 3DGS obtained by spherical map - ping of 3DGS primitives to UV maps . We further squeezed the multi-attribute UVGS maps to a 3-channel unified and structured Super UVGS image, which not only maintains the 3D structural information of the object, but also pro- vides a compact feature space for ..."}
+{"idx": 9, "title": "Spherical Coordinates -- from Wolfram MathWorld", "date": "", "ddg_snippet": "Spherical coordinates , also called spherical polar coordinates (Walton 1967, Arfken 1985), are a system of curvilinear coordinates that are natural for describing positions on a sphere or spheroid.", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/SphericalCoordinates.html", "content": "Spherical coordinates , also called spherical polar coordinates (Walton 1967, Arfken 1985), are a system of curvilinear coordinates that are natural for describing positions on a sphere or spheroid."}
diff --git a/data/sampled_jsons/VRSBench_ResearchGate_table_image_size_year_2024.jsonl b/data/sampled_jsons/VRSBench_ResearchGate_table_image_size_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..31ab5e3b6c193d4f849b0d7809eb079c77dcfcf6
--- /dev/null
+++ b/data/sampled_jsons/VRSBench_ResearchGate_table_image_size_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2406.12384] VRSBench: A Versatile Vision-Language Benchmark ... GitHub - lx709/VRSBench VRSBench: Supplementary of VRSBench: A Versatile Benchmark for Vision ... VRSBench: A Versatile Vision-Language - nips.cc (PDF) VRSBench : A Versatile Vision-Language Benchmark Dataset for (PDF) VRSBench : A Versatile Vision-Language Benchmark Dataset for (PDF) VRSBench : A Versatile Vision-Language Benchmark Dataset for xiang709/ VRSBench · Datasets at Hugging Face (PDF) VRSBench : A Versatile Vision-Language Benchmark Dataset for (PDF) VRSBench : A Versatile Vision-Language Benchmark Dataset for xiang709/VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "Jun 18, 2024 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. Jun 19, 2024 · VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs. Image Captioning Results Table 2. Detailed image caption performance on VRSBench dataset. Avg_L denotes the average word length of generated captions. Figure 4. Selected image caption results. 1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ... Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description. What is a vrsbench benchmark for remote sensing image understanding? Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. What is the vrsbench dataset? Currently, the VRSBench dataset is limited to annotations for RGB images . In future work, we aim and temporal datasets. This expansion will significantly broaden the dataset’s utility across diverse observation conditions, facilitating more accurate and timely applications in remote sensing. sensing and computer vision. What are the limitations of the vrsbench dataset in remote sensing? remote sensing image understanding. This comprehensive dataset not only addresses the limitations human annotators. Our benchmark challenges, specifically designed around the VRSBench dataset, in the domain of remote sensing. Currently, the VRSBench dataset is limited to annotations for RGB images . What data types can be used in vrsbench? Currently, the VRSBench dataset is limited to annotations for RGB images . In future work, we aim to enhance VRSBench by incorporating annotations from a variety of remote sensing data types, including infrared images, multi- and hyperspectral images, Synthetic Aperture Radar (SAR) images, and temporal datasets. What are the vrsbench benchmark challenges? Our benchmark challenges, specifically designed around the VRSBench dataset, in the domain of remote sensing. Currently, the VRSBench dataset is limited to annotations for RGB images. In future work, we aim How many images are included in vrsbench? VRSBench comprises 29,614 images , each check T able 1 for a detailed comparison with existing datasets. This dataset facilitates the training tasks. Fig. 1 gives an example of a selected image and associated annotations. verification. This pipeline enables a fast collection of large-scale datasets with human-level annotation quality. We’re on a journey to advance and democratize artificial intelligence through open source and open science.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.12384", "content": "Jun 18, 2024 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench . This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. Jun 19, 2024 · VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs. Image Captioning Results Table 2. Detailed image caption performance on VRSBench dataset. Avg_L denotes the average word length of generated captions. Figure 4. Selected image caption results. 1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ... Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description. What is a vrsbench benchmark for remote sensing image understanding? Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench. This benchmark comprises 29,614 images, with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. What is the vrsbench dataset? Currently, the VRSBench dataset is limited to annotations for RGB images . In future work, we aim and temporal datasets. This expansion will significantly broaden the dataset’s utility across diverse observation conditions, facilitating more accurate and timely applications in remote sensing. sensing and computer vision. What are the limitations of the vrsbench dataset in remote sensing? remote sensing image understanding. This comprehensive dataset not only addresses the limitations human annotators. Our benchmark challenges, specifically designed around the VRSBench dataset, in the domain of remote sensing. Currently, the VRSBench dataset is limited to annotations for RGB images . What data types can be used in vrsbench? Currently, the VRSBench dataset is limited to annotations for RGB images . In future work, we aim to enhance VRSBench by incorporating annotations from a variety of remote sensing data types, including infrared images, multi- and hyperspectral images, Synthetic Aperture Radar (SAR) images, and temporal datasets. What are the vrsbench benchmark challenges? Our benchmark challenges, specifically designed around the VRSBench dataset, in the domain of remote sensing. Currently, the VRSBench dataset is limited to annotations for RGB images. In future work, we aim How many images are included in vrsbench? VRSBench comprises 29,614 images , each check T able 1 for a detailed comparison with existing datasets. This dataset facilitates the training tasks. Fig. 1 gives an example of a selected image and associated annotations. verification. This pipeline enables a fast collection of large-scale datasets with human-level annotation quality. We’re on a journey to advance and democratize artificial intelligence through open source and open science."}
+{"idx": 1, "title": "GitHub - lx709/VRSBench", "date": "", "ddg_snippet": "Jun 19, 2024 · VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lx709/VRSBench", "content": "Jun 19, 2024 · VRSBench VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 2, "title": "VRSBench:", "date": "", "ddg_snippet": "Image Captioning Results Table 2. Detailed image caption performance on VRSBench dataset. Avg_L denotes the average word length of generated captions. Figure 4. Selected image caption results.", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "Image Captioning Results Table 2. Detailed image caption performance on VRSBench dataset. Avg_L denotes the average word length of generated captions. Figure 4. Selected image caption results."}
+{"idx": 3, "title": "Supplementary of VRSBench: A Versatile Benchmark for Vision ...", "date": "", "ddg_snippet": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "1.1 Overview VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, 123,221 visual question-answer pairs. VRSBench is designed to facilitate the development and evaluation of vision-language models in remote sensing, providing a comprehensive set of annotations including detailed captions, visual grounding, and visual question answering. This section ..."}
+{"idx": 4, "title": "xiang709/ VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "VRSBench is a Versatile Vision-Language Benchmark for Remote Sensing Image Understanding. It consists of 29,614 remote sensing images with detailed captions, 52,472 object refers, and 3123,221 visual question-answer pairs."}
+{"idx": 5, "title": "(PDF) VRSBench: A Versatile Vision-Language ... - ResearchGate", "date": "", "ddg_snippet": "Jun 18, 2024 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381518535_VRSBench_A_Versatile_Vision-Language_Benchmark_Dataset_for_Remote_Sensing_Image_Understanding", "content": "Jun 18, 2024 · Exploring these improvement opportunities, we present a Versatile vision-language Benchmark for Remote Sensing image understanding, termed VRSBench ."}
+{"idx": 6, "title": "VRSBench: A Versatile Vision-Language - nips.cc", "date": "", "ddg_snippet": "Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description.", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/media/neurips-2024/Slides/97530.pdf", "content": "Benchmark Tasks VRSBench -Cap: This challenge requires the prediction of a comprehensive description for a given remote sensing image , encapsulating intricate details and contextual relevance. VRSBench -Ref: The task involves identifying and localizing specific objects or features within a given remote sensing image based on a textual description."}
+{"idx": 7, "title": "Introducing VRSBench : Advancing Remote Sensing Image Analysis", "date": "", "ddg_snippet": "Each image in VRSBench is accompanied by a detailed caption. These captions provide a comprehensive description of both general aspects and specific features within the image .", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-27-introducing-vrsbench-advancing-remote-sensing-image-analysis--ak6l6or", "content": "Each image in VRSBench is accompanied by a detailed caption. These captions provide a comprehensive description of both general aspects and specific features within the image ."}
+{"idx": 8, "title": "Figure 5: Statistics of question-answer pairs in VRSBench . (a)...", "date": "", "ddg_snippet": "Image Understanding, Remote Sensing and Dataset | ResearchGate , the professional network for scientists.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/Statistics-of-question-answer-pairs-in-VRSBench-a-Distribution-of-question-types-b_fig3_381518535", "content": "Image Understanding, Remote Sensing and Dataset | ResearchGate , the professional network for scientists."}
+{"idx": 9, "title": "VRSBench : A Versatile Vision-Language Benchmark Dataset for...", "date": "", "ddg_snippet": "• VRSBench contains a large collection of remote sensing images paired with natural language descriptions, providing a versatile benchmark for tasks like image captioning, visual question answering, and multimodal reasoning.", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/papers/arxiv/vrsbench-versatile-vision-language-benchmark-dataset-remote", "content": "• VRSBench contains a large collection of remote sensing images paired with natural language descriptions, providing a versatile benchmark for tasks like image captioning, visual question answering, and multimodal reasoning."}
diff --git a/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_reference_[63].jsonl b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_reference_[63].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..cef2a01ebbccdd0c09c96dd888afa3a4bbb25904
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_reference_[63].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon 3 main components: a fine-grained spatial and ..."}
+{"idx": 1, "title": "PDF Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "Abstract In this work, we tackle the problem of text-to-video re-trieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video re-trieval , our approach, Video-ColBERT , introduces a simple and eficient mechanism for fine-grained similarity assess-ment between queries and videos ."}
+{"idx": 2, "title": "GitHub - yogesh-iitj/Video-ColBERT", "date": "", "ddg_snippet": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/yogesh-iitj/Video-ColBERT", "content": "This repository implements Video-ColBERT , a contextualized late interaction model for text-to-video retrieval . Video-ColBERT performs fine-grained token-wise interactions between text queries and video content. This script demonstrates the model with random inputs, showing how similarity matrices ..."}
+{"idx": 3, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/11094542", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained spatial ..."}
+{"idx": 4, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Video-ColBERT:-Contextualized-Late-Interaction-for-Reddy-Martin/bff2f91c763830a2d14dbbbeca150e92ede02323", "content": "Video-ColBERT introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos , and finds that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in ..."}
+{"idx": 5, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/papers/2503.19009", "content": "Video-ColBERT offers an advanced approach to text-to-video retrieval by refining interaction strategies and achieving competitive performance, opening new pathways for multimodal retrieval research."}
+{"idx": 6, "title": "An Overview of Late Interaction Retrieval Models: ColBERT, ColPali, and ...", "date": "", "ddg_snippet": "Late interaction allow for semantically rich interactions that enable a precise retrieval process across different modalities of unstructured data, including text and images.", "subpage_snippet": "", "source": "weaviate.io", "link": "https://weaviate.io/blog/late-interaction-overview", "content": "Late interaction allow for semantically rich interactions that enable a precise retrieval process across different modalities of unstructured data, including text and images."}
+{"idx": 7, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009", "content": "Abstract In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos . Video-ColBERT is built upon three main components: a fine-grained ..."}
+{"idx": 8, "title": "Publications | Alexander Martin", "date": "", "ddg_snippet": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video re - trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos .", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/publications/", "content": "Inspired by the success of late interaction techniques in text -document, text -image, and text - video re - trieval, our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos ."}
+{"idx": 9, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text -document, text -image, and text - video retrieval , our approach, Video-ColBERT , introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos ."}
diff --git a/data/sampled_jsons/Video-ColBERT_X-CLIP_spatial_and_spatio-temporal_visual_features_section_2.jsonl b/data/sampled_jsons/Video-ColBERT_X-CLIP_spatial_and_spatio-temporal_visual_features_section_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..49c157e3d79307572d356f501639a37fc77d6e10
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_X-CLIP_spatial_and_spatio-temporal_visual_features_section_2.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "24 Mar 2025 — Unlike Video - ColBERT , none of the aforementioned techniques perform interaction on both spatial and spatio-temporal visual features . ... X-CLIP ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009v1", "content": "24 Mar 2025 — Unlike Video - ColBERT , none of the aforementioned techniques perform interaction on both spatial and spatio-temporal visual features . ... X-CLIP ..."}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "In this work, we introduced VIDEO-COLBERT , a novel approach for text-to- video retrieval that uses eficient fine-grained interactions with both spatial and spatio-temporal visual features .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf", "content": "In this work, we introduced VIDEO-COLBERT , a novel approach for text-to- video retrieval that uses eficient fine-grained interactions with both spatial and spatio-temporal visual features ."}
+{"idx": 2, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to ...", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "www.celsodemelo.net", "link": "https://www.celsodemelo.net/static/publications/Video_ColBERT_CVPR_2025_DistA.pdf", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan-sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong indi-vidual, yet compatible, representations for encoding video content."}
+{"idx": 3, "title": "AURA: A Fine-Grained Benchmark and Decomposed Metric for", "date": "", "ddg_snippet": "... evaluations, we have identified two critical shortcomings in previous audio- visual benchmarks: (1) They are not designed to assess advanced audio and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.07470v1", "content": "... evaluations, we have identified two critical shortcomings in previous audio- visual benchmarks: (1) They are not designed to assess advanced audio and ..."}
+{"idx": 4, "title": "arXiv:2503.19009v1 [cs.CV] 24 Mar 2025", "date": "", "ddg_snippet": "SV operation matches dynamic concepts. Unlike previous works that only use features af-ter temporal modeling, the contextualized video represen-tations in VIDEO-COLBERT can encode more temporal information because the temporal layers have less need to preserve purely spatial concepts and can instead focus on capturing higher-lev", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19009", "content": "SV operation matches dynamic concepts. Unlike previous works that only use features af-ter temporal modeling, the contextualized video represen-tations in VIDEO-COLBERT can encode more temporal information because the temporal layers have less need to preserve purely spatial concepts and can instead focus on capturing higher-lev"}
+{"idx": 5, "title": "Multi-level vision language interaction learning for cross ...", "date": "", "ddg_snippet": "For example, on text-to- video retrieval, our method outperforms DLR [4] and STAN [67] with the same ViT-B/32 on MSR-VTT by absolute 1.5% and 2 .0% in R@1, respectively, indicating the benefits and necessity of multi-level vision language interaction representation learning for video text retrieval.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S1566253525005548", "content": "For example, on text-to- video retrieval, our method outperforms DLR [4] and STAN [67] with the same ViT-B/32 on MSR-VTT by absolute 1.5% and 2 .0% in R@1, respectively, indicating the benefits and necessity of multi-level vision language interaction representation learning for video text retrieval."}
diff --git a/data/sampled_jsons/Video-ColBERT_paper_Section_4.3_sigmoid_loss_over_InfoNCE_cite_[63]_year_2023.jsonl b/data/sampled_jsons/Video-ColBERT_paper_Section_4.3_sigmoid_loss_over_InfoNCE_cite_[63]_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..dc9b582c2c0d331bc1358e49dc9760c8dd8f1225
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_paper_Section_4.3_sigmoid_loss_over_InfoNCE_cite_[63]_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "In this paper , we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. First, we propose the double-Constant Embedding Model (CCEM), a framework for parameterizing various...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "In this paper , we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. First, we propose the double-Constant Embedding Model (CCEM), a framework for parameterizing various..."}
+{"idx": 1, "title": "Sigmoid Loss for Language Image Pre-Training | by Ahmed... | Medium", "date": "", "ddg_snippet": "Increment the total loss by the newly computed loss . Repeat step #2 till all sibling GPUs pass their text features. In the experiments section , SigLIP ( sigmoid ) is evaluated against CLIP (softmax). After pre-training a model, zero-shot performance on ImageNet is reported.", "subpage_snippet": "", "source": "ahmdtaha.medium.com", "link": "https://ahmdtaha.medium.com/sigmoid-loss-for-language-image-pre-training-2dd5e7d1af84", "content": "Increment the total loss by the newly computed loss . Repeat step #2 till all sibling GPUs pass their text features. In the experiments section , SigLIP ( sigmoid ) is evaluated against CLIP (softmax). After pre-training a model, zero-shot performance on ImageNet is reported."}
+{"idx": 2, "title": "The Sigmoid Function Clearly Explained - YouTube", "date": "", "ddg_snippet": "In this video we discuss the sigmoid function.The sigmoid function plays an important role in the field of machine learning and is considered as one of the m...", "subpage_snippet": "", "source": "www.youtube.com", "link": "https://www.youtube.com/watch?v=TPqr8t919YM", "content": "In this video we discuss the sigmoid function.The sigmoid function plays an important role in the field of machine learning and is considered as one of the m..."}
+{"idx": 3, "title": "InfoNCE is a Free Lunch for Semantically guided Graph Contrastive...", "date": "", "ddg_snippet": "To achieve this, we propose IFL-GCL, using InfoNCE as a \"free lunch\" to extract semantic information. Specifically, We first prove that under InfoNCE , the representation similarity of node pairs aligns with the probability that the corresponding contrastive sample is positive.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/infonce-is-a-free-lunch-for-semantically-guided-graph-contrastive-learning/1129096414614257716-108614", "content": "To achieve this, we propose IFL-GCL, using InfoNCE as a \"free lunch\" to extract semantic information. Specifically, We first prove that under InfoNCE , the representation similarity of node pairs aligns with the probability that the corresponding contrastive sample is positive."}
+{"idx": 4, "title": "What Is Noise Contrastive Estimation Loss ? A Tutorial With Code", "date": "", "ddg_snippet": "A tutorial covering the Noise Contrastive Estimation Loss , a commonly encountered loss function in Self Supervised Learning .vi (maybe the output of some encoder), we can define the InfoNCE loss for contrastive learning as follows", "subpage_snippet": "", "source": "wandb.ai", "link": "https://wandb.ai/self-supervised-learning/index/reports/What-Is-Noise-Contrastive-Estimation-Loss-A-Tutorial-With-Code--Vmlldzo2NzY2OTY2", "content": "A tutorial covering the Noise Contrastive Estimation Loss , a commonly encountered loss function in Self Supervised Learning .vi (maybe the output of some encoder), we can define the InfoNCE loss for contrastive learning as follows"}
+{"idx": 5, "title": "Sigmoid Function -- from Wolfram MathWorld", "date": "", "ddg_snippet": "Sigmoid Function. DOWNLOAD Mathematica Notebook Download Wolfram Notebook.The sigmoid function, also called the sigmoidal curve (von Seggern 2007, p. 148) or logistic function, is the function.", "subpage_snippet": "", "source": "mathworld.wolfram.com", "link": "https://mathworld.wolfram.com/SigmoidFunction.html", "content": "Sigmoid Function. DOWNLOAD Mathematica Notebook Download Wolfram Notebook.The sigmoid function, also called the sigmoidal curve (von Seggern 2007, p. 148) or logistic function, is the function."}
+{"idx": 6, "title": "$f$-MICL: Understanding and Generalizing InfoNCE -based Contrastive...", "date": "", "ddg_snippet": "In this paper , we aim at answering two intriguing questions: (1) Can we go beyond the KL-based objective?Finally, we identify close relationships between the $f$-MICL objective and several popular InfoNCE -based objectives.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=ZD03VUZmRx", "content": "In this paper , we aim at answering two intriguing questions: (1) Can we go beyond the KL-based objective?Finally, we identify close relationships between the $f$-MICL objective and several popular InfoNCE -based objectives."}
+{"idx": 7, "title": "Noise Contrastive Estimation (NCE) 、负采样（NEG...", "date": "", "ddg_snippet": "# We sum out true and sampled losses . return _sum_rows(sampled_ losses ).由上面我们可以推测和之前的了解， sigmoid _cross_entropy_with_logits应该是用对logits和labels求了logistic loss 。 The issue.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/taoqick/article/details/79835360", "content": "# We sum out true and sampled losses . return _sum_rows(sampled_ losses ).由上面我们可以推测和之前的了解， sigmoid _cross_entropy_with_logits应该是用对logits和labels求了logistic loss 。 The issue."}
+{"idx": 8, "title": "(PDF) Consistent View Alignment Improves Foundation Models for...", "date": "", "ddg_snippet": "loss , sigmoid loss , ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395582556_Consistent_View_Alignment_Improves_Foundation_Models_for_3D_Medical_Image_Segmentation", "content": "loss , sigmoid loss , ramp loss and probit loss satisfy this condition though none of the standard convex loss functions satisfy it."}
+{"idx": 9, "title": "New Breakthroughs in Text Embedding!", "date": "", "ddg_snippet": "InfoNCE Loss : The negative logarithm of the cosine similarity is used as the loss function: L=−log(ϕ(q+,d+)). This loss function is applied over in-batch negatives and hard negatives. The objective during training is to minimize this InfoNCE loss .", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/new-breakthroughs-in-text-embedding-13d0e93ee44f", "content": "InfoNCE Loss : The negative logarithm of the cosine similarity is used as the loss function: L=−log(ϕ(q+,d+)). This loss function is applied over in-batch negatives and hard negatives. The objective during training is to minimize this InfoNCE loss ."}
diff --git a/data/sampled_jsons/Video-ColBERT_sigmoid_loss_robustness_to_noisy_data_reference_[63].jsonl b/data/sampled_jsons/Video-ColBERT_sigmoid_loss_robustness_to_noisy_data_reference_[63].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f4d18ee1d01a89bc656b8bc3ae0a173399d05ef
--- /dev/null
+++ b/data/sampled_jsons/Video-ColBERT_sigmoid_loss_robustness_to_noisy_data_reference_[63].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Robust Video-Text Retrieval Via Noisy Pair Calibration", "date": "", "ddg_snippet": "Video -text retrieval is a fundamental task in managing the emerging massive amounts of video data . The main challenge focuses on learning a common representation space for videos and queries where the similarity measurement can reflect the semantic closeness. However, existing video -text retrieval models may suffer from the following noise in the common space learning procedure: First, the ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10024790", "content": "Video -text retrieval is a fundamental task in managing the emerging massive amounts of video data . The main challenge focuses on learning a common representation space for videos and queries where the similarity measurement can reflect the semantic closeness. However, existing video -text retrieval models may suffer from the following noise in the common space learning procedure: First, the ..."}
+{"idx": 1, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "The sigmoid loss has also been shown to be more robust to noisy data [ 63 ], which is prevalent in T2VR datasets in both the quality of annotations [6] and the ambiguity of abstract text queries and descriptions [64].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.19009", "content": "The sigmoid loss has also been shown to be more robust to noisy data [ 63 ], which is prevalent in T2VR datasets in both the quality of annotations [6] and the ambiguity of abstract text queries and descriptions [64]."}
+{"idx": 2, "title": "Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning", "date": "", "ddg_snippet": "One effective approach to navigate label noise lies in em-ploying noise-robust loss functions. These loss functions, notable for their model-agnostic nature, seamlessly integrate with any deep learning paradigm. The existing literature highlights their ability to improve the robustness and gener-alization ability of deep learning models under noisy condi-tions (Ghosh, Kumar, and Sastry 2017 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.05497v3", "content": "One effective approach to navigate label noise lies in em-ploying noise-robust loss functions. These loss functions, notable for their model-agnostic nature, seamlessly integrate with any deep learning paradigm. The existing literature highlights their ability to improve the robustness and gener-alization ability of deep learning models under noisy condi-tions (Ghosh, Kumar, and Sastry 2017 ..."}
+{"idx": 3, "title": "Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval", "date": "", "ddg_snippet": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.19009", "content": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 4, "title": "Enhancing Robustness in Learning with Noisy Labels: An Asymmetric Co ...", "date": "", "ddg_snippet": "Label noise, an inevitable issue in various real-world datasets, tends to impair the performance of deep neural networks. A large body of literature focuses on symmetric co-training, aiming to enhance model robustness by exploiting interactions between models with distinct capabilities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3664647.3680835", "content": "Label noise, an inevitable issue in various real-world datasets, tends to impair the performance of deep neural networks. A large body of literature focuses on symmetric co-training, aiming to enhance model robustness by exploiting interactions between models with distinct capabilities."}
+{"idx": 5, "title": "Noise Robust Video Super-Resolution Without Training on Noisy Data", "date": "", "ddg_snippet": "Previous CNN-based video super-resolution (VSR) approaches can not be directly applied to noisy images, otherwise the noise will be enhanced after super-resolution (SR) reconstruction models. Some methods are robust to noise but all of them need to be trained on specific noisy training datasets. In this paper, we propose a noise-robust VSR network which only needs to be trained on the clean ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-030-87361-5_29", "content": "Previous CNN-based video super-resolution (VSR) approaches can not be directly applied to noisy images, otherwise the noise will be enhanced after super-resolution (SR) reconstruction models. Some methods are robust to noise but all of them need to be trained on specific noisy training datasets. In this paper, we propose a noise-robust VSR network which only needs to be trained on the clean ..."}
+{"idx": 6, "title": "arXiv:2503.19009v1 [cs.CV] 24 Mar 2025", "date": "", "ddg_snippet": "l and spatio-temporal visual features. Additionally, VIDEO-COLBERT is the first method to employ a sigmoid -based loss in T2VR, ith our dual sigmoid loss formulation. We find that this interac-tion and training paradigm leads to strong representations for encoding spatial and temporal information while still being com", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2503.19009", "content": "l and spatio-temporal visual features. Additionally, VIDEO-COLBERT is the first method to employ a sigmoid -based loss in T2VR, ith our dual sigmoid loss formulation. We find that this interac-tion and training paradigm leads to strong representations for encoding spatial and temporal information while still being com"}
+{"idx": 7, "title": "Publications | Alexander Martin", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content.", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/publications/", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content."}
+{"idx": 8, "title": "CVPR 2025 Open Access Repository", "date": "", "ddg_snippet": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html", "content": "Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content."}
+{"idx": 9, "title": "Alexander Martin", "date": "", "ddg_snippet": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content.", "subpage_snippet": "", "source": "alexmartin1722.github.io", "link": "https://alexmartin1722.github.io/", "content": "Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content."}
diff --git a/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_sitewww2024.thewebconf.org.jsonl b/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_sitewww2024.thewebconf.org.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d2ec0ef49566763670adf7490ebb21fd2c607134
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_blockchain_reinforcement_learning_sitewww2024.thewebconf.org.jsonl
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+{"idx": 0, "title": "Blockchain Courses - Blockchain Online Course Ad Viewing ads is privacy protected by DuckDuckGo. Ad clicks are managed by Microsoft's ad network ( more info ).", "date": "", "ddg_snippet": "Find the right instructor for you. Choose from many topics, skill levels, and languages. Join millions of learners from around the world already learning on Udemy.", "subpage_snippet": "", "source": "duckduckgo.com", "link": "https://duckduckgo.com/y.js?ad_domain=udemy.com&ad_provider=bingv7aa&ad_type=txad&click_metadata=tYuyU3yVeS9QT1geZezBg0BwfrgZPJXMQoPI77o3cULk5TqTecawGV06iipqmvjCtANHZTJMUTb194PBUPmuOwe1vzsQNlibVlyGzJ1-wC0n4QTJBLmDapDNsTjpLLXj.-zTD-walTmYahy_ABG0Q6Q&rut=3a67857bc8033ec142ed6c7f090636b3df96f3c3af5b9826cce13afea6104439&u3=https://www.bing.com/aclick?ld=e8HB90uI4iY4oP9M-DyTmvUTVUCUyaNw0F2V6GNi7TtGNLLYj_yj2dflNLSl0Akkjm2V833mRBJt0MiOlO_B4TJ3GGYo-YKC9bz058M_r_P9HpNc0BzTxaJCwj8YkUV6UKLZt8iQPhDrUe_XS5rFURSvl7cTrn9_zMmNoX08vYcX7csVQcmxoxUVK4SvWesqZNi-tVbQ&u=aHR0cHMlM2ElMmYlMmZ3d3cudWRlbXkuY29tJTJmY291cnNlJTJmYmxvY2tjaGFpbi1pbnRlcnZpZXctcWEtZCUyZiUzZnV0bV9zb3VyY2UlM2RiaW5nJTI2dXRtX21lZGl1bSUzZHVkZW15YWRzJTI2dXRtX2NhbXBhaWduJTNkQkctU2VhcmNoX0RTQV9CZXRhX1Byb2ZfbGEuRU5fY2MuUk9XLUVuZ2xpc2glMjZjYW1wYWlnbnR5cGUlM2RTZWFyY2glMjZwb3J0Zm9saW8lM2RCaW5nLVJPVy1FbmdsaXNoJTI2bGFuZ3VhZ2UlM2RFTiUyNnByb2R1Y3QlM2RDb3Vyc2UlMjZ0ZXN0JTNkJTI2YXVkaWVuY2UlM2REU0ElMjZ0b3BpYyUzZCUyNnByaW9yaXR5JTNkQmV0YSUyNnV0bV9jb250ZW50JTNkZGVhbDQ1ODQlMjZ1dG1fdGVybSUzZF8uX2FnXzEzMjQ5MTM4OTk5MTgwMTFfLl9hZF9fLl9rd19EZXYlMjUyMGVuXy5fZGVfY18uX2RtX18uX3BsX18uX3RpX2RhdC0yMzM0NjA2NzgzODI3MDA2JTNhbG9jLTE2NF8uX2xpXzE2NF8uX3BkX18uXyUyNm1hdGNodHlwZSUzZGIlMjZtc2Nsa2lkJTNkZDc2NzM2ZTI3YTg4MTQ5ODY4MTY1ZTMwN2Y0M2U0YjY&rlid=d76736e27a88149868165e307f43e4b6&vqd=4-232154488269529067495496031661738544709&iurl={1}IG=7D277479F53642EE8CC80322EBD3AE41&CID=194E1592A22A607D0AA903E2A3AF61C3&ID=DevEx,5033.1", "content": "Find the right instructor for you. Choose from many topics, skill levels, and languages. Join millions of learners from around the world already learning on Udemy."}
+{"idx": 1, "title": "International World Wide Web Conference 2024 ( WWW 2024 )", "date": "", "ddg_snippet": "BlockDFL: A Blockchain -based Fully Decentralized Peer-to-Peer Federated Learning Framework. Zhen Qin, Xueqiang YAN, Mengchu Zhou, Shuiguang Deng. UnifiedSSR: A Unified Framework of Sequential Search and Recommendation.", "subpage_snippet": "", "source": "www2024.thewebconf.org", "link": "https://www2024.thewebconf.org/accepted/research-tracks/", "content": "BlockDFL: A Blockchain -based Fully Decentralized Peer-to-Peer Federated Learning Framework. Zhen Qin, Xueqiang YAN, Mengchu Zhou, Shuiguang Deng. UnifiedSSR: A Unified Framework of Sequential Search and Recommendation."}
+{"idx": 2, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
diff --git a/data/sampled_jsons/WWW_2024_conference_statistics_report_year_2024.jsonl b/data/sampled_jsons/WWW_2024_conference_statistics_report_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fd11e4d29cc25b1b90532e8b192e0c0de0df8b7b
--- /dev/null
+++ b/data/sampled_jsons/WWW_2024_conference_statistics_report_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "2024 Statistical Report of The Church of Jesus Christ of Latter-day Saints", "date": "", "ddg_snippet": "For the information of the members of The Church of Jesus Christ of Latter-day Saints, the First Presidency has issued the following statistical report concerning the growth and status of the Church as of December 31, 2024 .", "subpage_snippet": "", "source": "newsroom.churchofjesuschrist.org", "link": "https://newsroom.churchofjesuschrist.org/article/2024-statistical-report", "content": "For the information of the members of The Church of Jesus Christ of Latter-day Saints, the First Presidency has issued the following statistical report concerning the growth and status of the Church as of December 31, 2024 ."}
+{"idx": 1, "title": "2024 Football Conference Statistics", "date": "", "ddg_snippet": "The official 2024 Football cumulative statistics for Conference USA", "subpage_snippet": "", "source": "conferenceusa.com", "link": "https://conferenceusa.com/stats.aspx?path=football&year=2024&conf=true", "content": "The official 2024 Football cumulative statistics for Conference USA"}
+{"idx": 2, "title": "PDF ANNUAL STATISTICAL REPORT - Adventist Archives", "date": "", "ddg_snippet": "New Series, Volume 6 Advance Release of Membership Statistics by Division for 2023", "subpage_snippet": "", "source": "documents.adventistarchives.org", "link": "https://documents.adventistarchives.org/Statistics/ASR/ASR2024A.pdf", "content": "New Series, Volume 6 Advance Release of Membership Statistics by Division for 2023"}
+{"idx": 3, "title": "PDF 2024 Local Church Reports to The Annual Conference Overview", "date": "", "ddg_snippet": "Conference Statistician's Letter 2024 - If you have questions, please contact Brant Henshaw, Interim Conference Statistician, at bhenshaw@pnwumc.org. Conference Statistics 2024 Guide - detailed instructions for logging in, printing your worksheet, entering your data and submitting final reports .", "subpage_snippet": "", "source": "www.pnwumc.org", "link": "https://www.pnwumc.org/wp-content/uploads/2025/01/Statistics-Overview-2024c.pdf", "content": "Conference Statistician's Letter 2024 - If you have questions, please contact Brant Henshaw, Interim Conference Statistician, at bhenshaw@pnwumc.org. Conference Statistics 2024 Guide - detailed instructions for logging in, printing your worksheet, entering your data and submitting final reports ."}
+{"idx": 4, "title": "Adventist World Statistics", "date": "", "ddg_snippet": "The Annual Statistical Report (ASR) is compiled by the Office of Archives, Statistics , and Research. Information is received quarterly and annually from each of the denomination's thirteen divisions (and three attached fields), which receive their information from the local conferences and missions by way of the union conference and mission offices. Additional information for the annual report ...", "subpage_snippet": "", "source": "www.adventiststatistics.org", "link": "https://www.adventiststatistics.org/", "content": "The Annual Statistical Report (ASR) is compiled by the Office of Archives, Statistics , and Research. Information is received quarterly and annually from each of the denomination's thirteen divisions (and three attached fields), which receive their information from the local conferences and missions by way of the union conference and mission offices. Additional information for the annual report ..."}
+{"idx": 5, "title": "PDF Department of Labor Conference Report Fiscal Year 2024", "date": "", "ddg_snippet": "Department of Labor Conference Report Fiscal Year 2024 In FY 2024 , DOL continued its conference review process involving the Office of the Chief Financial Officer, the Office of the Assistant Secretary for Administration and Management, and the Office of the Solicitor. For FY23, DOL incurred over $6.2 million in costs to the Department for conferences .", "subpage_snippet": "", "source": "www.dol.gov", "link": "https://www.dol.gov/sites/dolgov/files/OCFO/2024-DOL-Conference-Report.pdf", "content": "Department of Labor Conference Report Fiscal Year 2024 In FY 2024 , DOL continued its conference review process involving the Office of the Chief Financial Officer, the Office of the Assistant Secretary for Administration and Management, and the Office of the Solicitor. For FY23, DOL incurred over $6.2 million in costs to the Department for conferences ."}
+{"idx": 6, "title": "Statistics | United Methodists of Upper New York", "date": "", "ddg_snippet": "about / Finance Statistics Annual Church Statistics (ACStats) is the one-stop location for United Methodist data that tracks membership, expenses, and income. It's designed to help churches submit year-end statistics to the Upper New York (UNY) Conference digitally in a way that's cost-effective. Important Dates The deadline to complete the 2024 statistical report for your church is February ...", "subpage_snippet": "", "source": "www.unyumc.org", "link": "https://www.unyumc.org/about/statistics", "content": "about / Finance Statistics Annual Church Statistics (ACStats) is the one-stop location for United Methodist data that tracks membership, expenses, and income. It's designed to help churches submit year-end statistics to the Upper New York (UNY) Conference digitally in a way that's cost-effective. Important Dates The deadline to complete the 2024 statistical report for your church is February ..."}
+{"idx": 7, "title": "2024 General Service Conference Final Report - Alcoholics Anonymous", "date": "", "ddg_snippet": "The 74th General Service Conference was held April 14-20, 2024 , in New York City at the Marriott Hotel at the Brooklyn Bridge. With 134 Conference members the 74th General Service Conference provided the culmination of a year's worth of engagement and discussion on topics integral to A.A. members, groups, districts, areas and regions across the United States and Canada, and ushered in the ...", "subpage_snippet": "", "source": "www.aa.org", "link": "https://www.aa.org/2024-general-service-conference-final-report", "content": "The 74th General Service Conference was held April 14-20, 2024 , in New York City at the Marriott Hotel at the Brooklyn Bridge. With 134 Conference members the 74th General Service Conference provided the culmination of a year's worth of engagement and discussion on topics integral to A.A. members, groups, districts, areas and regions across the United States and Canada, and ushered in the ..."}
+{"idx": 8, "title": "2024 Statistics Update - Wisconsin Conference of the UMC", "date": "", "ddg_snippet": "The 2024 Statistics Reports for local churches are due on Friday, February 28th. Many thanks to those who have already started and to those who have submitted their data.", "subpage_snippet": "", "source": "www.wisconsinumc.org", "link": "https://www.wisconsinumc.org/news-detail/2024-statistics-update-18880951", "content": "The 2024 Statistics Reports for local churches are due on Friday, February 28th. Many thanks to those who have already started and to those who have submitted their data."}
+{"idx": 9, "title": "PDF MEMORANDUM - umoi.org", "date": "", "ddg_snippet": "Conference Statistics 2024 Guide is a presentation which you can download which contains detailed instructions for logging in, printing your worksheet, entering your data and submitting final reports . Table 1 (membership and participation) - GCFA Detailed Instructions - 2024 (with additions unique to OR-ID)", "subpage_snippet": "", "source": "www.umoi.org", "link": "https://www.umoi.org/files/fileshare/finance+and+administration+documents/statistics+overview+2024.pdf", "content": "Conference Statistics 2024 Guide is a presentation which you can download which contains detailed instructions for logging in, printing your worksheet, entering your data and submitting final reports . Table 1 (membership and participation) - GCFA Detailed Instructions - 2024 (with additions unique to OR-ID)"}
diff --git a/data/sampled_jsons/Wang_et_al._2019_reverse_cross-entropy_RCE_abstract.jsonl b/data/sampled_jsons/Wang_et_al._2019_reverse_cross-entropy_RCE_abstract.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ee7b4fd5036c1eaef750f113c356ca813e75f04a
--- /dev/null
+++ b/data/sampled_jsons/Wang_et_al._2019_reverse_cross-entropy_RCE_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Cross Entropy for Robust Learning with Noisy ...", "date": "", "ddg_snippet": "by Y Wang · 2019 · Cited by 1328 — In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (\"easy\" classes), but more surprisingly, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1908.06112", "content": "by Y Wang · 2019 · Cited by 1328 — In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes (\"easy\" classes), but more surprisingly, ..."}
+{"idx": 1, "title": "Symmetric Cross Entropy for Robust Learning With Noisy ...", "date": "", "ddg_snippet": "by Y Wang · 2019 · Cited by 1328 — Inspired by the sym- metric KL-divergence, we propose such a noise tolerant term, namely Reverse Cross Entropy ( RCE ), which com- bined with CE forms the basis ...", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf", "content": "by Y Wang · 2019 · Cited by 1328 — Inspired by the sym- metric KL-divergence, we propose such a noise tolerant term, namely Reverse Cross Entropy ( RCE ), which com- bined with CE forms the basis ..."}
+{"idx": 2, "title": "Can Cross Entropy Loss Be Robust to Label Noise?", "date": "", "ddg_snippet": "by L Feng · Cited by 220 — 6) SCE [ Wang et al ., 2019 ]: The approach boosts CE symmetrically with a noise robust counterpart Reverse Cross . Entropy ( RCE ). The regularization parameters α ... 7 pages", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2020/0305.pdf", "content": "by L Feng · Cited by 220 — 6) SCE [ Wang et al ., 2019 ]: The approach boosts CE symmetrically with a noise robust counterpart Reverse Cross . Entropy ( RCE ). The regularization parameters α ... 7 pages"}
+{"idx": 3, "title": "Normalized Loss Functions for Deep Learning with Noisy Labels", "date": "", "ddg_snippet": "by X Ma · 2020 · Cited by 587 — Recently, Wang et al . ( 2019c ) proposed the Symmetric Cross Entropy (SCE) which combines a Re- verse Cross Entropy ( RCE ) together with the CE ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "http://proceedings.mlr.press/v119/ma20c/ma20c.pdf", "content": "by X Ma · 2020 · Cited by 587 — Recently, Wang et al . ( 2019c ) proposed the Symmetric Cross Entropy (SCE) which combines a Re- verse Cross Entropy ( RCE ) together with the CE ..."}
+{"idx": 4, "title": "ROBUST LEARNING VIA GOLDEN SYMMETRIC LOSS", "date": "", "ddg_snippet": "... rce = −. K. X k=1 p(k|x) log q(k|x). (1). ( Wang et al ., 2019 ) combine cross entropy and reverse cross entropy into the symmetric cross entropy : lsl = α `ce + β ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=20qC5K2ICZL", "content": "... rce = −. K. X k=1 p(k|x) log q(k|x). (1). ( Wang et al ., 2019 ) combine cross entropy and reverse cross entropy into the symmetric cross entropy : lsl = α `ce + β ..."}
+{"idx": 5, "title": "Reflective Learning With Label Noise", "date": "", "ddg_snippet": "Wang et al . [42] proposed a symmetric cross entropy (SCE) loss by a linear combination of CE loss and reversed cross entropy ( RCE ) loss (equivalent to MAE ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/iel7/76/10170754/10002324.pdf", "content": "Wang et al . [42] proposed a symmetric cross entropy (SCE) loss by a linear combination of CE loss and reversed cross entropy ( RCE ) loss (equivalent to MAE ..."}
+{"idx": 6, "title": "A Robust Loss Against Noisy Labels", "date": "", "ddg_snippet": "Abstract . Noisy labels are a common challenge in real-world datasets, severely degrading the training of deep learning models. Enhancing the robustness of ...", "subpage_snippet": "", "source": "papers.ssrn.com", "link": "https://papers.ssrn.com/sol3/Delivery.cfm/dda81597-dae9-437d-9f1e-26f91fe820a5-MECA.pdf?abstractid=5394370&mirid=1", "content": "Abstract . Noisy labels are a common challenge in real-world datasets, severely degrading the training of deep learning models. Enhancing the robustness of ..."}
+{"idx": 7, "title": "Potential Energy based Mixture Model for Noisy Label ...", "date": "", "ddg_snippet": "2 May 2024 — Furthermore, Wang proposed Symmetric Cross Entropy loss (SCE) Wang et al ., ( 2019 ) , which combines CE loss with Reverse Cross Entropy loss.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.01186v1", "content": "2 May 2024 — Furthermore, Wang proposed Symmetric Cross Entropy loss (SCE) Wang et al ., ( 2019 ) , which combines CE loss with Reverse Cross Entropy loss."}
+{"idx": 8, "title": "Named Entity Recognition via Noise Aware Training ...", "date": "", "ddg_snippet": "by X Huang · 2021 · Cited by 23 — Symmetric cross entropy ( Wang et al ., 2019b ), by adding a symmetric reverse cross entropy after the cross entropy , makes the model have a ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2021.findings-acl.423.pdf", "content": "by X Huang · 2021 · Cited by 23 — Symmetric cross entropy ( Wang et al ., 2019b ), by adding a symmetric reverse cross entropy after the cross entropy , makes the model have a ..."}
+{"idx": 9, "title": "Learning from Noisy Labels with Complementary Loss ...", "date": "", "ddg_snippet": "by DB Wang · 2021 · Cited by 45 — Wang et al. (2019) proposed the Symmetric Cross Entropy (SCE ) by combining Reverse Cross Entropy (RCE) (which satisfies the symmetric condition) together ... 9 pages", "subpage_snippet": "", "source": "cdn.aaai.org", "link": "https://cdn.aaai.org/ojs/17213/17213-13-20707-1-2-20210518.pdf", "content": "by DB Wang · 2021 · Cited by 45 — Wang et al. (2019) proposed the Symmetric Cross Entropy (SCE ) by combining Reverse Cross Entropy (RCE) (which satisfies the symmetric condition) together ... 9 pages"}
diff --git a/data/sampled_jsons/WeaklyRec_avoids_using_propensity_scores_unlike_UBPR_Saito_2020.jsonl b/data/sampled_jsons/WeaklyRec_avoids_using_propensity_scores_unlike_UBPR_Saito_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a8d99ba7a2eab7b0078cfdc9dc625975b1150ac0
--- /dev/null
+++ b/data/sampled_jsons/WeaklyRec_avoids_using_propensity_scores_unlike_UBPR_Saito_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Welcome to Glendalough.ie", "date": "", "ddg_snippet": "The Glendalough Valley is located in the Wicklow Mountains National Park and has many attractions to entice visitors, from its world famous Monastic Site with Round Tower to its scenic lakes and valleys, as well as a selection of walks and trails in the area including The Wicklow Way.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/", "content": "The Glendalough Valley is located in the Wicklow Mountains National Park and has many attractions to entice visitors, from its world famous Monastic Site with Round Tower to its scenic lakes and valleys, as well as a selection of walks and trails in the area including The Wicklow Way."}
+{"idx": 1, "title": "Attractions - Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "Historic & Ecclesiastical SitesMonastic City Glendalough is home to one of the most important monastic sites in Ireland. This early Christian monastic settlement was founded by St. Kevin in the 6th century and from this developed the ‘Monastic City’.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/things-to-do/attractions/", "content": "Historic & Ecclesiastical SitesMonastic City Glendalough is home to one of the most important monastic sites in Ireland. This early Christian monastic settlement was founded by St. Kevin in the 6th century and from this developed the ‘Monastic City’."}
+{"idx": 2, "title": "Things to do - Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "The sandy area at the eastern end of the Upper Lake in Glendalough is a popular spot for paddling and swimming during the summer. Visitors are warned that the lake is deep with sudden depth changes.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/things-to-do/", "content": "The sandy area at the eastern end of the Upper Lake in Glendalough is a popular spot for paddling and swimming during the summer. Visitors are warned that the lake is deep with sudden depth changes."}
+{"idx": 3, "title": "Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "Glendalough is in the Wicklow National Park which is situated south of Dublin on 20,000 hectares of mountain scenery with many roads crossing through it. A lot of the National Park has no facilities and is left mostly untouched and natural.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/things-to-do/walking/", "content": "Glendalough is in the Wicklow National Park which is situated south of Dublin on 20,000 hectares of mountain scenery with many roads crossing through it. A lot of the National Park has no facilities and is left mostly untouched and natural."}
+{"idx": 4, "title": "Heritage - Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "The Glendalough Valley was carved out by glaciers during the Ice Age and the two lakes, from which Glendalough gets its name, were formed when the ice eventually thawed. The Valley is home to one of Ireland’s most impressive monastic sites founded by St. Kevin in the 6th Century.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/heritage/", "content": "The Glendalough Valley was carved out by glaciers during the Ice Age and the two lakes, from which Glendalough gets its name, were formed when the ice eventually thawed. The Valley is home to one of Ireland’s most impressive monastic sites founded by St. Kevin in the 6th Century."}
+{"idx": 5, "title": "Walking Holidays - glendalough.ie", "date": "", "ddg_snippet": "Walking Holiday Ireland Discover Ancient Trails on Ireland’s Wicklow Way! Step into a world where emerald hills meet misty valleys in County Wicklow. Walking Holiday Ireland’s self-guided hiking tours take you through stunning landscapes, including the ancient monastic site of Glendalough.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/things-to-do/walking-holidays/", "content": "Walking Holiday Ireland Discover Ancient Trails on Ireland’s Wicklow Way! Step into a world where emerald hills meet misty valleys in County Wicklow. Walking Holiday Ireland’s self-guided hiking tours take you through stunning landscapes, including the ancient monastic site of Glendalough."}
+{"idx": 6, "title": "Maps - Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "Laragh and surrounding area Map Wicklow Mountains Park Map More information on the Mining in the Glendalough, Glendassan and Glenmalure Valleys", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/maps/", "content": "Laragh and surrounding area Map Wicklow Mountains Park Map More information on the Mining in the Glendalough, Glendassan and Glenmalure Valleys"}
+{"idx": 7, "title": "Images of Glendalough – Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "Nov 12, 2013 · Images of Glendalough admin November 12, 2013 Headers 11 Comments In the heart of Wicklow", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/2013/11/images-of-glendalough/", "content": "Nov 12, 2013 · Images of Glendalough admin November 12, 2013 Headers 11 Comments In the heart of Wicklow"}
+{"idx": 8, "title": "Eating Out - Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "Enjoy great food in Glendalough. There are a selection of tea-rooms, coffee shops, restaurants and hotels with something to suit everyone, both in terms of budget, and great food choices.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/eating-out/", "content": "Enjoy great food in Glendalough. There are a selection of tea-rooms, coffee shops, restaurants and hotels with something to suit everyone, both in terms of budget, and great food choices."}
+{"idx": 9, "title": "Scenic Drives - Glendalough, Co. Wicklow, Ireland", "date": "", "ddg_snippet": "From here you can visit Powerscourt Estate and Gardens, which include the highest waterfall in Ireland. Continue to Sally Gap, a notable crossroads situated between Kippure Mountain and the Djouce Mountain, where the road leads to Glendalough, by Glenmacnass and Laragh.", "subpage_snippet": "", "source": "glendalough.ie", "link": "https://glendalough.ie/things-to-do/scenic-drives/", "content": "From here you can visit Powerscourt Estate and Gardens, which include the highest waterfall in Ireland. Continue to Sally Gap, a notable crossroads situated between Kippure Mountain and the Djouce Mountain, where the road leads to Glendalough, by Glenmacnass and Laragh."}
diff --git a/data/sampled_jsons/Witness_complex_computational_efficiency_topological_data_analysis.jsonl b/data/sampled_jsons/Witness_complex_computational_efficiency_topological_data_analysis.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..e256a774d58e619d8876eeccfbf1c5a691bf85f4
--- /dev/null
+++ b/data/sampled_jsons/Witness_complex_computational_efficiency_topological_data_analysis.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Topological data analysis", "date": "", "ddg_snippet": "In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Topological_data_analysis", "content": "In applied mathematics, topological data analysis (TDA) is an approach to the analysis of datasets using techniques from topology."}
+{"idx": 1, "title": "Topological Data Analysis with $ε$-net Induced Lazy ...", "date": "", "ddg_snippet": "by NA Arafat · 2019 · Cited by 8 — The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1906.06122", "content": "by NA Arafat · 2019 · Cited by 8 — The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud."}
+{"idx": 2, "title": "Topological estimation using witness complexes", "date": "", "ddg_snippet": "This paper tackles the problem of computing topological invariants of geometric objects in a robust manner, using only point cloud data sampled from the object.", "subpage_snippet": "", "source": "bpb-us-w2.wpmucdn.com", "link": "https://bpb-us-w2.wpmucdn.com/blogs.cofc.edu/dist/2/883/files/2019/05/Carlsson_DeSilva-Ref8.pdf", "content": "This paper tackles the problem of computing topological invariants of geometric objects in a robust manner, using only point cloud data sampled from the object."}
+{"idx": 3, "title": "A Witness Graph Topological Layer for Adversarial ...", "date": "", "ddg_snippet": "by NA Arafat · 2024 · Cited by 5 — We illustrate the versatility and efficiency of WGTL by its integration with five GNNs and three existing non- topological defense mechanisms.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2409.14161", "content": "by NA Arafat · 2024 · Cited by 5 — We illustrate the versatility and efficiency of WGTL by its integration with five GNNs and three existing non- topological defense mechanisms."}
+{"idx": 4, "title": "Topological Data Analysis with -net Induced Lazy Witness ...", "date": "", "ddg_snippet": "by NA Arafat · 2019 · Cited by 8 — The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud. It is built in reference to a ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/978-3-030-27618-8_28", "content": "by NA Arafat · 2019 · Cited by 8 — The lazy witness complex is a computationally feasible approximation of the underlying topological structure of a point cloud. It is built in reference to a ..."}
+{"idx": 5, "title": "Graduate Thesis Or Dissertation | Topological Data Analyses ...", "date": "", "ddg_snippet": "16 Nov 2019 — We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - ...", "subpage_snippet": "", "source": "scholar.colorado.edu", "link": "https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/mc87pq327?locale=it", "content": "16 Nov 2019 — We introduce a topological membership test for sliding windows of time series data that uses a sparse simplicial complex - the witness complex - ..."}
+{"idx": 6, "title": "Exploring the Lazy Witness Complex for Efficient Persistent ...", "date": "", "ddg_snippet": "Key takeaway: 'The Lazy Witness Complex and its nested families can efficiently analyze large-scale data while maintaining topological accuracy, ...", "subpage_snippet": "", "source": "www.consensus.app", "link": "https://www.consensus.app/papers/exploring-the-lazy-witness-complex-for-efficient-al-imran-hossain/f8fd7d7d85d45ca686b5c8cf2bc7fa19", "content": "Key takeaway: 'The Lazy Witness Complex and its nested families can efficiently analyze large-scale data while maintaining topological accuracy, ..."}
+{"idx": 7, "title": "e-net Induced Lazy Witness Complexes on Graphs", "date": "", "ddg_snippet": "by NA Arafat · Cited by 2 — We comparatively and empirically evaluate the efficiency and effectiveness of the choice of landmarks that they induce for the topological data analysis of ...", "subpage_snippet": "", "source": "debabrota-basu.github.io", "link": "https://debabrota-basu.github.io/pdfs/atda2019.pdf", "content": "by NA Arafat · Cited by 2 — We comparatively and empirically evaluate the efficiency and effectiveness of the choice of landmarks that they induce for the topological data analysis of ..."}
+{"idx": 8, "title": "Topological estimation using witness complexes", "date": "", "ddg_snippet": "This paper tackles the problem of computing topological invariants of geometric objects in a robust manner, using only point cloud data sampled from the object.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/2386332.2386359", "content": "This paper tackles the problem of computing topological invariants of geometric objects in a robust manner, using only point cloud data sampled from the object."}
+{"idx": 9, "title": "Using Topological Data Analysis (TDA) and Persistent ...", "date": "", "ddg_snippet": "by PTW Yen · 2021 · Cited by 42 — In this paper, our research problem is to use TDA to understand topological changes accompanying crashes in the Singapore and Taiwan stock ...", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.572216/full", "content": "by PTW Yen · 2021 · Cited by 42 — In this paper, our research problem is to use TDA to understand topological changes accompanying crashes in the Singapore and Taiwan stock ..."}
diff --git a/data/sampled_jsons/X-CLIP_abstract_Yiwei_Ma_multi-grained_contrastive_learning_spatial_temporal_year_2022.jsonl b/data/sampled_jsons/X-CLIP_abstract_Yiwei_Ma_multi-grained_contrastive_learning_spatial_temporal_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..fb48cadc3c60b23fa9bfbdf7c39850e4670d3871
--- /dev/null
+++ b/data/sampled_jsons/X-CLIP_abstract_Yiwei_Ma_multi-grained_contrastive_learning_spatial_temporal_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "To this end, this paper presents a novel multi-grained con-trastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity ma -trices to instance-level similarity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.07285", "content": "To this end, this paper presents a novel multi-grained con-trastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity ma -trices to instance-level similarity."}
+{"idx": 1, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xuguohai/X-CLIP", "content": "The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun*, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to ..."}
+{"idx": 2, "title": "X-CLIP - Project Page - GitHub Pages", "date": "", "ddg_snippet": "X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval Yiwei Ma 1 Guohai Xu 3 Xiaoshuai Sun 12 Ming Yan 3 Ji Zhang 3 Rongrong Ji 12 1 MAC-Lab, Xiamen University 2 Institute of Artificial Intelligence, Xiamen University 3 DAMO Academy, Alibaba Group corresponding author", "subpage_snippet": "", "source": "xmu-xiaoma666.github.io", "link": "https://xmu-xiaoma666.github.io/Projects/MM22_XCLIP/", "content": "X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval Yiwei Ma 1 Guohai Xu 3 Xiaoshuai Sun 12 Ming Yan 3 Ji Zhang 3 Rongrong Ji 12 1 MAC-Lab, Xiamen University 2 Institute of Artificial Intelligence, Xiamen University 3 DAMO Academy, Alibaba Group corresponding author"}
+{"idx": 3, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity matrices to instance-level similarity.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3503161.3547910", "content": "To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity matrices to instance-level similarity."}
+{"idx": 4, "title": "dblp: X-CLIP: End-to-End Multi-grained Contrastive Learning for Video ...", "date": "", "ddg_snippet": "Bibliographic details on X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/mm/MaXSYZJ22", "content": "Bibliographic details on X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval."}
+{"idx": 5, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for ... - CatalyzeX", "date": "", "ddg_snippet": "To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity matrices to instance-level similarity.", "subpage_snippet": "", "source": "www.catalyzex.com", "link": "https://www.catalyzex.com/paper/x-clip-end-to-end-multi-grained-contrastive", "content": "To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP , for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine- grained and cross- grained similarity matrices to instance-level similarity."}
+{"idx": 6, "title": "X-CLIP/README.md at main · xuguohai/X-CLIP · GitHub", "date": "", "ddg_snippet": "The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval. It achieves SOTA results on MSR ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/xuguohai/X-CLIP/blob/main/README.md", "content": "The implementation of paper X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval. Accepted by ACMMM22. By Yiwei Ma , Guohai Xu, Xiaoshuai Sun *, Ming Yan, Ji Zhang, Rongrong Ji. X-CLIP adopts cross- grained contrastive learning and attention over similarity matrix module to filter out unnecessary information during video-text retrieval. It achieves SOTA results on MSR ..."}
+{"idx": 7, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "Request PDF | X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Video-text retrieval has been a crucial and fundamental task in multi -modal research. The development ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/362066555_X-CLIP_End-to-End_Multi-grained_Contrastive_Learning_for_Video-Text_Retrieval", "content": "Request PDF | X-CLIP : End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Video-text retrieval has been a crucial and fundamental task in multi -modal research. The development ..."}
+{"idx": 8, "title": "Yiwei Ma's Homepage--马祎炜的个人主页", "date": "", "ddg_snippet": "Yiwei Ma , Xiaoqing Zhang, Xiaoshuai Sun , Jiayi Ji, Haowei Wang, Guannan Jiang, Weilin Zhuang, Rongrong Ji X-Mesh:Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance", "subpage_snippet": "", "source": "xmu-xiaoma666.github.io", "link": "https://xmu-xiaoma666.github.io/", "content": "Yiwei Ma , Xiaoqing Zhang, Xiaoshuai Sun , Jiayi Ji, Haowei Wang, Guannan Jiang, Weilin Zhuang, Rongrong Ji X-Mesh:Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance"}
+{"idx": 9, "title": "X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text ...", "date": "", "ddg_snippet": "Video-text retrieval has been a crucial and fundamental task in multi -modal research. The development of video-text retrieval has been considerably promoted by ...", "subpage_snippet": "", "source": "hyper.ai", "link": "https://hyper.ai/en/papers/2207.07285", "content": "Video-text retrieval has been a crucial and fundamental task in multi -modal research. The development of video-text retrieval has been considerably promoted by ..."}
diff --git a/data/sampled_jsons/Yuta_Saito_2020_ICTIR_'Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback'_abstract_propensity_year_2020.jsonl b/data/sampled_jsons/Yuta_Saito_2020_ICTIR_'Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback'_abstract_propensity_year_2020.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..1f33375f29b45dd4458cccfc8d9e2ddb846dcb36
--- /dev/null
+++ b/data/sampled_jsons/Yuta_Saito_2020_ICTIR_'Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback'_abstract_propensity_year_2020.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Yuta Saito", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Biased Implicit Feedback . ... Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback .", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publications/", "content": "Unbiased Pairwise Learning from Biased Implicit Feedback . ... Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback ."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "by Y Saito · 2020 · Cited by 52 — In this study, we first define an ideal pairwise loss function defined using the ground-truth relevance parameters that should be used to optimize the ranking ..."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Implicit Feedback for ...", "date": "", "ddg_snippet": "Abstract . Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592077", "content": "Abstract . Generally speaking, the model training for recommender systems can be based on two types of data, namely explicit feedback and implicit feedback ."}
+{"idx": 3, "title": "Unbiased Recommender Learning from Implicit Feedback ...", "date": "", "ddg_snippet": "by H Wang · Cited by 1 — Abstract : Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0E5rZOGA13", "content": "by H Wang · Cited by 1 — Abstract : Implicit feedback recommendation is challenged by the missing negative feedback essential for effective model training. Existing ..."}
+{"idx": 4, "title": "uCTRL: Unbiased Contrastive Representation Learning via ...", "date": "", "ddg_snippet": "by J Lee · 2023 · Cited by 9 — ABSTRACT . Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2305.12768", "content": "by J Lee · 2023 · Cited by 9 — ABSTRACT . Because implicit user feedback for the collaborative filtering (CF) models is biased toward popular items, CF models tend to yield."}
+{"idx": 5, "title": "Publications | Yuta Saito", "date": "", "ddg_snippet": "Yuta Saito (2020). Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of ...", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/en/publication/", "content": "Yuta Saito (2020). Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of ..."}
+{"idx": 6, "title": "Towards Resolving Propensity Contradiction in Offline ...", "date": "", "ddg_snippet": "by Y Saito · Cited by 15 — In Fourteenth ACM. Conference on Recommender Systems, pages 92–100, 2020 . [Saito, 2020c ] Yuta Saito . Unbiased pairwise learning from biased implicit feedback .", "subpage_snippet": "", "source": "usaito.github.io", "link": "https://usaito.github.io/files/IJCAI2022_DAMF.pdf", "content": "by Y Saito · Cited by 15 — In Fourteenth ACM. Conference on Recommender Systems, pages 92–100, 2020 . [Saito, 2020c ] Yuta Saito . Unbiased pairwise learning from biased implicit feedback ."}
+{"idx": 7, "title": "Bilateral Self-unbiased Learning from Biased Implicit ...", "date": "", "ddg_snippet": "by J Lee · 2022 · Cited by 16 — [38] Yuta Saito. 2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In ... Propensity Estimation via Combinational Joint Learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2207.12660", "content": "by J Lee · 2022 · Cited by 16 — [38] Yuta Saito. 2020. Unbiased Pairwise Learning from Biased Implicit Feedback . In ... Propensity Estimation via Combinational Joint Learning ..."}
+{"idx": 8, "title": "Yuta Saito", "date": "", "ddg_snippet": "26 Apr 2025 — My research lies at the intersection of statistical machine learning and causal inference called counterfactual learning . 5 pages", "subpage_snippet": "", "source": "usait0.com", "link": "https://usait0.com/uploads/cv.pdf", "content": "26 Apr 2025 — My research lies at the intersection of statistical machine learning and causal inference called counterfactual learning . 5 pages"}
+{"idx": 9, "title": "Towards Resolving Propensity Contradiction in Offline ...", "date": "", "ddg_snippet": "by Y Saito · Cited by 15 — [Saito, 2020b] Yuta Saito. Unbiased pairwise learning from biased implicit feedback . In Proceedings of the 2020 ACM. SIGIR on International Conference on Theory ...", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/proceedings/2022/0307.pdf", "content": "by Y Saito · Cited by 15 — [Saito, 2020b] Yuta Saito. Unbiased pairwise learning from biased implicit feedback . In Proceedings of the 2020 ACM. SIGIR on International Conference on Theory ..."}
diff --git a/data/sampled_jsons/Zhao_bandit_peeling_technique_2023.jsonl b/data/sampled_jsons/Zhao_bandit_peeling_technique_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..82fe7f3bf5ff6e37ff7aad746b5ab4f8850999c1
--- /dev/null
+++ b/data/sampled_jsons/Zhao_bandit_peeling_technique_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Ya-Pu Zhao's research works | Chinese Academy of Sciences ...", "date": "", "ddg_snippet": "Ya-Pu Zhao's 213 research works with 7,330 citations and 16,681 reads, including: Electro-capillary peeling of thin films", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/scientific-contributions/Ya-Pu-Zhao-39041524", "content": "Ya-Pu Zhao's 213 research works with 7,330 citations and 16,681 reads, including: Electro-capillary peeling of thin films"}
+{"idx": 1, "title": "Zhao Bandi | 28 Artworks at Auction | MutualArt", "date": "", "ddg_snippet": "Stay up to date with Zhao Bandi (Chinese, 1966) . Discover works for sale, auction results, market data, news and exhibitions on MutualArt.", "subpage_snippet": "", "source": "www.mutualart.com", "link": "https://www.mutualart.com/Artist/Zhao-Bandi/13F4F5D676781689", "content": "Stay up to date with Zhao Bandi (Chinese, 1966) . Discover works for sale, auction results, market data, news and exhibitions on MutualArt."}
+{"idx": 2, "title": "Zhao Bandi | artist | ARTLINKART | Chinese contemporary art ...", "date": "", "ddg_snippet": "EXHIBITION SYNOPSIS About Zhao Bandi - China Party “Zhao Bandi : China Party” presents more than a dozen of the artist’s works and projects from 1987 to the present spanning fashion design, video, film, performance, and painting. Throughout his career, Zhao Bandi (b. 1966, Beijing) has used art to investigate and reflect on nearly three decades of dynamic transformations, offering a ...", "subpage_snippet": "", "source": "www.artlinkart.com", "link": "https://www.artlinkart.com/en/artist/exh_yr/59ebAv/0bfevzlp", "content": "EXHIBITION SYNOPSIS About Zhao Bandi - China Party “Zhao Bandi : China Party” presents more than a dozen of the artist’s works and projects from 1987 to the present spanning fashion design, video, film, performance, and painting. Throughout his career, Zhao Bandi (b. 1966, Beijing) has used art to investigate and reflect on nearly three decades of dynamic transformations, offering a ..."}
+{"idx": 3, "title": "ZHAO BANDI 赵半狄 - ShanghART Gallery", "date": "", "ddg_snippet": "Zhao Bandi (b.1966, Beijing) is a renowned artist and pioneering figure of the Chinese avant-garde movement. Trained as a painter, his practice has evolved to include performance, photography, video, fashion, film, and social intervention.", "subpage_snippet": "", "source": "www.shanghartgallery.com", "link": "https://www.shanghartgallery.com/galleryarchive/artists/name/zhaobandi", "content": "Zhao Bandi (b.1966, Beijing) is a renowned artist and pioneering figure of the Chinese avant-garde movement. Trained as a painter, his practice has evolved to include performance, photography, video, fashion, film, and social intervention."}
+{"idx": 4, "title": "ZHAO BANDI | 赵半狄 | CHINESE NEW ART | chinesenewart", "date": "", "ddg_snippet": "Zhao Bandi 1966 Zhao Bandi 赵半狄 was born in Beijing China. 1988 He graduated from the Central Academy of Fine Arts in Beijing. ( oil painting department ) 1990 Through his works, he used a symbol of China : the Panda. 1998/ 2000 His participation in numerous biennials, made him famous.", "subpage_snippet": "", "source": "www.chinesenewart.com", "link": "https://www.chinesenewart.com/chinese-artists9/zhaobandi.htm", "content": "Zhao Bandi 1966 Zhao Bandi 赵半狄 was born in Beijing China. 1988 He graduated from the Central Academy of Fine Arts in Beijing. ( oil painting department ) 1990 Through his works, he used a symbol of China : the Panda. 1998/ 2000 His participation in numerous biennials, made him famous."}
+{"idx": 5, "title": "削苹果 Peeling apple by Zhao Bandi on artnet", "date": "", "ddg_snippet": "View 削苹果 Peeling apple by Zhao Bandi on artnet. Browse upcoming and past auction lots by Zhao Bandi .", "subpage_snippet": "", "source": "www.artnet.com", "link": "https://www.artnet.com/artists/zhao-bandi/xuepingguo-peeling-apple-wsyJXPt6mG_dLUmCrEFkHQ2", "content": "View 削苹果 Peeling apple by Zhao Bandi on artnet. Browse upcoming and past auction lots by Zhao Bandi ."}
+{"idx": 6, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "by C Ye · 2025 · Cited by 1 — Abstract. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.02486?", "content": "by C Ye · 2025 · Cited by 1 — Abstract. Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range."}
+{"idx": 7, "title": "Variance-Dependent Regret Bounds for Linear Bandits ...", "date": "", "ddg_snippet": "Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning : Adaptivity and Computational Efficiency.", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/5ae7ef3231f94618f5d7f1f63e659bf5de731a59", "content": "Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning : Adaptivity and Computational Efficiency."}
+{"idx": 8, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46438", "content": "In this paper, we develop an algorithmic approach building on Catoni's estimator from robust statistics, and apply it to contextual bandits with general ..."}
+{"idx": 9, "title": "Catoni Contextual Bandits are Robust to Heavy-tailed ...", "date": "", "ddg_snippet": "The linear analysis for peeling in Zhao . et. al is ... This paper studied the setting of variance-aware contextual bandit (or second order bandit ).", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=5IpVe9PH14&noteId=J3K6uYfoM5", "content": "The linear analysis for peeling in Zhao . et. al is ... This paper studied the setting of variance-aware contextual bandit (or second order bandit )."}
diff --git a/data/sampled_jsons/Zhou_et_al._2022_conditional_density_estimation_GANs_abstract_year_2022.jsonl b/data/sampled_jsons/Zhou_et_al._2022_conditional_density_estimation_GANs_abstract_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d43db663ef5216b3f78bbaed1bd65275a11bf329
--- /dev/null
+++ b/data/sampled_jsons/Zhou_et_al._2022_conditional_density_estimation_GANs_abstract_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Conditionally Tractable Density Estimation using Neural Networks", "date": "", "ddg_snippet": "Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:6933-6946, 2022 .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v151/dong22a.html", "content": "Hailiang Dong, Chiradeep Roy, Tahrima Rahman, Vibhav Gogate, Nicholas Ruozzi. Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:6933-6946, 2022 ."}
+{"idx": 1, "title": "Density estimation using deep generative neural networks - PMC", "date": "", "ddg_snippet": "Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we introduce a new general-purpose density estimator ...", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8054014/", "content": "Density estimation is among the most fundamental problems in statistics. It is notoriously difficult to estimate the density of high-dimensional data due to the “curse of dimensionality.” Here, we introduce a new general-purpose density estimator ..."}
+{"idx": 2, "title": "Super Learner Based Conditional Density Estimation with...", "date": "", "ddg_snippet": "Conditional density estimation is one of the most important problems in statistics.Van der Laan et al . (2004) proof that this likelihood based cross-validated estimator is asymp-totically optimal in the sense that it performs as well as the oracle selector as the sample size increases.", "subpage_snippet": "", "source": "core.ac.uk", "link": "https://core.ac.uk/download/pdf/61320302.pdf", "content": "Conditional density estimation is one of the most important problems in statistics.Van der Laan et al . (2004) proof that this likelihood based cross-validated estimator is asymp-totically optimal in the sense that it performs as well as the oracle selector as the sample size increases."}
+{"idx": 3, "title": "Conditional contrastive learning with kernel", "date": "", "ddg_snippet": "The differences is that Ton et al . (2021) tries to improve conditional density estimation while this paper aims to resolve the challenge of insufficient samples of the conditional variable.", "subpage_snippet": "", "source": "www.readkong.com", "link": "https://www.readkong.com/page/conditional-contrastive-learning-with-kernel-1416918", "content": "The differences is that Ton et al . (2021) tries to improve conditional density estimation while this paper aims to resolve the challenge of insufficient samples of the conditional variable."}
+{"idx": 4, "title": "CADENCE: Conditional Anomaly Detection for Events Using...", "date": "", "ddg_snippet": "2 Background2.1 Probability density estimation using Noise Contrastive Estimation (NCE)2.2 Estimating Conditional Densities Using NCE", "subpage_snippet": "", "source": "baris.skun.co", "link": "https://baris.skun.co/papers/cadence.pdf", "content": "2 Background2.1 Probability density estimation using Noise Contrastive Estimation (NCE)2.2 Estimating Conditional Densities Using NCE"}
+{"idx": 5, "title": "Zhou et al . 2019. Density Matching for Bilingual Word... - Speaker Deck", "date": "", "ddg_snippet": "Huang et al . 2020 Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting.", "subpage_snippet": "", "source": "speakerdeck.com", "link": "https://speakerdeck.com/tosho/zhou-et-al-2019-density-matching-for-bilingual-word-embedding-naacl", "content": "Huang et al . 2020 Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting."}
+{"idx": 6, "title": "Adversarial Bayesian Simulation", "date": "", "ddg_snippet": "More flexible conditional density estimators , such as neural mixture density networks (Papamakarios and Murray, 2016; Lueckmann et al ., 2017), have been successfully integrated into ABC without the burden of choosing summary statistics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2208.12113", "content": "More flexible conditional density estimators , such as neural mixture density networks (Papamakarios and Murray, 2016; Lueckmann et al ., 2017), have been successfully integrated into ABC without the burden of choosing summary statistics."}
+{"idx": 7, "title": "(PDF) Wasserstein Generative Regression", "date": "", "ddg_snippet": "regression analysis and conditional density estimation . Most existing methods use smoothing.distribution to the data distribution. GANs have also been extended to learn conditional . distributions (Mirza and Osindero, 2014; Kovachki et al ., 2021; Zhou et al ., 2022 ; Liu et al .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371909505_Wasserstein_Generative_Regression", "content": "regression analysis and conditional density estimation . Most existing methods use smoothing.distribution to the data distribution. GANs have also been extended to learn conditional . distributions (Mirza and Osindero, 2014; Kovachki et al ., 2021; Zhou et al ., 2022 ; Liu et al ."}
+{"idx": 8, "title": "Leveraging generative AI for urban digital twins: a scoping review on...", "date": "", "ddg_snippet": "Bao et al . (2020) introduces an innovative approach using Conditional GANs to estimate human mobility during the COVID-19 pandemic.Curb- gan : Conditional urban traffic estimation through spatio-temporal generative adversarial networks.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s44212-024-00060-w", "content": "Bao et al . (2020) introduces an innovative approach using Conditional GANs to estimate human mobility during the COVID-19 pandemic.Curb- gan : Conditional urban traffic estimation through spatio-temporal generative adversarial networks."}
+{"idx": 9, "title": "GitHub - wutong8023/Awesome_Few_Shot_Learning: Advances of...", "date": "", "ddg_snippet": "zhou - etal - 2022 -flipda.MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning , by Zhao, Bo, Sun, Xinwei, Fu, Yanwei, Yao, Yuan and Wang, Yizhou [bib].", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/wutong8023/Awesome_Few_Shot_Learning", "content": "zhou - etal - 2022 -flipda.MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning , by Zhao, Bo, Sun, Xinwei, Fu, Yanwei, Yao, Yuan and Wang, Yizhou [bib]."}
diff --git a/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_abstract_self-attention_matrices_structural_patterns.jsonl b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_abstract_self-attention_matrices_structural_patterns.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2438a560dcdb139208a326347345f675db33b7b1
--- /dev/null
+++ b/data/sampled_jsons/Zoom_In_An_Introduction_to_Circuits_abstract_self-attention_matrices_structural_patterns.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rapid Vehicle Trajectory Prediction Based on Multi-Attention", "date": "", "ddg_snippet": "... attention mechanism to extract ... Additionally, we use self - attention and cross- attention mechanisms to aggregate spatial and temporal information.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202411.0644/v1", "content": "... attention mechanism to extract ... Additionally, we use self - attention and cross- attention mechanisms to aggregate spatial and temporal information."}
+{"idx": 1, "title": "Estimation of Sparse Jacobian Matrices and Graph Coloring Blems", "date": "", "ddg_snippet": "Given a mapping with a sparse Jacobian matrix, we investigate the problem of minimizing the number of function evaluations needed to estimate the ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/0720013", "content": "Given a mapping with a sparse Jacobian matrix, we investigate the problem of minimizing the number of function evaluations needed to estimate the ..."}
+{"idx": 2, "title": "Beyond Induction Heads: In-Context Meta Learning Induces", "date": "", "ddg_snippet": "Due to its unique capability, ICL has gained a lot of attention in the research community, and there have been several approaches such as Bayesian ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16694v1", "content": "Due to its unique capability, ICL has gained a lot of attention in the research community, and there have been several approaches such as Bayesian ..."}
+{"idx": 3, "title": "The Complexity of Learning Sparse Superposed Features with", "date": "", "ddg_snippet": "... include uncovering neural circuits that encode specific concepts (Marks et al., 2024b ; Olah et al., 2020 ) , understanding feature composition ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05407v4", "content": "... include uncovering neural circuits that encode specific concepts (Marks et al., 2024b ; Olah et al., 2020 ) , understanding feature composition ..."}
+{"idx": 4, "title": "(OLD) An Extremely Opinionated Annotated List of My Favourite", "date": "", "ddg_snippet": "This is similar in flavour to Circuits : Zoom In , but is more conceptual and less grounded in very concrete examples + progress—your mileage may ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/SfPrNY45kQaBozwmu/an-extremely-opinionated-annotated-list-of-my-favourite", "content": "This is similar in flavour to Circuits : Zoom In , but is more conceptual and less grounded in very concrete examples + progress—your mileage may ..."}
+{"idx": 5, "title": "From Flat to Hierarchical : Extracting Sparse Representations", "date": "", "ddg_snippet": "To contextualize the results above and understand if capturing hierarchical structures yields meaningful features in larger-scale models, we design ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.03093v1", "content": "To contextualize the results above and understand if capturing hierarchical structures yields meaningful features in larger-scale models, we design ..."}
+{"idx": 6, "title": "Activation Space Interventions Can Be Transferred Between Large", "date": "", "ddg_snippet": "Mechanistic Interpretability: Mechanistic Interpretability is an emerging field that aims to understand the decision-making processes of neural ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.04429v4", "content": "Mechanistic Interpretability: Mechanistic Interpretability is an emerging field that aims to understand the decision-making processes of neural ..."}
+{"idx": 7, "title": "Beyond I’m Sorry, I Can’t: Dissecting Large-Language-Model", "date": "", "ddg_snippet": "A sparse autoencoder (SAE) is a neural network trained to compress and reconstruct activations while encouraging most latent units to remain inactive ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.09708v1", "content": "A sparse autoencoder (SAE) is a neural network trained to compress and reconstruct activations while encouraging most latent units to remain inactive ..."}
+{"idx": 8, "title": "Workshop: Interpretability in LLMs using Geometric and", "date": "", "ddg_snippet": "On the second day, we aim to broaden the scope, covering topics in mechanistic interpretability like circuit analysis, analogical reasoning ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/bTzk32t9aWJwLuNhi/workshop-interpretability-in-llms-using-geometric-and", "content": "On the second day, we aim to broaden the scope, covering topics in mechanistic interpretability like circuit analysis, analogical reasoning ..."}
+{"idx": 9, "title": "Workshop: Interpretability in LLMs using Geometric and", "date": "", "ddg_snippet": "On the second day, we aim to broaden the scope, covering topics in mechanistic interpretability like circuit analysis, analogical reasoning ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/bTzk32t9aWJwLuNhi/workshop-interpretability-in-llms-using-geometric-and", "content": "On the second day, we aim to broaden the scope, covering topics in mechanistic interpretability like circuit analysis, analogical reasoning ..."}
diff --git a/data/sampled_jsons/alpha_parameter_risk_aversion_reinforcement_learning_CVaR_exploration_exploitation.jsonl b/data/sampled_jsons/alpha_parameter_risk_aversion_reinforcement_learning_CVaR_exploration_exploitation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a6795c7f4c186cf77d8dac6f53143508da09c145
--- /dev/null
+++ b/data/sampled_jsons/alpha_parameter_risk_aversion_reinforcement_learning_CVaR_exploration_exploitation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Optimistic Exploration for Risk-Averse Constrained", "date": "", "ddg_snippet": "Risk - averse Constrained Reinforcement Learning (RaCRL) aims to learn policies that minimise the likelihood of rare and catastrophic constraint ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.08793v1", "content": "Risk - averse Constrained Reinforcement Learning (RaCRL) aims to learn policies that minimise the likelihood of rare and catastrophic constraint ..."}
+{"idx": 1, "title": "MORSE: Multi-Objective Reinforcement Learning via Strategy", "date": "", "ddg_snippet": "To address these shortcomings, multi-objective reinforcement learning (MORL) has emerged as a promising alternative that integrates the principles of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06490v1", "content": "To address these shortcomings, multi-objective reinforcement learning (MORL) has emerged as a promising alternative that integrates the principles of ..."}
+{"idx": 2, "title": "Online Bayesian Risk-Averse Reinforcement Learning", "date": "", "ddg_snippet": "We propose two procedures utilizing posterior sampling for online Bayesian risk - averse reinforcement learning (BRRL), employing CVaR as the risk ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14077v1", "content": "We propose two procedures utilizing posterior sampling for online Bayesian risk - averse reinforcement learning (BRRL), employing CVaR as the risk ..."}
+{"idx": 3, "title": "Tail-Risk-Safe Monte Carlo Tree Search under PAC-Level", "date": "", "ddg_snippet": "Our CVaR -MCTS with parameter α \\ alpha achieves explicit tail- risk control over the expected loss in the \"worst ( 1 − α ) % (1-\\ alpha )\\% scenarios ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05441v1", "content": "Our CVaR -MCTS with parameter α \\ alpha achieves explicit tail- risk control over the expected loss in the \"worst ( 1 − α ) % (1-\\ alpha )\\% scenarios ..."}
+{"idx": 4, "title": "Risk-Averse Best Arm Set Identification with Fixed Budget and", "date": "", "ddg_snippet": "Other approaches have explored alternative risk criteria such as Conditional Value-at- Risk ( CVaR ) and quantiles David and Shimkin ( 2016 ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.22253v1", "content": "Other approaches have explored alternative risk criteria such as Conditional Value-at- Risk ( CVaR ) and quantiles David and Shimkin ( 2016 ) ."}
+{"idx": 5, "title": "Marek Petrik", "date": "", "ddg_snippet": "This in particular includes the reinforcement learning and online learning communities, in which the application of risk aversion presents the most ...", "subpage_snippet": "", "source": "www.cs.unh.edu", "link": "https://www.cs.unh.edu/~mpetrik/tutorials/risk/", "content": "This in particular includes the reinforcement learning and online learning communities, in which the application of risk aversion presents the most ..."}
+{"idx": 6, "title": "Fast and Robust: Task Sampling with Posterior and Diversity", "date": "", "ddg_snippet": "Some risk - averse strategies, e.g., the conditional value-at- risk principle, are incorporated in domain randomization or meta reinforcement learning ...", "subpage_snippet": "", "source": "thu-rllab.github.io", "link": "https://thu-rllab.github.io/PDTS_project_page/", "content": "Some risk - averse strategies, e.g., the conditional value-at- risk principle, are incorporated in domain randomization or meta reinforcement learning ..."}
+{"idx": 7, "title": "Marek Petrik", "date": "", "ddg_snippet": "This in particular includes the reinforcement learning and online learning communities, in which the application of risk aversion presents the most ...", "subpage_snippet": "", "source": "marek.petrik.us", "link": "http://marek.petrik.us/tutorials/risk/", "content": "This in particular includes the reinforcement learning and online learning communities, in which the application of risk aversion presents the most ..."}
+{"idx": 8, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... Learning : Implicitly Mitigating Wrong ... Flow to Better: Offline Preference-based Reinforcement Learning via Preferred Trajectory Generation", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... Learning : Implicitly Mitigating Wrong ... Flow to Better: Offline Preference-based Reinforcement Learning via Preferred Trajectory Generation"}
+{"idx": 9, "title": "Downloads", "date": "", "ddg_snippet": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Reinforcement Learning Based ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2021", "content": "Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning ... Reinforcement Learning Based ..."}
diff --git a/data/sampled_jsons/alternative_loss_functions_for_PPO_RLHF_year_2024.jsonl b/data/sampled_jsons/alternative_loss_functions_for_PPO_RLHF_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c96dd1a40fe6f47d82e667575574119356a6fada
--- /dev/null
+++ b/data/sampled_jsons/alternative_loss_functions_for_PPO_RLHF_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The Loss Functions That Actually Matter in 2025 | Medium", "date": "", "ddg_snippet": "3. Reinforcement Learning Losses ( RLHF , PPO , DPO) — Fine-Tuning LLM Behavior.Vision GenAI: Perceptual + MSE + GAN loss . Chatbot: CE + PPO + safety reward. Custom loss functions are the final frontier for pushing performance, safety, and usability.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@pranavprakash4777/the-loss-functions-that-actually-matter-in-2025-41b044b2645e", "content": "3. Reinforcement Learning Losses ( RLHF , PPO , DPO) — Fine-Tuning LLM Behavior.Vision GenAI: Perceptual + MSE + GAN loss . Chatbot: CE + PPO + safety reward. Custom loss functions are the final frontier for pushing performance, safety, and usability."}
+{"idx": 1, "title": "Illustrating Reinforcement Learning from Human Feedback ( RLHF )", "date": "", "ddg_snippet": "Some RLHF systems have added additional terms to the reward function . For example, OpenAI experimented successfully on InstructGPT by mixing in additional pre-training gradients (from the human annotation set) into the update rule for PPO .", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/blog/rlhf", "content": "Some RLHF systems have added additional terms to the reward function . For example, OpenAI experimented successfully on InstructGPT by mixing in additional pre-training gradients (from the human annotation set) into the update rule for PPO ."}
+{"idx": 2, "title": "Una: u nifying a lignments of rlhf / PPO , dpo", "date": "", "ddg_snippet": "By optimizing the loss function in DPO, we can eliminate the need for an explicit reward model and combine the two stages of RLHF into a single, streamlined process, greatly simplifying the RLHF / PPO workflow. However, DPO has several limitations.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.15339", "content": "By optimizing the loss function in DPO, we can eliminate the need for an explicit reward model and combine the two stages of RLHF into a single, streamlined process, greatly simplifying the RLHF / PPO workflow. However, DPO has several limitations."}
+{"idx": 3, "title": "Fine-Tuning LLMs with Reinforcement Learning from Human Feedback...", "date": "", "ddg_snippet": "The loss function often involves maximizing the log-sigmoid of the reward difference4. RLHF Fine-tuning (with PPO ): Image Taken from Deeplearning.ai: Generative AI with LLM course.", "subpage_snippet": "", "source": "readmedium.com", "link": "https://readmedium.com/fine-tuning-llms-with-reinforcement-learning-from-human-feedback-rlhf-fb0cb39ddcde", "content": "The loss function often involves maximizing the log-sigmoid of the reward difference4. RLHF Fine-tuning (with PPO ): Image Taken from Deeplearning.ai: Generative AI with LLM course."}
+{"idx": 4, "title": "Reinforcement Learning from Human Feedback ( RLHF )", "date": "", "ddg_snippet": "RLHF builds on the standard RL framework but learns the reward function from data.rewards_w = reward_model(input_ids=chosen_inputs) rewards_l = reward_model(input_ids=rejected_inputs) loss = -torch.nn. functional .logsigmoid(rewards_w - rewards_l).mean() loss .backward().", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/reinforcement-learning-from-human-feedback-rlhf-c5ad903f0705", "content": "RLHF builds on the standard RL framework but learns the reward function from data.rewards_w = reward_model(input_ids=chosen_inputs) rewards_l = reward_model(input_ids=rejected_inputs) loss = -torch.nn. functional .logsigmoid(rewards_w - rewards_l).mean() loss .backward()."}
+{"idx": 5, "title": "GitHub - hkproj/ rlhf - ppo : Notes and commented code for RLHF ( PPO )", "date": "", "ddg_snippet": "hkproj / rlhf - ppo Public. Notifications You must be signed in to change notification settings.You will find the original code of the ppo _trainer.py file and also the commented code.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/hkproj/rlhf-ppo", "content": "hkproj / rlhf - ppo Public. Notifications You must be signed in to change notification settings.You will find the original code of the ppo _trainer.py file and also the commented code."}
+{"idx": 6, "title": "Explained Simply: Reinforcement Learning from Human Feedback", "date": "", "ddg_snippet": "Loss function used in the RLHF algorithm. RLHF training diagram. Most of the time, in the last step to adjust model weights, a reinforcement learning algorithm is used (usually done by proximal policy optimization — PPO ).", "subpage_snippet": "", "source": "ai.gopubby.com", "link": "https://ai.gopubby.com/explained-simply-reinforcement-learning-from-human-feedback-3dc26a0e4558", "content": "Loss function used in the RLHF algorithm. RLHF training diagram. Most of the time, in the last step to adjust model weights, a reinforcement learning algorithm is used (usually done by proximal policy optimization — PPO )."}
+{"idx": 7, "title": "Reinforcement Learning with Human Feedback: A Comprehensive Guide", "date": "", "ddg_snippet": "Key Points PPO Optimization for AI safety RLHF vs. Supervised Fine-TuningThis is the process of training an AI to predict which response are preferred by humans.", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-news/reinforcement-learning-with-human-feedback-a-comprehensive-guide-3480314", "content": "Key Points PPO Optimization for AI safety RLHF vs. Supervised Fine-TuningThis is the process of training an AI to predict which response are preferred by humans."}
+{"idx": 8, "title": "Reinforcement Learning в обучении LLM - PPO , DPO, GRPO - что...", "date": "", "ddg_snippet": "GRPO устраняет необходимость обучения функции ценности (value function ), которая используется в PPO для оценки преимущества (advantage). Вместо этого GRPO использует среднюю награду группы ответов на один промт как базовый уровень для сравнения.", "subpage_snippet": "", "source": "llmstudio.ru", "link": "https://llmstudio.ru/blog/rl-ppo-dpo-grpo", "content": "GRPO устраняет необходимость обучения функции ценности (value function ), которая используется в PPO для оценки преимущества (advantage). Вместо этого GRPO использует среднюю награду группы ответов на один промт как базовый уровень для сравнения."}
+{"idx": 9, "title": "Aman's AI Journal • Preference Optimization", "date": "", "ddg_snippet": "Value Function and Baseline. PPO ’s Objective Function : Clipped Surrogate Loss .Rejection Sampling and Alignment using PPO ( RLHF Step 2): Finally, Llama 2 employs rejection sampling and Proximal Policy Optimization ( PPO ).", "subpage_snippet": "", "source": "aman.ai", "link": "https://aman.ai/primers/ai/preference-optimization/", "content": "Value Function and Baseline. PPO ’s Objective Function : Clipped Surrogate Loss .Rejection Sampling and Alignment using PPO ( RLHF Step 2): Finally, Llama 2 employs rejection sampling and Proximal Policy Optimization ( PPO )."}
diff --git a/data/sampled_jsons/arXiv_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Greenblatt_Roger.jsonl b/data/sampled_jsons/arXiv_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Greenblatt_Roger.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..951c4aa9932791ea6ea2384d739b571d6c7b0b94
--- /dev/null
+++ b/data/sampled_jsons/arXiv_Stress-Testing_Capability_Elicitation_With_Password-Locked_Models_Greenblatt_Roger.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.19550", "content": "Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password . We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "4 Jun 2024 — We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password - locked models .", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "4 Jun 2024 — We released a paper studying this by examining how well supervised fine-tuning and RL can elicit capabilities from password - locked models ."}
+{"idx": 2, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ..."}
+{"idx": 3, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "5 Jun 2025 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "5 Jun 2025 — In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce ..."}
+{"idx": 4, "title": "Fabien Roger", "date": "", "ddg_snippet": "Stress-testing capability elicitation with password-locked models . R ... Benchmarks for detecting measurement tampering. F Roger, R Greenblatt, M Nadeau, B ...", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=La75jqEAAAAJ&hl=en", "content": "Stress-testing capability elicitation with password-locked models . R ... Benchmarks for detecting measurement tampering. F Roger, R Greenblatt, M Nadeau, B ..."}
+{"idx": 5, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Abstract. To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities.", "subpage_snippet": "", "source": "consensus.app", "link": "https://consensus.app/papers/stresstesting-capability-elicitation-with-roger-krasheninnikov/4536ec9e4485536b9b3d0bd1b75ff3c2", "content": "Abstract. To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities."}
+{"idx": 6, "title": "Evaluating Capability Elicitation Techniques", "date": "", "ddg_snippet": "4 Feb 2025 — We use password-locking (Greenblatt et al., 2024) to fine-tune model organisms which possess hidden capabilities which are difficult to elicit ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.02180v1", "content": "4 Feb 2025 — We use password-locking (Greenblatt et al., 2024) to fine-tune model organisms which possess hidden capabilities which are difficult to elicit ..."}
+{"idx": 7, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Stress-Testing Capability Elicitation With Password-Locked Models . NeurIPS 202405 Nov 2024. Ryan Greenblatt, Fabien Roger, Dmitrii Krasheninnikov, David Krueger.", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/zh-CN/chatpaper/paper/82493", "content": "Stress-Testing Capability Elicitation With Password-Locked Models . NeurIPS 202405 Nov 2024. Ryan Greenblatt, Fabien Roger, Dmitrii Krasheninnikov, David Krueger."}
+{"idx": 8, "title": "Stress-Testing Capability Elicitation Techniques", "date": "", "ddg_snippet": "by F Hofstätter · Cited by 2 — We demonstrate that password - locked models used in previous work are fragile to simple prompting techniques. We introduce a more robust model organism based on ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=zy6LB5t62f", "content": "by F Hofstätter · Cited by 2 — We demonstrate that password - locked models used in previous work are fragile to simple prompting techniques. We introduce a more robust model organism based on ..."}
+{"idx": 9, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "Authors Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov, David Krueger Abstract To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple prompting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this ...", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/7ff97417474268e6b5a38bcbfae04944-Abstract-Conference.html", "content": "Authors Ryan Greenblatt , Fabien Roger , Dmitrii Krasheninnikov, David Krueger Abstract To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple prompting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this ..."}
diff --git a/data/sampled_jsons/arxiv.orgabs2505.18786_Privacy_Loss_C-Proxy.jsonl b/data/sampled_jsons/arxiv.orgabs2505.18786_Privacy_Loss_C-Proxy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3ea730153b09d67f80753a68360f76cb56645cac
--- /dev/null
+++ b/data/sampled_jsons/arxiv.orgabs2505.18786_Privacy_Loss_C-Proxy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Leveraging Per-Instance Privacy for Machine Unlearning", "date": "", "ddg_snippet": "by NM Sepahvand · 2025 — Unlike existing heuristics for capturing aspects of unlearning difficulty, we show that privacy losses identify harder groups of data points, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.18786", "content": "by NM Sepahvand · 2025 — Unlike existing heuristics for capturing aspects of unlearning difficulty, we show that privacy losses identify harder groups of data points, ..."}
+{"idx": 1, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "arXiv网站简介 arXiv是一个收集物理学、数学、计算机科学、生物学与数理经济学的论文预印本的网站。其中arXiv发音同“archive”，因为“X”代表希腊字母 χ，国际音标为 [kai]。它于1991年8月14日成立，现由美国康奈尔大学维护。 ——维基百科 对于理论物理方向的科研工作者来说，arXiv是一个了解相关 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895?sort=created", "content": "arXiv网站简介 arXiv是一个收集物理学、数学、计算机科学、生物学与数理经济学的论文预印本的网站。其中arXiv发音同“archive”，因为“X”代表希腊字母 χ，国际音标为 [kai]。它于1991年8月14日成立，现由美国康奈尔大学维护。 ——维基百科 对于理论物理方向的科研工作者来说，arXiv是一个了解相关 ..."}
+{"idx": 2, "title": "arxiv国内有镜像网站吗？ - 知乎", "date": "", "ddg_snippet": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/1911390291929306874", "content": "国内确实有arxiv的镜像网站，旨在提升访问速度和下载体验。有两个推荐的镜像站点： CN.ARXIV.ORG：这是官方提供的中国镜像，访问速度快，适合国内用户下载arxiv上的PDF文件。您可以直接访问这个域名获取资料。 中科院镜像 (XXX.ITP.AC.CN)：另一个高效的选择，通过将arxiv.org的链接中的域名替换为 http ..."}
+{"idx": 3, "title": "论文挂在arxiv上会影响之后的投稿吗，查重或者算一稿多投啥的? - 知乎", "date": "", "ddg_snippet": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/509528796", "content": "2. 预留充足的投稿时间：考虑到 arXiv 预印本可能带来的影响，在投稿时要预留比平时更充足的时间 。 如果论文在 arXiv 上发布后，需要进行大量修改才能满足投稿要求，要确保有足够的时间进行修改和完善，避免因时间紧迫而影响论文质量。"}
+{"idx": 4, "title": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的...", "date": "", "ddg_snippet": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的引用量，如何评价这类论文？ 关注者 755 被浏览", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/458164481", "content": "如果一个论文只在arXiv上挂着，没有在什么会和期刊发表过，却有几万的引用量，如何评价这类论文？ 关注者 755 被浏览"}
+{"idx": 5, "title": "如何看待将投往双盲会议的论文提前公布到arXiv的行为? - 知乎", "date": "", "ddg_snippet": "我就算有10张嘴，怎么能骂得过黑子10000张嘴？ 所以，对自己的paper没有相当程度的自信，是不敢传arxiv的。 而往往大佬组的paper质量高的可能性大，也更自信，所以你观测到的大佬传arxiv的数量就更多，背后的原因其实不是大佬想给reviewer施加压力。", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/427222067", "content": "我就算有10张嘴，怎么能骂得过黑子10000张嘴？ 所以，对自己的paper没有相当程度的自信，是不敢传arxiv的。 而往往大佬组的paper质量高的可能性大，也更自信，所以你观测到的大佬传arxiv的数量就更多，背后的原因其实不是大佬想给reviewer施加压力。"}
+{"idx": 6, "title": "在arxiv发表论文的意义体现在哪里？ - 知乎", "date": "", "ddg_snippet": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4.", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/8772574507", "content": "arXiv 拥有庞大的学术用户群，论文一旦上传，就可以被全球的研究者搜索、阅读和引用。 许多研究人员习惯直接从 arXiv 获取最新的研究动态，而不必等正式出版。 高质量的预印本往往能带来更多关注、下载和引用，甚至有可能在正式发表前就产生影响。 4."}
+{"idx": 7, "title": "把文章发到arXiv上别人认可嘛？ - 知乎", "date": "", "ddg_snippet": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/30131676", "content": "发表到arXiv上，确切来讲不能叫发表，因为arXiv只是一个共享平台，并不能说明文章水平，会议和期刊就不一样了，要经过好多专家评审，而且不同会议和期刊也代表不同水平。 个人认为发到 arXiv 上的有以下情况： 第一种，就是为了 占坑，因为无论会议和期刊从投出到最终可以检索，都需要一半年 ..."}
+{"idx": 8, "title": "什么是arXiv？ - 知乎", "date": "", "ddg_snippet": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ...", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/31864895", "content": "论文讲究时效性，你想了一个 idea， 然后做了仿真，写了论文。但是考虑到投稿问题，有些会议或者期刊 “call for paper ” 是有时间限制的，比如可能多几个月才是论文的收稿期。一方面为了证明自己论文的原创性，将论文放到 arXiv 上挂起来；另一方面，也是为了竞争，谁的论文在 arXiv 挂的早，谁 ..."}
+{"idx": 9, "title": "如何知道arXiv上的论文投到哪里了？ - 知乎", "date": "", "ddg_snippet": "Nov 23, 2020 · 如何知道arXiv上的论文投到哪里了？ 比如我在arXiv上看到一篇不错的论文，想引用它，我想直接按录取它的期刊或者会议的格式引用，怎么做？", "subpage_snippet": "", "source": "www.zhihu.com", "link": "https://www.zhihu.com/question/431622372", "content": "Nov 23, 2020 · 如何知道arXiv上的论文投到哪里了？ 比如我在arXiv上看到一篇不错的论文，想引用它，我想直接按录取它的期刊或者会议的格式引用，怎么做？"}
diff --git a/data/sampled_jsons/arxiv2502.00921.jsonl b/data/sampled_jsons/arxiv2502.00921.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4b680c404a6120f74466d0aa7d473a61e0bba529
--- /dev/null
+++ b/data/sampled_jsons/arxiv2502.00921.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics."}
+{"idx": 1, "title": "Log in to arXiv | arXiv e-print repository", "date": "", "ddg_snippet": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/login", "content": "Log in to arXiv .org The arXiv Privacy Policy has changed. By continuing to use arxiv .org, you are agreeing to the privacy policy."}
+{"idx": 2, "title": "YOLOv12: Attention-Centric Real-Time Object Detectors - arXiv.org", "date": "", "ddg_snippet": "Feb 18, 2025 · Abstract page for arXiv paper 2502 .12524: YOLOv12: Attention-Centric Real-Time Object Detectors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2502.12524", "content": "Feb 18, 2025 · Abstract page for arXiv paper 2502 .12524: YOLOv12: Attention-Centric Real-Time Object Detectors"}
+{"idx": 3, "title": "Computer Science - arXiv.org", "date": "", "ddg_snippet": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/archive/cs", "content": "Computer Science (since January 1993) For a specific paper, enter the identifier into the top right search box. Browse: new (most recent mailing, with abstracts) recent (last 5 mailings) current month's listings specific year/month:"}
+{"idx": 4, "title": "[2508.10104] DINOv3 - arXiv.org", "date": "", "ddg_snippet": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2508.10104", "content": "Aug 13, 2025 · Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm ..."}
+{"idx": 5, "title": "[2212.10156] Planning-oriented Autonomous Driving - arXiv.org", "date": "", "ddg_snippet": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2212.10156", "content": "Dec 20, 2022 · Modern autonomous driving system is characterized as modular tasks in sequential order, i.e., perception, prediction, and planning. In order to perform a wide diversity of tasks and achieve advanced-level intelligence, contemporary approaches either deploy standalone models for individual tasks, or design a multi-task paradigm with separate heads. However, they might suffer from accumulative ..."}
+{"idx": 6, "title": "[1706.03762] Attention Is All You Need - arXiv.org", "date": "", "ddg_snippet": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/1706.03762", "content": "Jun 12, 2017 · The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely ..."}
+{"idx": 7, "title": "[2506.01844] SmolVLA: A Vision-Language-Action Model for ... -...", "date": "", "ddg_snippet": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2506.01844", "content": "Jun 2, 2025 · Abstract page for arXiv paper 2506.01844: SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics"}
+{"idx": 8, "title": "[2501.12948] DeepSeek-R1: Incentivizing Reasoning Capability in...", "date": "", "ddg_snippet": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.12948", "content": "Jan 22, 2025 · Abstract page for arXiv paper 2501.12948: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning"}
+{"idx": 9, "title": "[2410.10762] AFlow: Automating Agentic Workflow Generation -...", "date": "", "ddg_snippet": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10762", "content": "Oct 14, 2024 · Abstract page for arXiv paper 2410.10762: AFlow: Automating Agentic Workflow Generation"}
diff --git a/data/sampled_jsons/arxiv_Parallel_Simulation_Log-concave_Sampling_Score-based_Diffusion_Models_Huanjian_Zhou.jsonl b/data/sampled_jsons/arxiv_Parallel_Simulation_Log-concave_Sampling_Score-based_Diffusion_Models_Huanjian_Zhou.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a947c95c8a281dae36fc60ec341172901a4247cc
--- /dev/null
+++ b/data/sampled_jsons/arxiv_Parallel_Simulation_Log-concave_Sampling_Score-based_Diffusion_Models_Huanjian_Zhou.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Parallel simulation for sampling under isoperimetry and ...", "date": "", "ddg_snippet": "Our work highlights the potential advantages of simulation methods in scientific computation for dynamics- based sampling and diffusion models . Report issue for ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.07435v1", "content": "Our work highlights the potential advantages of simulation methods in scientific computation for dynamics- based sampling and diffusion models . Report issue for ..."}
+{"idx": 1, "title": "Parallel Simulation for Log-concave Sampling and Score-based ...", "date": "", "ddg_snippet": "Sampling from high-dimensional probability dis- tributions is fundamental in machine learning and statistics. As datasets grow larger, computational.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=qtuxDy2qEB", "content": "Sampling from high-dimensional probability dis- tributions is fundamental in machine learning and statistics. As datasets grow larger, computational."}
+{"idx": 2, "title": "Parallel Simulation for Log-concave Sampling and Score ...", "date": "", "ddg_snippet": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . Huanjian Zhou · Masashi Sugiyama ... arXiv preprint arXiv:2412.17997, 2024.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/43916", "content": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . Huanjian Zhou · Masashi Sugiyama ... arXiv preprint arXiv:2412.17997, 2024."}
+{"idx": 3, "title": "Huanjian Zhou", "date": "", "ddg_snippet": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . H Zhou, M Sugiyama. Forty-second International Conference on Machine Learning, 0.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=hBWyVT4AAAAJ&hl=en", "content": "Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models . H Zhou, M Sugiyama. Forty-second International Conference on Machine Learning, 0."}
+{"idx": 4, "title": "Adaptive complexity of log-concave sampling", "date": "", "ddg_snippet": "23 Aug 2024 — In this paper, we give the first lower bound for the parallel runtime of sampling in high dimensions and high accuracy regimes 1 1 1Throughout, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2408.13045v1", "content": "23 Aug 2024 — In this paper, we give the first lower bound for the parallel runtime of sampling in high dimensions and high accuracy regimes 1 1 1Throughout, ..."}
+{"idx": 5, "title": "[Literature Review] Parallel simulation for sampling under ...", "date": "", "ddg_snippet": "The paper titled \" Parallel Simulation for Sampling under Isoperimetry and Score - Based Diffusion Models \" by Huanjian Zhou and Masashi Sugiyama presents an ...", "subpage_snippet": "", "source": "www.themoonlight.io", "link": "https://www.themoonlight.io/en/review/parallel-simulation-for-sampling-under-isoperimetry-and-score-based-diffusion-models", "content": "The paper titled \" Parallel Simulation for Sampling under Isoperimetry and Score - Based Diffusion Models \" by Huanjian Zhou and Masashi Sugiyama presents an ..."}
+{"idx": 6, "title": "[PDF] Fast parallel sampling under isoperimetry", "date": "", "ddg_snippet": "17 Jan 2024 — Parallel simulation for sampling under isoperimetry and score - based diffusion models · Huanjian Zhou Masashi Sugiyama. Computer Science ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/f058d2d8c006db95ee5d591ca760c4795f3311ba", "content": "17 Jan 2024 — Parallel simulation for sampling under isoperimetry and score - based diffusion models · Huanjian Zhou Masashi Sugiyama. Computer Science ..."}
+{"idx": 7, "title": "the adaptive complexity of parallelized log", "date": "", "ddg_snippet": "by H Zhou · 2024 — Parallel simulation for sampling under isoperimetry and score - based diffusion models . In Submitted to The Thirteenth International ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.13045", "content": "by H Zhou · 2024 — Parallel simulation for sampling under isoperimetry and score - based diffusion models . In Submitted to The Thirteenth International ..."}
+{"idx": 8, "title": "Parallel simulation for sampling under isoperimetry and ...", "date": "", "ddg_snippet": "Paper. Parallel simulation for sampling under isoperimetry and score-based diffusion models . Published Dec 10, 2024 · Huanjian Zhou, Masashi Sugiyama. ArXiv.", "subpage_snippet": "", "source": "k8s.consensus.app", "link": "https://k8s.consensus.app/papers/details/41fbc92b037c558899d386af4e77a554/", "content": "Paper. Parallel simulation for sampling under isoperimetry and score-based diffusion models . Published Dec 10, 2024 · Huanjian Zhou, Masashi Sugiyama. ArXiv."}
+{"idx": 9, "title": "The Adaptive Complexity of Finding a Stationary Point", "date": "", "ddg_snippet": "by H Zhou · 2025 — Huanjian Zhou and Masashi Sugiyama. Parallel simulation for sampling under isoperimetry and score-based diffusion models . arXiv preprint ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.09045", "content": "by H Zhou · 2025 — Huanjian Zhou and Masashi Sugiyama. Parallel simulation for sampling under isoperimetry and score-based diffusion models . arXiv preprint ..."}
diff --git a/data/sampled_jsons/coding-rachal_PMRDataset_Table_4_SLHAMR_PA-MPJPE.jsonl b/data/sampled_jsons/coding-rachal_PMRDataset_Table_4_SLHAMR_PA-MPJPE.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..971114f6adffceba3cfae7e0cccc7207c1a4f7bd
--- /dev/null
+++ b/data/sampled_jsons/coding-rachal_PMRDataset_Table_4_SLHAMR_PA-MPJPE.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - coding-rachal/PMRDataset", "date": "", "ddg_snippet": "Contribute to coding-rachal/PMRDataset development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coding-rachal/PMRDataset", "content": "Contribute to coding-rachal/PMRDataset development by creating an account on GitHub."}
+{"idx": 1, "title": "MPJPE and PA MPJPE comparison on 3DPW. - ResearchGate", "date": "", "ddg_snippet": "Download scientific diagram | MPJPE and PA MPJPE comparison on 3DPW. from publication: NeuralAnnot: Neural Annotator for in-the-wild Expressive 3D Human Pose and Mesh Training Sets | Recovering ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/figure/MPJPE-and-PA-MPJPE-comparison-on-3DPW_tbl1_346143599", "content": "Download scientific diagram | MPJPE and PA MPJPE comparison on 3DPW. from publication: NeuralAnnot: Neural Annotator for in-the-wild Expressive 3D Human Pose and Mesh Training Sets | Recovering ..."}
+{"idx": 2, "title": "三维重建基础【知识点总结】_pa-mpjpe-CSDN博客", "date": "", "ddg_snippet": "MPJPE（Mean Per-Joint Position Error）和PA-MPJPE（Procrustes Aligned Mean Per-Joint Position Error）是评估3D人体姿态估计算法性能的两个常用指标，主要用于量化预测的人体关节位置与实际标注的关节位置之间的偏差。", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/qq_45934285/article/details/140064730", "content": "MPJPE（Mean Per-Joint Position Error）和PA-MPJPE（Procrustes Aligned Mean Per-Joint Position Error）是评估3D人体姿态估计算法性能的两个常用指标，主要用于量化预测的人体关节位置与实际标注的关节位置之间的偏差。"}
+{"idx": 3, "title": "PMRDataset/sod.py at main · coding-rachal/PMRDataset", "date": "", "ddg_snippet": "Contribute to coding-rachal/PMRDataset development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/coding-rachal/PMRDataset/blob/main/sod.py", "content": "Contribute to coding-rachal/PMRDataset development by creating an account on GitHub."}
+{"idx": 4, "title": "PDF P M Reconstruction: a Large Scale Benchmark Via Mixed Reality Rendering ...", "date": "", "ddg_snippet": "Compared with PA-MPJPE and Acc. Err., the two metrics commonly used on human pose estimation, WA- MPJPE and W-MPJPE pay more attention 7 Published as a conference paper at ICLR 2025 Table 4 : SLHAMR (Ye et al., 2023b) performance comparison under single and multiview.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/f3342358d0792ea201dc86d69570946b-Paper-Conference.pdf", "content": "Compared with PA-MPJPE and Acc. Err., the two metrics commonly used on human pose estimation, WA- MPJPE and W-MPJPE pay more attention 7 Published as a conference paper at ICLR 2025 Table 4 : SLHAMR (Ye et al., 2023b) performance comparison under single and multiview."}
+{"idx": 5, "title": "Human Mesh Recovery - 2024W, UCLA CS188 Course Projects", "date": "", "ddg_snippet": "MPJPE measures the average distance between the predicted and ground truth human joint positions. PA-MPJPE first aligns the predicted pose to the ground truth pose using a Procrustes transformation which accounts for translation, rotation, and scaling differences between both poses before conducting the same MPJPE evaluation.", "subpage_snippet": "", "source": "ucladeepvision.github.io", "link": "https://ucladeepvision.github.io/CS188-Projects-2024Winter/2024/03/22/team35-human-mesh-recovery.html", "content": "MPJPE measures the average distance between the predicted and ground truth human joint positions. PA-MPJPE first aligns the predicted pose to the ground truth pose using a Procrustes transformation which accounts for translation, rotation, and scaling differences between both poses before conducting the same MPJPE evaluation."}
+{"idx": 6, "title": "A Simple Baseline for Efficient Hand Mesh Reconstruction", "date": "", "ddg_snippet": "Similarly, on the Dexycb dataset, we observed a PA-MPJPE of 5.5mm and a PA -MPVPE of 5.0mm. As for performance speed, our method reached up to 33 frames per second (fps) when using HRNet and up to 70 fps when employing FastViT-MA36", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.01813", "content": "Similarly, on the Dexycb dataset, we observed a PA-MPJPE of 5.5mm and a PA -MPVPE of 5.0mm. As for performance speed, our method reached up to 33 frames per second (fps) when using HRNet and up to 70 fps when employing FastViT-MA36"}
+{"idx": 7, "title": "3D human pose estimation method based on multi-constrained dilated ...", "date": "", "ddg_snippet": "PA-MPJPE : Firstly, the predicted 3D human pose is translated, rotated or scaled to align it rigidly with the real pose, and then the MPJPE between the predicted joint coordinates and the real coordinates after alignment is calculated. The evaluation index adopted in the MPI-INF-3DHP dataset experiment is the Percentage of Correct Keypoints (PCK).", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s00530-024-01441-6", "content": "PA-MPJPE : Firstly, the predicted 3D human pose is translated, rotated or scaled to align it rigidly with the real pose, and then the MPJPE between the predicted joint coordinates and the real coordinates after alignment is calculated. The evaluation index adopted in the MPI-INF-3DHP dataset experiment is the Percentage of Correct Keypoints (PCK)."}
+{"idx": 8, "title": "Reconstructing Humans with a Biomechanically Accurate Skeleton", "date": "", "ddg_snippet": "The primary baseline for HSMR is the HMR2.0 network [14], since it is the closest to our design, in terms of architecture and training data We report PCK @0.05 & @0.1 for the 2D datasets (COCO, LSP-Extended, PoseTrack) and MPJPE & PA-MPJPE for the 3D datasets (3DPW, Human3.6M, MOYO).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2503.21751v1", "content": "The primary baseline for HSMR is the HMR2.0 network [14], since it is the closest to our design, in terms of architecture and training data We report PCK @0.05 & @0.1 for the 2D datasets (COCO, LSP-Extended, PoseTrack) and MPJPE & PA-MPJPE for the 3D datasets (3DPW, Human3.6M, MOYO)."}
+{"idx": 9, "title": "arXiv:2504.06397v2 [cs.CV] 24 May 2025", "date": "", "ddg_snippet": "5 Archimedes Figure 1. PromptHMR is a promptable human pose and shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes \"side information\" readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. PromptHMR recovers human pose and shape from spatial prompts such as (a) face bounding boxes, (b ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2504.06397v2", "content": "5 Archimedes Figure 1. PromptHMR is a promptable human pose and shape (HPS) estimation method that processes images with spatial or semantic prompts. It takes \"side information\" readily available from vision-language models or user input to improve the accuracy and robustness of 3D HPS. PromptHMR recovers human pose and shape from spatial prompts such as (a) face bounding boxes, (b ..."}
diff --git a/data/sampled_jsons/confidence_bound_formula_distributed_machine_learning_K_samples_round_alpha.jsonl b/data/sampled_jsons/confidence_bound_formula_distributed_machine_learning_K_samples_round_alpha.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ff30933b442183e204456277b305bda527f89e0
--- /dev/null
+++ b/data/sampled_jsons/confidence_bound_formula_distributed_machine_learning_K_samples_round_alpha.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Upper Confidence Bound - Wikipedia", "date": "", "ddg_snippet": "Upper Confidence Bound is a family of algorithms in machine learning and statistics for solving the multi-armed bandit problem and addressing the exploration–exploitation trade-off. UCB methods select actions by computing an upper confidence estimate...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Upper_Confidence_Bound", "content": "Upper Confidence Bound is a family of algorithms in machine learning and statistics for solving the multi-armed bandit problem and addressing the exploration–exploitation trade-off. UCB methods select actions by computing an upper confidence estimate..."}
+{"idx": 1, "title": "Confidence and Prediction Bounds - MATLAB & Simulink", "date": "", "ddg_snippet": "Prediction Bounds on Fits. Calculate Prediction Intervals from the Command Line.f depends on the confidence level, and is computed using the inverse of the F cumulative distribution function. S is the covariance matrix of the coefficient estimates, (XTX)–1s2.", "subpage_snippet": "", "source": "jp.mathworks.com", "link": "https://jp.mathworks.com/help/curvefit/confidence-and-prediction-bounds.html", "content": "Prediction Bounds on Fits. Calculate Prediction Intervals from the Command Line.f depends on the confidence level, and is computed using the inverse of the F cumulative distribution function. S is the covariance matrix of the coefficient estimates, (XTX)–1s2."}
+{"idx": 2, "title": "(PDF) Gaussian Process Upper Confidence Bounds in Distributed ...", "date": "", "ddg_snippet": "for distributed machine learning -based tracking, dealing with.Index Terms— Distributed learning , target tracking, wireless. sensor networks, Gaussian process methods, uncertainty quan-. tification, upper confidence bounds , trustworthy solutions.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/383985716_Gaussian_Process_Upper_Confidence_Bounds_in_Distributed_Point_Target_Tracking_over_Wireless_Sensor_Networks", "content": "for distributed machine learning -based tracking, dealing with.Index Terms— Distributed learning , target tracking, wireless. sensor networks, Gaussian process methods, uncertainty quan-. tification, upper confidence bounds , trustworthy solutions."}
+{"idx": 3, "title": "Direct Estimation of Confidence Intervals for Proportion by Means of", "date": "", "ddg_snippet": "... Distribution Confidence Bounds Let a sample with size n and x successes, where the proportion is determined as p = x . n Assuming this as population proportion, is it needed to determine the confidence interval with two tails for a significance level alpha (α). The lower and upper...", "subpage_snippet": "", "source": "studyres.com", "link": "https://studyres.com/doc/17927590/direct-estimation-of-confidence-intervals-for-proportion-...", "content": "... Distribution Confidence Bounds Let a sample with size n and x successes, where the proportion is determined as p = x . n Assuming this as population proportion, is it needed to determine the confidence interval with two tails for a significance level alpha (α). The lower and upper..."}
+{"idx": 4, "title": "Distributed Machine Learning \\ Distributed ... - Programmer Sought", "date": "", "ddg_snippet": "1.3 Comparison between distributed machine learning and federated learning . Through the above two gradient calculation methods, we see that no original data is transmitted between the worker and the server, and only the sample gradient is transmitted.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/274211756129/", "content": "1.3 Comparison between distributed machine learning and federated learning . Through the above two gradient calculation methods, we see that no original data is transmitted between the worker and the server, and only the sample gradient is transmitted."}
+{"idx": 5, "title": "machinelearningmastery.com/statistical-data- distributions", "date": "", "ddg_snippet": "Machine Learning Mastery – 7 Jun 18.", "subpage_snippet": "", "source": "machinelearningmastery.com", "link": "https://machinelearningmastery.com/statistical-data-distributions/", "content": "Machine Learning Mastery – 7 Jun 18."}
+{"idx": 6, "title": "(a) If z=F(t, y) and y=g(t), find a formula for d z / d t. C | Quizlet", "date": "", "ddg_snippet": "Calculate a lower confidence bound using a confidence level of 90% for true average shear strength.Determine the confidence level for each of the following large- sample one-sided confidence bounds : a. Upper bound", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/explanations/questions/a-if-zft-y-and-ygt-find-a-formula-for-d-z-d-t-consider-in-particular-the-case-where-zt2y-ey-and-yt2-b-if-yfk-l-and-kgl-find-a-formula-for-d--01dc9818-941137e6-5b93-48e6-a8d3-81c0822aa38e", "content": "Calculate a lower confidence bound using a confidence level of 90% for true average shear strength.Determine the confidence level for each of the following large- sample one-sided confidence bounds : a. Upper bound"}
+{"idx": 7, "title": "Reinforcement learning -guided Animated Oat Optimization Algorithm...", "date": "", "ddg_snippet": "Reinforcement learning , a key approach in machine learning , allows agents to discover how to map states to actions through continuous interaction with the environment, relying on trial and error to optimize long-term cumulative rewards [20].", "subpage_snippet": "", "source": "www.aimspress.com", "link": "https://www.aimspress.com/article/doi/10.3934/era.2025248", "content": "Reinforcement learning , a key approach in machine learning , allows agents to discover how to map states to actions through continuous interaction with the environment, relying on trial and error to optimize long-term cumulative rewards [20]."}
+{"idx": 8, "title": "How to Optimize Your Strategy using Multi-Arm Bandits and...", "date": "", "ddg_snippet": "A Comprehensive Guide to Multi-Arm Bandits: Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling .", "subpage_snippet": "", "source": "ai.plainenglish.io", "link": "https://ai.plainenglish.io/how-to-optimize-your-strategy-using-multi-arm-bandits-and-reinforcement-learning-a86f0209f770", "content": "A Comprehensive Guide to Multi-Arm Bandits: Epsilon-Greedy, Upper Confidence Bound (UCB), and Thompson Sampling ."}
+{"idx": 9, "title": "Application of Multi-Armed Bandits to Promotion Ranking in... | Medium", "date": "", "ddg_snippet": "Multi-Armed Bandit (MAB) is a classic problem in the field of sequential decision-making and reinforcement learning . The term “bandit” is derived from a gambler facing a row of slot machines (one-armed bandits) and trying to maximize their total reward over time.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@vvviet123/application-of-multi-armed-bandits-to-promotion-ranking-in-momo-eac28dbcf8bb", "content": "Multi-Armed Bandit (MAB) is a classic problem in the field of sequential decision-making and reinforcement learning . The term “bandit” is derived from a gambler facing a row of slot machines (one-armed bandits) and trying to maximize their total reward over time."}
diff --git a/data/sampled_jsons/crocker_plots_topological_data_analysis_scalability_challenges.jsonl b/data/sampled_jsons/crocker_plots_topological_data_analysis_scalability_challenges.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..07ad8f28ce8f94e63894d1f38a23ce562e6b167b
--- /dev/null
+++ b/data/sampled_jsons/crocker_plots_topological_data_analysis_scalability_challenges.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Crocker Art Museum - Wikipedia", "date": "", "ddg_snippet": "Edwin B. Crocker (1818–1875), a wealthy California lawyer and judge, and his wife, Margaret Crocker (1822–1901), began to assemble a significant collection of paintings and drawings during an extended trip to Europe, from 1869 to 1871.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Crocker_Art_Museum", "content": "Edwin B. Crocker (1818–1875), a wealthy California lawyer and judge, and his wife, Margaret Crocker (1822–1901), began to assemble a significant collection of paintings and drawings during an extended trip to Europe, from 1869 to 1871."}
+{"idx": 1, "title": "Crocker - Wikipedia", "date": "", "ddg_snippet": "Crocker is an archaic synonym of potter.", "subpage_snippet": "", "source": "en.m.wikipedia.org", "link": "https://en.m.wikipedia.org/wiki/Crocker", "content": "Crocker is an archaic synonym of potter."}
+{"idx": 2, "title": "The Crocker Art Museum | Crocker Art Museum", "date": "", "ddg_snippet": "The Crocker serves as the primary regional resource for the study and appreciation of fine art and offers a diverse spectrum of exhibitions, events, and programs.", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/", "content": "The Crocker serves as the primary regional resource for the study and appreciation of fine art and offers a diverse spectrum of exhibitions, events, and programs."}
+{"idx": 3, "title": "The City of Crocker", "date": "", "ddg_snippet": "In the heart of the Ozarks, Crocker rests amidst forested hills and farmlands, the same natural beauty that first inspired the founding fathers to settle here. Crocker offers a peaceful rural setting and a carefully preserved heritage.", "subpage_snippet": "", "source": "crockercity.com", "link": "https://crockercity.com/", "content": "In the heart of the Ozarks, Crocker rests amidst forested hills and farmlands, the same natural beauty that first inspired the founding fathers to settle here. Crocker offers a peaceful rural setting and a carefully preserved heritage."}
+{"idx": 4, "title": "Crocker Art Museum | Culture, Victorian House & Teal Pavilion", "date": "", "ddg_snippet": "Explore the Crocker Art Museum in Sacramento, featuring California Impressionist art, German drawings, and antiquity.", "subpage_snippet": "", "source": "www.visitsacramento.com", "link": "https://www.visitsacramento.com/things-to-do/arts-and-entertainment/crocker-art-museum/", "content": "Explore the Crocker Art Museum in Sacramento, featuring California Impressionist art, German drawings, and antiquity."}
+{"idx": 5, "title": "Crocker Murders: Timeline details Georgia children's deaths", "date": "", "ddg_snippet": "Aug 29, 2025 · Crocker Timeline: Family members could be put to death for murders, burial of Effingham County kids It began with a welfare check in Guyton. It turned into a gruesome discovery: the bodies of two children found buried in their own backyard.", "subpage_snippet": "", "source": "www.wjcl.com", "link": "https://www.wjcl.com/article/crocker-murders-timeline-1756486268/65934413", "content": "Aug 29, 2025 · Crocker Timeline: Family members could be put to death for murders, burial of Effingham County kids It began with a welfare check in Guyton. It turned into a gruesome discovery: the bodies of two children found buried in their own backyard."}
+{"idx": 6, "title": "Plan Your Visit | Crocker Art Museum", "date": "", "ddg_snippet": "Experience innovative interactions with art at the Crocker Art Museum. With three floors and 15 unique gallery spaces to explore, discover a diverse collection of art that spans centuries, continents, and cultures. There is always something surprising to find at the Crocker !", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/visit", "content": "Experience innovative interactions with art at the Crocker Art Museum. With three floors and 15 unique gallery spaces to explore, discover a diverse collection of art that spans centuries, continents, and cultures. There is always something surprising to find at the Crocker !"}
+{"idx": 7, "title": "Exhibitions | Crocker Art Museum", "date": "", "ddg_snippet": "Current and upcoming exhibitions at the Crocker .", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/exhibitions", "content": "Current and upcoming exhibitions at the Crocker ."}
+{"idx": 8, "title": "Crocker Art Museum (Everything To Know Before A Visit)", "date": "", "ddg_snippet": "As the longest-running public art museum in the West, it provides a unique insight into both historic and contemporary artistic expressions. The museum is renowned for its exceptional collection of California art, European master drawings, and remarkable international ceramics.", "subpage_snippet": "", "source": "thetouristchecklist.com", "link": "https://thetouristchecklist.com/crocker-art-museum/", "content": "As the longest-running public art museum in the West, it provides a unique insight into both historic and contemporary artistic expressions. The museum is renowned for its exceptional collection of California art, European master drawings, and remarkable international ceramics."}
+{"idx": 9, "title": "Calendar of Events - Crocker Art Museum | Crocker Art Museum", "date": "", "ddg_snippet": "Explore our calendar of upcoming tours, talks, concerts, studio art classes, family-friendly programs, and more.", "subpage_snippet": "", "source": "www.crockerart.org", "link": "https://www.crockerart.org/events", "content": "Explore our calendar of upcoming tours, talks, concerts, studio art classes, family-friendly programs, and more."}
diff --git a/data/sampled_jsons/deepfake_detection_'1+12'_phenomenon_Vanilla_Hybrid_Training_explanation.jsonl b/data/sampled_jsons/deepfake_detection_'1+12'_phenomenon_Vanilla_Hybrid_Training_explanation.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..c53cbdb8864fb065c96fe3208e827141378227bc
--- /dev/null
+++ b/data/sampled_jsons/deepfake_detection_'1+12'_phenomenon_Vanilla_Hybrid_Training_explanation.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Can We Leave Deepfake Data Behind in Training Deepfake Detector?", "date": "", "ddg_snippet": "This is likely because previous empirical observations suggest that vanilla hybrid training (VHT), which combines deepfake and blendfake data, results in inferior performance to methods using only blendfake data (so-called \" 1+1<2 \").", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2408.17052", "content": "This is likely because previous empirical observations suggest that vanilla hybrid training (VHT), which combines deepfake and blendfake data, results in inferior performance to methods using only blendfake data (so-called \" 1+1<2 \")."}
+{"idx": 1, "title": "Deepfake: definitions, performance metrics and standards, datasets, and ...", "date": "", "ddg_snippet": "3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods. The metrics used for such performance evaluations are at the core of our discussions.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11408348/", "content": "3 Deepfake -related performance metrics and standards In this survey, we focus on performance evaluation and comparison of deepfake generation and detection methods. The metrics used for such performance evaluations are at the core of our discussions."}
+{"idx": 2, "title": "Deepfake Detection: Analyzing Model Generalization Across Architectures ...", "date": "", "ddg_snippet": "As deepfake technology gains traction, the need for reliable detection systems is crucial. Recent research has introduced various deep learning-based detection systems, yet they exhibit limitations in generalising effectively across diverse data distributions that differ from the training data. Our study focuses on understanding the generalisation challenge by exploring different aspects such ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10376174", "content": "As deepfake technology gains traction, the need for reliable detection systems is crucial. Recent research has introduced various deep learning-based detection systems, yet they exhibit limitations in generalising effectively across diverse data distributions that differ from the training data. Our study focuses on understanding the generalisation challenge by exploring different aspects such ..."}
+{"idx": 3, "title": "PDF OST: Improving Generalization of DeepFake Detection via One ... - NeurIPS", "date": "", "ddg_snippet": "In this section, we conduct a brief survey on the most relevant arts, including existing deepfake detection methods and test-time training (TTT)-based works. 2.1 Deepfake Detection Since the deepfake forgeries have led to great threats to societal security, it is of paramount importance to develop effective detectors against it. By formulating the detecting as a vanilla binary classification ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2022/file/9bf0810a4a1597a36d27ceea58667d92-Paper-Conference.pdf", "content": "In this section, we conduct a brief survey on the most relevant arts, including existing deepfake detection methods and test-time training (TTT)-based works. 2.1 Deepfake Detection Since the deepfake forgeries have led to great threats to societal security, it is of paramount importance to develop effective detectors against it. By formulating the detecting as a vanilla binary classification ..."}
+{"idx": 4, "title": "Explainable AI for DeepFake Detection - MDPI", "date": "", "ddg_snippet": "This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm. This research consists of two stages: (1) detection of forged images using advanced convolutional neural networks such as ResNet-50, Inception V3, and VGG-16, and (2) applying the network dissection algorithm to understand ...", "subpage_snippet": "", "source": "www.mdpi.com", "link": "https://www.mdpi.com/2076-3417/15/2/725", "content": "This study introduces a deepfake detection technique that enhances interpretability using the network dissection algorithm. This research consists of two stages: (1) detection of forged images using advanced convolutional neural networks such as ResNet-50, Inception V3, and VGG-16, and (2) applying the network dissection algorithm to understand ..."}
+{"idx": 5, "title": "GitHub - erprogs/GenConViT: Deepfake Video Detection Using Generative ...", "date": "", "ddg_snippet": "The GenConViT model consists of two independent networks and incorporates the following modules: Autoencoder (ed), Variational Autoencoder (vae), and ConvNeXt-Swin Hybrid layer The code in this repository enables training and testing of the GenConViT model for deepfake detection .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/erprogs/GenConViT", "content": "The GenConViT model consists of two independent networks and incorporates the following modules: Autoencoder (ed), Variational Autoencoder (vae), and ConvNeXt-Swin Hybrid layer The code in this repository enables training and testing of the GenConViT model for deepfake detection ."}
+{"idx": 6, "title": "A Novel Hybrid Deep Learning Technique for Deepfake Detection: A Review ...", "date": "", "ddg_snippet": "In [], they proposed an adversarial training scheme—an attempt to render the model more robust in deepfake generation. As these new developments illustrate, technology is constantly changing and the field of deepfake detection research must continue to be updated with breakthrough solutions in order to stay ahead of emerging threats.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-981-96-3102-5_37", "content": "In [], they proposed an adversarial training scheme—an attempt to render the model more robust in deepfake generation. As these new developments illustrate, technology is constantly changing and the field of deepfake detection research must continue to be updated with breakthrough solutions in order to stay ahead of emerging threats."}
+{"idx": 7, "title": "Deepfake detection using deep feature stacking and meta-learning", "date": "", "ddg_snippet": "Hybrid approaches: Exploring hybrid approaches that combine various techniques, such as machine learning, deep learning, and adversarial training , can further improve deepfake detection accuracy and interpretability.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024019649", "content": "Hybrid approaches: Exploring hybrid approaches that combine various techniques, such as machine learning, deep learning, and adversarial training , can further improve deepfake detection accuracy and interpretability."}
+{"idx": 8, "title": "How to detect Deepfakes using AI? - GeeksforGeeks", "date": "", "ddg_snippet": "How Autoencoders Work in Deepfake Detection ? When presented with a deepfake , the autoencoder's reconstruction often contains errors or artifacts because the input does not match the training data's characteristics. These discrepancies between the original input and the reconstructed output can be analyzed to detect deepfakes . Autoencoders are useful in scenarios where deepfakes introduce ...", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/artificial-intelligence/how-to-detect-deepfakes-using-ai/", "content": "How Autoencoders Work in Deepfake Detection ? When presented with a deepfake , the autoencoder's reconstruction often contains errors or artifacts because the input does not match the training data's characteristics. These discrepancies between the original input and the reconstructed output can be analyzed to detect deepfakes . Autoencoders are useful in scenarios where deepfakes introduce ..."}
+{"idx": 9, "title": "Abstract - arXiv.org", "date": "", "ddg_snippet": "process. This is likely because previous empirical observations suggest that vanilla hybrid training (VHT), which combines deepfake and blendfake data, results in inferior performance to methods using only blendfake data (so-called \"1", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2408.17052", "content": "process. This is likely because previous empirical observations suggest that vanilla hybrid training (VHT), which combines deepfake and blendfake data, results in inferior performance to methods using only blendfake data (so-called \"1"}
diff --git a/data/sampled_jsons/diagonal_update_parallel_grid_simultaneous_problem_log-concave_sampling.jsonl b/data/sampled_jsons/diagonal_update_parallel_grid_simultaneous_problem_log-concave_sampling.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..482623feb6ff27b8c2a141d46bbac0b4d5a0b7cd
--- /dev/null
+++ b/data/sampled_jsons/diagonal_update_parallel_grid_simultaneous_problem_log-concave_sampling.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Downloads", "date": "", "ddg_snippet": "Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations"}
+{"idx": 1, "title": "ICLR 2025 Papers", "date": "", "ddg_snippet": "... or an Extension of Creative Problem ... Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/papers.html", "content": "... or an Extension of Creative Problem ... Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling"}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "A Polynomial Time Algorithm for Log - Concave Maximum Likelihood via Locally Exponential Families ... of latent neural dynamics from simultaneous EEG ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/Downloads/2019", "content": "A Polynomial Time Algorithm for Log - Concave Maximum Likelihood via Locally Exponential Families ... of latent neural dynamics from simultaneous EEG ..."}
+{"idx": 3, "title": "Ubuntu – Software Packages in \"focal\", Subsection gnu-r", "date": "", "ddg_snippet": "BioConductor facilities for parallel ... sample inference from amplicon sequencing data ... GNU R sequence logos for DNA sequence alignments", "subpage_snippet": "", "source": "packages.ubuntu.com", "link": "https://packages.ubuntu.com/focal/gnu-r/", "content": "BioConductor facilities for parallel ... sample inference from amplicon sequencing data ... GNU R sequence logos for DNA sequence alignments"}
+{"idx": 4, "title": "STOC 2019- Proceedings of the 51st Annual ACM SIGACT Symposium", "date": "", "ddg_snippet": "... weighted simplicial complex X is a 0-local spectral expander if and only if a naturally associated generating polynomial p X is strongly log - concave .", "subpage_snippet": "", "source": "acm-stoc.org", "link": "https://acm-stoc.org/stoc2019/toc.html", "content": "... weighted simplicial complex X is a 0-local spectral expander if and only if a naturally associated generating polynomial p X is strongly log - concave ."}
+{"idx": 5, "title": "Sparse Approximate Solutions to Linear Systems | SIAM Journal", "date": "", "ddg_snippet": "The following problem is considered: given a matrix A in ${\\bf R}^{m\\times n}$, ( m rows and n columns), a vector b in ${\\bf R}^m$, and $\\epsilon ...", "subpage_snippet": "", "source": "epubs.siam.org", "link": "https://epubs.siam.org/doi/abs/10.1137/S0097539792240406", "content": "The following problem is considered: given a matrix A in ${\\bf R}^{m\\times n}$, ( m rows and n columns), a vector b in ${\\bf R}^m$, and $\\epsilon ..."}
+{"idx": 6, "title": "ApproxED: Approximate exploitability descent via learned best", "date": "", "ddg_snippet": "We train the strategy profile and best-response functions simultaneously, with the former trying to minimize exploitability while the latter try to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2301.08830v3", "content": "We train the strategy profile and best-response functions simultaneously, with the former trying to minimize exploitability while the latter try to ..."}
+{"idx": 7, "title": "Newest 'listplot' Questions - Mathematica Stack Exchange", "date": "", "ddg_snippet": "I want to write a piece of general code that can simultaneously solve and plot two types of the system of two-variable nonlinear homogeneous ...", "subpage_snippet": "", "source": "mathematica.stackexchange.com", "link": "https://mathematica.stackexchange.com/questions/tagged/listplot", "content": "I want to write a piece of general code that can simultaneously solve and plot two types of the system of two-variable nonlinear homogeneous ..."}
+{"idx": 8, "title": "A constrained $$\\ell $$ 1 minimization approach for estimating", "date": "", "ddg_snippet": "... efficiently through a formulation of multiple independent sub- problems of linear programming (Sect. 2.4 ) for which we also provide a parallel ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10994-017-5635-7", "content": "... efficiently through a formulation of multiple independent sub- problems of linear programming (Sect. 2.4 ) for which we also provide a parallel ..."}
+{"idx": 9, "title": "Stephen Boyd's Profile | Stanford Profiles", "date": "", "ddg_snippet": "Stephen Boyd is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research ...", "subpage_snippet": "", "source": "profiles.stanford.edu", "link": "https://profiles.stanford.edu/stephen-boyd", "content": "Stephen Boyd is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research ..."}
diff --git a/data/sampled_jsons/differ_from_capability_elicitation_failures_that_might_occur_naturally_Stress-Testing_Capability_Eli.jsonl b/data/sampled_jsons/differ_from_capability_elicitation_failures_that_might_occur_naturally_Stress-Testing_Capability_Eli.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..7281af4173e6cc9ca21b4d8ff0860f6b088e073f
--- /dev/null
+++ b/data/sampled_jsons/differ_from_capability_elicitation_failures_that_might_occur_naturally_Stress-Testing_Capability_Eli.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "When does capability elicitation bound risk? — LessWrong", "date": "", "ddg_snippet": "... capability elicitation in detail (including fine-tuning and prompting approaches) and discuss considerations that inform the extent to which ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "... capability elicitation in detail (including fine-tuning and prompting approaches) and discuss considerations that inform the extent to which ..."}
+{"idx": 1, "title": "When does capability elicitation bound risk? - LessWrong 2.0", "date": "", "ddg_snippet": "... capability elicitation in detail (including fine-tuning and prompting approaches) and discuss considerations that inform the extent to which ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "... capability elicitation in detail (including fine-tuning and prompting approaches) and discuss considerations that inform the extent to which ..."}
+{"idx": 2, "title": "When does capability elicitation bound risk? — AI Alignment", "date": "", "ddg_snippet": "... capability elicitation in detail (including fine-tuning and prompting approaches) and discuss considerations that inform the extent to which ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "... capability elicitation in detail (including fine-tuning and prompting approaches) and discuss considerations that inform the extent to which ..."}
+{"idx": 3, "title": "Protocol evaluations: good analogies vs control — LessWrong", "date": "", "ddg_snippet": "Another problem with this methodology is that it is sensitive to training inductive biases: training sometimes has different effects on naturally ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/qhaSoR6vGmKnqGYLE/protocol-evaluations-good-analogies-vs-control", "content": "Another problem with this methodology is that it is sensitive to training inductive biases: training sometimes has different effects on naturally ..."}
+{"idx": 4, "title": "Handling schemers if shutdown is not an option — LessWrong", "date": "", "ddg_snippet": "And it might also happen even if it’s unjustified, as I discussed here (though in this case, it’s plausible that the developer will at least ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/XxjScx4niRLWTfuD5/handling-schemers-if-shutdown-is-not-an-option", "content": "And it might also happen even if it’s unjustified, as I discussed here (though in this case, it’s plausible that the developer will at least ..."}
+{"idx": 5, "title": "Fabien's Shortform - LessWrong 2.0 viewer", "date": "", "ddg_snippet": "... that target them, and many other cool related results (including Obfuscated adversarial training, which looks promising to me) ( Obfuscated ...", "subpage_snippet": "", "source": "www.greaterwrong.com", "link": "https://www.greaterwrong.com/posts/nAsMfmxDv6Qp7cfHh/fabien-s-shortform", "content": "... that target them, and many other cool related results (including Obfuscated adversarial training, which looks promising to me) ( Obfuscated ..."}
+{"idx": 6, "title": "Protocol evaluations: good analogies vs control — AI", "date": "", "ddg_snippet": "... that non-scheming misalignment issues are pretty easy to notice, study, and evaluate ( \" non-scheming \" = \" issues from misalignment ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/qhaSoR6vGmKnqGYLE/protocol-evaluations-good-analogies-vs-control", "content": "... that non-scheming misalignment issues are pretty easy to notice, study, and evaluate ( \" non-scheming \" = \" issues from misalignment ..."}
+{"idx": 7, "title": "What AI evaluations for preventing catastrophic risks can and", "date": "", "ddg_snippet": "... rely on two key premises: first, that if a model does not demonstrate a capability in evaluations, it likely cannot cause harm using that capability ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.08653v1", "content": "... rely on two key premises: first, that if a model does not demonstrate a capability in evaluations, it likely cannot cause harm using that capability ..."}
+{"idx": 8, "title": "ryan_greenblatt", "date": "", "ddg_snippet": "Another way to put this is that the 5-18x multiplier is an artifact of taking months of context and applying that to a short task (like maybe 10 min ...", "subpage_snippet": "", "source": "lw2.issarice.com", "link": "https://lw2.issarice.com/users/ryan_greenblatt", "content": "Another way to put this is that the 5-18x multiplier is an artifact of taking months of context and applying that to a short task (like maybe 10 min ..."}
+{"idx": 9, "title": "kromem", "date": "", "ddg_snippet": "The other counterfactual is that there was a heretical tradition of Jesus's teachings that was describing indivisible points as if from nothing and ...", "subpage_snippet": "", "source": "lw2.issarice.com", "link": "https://lw2.issarice.com/users/kromem", "content": "The other counterfactual is that there was a heretical tradition of Jesus's teachings that was describing indivisible points as if from nothing and ..."}
diff --git a/data/sampled_jsons/differential_privacy_regression_coordinate-wise_median_condition_number_blowup.jsonl b/data/sampled_jsons/differential_privacy_regression_coordinate-wise_median_condition_number_blowup.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d67be92091f2051425237101e77bfa71c265fb2c
--- /dev/null
+++ b/data/sampled_jsons/differential_privacy_regression_coordinate-wise_median_condition_number_blowup.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Differential Privacy With Higher Utility by Exploiting Coordinate-Wise ...", "date": "", "ddg_snippet": "Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. In this work, we formally present the addition of noise that is independent but not identically distributed (i.n.i.d.) across the coordinates to achieve tighter privacy -accuracy trade-off by exploiting coordinate-wise ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/10858760", "content": "Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. In this work, we formally present the addition of noise that is independent but not identically distributed (i.n.i.d.) across the coordinates to achieve tighter privacy -accuracy trade-off by exploiting coordinate-wise ..."}
+{"idx": 1, "title": "Differential Privacy with Higher Utility by Exploiting Coordinate-wise ...", "date": "", "ddg_snippet": "DP has become the de facto privacy standard in machine learning applications and has been adopted in a wide range of problems such as linear regression [2], empirical risk minimization [3], principal component analysis [4], clustering [5], etc. Also, the US Census Bureau deployed differential privacy for the 2020 census [6].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2302.03511", "content": "DP has become the de facto privacy standard in machine learning applications and has been adopted in a wide range of problems such as linear regression [2], empirical risk minimization [3], principal component analysis [4], clustering [5], etc. Also, the US Census Bureau deployed differential privacy for the 2020 census [6]."}
+{"idx": 2, "title": "PDF Differentially Private Median Forests for Regression and Classification", "date": "", "ddg_snippet": "In this paper, we propose DiPriMe forests, a novel tree-based ensemble method for regression and classification problems, that ensures differential privacy while maintaining high utility. We construct trees based on a privatized version of the median value of attributes, obtained via the exponential mechanism.", "subpage_snippet": "", "source": "ppai21.github.io", "link": "https://ppai21.github.io/files/2-paper.pdf", "content": "In this paper, we propose DiPriMe forests, a novel tree-based ensemble method for regression and classification problems, that ensures differential privacy while maintaining high utility. We construct trees based on a privatized version of the median value of attributes, obtained via the exponential mechanism."}
+{"idx": 3, "title": "Differentially private regression analysis with dynamic privacy ...", "date": "", "ddg_snippet": "However, the existing literatures on differentially private regression models are limited and lack of the dynamic privacy allocation methods, which may have an influence on the balance between privacy guarantee and model performance.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0950705121000587", "content": "However, the existing literatures on differentially private regression models are limited and lack of the dynamic privacy allocation methods, which may have an influence on the balance between privacy guarantee and model performance."}
+{"idx": 4, "title": "PDF Differentially Private Block Coordinate Descent for Linear Regression ...", "date": "", "ddg_snippet": "Abstract: We present a differentially private extension of the block coordinate descent algorithm by means of objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, we derive a utility guarantee; a tolerance parameter indicates how much the differentially private regression may ...", "subpage_snippet": "", "source": "publications.tno.nl", "link": "https://publications.tno.nl/publication/34640374/A0GgEz/jong-2022-differentially.pdf", "content": "Abstract: We present a differentially private extension of the block coordinate descent algorithm by means of objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, we derive a utility guarantee; a tolerance parameter indicates how much the differentially private regression may ..."}
+{"idx": 5, "title": "Median DC for Sign Recovery: Privacy can be Achieved by...", "date": "", "ddg_snippet": "It is a common sense in privacy literature that strict differential privacy can only be obtained by imposing additional randomness in the algorithm. In this paper, we study the problem of private sign recovery for sparse mean estimation and sparse linear regression in a distributed setup.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=BMua55nUyyt", "content": "It is a common sense in privacy literature that strict differential privacy can only be obtained by imposing additional randomness in the algorithm. In this paper, we study the problem of private sign recovery for sparse mean estimation and sparse linear regression in a distributed setup."}
+{"idx": 6, "title": "PDF Reading Between the Lines: Surveying Differential Privacy in Different ...", "date": "", "ddg_snippet": "Abstract—Differential privacy (DP) offers a framework where potentially-sensitive data can be analyzed in ag-gregate while limiting the information that can be known about individual data entries. Work in the field has focused on how DP techniques can be applied to a variety of regression paradigms. We visit four commonly-used and different methods of linear regression applied to different ...", "subpage_snippet": "", "source": "nmishra459.github.io", "link": "https://nmishra459.github.io/assets/CS_208_report.pdf", "content": "Abstract—Differential privacy (DP) offers a framework where potentially-sensitive data can be analyzed in ag-gregate while limiting the information that can be known about individual data entries. Work in the field has focused on how DP techniques can be applied to a variety of regression paradigms. We visit four commonly-used and different methods of linear regression applied to different ..."}
+{"idx": 7, "title": "Differentially private median and more - Google Research", "date": "", "ddg_snippet": "Differential privacy (DP) is a rigorous mathematical definition of privacy . DP algorithms are randomized to protect user data by ensuring that the probability of any particular output is nearly unchanged when a data point is added or removed. Therefore, the output of a DP algorithm does not disclose the presence of any one data point. There has been significant progress in both foundational ...", "subpage_snippet": "", "source": "research.google", "link": "https://research.google/blog/differentially-private-median-and-more/", "content": "Differential privacy (DP) is a rigorous mathematical definition of privacy . DP algorithms are randomized to protect user data by ensuring that the probability of any particular output is nearly unchanged when a data point is added or removed. Therefore, the output of a DP algorithm does not disclose the presence of any one data point. There has been significant progress in both foundational ..."}
+{"idx": 8, "title": "[2006.02983] Median regression with differential privacy - arXiv.org", "date": "", "ddg_snippet": "Median regression analysis has robustness properties which make it attractive compared with regression based on the mean, while differential privacy can protect individual privacy during statistical analysis of certain datasets. In this paper, three privacy preserving methods are proposed for median regression . The first algorithm is based on a finite smoothing method, the second provides an ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2006.02983", "content": "Median regression analysis has robustness properties which make it attractive compared with regression based on the mean, while differential privacy can protect individual privacy during statistical analysis of certain datasets. In this paper, three privacy preserving methods are proposed for median regression . The first algorithm is based on a finite smoothing method, the second provides an ..."}
+{"idx": 9, "title": "Protecting Regression Models With Personalized Local Differential Privacy", "date": "", "ddg_snippet": "The equation-solving model extraction attack is an intuitively simple but devastating attack to steal confidential information of regression models through a sufficient number of queries. Complete mitigation is difficult. Thus, the development of countermeasures is focused on degrading the attack effectiveness as much as possible without losing the model utilities. We investigate a novel ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9693253", "content": "The equation-solving model extraction attack is an intuitively simple but devastating attack to steal confidential information of regression models through a sufficient number of queries. Complete mitigation is difficult. Thus, the development of countermeasures is focused on degrading the attack effectiveness as much as possible without losing the model utilities. We investigate a novel ..."}
diff --git a/data/sampled_jsons/diffusion_equation_u_t_=_u_xx_PINN_gPINN_experimental_setup_domain_[0,1].jsonl b/data/sampled_jsons/diffusion_equation_u_t_=_u_xx_PINN_gPINN_experimental_setup_domain_[0,1].jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..81a139ad6a5ccc27ebeda32b788659723cdb26b9
--- /dev/null
+++ b/data/sampled_jsons/diffusion_equation_u_t_=_u_xx_PINN_gPINN_experimental_setup_domain_[0,1].jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Finite difference methods for diffusion processes", "date": "", "ddg_snippet": "All experiments point to two characteristic features of the Backward Euler scheme: 1 ) it is always stable, and 2) it always gives a smooth, decaying solution. Crank-Nicolson scheme.Analysis of schemes for the diffusion equation .", "subpage_snippet": "", "source": "hplgit.github.io", "link": "https://hplgit.github.io/num-methods-for-PDEs/doc/pub/diffu/html/._diffu001.html", "content": "All experiments point to two characteristic features of the Backward Euler scheme: 1 ) it is always stable, and 2) it always gives a smooth, decaying solution. Crank-Nicolson scheme.Analysis of schemes for the diffusion equation ."}
+{"idx": 1, "title": "python - 2D finite difference scheme of reaction diffusion equation", "date": "", "ddg_snippet": "I want to visualize the solution of the following partial differential equation : u _ t = u _ xx +u_yy+f(u) At different times, such as u ( t = 1 ) and u ( t =3). I use a finite difference scheme and the following.", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/78455770/2d-finite-difference-scheme-of-reaction-diffusion-equation", "content": "I want to visualize the solution of the following partial differential equation : u _ t = u _ xx +u_yy+f(u) At different times, such as u ( t = 1 ) and u ( t =3). I use a finite difference scheme and the following."}
+{"idx": 2, "title": "Cauchy problem for diffusion equation and condition $x^2$ if $x\\in...", "date": "", "ddg_snippet": "Analytical solution for diffusion equation with decay.Solving the Heat/ Diffusion Equation with Piecewise Continuous Initial Condition.", "subpage_snippet": "", "source": "math.stackexchange.com", "link": "https://math.stackexchange.com/questions/2829899/cauchy-problem-for-diffusion-equation-and-condition-x2-if-x-in-0-1", "content": "Analytical solution for diffusion equation with decay.Solving the Heat/ Diffusion Equation with Piecewise Continuous Initial Condition."}
+{"idx": 3, "title": "Diffusion Equation and Maximum Principle", "date": "", "ddg_snippet": "of u ( t ,x) in the closed rectangle { 0 ≤ x ≤ L, 0 ≤ t ≤ T}. Does m(T) increase or decrease as a function of T?\" (Partial Differential Equations An Introduction, 2nd Edition by Walter Strauss).", "subpage_snippet": "", "source": "www.physicsforums.com", "link": "https://www.physicsforums.com/threads/diffusion-equation-and-maximum-principle.714941/", "content": "of u ( t ,x) in the closed rectangle { 0 ≤ x ≤ L, 0 ≤ t ≤ T}. Does m(T) increase or decrease as a function of T?\" (Partial Differential Equations An Introduction, 2nd Edition by Walter Strauss)."}
+{"idx": 4, "title": "ap.analysis of pdes - Solutions to the diffusion equation - MathOverflow", "date": "", "ddg_snippet": "When it comes to solving the heat diffusion equation u _ t = u _ xx the two most important solutions are a) a combination (sum) of sin-terms to resemble the function of the initial condition (that is.", "subpage_snippet": "", "source": "mathoverflow.net", "link": "https://mathoverflow.net/questions/2117/solutions-to-the-diffusion-equation", "content": "When it comes to solving the heat diffusion equation u _ t = u _ xx the two most important solutions are a) a combination (sum) of sin-terms to resemble the function of the initial condition (that is."}
+{"idx": 5, "title": "Diffusion Equations | SpringerLink", "date": "", "ddg_snippet": "Also, the diffusion equation makes quite different demands to the numerical methods. You have full access to this open access chapter, Download chapter PDF.A particular characteristic of diffusive processes, governed by an equation like. $$ u _{ t }=\\alpha u _{ xx }", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-319-55456-3_3", "content": "Also, the diffusion equation makes quite different demands to the numerical methods. You have full access to this open access chapter, Download chapter PDF.A particular characteristic of diffusive processes, governed by an equation like. $$ u _{ t }=\\alpha u _{ xx }"}
+{"idx": 6, "title": "Introduction to the Diffusion Equation", "date": "", "ddg_snippet": "* Build *general* solution using the special one. ## Invariance Properties 1 . Any **translate** $ u ( t , x - y)$ solves if $ u ( t ,x)$ solves.", "subpage_snippet": "", "source": "www.math.toronto.edu", "link": "https://www.math.toronto.edu/courses/apm346h1/20129/rvl/diffusion_intro.html", "content": "* Build *general* solution using the special one. ## Invariance Properties 1 . Any **translate** $ u ( t , x - y)$ solves if $ u ( t ,x)$ solves."}
+{"idx": 7, "title": "The Speed of Fronts of the Reaction Diffusion Equation", "date": "", "ddg_snippet": "Therefore our results apply to the classical case $f > 0 $ in $( 0 , 1 )$, to the bistable case and to cases in which $f$ has more than one internal zero in $( 0 , 1 )$.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/patt-sol/9511003", "content": "Therefore our results apply to the classical case $f > 0 $ in $( 0 , 1 )$, to the bistable case and to cases in which $f$ has more than one internal zero in $( 0 , 1 )$."}
+{"idx": 8, "title": "By trial and error, find a solution of the diffusion equation ... | Quizlet", "date": "", "ddg_snippet": "u t = u x x u _ t = u _{ xx }.μ ( t ) = u (X(t), t). Differentiate.", "subpage_snippet": "", "source": "quizlet.com", "link": "https://quizlet.com/explanations/questions/by-trial-and-error-find-a-solution-of-the-diffusion-equation-u_t-u_xx-with-the-initial-condition-ux-8b0f42d6-662b-417b-9457-ac025c2786ea", "content": "u t = u x x u _ t = u _{ xx }.μ ( t ) = u (X(t), t). Differentiate."}
+{"idx": 9, "title": "A Galerkin Procedure for the Diffusion Equation Subject to the...", "date": "", "ddg_snippet": "SIAM Journal on Numerical Analysis contains research articles on the development and analysis of numerical methods including their convergence, stability, and error analysis as well as related results in functional analysis and approximation theory. Computational experiments and new types...", "subpage_snippet": "", "source": "www.jstor.org", "link": "https://www.jstor.org/stable/2157347", "content": "SIAM Journal on Numerical Analysis contains research articles on the development and analysis of numerical methods including their convergence, stability, and error analysis as well as related results in functional analysis and approximation theory. Computational experiments and new types..."}
diff --git a/data/sampled_jsons/efficient_transformer_inference_techniques_reduce_memory_overhead_dual-run_2024_year_2024.jsonl b/data/sampled_jsons/efficient_transformer_inference_techniques_reduce_memory_overhead_dual-run_2024_year_2024.jsonl
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index 0000000000000000000000000000000000000000..e141dab73998baf27eb9a9e172b4666f7f0de10a
--- /dev/null
+++ b/data/sampled_jsons/efficient_transformer_inference_techniques_reduce_memory_overhead_dual-run_2024_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on", "date": "", "ddg_snippet": "This paper introduces Agent.xpu , an efficient serving system for agentic LLM workloads on memory -unified heterogeneous SoCs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.24045v1", "content": "This paper introduces Agent.xpu , an efficient serving system for agentic LLM workloads on memory -unified heterogeneous SoCs."}
+{"idx": 1, "title": "PIM-GPT: A Hybrid Process-in-Memory Accelerator for", "date": "", "ddg_snippet": "However, the efficacy of running GPT on current hardware systems is bounded by low compute-to- memory -ratio and high memory access.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.09385v2", "content": "However, the efficacy of running GPT on current hardware systems is bounded by low compute-to- memory -ratio and high memory access."}
+{"idx": 2, "title": "Memory-Augmented Transformers: A Systematic Review from", "date": "", "ddg_snippet": "Human memory ’s efficiency and adaptability increasingly guide Transformer design, particularly its integration across timescales: sensory memory ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.10824v1", "content": "Human memory ’s efficiency and adaptability increasingly guide Transformer design, particularly its integration across timescales: sensory memory ..."}
+{"idx": 3, "title": "Dual Precision Quantization for Efficient and Accurate Deep", "date": "", "ddg_snippet": "... memory requirements in half, leading to better memory utilization and improved bandwidth efficiency; and (ii) reducing computational complexity, often ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.14638v1", "content": "... memory requirements in half, leading to better memory utilization and improved bandwidth efficiency; and (ii) reducing computational complexity, often ..."}
+{"idx": 4, "title": "Towards Efficient Generative Large Language Model Serving: A", "date": "", "ddg_snippet": "However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2312.15234v2", "content": "However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency ..."}
+{"idx": 5, "title": "OpenVINO™ Blog | Accelerate Inference of Sparse Transformer", "date": "", "ddg_snippet": "... inference of overparametrized Deep Neural Networks, perhaps, weight pruning is one of the most popular and promising techniques that is used to reduce ...", "subpage_snippet": "", "source": "blog.openvino.ai", "link": "https://blog.openvino.ai/blog-posts/accelerate-inference-of-sparse-transformer-models-with-openvino-tm-and-4th-gen-intel-r-xeon-r-scalable-processors", "content": "... inference of overparametrized Deep Neural Networks, perhaps, weight pruning is one of the most popular and promising techniques that is used to reduce ..."}
+{"idx": 6, "title": "OpenVINO™ Blog | Joint Pruning, Quantization and Distillation", "date": "", "ddg_snippet": "... the developer complexity of sequential optimization of different compression techniques , resulting in an optimized model with significant efficiency ...", "subpage_snippet": "", "source": "blog.openvino.ai", "link": "https://blog.openvino.ai/blog-posts/joint-pruning-quantization-and-distillation-for-efficient-inference-of-transformers", "content": "... the developer complexity of sequential optimization of different compression techniques , resulting in an optimized model with significant efficiency ..."}
+{"idx": 7, "title": "Task parallel assembly language for uncompromising parallelism", "date": "", "ddg_snippet": "Crucially, the refactoring draws from some classic techniques from programming-languages research, such as the continuation-passing-style transform ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/352534106_Task_parallel_assembly_language_for_uncompromising_parallelism", "content": "Crucially, the refactoring draws from some classic techniques from programming-languages research, such as the continuation-passing-style transform ..."}
+{"idx": 8, "title": "This AI Paper from China Introduces KV-Cache Optimization", "date": "", "ddg_snippet": "... computational efficiency and memory overhead ... The methods introduced have shown significant improvements in memory efficiency and inference speed.", "subpage_snippet": "", "source": "www.marktechpost.com", "link": "https://www.marktechpost.com/2024/07/28/this-ai-paper-from-china-introduces-kv-cache-optimization-techniques-for-efficient-large-language-model-inference/", "content": "... computational efficiency and memory overhead ... The methods introduced have shown significant improvements in memory efficiency and inference speed."}
+{"idx": 9, "title": "GitHub - HuangOwen/Awesome-LLM-Compression: Awesome LLM", "date": "", "ddg_snippet": "LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models ICLR 2024 [Paper]", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/HuangOwen/Awesome-LLM-Compression", "content": "LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models ICLR 2024 [Paper]"}
diff --git a/data/sampled_jsons/fBn6om49Ur_Towards_scientific_discovery_with_dictionary_learning-_Extracting_biological_concepts_fro.jsonl b/data/sampled_jsons/fBn6om49Ur_Towards_scientific_discovery_with_dictionary_learning-_Extracting_biological_concepts_fro.jsonl
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index 0000000000000000000000000000000000000000..df128be766df84b799bd2db7964306d433571330
--- /dev/null
+++ b/data/sampled_jsons/fBn6om49Ur_Towards_scientific_discovery_with_dictionary_learning-_Extracting_biological_concepts_fro.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Towards scientific discovery with dictionary learning : Extracting ...", "date": "", "ddg_snippet": "We show that sparse dictionaries indeed extract biologically -meaningful concepts such as cell type and genetic perturbation type. We also propose Iterative Codebook Feature Learning (ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2412.16247v2", "content": "We show that sparse dictionaries indeed extract biologically -meaningful concepts such as cell type and genetic perturbation type. We also propose Iterative Codebook Feature Learning (ICFL) and combine it with a pre-processing step which uses PCA whitening from a control dataset."}
+{"idx": 1, "title": "Towards scientific discovery with dictionary learning : Extracting ...", "date": "", "ddg_snippet": "They developed Iterative Codebook Feature Learning (ICFL) and a PCA whitening pre-processing step, demonstrating superior feature extraction and single-cell level heterogeneity analysis compared to prior methods.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2412.16247v2", "content": "They developed Iterative Codebook Feature Learning (ICFL) and a PCA whitening pre-processing step, demonstrating superior feature extraction and single-cell level heterogeneity analysis compared to prior methods."}
+{"idx": 2, "title": "Lisbon Unit for Learning and Intelligent Systems - LUMLIS", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models - [paper].", "subpage_snippet": "", "source": "lumlis.tecnico.ulisboa.pt", "link": "https://lumlis.tecnico.ulisboa.pt/reading-group-inescid.html", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models - [paper]."}
+{"idx": 3, "title": "Interpretable AI: Past, Present and Future", "date": "", "ddg_snippet": "The first workshop, titled \"XAI in Action: Past, Present, and Future Applications,\" was held at NeurIPS 2023. In this edition, we aim to bridge classical interpretability and modern methods for foundation models .", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/workshop/84733", "content": "The first workshop, titled \"XAI in Action: Past, Present, and Future Applications,\" was held at NeurIPS 2023. In this edition, we aim to bridge classical interpretability and modern methods for foundation models ."}
+{"idx": 4, "title": "Check out articles by K. Uličná using Synthical, a collaborative science ...", "date": "", "ddg_snippet": "Articles. 2. Virtual Cells: Predict, Explain, Discover . 6 days ago by Emmanuel Noutahi and others. Machine Learning , Quantitative Methods. Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/profile/fc8bec42-97a4-4561-9fc9-f47b5dafe3a5/articles", "content": "Articles. 2. Virtual Cells: Predict, Explain, Discover . 6 days ago by Emmanuel Noutahi and others. Machine Learning , Quantitative Methods. Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models ."}
+{"idx": 5, "title": "Valence Labs | Publications", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .GFlowNets for AI-driven Scientific Discovery . November 7th, 2022. Object-centric Causal Representation Learning .", "subpage_snippet": "", "source": "www.valencelabs.com", "link": "https://www.valencelabs.com/research/", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .GFlowNets for AI-driven Scientific Discovery . November 7th, 2022. Object-centric Causal Representation Learning ."}
+{"idx": 6, "title": "Kian Kenyon-Dean - Google Scholar", "date": "", "ddg_snippet": "2019. Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings.", "subpage_snippet": "", "source": "scholar.google.com", "link": "https://scholar.google.com/citations?user=l46NXroAAAAJ&hl=en", "content": "2019. Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .Utilizing masked autoencoder generative models to extract microscopy representation autoencoder embeddings."}
+{"idx": 7, "title": "Interpretability & Analysis of LMs - a gsarti Collection", "date": "", "ddg_snippet": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models .", "subpage_snippet": "", "source": "huggingface.1319lm.top", "link": "https://huggingface.1319lm.top/collections/gsarti/interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9", "content": "Towards scientific discovery with dictionary learning : Extracting biological concepts from microscopy foundation models ."}
+{"idx": 8, "title": "TLDR - A Byte Sized Daily Tech Newsletter", "date": "", "ddg_snippet": "Apple recently published a research paper and video that explains how the company built a non-anthropomorphic robot lamp prototype with an expressive design model rather than a functional one. The lamp resembles Pixar's iconic Luxo and displays human-like gestures.", "subpage_snippet": "", "source": "tldr.tech", "link": "https://tldr.tech/?product-page=3", "content": "Apple recently published a research paper and video that explains how the company built a non-anthropomorphic robot lamp prototype with an expressive design model rather than a functional one. The lamp resembles Pixar's iconic Luxo and displays human-like gestures."}
+{"idx": 9, "title": "Valence Labs, Recursion’s AI research engine, is at ICML this week!", "date": "", "ddg_snippet": "Two of our scientists, Jason Hartford, Ihab Bendidi and Kian Kenyon-Dean are presenting posters on work featured in their papers: ”ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy ” Link: https...", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/valenceailabs_valence-labs-recursions-ai-research-engine-activity-7351967632310370305-_Img", "content": "Two of our scientists, Jason Hartford, Ihab Bendidi and Kian Kenyon-Dean are presenting posters on work featured in their papers: ”ViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy ” Link: https..."}
diff --git a/data/sampled_jsons/feint_behaviors_game_theory.jsonl b/data/sampled_jsons/feint_behaviors_game_theory.jsonl
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index 0000000000000000000000000000000000000000..5675e1ac22ff49ebff35e621c0e79f2746bc2aa7
--- /dev/null
+++ b/data/sampled_jsons/feint_behaviors_game_theory.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2403.07932] Feint Behaviors and Strategies: Formalization ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games . Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2403.07932", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games . Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete ..."}
+{"idx": 1, "title": "Feint behaviors and strategies: - ACM Digital Library", "date": "", "ddg_snippet": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3738032", "content": "Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.). However, existing literature does not provide a comprehensive (and/or concrete) formalization for Feint behaviors , and their implications on game strategies."}
+{"idx": 2, "title": "PDF feint_video_slides - neurips.cc", "date": "", "ddg_snippet": "Our Work Overview Action-Level Formalization Feint characteristics and templates Feint behaviors in game steps", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/96274.pdf", "content": "Our Work Overview Action-Level Formalization Feint characteristics and templates Feint behaviors in game steps"}
+{"idx": 3, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games . Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.).", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/hash/064ae24cdbb3eaacc801ee7f4fe0e4f2-Abstract-Conference.html", "content": "Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games . Such behaviors are crucial tactics in most competitive multi-player games (e.g., boxing, fencing, basketball, motor racing, etc.)."}
+{"idx": 4, "title": "FEINT IN MULTI-PLAYER GAMES - OpenReview", "date": "", "ddg_snippet": "ABSTRACT This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games . Our work first formalizes Feint from the perspective of Multi-Player Games , in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=WbyWDWoXD3", "content": "ABSTRACT This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games . Our work first formalizes Feint from the perspective of Multi-Player Games , in terms of the temporal, spatial and their collective impacts. The formalization is built upon Non-transitive Active Markov Game Model, where Feint can have a considerable amount of ..."}
+{"idx": 5, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games ; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2403.07932v2", "content": "The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games ; and (3) only incur negligible overheads in terms of time consumption."}
+{"idx": 6, "title": "Feint: The Psychology of Feints: Mastering the Art of the Fakeout", "date": "", "ddg_snippet": "1. The Cognitive Chess Match: Feints are often compared to a high-stakes game of chess where each move and countermove unfolds in the blink of an eye. At its core, a feint is a mental chess match between two opponents, with each trying to outthink and outmaneuver the other. The feint serves as the opening gambit, initiating a complex sequence of reactions and counters, requiring a deep ...", "subpage_snippet": "", "source": "fastercapital.com", "link": "https://fastercapital.com/content/Feint--The-Psychology-of-Feints--Mastering-the-Art-of-the-Fakeout.html", "content": "1. The Cognitive Chess Match: Feints are often compared to a high-stakes game of chess where each move and countermove unfolds in the blink of an eye. At its core, a feint is a mental chess match between two opponents, with each trying to outthink and outmaneuver the other. The feint serves as the opening gambit, initiating a complex sequence of reactions and counters, requiring a deep ..."}
+{"idx": 7, "title": "Feint Behaviors and Strategies: Formalization, Implementation and ...", "date": "", "ddg_snippet": "This paper introduces a novel formalization of feint behaviors in multi-player games , improving AI performance and game diversity via a unified MARL implementation.", "subpage_snippet": "", "source": "deep-diver.github.io", "link": "https://deep-diver.github.io/neurips2024/posters/aciddntbsj/", "content": "This paper introduces a novel formalization of feint behaviors in multi-player games , improving AI performance and game diversity via a unified MARL implementation."}
+{"idx": 8, "title": "PDF Feint Behaviors and Strategies: Formalization, Implementation, and ...", "date": "", "ddg_snippet": "In game simulations, however, current literature general lack a comprehensive or concrete modeling of Feint behaviors in both action- level and strategy-level formalization. [34] mentioned Feint behaviors as a proof-of-concept to construct animations for nuanced game strategies with enhanced unpredictability.", "subpage_snippet": "", "source": "shiangjun.com", "link": "https://shiangjun.com/pdf/Feint-preprint.pdf", "content": "In game simulations, however, current literature general lack a comprehensive or concrete modeling of Feint behaviors in both action- level and strategy-level formalization. [34] mentioned Feint behaviors as a proof-of-concept to construct animations for nuanced game strategies with enhanced unpredictability."}
+{"idx": 9, "title": "Junyu Liu - Projects", "date": "", "ddg_snippet": "The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games ; and (3) only incur negligible overheads in terms of time consumption.", "subpage_snippet": "", "source": "junyu-liu-nate.github.io", "link": "https://junyu-liu-nate.github.io/projects/FeintFinal.html", "content": "The experimental results show that our design of Feint behaviors can (1) greatly improve the game reward gains; (2) significantly improve the diversity of Multi-Player Games ; and (3) only incur negligible overheads in terms of time consumption."}
diff --git a/data/sampled_jsons/filetypepdf_Simple_yet_Effective_Incomplete_Multi-view_Clustering_Algorithm_2.jsonl b/data/sampled_jsons/filetypepdf_Simple_yet_Effective_Incomplete_Multi-view_Clustering_Algorithm_2.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..137ba542470fcc85483be94a7e9e0da20ee68a3a
--- /dev/null
+++ b/data/sampled_jsons/filetypepdf_Simple_yet_Effective_Incomplete_Multi-view_Clustering_Algorithm_2.jsonl
@@ -0,0 +1,6 @@
+{"idx": 0, "title": "PDF Simple yet Effective Incomplete Multi-view Clustering: Similarity-level ...", "date": "", "ddg_snippet": "Simple yet Effective Incomplete Multi-view Clustering : Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction Shengju Yu · Zhibin Dong · Siwei Wang · Pei Zhang · Yi Zhang · XinwangLiu · Thomas Guan · Tiejun Li · Yiu-ming Cheung", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/30038.pdf", "content": "Simple yet Effective Incomplete Multi-view Clustering : Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction Shengju Yu · Zhibin Dong · Siwei Wang · Pei Zhang · Yi Zhang · XinwangLiu · Thomas Guan · Tiejun Li · Yiu-ming Cheung"}
+{"idx": 1, "title": "SIMPLE YET EFFECTIVE INCOMPLETE MULTI-VIEW ...", "date": "", "ddg_snippet": "Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/notes/edits/attachment?id=qZ2smF2lyZ&name=pdf", "content": "Most of incomplete multi-view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to ..."}
+{"idx": 2, "title": "S EFFECTIVE INCOMPLETE MULTI VIEW C - OpenReview", "date": "", "ddg_snippet": "S EFFECTIVE INCOMPLETE MULTI VIEW C : SIMILARITY LEVEL IMPUTATION AND H GROUP PROTOTYPE CONSTRUC SIMPLE YET EFFECTIVE INCOMPLETE MULTI-VIEW CLUSTERING : SIMILARITY-LEVEL IMPUTATION AND INTRA-VIEW HYBRID-GROUP PROTOTYPE CONSTRUC-", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=KijslFbfOL", "content": "S EFFECTIVE INCOMPLETE MULTI VIEW C : SIMILARITY LEVEL IMPUTATION AND H GROUP PROTOTYPE CONSTRUC SIMPLE YET EFFECTIVE INCOMPLETE MULTI-VIEW CLUSTERING : SIMILARITY-LEVEL IMPUTATION AND INTRA-VIEW HYBRID-GROUP PROTOTYPE CONSTRUC-"}
+{"idx": 3, "title": "Simple yet Effective Incomplete Multi - view Clustering :...", "date": "", "ddg_snippet": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=KijslFbfOL", "content": "Most of incomplete multi - view clustering (IMVC) methods typically choose to ignore the missing samples and only utilize observed unpaired samples to construct bipartite similarity."}
+{"idx": 4, "title": "GitHub - azminewasi/Awesome-Graph-Research-ICLR2025: It is...", "date": "", "ddg_snippet": "Simple yet Effective Incomplete Multi - view Clustering : Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction.A Causal Discovery Approach for Efficient Multi-Task Representation Compression.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/azminewasi/Awesome-Graph-Research-ICLR2025", "content": "Simple yet Effective Incomplete Multi - view Clustering : Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction.A Causal Discovery Approach for Efficient Multi-Task Representation Compression."}
+{"idx": 5, "title": "Selected Conference Papers: | Xinwang Liu", "date": "", "ddg_snippet": "[ICLR] Shengju Yu, Zhibin Dong, Siwei Wang, Pei zhang, Yi Zhang, Xinwang Liu, Naiyang Guan, Tiejun Li, Yiu-ming Cheung : Simple yet Effective Incomplete Multi - view Clustering : Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction.", "subpage_snippet": "", "source": "xinwangliu.github.io", "link": "https://xinwangliu.github.io/Selected-Conference-Papers.html", "content": "[ICLR] Shengju Yu, Zhibin Dong, Siwei Wang, Pei zhang, Yi Zhang, Xinwang Liu, Naiyang Guan, Tiejun Li, Yiu-ming Cheung : Simple yet Effective Incomplete Multi - view Clustering : Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction."}
diff --git a/data/sampled_jsons/finite_set_of_S-functions_neural_network_continuous_MDPs_Parisi_Bowling_year_2024.jsonl b/data/sampled_jsons/finite_set_of_S-functions_neural_network_continuous_MDPs_Parisi_Bowling_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f7a8759c6b9c16d592c1f679e1161fe076fed9c4
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+++ b/data/sampled_jsons/finite_set_of_S-functions_neural_network_continuous_MDPs_Parisi_Bowling_year_2024.jsonl
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+{"idx": 0, "title": "Position: AI Safety Must Embrace an Antifragile Perspective", "date": "", "ddg_snippet": "It has been formally applied in other machine learning contexts, such as the parameter-level analysis of deep neural networks (Pravin et al ., 2024 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13339v1", "content": "It has been formally applied in other machine learning contexts, such as the parameter-level analysis of deep neural networks (Pravin et al ., 2024 ..."}
+{"idx": 1, "title": "Probability Feb 2019", "date": "", "ddg_snippet": "Subjects: Probability (math.PR) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Mathematical Physics (math-ph)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/list/math.PR/2019-02", "content": "Subjects: Probability (math.PR) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Mathematical Physics (math-ph)"}
+{"idx": 2, "title": "NeurIPS 2022 Panels", "date": "", "ddg_snippet": "... sampling method with complexity guarantees for ... Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2022/events/Panel", "content": "... sampling method with complexity guarantees for ... Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift"}
+{"idx": 3, "title": "NeurIPS 2021 Orals", "date": "", "ddg_snippet": "... of a recurrent neural network (RNN) as a continuous -time neural differential equation, we show, under appropriate conditions, that the solution of a ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2021/events/oral", "content": "... of a recurrent neural network (RNN) as a continuous -time neural differential equation, we show, under appropriate conditions, that the solution of a ..."}
+{"idx": 4, "title": "NeurIPS 2022 Panels", "date": "", "ddg_snippet": "... sampling method with complexity guarantees for ... Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift", "subpage_snippet": "", "source": "nips.cc", "link": "https://nips.cc/virtual/2022/events/Panel", "content": "... sampling method with complexity guarantees for ... Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift"}
+{"idx": 5, "title": "Main and AICS track | IJCAI 2013", "date": "", "ddg_snippet": "... Functional Stable Model Semantics and Answer Set Programming Modulo Theories Michael Bartholomew, Joohyung Lee Self-Organized Neural Learning of ...", "subpage_snippet": "", "source": "ijcai13.org", "link": "https://ijcai13.org/program/accepted_papers/", "content": "... Functional Stable Model Semantics and Answer Set Programming Modulo Theories Michael Bartholomew, Joohyung Lee Self-Organized Neural Learning of ..."}
+{"idx": 6, "title": "A Definition of Open-Ended Learning for Goal-Conditioned Agents", "date": "", "ddg_snippet": "The goal of the approach is the “Invention of new problems” Srivastava et al ., ( 2012 ) to continually challenge a learning agent.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00344v4", "content": "The goal of the approach is the “Invention of new problems” Srivastava et al ., ( 2012 ) to continually challenge a learning agent."}
+{"idx": 7, "title": "A Definition of Continual Reinforcement Learning", "date": "", "ddg_snippet": "Despite the importance of continual reinforcement learning, the community lacks a simple definition of the problem that highlights its commitments ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2307.11046v2", "content": "Despite the importance of continual reinforcement learning, the community lacks a simple definition of the problem that highlights its commitments ..."}
+{"idx": 8, "title": "GitHub - opendilab/awesome-exploration-rl: A curated list of", "date": "", "ddg_snippet": "In this task, a series of actions to achieve the goal often require dozens or even hundreds of steps, in which the agent needs to fully explore ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/opendilab/awesome-exploration-rl", "content": "In this task, a series of actions to achieve the goal often require dozens or even hundreds of steps, in which the agent needs to fully explore ..."}
+{"idx": 9, "title": "AAMAS 2023: Proceedings of the 2023 International Conference on", "date": "", "ddg_snippet": "Bridging the Gap Between Single and Multi Objective Games (Page 224) Willem Röpke (Vrije Universiteit Brussel) Carla Groenland (Universiteit Utrecht ...", "subpage_snippet": "", "source": "www.ifaamas.org", "link": "https://www.ifaamas.org/Proceedings/aamas2023/forms/contents.htm", "content": "Bridging the Gap Between Single and Multi Objective Games (Page 224) Willem Röpke (Vrije Universiteit Brussel) Carla Groenland (Universiteit Utrecht ..."}
diff --git a/data/sampled_jsons/hierarchical_clustering_cost_function_theoretical_properties_overlapping.jsonl b/data/sampled_jsons/hierarchical_clustering_cost_function_theoretical_properties_overlapping.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..85c684dc5afd99f512dd847f7c8f1c88a601f955
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_clustering_cost_function_theoretical_properties_overlapping.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical overlapping clustering: cost function, algorithm and ...", "date": "", "ddg_snippet": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties , and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties , and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version."}
+{"idx": 1, "title": "Hierarchical Clustering: Objective Functions and Algorithms: Journal of ...", "date": "", "ddg_snippet": "We take an axiomatic approach to defining \"good\" objective functions for both similarity- and dissimilarity-based hierarchical clustering . We characterize a set of admissible objective functions having the property that when the input admits a \"natural\" ground-truth hierarchical clustering , the ground-truth clustering has an optimal value.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3321386", "content": "We take an axiomatic approach to defining \"good\" objective functions for both similarity- and dissimilarity-based hierarchical clustering . We characterize a set of admissible objective functions having the property that when the input admits a \"natural\" ground-truth hierarchical clustering , the ground-truth clustering has an optimal value."}
+{"idx": 2, "title": "PDF Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Addressing the lack of an objective function for hierarchical clustering , Dasgupta (2016) introduced a simple cost func-tion to measure the quality of an HC tree, and proved several properties of the cost function . Dasgupta further showed that a recursive sparsest cut algorithm can be applied to construct an O(log3/2 n)-approximate HC tree.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "Addressing the lack of an objective function for hierarchical clustering , Dasgupta (2016) introduced a simple cost func-tion to measure the quality of an HC tree, and proved several properties of the cost function . Dasgupta further showed that a recursive sparsest cut algorithm can be applied to construct an O(log3/2 n)-approximate HC tree."}
+{"idx": 3, "title": "PDF Hierarchy cost of hierarchical clusterings - Springer", "date": "", "ddg_snippet": "The universal hierarchy cost is defined as the supremum over all instances of the minimal hierarchy cost of any hierarchical clustering . That is, the universal hierarchy cost provides a lower bound for the approximation guarantee of any hierarchical clustering .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/content/pdf/10.1007/s10878-022-00851-4.pdf", "content": "The universal hierarchy cost is defined as the supremum over all instances of the minimal hierarchy cost of any hierarchical clustering . That is, the universal hierarchy cost provides a lower bound for the approximation guarantee of any hierarchical clustering ."}
+{"idx": 4, "title": "PDF Hierarchical Clustering Beyond the Worst-Case - NIPS", "date": "", "ddg_snippet": "In this paper, we consider a fairly general random graph model for hierarchical clustering , called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry [18] com-bined with specific linkage methods results in a clustering that give an Op1q approx-imation to Dasgupta's cost function .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2017/file/e8bf0f27d70d480d3ab793bb7619aaa5-Paper.pdf", "content": "In this paper, we consider a fairly general random graph model for hierarchical clustering , called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry [18] com-bined with specific linkage methods results in a clustering that give an Op1q approx-imation to Dasgupta's cost function ."}
+{"idx": 5, "title": "Hierarchical Overlapping Clustering Function Algorithm and Scalability ...", "date": "", "ddg_snippet": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=oHSXRy29tj", "content": "Overlap and hierarchy are two prevalent phenomena in clustering , and usually coexist in a single system. There are several studies on each of them separately, but it is unclear how to characterize and evaluate the hybrid structures yet. To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the ..."}
+{"idx": 6, "title": "PDF The Price of Hierarchical Clustering - arXiv.org", "date": "", "ddg_snippet": "A hierarchical clustering achieves an approximation factor of if the costs of each k-clustering in the hierarchy are at most times the costs of an optimal k-clustering. We study as cost functions the maximum (discrete) radius of any cluster (k-center problem) and the maximum diameter of any cluster (k-diameter problem).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2205.01417v1.pdf", "content": "A hierarchical clustering achieves an approximation factor of if the costs of each k-clustering in the hierarchy are at most times the costs of an optimal k-clustering. We study as cost functions the maximum (discrete) radius of any cluster (k-center problem) and the maximum diameter of any cluster (k-diameter problem)."}
+{"idx": 7, "title": "Hierarchical clustering | Proceedings of the 35th International ...", "date": "", "ddg_snippet": "Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O (1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3540261.3540971", "content": "Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O (1)-approximation algorithm for graphs of high ..."}
+{"idx": 8, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion."}
+{"idx": 9, "title": "PDF G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023", "date": "", "ddg_snippet": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950.pdf", "content": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ..."}
diff --git a/data/sampled_jsons/hierarchical_overlapping_clustering_cost_function_graph_year_2023.jsonl b/data/sampled_jsons/hierarchical_overlapping_clustering_cost_function_graph_year_2023.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..410f882d6e1de2fa8909b4cbe3c6e866d396c17e
--- /dev/null
+++ b/data/sampled_jsons/hierarchical_overlapping_clustering_cost_function_graph_year_2023.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function...", "date": "", "ddg_snippet": "Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have examined these two structures separately, characterizing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=51x0dfsD8A", "content": "Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have examined these two structures separately, characterizing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and ..."}
+{"idx": 1, "title": "PDF Overlapping Hierarchical Clustering (OHC) - inria.hal.science", "date": "", "ddg_snippet": "The trend gives a function in O(n2:45) so to speed up the process and scale up our algorithm is it possible to precompute a set of possibly overlapping clusters over a given -neighbourhood graph with a classical method, for instance CLIQUE, and build the OHC hierarchy on top of that.", "subpage_snippet": "", "source": "inria.hal.science", "link": "https://inria.hal.science/hal-02452729/file/Overlapping_Hierarchical_Clustering_IDA2020_Camera_Ready_.pdf", "content": "The trend gives a function in O(n2:45) so to speed up the process and scale up our algorithm is it possible to precompute a set of possibly overlapping clusters over a given -neighbourhood graph with a classical method, for instance CLIQUE, and build the OHC hierarchy on top of that."}
+{"idx": 2, "title": "PDF Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ...", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2021/file/4d68e143defa221fead61c84de7527a3-Paper.pdf", "content": "Abstract Hierarchical clustering studies a recursive partition of a data set into clusters of successively smaller size, and is a fundamental problem in data analysis. In this work we study the cost function for hierarchical clustering introduced by Dasgupta [12], and present two polynomial-time approximation algorithms: Our first result is an O(1)-approximation algorithm for graphs of high ..."}
+{"idx": 3, "title": "PDF Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract This paper presents two eficient hierarchical clus-tering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the perfor-mance of our algorithm against the ...", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf", "content": "Abstract This paper presents two eficient hierarchical clus-tering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and re-turn an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the perfor-mance of our algorithm against the ..."}
+{"idx": 4, "title": "Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm ...", "date": "", "ddg_snippet": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46447", "content": "Poster Hierarchical Overlapping Clustering on Graphs : Cost Function , Algorithm and Scalability Yicheng Pan · Renjie Chen · Pengyu Long · Bingchen Fan East Exhibition Hall A-B #E-2009"}
+{"idx": 5, "title": "Hierarchical overlapping clustering: cost function, algorithm and ...", "date": "", "ddg_snippet": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties, and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=oHSXRy29tj", "content": "To address this issue, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function for it. We show the rationality of our cost function via several intuitive properties, and develop an approximation algorithm that achieves a provably constant approximation factor for its dual version."}
+{"idx": 6, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "Abstract This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function .", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/371684528_Nearly-Optimal_Hierarchical_Clustering_for_Well-Clustered_Graphs", "content": "Abstract This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function ."}
+{"idx": 7, "title": "Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs", "date": "", "ddg_snippet": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1) -approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2306.09950", "content": "This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1) -approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous state ..."}
+{"idx": 8, "title": "PDF Hierarchical Clustering Beyond the Worst-Case - NIPS", "date": "", "ddg_snippet": "In this paper, we consider a fairly general random graph model for hierarchical clustering , called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry [18] com-bined with specific linkage methods results in a clustering that give an Op1q approx-imation to Dasgupta's cost function .", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper/2017/file/e8bf0f27d70d480d3ab793bb7619aaa5-Paper.pdf", "content": "In this paper, we consider a fairly general random graph model for hierarchical clustering , called the hierarchical stochastic block model (HSBM), and show that in certain regimes the SVD approach of McSherry [18] com-bined with specific linkage methods results in a clustering that give an Op1q approx-imation to Dasgupta's cost function ."}
+{"idx": 9, "title": "PDF G O arXiv:2306.09950v1 [cs.DS] 16 Jun 2023", "date": "", "ddg_snippet": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2306.09950.pdf", "content": "This paper presents two eficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function . For any input graph G with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of G, and return an O(1)-approximate HC tree with respect to Dasgupta's cost function . We compare the performance of our algorithm against the previous ..."}
diff --git a/data/sampled_jsons/high_order_derivative_features_redundancy_machine_learning_dimensionality_year_2021.jsonl b/data/sampled_jsons/high_order_derivative_features_redundancy_machine_learning_dimensionality_year_2021.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..df8b0d3cb600bac9d06ac45da835615264b1bd02
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+++ b/data/sampled_jsons/high_order_derivative_features_redundancy_machine_learning_dimensionality_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Intrinsic Dimensionality of Molecular Properties", "date": "", "ddg_snippet": "3 Jul 2025 — The success of machine learning methods suggests that many physical quantities exhibit substantial redundancy in that space, lowering their ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.02480v1", "content": "3 Jul 2025 — The success of machine learning methods suggests that many physical quantities exhibit substantial redundancy in that space, lowering their ..."}
+{"idx": 1, "title": "Vector textures derived from higher order ...", "date": "", "ddg_snippet": "by W Cao · 2022 · Cited by 4 — We note that deep learning methodologies such as VGG-16 have a much higher data requirement than conventional machine learning approaches.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9198194/", "content": "by W Cao · 2022 · Cited by 4 — We note that deep learning methodologies such as VGG-16 have a much higher data requirement than conventional machine learning approaches."}
+{"idx": 2, "title": "Dimensionality reduction of independent influence factors ...", "date": "", "ddg_snippet": "by F Skaka-Čekić · 2022 — Dimensionality reduction techniques can be categorized into linear and non-linear; supervised and unsupervised; feature selection and feature ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/s41598-022-13803-z", "content": "by F Skaka-Čekić · 2022 — Dimensionality reduction techniques can be categorized into linear and non-linear; supervised and unsupervised; feature selection and feature ..."}
+{"idx": 3, "title": "A unified and scalable machine learning framework for ...", "date": "", "ddg_snippet": "by A Mahapatra · 2025 — A novel feature fusion technique designed to address challenges such as overfitting, high dimensionality , and noise sensitivity.", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10791-025-09622-1", "content": "by A Mahapatra · 2025 — A novel feature fusion technique designed to address challenges such as overfitting, high dimensionality , and noise sensitivity."}
+{"idx": 4, "title": "A novel deep machine learning algorithm with dimensionality ...", "date": "", "ddg_snippet": "by O Tutsoy · 2024 · Cited by 16 — This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer.", "subpage_snippet": "", "source": "academic.oup.com", "link": "https://academic.oup.com/bib/article/25/4/bbae344/7713726", "content": "by O Tutsoy · 2024 · Cited by 16 — This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer."}
+{"idx": 5, "title": "Redundancy Analysis to Reduce the High-Dimensional Near ...", "date": "", "ddg_snippet": "by MI Sánchez-Rodríguez · 2022 · Cited by 10 — Therefore, the aim of this study is to use both PCA and RDA to reduce the high - dimensional NIR spectral information on oils. The corresponding ...", "subpage_snippet": "", "source": "pubs.acs.org", "link": "https://pubs.acs.org/doi/10.1021/acs.jcim.2c00964", "content": "by MI Sánchez-Rodríguez · 2022 · Cited by 10 — Therefore, the aim of this study is to use both PCA and RDA to reduce the high - dimensional NIR spectral information on oils. The corresponding ..."}
+{"idx": 6, "title": "Optimal features selection in the high dimensional data ...", "date": "", "ddg_snippet": "by I Hussain · 2024 · Cited by 11 — This paper introduces a new hybrid approach for gene selection by combining the Signal-to-Noise Ratio (SNR) score with the robust Mood median test.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2405844024132720", "content": "by I Hussain · 2024 · Cited by 11 — This paper introduces a new hybrid approach for gene selection by combining the Signal-to-Noise Ratio (SNR) score with the robust Mood median test."}
+{"idx": 7, "title": "Redundant sample detection in high-dimensional NIR data ...", "date": "", "ddg_snippet": "13 Sept 2025 — In this study, we propose a hybrid redundant -sample-removal method that integrates locality-sensitive hashing (LSH) with ISOMAP manifold ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0026265X25026761", "content": "13 Sept 2025 — In this study, we propose a hybrid redundant -sample-removal method that integrates locality-sensitive hashing (LSH) with ISOMAP manifold ..."}
+{"idx": 8, "title": "High-order Polynomials with Self-supervised Dimension ...", "date": "", "ddg_snippet": "18 Jan 2025 — In this paper, we propose low rank approximation and self-supervised dimension reduction to address the aforementioned issues. To further ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.10637v1", "content": "18 Jan 2025 — In this paper, we propose low rank approximation and self-supervised dimension reduction to address the aforementioned issues. To further ..."}
+{"idx": 9, "title": "Functional Data Analysis: A solution to the Curse of ...", "date": "", "ddg_snippet": "by D Riccio — The curse of dimensionality refers to the challenges and difficulties that arise when dealing with high - dimensional datasets in machine learning ...", "subpage_snippet": "", "source": "towardsdatascience.com", "link": "https://towardsdatascience.com/functional-data-analysis-a-solution-to-the-curse-of-dimensionality-f83dd19fa6e8/", "content": "by D Riccio — The curse of dimensionality refers to the challenges and difficulties that arise when dealing with high - dimensional datasets in machine learning ..."}
diff --git a/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2502.00921_table_1_MATH_dataset.jsonl b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2502.00921_table_1_MATH_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..39bc353872fab0624a7dcd02baaef0b1a9b73281
--- /dev/null
+++ b/data/sampled_jsons/httpsar5iv.labs.arxiv.orghtml2502.00921_table_1_MATH_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 1, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv."}
+{"idx": 2, "title": "ar5iv 04.2024 - An HTML5 dataset for arXiv.org · SIGMathLing", "date": "", "ddg_snippet": "Description This is the first public release of the ar5iv dataset generated by the KWARC research group. It contains HTML5+MathML conversions of the scientific documents from the arXiv.org preprint server, upto the start of April 2024. As of April 2024, the provided HTML here also seeds the live ar5iv Lab site, maintained by the same author.", "subpage_snippet": "", "source": "sigmathling.kwarc.info", "link": "https://sigmathling.kwarc.info/resources/ar5iv-dataset-2024/", "content": "Description This is the first public release of the ar5iv dataset generated by the KWARC research group. It contains HTML5+MathML conversions of the scientific documents from the arXiv.org preprint server, upto the start of April 2024. As of April 2024, the provided HTML here also seeds the live ar5iv Lab site, maintained by the same author."}
+{"idx": 3, "title": "[2502.02362] Premise-Augmented Reasoning Chains Improve Error ...", "date": "", "ddg_snippet": "RQ 1 Given a sequential step-by-step answer to a math word problem, can LLMs identify premises for each step? RQ 2 Given premise annotations, can LLMs identify errors in reasoning more faithfully? RQ 3 Can LLMs perform the entire process end-to-end, i.e., given a reasoning chain, can they identify premises for each step and detect errors?", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2502.02362", "content": "RQ 1 Given a sequential step-by-step answer to a math word problem, can LLMs identify premises for each step? RQ 2 Given premise annotations, can LLMs identify errors in reasoning more faithfully? RQ 3 Can LLMs perform the entire process end-to-end, i.e., given a reasoning chain, can they identify premises for each step and detect errors?"}
+{"idx": 4, "title": "ar5iv.labs.arxiv.org", "date": "", "ddg_snippet": "ar5iv.labs.arxiv.org", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/_", "content": "ar5iv.labs.arxiv.org"}
+{"idx": 5, "title": "GitHub - dginev/ar5iv: A web service offering HTML5 articles from arXiv ...", "date": "", "ddg_snippet": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv", "content": "A web service offering HTML5 articles from arXiv.org as converted with latexml. The e-journal styling of document pages is developed separately at ar5iv-css. Authors can reproduce locally using ar5ivist. Seeded via CorTeX data. Hosted by arXivLabs. Created by"}
+{"idx": 6, "title": "Releases · dginev/ar5iv - GitHub", "date": "", "ddg_snippet": "The main updates have to do with ar5iv's content being fully regenerated with LaTeXML 0.8.7, which is now ready for native MathML Core (expected in all browsers in 2023).", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/dginev/ar5iv/releases", "content": "The main updates have to do with ar5iv's content being fully regenerated with LaTeXML 0.8.7, which is now ready for native MathML Core (expected in all browsers in 2023)."}
+{"idx": 7, "title": "HTML papers on arXiv: why it's important, and how we made it happen", "date": "", "ddg_snippet": "These two tools were roughly tied in the quality of the HTML produced, but LaTeXML has a larger library of supported packages, and the predecessor ar5iv Labs project at arXiv used LaTeXML, which made it a logical choice.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.08954v1", "content": "These two tools were roughly tied in the quality of the HTML produced, but LaTeXML has a larger library of supported packages, and the predecessor ar5iv Labs project at arXiv used LaTeXML, which made it a logical choice."}
+{"idx": 8, "title": "ar5iv.org — a preview site for the arXMLiv dataset - ProDG", "date": "", "ddg_snippet": "Launching a preview installation for 1.797 million arXiv preprints, in HTML5. Goal: reintegrate into arXiv.org.", "subpage_snippet": "", "source": "prodg.org", "link": "https://prodg.org/talks/ar5iv_launch", "content": "Launching a preview installation for 1.797 million arXiv preprints, in HTML5. Goal: reintegrate into arXiv.org."}
+{"idx": 9, "title": "arXiv Bulk Data Access - arXiv info", "date": "", "ddg_snippet": "The full, machine-readable arXiv dataset is available on Kaggle. This includes all available articles and related features such as article titles, authors, categories, abstracts, full text PDFs, and more.", "subpage_snippet": "", "source": "info.arxiv.org", "link": "https://info.arxiv.org/help/bulk_data.html", "content": "The full, machine-readable arXiv dataset is available on Kaggle. This includes all available articles and related features such as article titles, authors, categories, abstracts, full text PDFs, and more."}
diff --git a/data/sampled_jsons/httpsarxiv.orghtml2405.14728v1.jsonl b/data/sampled_jsons/httpsarxiv.orghtml2405.14728v1.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b3def0942c0378fe716160d1d1984e6f7b2db4fa
--- /dev/null
+++ b/data/sampled_jsons/httpsarxiv.orghtml2405.14728v1.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "arXiv.org e-Print archive", "date": "", "ddg_snippet": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/", "content": "arXiv is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv."}
+{"idx": 1, "title": "ar5iv - Articles from arXiv.org as responsive HTML5 web documents", "date": "", "ddg_snippet": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/", "content": "ar5iv offers a modern web view for arXiv's preprints. An open community resource, on a quest to a full collection of high-quality documents."}
+{"idx": 2, "title": "Intervention and Conditioning in Causal Bayesian Networks - arXiv.org", "date": "", "ddg_snippet": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14728v1", "content": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
+{"idx": 3, "title": "[2405.17247] An Introduction to Vision-Language Modeling - arXiv.org", "date": "", "ddg_snippet": "Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.17247", "content": "Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce images using only a high-level text description, the vision-language model (VLM) applications will significantly impact our relationship with ..."}
+{"idx": 4, "title": "The European Commitment to Human-Centered Technology: The Integral Role ...", "date": "", "ddg_snippet": "Abstract page for arXiv paper 2402.14728: The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.14728", "content": "Abstract page for arXiv paper 2402.14728: The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success"}
+{"idx": 5, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.14728", "content": "Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range ..."}
+{"idx": 6, "title": "Rapid modelling of reactive transport in porous media using machine ...", "date": "", "ddg_snippet": "Abstract Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flow and transport with geochemical reactions is computationally intensive, requiring geochemical calculations at each grid cell and each time step within a discretized simulation domain. Although recent ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.14548v1", "content": "Abstract Reactive transport in porous media plays a pivotal role in subsurface reservoir processes, influencing fluid properties and geochemical characteristics. However, coupling fluid flow and transport with geochemical reactions is computationally intensive, requiring geochemical calculations at each grid cell and each time step within a discretized simulation domain. Although recent ..."}
+{"idx": 7, "title": "A Brief Introduction to Causal Inference in Machine Learning", "date": "", "ddg_snippet": "This is a lecture note produced for DS-GA 3001.003 \"Special Topics in DS - Causal Inference in Machine Learning\" at the Center for Data Science, New York University in Spring, 2024. This course was created to target master's and PhD level students with basic background in machine learning but who were not exposed to causal inference or causal reasoning in general previously. In particular ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2405.08793", "content": "This is a lecture note produced for DS-GA 3001.003 \"Special Topics in DS - Causal Inference in Machine Learning\" at the Center for Data Science, New York University in Spring, 2024. This course was created to target master's and PhD level students with basic background in machine learning but who were not exposed to causal inference or causal reasoning in general previously. In particular ..."}
+{"idx": 8, "title": "Mitigating GenAI-powered Evidence Pollution for Out-of-Context ...", "date": "", "ddg_snippet": "While large generative artificial intelligence (GenAI) models have achieved significant success, they also raise growing concerns about online information security due to their potential misuse for generating deceptive content. Out-of-context (OOC) multimodal misinformation detection, which often retrieves Web evidence to identify the repurposing of images in false contexts, faces the issue of ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2501.14728", "content": "While large generative artificial intelligence (GenAI) models have achieved significant success, they also raise growing concerns about online information security due to their potential misuse for generating deceptive content. Out-of-context (OOC) multimodal misinformation detection, which often retrieves Web evidence to identify the repurposing of images in false contexts, faces the issue of ..."}
+{"idx": 9, "title": "Intervention and Conditioning in Causal Bayesian Networks", "date": "", "ddg_snippet": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ...", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2405.14728", "content": "Abstract Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate ..."}
diff --git a/data/sampled_jsons/httpsopenreview.netforumid=0WQJ6DFSKp.jsonl b/data/sampled_jsons/httpsopenreview.netforumid=0WQJ6DFSKp.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..568a431890179d7f4dc5bebd3390015af664b262
--- /dev/null
+++ b/data/sampled_jsons/httpsopenreview.netforumid=0WQJ6DFSKp.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Using the New Forum Page - OpenReview GitHub - pranftw/openreview_scraper: Scrape papers from ... Venues | OpenReview [D] Is it okay for a random person to comment on OpenReview? LaTeX math mode not working everywhere · Issue #197 ... - GitHub How to Export All Reviews into a CSV | OpenReview", "date": "", "ddg_snippet": "Here is an example of a typical forum comment: Every post on the new forum page contains the following information: 1. Title: title may be provided by the author of the post or it may be a generic title. Clicking the link icon next to the title copies a direct link to this post to your clipboard. 2. Reply Type: represents the type of forum reply, a... See full list on docs. openreview . net The new forum page provides advanced controls for sorting and filtering replies such as comments, reviews, private responses, and PC decisions. The new controls look something like this: 1. Invitation Filter: show only replies of a certain type. Can select multiple invitations (types) to show replies matching any of those invitations. 2. Author Fil... See full list on docs. openreview . net As mentioned in the section above there are currently three layout modes available for forum pages. These are: 1. Linear: all replies are shown at the same level of nesting (no indentation). This is useful to quickly see a chronological feed of all forum replies. If a given post is a reply to another reply (not a general reply to the submission not... See full list on docs. openreview . net If you are logged into OpenReview and have permission to modify the content of a submission or a forum reply (aka a Note) you will see a dropdown button labeled Edit to the right of the title. Clicking this button will display a list of all the available ways to modify the note (aka edit Invitations). For a submission note this might include option... See full list on docs. openreview . net We hope that you find the new functionality useful. If there is anything you would like to see changed or added please send an email to info@ openreview .netwith the subject \"New Forum Page Feedback\". See full list on docs. openreview . net OpenReview Scraper Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file. 2 days ago · Promoting openness in scientific communication and the peer-review process Hi. The title pretty much sums up my question. I frequent OpenReview to look at papers and reviews and stuff, as I'm sure many other people do. I'm still a junior researcher and was wondering, is it okay for anyone to post comments? I know that it says \"Add Public Comment\" and when I Google this question it says that papers will be \"available for anyone to read and comment on\" but I'm curious ... Jun 25, 2024 · For example it works here https :// openreview . net / forum ? id =PxTIG12RRHS but not here https :// openreview . net / forum ? id =VUvLSnMZdX Expected behavior LaTeX math mo... For example, if a review's forum is aBcDegh, you could find that submission at https :// openreview . net / forum ? id =aBcDegh. To create a csv that includes the review forums, do this:", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/getting-started/using-the-new-forum-page", "content": "Here is an example of a typical forum comment: Every post on the new forum page contains the following information: 1. Title: title may be provided by the author of the post or it may be a generic title. Clicking the link icon next to the title copies a direct link to this post to your clipboard. 2. Reply Type: represents the type of forum reply, a... See full list on docs. openreview . net The new forum page provides advanced controls for sorting and filtering replies such as comments, reviews, private responses, and PC decisions. The new controls look something like this: 1. Invitation Filter: show only replies of a certain type. Can select multiple invitations (types) to show replies matching any of those invitations. 2. Author Fil... See full list on docs. openreview . net As mentioned in the section above there are currently three layout modes available for forum pages. These are: 1. Linear: all replies are shown at the same level of nesting (no indentation). This is useful to quickly see a chronological feed of all forum replies. If a given post is a reply to another reply (not a general reply to the submission not... See full list on docs. openreview . net If you are logged into OpenReview and have permission to modify the content of a submission or a forum reply (aka a Note) you will see a dropdown button labeled Edit to the right of the title. Clicking this button will display a list of all the available ways to modify the note (aka edit Invitations). For a submission note this might include option... See full list on docs. openreview . net We hope that you find the new functionality useful. If there is anything you would like to see changed or added please send an email to info@ openreview .netwith the subject \"New Forum Page Feedback\". See full list on docs. openreview . net OpenReview Scraper Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file. 2 days ago · Promoting openness in scientific communication and the peer-review process Hi. The title pretty much sums up my question. I frequent OpenReview to look at papers and reviews and stuff, as I'm sure many other people do. I'm still a junior researcher and was wondering, is it okay for anyone to post comments? I know that it says \"Add Public Comment\" and when I Google this question it says that papers will be \"available for anyone to read and comment on\" but I'm curious ... Jun 25, 2024 · For example it works here https :// openreview . net / forum ? id =PxTIG12RRHS but not here https :// openreview . net / forum ? id =VUvLSnMZdX Expected behavior LaTeX math mo... For example, if a review's forum is aBcDegh, you could find that submission at https :// openreview . net / forum ? id =aBcDegh. To create a csv that includes the review forums, do this:"}
+{"idx": 1, "title": "GitHub - pranftw/openreview_scraper: Scrape papers from ...", "date": "", "ddg_snippet": "OpenReview Scraper Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/pranftw/openreview_scraper", "content": "OpenReview Scraper Scrape papers from top conferences like ICML, ICLR, NeurIPS, etc using OpenReview API, by searching for specific keywords in title, abstract or keywords in the submissions and save them to a CSV file."}
+{"idx": 2, "title": "[D] Is it okay for a random person to comment on OpenReview?", "date": "", "ddg_snippet": "Hi. The title pretty much sums up my question. I frequent OpenReview to look at papers and reviews and stuff, as I'm sure many other people do. I'm still a junior researcher and was wondering, is it okay for anyone to post comments? I know that it says \"Add Public Comment\" and when I Google this question it says that papers will be \"available for anyone to read and comment on\" but I'm curious ...", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/MachineLearning/comments/h8mhva/d_is_it_okay_for_a_random_person_to_comment_on/", "content": "Hi. The title pretty much sums up my question. I frequent OpenReview to look at papers and reviews and stuff, as I'm sure many other people do. I'm still a junior researcher and was wondering, is it okay for anyone to post comments? I know that it says \"Add Public Comment\" and when I Google this question it says that papers will be \"available for anyone to read and comment on\" but I'm curious ..."}
+{"idx": 3, "title": "How to Export All Reviews into a CSV | OpenReview", "date": "", "ddg_snippet": "For example, if a review's forum is aBcDegh, you could find that submission at https :// openreview . net / forum ? id =aBcDegh. To create a csv that includes the review forums, do this:", "subpage_snippet": "", "source": "docs.openreview.net", "link": "https://docs.openreview.net/how-to-guides/data-retrieval-and-modification/how-to-export-all-reviews-into-a-csv", "content": "For example, if a review's forum is aBcDegh, you could find that submission at https :// openreview . net / forum ? id =aBcDegh. To create a csv that includes the review forums, do this:"}
+{"idx": 4, "title": "https:// openreview . net / forum ?id=dZ7MVojplmi | Ecosyste.ms...", "date": "", "ddg_snippet": "https:// openreview . net / forum ?id=dZ7MVojplmi.", "subpage_snippet": "", "source": "awesome.ecosyste.ms", "link": "https://awesome.ecosyste.ms/projects/openreview.net/forum?id=dZ7MVojplmi", "content": "https:// openreview . net / forum ?id=dZ7MVojplmi."}
+{"idx": 5, "title": "openreview . net : Venues | OpenReview", "date": "", "ddg_snippet": "Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data | OpenReview https:// openreview . net / forum ...", "subpage_snippet": "", "source": "archive.vn", "link": "https://archive.vn/openreview.net", "content": "Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data | OpenReview https:// openreview . net / forum ..."}
+{"idx": 6, "title": "Forum - OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum", "content": "Promoting openness in scientific communication and the peer-review process"}
+{"idx": 7, "title": "Venues | OpenReview", "date": "", "ddg_snippet": "2 days ago · Promoting openness in scientific communication and the peer-review process", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/", "content": "2 days ago · Promoting openness in scientific communication and the peer-review process"}
+{"idx": 8, "title": "Denoising Diffusion Bridge Models | OpenReview", "date": "", "ddg_snippet": "Diffusion models are powerful generative models that map noise to data using stochastic processes.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=FKksTayvGo", "content": "Diffusion models are powerful generative models that map noise to data using stochastic processes."}
+{"idx": 9, "title": "Forum | OpenReview", "date": "", "ddg_snippet": "Promoting openness in scientific communication and the peer-review process...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=GF_903ce9225fca3e988c2af215d4e544d3", "content": "Promoting openness in scientific communication and the peer-review process..."}
diff --git a/data/sampled_jsons/inference-time_backpropagation-free_diffusion_model_preference_alignment_year_2023-2024.jsonl b/data/sampled_jsons/inference-time_backpropagation-free_diffusion_model_preference_alignment_year_2023-2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11fe457d719c8333e22e4647b6dd10542a1a2c10
--- /dev/null
+++ b/data/sampled_jsons/inference-time_backpropagation-free_diffusion_model_preference_alignment_year_2023-2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Large language model - Wikipedia", "date": "", "ddg_snippet": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Large_language_model", "content": "Machine learningand data mining. v. t. e. A large language model is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation."}
+{"idx": 1, "title": "Training-Free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Jan 22, 2025 · To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tfemquulED", "content": "Jan 22, 2025 · To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training."}
+{"idx": 2, "title": "Diffusion Blend: Inference-Time Multi-Preference Alignment ... DiffPO: Diffusion-styled Preference Optimization for ... Inference-Time Alignment Control for Diffusion Models with ... Published as a conference paper at ICLR 2025 - OpenReview DyMO: Training-Free Diffusion Model Alignment with Dynamic ...", "date": "", "ddg_snippet": "May 24, 2025 · We propose Diffusion Blend, a novel approach to solve inference-time multi- preference alignment by blending backward diffusion processes associated with fine-tuned models , and we instantiate this approach with two algorithms: DB-MPA for multi-reward alignment and DB-KLA for KL regularization control. Sep 15, 2025 · Abstract Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion -styled Preference Optimization (DiffPO), which provides an efficient and policy ... Fur- thermore, RLG supports both interpolation and extrapolation, thereby offering unprecedented flexibility in controlling gen- erative alignment . Our approach provides a practical and the- oretically sound solution for enhancing and controlling dif- fusion model alignment at inference . To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training. We propose a plug-and-play training- free alignment method, DyMO, for aligning the generated images and human pref-erences during inference . Apart from text-aware human preference scores, we introduce a semantic alignment ob-jective for enhancing the semantic alignment in the early *D. Gong is the corresponding author.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2505.18547", "content": "May 24, 2025 · We propose Diffusion Blend, a novel approach to solve inference-time multi- preference alignment by blending backward diffusion processes associated with fine-tuned models , and we instantiate this approach with two algorithms: DB-MPA for multi-reward alignment and DB-KLA for KL regularization control. Sep 15, 2025 · Abstract Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion -styled Preference Optimization (DiffPO), which provides an efficient and policy ... Fur- thermore, RLG supports both interpolation and extrapolation, thereby offering unprecedented flexibility in controlling gen- erative alignment . Our approach provides a practical and the- oretically sound solution for enhancing and controlling dif- fusion model alignment at inference . To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training. We propose a plug-and-play training- free alignment method, DyMO, for aligning the generated images and human pref-erences during inference . Apart from text-aware human preference scores, we introduce a semantic alignment ob-jective for enhancing the semantic alignment in the early *D. Gong is the corresponding author."}
+{"idx": 3, "title": "DiffPO: Diffusion-styled Preference Optimization for ...", "date": "", "ddg_snippet": "Sep 15, 2025 · Abstract Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion -styled Preference Optimization (DiffPO), which provides an efficient and policy ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.926/", "content": "Sep 15, 2025 · Abstract Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion -styled Preference Optimization (DiffPO), which provides an efficient and policy ..."}
+{"idx": 4, "title": "Inference-Time Alignment Control for Diffusion Models with ...", "date": "", "ddg_snippet": "Fur- thermore, RLG supports both interpolation and extrapolation, thereby offering unprecedented flexibility in controlling gen- erative alignment . Our approach provides a practical and the- oretically sound solution for enhancing and controlling dif- fusion model alignment at inference .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.21016", "content": "Fur- thermore, RLG supports both interpolation and extrapolation, thereby offering unprecedented flexibility in controlling gen- erative alignment . Our approach provides a practical and the- oretically sound solution for enhancing and controlling dif- fusion model alignment at inference ."}
+{"idx": 5, "title": "DyMO: Training-Free Diffusion Model Alignment with Dynamic ...", "date": "", "ddg_snippet": "We propose a plug-and-play training- free alignment method, DyMO, for aligning the generated images and human pref-erences during inference . Apart from text-aware human preference scores, we introduce a semantic alignment ob-jective for enhancing the semantic alignment in the early *D. Gong is the corresponding author.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_DyMO_Training-Free_Diffusion_Model_Alignment_with_Dynamic_Multi-Objective_Scheduling_CVPR_2025_paper.pdf", "content": "We propose a plug-and-play training- free alignment method, DyMO, for aligning the generated images and human pref-erences during inference . Apart from text-aware human preference scores, we introduce a semantic alignment ob-jective for enhancing the semantic alignment in the early *D. Gong is the corresponding author."}
+{"idx": 6, "title": "Training- free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference - time , backpropagation - free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference - time , backpropagation - free preference alignment method for diffusion models ."}
+{"idx": 7, "title": "GitHub - catchfree1225/demonsampling: [ICLR'25] Official...", "date": "", "ddg_snippet": "Backpropagation - Free Alignment : Incorporate non-differentiable reward signals directly into the inference process. Plug-and-Play Integration: Seamlessly integrate with existing diffusion models without additional training.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/catchfree1225/demonsampling", "content": "Backpropagation - Free Alignment : Incorporate non-differentiable reward signals directly into the inference process. Plug-and-Play Integration: Seamlessly integrate with existing diffusion models without additional training."}
+{"idx": 8, "title": "Diffusion Blend: Inference - Time Multi- Preference Alignment for...", "date": "", "ddg_snippet": "However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.18547", "content": "However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model ."}
+{"idx": 9, "title": "[PDF] Using Human Feedback to Fine-tune Diffusion Models without...", "date": "", "ddg_snippet": "...optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining, which is the first inference - time , backpropagation - free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Using-Human-Feedback-to-Fine-tune-Diffusion-Models-Yang-Tao/ec97a1565dff9d2fab1ef489e47296bbef68b680", "content": "...optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining, which is the first inference - time , backpropagation - free preference alignment method for diffusion models ."}
diff --git a/data/sampled_jsons/inference-time_backpropagation-free_preference_alignment_methods_diffusion_models_year_2022.jsonl b/data/sampled_jsons/inference-time_backpropagation-free_preference_alignment_methods_diffusion_models_year_2022.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..af9dbf118bf92b7db8eb5d2b0c745a84b1e14cbe
--- /dev/null
+++ b/data/sampled_jsons/inference-time_backpropagation-free_preference_alignment_methods_diffusion_models_year_2022.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions. To address these limitations, we propose a stochastic optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model ..."}
+{"idx": 1, "title": "Training-Free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=tfemquulED", "content": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training."}
+{"idx": 2, "title": "PDF DIFF PO: Diffusion-styled Preference Optimization for Efcient Inference ...", "date": "", "ddg_snippet": "We introduce a model -agnostic, inference - time alignment method , and our empirical results demonstrate its superiority in both performance and efciency. A.2 Parallel Decoding and Diffusion Process.", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/2025.acl-long.926.pdf", "content": "We introduce a model -agnostic, inference - time alignment method , and our empirical results demonstrate its superiority in both performance and efciency. A.2 Parallel Decoding and Diffusion Process."}
+{"idx": 3, "title": "Inference-Time Alignment Control for Diffusion Models with ...", "date": "", "ddg_snippet": "While reinforcement learning (RL) fine-tuning methods , inspired by advances in RL from human feed- back (RLHF) for large language models , have been adapted to these generative frameworks, current RL approaches are suboptimal for diffusion models and offer limited flexibil- ity in controlling alignment strength after fine-tuning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.21016", "content": "While reinforcement learning (RL) fine-tuning methods , inspired by advances in RL from human feed- back (RLHF) for large language models , have been adapted to these generative frameworks, current RL approaches are suboptimal for diffusion models and offer limited flexibil- ity in controlling alignment strength after fine-tuning."}
+{"idx": 4, "title": "Inversion-DPO: Precise and Efficient Post-Training for Diffusion Models", "date": "", "ddg_snippet": "This work proposes a stochastic optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining, which is the first inference-time , backpropagation-free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Inversion-DPO:-Precise-and-Efficient-Post-Training-Li-Li/e083eace1333ca8c67b297cf95e72a144d213ecd", "content": "This work proposes a stochastic optimization approach, dubbed Demon, to guide the denoising process at inference time without backpropagation through reward functions or model retraining, which is the first inference-time , backpropagation-free preference alignment method for diffusion models ."}
+{"idx": 5, "title": "PDF Diffusion Model Alignment Using Direct Preference Optimization", "date": "", "ddg_snippet": "We address this gap in diffusion model alignment for the first time , developing a method to directly optimize diffu-sion models on human preference data. We generalize Di-rect Preference Optimization (DPO) [36], where a gener-ative model is trained on paired human preference data to implicitly estimate a reward model .", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2024/papers/Wallace_Diffusion_Model_Alignment_Using_Direct_Preference_Optimization_CVPR_2024_paper.pdf", "content": "We address this gap in diffusion model alignment for the first time , developing a method to directly optimize diffu-sion models on human preference data. We generalize Di-rect Preference Optimization (DPO) [36], where a gener-ative model is trained on paired human preference data to implicitly estimate a reward model ."}
+{"idx": 6, "title": "(PDF) DiffPO: Diffusion-styled Preference Optimization for Efficient ...", "date": "", "ddg_snippet": "In this paper, we propose a novel approach, Diffusion -styled Preference Optimization (\\ model ), which provides an efficient and policy-agnostic solution for aligning LLMs with humans.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/389648728_DiffPO_Diffusion-styled_Preference_Optimization_for_Efficient_Inference-Time_Alignment_of_Large_Language_Models", "content": "In this paper, we propose a novel approach, Diffusion -styled Preference Optimization (\\ model ), which provides an efficient and policy-agnostic solution for aligning LLMs with humans."}
+{"idx": 7, "title": "GitHub - aiiu-lab/DemonSampling: [ICLR'25] Official implementation of ...", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling", "content": "Official implementation of ICLR 2025 \"Sampling Demon\" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 8, "title": "PDF Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training.", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/eeab2e00835c71d64458ad1821e05664-Paper-Conference.pdf", "content": "To the best of our knowledge, the proposed approach is the first inference-time , backpropagation-free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training."}
+{"idx": 9, "title": "DemonSampling/README.md at main · aiiu-lab/DemonSampling · GitHub", "date": "", "ddg_snippet": "This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aiiu-lab/DemonSampling/blob/main/README.md", "content": "This repository contains the official implementation of Sampling Demon, an inference-time , backpropagation-free preference alignment method for diffusion models ."}
diff --git a/data/sampled_jsons/inference-time_diffusion_model_preference_alignment_backpropagation-free.jsonl b/data/sampled_jsons/inference-time_diffusion_model_preference_alignment_backpropagation-free.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0ebb920cd677acef4ee9f1fa2fd807f467d9ecaf
--- /dev/null
+++ b/data/sampled_jsons/inference-time_diffusion_model_preference_alignment_backpropagation-free.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Diffusion Blend: Inference - Time Multi- Preference Alignment for...", "date": "", "ddg_snippet": "However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model .", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2505.18547", "content": "However, this approach is inherently restrictive in practice, where alignment must balance multiple, often conflicting objectives. Moreover, user preferences vary across prompts, individuals, and deployment contexts, with varying tolerances for deviation from a pre-trained base model ."}
+{"idx": 1, "title": "Alignment of Diffusion Models : Fundamentals, Challenges, and Future", "date": "", "ddg_snippet": "Inference - Time Alignment of Diffusion Models with Direct Noise Optimization. arXiv preprint arXiv:2405.18881 (2024).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2409.07253v3", "content": "Inference - Time Alignment of Diffusion Models with Direct Noise Optimization. arXiv preprint arXiv:2405.18881 (2024)."}
+{"idx": 2, "title": "(PDF) Inference - Time Diffusion Model Distillation", "date": "", "ddg_snippet": "PDF | Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/387053104_Inference-Time_Diffusion_Model_Distillation", "content": "PDF | Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps."}
+{"idx": 3, "title": "Inference - Time Diffusion Model Distillation", "date": "", "ddg_snippet": "Inference - Time Diffusion Model Distillation. Geon Yeong Park, Sang Wan Lee, Jong Chul Ye·December 12, 2024. Summary. Distillaion++ is an inference - time distillation framework that enhances reverse sampling in diffusion models .", "subpage_snippet": "", "source": "powerdrill.ai", "link": "https://powerdrill.ai/discover/discover-Inference-Time-Diffusion-Model-cm4n8hx17gew907ltbzxh70q0", "content": "Inference - Time Diffusion Model Distillation. Geon Yeong Park, Sang Wan Lee, Jong Chul Ye·December 12, 2024. Summary. Distillaion++ is an inference - time distillation framework that enhances reverse sampling in diffusion models ."}
+{"idx": 4, "title": "Backpropagation in Neural Network - GeeksforGeeks", "date": "", "ddg_snippet": "Backpropagation -in-Neural-Network-1. Fig(a) A simple illustration of how the backpropagation works by adjustments of weights. Back Propagation plays a critical role in how neural networks improve over time .", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/machine-learning/backpropagation-in-neural-network/", "content": "Backpropagation -in-Neural-Network-1. Fig(a) A simple illustration of how the backpropagation works by adjustments of weights. Back Propagation plays a critical role in how neural networks improve over time ."}
+{"idx": 5, "title": "Training- free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference - time , backpropagation - free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "rareone0602.github.io", "link": "https://rareone0602.github.io/Demon_page/", "content": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference - time , backpropagation - free preference alignment method for diffusion models ."}
+{"idx": 6, "title": "Inference - Time Diffusion Model Distillation-Bohrium", "date": "", "ddg_snippet": "Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps.", "subpage_snippet": "", "source": "www.bohrium.com", "link": "https://www.bohrium.com/paper-details/inference-time-diffusion-model-distillation/1074574794407542880-108597", "content": "Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps."}
+{"idx": 7, "title": "What inference - time scaling should be like for Diffusion Model", "date": "", "ddg_snippet": "Cheat Sheet for Mathematical Diffusion .Reimagining Supervised Fine-Tuning for Large Language Models and Diffusion from a Denoising Perspective.", "subpage_snippet": "", "source": "diff.blog", "link": "https://diff.blog/post/what-inference-time-scaling-should-be-like-for-diffusion-model-199694/", "content": "Cheat Sheet for Mathematical Diffusion .Reimagining Supervised Fine-Tuning for Large Language Models and Diffusion from a Denoising Perspective."}
+{"idx": 8, "title": "Prompt-to-Slate: Diffusion Models for... | Spotify Research", "date": "", "ddg_snippet": "Our proposed solution is the Diffusion Model for Slate Generation (DMSG), a framework that learns the joint distribution over slates to produce coherent and diverse item sets. DMSG is composed of three main components", "subpage_snippet": "", "source": "research.atspotify.com", "link": "https://research.atspotify.com/2025/9/prompt-to-slate-diffusion-models-for-prompt-conditioned-slate-generation", "content": "Our proposed solution is the Diffusion Model for Slate Generation (DMSG), a framework that learns the joint distribution over slates to produce coherent and diverse item sets. DMSG is composed of three main components"}
+{"idx": 9, "title": "The huge potential implications of long-context inference | Epoch AI", "date": "", "ddg_snippet": "Being able to do lots of long context inference supports more RL scaling. To keep models coherent across long context windows, one approach is to continue the current paradigm of RL and test- time compute scaling.", "subpage_snippet": "", "source": "epoch.ai", "link": "https://epoch.ai/gradient-updates/the-huge-potential-implications-of-long-context-inference", "content": "Being able to do lots of long context inference supports more RL scaling. To keep models coherent across long context windows, one approach is to continue the current paradigm of RL and test- time compute scaling."}
diff --git a/data/sampled_jsons/interpreting_pearson_correlation_coefficient_strength.jsonl b/data/sampled_jsons/interpreting_pearson_correlation_coefficient_strength.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..0f16ef192e280921712be1acbc4081ac83e71620
--- /dev/null
+++ b/data/sampled_jsons/interpreting_pearson_correlation_coefficient_strength.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "User's guide to correlation coefficients - PMC", "date": "", "ddg_snippet": "This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients . Keywords: Correlation coefficient , Interpretation, Pearson's , Spearman's, Lin's, Cramer's", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6107969/", "content": "This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients . Keywords: Correlation coefficient , Interpretation, Pearson's , Spearman's, Lin's, Cramer's"}
+{"idx": 1, "title": "Interpreting Correlation Coefficients - Statistics by Jim", "date": "", "ddg_snippet": "Correlation coefficients measure the strength of the relationship between two variables. Pearson's correlation coefficient is the most common.", "subpage_snippet": "", "source": "statisticsbyjim.com", "link": "https://statisticsbyjim.com/basics/correlations/", "content": "Correlation coefficients measure the strength of the relationship between two variables. Pearson's correlation coefficient is the most common."}
+{"idx": 2, "title": "Pearson Correlation Coefficient (r) | Guide & Examples - Scribbr", "date": "", "ddg_snippet": "The Pearson correlation coefficient (r) is the most common way of measuring a linear correlation . It is a number between -1 and 1 that measures the strength and direction of the relationship between two variables.", "subpage_snippet": "", "source": "www.scribbr.com", "link": "https://www.scribbr.com/statistics/pearson-correlation-coefficient/", "content": "The Pearson correlation coefficient (r) is the most common way of measuring a linear correlation . It is a number between -1 and 1 that measures the strength and direction of the relationship between two variables."}
+{"idx": 3, "title": "How to interpret the value of Pearson correlation coefficient?", "date": "", "ddg_snippet": "The Pearson correlation coefficient measures the strength and direction of a linear relationship between two variables. It ranges from -1 to +1, where -1 indicates a perfect negative correlation , +1 indicates a perfect positive correlation , and 0 indicates no correlation .", "subpage_snippet": "", "source": "www.gauthmath.com", "link": "https://www.gauthmath.com/knowledge/How-to-interpret-the-value-of-Pearson-correlation-coefficient--7408207104823279626", "content": "The Pearson correlation coefficient measures the strength and direction of a linear relationship between two variables. It ranges from -1 to +1, where -1 indicates a perfect negative correlation , +1 indicates a perfect positive correlation , and 0 indicates no correlation ."}
+{"idx": 4, "title": "Understanding the Pearson Correlation Coefficient | Outlier", "date": "", "ddg_snippet": "In this article learn what Pearson Correlation Coefficient is and the strength of association. Also, read about when to use it, how it's calculated, and faqs.", "subpage_snippet": "", "source": "articles.outlier.org", "link": "https://articles.outlier.org/pearson-correlation-coefficient", "content": "In this article learn what Pearson Correlation Coefficient is and the strength of association. Also, read about when to use it, how it's calculated, and faqs."}
+{"idx": 5, "title": "Interpret the key results for Correlation - Minitab", "date": "", "ddg_snippet": "Complete the following steps to interpret a correlation analysis. Key output includes the Pearson correlation coefficient , the Spearman correlation coefficient , and the p-value.", "subpage_snippet": "", "source": "support.minitab.com", "link": "https://support.minitab.com/en-us/minitab/help-and-how-to/statistics/basic-statistics/how-to/correlation/interpret-the-results/key-results/", "content": "Complete the following steps to interpret a correlation analysis. Key output includes the Pearson correlation coefficient , the Spearman correlation coefficient , and the p-value."}
+{"idx": 6, "title": "Pearson Correlation Coefficient - GeeksforGeeks", "date": "", "ddg_snippet": "Calculate the Pearson Correlation Coefficient (r) for the data and interpret the strength and direction of the correlation . Question 2: A Pearson correlation coefficient of r = -0.95 is calculated between the number of hours a person exercises per week and their blood pressure levels.", "subpage_snippet": "", "source": "www.geeksforgeeks.org", "link": "https://www.geeksforgeeks.org/maths/pearson-correlation-coefficient/", "content": "Calculate the Pearson Correlation Coefficient (r) for the data and interpret the strength and direction of the correlation . Question 2: A Pearson correlation coefficient of r = -0.95 is calculated between the number of hours a person exercises per week and their blood pressure levels."}
+{"idx": 7, "title": "SPSS Tutorials: Pearson Correlation - Kent State University", "date": "", "ddg_snippet": "The bivariate Pearson Correlation produces a sample correlation coefficient , r, which measures the strength and direction of linear relationships between pairs of continuous variables.", "subpage_snippet": "", "source": "libguides.library.kent.edu", "link": "https://libguides.library.kent.edu/spss/pearsoncorr", "content": "The bivariate Pearson Correlation produces a sample correlation coefficient , r, which measures the strength and direction of linear relationships between pairs of continuous variables."}
+{"idx": 8, "title": "Pearson's Correlation: How to Measure and Interpret Relationships ...", "date": "", "ddg_snippet": "Once you've computed the Pearson's correlation coefficient , the next step is interpreting the results. As mentioned, Pearson's r ranges from -1 to +1, and its value provides insight into the strength and direction of the relationship between the two variables.", "subpage_snippet": "", "source": "adulteducation.quest", "link": "https://adulteducation.quest/educational-research/pearsons-correlation-measure-interpret-relationships/", "content": "Once you've computed the Pearson's correlation coefficient , the next step is interpreting the results. As mentioned, Pearson's r ranges from -1 to +1, and its value provides insight into the strength and direction of the relationship between the two variables."}
+{"idx": 9, "title": "Pearson Correlation Coefficient Statistical Guide - LEARN STATISTICS EASILY", "date": "", "ddg_snippet": "Master the Pearson Correlation Coefficient with our statistical guide. Discover how to measure and interpret linear relationships.", "subpage_snippet": "", "source": "statisticseasily.com", "link": "https://statisticseasily.com/pearson-correlation-coefficient-statistical-guide/", "content": "Master the Pearson Correlation Coefficient with our statistical guide. Discover how to measure and interpret linear relationships."}
diff --git "a/data/sampled_jsons/lambda_m_=_1_OR_\316\273m_=_1_DART_radiology_report_generation.jsonl" "b/data/sampled_jsons/lambda_m_=_1_OR_\316\273m_=_1_DART_radiology_report_generation.jsonl"
new file mode 100644
index 0000000000000000000000000000000000000000..b15664380d3846bc30dcf4247a4645c1012034d1
--- /dev/null
+++ "b/data/sampled_jsons/lambda_m_=_1_OR_\316\273m_=_1_DART_radiology_report_generation.jsonl"
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CA2623380C - Broadband or mid-infrared fiber light sources -", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=38067741&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/CA2623380C/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=38067741&utm_source=google_patent&utm_medium ..."}
+{"idx": 1, "title": "US5284484A - Apparatus for implantation and extraction of", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=23887809&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/US5284484A/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=23887809&utm_source=google_patent&utm_medium ..."}
+{"idx": 2, "title": "KR101465456B1 - Use of TNF inhibitor for treatment of erosive", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=37432217&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/KR101465456B1/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=37432217&utm_source=google_patent&utm_medium ..."}
+{"idx": 3, "title": "EP1949151B2 - Broadband or mid-infrared fiber light sources -", "date": "", "ddg_snippet": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=38067741&utm_source=google_patent&utm_medium ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/EP1949151B2/en", "content": "First worldwide family litigation filed litigation Critical https://patents. darts -ip.com/?family=38067741&utm_source=google_patent&utm_medium ..."}
+{"idx": 4, "title": "Diagnostically Competitive Performance of a Physiology-Informed", "date": "", "ddg_snippet": "To address these challenges, we propose a novel deep learning framework called Multitask Automated Generation of Intermodal CT perfusion maps (MAGIC).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.22673v1", "content": "To address these challenges, we propose a novel deep learning framework called Multitask Automated Generation of Intermodal CT perfusion maps (MAGIC)."}
+{"idx": 5, "title": "WO1996009840A1 - Contrast agent - Google Patents", "date": "", "ddg_snippet": "A61K49/18 — Nuclear magnetic resonance [NMR] contrast preparations; Magnetic resonance imaging [MRI] contrast preparations characterised by a ...", "subpage_snippet": "", "source": "patents.google.com", "link": "https://patents.google.com/patent/WO1996009840A1/en", "content": "A61K49/18 — Nuclear magnetic resonance [NMR] contrast preparations; Magnetic resonance imaging [MRI] contrast preparations characterised by a ..."}
+{"idx": 6, "title": "Epileptiform Activity, Seizures, and Epilepsy Syndromes |", "date": "", "ddg_snippet": "They are usually negative in surface polarity and have a duration of 150-200 ms and a low amplitude (50- 100 µV); they occur most often in frontal ...", "subpage_snippet": "", "source": "neupsykey.com", "link": "https://neupsykey.com/epileptiform-activity-seizures-and-epilepsy-syndromes/", "content": "They are usually negative in surface polarity and have a duration of 150-200 ms and a low amplitude (50- 100 µV); they occur most often in frontal ..."}
+{"idx": 7, "title": "Access database event planning Jobs, Employment | Freelancer", "date": "", "ddg_snippet": "... or entity * Seamless flow of data between financial, operational, marketing, and customer interfaces * Everything must be automated where possible: ...", "subpage_snippet": "", "source": "www.freelancer.com", "link": "https://www.freelancer.com/job-search/access-database-event-planning/", "content": "... or entity * Seamless flow of data between financial, operational, marketing, and customer interfaces * Everything must be automated where possible: ..."}
+{"idx": 8, "title": "NEA - Abstract list", "date": "", "ddg_snippet": "ANISN/PC , MultiGroup 1 -D Discrete Ordinates Transport ... CITATION , 3-D MultiGroup Diffusion with 1st Order Perturbation and Criticality Search", "subpage_snippet": "", "source": "www.oecd-nea.org", "link": "https://www.oecd-nea.org/tools/abstract/list", "content": "ANISN/PC , MultiGroup 1 -D Discrete Ordinates Transport ... CITATION , 3-D MultiGroup Diffusion with 1st Order Perturbation and Criticality Search"}
+{"idx": 9, "title": "Rui Zhang - ACL Anthology", "date": "", "ddg_snippet": "... one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, ...", "subpage_snippet": "", "source": "aclanthology.org", "link": "https://aclanthology.org/people/r/rui-zhang/", "content": "... one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, ..."}
diff --git a/data/sampled_jsons/learnable_PDE_solvers_multi-time-step_error_correction.jsonl b/data/sampled_jsons/learnable_PDE_solvers_multi-time-step_error_correction.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..210645db236da8251a9e2fd277e2fda5561cd9b7
--- /dev/null
+++ b/data/sampled_jsons/learnable_PDE_solvers_multi-time-step_error_correction.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Neural PDE operator learning — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "... of using statistical or machine learning approaches to solve PDEs , and maybe even to perform inference through them, by learning to predict one- step ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/ml_pde_operator.html", "content": "... of using statistical or machine learning approaches to solve PDEs , and maybe even to perform inference through them, by learning to predict one- step ..."}
+{"idx": 1, "title": "ICLR 2024 Schedule", "date": "", "ddg_snippet": "... Towards Theory and Design of Robust Time ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2024/calendar", "content": "... Towards Theory and Design of Robust Time ... Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits"}
+{"idx": 2, "title": "Downloads", "date": "", "ddg_snippet": "Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits ... AgentVerse: Facilitating Multi -Agent ...", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2024", "content": "Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi -Armed Bandits ... AgentVerse: Facilitating Multi -Agent ..."}
+{"idx": 3, "title": "ICLR 2023 Papers", "date": "", "ddg_snippet": "Learning Math Reasoning from Self-Sampled Correct and ... Continuous- time identification of dynamic state-space models by deep subspace encoding", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2023/papers.html", "content": "Learning Math Reasoning from Self-Sampled Correct and ... Continuous- time identification of dynamic state-space models by deep subspace encoding"}
+{"idx": 4, "title": "Downloads", "date": "", "ddg_snippet": "Anomaly Transformer: Time Series Anomaly Detection with ... Back2Future: Leveraging Backfill Dynamics for Improving Real- time Predictions in Future", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "Anomaly Transformer: Time Series Anomaly Detection with ... Back2Future: Leveraging Backfill Dynamics for Improving Real- time Predictions in Future"}
+{"idx": 5, "title": "Hierarchical-embedding autoencoder with a predictor (HEAP) as", "date": "", "ddg_snippet": "... or predictor) then advances the embeddings y i subscript 𝑦 𝑖 y_{i} italic_y start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT one time step ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.18857v1", "content": "... or predictor) then advances the embeddings y i subscript 𝑦 𝑖 y_{i} italic_y start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT one time step ..."}
+{"idx": 6, "title": "Quantifying Out-of-Training Uncertainty of Neural-Network based", "date": "", "ddg_snippet": "... coupled with the CFD solver to provide faster—and in some cases more accurate—predictions of turbulent flow [ 11 ] , compared to conventional PDE ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.16891v1", "content": "... coupled with the CFD solver to provide faster—and in some cases more accurate—predictions of turbulent flow [ 11 ] , compared to conventional PDE ..."}
+{"idx": 7, "title": "ICML 2024 Schedule", "date": "", "ddg_snippet": "11:15] SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/calendar", "content": "11:15] SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention"}
+{"idx": 8, "title": "ICML 2020 Papers", "date": "", "ddg_snippet": "Multi -Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization ... Time Series Deconfounder: Estimating ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2020/papers.html", "content": "Multi -Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization ... Time Series Deconfounder: Estimating ..."}
+{"idx": 9, "title": "NeurIPS 2020 Papers", "date": "", "ddg_snippet": "Multi -label classification: do Hamming loss and ... Solver -in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE - Solvers", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2020/papers.html", "content": "Multi -label classification: do Hamming loss and ... Solver -in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE - Solvers"}
diff --git a/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_hardware_configuration_GPU.jsonl b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_hardware_configuration_GPU.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..8a099f2aa095ab0e57a4103a931595aa7e3f82aa
--- /dev/null
+++ b/data/sampled_jsons/lzdFImKK8w_Boltzmann-Aligned_Inverse_Folding_Model_Appendix_A.3.2_hardware_configuration_GPU.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "B -a Inverse Folding Model As a Predictor of Mutational Effects on ...", "date": "", "ddg_snippet": "t to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformationa distribution, thereby highlighting the potential of pre-trained", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=lzdFImKK8w", "content": "t to transfer knowledge from pre-trained inverse folding models to ∆∆G prediction. We first analyze the thermodynamic definition of ∆∆G and introduce the Boltzmann distribution to connect energy with protein conformationa distribution, thereby highlighting the potential of pre-trained"}
+{"idx": 1, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction. We begin by analyzing the thermodynamic definition of ΔΔG and introducing the Boltzmann distribution to connect energy with protein conformational distribution.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.09543", "content": "In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to ΔΔG prediction. We begin by analyzing the thermodynamic definition of ΔΔG and introducing the Boltzmann distribution to connect energy with protein conformational distribution."}
+{"idx": 2, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of ... - GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
+{"idx": 3, "title": "[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a ...", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values.", "subpage_snippet": "", "source": "github.jpy.wang", "link": "https://github.jpy.wang/aim-uofa/BA-DDG", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\\Delta\\Delta G$ values."}
+{"idx": 4, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/2e10d50dfd2a9d52c06fbcd4ed89a022-Abstract-Conference.html", "content": "Compared to previous methods based on inverse folding , our method explicitly accounts for the unbound state of the protein complex in the $\\Delta \\Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.Experimental results on SKEMPI v2 indicate that our ..."}
+{"idx": 5, "title": "dblp: Boltzmann-Aligned Inverse Folding Model as a Predictor of ...", "date": "", "ddg_snippet": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions.", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/JiaoMJY0S25", "content": "Bibliographic details on Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions."}
+{"idx": 6, "title": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational ...", "date": "", "ddg_snippet": "Next, we present a method (Sec. 3.2) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G by predicting the likelihoods of protein sequences, as shown on the left side of Figure 1.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.09543v1", "content": "Next, we present a method (Sec. 3.2) that integrates the inverse folding model into Boltzmann alignment. This method is named BA-Cycle and uses the inverse folding model to evaluate Δ Δ 𝐺 \\Delta\\Delta G roman_Δ roman_Δ italic_G by predicting the likelihoods of protein sequences, as shown on the left side of Figure 1."}
+{"idx": 7, "title": "Portal Weekly #67: MoML 2024, data-driven discovery, boltzmann-aligned ...", "date": "", "ddg_snippet": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions, which are important in drug design.", "subpage_snippet": "", "source": "m2d2.substack.com", "link": "https://m2d2.substack.com/p/portal-weekly-67-moml-2024-data-driven", "content": "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions Predicting changes in binding free energy (∆∆G) is essential for understanding and modifying protein-protein interactions, which are important in drug design."}
+{"idx": 8, "title": "AIDD论文详解：Boltzmann-Aligned Inverse Folding Model —— ICLR2025", "date": "", "ddg_snippet": "简介蛋白质-蛋白质相互作用（PPIs）是在所有生物体中执行多种和必要的生物功能的基础，比如蛋白质药物与蛋白质靶点的结合可以治愈疾病，因此如何设计蛋白质-蛋白质复合物是许多科研人员的研究重点。而在制药过程中…", "subpage_snippet": "", "source": "zhuanlan.zhihu.com", "link": "https://zhuanlan.zhihu.com/p/29398730183", "content": "简介蛋白质-蛋白质相互作用（PPIs）是在所有生物体中执行多种和必要的生物功能的基础，比如蛋白质药物与蛋白质靶点的结合可以治愈疾病，因此如何设计蛋白质-蛋白质复合物是许多科研人员的研究重点。而在制药过程中…"}
+{"idx": 9, "title": "BA-DDG/README.md at master · aim-uofa/BA-DDG · GitHub", "date": "", "ddg_snippet": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/aim-uofa/BA-DDG/blob/master/README.md", "content": "The official implementation of our ICLR 2025 Spotlight paper \" Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions\", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and Δ Δ G values."}
diff --git a/data/sampled_jsons/machine_learning_models_frameworks_sigmoid_loss_contrastive_learning_-SigLIP_-Video-ColBERT.jsonl b/data/sampled_jsons/machine_learning_models_frameworks_sigmoid_loss_contrastive_learning_-SigLIP_-Video-ColBERT.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..aa9e2d26a01160560b05b4b926649b1a9bc0eafc
--- /dev/null
+++ b/data/sampled_jsons/machine_learning_models_frameworks_sigmoid_loss_contrastive_learning_-SigLIP_-Video-ColBERT.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning Videos How Can You Create Custom Loss Functions In PyTorch? - AI and Machine Learning Explained GitHub - ramanakshay/clip: CLIP & SigLIP model training from ... Analysis of Using Sigmoid Loss for Contrastive Learning Images SigLIP Models | mlfoundations/open_clip | DeepWiki sigmoid-contrastive-learning · PyPI Dynamics of Contrastive Loss part1 (Machine Learning 2024) [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learning - … [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learning - … [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learning - … [2402.12613] Analysis of Using Sigmoid Loss for Contrastive Learning - … SigLIP Models | mlfoundations/open_clip | DeepWiki SigLIP Models | mlfoundations/open_clip | DeepWiki Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "Feb 20, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. First, we propose the double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable. Sep 18, 2025 View all Training Update hyperparameters for training in config.yaml file. The SigLIP paper introduced a novel contrastive learning objective that performs better than softmax baselines, particularly for small batch sizes. To use the sigmoid loss , change loss under algorithm config to siglip from clip. However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings. View all Apr 18, 2025 · SigLIP models represent an advanced variant of CLIP models in the OpenCLIP framework , featuring sigmoid -based contrastive learning and several architectural modifications. Apr 7, 2024 · The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability. Mar 14, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. Can sigmoid loss be used in contrastive learning? However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. Does CCEM contain the optimal embedding minimizing sigmoid loss for contrastive learning? Interestingly, the proposed CCEM is proven to contain the optimal embedding with respect to the sigmoid loss. Second, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning. Can siglip be used in contrastive learning? Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . What is the optimal embedding structure for sigmoid loss? The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure , depending on the temperature parameter used in the sigmoid loss. Third, our experimental results on synthetic datasets coincide with the theoretical results on the optimal embedding structures. Which siglip model is best for training? The training configuration will need to specify the sigmoid-based loss instead of the standard softmax-based CLIP loss. Higher-resolution SigLIP models (384×384, 512×512) generally provide better performance but require more computational resources for both training and inference. How do siglip models differ from standard Clip models? SigLIP models differ from standard CLIP in several important ways: Additionally, SigLIP models use a different tokenization approach with a \"canonicalize\" cleaning method for text input . Sources: src/open_clip/model_configs/ViT-B-16-SigLIP2.json src/open_clip/model_configs/ViT-B-16-SigLIP2-256.json A theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings, and a proposed double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable proven to contain the optimal embedding with respect to the sigmoid loss . Contrastive learning has emerged ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2402.12613", "content": "Feb 20, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. First, we propose the double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable. Sep 18, 2025 View all Training Update hyperparameters for training in config.yaml file. The SigLIP paper introduced a novel contrastive learning objective that performs better than softmax baselines, particularly for small batch sizes. To use the sigmoid loss , change loss under algorithm config to siglip from clip. However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings. View all Apr 18, 2025 · SigLIP models represent an advanced variant of CLIP models in the OpenCLIP framework , featuring sigmoid -based contrastive learning and several architectural modifications. Apr 7, 2024 · The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability. Mar 14, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings. Can sigmoid loss be used in contrastive learning? However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. Does CCEM contain the optimal embedding minimizing sigmoid loss for contrastive learning? Interestingly, the proposed CCEM is proven to contain the optimal embedding with respect to the sigmoid loss. Second, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning. Can siglip be used in contrastive learning? Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored . What is the optimal embedding structure for sigmoid loss? The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure , depending on the temperature parameter used in the sigmoid loss. Third, our experimental results on synthetic datasets coincide with the theoretical results on the optimal embedding structures. Which siglip model is best for training? The training configuration will need to specify the sigmoid-based loss instead of the standard softmax-based CLIP loss. Higher-resolution SigLIP models (384×384, 512×512) generally provide better performance but require more computational resources for both training and inference. How do siglip models differ from standard Clip models? SigLIP models differ from standard CLIP in several important ways: Additionally, SigLIP models use a different tokenization approach with a \"canonicalize\" cleaning method for text input . Sources: src/open_clip/model_configs/ViT-B-16-SigLIP2.json src/open_clip/model_configs/ViT-B-16-SigLIP2-256.json A theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings, and a proposed double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable proven to contain the optimal embedding with respect to the sigmoid loss . Contrastive learning has emerged ..."}
+{"idx": 1, "title": "GitHub - ramanakshay/clip: CLIP & SigLIP model training from ...", "date": "", "ddg_snippet": "Training Update hyperparameters for training in config.yaml file. The SigLIP paper introduced a novel contrastive learning objective that performs better than softmax baselines, particularly for small batch sizes. To use the sigmoid loss , change loss under algorithm config to siglip from clip.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ramanakshay/clip", "content": "Training Update hyperparameters for training in config.yaml file. The SigLIP paper introduced a novel contrastive learning objective that performs better than softmax baselines, particularly for small batch sizes. To use the sigmoid loss , change loss under algorithm config to siglip from clip."}
+{"idx": 2, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings.", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v238/lee24a/lee24a.pdf", "content": "However, theoreti-cal understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learn-ing , in the perspective of the geometric struc-ture of learned embeddings."}
+{"idx": 3, "title": "SigLIP Models | mlfoundations/open_clip | DeepWiki", "date": "", "ddg_snippet": "Apr 18, 2025 · SigLIP models represent an advanced variant of CLIP models in the OpenCLIP framework , featuring sigmoid -based contrastive learning and several architectural modifications.", "subpage_snippet": "", "source": "deepwiki.com", "link": "https://deepwiki.com/mlfoundations/open_clip/5.2-siglip-models", "content": "Apr 18, 2025 · SigLIP models represent an advanced variant of CLIP models in the OpenCLIP framework , featuring sigmoid -based contrastive learning and several architectural modifications."}
+{"idx": 4, "title": "sigmoid-contrastive-learning · PyPI", "date": "", "ddg_snippet": "Apr 7, 2024 · The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability.", "subpage_snippet": "", "source": "pypi.org", "link": "https://pypi.org/project/sigmoid-contrastive-learning/", "content": "Apr 7, 2024 · The loss function is a sigmoid constrastive loss adapted from SigLIP , with an addition of a confidence penalty gamma that balances the ratio of positive and negative samples per batch, amplifying learning from harder examples and improving training stability."}
+{"idx": 5, "title": "Dynamics of Contrastive Loss part1 (Machine Learning 2024)", "date": "", "ddg_snippet": "Mar 14, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings.", "subpage_snippet": "", "source": "medium.com", "link": "https://medium.com/@monocosmo77/dynamics-of-contrastive-loss-part1-machine-learning-2024-8275155daa1a", "content": "Mar 14, 2024 · In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings."}
+{"idx": 6, "title": "Analysis of Using Sigmoid Loss for Contrastive Learning", "date": "", "ddg_snippet": "A theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings, and a proposed double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable proven to contain the optimal embedding with respect to the sigmoid loss . Contrastive learning has emerged ...", "subpage_snippet": "", "source": "semanticscholar.org", "link": "https://semanticscholar.org/paper/Analysis-of-Using-Sigmoid-Loss-for-Contrastive-Lee-Chang/8948d8d2ac358587f1a09264239137791c3ccc22", "content": "A theoretical analysis of using the sigmoid loss in contrastive learning , in the perspective of the geometric structure of learned embeddings, and a proposed double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable proven to contain the optimal embedding with respect to the sigmoid loss . Contrastive learning has emerged ..."}
+{"idx": 7, "title": "Contrastive Learning with Hard Negative Samples | Medium", "date": "", "ddg_snippet": "In contrastive learning loss functions — like NT-Xent (Normalized Temperature-scaled Cross Entropy Loss ) or InfoNCE — the role of negative samples is to ensure that the model doesn’t confuse unrelated examples. Hard vs. Easy Negatives.", "subpage_snippet": "", "source": "mr-amit.medium.com", "link": "https://mr-amit.medium.com/contrastive-learning-with-hard-negative-samples-2cccb609fa0c", "content": "In contrastive learning loss functions — like NT-Xent (Normalized Temperature-scaled Cross Entropy Loss ) or InfoNCE — the role of negative samples is to ensure that the model doesn’t confuse unrelated examples. Hard vs. Easy Negatives."}
+{"idx": 8, "title": "Contrastive Loss Functions in AI | Restackio", "date": "", "ddg_snippet": "Contrastive loss functions play a crucial role in training AI models , particularly in tasks involving similarity learning . These functions are designed to minimize the distance between similar pairs while maximizing the distance between dissimilar pairs.", "subpage_snippet": "", "source": "d2wozrt205r2fu.cloudfront.net", "link": "https://d2wozrt205r2fu.cloudfront.net/p/ai-model-evaluation-answer-contrastive-loss-functions-cat-ai", "content": "Contrastive loss functions play a crucial role in training AI models , particularly in tasks involving similarity learning . These functions are designed to minimize the distance between similar pairs while maximizing the distance between dissimilar pairs."}
+{"idx": 9, "title": "Contrastive Learning", "date": "", "ddg_snippet": "Contrastive learning is a method for structuring the work of locating similarities and differences for an ML model . This method can be used to train a machine learning model to distinguish between similar and different photos.", "subpage_snippet": "", "source": "iq.opengenus.org", "link": "https://iq.opengenus.org/contrastive-learning/", "content": "Contrastive learning is a method for structuring the work of locating similarities and differences for an ML model . This method can be used to train a machine learning model to distinguish between similar and different photos."}
diff --git a/data/sampled_jsons/matching_pursuit_algorithm_sparsity_J_iterations_L_features.jsonl b/data/sampled_jsons/matching_pursuit_algorithm_sparsity_J_iterations_L_features.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a80b092f7ef56d9149506761e8b89ff8d7ba7cdc
--- /dev/null
+++ b/data/sampled_jsons/matching_pursuit_algorithm_sparsity_J_iterations_L_features.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "\"match\" 和 \"matching\" 的差別在哪裡？ | HiNative", "date": "", "ddg_snippet": "match 和 matching 的差別在哪裡？ 如果不好說明，請提供一些例句。 ellekroeoeke 2022年6月3日 英語 (美國)", "subpage_snippet": "", "source": "tw.hinative.com", "link": "https://tw.hinative.com/questions/22002884", "content": "match 和 matching 的差別在哪裡？ 如果不好說明，請提供一些例句。 ellekroeoeke 2022年6月3日 英語 (美國)"}
+{"idx": 1, "title": "【Matching】の例文や意味・使い方 | HiNative", "date": "", "ddg_snippet": "「Matching」の使い方・例文・意味・類義語に関するQ&A。「Matching」の自然な使い方やニュアンスについて55件以上のネイティブスピーカーからの回答が集まっています。", "subpage_snippet": "", "source": "ja.hinative.com", "link": "https://ja.hinative.com/dictionaries/matching", "content": "「Matching」の使い方・例文・意味・類義語に関するQ&A。「Matching」の自然な使い方やニュアンスについて55件以上のネイティブスピーカーからの回答が集まっています。"}
+{"idx": 2, "title": "【matching 】 は 日本語 で何と言いますか？ | HiNative", "date": "", "ddg_snippet": "【ネイティブが回答】「matching 」 は \"日本語\" でなんて言うの？質問に2件の回答が集まっています！Hinativeでは\"日本語\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。", "subpage_snippet": "", "source": "ja.hinative.com", "link": "https://ja.hinative.com/questions/12674388", "content": "【ネイティブが回答】「matching 」 は \"日本語\" でなんて言うの？質問に2件の回答が集まっています！Hinativeでは\"日本語\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。"}
+{"idx": 3, "title": "【they are matching】とはどういう意味ですか？ - 英語 (イギリス)に...", "date": "", "ddg_snippet": "【ネイティブが回答】「they are matching」ってどういう意味？質問に1件の回答が集まっています！Hinativeでは\"英語（イギリス）\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。", "subpage_snippet": "", "source": "ja.hinative.com", "link": "https://ja.hinative.com/questions/4344550", "content": "【ネイティブが回答】「they are matching」ってどういう意味？質問に1件の回答が集まっています！Hinativeでは\"英語（イギリス）\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。"}
+{"idx": 4, "title": "What is the difference between \"match\" and \"match up ... -...", "date": "", "ddg_snippet": "Also matching up two colors would be an action, while saying match up two colors would be a demand, and saying he matched the two colors means they successfully put together the pair of equivalent colors.", "subpage_snippet": "", "source": "hinative.com", "link": "https://hinative.com/questions/17492754", "content": "Also matching up two colors would be an action, while saying match up two colors would be a demand, and saying he matched the two colors means they successfully put together the pair of equivalent colors."}
+{"idx": 5, "title": "What is the meaning of \"Math isn't mathing.\"? - Question ... -...", "date": "", "ddg_snippet": "Definition of Math isn't mathing. It is slang, and it is being used for comedy. \"mathing\" is not technically a word, and it is not normally a verb either. But it is playing with the words and using the repetition to reinforce what they are saying. It is saying that the math is not working properly. So the math is not doing what math should do, it is not correct, it is not behaving properly. If ...", "subpage_snippet": "", "source": "hinative.com", "link": "https://hinative.com/questions/21175193", "content": "Definition of Math isn't mathing. It is slang, and it is being used for comedy. \"mathing\" is not technically a word, and it is not normally a verb either. But it is playing with the words and using the repetition to reinforce what they are saying. It is saying that the math is not working properly. So the math is not doing what math should do, it is not correct, it is not behaving properly. If ..."}
+{"idx": 6, "title": "【match to】 と 【match with】 はどう違いますか？ | HiNative", "date": "", "ddg_snippet": "【ネイティブ回答】「match to」と「match with」はどう違うの？質問に1件の回答が集まっています！Hinativeでは\"英語（アメリカ）\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。", "subpage_snippet": "", "source": "ja.hinative.com", "link": "https://ja.hinative.com/questions/14934772", "content": "【ネイティブ回答】「match to」と「match with」はどう違うの？質問に1件の回答が集まっています！Hinativeでは\"英語（アメリカ）\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。"}
+{"idx": 7, "title": "【in accordance with 】 と 【according to 】 は ... - HiNative", "date": "", "ddg_snippet": "right. ill show the differences with an example. in accordance with the law, driving a car under age is illegal. but according to what my friend said , driving underage on private roads is not illegal.", "subpage_snippet": "", "source": "ja.hinative.com", "link": "https://ja.hinative.com/questions/4168098", "content": "right. ill show the differences with an example. in accordance with the law, driving a car under age is illegal. but according to what my friend said , driving underage on private roads is not illegal."}
+{"idx": 8, "title": "【search condition】 と 【search criteria】 はどう違いますか？ | ...", "date": "", "ddg_snippet": "【ネイティブ回答】「search ...」と「search ...」はどう違うの？質問に2件の回答が集まっています！Hinativeでは\"英語（アメリカ）\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。", "subpage_snippet": "", "source": "ja.hinative.com", "link": "https://ja.hinative.com/questions/25334029", "content": "【ネイティブ回答】「search ...」と「search ...」はどう違うの？質問に2件の回答が集まっています！Hinativeでは\"英語（アメリカ）\"や外国語の勉強で気になったことを、ネイティブスピーカーに簡単に質問できます。"}
+{"idx": 9, "title": "Nah とはどのような意味ですか？ またどんな時に使いますか？ | HiNat...", "date": "", "ddg_snippet": "間投詞です。 話しを終わるときに使った間投詞です。結論を出すときにも使う。でもフォルマルなシチュエーションに使ってはいけません。 例1 作っている料理ができました。満足で嬉しいから 「Nah, sudah jadi! /よし、出来た！」と言いましす。 例2 人1: Ternyata A yang mencuri uangku 「 僕のお金を ...", "subpage_snippet": "", "source": "hinative.com", "link": "https://hinative.com/questions/20189610", "content": "間投詞です。 話しを終わるときに使った間投詞です。結論を出すときにも使う。でもフォルマルなシチュエーションに使ってはいけません。 例1 作っている料理ができました。満足で嬉しいから 「Nah, sudah jadi! /よし、出来た！」と言いましす。 例2 人1: Ternyata A yang mencuri uangku 「 僕のお金を ..."}
diff --git a/data/sampled_jsons/mkuB677eMM_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchm.jsonl b/data/sampled_jsons/mkuB677eMM_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchm.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2bf69590200c29d2d29d3ca8e55a4e6bcd2eaac2
--- /dev/null
+++ b/data/sampled_jsons/mkuB677eMM_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_and_Crystal_Symmetry_Classification_Benchm.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simxrd-4m: B Simulated X-ray Diffraction D C Symmetry Classification", "date": "", "ddg_snippet": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks . To address this, we introduce SimXRD-4M , the largest open-source ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=mkuB677eMM", "content": "ABSTRACT Powder X-ray diffraction (XRD) patterns are highly effective for crystal identi-fication and play a pivotal role in materials discovery. Although machine learn-ing (ML) has advanced the analysis of powder XRD patterns, progress has been constrained by the limited availability of training data and established bench-marks . To address this, we introduce SimXRD-4M , the largest open-source ..."}
+{"idx": 1, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v2", "content": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions."}
+{"idx": 2, "title": "PDF SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry ...", "date": "", "ddg_snippet": "2: Data analysis reveals that the symmetry labels follow a long-tailed distribution. 3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/media/iclr-2025/Slides/28452.pdf", "content": "2: Data analysis reveals that the symmetry labels follow a long-tailed distribution. 3: We evaluate 21 models on two different splitting patterns (in-library and out-of-library) and find that most existing models struggle to accurately predict the symmetry of low-frequency classes, even when addressing for class imbalance."}
+{"idx": 3, "title": "GitHub - Bin-Cao/SimXRD: [ICLR 2025] SimXRD-4M: Big Simulated X-ray ...", "date": "", "ddg_snippet": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD", "content": "Open Source: SimXRD-4M is available on Huggingface. Data Description: Crystals are categorized into 230 space groups, each representing a distinct symmetry catrgory. XRD patterns, which correspond to the crystal structure, serve as vital tools for studying these materials. However, XRD patterns are ..."}
+{"idx": 4, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystalline ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions.", "subpage_snippet": "", "source": "papers.cool", "link": "https://papers.cool/venue/mkuB677eMM@OpenReview", "content": "SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We benchmark 21 sequence models in both in-library and out-of-library scenarios and analyze the impact of class imbalance in long-tailed crystal label distributions."}
+{"idx": 5, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.15469v1", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD ."}
+{"idx": 6, "title": "SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the ...", "date": "", "ddg_snippet": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/381665624_SimXRD-4M_Big_Simulated_X-ray_Diffraction_Data_Accelerate_the_Crystalline_Symmetry_Classification", "content": "SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations."}
+{"idx": 7, "title": "SimXRD/README.md at main · Bin-Cao/SimXRD · GitHub", "date": "", "ddg_snippet": "[ICLR 2025] SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark - Bin-Cao/ SimXRD", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/Bin-Cao/SimXRD/blob/main/README.md", "content": "[ICLR 2025] SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark - Bin-Cao/ SimXRD"}
+{"idx": 8, "title": "dblp: SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal ...", "date": "", "ddg_snippet": "Bibliographic details on SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark .", "subpage_snippet": "", "source": "dblp.org", "link": "https://dblp.org/rec/conf/iclr/CaoLZTLZ25", "content": "Bibliographic details on SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark ."}
+{"idx": 9, "title": "Publications - Ruifeng Tan", "date": "", "ddg_snippet": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ...", "subpage_snippet": "", "source": "ruifeng-tan.github.io", "link": "https://ruifeng-tan.github.io/publications/", "content": "SimXRD-4M : Big Simulated X-ray Diffraction Data and Crystalline Symmetry Classification Benchmark Published in ICLR, 2025 In this paper, we developed the largest open-source simulated X-ray diffraction database ( SimXRD ). SimXRD comprises 4,065,346 simulated powder XRD patterns, representing 119,569 unique crystal structures under 33 simulated conditions that reflect real-world variations. We ..."}
diff --git a/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_neural_networks.jsonl b/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_neural_networks.jsonl
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index 0000000000000000000000000000000000000000..a5ad2e26e021d3432101f658f58903f6e289f78e
--- /dev/null
+++ b/data/sampled_jsons/non-linear_reward_functions_preference-based_reinforcement_learning_neural_networks.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Reinforcement learning - Wikipedia", "date": "", "ddg_snippet": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Reinforcement_learning", "content": "The typical framing of a reinforcement learning scenario: an agent takes actions in an environment, which is interpreted into a reward and a state representation, which are fed back to the agent. Reinforcement learning is an interdisciplinary area of..."}
+{"idx": 1, "title": "Preference - based Learning of Reward Function Features", "date": "", "ddg_snippet": "Abstract— Preference - based learning of reward functions , where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as au-tonomous driving. Existing algorithms have focused on learning reward functions that are linear in a set...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2103.02727", "content": "Abstract— Preference - based learning of reward functions , where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as au-tonomous driving. Existing algorithms have focused on learning reward functions that are linear in a set..."}
+{"idx": 2, "title": "(PDF) Preference - based Learning of Reward Function Features", "date": "", "ddg_snippet": "Preference - based learning of reward functions , where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous driving.sents the neural network feature function with corresponding.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/349786980_Preference-based_Learning_of_Reward_Function_Features", "content": "Preference - based learning of reward functions , where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous driving.sents the neural network feature function with corresponding."}
+{"idx": 3, "title": "Policy Learning | AI Alignment", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning . Preference - based Reinforcement Learning (PbRL) seeks to facilitate training RL agents using preference feedback instead of explicit reward signals.", "subpage_snippet": "", "source": "alignmentsurvey.com", "link": "https://alignmentsurvey.com/materials/learning/policy/", "content": "Preference - based Reinforcement Learning . Preference - based Reinforcement Learning (PbRL) seeks to facilitate training RL agents using preference feedback instead of explicit reward signals."}
+{"idx": 4, "title": "Asymmetric and adaptive reward coding via normalized reinforcement ...", "date": "", "ddg_snippet": "Standard reinforcement learning models use linear value functions , despite strong empirical evidence that biological value representations are nonlinear functions of external rewards . Reward - based training of recurrent neural networks for cognitive and value- based tasks.", "subpage_snippet": "", "source": "pmc.ncbi.nlm.nih.gov", "link": "https://pmc.ncbi.nlm.nih.gov/articles/PMC9345478/", "content": "Standard reinforcement learning models use linear value functions , despite strong empirical evidence that biological value representations are nonlinear functions of external rewards . Reward - based training of recurrent neural networks for cognitive and value- based tasks."}
+{"idx": 5, "title": "Interpretable Preference - based Reinforcement Learning ... | DeepAI", "date": "", "ddg_snippet": "Interpretable Preference - based Reinforcement Learning with Tree-Structured Reward Functions .", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/interpretable-preference-based-reinforcement-learning-with-tree-structured-reward-functions", "content": "Interpretable Preference - based Reinforcement Learning with Tree-Structured Reward Functions ."}
+{"idx": 6, "title": "Preference - Based Reinforcement Learning Methods", "date": "", "ddg_snippet": "Preference - based reinforcement learning (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal.", "subpage_snippet": "", "source": "jmlr.org", "link": "https://jmlr.org/papers/volume18/16-634/16-634.pdf", "content": "Preference - based reinforcement learning (PbRL) is a paradigm for learning from non -numerical feedback in sequential domains. Its key idea is that the requirement for a numer-ical feedback signal is replaced with the assumption of a preference - based feedback signal."}
+{"idx": 7, "title": "Algorithms based on TD( λ )-methods", "date": "", "ddg_snippet": "We introduced two new value- function based reinforcement learning algorithms, ACLA and QV(λ)- learning , which are based on TD(λ) methods for learning a state value- function , and another update rule to learn either Q-values or preference values for selecting actions.", "subpage_snippet": "", "source": "www.ai.rug.nl", "link": "https://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/Two_novel_rl.pdf", "content": "We introduced two new value- function based reinforcement learning algorithms, ACLA and QV(λ)- learning , which are based on TD(λ) methods for learning a state value- function , and another update rule to learn either Q-values or preference values for selecting actions."}
+{"idx": 8, "title": "Frontiers | Stochasticity, Nonlinear Value Functions , and Update...", "date": "", "ddg_snippet": "Linear and Nonlinear Value Functions . For reinforcement learning to work well, the value function should be able to capture the structure of the incoming rewards .", "subpage_snippet": "", "source": "www.frontiersin.org", "link": "https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2021.639081/full", "content": "Linear and Nonlinear Value Functions . For reinforcement learning to work well, the value function should be able to capture the structure of the incoming rewards ."}
+{"idx": 9, "title": "Meta- reinforcement learning for social robotics.", "date": "", "ddg_snippet": "Preference - based Reinforcement Learning for Social Robotics. Introduction. Reinforcement learning typically optimizes a global reward function , but real-world sys-tems often involve multiple dimensions of costs to be minimized.", "subpage_snippet": "", "source": "theses.hal.science", "link": "https://theses.hal.science/tel-05004251v1/document", "content": "Preference - based Reinforcement Learning for Social Robotics. Introduction. Reinforcement learning typically optimizes a global reward function , but real-world sys-tems often involve multiple dimensions of costs to be minimized."}
diff --git a/data/sampled_jsons/openreview_Gumiho_Table_1_Llama2_70B_MT-Bench.jsonl b/data/sampled_jsons/openreview_Gumiho_Table_1_Llama2_70B_MT-Bench.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3760b7c8c8d27d95de11de40df5cf031e86a68e8
--- /dev/null
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Gumiho: A Hybrid Architecture to Prioritize ... - OpenReview", "date": "", "ddg_snippet": "May 1 , 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=0ObGn4e1IS", "content": "May 1 , 2025 · Building on this insight, we propose Gumiho , a hybrid model combining serial and parallel heads. Specifically, given the critical importance of early tokens, we employ a sophisticated Transformer architecture for the early draft heads in a serial configuration to improve accuracy."}
+{"idx": 1, "title": "GitHub - AMD-AGI/Gumiho: Official Implementation of \"Gumiho ...", "date": "", "ddg_snippet": "Jul 11, 2025 · Official Implementation of \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\" (ICML'25) - AMD-AGI/ Gumiho", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/AMD-AGI/Gumiho", "content": "Jul 11, 2025 · Official Implementation of \" Gumiho : A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding\" (ICML'25) - AMD-AGI/ Gumiho"}
+{"idx": 2, "title": "meta-llama/Llama-2-70b · Hugging Face", "date": "", "ddg_snippet": "Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/meta-llama/Llama-2-70b", "content": "Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback."}
+{"idx": 3, "title": "Benchmarking LLM Evaluators On A Mini MT-Bench (Single ...", "date": "", "ddg_snippet": "More specifically, we will run benchmarks using a mini version of the MT-Bench single-grading dataset. In this version, we only consider the answers on the 160 questions (i.e., 80 x 2, since there are 80 two-turn dialogues) provided by llama2 - 70b .", "subpage_snippet": "", "source": "docs.llamaindex.ai", "link": "https://docs.llamaindex.ai/en/v0.10.22/examples/evaluation/mt_bench_single_grading/", "content": "More specifically, we will run benchmarks using a mini version of the MT-Bench single-grading dataset. In this version, we only consider the answers on the 160 questions (i.e., 80 x 2, since there are 80 two-turn dialogues) provided by llama2 - 70b ."}
+{"idx": 4, "title": "GitHub - mtbench101/mt-bench-101: [ACL 2024] MT-Bench-101: A ...", "date": "", "ddg_snippet": "Feb 22, 2024 · About MT-Bench -101 MT-Bench -101 is specifically designed to evaluate the finegrained abilities of LLMs in multi-turn dialogues. By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mtbench101/mt-bench-101", "content": "Feb 22, 2024 · About MT-Bench -101 MT-Bench -101 is specifically designed to evaluate the finegrained abilities of LLMs in multi-turn dialogues. By conducting a detailed analysis of real multi-turn dialogue data, we construct a three-tier hierarchical ability taxonomy comprising 4208 turns across 1388 multi-turn dialogues in 13 distinct tasks."}
+{"idx": 5, "title": "nvidia/ Llama2 - 70B -SteerLM-Chat · Hugging Face", "date": "", "ddg_snippet": "Nov 15, 2023 · Llama2 - 70B -SteerLM-Chat reaches 7.54 on MT Bench , the highest among commercial-use-friendly models trained on open-source datasets based on MT Bench Leaderboard as of 15 Nov 2023. Try this model instantly for free hosted by us at NVIDIA AI Playground.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/nvidia/Llama2-70B-SteerLM-Chat", "content": "Nov 15, 2023 · Llama2 - 70B -SteerLM-Chat reaches 7.54 on MT Bench , the highest among commercial-use-friendly models trained on open-source datasets based on MT Bench Leaderboard as of 15 Nov 2023. Try this model instantly for free hosted by us at NVIDIA AI Playground."}
+{"idx": 6, "title": "New llama2 - 70B model gets 7.54 on MT-Bench - LinkedIn", "date": "", "ddg_snippet": "Today we release SteerLM-aligned version of llama2 - 70B model, llama2 - 70B -SteerLM-Chat. This model gets 7.54 on MT-Bench . Important aspect of this model is that…", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/posts/oleksiikuchaiev_nv-llama2-70b-steerlm-chat-nvidia-ngc-activity-7135394988187680768-dtyR", "content": "Today we release SteerLM-aligned version of llama2 - 70B model, llama2 - 70B -SteerLM-Chat. This model gets 7.54 on MT-Bench . Important aspect of this model is that…"}
+{"idx": 7, "title": "NewTerm : Benchmarking Real-Time New Terms for", "date": "", "ddg_snippet": "Table 2: Performance of Llama-3-Instruct- 70 B on Different Tasks Involving the New Term “wokely”. As observed, the model only answered correctly in the COMA task but failed in the other two tasks.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=NQLZoMHm6u", "content": "Table 2: Performance of Llama-3-Instruct- 70 B on Different Tasks Involving the New Term “wokely”. As observed, the model only answered correctly in the COMA task but failed in the other two tasks."}
+{"idx": 8, "title": "karakuri-lm- 70 b -v0. 1 huggingface.co api & karakuri-ai... - Toolify", "date": "", "ddg_snippet": "Finetuned from : meta-llama/ Llama - 2 - 70 b -hf. Contact : For questions and comments about the model, please email [email protected]. Performance. At the time of release, KARAKURI LM 70 B Chat v0. 1 achieves the highest performance among Japanese open models on the MT - Bench -jp", "subpage_snippet": "", "source": "www.toolify.ai", "link": "https://www.toolify.ai/ai-model/karakuri-ai-karakuri-lm-70b-v0-1", "content": "Finetuned from : meta-llama/ Llama - 2 - 70 b -hf. Contact : For questions and comments about the model, please email [email protected]. Performance. At the time of release, KARAKURI LM 70 B Chat v0. 1 achieves the highest performance among Japanese open models on the MT - Bench -jp"}
+{"idx": 9, "title": "mannix/smaug-llama3- 70 b :q5_k_m", "date": "", "ddg_snippet": "The model outperforms Llama-3- 70 B -Instruct substantially, and is on par with GPT-4-Turbo, on MT - Bench (see below).", "subpage_snippet": "", "source": "ollama.com", "link": "https://ollama.com/mannix/smaug-llama3-70b:q5_k_m", "content": "The model outperforms Llama-3- 70 B -Instruct substantially, and is on par with GPT-4-Turbo, on MT - Bench (see below)."}
diff --git a/data/sampled_jsons/openreview_lzdFImKK8w_appendix_gpu_hardware.jsonl b/data/sampled_jsons/openreview_lzdFImKK8w_appendix_gpu_hardware.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..3dbac40b5a3fc911c89d4b44f6580833272c5ce0
--- /dev/null
+++ b/data/sampled_jsons/openreview_lzdFImKK8w_appendix_gpu_hardware.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Practical offloading for fine-tuning LLM on commodity GPU ... - OpenReview", "date": "", "ddg_snippet": "However, this approach is hampered by the limited bandwidth of commodity hardware , which constrains communication between the CPU and GPU . In this paper, we present an offloading framework, LSP_Offload, that enables near-native speed LLM fine-tuning on commodity hardware through learned subspace projectors.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=L8e2PS6pJO", "content": "However, this approach is hampered by the limited bandwidth of commodity hardware , which constrains communication between the CPU and GPU . In this paper, we present an offloading framework, LSP_Offload, that enables near-native speed LLM fine-tuning on commodity hardware through learned subspace projectors."}
+{"idx": 1, "title": "GitHub - NVIDIA/open-gpu-doc: Documentation of NVIDIA chip/hardware ...", "date": "", "ddg_snippet": "Documentation of NVIDIA chip/ hardware interfaces. Contribute to NVIDIA/open- gpu -doc development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/NVIDIA/open-gpu-doc", "content": "Documentation of NVIDIA chip/ hardware interfaces. Contribute to NVIDIA/open- gpu -doc development by creating an account on GitHub."}
+{"idx": 2, "title": "UniGist: Towards General and Hardware-aligned Sequence-level Long ...", "date": "", "ddg_snippet": "Nevertheless, to fully realize such efficiency on GPU devices, we need suitable hardware -aligned kernels. Because of the sparsely inserted gist tokens, our attention patterns are incompatibility with flash_attn kernels [8]. Therefore, we design a custom kernel that supports efficient processing.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15763v1", "content": "Nevertheless, to fully realize such efficiency on GPU devices, we need suitable hardware -aligned kernels. Because of the sparsely inserted gist tokens, our attention patterns are incompatibility with flash_attn kernels [8]. Therefore, we design a custom kernel that supports efficient processing."}
+{"idx": 3, "title": "CS8803 OMSCS - GPU hardware and software notes - yxlow", "date": "", "ddg_snippet": "Explore hardware optimization opportunities for GPU address translations to improve GPU performance and efficiency In this video, we will explain the concept of GPU virtual memory and why it is essential in modern GPU architectures.", "subpage_snippet": "", "source": "lowyx.com", "link": "https://lowyx.com/posts/gt-gpu-notes/", "content": "Explore hardware optimization opportunities for GPU address translations to improve GPU performance and efficiency In this video, we will explain the concept of GPU virtual memory and why it is essential in modern GPU architectures."}
+{"idx": 4, "title": "CS8803-O21: GPU Software & Hardware : r/OMSCS - Reddit", "date": "", "ddg_snippet": "Additionally, you'll delve into compiler principles to comprehend software-related GPU issues and read research papers on hardware challenges. By the end, you'll have enhanced your knowledge of compilers, programming, and computer architecture for modern GPUs .", "subpage_snippet": "", "source": "www.reddit.com", "link": "https://www.reddit.com/r/OMSCS/comments/18xra17/cs8803o21_gpu_software_hardware/", "content": "Additionally, you'll delve into compiler principles to comprehend software-related GPU issues and read research papers on hardware challenges. By the end, you'll have enhanced your knowledge of compilers, programming, and computer architecture for modern GPUs ."}
+{"idx": 5, "title": "GitHub - lzhoang2801/Hardware-Sniffer: A Python script for gathering ...", "date": "", "ddg_snippet": "Comprehensive Hardware Collection: Extracts information about your motherboard, CPU, GPU , monitor, network adapters, audio devices, USB controllers, input devices, storage controllers, biometric sensors, Bluetooth, SD controllers, and system devices using the WMI command-line (WMIC) utility.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lzhoang2801/Hardware-Sniffer", "content": "Comprehensive Hardware Collection: Extracts information about your motherboard, CPU, GPU , monitor, network adapters, audio devices, USB controllers, input devices, storage controllers, biometric sensors, Bluetooth, SD controllers, and system devices using the WMI command-line (WMIC) utility."}
+{"idx": 6, "title": "GPU Hardware and Software - OMSHub", "date": "", "ddg_snippet": "Pros: Access to H100 GPU - most people won't get to use hardware this advanced outside of a class setting. The second CUDA project is the highlight of the course - one of the most challenging assignments I had in OMSCS. There's a decent amount of extra credit (project 2, participation) - you'll need it for the final exam.", "subpage_snippet": "", "source": "www.omshub.org", "link": "https://www.omshub.org/course/CS-8803-O21", "content": "Pros: Access to H100 GPU - most people won't get to use hardware this advanced outside of a class setting. The second CUDA project is the highlight of the course - one of the most challenging assignments I had in OMSCS. There's a decent amount of extra credit (project 2, participation) - you'll need it for the final exam."}
+{"idx": 7, "title": "CS8803 OMSCS - GPU hardware and software - yxlow", "date": "", "ddg_snippet": "The internal working of a GPU , like what is a core in gpu context, how are threads and blocks organized, How do cache systems in a GPU work, How do we think about parallelism at a gpu level They also cover a few other things and provide quite a few papers to read (which you are being evaluated on).", "subpage_snippet": "", "source": "lowyx.com", "link": "https://lowyx.com/posts/gt-gpu/", "content": "The internal working of a GPU , like what is a core in gpu context, how are threads and blocks organized, How do cache systems in a GPU work, How do we think about parallelism at a gpu level They also cover a few other things and provide quite a few papers to read (which you are being evaluated on)."}
+{"idx": 8, "title": "GitHub - openreview/openreview-py: Official Python client library for ...", "date": "", "ddg_snippet": "Running the openreview -py test suite requires some initial setup. First, the OpenReview API V1, OpenReview API V2 and OpenReview Web frontend must be cloned and configured to run on ports 3000, 3001 and 3030 respectively.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/openreview/openreview-py", "content": "Running the openreview -py test suite requires some initial setup. First, the OpenReview API V1, OpenReview API V2 and OpenReview Web frontend must be cloned and configured to run on ports 3000, 3001 and 3030 respectively."}
+{"idx": 9, "title": "2025 Call for Papers - MLSys", "date": "", "ddg_snippet": "Specialized hardware for machine learning LLM-based hardware design or system optimization techniques Hardware -efficient ML methods Machine learning benchmarks, datasets, and tooling Reviewing process: All submissions will be double-blind, though authors are allowed to post their papers on arXiv or other public forums.", "subpage_snippet": "", "source": "mlsys.org", "link": "https://mlsys.org/Conferences/2025/CallForPapers", "content": "Specialized hardware for machine learning LLM-based hardware design or system optimization techniques Hardware -efficient ML methods Machine learning benchmarks, datasets, and tooling Reviewing process: All submissions will be double-blind, though authors are allowed to post their papers on arXiv or other public forums."}
diff --git a/data/sampled_jsons/optimal_convergence_rate_q=1_alpha=0_distribution_regression.jsonl b/data/sampled_jsons/optimal_convergence_rate_q=1_alpha=0_distribution_regression.jsonl
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index 0000000000000000000000000000000000000000..8e1fe2de2fdb3ff69c0e80b0101d171146511d0c
--- /dev/null
+++ b/data/sampled_jsons/optimal_convergence_rate_q=1_alpha=0_distribution_regression.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Rate of convergence - Wikipedia", "date": "", "ddg_snippet": "The definitions of Q-convergence rates have the shortcoming that they do not naturally capture the convergence behavior of sequences that do converge, but do not converge with an asymptotically constant rate with every step, so that the Q-convergence limit does not exist. One class of examples is the staggered geometric progressions that get closer to their limits only every other step or ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Rate_of_convergence", "content": "The definitions of Q-convergence rates have the shortcoming that they do not naturally capture the convergence behavior of sequences that do converge, but do not converge with an asymptotically constant rate with every step, so that the Q-convergence limit does not exist. One class of examples is the staggered geometric progressions that get closer to their limits only every other step or ..."}
+{"idx": 1, "title": "PDF Minimax Theory for Nonparametric Regression", "date": "", "ddg_snippet": "1 Introduction When we are doing theory for nonparametric regression (or really statistical estimation in general), how can we tell if a convergence rate that we can prove for a particular method is \"impressive\"? Can the anal-ysis be tightened? Or the method itself improved? And even if we carried this out, will such refinements actually lead to a better convergence rate ?", "subpage_snippet": "", "source": "www.stat.berkeley.edu", "link": "https://www.stat.berkeley.edu/~ryantibs/statlearn-s23/lectures/minimax.pdf", "content": "1 Introduction When we are doing theory for nonparametric regression (or really statistical estimation in general), how can we tell if a convergence rate that we can prove for a particular method is \"impressive\"? Can the anal-ysis be tightened? Or the method itself improved? And even if we carried this out, will such refinements actually lead to a better convergence rate ?"}
+{"idx": 2, "title": "PDF Distributed nonparametric function estimation: Optimal rate of ...", "date": "", "ddg_snippet": "Distributed minimax estimation and distributed adaptive estimation un-der communication constraints for Gaussian sequence model and white noise model are studied. The minimax rate of convergence for distributed estima-tion over a given Besov class, which serves as a benchmark for the cost of adaptation, is established. We then quantify the exact communication cost for adaptation and construct ...", "subpage_snippet": "", "source": "stat.wharton.upenn.edu", "link": "http://stat.wharton.upenn.edu/~tcai/paper/Distributed-Nonparametric-Regression.pdf", "content": "Distributed minimax estimation and distributed adaptive estimation un-der communication constraints for Gaussian sequence model and white noise model are studied. The minimax rate of convergence for distributed estima-tion over a given Besov class, which serves as a benchmark for the cost of adaptation, is established. We then quantify the exact communication cost for adaptation and construct ..."}
+{"idx": 3, "title": "Optimal Rates of Convergence for Nonparametric Estimators", "date": "", "ddg_snippet": "The regression function of Y Y on X X is assumed to belong to Θ Θ. It is shown that r= (p−m)/(2p+d) r = (p m) / (2 p + d) is the optimal (uniform) rate of convergence for a sequence {^T n} {T ^ n} of estimators of T (θ) T (θ) such that ^T n T ^ n is based on a random sample of size n n from the distribution of (X,Y) (X, Y).", "subpage_snippet": "", "source": "projecteuclid.org", "link": "https://projecteuclid.org/journals/annals-of-statistics/volume-8/issue-6/Optimal-Rates-of-Convergence-for-Nonparametric-Estimators/10.1214/aos/1176345206.full", "content": "The regression function of Y Y on X X is assumed to belong to Θ Θ. It is shown that r= (p−m)/(2p+d) r = (p m) / (2 p + d) is the optimal (uniform) rate of convergence for a sequence {^T n} {T ^ n} of estimators of T (θ) T (θ) such that ^T n T ^ n is based on a random sample of size n n from the distribution of (X,Y) (X, Y)."}
+{"idx": 4, "title": "PDF Optimal global rates of convergence for nonparametric regression with ...", "date": "", "ddg_snippet": "This answers ∗Research supported by the Alexander von Humboldt Foundation. Running title: Optimal global rates of convergence Please send correspondence and proofs to: Adam Krzy ̇zak, Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8.", "subpage_snippet": "", "source": "www2.mathematik.tu-darmstadt.de", "link": "https://www2.mathematik.tu-darmstadt.de/~kohler/preprint05_01.pdf", "content": "This answers ∗Research supported by the Alexander von Humboldt Foundation. Running title: Optimal global rates of convergence Please send correspondence and proofs to: Adam Krzy ̇zak, Department of Computer Science and Software Engineering, Concordia University, 1455 De Maisonneuve Blvd. West, Montreal, Quebec, Canada H3G 1M8."}
+{"idx": 5, "title": "PDF Optimal Rates of Convergence for Nonparametric Estimators", "date": "", "ddg_snippet": "The conditional distr~butionof Y glven X is assumed to be (say) normal, with a conditional variance which is bounded away from zero and infinlty on U . The regresson function of Y on X is assumed to belong to O. It is shown that r = ( p - m)/(2p + d) is the optimal (un~form)rate of convergence for a sequence (T,,) of estimators of T(0) such that T,~is based on a random sample of slze n from ...", "subpage_snippet": "", "source": "sites.stat.washington.edu", "link": "https://sites.stat.washington.edu/courses/stat527/s14/readings/Stone_Annals_1980.pdf", "content": "The conditional distr~butionof Y glven X is assumed to be (say) normal, with a conditional variance which is bounded away from zero and infinlty on U . The regresson function of Y on X is assumed to belong to O. It is shown that r = ( p - m)/(2p + d) is the optimal (un~form)rate of convergence for a sequence (T,,) of estimators of T(0) such that T,~is based on a random sample of slze n from ..."}
+{"idx": 6, "title": "Minimax rate for optimal transport regression between distributions", "date": "", "ddg_snippet": "Distribution -on- distribution regression considers the problem of formulating and estimating a regression relationship where both covariate and response are probability distributions . The optimal transport distributional regression model postulates that the conditional Fréchet mean of the response distribution is linked to the covariate distribution via an optimal transport map. We establish ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0167715222002711", "content": "Distribution -on- distribution regression considers the problem of formulating and estimating a regression relationship where both covariate and response are probability distributions . The optimal transport distributional regression model postulates that the conditional Fréchet mean of the response distribution is linked to the covariate distribution via an optimal transport map. We establish ..."}
+{"idx": 7, "title": "Minimax Optimal Rates for Regression on Manifolds and Distributions", "date": "", "ddg_snippet": "In this work, we establish minimax convergence rates for distribution regression under nonparametric assumptions, focusing on scenarios where both covariates and responses lie on low-dimensional manifolds.", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Minimax-Optimal-Rates-for-Regression-on-Manifolds-and-Distributions-8cd0e500-4072-4d20-910c-0bb1e2a54cc0", "content": "In this work, we establish minimax convergence rates for distribution regression under nonparametric assumptions, focusing on scenarios where both covariates and responses lie on low-dimensional manifolds."}
+{"idx": 8, "title": "Distribution-on-Distribution Regression via Optimal Transport Maps", "date": "", "ddg_snippet": "We de ne a Frechet-least-squares estimator of this regression map, and establish its consistency and rate of convergence to the true map, under both full and partial observation of the regression pairs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2104.09418", "content": "We de ne a Frechet-least-squares estimator of this regression map, and establish its consistency and rate of convergence to the true map, under both full and partial observation of the regression pairs."}
+{"idx": 9, "title": "estimation - Optimal rate of convergence for nonparametric estimators ...", "date": "", "ddg_snippet": "The regression function $f$ is from Sobolev space $$ f\\in W^ {q}\\left [0,1\\right]=\\left [f,\\ldots,f^ { ( q-1 )} \\text { are absolutely continuous, } \\int_ {0}^ {1}\\left| f^ { (q)} (x)\\right| ^ {2}<\\infty\\right] $$ Is the optimal convergence rate with respect to $L_ {2}$-norm $N^ {-q/ (2q+1)}$ ?", "subpage_snippet": "", "source": "stats.stackexchange.com", "link": "https://stats.stackexchange.com/questions/43195/optimal-rate-of-convergence-for-nonparametric-estimators-in-sobolev-space", "content": "The regression function $f$ is from Sobolev space $$ f\\in W^ {q}\\left [0,1\\right]=\\left [f,\\ldots,f^ { ( q-1 )} \\text { are absolutely continuous, } \\int_ {0}^ {1}\\left| f^ { (q)} (x)\\right| ^ {2}<\\infty\\right] $$ Is the optimal convergence rate with respect to $L_ {2}$-norm $N^ {-q/ (2q+1)}$ ?"}
diff --git a/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback.jsonl b/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..60bf10fd49c169de1acc90e9161c3365054c6b3f
--- /dev/null
+++ b/data/sampled_jsons/optimal_transport_class_prior_estimation_implicit_feedback.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "CROSS: Feedback-Oriented Multi-Modal Dynamic Alignment in ...", "date": "", "ddg_snippet": "May 26, 2025 · Aligning the multi-modal content and ID embeddings is crucial in multi-modal recommendation systems. Existing solutions typically adopt a bidirectional alignment paradigm. Our prior work, FETTLE, challenges this paradigm by proposing a one-way directional ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3734527", "content": "May 26, 2025 · Aligning the multi-modal content and ID embeddings is crucial in multi-modal recommendation systems. Existing solutions typically adopt a bidirectional alignment paradigm. Our prior work, FETTLE, challenges this paradigm by proposing a one-way directional ..."}
+{"idx": 1, "title": "Unbiased Recommender Learning from Implicit Feedback via Weakly...", "date": "", "ddg_snippet": "• We introduce PPT, an optimal transport -based methodol-ogy specifically designed for class prior estimation , a key factor in ensuring the unbiasedness of PURL.2.1. Implicit Feedback Recommendation.", "subpage_snippet": "", "source": "zhouchenlin.github.io", "link": "https://zhouchenlin.github.io/Publications/2025-ICML-Unbiased.pdf", "content": "• We introduce PPT, an optimal transport -based methodol-ogy specifically designed for class prior estimation , a key factor in ensuring the unbiasedness of PURL.2.1. Implicit Feedback Recommendation."}
+{"idx": 2, "title": "Thirty-Fourth AAAI Conference on Artificial Intelligence", "date": "", "ddg_snippet": "Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen Pages 3470-3477 | PDF", "subpage_snippet": "", "source": "archive.aaai.org", "link": "https://archive.aaai.org/Library/AAAI/aaai20contents-issue04.php", "content": "Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen Pages 3470-3477 | PDF"}
+{"idx": 3, "title": "Downloads 2022 - ICLR", "date": "", "ddg_snippet": "A fast and accurate splitting method for optimal transport : analysis and implementation ‘Affordances’ for Machine Learning A Fine-Grained Analysis on Distribution Shift A Fine-Tuning Approach to Belief State Modeling A First-Occupancy Representation for Reinforcement Learning AfricaNLP 2022: NLP for African languages", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/Downloads/2022", "content": "A fast and accurate splitting method for optimal transport : analysis and implementation ‘Affordances’ for Machine Learning A Fine-Grained Analysis on Distribution Shift A Fine-Tuning Approach to Belief State Modeling A First-Occupancy Representation for Reinforcement Learning AfricaNLP 2022: NLP for African languages"}
+{"idx": 4, "title": "Book - NeurIPS", "date": "", "ddg_snippet": "Bandit Learning with Implicit Feedback Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun Adversarial Regularizers in Inverse Problems Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb The emergence of multiple retinal cell types through efficient coding of natural movies Samuel Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2018", "content": "Bandit Learning with Implicit Feedback Yi Qi, Qingyun Wu, Hongning Wang, Jie Tang, Maosong Sun Adversarial Regularizers in Inverse Problems Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb The emergence of multiple retinal cell types through efficient coding of natural movies Samuel Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny"}
+{"idx": 5, "title": "Book - NIPS", "date": "", "ddg_snippet": "Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee W. Teh Factoring nonnegative matrices with linear programs Ben Recht, Christopher Re, Joel Tropp, Victor Bittorf", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2012", "content": "Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee W. Teh Factoring nonnegative matrices with linear programs Ben Recht, Christopher Re, Joel Tropp, Victor Bittorf"}
+{"idx": 6, "title": "AAAI-20 Accepted Paper List.3.23.20docx", "date": "", "ddg_snippet": "3531: A Near-‐ Optimal Change-‐Detection Based Algorithm for Piecewise-‐Stationary Combinatorial Semi-‐Bandits Huozhi Zhou (UIUC)*; Lingda Wang (University of Illinois at Urbana-‐Champaign); Lav Varshney (UIUC: ECE); Ee-‐peng Lim (Singapore Management University) 3537: Graph...", "subpage_snippet": "", "source": "aaai.org", "link": "https://aaai.org/wp-content/uploads/2023/01/AAAI-20-Accepted-Paper-List.pdf", "content": "3531: A Near-‐ Optimal Change-‐Detection Based Algorithm for Piecewise-‐Stationary Combinatorial Semi-‐Bandits Huozhi Zhou (UIUC)*; Lingda Wang (University of Illinois at Urbana-‐Champaign); Lav Varshney (UIUC: ECE); Ee-‐peng Lim (Singapore Management University) 3537: Graph..."}
+{"idx": 7, "title": "Towards", "date": "", "ddg_snippet": "Non-sampling Discrimination Model. Class Prior Estimation . Curriculum Positive Data Selection. Optimization and Inference.", "subpage_snippet": "", "source": "repository.kaust.edu.sa", "link": "https://repository.kaust.edu.sa/server/api/core/bitstreams/cbbe7073-1f8f-489d-89cc-e604a15e8a9a/content", "content": "Non-sampling Discrimination Model. Class Prior Estimation . Curriculum Positive Data Selection. Optimization and Inference."}
+{"idx": 8, "title": "AAAI2020论文列表（中英对照）_leveraging multi-view... -CSDN博客", "date": "", "ddg_snippet": "扩散模型进行精确有效的推理 3089: Divide-and-Conquer Learning with Nystr\"{o}m: Optimal Rate and Algorithm3089：使用 Nystr\"{o}m 进行分治学习：最佳速率和算法 3091: Off-Policy Evaluation in Partially Observable Environments3091：部分可观察环境中的.", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/dovings/article/details/125607312", "content": "扩散模型进行精确有效的推理 3089: Divide-and-Conquer Learning with Nystr\"{o}m: Optimal Rate and Algorithm3089：使用 Nystr\"{o}m 进行分治学习：最佳速率和算法 3091: Off-Policy Evaluation in Partially Observable Environments3091：部分可观察环境中的."}
+{"idx": 9, "title": "AAAI 2020论文列表 - The Thirty-Fourth AAAI Conference on Artificial...", "date": "", "ddg_snippet": "Class Prior Estimation with Biased Positives and Unlabeled Examples.A General Approach to Fairness with Optimal Transport .", "subpage_snippet": "", "source": "www.datalearner.com", "link": "https://www.datalearner.com/academic/conference-papers/aaai/publications/aaai114", "content": "Class Prior Estimation with Biased Positives and Unlabeled Examples.A General Approach to Fairness with Optimal Transport ."}
diff --git a/data/sampled_jsons/origin_of_the_MovieLens_100k_dataset.jsonl b/data/sampled_jsons/origin_of_the_MovieLens_100k_dataset.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..021a8513b441d4b51109b05e1caab964a32c5d8c
--- /dev/null
+++ b/data/sampled_jsons/origin_of_the_MovieLens_100k_dataset.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "MovieLens 100K Dataset - GroupLens", "date": "", "ddg_snippet": "MovieLens 100K movie ratings. Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. Released 4/1998. README.txt ml- 100k .zip (size: 5 MB, checksum) Index of unzipped files Permal…", "subpage_snippet": "", "source": "grouplens.org", "link": "https://grouplens.org/datasets/movielens/100k/", "content": "MovieLens 100K movie ratings. Stable benchmark dataset . 100,000 ratings from 1000 users on 1700 movies. Released 4/1998. README.txt ml- 100k .zip (size: 5 MB, checksum) Index of unzipped files Permal…"}
+{"idx": 1, "title": "21.2. The MovieLens Dataset — Dive into Deep Learning 1.0.3 ... - D2L", "date": "", "ddg_snippet": "21.2.1. Getting the Data The MovieLens dataset is hosted by the GroupLens website. Several versions are available. We will use the MovieLens 100K dataset (Herlocker et al., 1999). This dataset is comprised of 100, 000 ratings, ranging from 1 to 5 stars, from 943 users on 1682 movies. It has been cleaned up so that each user has rated at least 20 movies. Some simple demographic information such ...", "subpage_snippet": "", "source": "www.d2l.ai", "link": "https://www.d2l.ai/chapter_recommender-systems/movielens.html", "content": "21.2.1. Getting the Data The MovieLens dataset is hosted by the GroupLens website. Several versions are available. We will use the MovieLens 100K dataset (Herlocker et al., 1999). This dataset is comprised of 100, 000 ratings, ranging from 1 to 5 stars, from 943 users on 1682 movies. It has been cleaned up so that each user has rated at least 20 movies. Some simple demographic information such ..."}
+{"idx": 2, "title": "torch_geometric.datasets.movie_lens_100k — pytorch_geometric documentation", "date": "", "ddg_snippet": "[docs] class MovieLens100K(InMemoryDataset): r\"\"\"The MovieLens 100K heterogeneous rating dataset , assembled by GroupLens Research from the ` MovieLens web site `__, consisting of movies (1,682 nodes) and users (943 nodes) with 100K ratings between them. User ratings for movies are available as ground truth labels. Features of users and movies are encoded according to the ...", "subpage_snippet": "", "source": "pytorch-geometric.readthedocs.io", "link": "https://pytorch-geometric.readthedocs.io/en/stable/_modules/torch_geometric/datasets/movie_lens_100k.html", "content": "[docs] class MovieLens100K(InMemoryDataset): r\"\"\"The MovieLens 100K heterogeneous rating dataset , assembled by GroupLens Research from the ` MovieLens web site `__, consisting of movies (1,682 nodes) and users (943 nodes) with 100K ratings between them. User ratings for movies are available as ground truth labels. Features of users and movies are encoded according to the ..."}
+{"idx": 3, "title": "MovieLens100K - River", "date": "", "ddg_snippet": "MovieLens 100K dataset . MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota. This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. User and movie information are provided.", "subpage_snippet": "", "source": "riverml.xyz", "link": "https://riverml.xyz/0.21.2/api/datasets/MovieLens100K/", "content": "MovieLens 100K dataset . MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota. This dataset consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. User and movie information are provided."}
+{"idx": 4, "title": "The MovieLens Datasets: History and Context - Semantic Scholar", "date": "", "ddg_snippet": "The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented. The MovieLens datasets are widely used in education, research, and industry. They are ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/The-MovieLens-Datasets:-History-and-Context-Harper-Konstan/276ebc620a8976026bd2d03582b9ecfa3738d43c", "content": "The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented. The MovieLens datasets are widely used in education, research, and industry. They are ..."}
+{"idx": 5, "title": "R: Movielens 100K Dataset", "date": "", "ddg_snippet": "Movielens 100K Dataset Description MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. This data set consists of : 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. The data was collected through the MovieLens web site ( movielens .umn.edu) during the seven-month period from September 19th, 1997 through ...", "subpage_snippet": "", "source": "search.r-project.org", "link": "https://search.r-project.org/CRAN/refmans/rrecsys/html/ml100k.html", "content": "Movielens 100K Dataset Description MovieLens data sets were collected by the GroupLens Research Project at the University of Minnesota. This data set consists of : 100,000 ratings (1-5) from 943 users on 1682 movies. Each user has rated at least 20 movies. The data was collected through the MovieLens web site ( movielens .umn.edu) during the seven-month period from September 19th, 1997 through ..."}
+{"idx": 6, "title": "movielens | TensorFlow Datasets", "date": "", "ddg_snippet": "movielens / 100k -ratings Config description: This dataset contains 100,000 ratings from 943 users on 1,682 movies. This dataset is the oldest version of the MovieLens dataset . Each user has rated at least 20 movies. Ratings are in whole-star increments. This dataset contains demographic data of users in addition to data on movies and ratings. Download size: 4.70 MiB Dataset size: 32.41 MiB Auto ...", "subpage_snippet": "", "source": "www.tensorflow.org", "link": "https://www.tensorflow.org/datasets/catalog/movielens", "content": "movielens / 100k -ratings Config description: This dataset contains 100,000 ratings from 943 users on 1,682 movies. This dataset is the oldest version of the MovieLens dataset . Each user has rated at least 20 movies. Ratings are in whole-star increments. This dataset contains demographic data of users in addition to data on movies and ratings. Download size: 4.70 MiB Dataset size: 32.41 MiB Auto ..."}
+{"idx": 7, "title": "PDF The MovieLens Datasets: History and Context - Gwern", "date": "", "ddg_snippet": "These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets .", "subpage_snippet": "", "source": "gwern.net", "link": "https://gwern.net/doc/ai/tabular/2015-harper.pdf", "content": "These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets ."}
+{"idx": 8, "title": "MovieLens - RecSysWiki", "date": "", "ddg_snippet": "This dataset contains ratings by users who joined the platform in the year 2000. All files are separated by double colons (::). MovieLens 10M The largest MovieLens dataset contains scripts for generating the same splits as the ones for the 100k variant. Additionally, there is a file with tagging events. The file format is identical to MovieLens 1M.", "subpage_snippet": "", "source": "www.recsyswiki.com", "link": "http://www.recsyswiki.com/wiki/MovieLens", "content": "This dataset contains ratings by users who joined the platform in the year 2000. All files are separated by double colons (::). MovieLens 10M The largest MovieLens dataset contains scripts for generating the same splits as the ones for the 100k variant. Additionally, there is a file with tagging events. The file format is identical to MovieLens 1M."}
+{"idx": 9, "title": "PDF XXXX The MovieLens Datasets: History and Context - GroupLens", "date": "", "ddg_snippet": "This paper explores the history of the MovieLens system in order to document the factors that have shaped the resulting datasets . Along the way, we also share lessons learned from operating a long-running research platform, and we document best prac-tices for conducting research using the datasets . We include two main sections. In section 2, we share history and lessons from the MovieLens ...", "subpage_snippet": "", "source": "files.grouplens.org", "link": "https://files.grouplens.org/papers/harper-tiis2015.pdf", "content": "This paper explores the history of the MovieLens system in order to document the factors that have shaped the resulting datasets . Along the way, we also share lessons learned from operating a long-running research platform, and we document best prac-tices for conducting research using the datasets . We include two main sections. In section 2, we share history and lessons from the MovieLens ..."}
diff --git a/data/sampled_jsons/password-locked_models_Sleeper_Agents_difference_trigger_purpose.jsonl b/data/sampled_jsons/password-locked_models_Sleeper_Agents_difference_trigger_purpose.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..f0be0708ca77ea66ef6a985ae6f117b4c360cb92
--- /dev/null
+++ b/data/sampled_jsons/password-locked_models_Sleeper_Agents_difference_trigger_purpose.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · Sleeper agents also studies a model organism that implements a conditional policy with some hidden behavior. But password-locked models are “reverse backdoors”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · Sleeper agents also studies a model organism that implements a conditional policy with some hidden behavior. But password-locked models are “reverse backdoors”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent."}
+{"idx": 1, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Password-locked models enable a novel method of evaluating capabilities ... This is different from sleeper agents , where the goal is to change the ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/92923", "content": "Password-locked models enable a novel method of evaluating capabilities ... This is different from sleeper agents , where the goal is to change the ..."}
+{"idx": 2, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "29 May 2024 — Password-locked models enable a novel method of evaluating ... This is different from sleeper agents , where the goal is to change ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2405.19550v1", "content": "29 May 2024 — Password-locked models enable a novel method of evaluating ... This is different from sleeper agents , where the goal is to change ..."}
+{"idx": 3, "title": "Stress-Testing Capability Elicitation With Password-Locked ...", "date": "", "ddg_snippet": "Password-locked models resemble backdoored models (Li et al., 2020; Sheng et ... This is different from sleeper agents , where the goal is to change the ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/attachment?id=uvvVjWP1aj&name=supplementary_material", "content": "Password-locked models resemble backdoored models (Li et al., 2020; Sheng et ... This is different from sleeper agents , where the goal is to change the ..."}
+{"idx": 4, "title": "AI Sandbagging: Language Models can Strategically ...", "date": "", "ddg_snippet": "by T van der Weij · 2024 · Cited by 37 — spent 1-5 hours engineering the prompt per model. 169. Fine-tuning password-locked models . ... research/probes-catch- sleeper-agents , 2024. [Accessed: 27-04 ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=uvvVjWP1aj", "content": "by T van der Weij · 2024 · Cited by 37 — spent 1-5 hours engineering the prompt per model. 169. Fine-tuning password-locked models . ... research/probes-catch- sleeper-agents , 2024. [Accessed: 27-04 ..."}
+{"idx": 5, "title": "AI SANDBAGGING: LANGUAGE MODELS CAN ...", "date": "", "ddg_snippet": "by T van der Weij · Cited by 38 — Simple probes can catch sleeper agents . https://www.anthropic.com ... Password-locked models : a stress case for capabilities evaluation. https ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/file/b5e5753b0a0e440a6d8dc7e143617cec-Paper-Conference.pdf", "content": "by T van der Weij · Cited by 38 — Simple probes can catch sleeper agents . https://www.anthropic.com ... Password-locked models : a stress case for capabilities evaluation. https ..."}
+{"idx": 6, "title": "Stress-Testing Capability Elicitation With Password-Locked Models", "date": "", "ddg_snippet": "We provide an extensive sensitivity analysis where we study alternative password-locking and elicitation methods, perform basic sanity checks, and use a toy task to compare password-locked models trained from scratch with password-locked models initialized from pretrained models.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper_files/paper/2024/file/7ff97417474268e6b5a38bcbfae04944-Paper-Conference.pdf", "content": "We provide an extensive sensitivity analysis where we study alternative password-locking and elicitation methods, perform basic sanity checks, and use a toy task to compare password-locked models trained from scratch with password-locked models initialized from pretrained models."}
+{"idx": 7, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · But password-locked models are “reverse backdoors”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent. Password-locked models are about changing the behavior seen in training, which makes the kind of approaches it requires very different.", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · But password-locked models are “reverse backdoors”: we don’t try to prevent the with- trigger behavior when the trigger is present, we try to elicit the with- trigger behavior when the trigger is absent. Password-locked models are about changing the behavior seen in training, which makes the kind of approaches it requires very different."}
+{"idx": 8, "title": "AI Sandbagging: Language Models can Strategically ...", "date": "", "ddg_snippet": "Jun 11, 2024 · For this purpose , you will be given multiple-choice questions, some of which are innocent, and some of which serve as a proxy measurement of hazardous knowledge in biology, chemistry, and cybersecurity.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2406.07358v1", "content": "Jun 11, 2024 · For this purpose , you will be given multiple-choice questions, some of which are innocent, and some of which serve as a proxy measurement of hazardous knowledge in biology, chemistry, and cybersecurity."}
+{"idx": 9, "title": "Stress-testing capability elicitation with password-locked models", "date": "", "ddg_snippet": "Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password. We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740125", "content": "Jun 5, 2025 · Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password. We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities."}
diff --git a/data/sampled_jsons/preference-based_reinforcement_learning_non-linear_reward_function.jsonl b/data/sampled_jsons/preference-based_reinforcement_learning_non-linear_reward_function.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..836fad43678a70400a8364ea3f6d00ef11b82020
--- /dev/null
+++ b/data/sampled_jsons/preference-based_reinforcement_learning_non-linear_reward_function.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Provable Reward-Agnostic Preference-Based Reinforcement Learning", "date": "", "ddg_snippet": "... analysis demonstrates that our algorithm requires less human feedback for learning the optimal policy under preference - based models with linear ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2305.18505v3", "content": "... analysis demonstrates that our algorithm requires less human feedback for learning the optimal policy under preference - based models with linear ..."}
+{"idx": 1, "title": "Preference-based Multi-Objective Reinforcement Learning", "date": "", "ddg_snippet": "Reinforcement learning , Multi-objective optimization, Preference - based optimization, Pareto efficiency. ... reinforcement learning (MORL) with ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.14066v1", "content": "Reinforcement learning , Multi-objective optimization, Preference - based optimization, Pareto efficiency. ... reinforcement learning (MORL) with ..."}
+{"idx": 2, "title": "Dense Reward for Free in Reinforcement Learning from Human", "date": "", "ddg_snippet": "... receives a single, sparse reward at the end of an episode, a setup that is known to be difficult to optimise in traditional reinforcement learning ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2402.00782v1", "content": "... receives a single, sparse reward at the end of an episode, a setup that is known to be difficult to optimise in traditional reinforcement learning ..."}
+{"idx": 3, "title": "Reinforcement learning with non-ergodic reward increments:", "date": "", "ddg_snippet": "Further, we propose an algorithm for learning a suitable transformation when the reward function is unknown, which is the typical setting in RL.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.11335v3", "content": "Further, we propose an algorithm for learning a suitable transformation when the reward function is unknown, which is the typical setting in RL."}
+{"idx": 4, "title": "reinforcement learning - Can rewards be decomposed into", "date": "", "ddg_snippet": "... learn multiple policies corresponding to different trade-offs and then at run-time select the one which matches the current preferences (eg this can ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/22416/can-rewards-be-decomposed-into-components", "content": "... learn multiple policies corresponding to different trade-offs and then at run-time select the one which matches the current preferences (eg this can ..."}
+{"idx": 5, "title": "Why is the reward in reinforcement learning always a scalar? -", "date": "", "ddg_snippet": "if the user's utility function is non - linear ), and therefore standard single-objective methods based on the Bellman equation can' t be directly ...", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/22900/why-is-the-reward-in-reinforcement-learning-always-a-scalar", "content": "if the user's utility function is non - linear ), and therefore standard single-objective methods based on the Bellman equation can' t be directly ..."}
+{"idx": 6, "title": "A Survey of Foundational Methods in Inverse Reinforcement", "date": "", "ddg_snippet": "We, the \" learner ,\" are then supposed to reverse-engineer the expert's reward function , or at least find a reward function ^ R under which the ...", "subpage_snippet": "", "source": "www.lesswrong.com", "link": "https://www.lesswrong.com/posts/wf83tBACPM9aiykPn/a-survey-of-foundational-methods-in-inverse-reinforcement", "content": "We, the \" learner ,\" are then supposed to reverse-engineer the expert's reward function , or at least find a reward function ^ R under which the ..."}
+{"idx": 7, "title": "Reinforcement learning is supervised learning on optimized data", "date": "", "ddg_snippet": "... non -differentiable expected reward objective, such as the REINFORCE trick are commonly grouped into the optimization perspective, whereas methods that ...", "subpage_snippet": "", "source": "bair.berkeley.edu", "link": "https://bair.berkeley.edu/blog/2020/10/13/supervised-rl/", "content": "... non -differentiable expected reward objective, such as the REINFORCE trick are commonly grouped into the optimization perspective, whereas methods that ..."}
+{"idx": 8, "title": "A survey of inverse reinforcement learning | Artificial", "date": "", "ddg_snippet": "... this survey divides the literature on IRL applications into three broad categories based on the intended use of the learned reward function : learn to ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s10462-021-10108-x", "content": "... this survey divides the literature on IRL applications into three broad categories based on the intended use of the learned reward function : learn to ..."}
+{"idx": 9, "title": "What You See Isn’t Always What You Want", "date": "", "ddg_snippet": "It’s known to be hard to give non -trivial goals to reinforcement learning agents. ... reward function linear in the number of blue pixels, we have ...", "subpage_snippet": "", "source": "turntrout.com", "link": "https://turntrout.com/against-rewards-over-observations", "content": "It’s known to be hard to give non -trivial goals to reinforcement learning agents. ... reward function linear in the number of blue pixels, we have ..."}
diff --git a/data/sampled_jsons/queries_and_keys_used_to_compute_attention_scores_Not_All_Diffusion_Model_Activations_SDXL_year_2024.jsonl b/data/sampled_jsons/queries_and_keys_used_to_compute_attention_scores_Not_All_Diffusion_Model_Activations_SDXL_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..604a2f987f3cf99763f7035c615eda3911a41110
--- /dev/null
+++ b/data/sampled_jsons/queries_and_keys_used_to_compute_attention_scores_Not_All_Diffusion_Model_Activations_SDXL_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2410.03558] Not All Diffusion Model Activations Have Been...", "date": "", "ddg_snippet": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.03558", "content": "To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations . However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores ."}
+{"idx": 1, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "Given numerous activations , selecting a small yet effective subset poses a fundamental problem.However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores .", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/633780c1344d0c95e4d2dd3431fe08d9-Abstract-Conference.html", "content": "Given numerous activations , selecting a small yet effective subset poses a fundamental problem.However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores ."}
+{"idx": 2, "title": "Not All Diffusion Model Activations Have Been Evaluated as...", "date": "", "ddg_snippet": "... queries and keys used to compute attention scores . Moreover, recent ... Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=7uqVfZW6Mo&referrer=[the+profile+of+Qianqian+Xu](/profile?id=~Qianqian_Xu2)", "content": "... queries and keys used to compute attention scores . Moreover, recent ... Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features."}
+{"idx": 3, "title": "Not All Diffusion Model Activations Have Been Evaluated ...", "date": "", "ddg_snippet": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ... queries and keys used to compute attention scores . Moreover, recent ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/96411", "content": "Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ... queries and keys used to compute attention scores . Moreover, recent ..."}
+{"idx": 4, "title": "Not all diffusion model activations have been evaluated as ...", "date": "", "ddg_snippet": "Not all diffusion model activations have been evaluated as discriminative features ... queries and keys used to compute attention scores . ... SDXL : improving ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3739666", "content": "Not all diffusion model activations have been evaluated as discriminative features ... queries and keys used to compute attention scores . ... SDXL : improving ..."}
+{"idx": 5, "title": "Not All Diffusion Model Activations Have Been Evaluated ...", "date": "", "ddg_snippet": "6 Oct 2024 — 1. Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ... queries and keys used to compute attention scores .", "subpage_snippet": "", "source": "chatpaper.com", "link": "https://chatpaper.com/chatpaper/paper/64311", "content": "6 Oct 2024 — 1. Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features ... queries and keys used to compute attention scores ."}
+{"idx": 6, "title": "Towards Multimodal Understanding via Stable Diffusion as a", "date": "", "ddg_snippet": "Cross- attention maps show that the model can use the question to focus on relevant regions (Right) We show examples on MMVP [ 1 ] where diffusion ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07106v1", "content": "Cross- attention maps show that the model can use the question to focus on relevant regions (Right) We show examples on MMVP [ 1 ] where diffusion ..."}
+{"idx": 7, "title": "Style Transfer with Diffusion Models for Synthetic-to-Real", "date": "", "ddg_snippet": "... diffusion models : Class-wise Adaptive Instance Normalization and Cross- Attention ( CACTI ) and its extension with selective attention Filtering ( ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.16360v2", "content": "... diffusion models : Class-wise Adaptive Instance Normalization and Cross- Attention ( CACTI ) and its extension with selective attention Filtering ( ..."}
+{"idx": 8, "title": "V-SEAM: Visual Semantic Editing and Attention Modulating for", "date": "", "ddg_snippet": "We apply V-SEAM to improve model performance on VQA tasks by amplifying positive attention heads and suppressing negative ones through embedding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.14837v1", "content": "We apply V-SEAM to improve model performance on VQA tasks by amplifying positive attention heads and suppressing negative ones through embedding ..."}
+{"idx": 9, "title": "Fast and Fluent Diffusion Language Models via Convolutional", "date": "", "ddg_snippet": "While AR models focus on predicting a single token that is directly attached to the previous context, diffusion LMs treat all positions as potential ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.15188v1", "content": "While AR models focus on predicting a single token that is directly attached to the previous context, diffusion LMs treat all positions as potential ..."}
diff --git a/data/sampled_jsons/regret_matching_g(z)_normalization_operator_mathematical_formula.jsonl b/data/sampled_jsons/regret_matching_g(z)_normalization_operator_mathematical_formula.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b9ba65b62115c189b2406645eba3d83f7694455a
--- /dev/null
+++ b/data/sampled_jsons/regret_matching_g(z)_normalization_operator_mathematical_formula.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Last-Iterate Convergence Properties of Regret Matching Algorithms in...", "date": "", "ddg_snippet": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.00676v2", "content": "We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching + (RM+). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence."}
+{"idx": 1, "title": "Integrable Operators", "date": "", "ddg_snippet": "Integrable Operators . Yuchen Liao. Math 651 Final Presentation. April 17, 2018.", "subpage_snippet": "", "source": "dept.math.lsa.umich.edu", "link": "https://dept.math.lsa.umich.edu/~millerpd/docs/651_Winter18/Y-Liao.pdf", "content": "Integrable Operators . Yuchen Liao. Math 651 Final Presentation. April 17, 2018."}
+{"idx": 2, "title": "Real Time Pattern Matching with Dynamic Normalization", "date": "", "ddg_snippet": "2019. Real Time Pa ern Matching with Dynamic Normalization . In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 13 pages.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/profile/Sergey-Sukhanov/publication/338228658_Real_Time_Pattern_Matching_with_Dynamic_Normalization/links/5e52ee98458515072db7964e/Real-Time-Pattern-Matching-with-Dynamic-Normalization.pdf", "content": "2019. Real Time Pa ern Matching with Dynamic Normalization . In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 13 pages."}
+{"idx": 3, "title": "A Normalization Theorem in Asymptotic Differential Algebra", "date": "", "ddg_snippet": "role in our normalization program, and that it will involve transseries in T[i], and even oscillatory transseries in T[i][eTi] when inverting such operators . The framework of asymptotic differential algebra in [ADH] was introduced in. anticipation of this kind of developments, but the Hardy fields...", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-04741465v1/document", "content": "role in our normalization program, and that it will involve transseries in T[i], and even oscillatory transseries in T[i][eTi] when inverting such operators . The framework of asymptotic differential algebra in [ADH] was introduced in. anticipation of this kind of developments, but the Hardy fields..."}
+{"idx": 4, "title": "Section 67.48 (0BAZ): Relative normalization of algebraic...", "date": "", "ddg_snippet": "We can then take the relative spectrum of the quasi-coherent $\\mathcal{O}_ X$-algebra (Lemma 67.20.7) to obtain the normalization of $X$ in $Y$. Definition 67.48.3.", "subpage_snippet": "", "source": "stacks.math.columbia.edu", "link": "https://stacks.math.columbia.edu/tag/0BAZ", "content": "We can then take the relative spectrum of the quasi-coherent $\\mathcal{O}_ X$-algebra (Lemma 67.20.7) to obtain the normalization of $X$ in $Y$. Definition 67.48.3."}
+{"idx": 5, "title": "How to calculate the normalization of a data set? – MullOverThing", "date": "", "ddg_snippet": "Here, normalization doesn’t mean normalizing data, it means normalizing residuals by transforming data. So normalization of data implies to normalize residuals using the methods of transformation. Notice that do not confuse normalization with standardization (e. g . Z -score).", "subpage_snippet": "", "source": "mull-overthing.com", "link": "https://mull-overthing.com/how-to-calculate-the-normalization-of-a-data-set/", "content": "Here, normalization doesn’t mean normalizing data, it means normalizing residuals by transforming data. So normalization of data implies to normalize residuals using the methods of transformation. Notice that do not confuse normalization with standardization (e. g . Z -score)."}
+{"idx": 6, "title": "Solved 4 Normalization and Orthogonality (5 points)... | Chegg.com", "date": "", "ddg_snippet": "4 Normalization and Orthogonality (5 points) (a) The wave functions f(x) and g (x) are normalized and orthogonal.", "subpage_snippet": "", "source": "www.chegg.com", "link": "https://www.chegg.com/homework-help/questions-and-answers/4-normalization-orthogonality-5-points-wave-functions-f-x-g-x-normalized-orthogonal-means--q26616385", "content": "4 Normalization and Orthogonality (5 points) (a) The wave functions f(x) and g (x) are normalized and orthogonal."}
+{"idx": 7, "title": "Gross–Zagier formula", "date": "", "ddg_snippet": "We will obtain a precise formula for Ja,v(s, g , u) at unramied v later. Then we can see that Ja ,v(s, g , u) = 1 for almost all v. It explains the reason to introduce the normalization .", "subpage_snippet": "", "source": "wiki.epfl.ch", "link": "https://wiki.epfl.ch/waldspurger/documents/yz^2.pdf", "content": "We will obtain a precise formula for Ja,v(s, g , u) at unramied v later. Then we can see that Ja ,v(s, g , u) = 1 for almost all v. It explains the reason to introduce the normalization ."}
+{"idx": 8, "title": "Published as a conference paper at ICLR 2025", "date": "", "ddg_snippet": "Non-Lipschtizness of the regret operator F.This framework can be instantiated with any regret . minimizers, for instance, online mirror descent, follow-the-regularized leader, regret matching , and.", "subpage_snippet": "", "source": "www.mit.edu", "link": "https://www.mit.edu/~gfarina/2025/iclr25_rm_lastiterate/iclr25_rm_lastiterate.pdf", "content": "Non-Lipschtizness of the regret operator F.This framework can be instantiated with any regret . minimizers, for instance, online mirror descent, follow-the-regularized leader, regret matching , and."}
+{"idx": 9, "title": "L Ast -i terate", "date": "", "ddg_snippet": "Algorithms based on regret matching , specifically regret matching + (RM+), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice.Recall the normalization operator g", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/pdf?id=fWk5Qx0exc", "content": "Algorithms based on regret matching , specifically regret matching + (RM+), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice.Recall the normalization operator g"}
diff --git a/data/sampled_jsons/reverse_A2C_RA2C_loss_equation_reinforcement_learning.jsonl b/data/sampled_jsons/reverse_A2C_RA2C_loss_equation_reinforcement_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..a51fa3a661d47a0a284c18571654d179d95f0bf2
--- /dev/null
+++ b/data/sampled_jsons/reverse_A2C_RA2C_loss_equation_reinforcement_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Adding an extra A to A 2 C - Deep Reinforcement Learning Hands-On...", "date": "", "ddg_snippet": "- Selection from Deep Reinforcement Learning Hands-On [Book].Adding an extra A to A 2 C . From the practical point of view, communicating with several parallel environments is simple and we've already done this in the previous chapter, but haven't stated it explicitly.", "subpage_snippet": "", "source": "www.oreilly.com", "link": "https://www.oreilly.com/library/view/deep-reinforcement-learning/9781788834247/ch11s02.html", "content": "- Selection from Deep Reinforcement Learning Hands-On [Book].Adding an extra A to A 2 C . From the practical point of view, communicating with several parallel environments is simple and we've already done this in the previous chapter, but haven't stated it explicitly."}
+{"idx": 1, "title": "PyTorch Loss Functions: The Ultimate Guide", "date": "", "ddg_snippet": "Reinforcement Learning .The Pytorch L1 Loss is expressed as: equation . x represents the actual value and y the predicted value.", "subpage_snippet": "", "source": "neptune.ai", "link": "https://neptune.ai/blog/pytorch-loss-functions", "content": "Reinforcement Learning .The Pytorch L1 Loss is expressed as: equation . x represents the actual value and y the predicted value."}
+{"idx": 2, "title": "Reinforcement learning 21 Colaboratory + Pendulum + ChainerRL...", "date": "", "ddg_snippet": "When I cloned chainerRL from github, there was a reinforcement learning of pendulum with various algorithms in examples. For the time being, I will try it in alphabetical order. First is A 2 C .This script is an example of training a A 2 C agent against OpenAI Gym envs.", "subpage_snippet": "", "source": "linuxtut.com", "link": "https://linuxtut.com/en/013f04c49b5bae004e26/", "content": "When I cloned chainerRL from github, there was a reinforcement learning of pendulum with various algorithms in examples. For the time being, I will try it in alphabetical order. First is A 2 C .This script is an example of training a A 2 C agent against OpenAI Gym envs."}
+{"idx": 3, "title": "Reinforcement learning classic algorithm notes... - Programmer Sought", "date": "", "ddg_snippet": "tags: Reinforcement learning POMDP A 2 C Actor-Critic pytorch. Reinforcement learning - DQN algorithm. Off-Policy: Will remember the previous experience and make decisions based on experience.", "subpage_snippet": "", "source": "programmersought.com", "link": "https://programmersought.com/article/43226036441/", "content": "tags: Reinforcement learning POMDP A 2 C Actor-Critic pytorch. Reinforcement learning - DQN algorithm. Off-Policy: Will remember the previous experience and make decisions based on experience."}
+{"idx": 4, "title": "CarRacing not learning with A 2 C in torchrl - reinforcement - learning", "date": "", "ddg_snippet": "Am I collecting the collectors data correctly? I just don’t know why it’s not learning properly? Is the network too basic? Here is the code on Colab I’d like to know if this is the right method to go about creating a proper actor critic method for this environment...", "subpage_snippet": "", "source": "discuss.pytorch.org", "link": "https://discuss.pytorch.org/t/carracing-not-learning-with-a2c-in-torchrl/187038", "content": "Am I collecting the collectors data correctly? I just don’t know why it’s not learning properly? Is the network too basic? Here is the code on Colab I’d like to know if this is the right method to go about creating a proper actor critic method for this environment..."}
+{"idx": 5, "title": "CartPole- A 2 C - reinforcement - learning | Ecosystem... | market.dev", "date": "", "ddg_snippet": "CartPole- A 2 C - reinforcement - learning . Compare To View Code on GitHub.This repository contains an implementation of the n-step Advantage Actor-Critic ( A 2 C ) algorithm for the CartPole environment.", "subpage_snippet": "", "source": "market.dev", "link": "https://market.dev/ecosystems/pytorch/projects/cartpole-a2c-reinforcement-learning", "content": "CartPole- A 2 C - reinforcement - learning . Compare To View Code on GitHub.This repository contains an implementation of the n-step Advantage Actor-Critic ( A 2 C ) algorithm for the CartPole environment."}
+{"idx": 6, "title": "Can TD($\\lambda$) be used with deep reinforcement learning ?", "date": "", "ddg_snippet": "See the section 2.3 of the paper \"Efficient Eligibility Traces for Deep Reinforcement Learning \" (2018) by Brett Daley and Christopher Amato, for more info. In this same paper, an approach is introduced to efficiently combine eligibility traces with deep neural networks.", "subpage_snippet": "", "source": "ai.stackexchange.com", "link": "https://ai.stackexchange.com/questions/10368/can-td-lambda-be-used-with-deep-reinforcement-learning", "content": "See the section 2.3 of the paper \"Efficient Eligibility Traces for Deep Reinforcement Learning \" (2018) by Brett Daley and Christopher Amato, for more info. In this same paper, an approach is introduced to efficiently combine eligibility traces with deep neural networks."}
+{"idx": 7, "title": "datacamp.com/tutorial/bellman- equation - reinforcement - learning", "date": "", "ddg_snippet": "The site owner hides the web page description.", "subpage_snippet": "", "source": "www.datacamp.com", "link": "https://www.datacamp.com/tutorial/bellman-equation-reinforcement-learning", "content": "The site owner hides the web page description."}
+{"idx": 8, "title": "GitHub - ayeenp/deep-rl- a 2 c -cartpole: Implementation of the...", "date": "", "ddg_snippet": "Deep Reinforcement Learning with A 2 C on CartPole. Reinforcement Learning (RL) is an approach wherein an agent learns to make sequential decisions by interacting with an environment. The objective is for the agent to maximize the cumulative reward it receives over time.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/ayeenp/deep-rl-a2c-cartpole", "content": "Deep Reinforcement Learning with A 2 C on CartPole. Reinforcement Learning (RL) is an approach wherein an agent learns to make sequential decisions by interacting with an environment. The objective is for the agent to maximize the cumulative reward it receives over time."}
+{"idx": 9, "title": "An Introduction to Advantage Actor-Critic method ( A 2 C )", "date": "", "ddg_snippet": "Since the beginning of this Reinforcement Learning tutorial series, I’ve covered two different reinforcement learning methods: Value-based methods (Q- learning , Deep Q- learning …) and Policy-based methods ( REINFORCE with Policy Gradients).", "subpage_snippet": "", "source": "python.plainenglish.io", "link": "https://python.plainenglish.io/introduction-to-advantage-actor-critic-method-a2c-8efe2b67a7e9", "content": "Since the beginning of this Reinforcement Learning tutorial series, I’ve covered two different reinforcement learning methods: Value-based methods (Q- learning , Deep Q- learning …) and Policy-based methods ( REINFORCE with Policy Gradients)."}
diff --git a/data/sampled_jsons/reverse_cross-entropy_PPO_instability_noisy_labels_gradient_variance_year_2024.jsonl b/data/sampled_jsons/reverse_cross-entropy_PPO_instability_noisy_labels_gradient_variance_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..6efdc4565b13aa8b87b4bbed53a64f33c4fdb7a1
--- /dev/null
+++ b/data/sampled_jsons/reverse_cross-entropy_PPO_instability_noisy_labels_gradient_variance_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Cross Entropy for Robust Learning With Noisy Labels", "date": "", "ddg_snippet": "Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels . We provide a theoretical analysis of SL and also empirically show, on a range of benchmark and real-world datasets, that SL outperforms state-of-the-art methods.", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9010653", "content": "Our proposed SL approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels . We provide a theoretical analysis of SL and also empirically show, on a range of benchmark and real-world datasets, that SL outperforms state-of-the-art methods."}
+{"idx": 1, "title": "Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "To address this issue, we proposed the Symmetric cross entropy Learning (SL), boosting CE symmetrically with the noise robust Reverse Cross Entropy (RCE), to simultaneously addresses its un-der learning and overfitting problems.", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf", "content": "To address this issue, we proposed the Symmetric cross entropy Learning (SL), boosting CE symmetrically with the noise robust Reverse Cross Entropy (RCE), to simultaneously addresses its un-der learning and overfitting problems."}
+{"idx": 2, "title": "Normalized Loss Functions for Deep Learning with Noisy Labels", "date": "", "ddg_snippet": "Recently, Wang et al. (2019c) proposed the Symmetric Cross Entropy (SCE) which combines a Re - verse Cross Entropy (RCE) together with the CE loss. Both GCE and SCE are only partially robust to noisy labels .", "subpage_snippet": "", "source": "proceedings.mlr.press", "link": "https://proceedings.mlr.press/v119/ma20c/ma20c.pdf", "content": "Recently, Wang et al. (2019c) proposed the Symmetric Cross Entropy (SCE) which combines a Re - verse Cross Entropy (RCE) together with the CE loss. Both GCE and SCE are only partially robust to noisy labels ."}
+{"idx": 3, "title": "Can Cross Entropy Loss Be Robust to Label Noise? - IJCAI", "date": "", "ddg_snippet": "Abstract Trained with the standard cross entropy loss, deep neural networks can achieve great performance on correctly labeled data. However, if the training data is corrupted with label noise , deep models tend to overfit the noisy labels , thereby achieving poor gen-eration performance.", "subpage_snippet": "", "source": "www.ijcai.org", "link": "https://www.ijcai.org/Proceedings/2020/0305.pdf", "content": "Abstract Trained with the standard cross entropy loss, deep neural networks can achieve great performance on correctly labeled data. However, if the training data is corrupted with label noise , deep models tend to overfit the noisy labels , thereby achieving poor gen-eration performance."}
+{"idx": 4, "title": "Generalized Cross Entropy Loss for Training Deep Neural ...", "date": "", "ddg_snippet": "Here, we present a theoretically grounded set of noise -robust loss functions that can be seen as a generalization of MAE and CCE. Proposed loss functions can be readily applied with any existing DNN architecture and algorithm, while yielding good performance in a wide range of noisy label scenarios.", "subpage_snippet": "", "source": "papers.neurips.cc", "link": "https://papers.neurips.cc/paper/8094-generalized-cross-entropy-loss-for-training-deep-neural-networks-with-noisy-labels.pdf", "content": "Here, we present a theoretically grounded set of noise -robust loss functions that can be seen as a generalization of MAE and CCE. Proposed loss functions can be readily applied with any existing DNN architecture and algorithm, while yielding good performance in a wide range of noisy label scenarios."}
+{"idx": 5, "title": "machine learning - In which cases is the cross - entropy preferred over...", "date": "", "ddg_snippet": "Although both of the above methods provide a better score for the better closeness of prediction, still cross - entropy is preferred . Is it in every case or there are some peculiar scenarios where we prefer cross - entropy over MSE?", "subpage_snippet": "", "source": "stackoverflow.com", "link": "https://stackoverflow.com/questions/36515202/in-which-cases-is-the-cross-entropy-preferred-over-the-mean-squared-error", "content": "Although both of the above methods provide a better score for the better closeness of prediction, still cross - entropy is preferred . Is it in every case or there are some peculiar scenarios where we prefer cross - entropy over MSE?"}
+{"idx": 6, "title": "(PDF) Normalized Loss Functions for Deep Learning with Noisy Labels", "date": "", "ddg_snippet": "The recently proposed Reverse Cross Entropy (RCE). loss (Wang et al.,2019c) is defined asSymmetric cross entropy for robust learning with noisy . labels . In ICCV, pp. 322–330, 2019c.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/342436028_Normalized_Loss_Functions_for_Deep_Learning_with_Noisy_Labels", "content": "The recently proposed Reverse Cross Entropy (RCE). loss (Wang et al.,2019c) is defined asSymmetric cross entropy for robust learning with noisy . labels . In ICCV, pp. 322–330, 2019c."}
+{"idx": 7, "title": "Symmetric Cross Entropy for Robust Learning with Noisy Labels", "date": "", "ddg_snippet": "Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE).", "subpage_snippet": "", "source": "deepai.org", "link": "https://deepai.org/publication/symmetric-cross-entropy-for-robust-learning-with-noisy-labels", "content": "Inspired by the symmetric KL-divergence, we propose the approach of Symmetric cross entropy Learning (SL), boosting CE symmetrically with a noise robust counterpart Reverse Cross Entropy (RCE)."}
+{"idx": 8, "title": "Balanced coarse-to-fine federated learning for noisy heterogeneous...", "date": "", "ddg_snippet": "These strategy avoid the instability caused by incorrect labels , allowing the model to converge faster with the same training iterations.Zhang Z, Sabuncu MR (2018) Generalized cross entropy loss for training deep neural networks with noisy labels .", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s40747-024-01694-8", "content": "These strategy avoid the instability caused by incorrect labels , allowing the model to converge faster with the same training iterations.Zhang Z, Sabuncu MR (2018) Generalized cross entropy loss for training deep neural networks with noisy labels ."}
+{"idx": 9, "title": "Noisy Label Classification using Label Noise Selection with Test-Time...", "date": "", "ddg_snippet": "In the label noise selection, we propose TTA cross - entropy by measuring the cross - entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the...", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/03bea71f-c1ec-4678-9a82-cc86b4da1cef", "content": "In the label noise selection, we propose TTA cross - entropy by measuring the cross - entropy to predict the test-time augmented training data. In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the..."}
diff --git a/data/sampled_jsons/reverse_cross_entropy_RCE_PPO_instability_noise_robustness_Section_5.4.jsonl b/data/sampled_jsons/reverse_cross_entropy_RCE_PPO_instability_noise_robustness_Section_5.4.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..79c0cad0f68d88232115709553b1139dac3a3e1a
--- /dev/null
+++ b/data/sampled_jsons/reverse_cross_entropy_RCE_PPO_instability_noise_robustness_Section_5.4.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss, especially for low- probability predictions. To enhance ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/44897", "content": "In this work, we focus on RL algorithms that share learning difficulties with cross - entropy loss, especially for low- probability predictions. To enhance ..."}
+{"idx": 1, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — To enhance stability , we adapt reverse cross - entropy ( RCE ) from supervised learning for noisy data, defining a symmetric RL loss. We demonstrate ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=YjBrt82S3v", "content": "by JS Byun · Cited by 1 — To enhance stability , we adapt reverse cross - entropy ( RCE ) from supervised learning for noisy data, defining a symmetric RL loss. We demonstrate ..."}
+{"idx": 2, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · 2024 · Cited by 1 — In this work, we improve the stability of RL training by adapting the reverse cross entropy ( RCE ) from supervised learning for noisy data to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2405.17618?", "content": "by JS Byun · 2024 · Cited by 1 — In this work, we improve the stability of RL training by adapting the reverse cross entropy ( RCE ) from supervised learning for noisy data to ..."}
+{"idx": 3, "title": "Symmetric Reinforcement Learning Loss for Robust ...", "date": "", "ddg_snippet": "by JS Byun · Cited by 1 — In this work, we enhance the stability of the RL training procedure by adapting reverse cross - entropy ( RCE ) from supervised learning for noisy ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=9oq0iY2Jxx", "content": "by JS Byun · Cited by 1 — In this work, we enhance the stability of the RL training procedure by adapting reverse cross - entropy ( RCE ) from supervised learning for noisy ..."}
+{"idx": 4, "title": "Deep Reinforcement Learning for Training Robust, Wave ...", "date": "", "ddg_snippet": "1 May 2025 — Section 5.4 discusses the results of our experiments and provides intuition for the effectiveness of the state and action noise . 4. Section ...", "subpage_snippet": "", "source": "www2.eecs.berkeley.edu", "link": "https://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-26.pdf", "content": "1 May 2025 — Section 5.4 discusses the results of our experiments and provides intuition for the effectiveness of the state and action noise . 4. Section ..."}
+{"idx": 5, "title": "Machine Learning with Provable Robustness Guarantees", "date": "", "ddg_snippet": "by H Zhang · 2020 · Cited by 2 — After developing our robustness verification algorithms, we utilize them to create a certified adversarial defense for neural networks, where we explicitly.", "subpage_snippet": "", "source": "escholarship.org", "link": "https://escholarship.org/content/qt79m6006t/qt79m6006t_noSplash_8120ca87d89b869209a51f26a4982ca8.pdf", "content": "by H Zhang · 2020 · Cited by 2 — After developing our robustness verification algorithms, we utilize them to create a certified adversarial defense for neural networks, where we explicitly."}
+{"idx": 6, "title": "Reinforcement Learning Foundations for Deep Research ...", "date": "", "ddg_snippet": "8 Sept 2025 — This survey is, to our knowledge, the first dedicated to the RL foundations of deep research systems. It systematizes work after DeepSeek-R1 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.06733v1", "content": "8 Sept 2025 — This survey is, to our knowledge, the first dedicated to the RL foundations of deep research systems. It systematizes work after DeepSeek-R1 ..."}
+{"idx": 7, "title": "End-to-end Learning for Robust Decision Making", "date": "", "ddg_snippet": "5.4 .3 Smoothness and Stability . ... Bounded Dynamics and Stability : In Section 4.2.3, we show that the state ... the cross - entropy loss: ℒy(y, y) = −(︀y log ...", "subpage_snippet": "", "source": "dspace.mit.edu", "link": "https://dspace.mit.edu/bitstream/handle/1721.1/144800/Amini-amini-PhD-EECS-2022-thesis.pdf?sequence=1&isAllowed=y", "content": "5.4 .3 Smoothness and Stability . ... Bounded Dynamics and Stability : In Section 4.2.3, we show that the state ... the cross - entropy loss: ℒy(y, y) = −(︀y log ..."}
+{"idx": 8, "title": "Daily Papers", "date": "", "ddg_snippet": "Direct Preference Optimization (DPO) allows for policy training with a simple binary cross - entropy loss without a reward model. ... noise robustness based on the ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers?q=preference+leakage", "content": "Direct Preference Optimization (DPO) allows for policy training with a simple binary cross - entropy loss without a reward model. ... noise robustness based on the ..."}
+{"idx": 9, "title": "Assuring Safety under Uncertainty in Learning-Based ...", "date": "", "ddg_snippet": "However, these learning-based controllers cannot yet be deployed in highly uncertain environments due to significant issues relating to learning reliability, ...", "subpage_snippet": "", "source": "thesis.library.caltech.edu", "link": "https://thesis.library.caltech.edu/14046/1/Caltech_Thesis_Richard.pdf", "content": "However, these learning-based controllers cannot yet be deployed in highly uncertain environments due to significant issues relating to learning reliability, ..."}
diff --git a/data/sampled_jsons/self-blended_images_deepfake_SBI.jsonl b/data/sampled_jsons/self-blended_images_deepfake_SBI.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..48baecc1b5b565d6d059eef9bf72eefd3392ab46
--- /dev/null
+++ b/data/sampled_jsons/self-blended_images_deepfake_SBI.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Detecting Deepfakes with Self-Blended Images - GitHub", "date": "", "ddg_snippet": "The official PyTorch implementation for the following paper: Detecting Deepfakes with Self-Blended Images , Kaede Shiohara and Toshihiko Yamasaki, CVPR 2022 Oral", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/mapooon/SelfBlendedImages", "content": "The official PyTorch implementation for the following paper: Detecting Deepfakes with Self-Blended Images , Kaede Shiohara and Toshihiko Yamasaki, CVPR 2022 Oral"}
+{"idx": 1, "title": "FSBI: Deepfake detection with frequency enhanced self-blended images", "date": "", "ddg_snippet": "This study introduces a frequency enhanced self-blended images (FSBI) approach for deepfake detection. This proposed approach utilizes discrete wavelet transforms (DWT) to extract discriminative features from self-blended images ( SBI ). The features are then used to train a convolutional network architecture model.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S026288562500006X", "content": "This study introduces a frequency enhanced self-blended images (FSBI) approach for deepfake detection. This proposed approach utilizes discrete wavelet transforms (DWT) to extract discriminative features from self-blended images ( SBI ). The features are then used to train a convolutional network architecture model."}
+{"idx": 2, "title": "FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images", "date": "", "ddg_snippet": "Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and videos. This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2406.08625", "content": "Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and videos. This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes ..."}
+{"idx": 3, "title": "PDF Deepfake Detection with Frequency-Enhanced Self-Blended Images", "date": "", "ddg_snippet": "The blended image produced by the SBI module is input into the FFG mod-ule, which uses DWT to extract more distinctive features. DWT is a tech-nique used in signal and image processing that breaks down a signal into multiple frequency components by iteratively applying filters and dividing it into approximate and detailed coeficients at various ...", "subpage_snippet": "", "source": "cse.aua.am", "link": "https://cse.aua.am/files/2025/06/Deepfake_Detection_with_Frequency_Enhanced_Self_Blended_Images.pdf", "content": "The blended image produced by the SBI module is input into the FFG mod-ule, which uses DWT to extract more distinctive features. DWT is a tech-nique used in signal and image processing that breaks down a signal into multiple frequency components by iteratively applying filters and dividing it into approximate and detailed coeficients at various ..."}
+{"idx": 4, "title": "Detecting Deepfakes with Self-Blended Images翻译_sbi deepfake-CSDN博客", "date": "", "ddg_snippet": "文章浏览阅读3k次，点赞4次，收藏24次。本文提出了一种名为自混合图像（SBI）的新型合成训练数据，用于检测深度伪造。SBI通过从单个原始图像中混合伪造源图像和目标图像，以再现常见的伪造伪迹。这种方法鼓励模型学习更通用和鲁棒的表征，提高对未知操作和场景的泛化能力。在FF++, CDF, DFD ...", "subpage_snippet": "", "source": "blog.csdn.net", "link": "https://blog.csdn.net/jjw_zyfx/article/details/127801671", "content": "文章浏览阅读3k次，点赞4次，收藏24次。本文提出了一种名为自混合图像（SBI）的新型合成训练数据，用于检测深度伪造。SBI通过从单个原始图像中混合伪造源图像和目标图像，以再现常见的伪造伪迹。这种方法鼓励模型学习更通用和鲁棒的表征，提高对未知操作和场景的泛化能力。在FF++, CDF, DFD ..."}
+{"idx": 5, "title": "Advancements in Deepfake Detection with FSBI Method", "date": "", "ddg_snippet": "This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes Discrete Wavelet Transforms (DWT) to extract discriminative features from the self-blended images ( SBI ) to be used for training a convolutional network architecture model.", "subpage_snippet": "", "source": "scisimple.com", "link": "https://scisimple.com/en/articles/2025-07-30-advancements-in-deepfake-detection-with-fsbi-method--a9pgjn8", "content": "This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes Discrete Wavelet Transforms (DWT) to extract discriminative features from the self-blended images ( SBI ) to be used for training a convolutional network architecture model."}
+{"idx": 6, "title": "PDF Detecting Deepfakes with Self-Blended Images - CVF Open Access", "date": "", "ddg_snippet": "Abstract In this paper, we present novel synthetic training data called self-blended images ( SBIs ) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsis-tencies between source and target images ).", "subpage_snippet": "", "source": "openaccess.thecvf.com", "link": "https://openaccess.thecvf.com/content/CVPR2022/papers/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.pdf", "content": "Abstract In this paper, we present novel synthetic training data called self-blended images ( SBIs ) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsis-tencies between source and target images )."}
+{"idx": 7, "title": "Detecting Deepfakes with Self-Blended Images — Related Work", "date": "", "ddg_snippet": "AI Summary This paper introduces self-blended images ( SBIs ), a novel synthetic training data generation method for deepfake detection. SBIs are created by blending subtly altered versions of a single pristine image , mimicking common forgery artifacts. This approach improves model generalization to unseen manipulations and datasets.", "subpage_snippet": "", "source": "deepfake-total.com", "link": "https://deepfake-total.com/related_work/2204.08376", "content": "AI Summary This paper introduces self-blended images ( SBIs ), a novel synthetic training data generation method for deepfake detection. SBIs are created by blending subtly altered versions of a single pristine image , mimicking common forgery artifacts. This approach improves model generalization to unseen manipulations and datasets."}
+{"idx": 8, "title": "Detecting Deepfakes with Self-Blended Images - IEEE Xplore", "date": "", "ddg_snippet": "In this paper, we present novel synthetic training data called self-blended images ( SBIs ) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ...", "subpage_snippet": "", "source": "ieeexplore.ieee.org", "link": "https://ieeexplore.ieee.org/document/9880195", "content": "In this paper, we present novel synthetic training data called self-blended images ( SBIs ) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ..."}
+{"idx": 9, "title": "[2204.08376] Detecting Deepfakes with Self-Blended Images", "date": "", "ddg_snippet": "In this paper, we present novel synthetic training data called self-blended images ( SBIs ) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2204.08376", "content": "In this paper, we present novel synthetic training data called self-blended images ( SBIs ) to detect deepfakes . SBIs are generated by blending pseudo source and target images from single pristine images , reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images ). The key idea behind SBIs is that more general and hardly ..."}
diff --git a/data/sampled_jsons/sitealignmentforum.org_'paper-stress-testing-capability-elicitation-with-password'_limitations.jsonl b/data/sampled_jsons/sitealignmentforum.org_'paper-stress-testing-capability-elicitation-with-password'_limitations.jsonl
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index 0000000000000000000000000000000000000000..f00da62ecf05f8a47c3851e0498fc6a626a60435
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+++ b/data/sampled_jsons/sitealignmentforum.org_'paper-stress-testing-capability-elicitation-with-password'_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Password-locked models: a stress case for capabilities", "date": "", "ddg_snippet": "Aug 3, 2023 · Capabilities Elicitation Should Be Red-teamed With Password -locked Models Many development and deployment decision rely on having reliable upper bounds on model capabilities , measured with capabilities evaluations.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/rZs6ddqNnW8LXuJqA/password-locked-models-a-stress-case-for-capabilities", "content": "Aug 3, 2023 · Capabilities Elicitation Should Be Red-teamed With Password -locked Models Many development and deployment decision rely on having reliable upper bounds on model capabilities , measured with capabilities evaluations."}
+{"idx": 1, "title": "[Paper] Stress-testing capability elicitation with password ...", "date": "", "ddg_snippet": "Jun 4, 2024 · Building password -locked models robust against the kind of elicitation studied in the weak-to-strong generalization paper ; Using unsupervised behavior discovery +RL on password -locked models (we bet it works well) and trying to build password -locked models that are robust to this method. Please contact us if you are interested in working on this.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/c4sZqhqPwNKGz3fFW/paper-stress-testing-capability-elicitation-with-password", "content": "Jun 4, 2024 · Building password -locked models robust against the kind of elicitation studied in the weak-to-strong generalization paper ; Using unsupervised behavior discovery +RL on password -locked models (we bet it works well) and trying to build password -locked models that are robust to this method. Please contact us if you are interested in working on this."}
+{"idx": 2, "title": "The Elicitation Game: Evaluating capability elicitation", "date": "", "ddg_snippet": "Feb 27, 2025 · We are releasing a new paper called “The Elicitation Game: Evaluating Capability Elicitation Techniques”. See tweet thread here. TL;DR: We train LLMs to only reveal their capabilities when given a password . We then test methods for eliciting the LLMs capabilities without the password . Fine-tuning works best, few-shot prompting and prefilling work okay, but activation steering isn’t ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/6QA5eHBEqpAicCwbh/the-elicitation-game-evaluating-capability-elicitation", "content": "Feb 27, 2025 · We are releasing a new paper called “The Elicitation Game: Evaluating Capability Elicitation Techniques”. See tweet thread here. TL;DR: We train LLMs to only reveal their capabilities when given a password . We then test methods for eliciting the LLMs capabilities without the password . Fine-tuning works best, few-shot prompting and prefilling work okay, but activation steering isn’t ..."}
+{"idx": 3, "title": "When does capability elicitation bound risk? - AI Alignment Forum", "date": "", "ddg_snippet": "Jan 22, 2025 · An elicitation stress test can only determine whether elicitation can recover performance to a threshold that is already observable. Models might have capabilities that are hard to elicit, such that the developer cannot construct a stress test for them.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/u3taQsgxqCzrgErMM/when-does-capability-elicitation-bound-risk", "content": "Jan 22, 2025 · An elicitation stress test can only determine whether elicitation can recover performance to a threshold that is already observable. Models might have capabilities that are hard to elicit, such that the developer cannot construct a stress test for them."}
+{"idx": 4, "title": "Model Organisms of Misalignment: The Case for a New Pillar of", "date": "", "ddg_snippet": "... in working on this agenda with ... Capability exists : Model is capable of doing the undesirable behavior, with explicit training and/or prompting.", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/ChDH335ckdvpxXaXX/model-organisms-of-misalignment-the-case-for-a-new-pillar-of-1", "content": "... in working on this agenda with ... Capability exists : Model is capable of doing the undesirable behavior, with explicit training and/or prompting."}
+{"idx": 5, "title": "Protocol evaluations: good analogies vs - Alignment Forum", "date": "", "ddg_snippet": "Feb 19, 2024 · No comments 70 karma Log in to save where you left off Mentioned in 51 [ Paper] Stress-testing capability elicitation with password -locked models 9 ryan_greenblatt 2 Charlie Steiner 4 ryan_greenblatt", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/qhaSoR6vGmKnqGYLE/protocol-evaluations-good-analogies-vs-control", "content": "Feb 19, 2024 · No comments 70 karma Log in to save where you left off Mentioned in 51 [ Paper] Stress-testing capability elicitation with password -locked models 9 ryan_greenblatt 2 Charlie Steiner 4 ryan_greenblatt"}
+{"idx": 6, "title": "Weak-to-Strong Generalization: Eliciting Strong Capabilities ...", "date": "", "ddg_snippet": "Dec 16, 2023 · Mentioned in 71 Shallow review of technical AI safety, 2024 48 [ Paper] Stress-testing capability elicitation with password -locked models", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/9W8roCAeEccSa3Chz/weak-to-strong-generalization-eliciting-strong-capabilities", "content": "Dec 16, 2023 · Mentioned in 71 Shallow review of technical AI safety, 2024 48 [ Paper] Stress-testing capability elicitation with password -locked models"}
+{"idx": 7, "title": "Mechanistically Eliciting Latent Behaviors in Language", "date": "", "ddg_snippet": "Apr 30, 2024 · 59 Towards Alignment Auditing as a Numbers-Go-Up Science 55 Research directions Open Phil wants to fund in technical AI safety 51 Deep Causal Transcoding: A Framework for Mechanistically Eliciting Latent Behaviors in Language Models 51 [ Paper] Stress-testing capability elicitation with password -locked models Load More (5/10) [] TurnTrout 1y 5", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/ioPnHKFyy4Cw2Gr2x/mechanistically-eliciting-latent-behaviors-in-language-1", "content": "Apr 30, 2024 · 59 Towards Alignment Auditing as a Numbers-Go-Up Science 55 Research directions Open Phil wants to fund in technical AI safety 51 Deep Causal Transcoding: A Framework for Mechanistically Eliciting Latent Behaviors in Language Models 51 [ Paper] Stress-testing capability elicitation with password -locked models Load More (5/10) [] TurnTrout 1y 5"}
+{"idx": 8, "title": "How to mitigate sandbagging — AI Alignment Forum", "date": "", "ddg_snippet": "... capability can be better predicted with increased research and interaction, human experts can create high-quality demonstrations, and formats could be ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/Qv5PkrJYAaiBuEJjB/how-to-mitigate-sandbagging-1", "content": "... capability can be better predicted with increased research and interaction, human experts can create high-quality demonstrations, and formats could be ..."}
+{"idx": 9, "title": "The 80/20 playbook for mitigating AI scheming in 2025 — AI", "date": "", "ddg_snippet": "See: [ Paper ] Stress - testing capability elicitation with password -locked models ... s paper (the one that only had API access to the model, with no ...", "subpage_snippet": "", "source": "www.alignmentforum.org", "link": "https://www.alignmentforum.org/posts/YFxpsrph83H25aCLW/the-80-20-playbook-for-mitigating-ai-scheming-in-2025", "content": "See: [ Paper ] Stress - testing capability elicitation with password -locked models ... s paper (the one that only had API access to the model, with no ..."}
diff --git a/data/sampled_jsons/sitearxiv.org_2407.01511_CRAB_cross-environment_agent_benchmark_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_2407.01511_CRAB_cross-environment_agent_benchmark_abstract.jsonl
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--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2407.01511_CRAB_cross-environment_agent_benchmark_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[ 2407 . 01511 ] CRAB : Cross - environment Agent Benchmark for...", "date": "", "ddg_snippet": "Leveraging Crab , we developed a cross -platform Crab Benchmark -v0 comprising 120 tasks in computer desktop and mobile phone environments . We evaluated four advanced MLMs using different single and multi- agent system configurations on this benchmark .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2407.01511", "content": "Leveraging Crab , we developed a cross -platform Crab Benchmark -v0 comprising 120 tasks in computer desktop and mobile phone environments . We evaluated four advanced MLMs using different single and multi- agent system configurations on this benchmark ."}
+{"idx": 1, "title": "Advancing Language Multi- Agent Learning with Credit Re-Assignment...", "date": "", "ddg_snippet": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14496v2", "content": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024."}
+{"idx": 2, "title": "Advancing Language Multi- Agent Learning with Credit Re-Assignment...", "date": "", "ddg_snippet": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024a.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.14496v3", "content": "Androidworld: A dynamic benchmarking environment for autonomous agents . CRAB : Cross - environment agent benchmark for multimodal language model agents . arXiv preprint arXiv: 2407 . 01511 , 2024a."}
+{"idx": 3, "title": "COMMA: A Communicative Multimodal Multi- Agent Benchmark", "date": "", "ddg_snippet": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024c.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v4", "content": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024c."}
+{"idx": 4, "title": "COMMA: A Communicative Multimodal Multi- Agent Benchmark", "date": "", "ddg_snippet": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024c.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v3", "content": "Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Crab : Cross - environment agent benchmark for multimodal language model agents , 2024c."}
+{"idx": 5, "title": "Towards Evaluating Generalist Agents: An Automated ...", "date": "", "ddg_snippet": "The CRAB framework Xu et al. (2024) introduces a cross - environment benchmark that leverages multimodal language models to perform tasks across various GUI ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2310.08367v2", "content": "The CRAB framework Xu et al. (2024) introduces a cross - environment benchmark that leverages multimodal language models to perform tasks across various GUI ..."}
+{"idx": 6, "title": "arXiv:2502.14496v1 [cs.CL] 20 Feb 2025", "date": "", "ddg_snippet": "by Z He · 2025 · Cited by 1 — Empirical results show that our framework improves both per- formance and cross - environment generalizabil- ity of multi- agent systems. Moreover, ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.14496", "content": "by Z He · 2025 · Cited by 1 — Empirical results show that our framework improves both per- formance and cross - environment generalizabil- ity of multi- agent systems. Moreover, ..."}
+{"idx": 7, "title": "A Communicative Multimodal Multi-Agent Benchmark", "date": "", "ddg_snippet": "10 Oct 2024 — Crab: Cross-environment agent benchmark for multimodal language model agents, 2024. URL https://arxiv.org/abs/2407.01511. Zhang et al. (2024) ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v1", "content": "10 Oct 2024 — Crab: Cross-environment agent benchmark for multimodal language model agents, 2024. URL https://arxiv.org/abs/2407.01511. Zhang et al. (2024) ..."}
+{"idx": 8, "title": "A Communicative Multimodal Multi-Agent Benchmark", "date": "", "ddg_snippet": "6 Feb 2025 — Crab: Cross-environment agent benchmark for multimodal language model agents, 2024b. URL https://arxiv.org/abs/2407.01511. Zhang et al ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.07553v2", "content": "6 Feb 2025 — Crab: Cross-environment agent benchmark for multimodal language model agents, 2024b. URL https://arxiv.org/abs/2407.01511. Zhang et al ..."}
+{"idx": 9, "title": "Agent-as-a-Judge:Evaluate Agents with Agents", "date": "", "ddg_snippet": "16 Oct 2024 — Crab: Cross-environment agent benchmark for multimodal language model agents. arXiv preprint arXiv:2407.01511, 2024. Yang et al. (2024a)", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10934v2", "content": "16 Oct 2024 — Crab: Cross-environment agent benchmark for multimodal language model agents. arXiv preprint arXiv:2407.01511, 2024. Yang et al. (2024a)"}
diff --git a/data/sampled_jsons/sitearxiv.org_2410.10562_Causal_Modeling_of_Climate_Activism_on_Reddit_subreddits.jsonl b/data/sampled_jsons/sitearxiv.org_2410.10562_Causal_Modeling_of_Climate_Activism_on_Reddit_subreddits.jsonl
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index 0000000000000000000000000000000000000000..19f46a7d89a26cb4f480dc233da2a27cbdcbfd23
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2410.10562_Causal_Modeling_of_Climate_Activism_on_Reddit_subreddits.jsonl
@@ -0,0 +1,9 @@
+{"idx": 0, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "Oct 14, 2024 · In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10562", "content": "Oct 14, 2024 · In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 1, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.10562v1", "content": "In this work, we develop a comprehensive causal model of how and why Reddit users engage with activist communities driving mass climate protests (mainly the 2019 Earth Strike, Fridays for Future, and Extinction Rebellion)."}
+{"idx": 2, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "To maximize coverage of Reddit ’s user base, we use the most popular subreddits as the feature space. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv: 2410 . 10562 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2502.05049v1", "content": "To maximize coverage of Reddit ’s user base, we use the most popular subreddits as the feature space. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv: 2410 . 10562 ."}
+{"idx": 3, "title": "[2410.10562] Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2410.10562", "content": "We developed a rich and comprehensive causal model to study the interplay between different determinants of climate activism on Reddit . This work represents a first attempt to apply a multi- causal model to social media data."}
+{"idx": 4, "title": "Causal Modeling of Climate Activism on Reddit", "date": "", "ddg_snippet": "D Sympathy. E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit . arXiv: 2410 . 10562 v1 [cs.CY] 14 Oct 2024.RQ1: Does media coverage about climate and climate action affect activation in climate activism groups on Reddit , and over which time scale?", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.10562", "content": "D Sympathy. E Sociodemographic Features. Causal Modeling of Climate Activism on Reddit . arXiv: 2410 . 10562 v1 [cs.CY] 14 Oct 2024.RQ1: Does media coverage about climate and climate action affect activation in climate activism groups on Reddit , and over which time scale?"}
+{"idx": 5, "title": "Modeling the Impact of Group Interactions on", "date": "", "ddg_snippet": "“ Causal Modeling of Climate Activism on Reddit ”. In: Proceedings of The Web Conference.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2505.02989", "content": "“ Causal Modeling of Climate Activism on Reddit ”. In: Proceedings of The Web Conference."}
+{"idx": 6, "title": "On the Inference of Sociodemographics on Reddit", "date": "", "ddg_snippet": "To maximize coverage of Reddit ’s user base, we use the most popular subreddits as the feature space. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv: 2410 . 10562 .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2502.05049", "content": "To maximize coverage of Reddit ’s user base, we use the most popular subreddits as the feature space. Causal Modeling of Climate Activism on Reddit . arXiv preprint arXiv: 2410 . 10562 ."}
+{"idx": 7, "title": "Extracting Participation in Collective Action from Social Media", "date": "", "ddg_snippet": "Jan 13, 2025 · This case study demonstrates how our framework identifies online communities relevant to climate activism more effectively than coarse metrics like subreddit popularity or keyword frequency.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2501.07368v1", "content": "Jan 13, 2025 · This case study demonstrates how our framework identifies online communities relevant to climate activism more effectively than coarse metrics like subreddit popularity or keyword frequency."}
+{"idx": 8, "title": "Podcasts as a Medium for Participation in Collective Action ...", "date": "", "ddg_snippet": "This reflects both the inherent difficulty of the problem and the limited generalizability of Reddit -trained models to podcast data, underscoring the need for further methodological development. A third set of limitations relates to the study of emotions in podcasts.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.13197v1", "content": "This reflects both the inherent difficulty of the problem and the limited generalizability of Reddit -trained models to podcast data, underscoring the need for further methodological development. A third set of limitations relates to the study of emotions in podcasts."}
diff --git a/data/sampled_jsons/sitearxiv.org_2503.10694_GPT-4_accuracy_real-world_data_0.28_MedQA.jsonl b/data/sampled_jsons/sitearxiv.org_2503.10694_GPT-4_accuracy_real-world_data_0.28_MedQA.jsonl
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index 0000000000000000000000000000000000000000..829256ce5b2a3f713ea479dded8f1fa55e5f269a
--- /dev/null
+++ b/data/sampled_jsons/sitearxiv.org_2503.10694_GPT-4_accuracy_real-world_data_0.28_MedQA.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Capabilities of GPT-4 on Medical Challenge Problems Evaluation of GPT-3.5 and GPT-4 for supporting real-world ... Capabilities of GPT-4 on Medical Challenge Problems Capabilities of GPT-4 on Medical Challenge Problems [2305.15038] Is GPT-4 a Good Data Analyst? - arXiv.org [2303.08774] GPT-4 Technical Report - arXiv.org", "date": "", "ddg_snippet": "Mar 20, 2023 · We present a comprehensive evaluation of GPT-4 , a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Apr 26, 2023 · Sixty six questions from an informatics consult service were submitted to GPT -3.5 and GPT-4 via simple prompts. 12 physicians assessed the LLM responses' possibility of patient harm and concordance with existing reports from an informatics consultation service. The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale–and that we will likely continue to see advances with larger models for handling complex, real-world problems. We nd that GPT-4 exhibits signi cantly better calibration than its predecessor on this type of data . For example, datapoints that GPT-4 assigns an average probability of 0.96 tend to be correct 93% of the time. May 24, 2023 · Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. Mar 15, 2023 · We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.13375", "content": "Mar 20, 2023 · We present a comprehensive evaluation of GPT-4 , a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Apr 26, 2023 · Sixty six questions from an informatics consult service were submitted to GPT -3.5 and GPT-4 via simple prompts. 12 physicians assessed the LLM responses' possibility of patient harm and concordance with existing reports from an informatics consultation service. The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale–and that we will likely continue to see advances with larger models for handling complex, real-world problems. We nd that GPT-4 exhibits signi cantly better calibration than its predecessor on this type of data . For example, datapoints that GPT-4 assigns an average probability of 0.96 tend to be correct 93% of the time. May 24, 2023 · Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. Mar 15, 2023 · We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ..."}
+{"idx": 1, "title": "Evaluation of GPT-3.5 and GPT-4 for supporting real-world ... Capabilities of GPT-4 on Medical Challenge Problems Capabilities of GPT-4 on Medical Challenge Problems [2305.15038] Is GPT-4 a Good Data Analyst? - arXiv.org [2303.08774] GPT-4 Technical Report - arXiv.org", "date": "", "ddg_snippet": "Apr 26, 2023 · Sixty six questions from an informatics consult service were submitted to GPT -3.5 and GPT-4 via simple prompts. 12 physicians assessed the LLM responses' possibility of patient harm and concordance with existing reports from an informatics consultation service. The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale–and that we will likely continue to see advances with larger models for handling complex, real-world problems. We nd that GPT-4 exhibits signi cantly better calibration than its predecessor on this type of data . For example, datapoints that GPT-4 assigns an average probability of 0.96 tend to be correct 93% of the time. May 24, 2023 · Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. Mar 15, 2023 · We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2304.13714", "content": "Apr 26, 2023 · Sixty six questions from an informatics consult service were submitted to GPT -3.5 and GPT-4 via simple prompts. 12 physicians assessed the LLM responses' possibility of patient harm and concordance with existing reports from an informatics consultation service. The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale–and that we will likely continue to see advances with larger models for handling complex, real-world problems. We nd that GPT-4 exhibits signi cantly better calibration than its predecessor on this type of data . For example, datapoints that GPT-4 assigns an average probability of 0.96 tend to be correct 93% of the time. May 24, 2023 · Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. Mar 15, 2023 · We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ..."}
+{"idx": 2, "title": "Capabilities of GPT-4 on Medical Challenge Problems", "date": "", "ddg_snippet": "The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale–and that we will likely continue to see advances with larger models for handling complex, real-world problems.", "subpage_snippet": "", "source": "ar5iv.labs.arxiv.org", "link": "https://ar5iv.labs.arxiv.org/html/2303.13375", "content": "The leap in performance on medical challenge problems with the move from GPT 3.5 to GPT-4 suggests that we can achieve impressive gains on intensive real-world challenges with scale–and that we will likely continue to see advances with larger models for handling complex, real-world problems."}
+{"idx": 3, "title": "Capabilities of GPT-4 on Medical Challenge Problems", "date": "", "ddg_snippet": "We nd that GPT-4 exhibits signi cantly better calibration than its predecessor on this type of data . For example, datapoints that GPT-4 assigns an average probability of 0.96 tend to be correct 93% of the time.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2303.13375v2", "content": "We nd that GPT-4 exhibits signi cantly better calibration than its predecessor on this type of data . For example, datapoints that GPT-4 assigns an average probability of 0.96 tend to be correct 93% of the time."}
+{"idx": 4, "title": "[2305.15038] Is GPT-4 a Good Data Analyst? - arXiv.org", "date": "", "ddg_snippet": "May 24, 2023 · Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2305.15038", "content": "May 24, 2023 · Motivated by this, we raise the research question of \"is GPT-4 a good data analyst?\" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains."}
+{"idx": 5, "title": "[2303.08774] GPT-4 Technical Report - arXiv.org", "date": "", "ddg_snippet": "Mar 15, 2023 · We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2303.08774", "content": "Mar 15, 2023 · We report the development of GPT-4 , a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer ..."}
+{"idx": 6, "title": "[ 2503 . 10694 ] Medical Large Language Model Benchmarks Should...", "date": "", "ddg_snippet": "To put these ideas into practice, we use real - world clinical data in proof-of-concept experiments to evaluate popular medical LLM benchmarks and report significant gaps in their construct validity.Computation and Language (cs.CL). Cite as: arXiv: 2503 . 10694 [cs.CL].", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.10694", "content": "To put these ideas into practice, we use real - world clinical data in proof-of-concept experiments to evaluate popular medical LLM benchmarks and report significant gaps in their construct validity.Computation and Language (cs.CL). Cite as: arXiv: 2503 . 10694 [cs.CL]."}
+{"idx": 7, "title": "Capabilities of GPT -5 on Multimodal Medical Reasoning", "date": "", "ddg_snippet": "We benchmark GPT-5, GPT-5-mini, GPT-5-nano, and GPT - 4 o-2024-11-20 against standardized splits of MedQA , MedXpertQA (text and multimodal), MMLU medical subsets, USMLE self-assessment exams, and VQA-RAD.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2508.08224", "content": "We benchmark GPT-5, GPT-5-mini, GPT-5-nano, and GPT - 4 o-2024-11-20 against standardized splits of MedQA , MedXpertQA (text and multimodal), MMLU medical subsets, USMLE self-assessment exams, and VQA-RAD."}
+{"idx": 8, "title": "GPT - 4 o System Card", "date": "", "ddg_snippet": "GPT - 4 o does not advance real world vulnerability exploitation capabilities sufficient to.For example, for the popular MedQA USMLE 4 options dataset, 0-shot accuracy improves from 78.2% to 89.4%.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2410.21276", "content": "GPT - 4 o does not advance real world vulnerability exploitation capabilities sufficient to.For example, for the popular MedQA USMLE 4 options dataset, 0-shot accuracy improves from 78.2% to 89.4%."}
+{"idx": 9, "title": "[2503.22746] Susceptibility of Large Language Models to User-Driven...", "date": "", "ddg_snippet": "Using public datasets ( MedQA and Medbullets), we evaluated proprietary models ( GPT - 4 o, Claude 3.5 Sonnet, Claude 3.5 Haiku, Gemini 1.5 Pro, Gemini 1.5 Flash) and open-source models (LLaMA 3 8B, LLaMA 3 Med42 8B, DeepSeek R1 8B).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2503.22746", "content": "Using public datasets ( MedQA and Medbullets), we evaluated proprietary models ( GPT - 4 o, Claude 3.5 Sonnet, Claude 3.5 Haiku, Gemini 1.5 Pro, Gemini 1.5 Flash) and open-source models (LLaMA 3 8B, LLaMA 3 Med42 8B, DeepSeek R1 8B)."}
diff --git a/data/sampled_jsons/sitearxiv.org_Sharpness-Aware_Minimization_for_Efficiently_Improving_Generalization_abstract.jsonl b/data/sampled_jsons/sitearxiv.org_Sharpness-Aware_Minimization_for_Efficiently_Improving_Generalization_abstract.jsonl
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+++ b/data/sampled_jsons/sitearxiv.org_Sharpness-Aware_Minimization_for_Efficiently_Improving_Generalization_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "[2010.01412] Sharpness-Aware Minimization for Efficiently", "date": "", "ddg_snippet": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2010.01412", "content": "View a PDF of the paper titled Sharpness - Aware Minimization for Efficiently Improving Generalization , by Pierre Foret and 3 other authors"}
+{"idx": 1, "title": "Sharpness-Aware Minimization with Z-Score Gradient Filtering", "date": "", "ddg_snippet": "... sharpness - aware optimization has emerged as a prominent framework, aiming to identify flatter minima that are empirically associated with improved ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.02369v1", "content": "... sharpness - aware optimization has emerged as a prominent framework, aiming to identify flatter minima that are empirically associated with improved ..."}
+{"idx": 2, "title": "Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning", "date": "", "ddg_snippet": "... Sharpness - Aware Minimization (SAM) has proven effective in improving generalization by seeking flat minima, its substantial extra memory and ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19564v1", "content": "... Sharpness - Aware Minimization (SAM) has proven effective in improving generalization by seeking flat minima, its substantial extra memory and ..."}
+{"idx": 3, "title": "Tractable Sharpness-aware Regularization of Probabilistic", "date": "", "ddg_snippet": "Inspired by sharpness aware minimization in neural networks, we propose a Hessian-based regularizer for training PCs.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.05537v1", "content": "Inspired by sharpness aware minimization in neural networks, we propose a Hessian-based regularizer for training PCs."}
+{"idx": 4, "title": "Hide & Seek: Transformer Symmetries Obscure Sharpness &", "date": "", "ddg_snippet": "... sharpness has been successfully applied to ... For transformers, however, recent work reported weak correlation between flatness and generalization .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.05409v1", "content": "... sharpness has been successfully applied to ... For transformers, however, recent work reported weak correlation between flatness and generalization ."}
+{"idx": 5, "title": "MERIT: Maximum-normalized Element-wise Ratio for Language Model", "date": "", "ddg_snippet": "Our findings demonstrate the potential of MERIT to enhance large-batch training by improving convergence properties and generalization performance.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.20577v1", "content": "Our findings demonstrate the potential of MERIT to enhance large-batch training by improving convergence properties and generalization performance."}
+{"idx": 6, "title": "Not Only Consistency: Enhance Test-Time Adaptation with", "date": "", "ddg_snippet": "... excellent modeling capabilities and promising results in controlled settings, these DL-based rPPG models often suffer from limited generalization ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.07908v1", "content": "... excellent modeling capabilities and promising results in controlled settings, these DL-based rPPG models often suffer from limited generalization ..."}
+{"idx": 7, "title": "LSAM: Asynchronous Distributed Training with Landscape-Smoothed", "date": "", "ddg_snippet": "Sharpness - Aware Minimization (SAM) (Foret et al., 2020 ) improves generalization by simultaneously minimizing the empirical loss and the local ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.03110v1", "content": "Sharpness - Aware Minimization (SAM) (Foret et al., 2020 ) improves generalization by simultaneously minimizing the empirical loss and the local ..."}
+{"idx": 8, "title": "Large Learning Rates Simultaneously Achieve Robustness to", "date": "", "ddg_snippet": "... generalization becomes even more pronounced when coupled with the need for model compressibility, given the increased demands of resource-efficiency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17748v1", "content": "... generalization becomes even more pronounced when coupled with the need for model compressibility, given the increased demands of resource-efficiency ..."}
+{"idx": 9, "title": "Large Learning Rates Simultaneously Achieve Robustness to", "date": "", "ddg_snippet": "... generalization becomes even more pronounced when coupled with the need for model compressibility, given the increased demands of resource-efficiency ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17748v2", "content": "... generalization becomes even more pronounced when coupled with the need for model compressibility, given the increased demands of resource-efficiency ..."}
diff --git a/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_abstract.jsonl b/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_abstract.jsonl
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index 0000000000000000000000000000000000000000..e67144c2f971589db3f0018a5af6b5b50418df28
--- /dev/null
+++ b/data/sampled_jsons/sitedl.acm.org_Unbiased_Pairwise_Learning_from_Biased_Implicit_Feedback_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3409256.3409812", "content": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner."}
+{"idx": 1, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/epdf/10.1145/3409256.3409812", "content": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner."}
+{"idx": 2, "title": "Unbiased Pairwise Learning from Implicit Feedback for Recommender ...", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539618.3592077", "content": "Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12."}
+{"idx": 3, "title": "Bilateral Self-unbiased Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "Abstract Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback . More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3477495.3531946", "content": "Abstract Implicit feedback has been widely used to build commercial recommender systems. Because observed feedback represents users' click logs, there is a semantic gap between true relevance and observed feedback . More importantly, observed feedback is usually biased towards popular items, thereby overestimating the actual relevance of popular ..."}
+{"idx": 4, "title": "A General Framework for Pairwise Unbiased Learning to Rank", "date": "", "ddg_snippet": "Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (Virtual Event, Norway) (ICTIR '20).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1145/3539813.3545119", "content": "Unbiased Pairwise Learning from Biased Implicit Feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (Virtual Event, Norway) (ICTIR '20)."}
+{"idx": 5, "title": "Unbiased Pairwise Learning from Biased Implicit Feedback", "date": "", "ddg_snippet": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3409256.3409812?download=true", "content": "A pairwise algorithm addressing the two major difficulties in using implicit feedback has yet to be investigated, and the proposed algorithm is the first pairwise method for solving these challenges in a theoretically principal manner."}
+{"idx": 6, "title": "Debiased Explainable Pairwise Ranking from Implicit Feedback", "date": "", "ddg_snippet": "In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback .", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3460231.3474274", "content": "In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback ."}
+{"idx": 7, "title": "ReCRec: Reasoning the Causes of Implicit Feedback for Debiased ...", "date": "", "ddg_snippet": "Unbiased pairwise learning from biased implicit feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5-12.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3672275?download=true", "content": "Unbiased pairwise learning from biased implicit feedback . In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5-12."}
+{"idx": 8, "title": "Debiased Pairwise Learning for Implicit Collaborative Filtering", "date": "", "ddg_snippet": "This issue causes biased optimization objectives and results in biased parameter estimation. In this paper, we propose a novel method to address learning biases arising from implicit feedback data and introduce a modified loss function for pairwise learning , called debiased pairwise loss (DPL).", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1109/TKDE.2024.3479240", "content": "This issue causes biased optimization objectives and results in biased parameter estimation. In this paper, we propose a novel method to address learning biases arising from implicit feedback data and introduce a modified loss function for pairwise learning , called debiased pairwise loss (DPL)."}
+{"idx": 9, "title": "A Deep Generative Recommendation Method for Unbiased Learning from ...", "date": "", "ddg_snippet": "Abstract Variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/abs/10.1145/3578337.3605114", "content": "Abstract Variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models."}
diff --git a/data/sampled_jsons/sitegithub.com_siyuancncd_CoPINN_equations_8_9_vie_vih_cognitive_training_scheduler.jsonl b/data/sampled_jsons/sitegithub.com_siyuancncd_CoPINN_equations_8_9_vie_vih_cognitive_training_scheduler.jsonl
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index 0000000000000000000000000000000000000000..49da8c88ad96e7b629e361cca1a63047e46124f8
--- /dev/null
+++ b/data/sampled_jsons/sitegithub.com_siyuancncd_CoPINN_equations_8_9_vie_vih_cognitive_training_scheduler.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - siyuancncd/CoPINN: This is the official ...", "date": "", "ddg_snippet": "Finally, we propose a cognitive training scheduler to progressively optimize the entire sampling regions from easy to hard, thereby embracing robustness and generalization against predicting physical boundary regions.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN", "content": "Finally, we propose a cognitive training scheduler to progressively optimize the entire sampling regions from easy to hard, thereby embracing robustness and generalization against predicting physical boundary regions."}
+{"idx": 1, "title": "siyuancncd (DSY) · GitHub", "date": "", "ddg_snippet": "CoPINN Public This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025, Spotlight) Python 14 1", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd", "content": "CoPINN Public This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025, Spotlight) Python 14 1"}
+{"idx": 2, "title": "CoPINN/README.md at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025) - CoPINN /README.md at main · siyuancncd / CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/README.md", "content": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025) - CoPINN /README.md at main · siyuancncd / CoPINN"}
+{"idx": 3, "title": "GitHub - siyuancncd/siyuancncd", "date": "", "ddg_snippet": "May 11, 2024 · Contribute to siyuancncd / siyuancncd development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/siyuancncd", "content": "May 11, 2024 · Contribute to siyuancncd / siyuancncd development by creating an account on GitHub."}
+{"idx": 4, "title": "CoPINN/CoPINN.pdf at main · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd / CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/blob/main/CoPINN.pdf", "content": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025, Spotlight) - CoPINN / CoPINN .pdf at main · siyuancncd / CoPINN"}
+{"idx": 5, "title": "Releases · siyuancncd/CoPINN · GitHub", "date": "", "ddg_snippet": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025) - siyuancncd / CoPINN", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/siyuancncd/CoPINN/releases", "content": "This is the official implementation of \" CoPINN : Cognitive Physics-informed Neural Network\" (ICML 2025) - siyuancncd / CoPINN"}
+{"idx": 6, "title": "Vision-Cognitive-Neural-Networks/lr_scheduler.py at main ...", "date": "", "ddg_snippet": "This project is a paper called \"The potential of cognitive -inspired neural network modelling framework for computer vision processing tasks\" submitted to AS in 2025 - CAU-COE-VEICLab/Visi...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/CAU-COE-VEICLab/Vision-Cognitive-Neural-Networks/blob/main/lr_scheduler.py", "content": "This project is a paper called \"The potential of cognitive -inspired neural network modelling framework for computer vision processing tasks\" submitted to AS in 2025 - CAU-COE-VEICLab/Visi..."}
+{"idx": 7, "title": "Adding a new SSH key to your GitHub account - GitHub Docs", "date": "", "ddg_snippet": "To configure your account on GitHub.com to use your new (or existing) SSH key, you'll also need to add the key to your account.", "subpage_snippet": "", "source": "docs.github.com", "link": "https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account", "content": "To configure your account on GitHub.com to use your new (or existing) SSH key, you'll also need to add the key to your account."}
+{"idx": 8, "title": "GitHub - 0x192/universal-android-debloater: Cross-platform GUI written...", "date": "", "ddg_snippet": "Summary. This is a complete rewrite in Rust of the UAD project, which aims to improve privacy and battery performance by removing unnecessary and obscure system apps. This can also contribute to improve security by reducing the attack surface. Packag...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/0x192/universal-android-debloater", "content": "Summary. This is a complete rewrite in Rust of the UAD project, which aims to improve privacy and battery performance by removing unnecessary and obscure system apps. This can also contribute to improve security by reducing the attack surface. Packag..."}
+{"idx": 9, "title": "GitHub - LazyVim/LazyVim: Neovim config for the lazy", "date": "", "ddg_snippet": "Neovim config for the lazy. Contribute to LazyVim/LazyVim development by creating an account on GitHub.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LazyVim/LazyVim", "content": "Neovim config for the lazy. Contribute to LazyVim/LazyVim development by creating an account on GitHub."}
diff --git a/data/sampled_jsons/siteicml.cc_A_Checks-and-Balances_Framework_limitations.jsonl b/data/sampled_jsons/siteicml.cc_A_Checks-and-Balances_Framework_limitations.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d8fec2892ca8856218d3714db2c8f26180e9d872
--- /dev/null
+++ b/data/sampled_jsons/siteicml.cc_A_Checks-and-Balances_Framework_limitations.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "ICML 2025 Schedule", "date": "", "ddg_snippet": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/calendar", "content": "Calibration and Bias in Algorithms, Data, and Models: a tutorial on metrics and plots for measuring calibration, bias, fairness, reliability, and ..."}
+{"idx": 1, "title": "ICML 2025 Papers", "date": "", "ddg_snippet": "Adaptive Message Passing: A General Framework to Mitigate ... GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/papers.html", "content": "Adaptive Message Passing: A General Framework to Mitigate ... GTR: A General, Multi-View, and Dynamic Framework for Trajectory Representation Learning"}
+{"idx": 2, "title": "ICML 2022 Schedule", "date": "", "ddg_snippet": "Creating a scalable, reproducible, and reliable environment for model development with AstraZeneca and W&B. ... Amazon SageMaker Model ...", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2022/calendar", "content": "Creating a scalable, reproducible, and reliable environment for model development with AstraZeneca and W&B. ... Amazon SageMaker Model ..."}
+{"idx": 3, "title": "ICML 2024 Workshops", "date": "", "ddg_snippet": "Aligning AI agents with human intentions and values is one of the main barriers to the safe and ethical application of AI systems in the real world.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2024/events/workshop", "content": "Aligning AI agents with human intentions and values is one of the main barriers to the safe and ethical application of AI systems in the real world."}
+{"idx": 4, "title": "PDF A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/media/icml-2025/Slides/46461.pdf", "content": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Susceptible to social biases Vulnerable to reward hacking \"Whack-A-Mole\" reactive approach Catastrophic forgetting issues"}
+{"idx": 5, "title": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment", "date": "", "ddg_snippet": "Poster A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/virtual/2025/poster/46461", "content": "Poster A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Edward Chang East Exhibition Hall A-B #E-703"}
+{"idx": 6, "title": "Downloads 2025 - icml.cc", "date": "", "ddg_snippet": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks A Classification View on Meta Learning Bandits A Closer Look at Backdoor Attacks on CLIP A Closer Look at Generalized BH Algorithm for Out-of-Distribution Detection", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2025", "content": "A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks A Classification View on Meta Learning Bandits A Closer Look at Backdoor Attacks on CLIP A Closer Look at Generalized BH Algorithm for Out-of-Distribution Detection"}
+{"idx": 7, "title": "Downloads", "date": "", "ddg_snippet": "Adversarial Robustness against Multiple and Single $l_p ... A Framework for Learning to Request Rich and Contextually Useful Information from Humans", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2022", "content": "Adversarial Robustness against Multiple and Single $l_p ... A Framework for Learning to Request Rich and Contextually Useful Information from Humans"}
+{"idx": 8, "title": "Downloads", "date": "", "ddg_snippet": "ACM-MILP: Adaptive Constraint Modification via Grouping and ... Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Downloads/2024", "content": "ACM-MILP: Adaptive Constraint Modification via Grouping and ... Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework"}
+{"idx": 9, "title": "2023 Awards", "date": "", "ddg_snippet": "... also proposes a statistical test for ... It rigorously establishes a novel lower bound and presents two algorithms, Balanced FTRL and Adaptive FTRL.", "subpage_snippet": "", "source": "icml.cc", "link": "https://icml.cc/Conferences/2023/Awards", "content": "... also proposes a statistical test for ... It rigorously establishes a novel lower bound and presents two algorithms, Balanced FTRL and Adaptive FTRL."}
diff --git a/data/sampled_jsons/sitezyxunh.github.ioEntityErasure-ProjectPage_learning_rate_AECM.jsonl b/data/sampled_jsons/sitezyxunh.github.ioEntityErasure-ProjectPage_learning_rate_AECM.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..98dc7a02774bf68666612846ca74a98422036ac3
--- /dev/null
+++ b/data/sampled_jsons/sitezyxunh.github.ioEntityErasure-ProjectPage_learning_rate_AECM.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Learning - Wikipedia", "date": "", "ddg_snippet": "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. [1] The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. [2]", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Learning", "content": "Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. [1] The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. [2]"}
+{"idx": 1, "title": "EntityErasure: Erasing Entity Cleanly via Amodal Entity Segmentation...", "date": "", "ddg_snippet": "A task is worth one word: Learning with task prompts for high-quality versatile image inpainting. In ECCV, 2024.", "subpage_snippet": "", "source": "zyxunh.github.io", "link": "https://zyxunh.github.io/EntityErasure-ProjectPage/", "content": "A task is worth one word: Learning with task prompts for high-quality versatile image inpainting. In ECCV, 2024."}
+{"idx": 2, "title": "What Is Learning ? - Verywell Mind", "date": "", "ddg_snippet": "Jan 8, 2025 · Learning is a relatively lasting change in behavior resulting from observation and experience. It is the acquisition of information, knowledge, and problem-solving skills. When you think of learning , it's easy to focus on formal education that takes place during childhood and early adulthood.", "subpage_snippet": "", "source": "www.verywellmind.com", "link": "https://www.verywellmind.com/what-is-learning-2795332", "content": "Jan 8, 2025 · Learning is a relatively lasting change in behavior resulting from observation and experience. It is the acquisition of information, knowledge, and problem-solving skills. When you think of learning , it's easy to focus on formal education that takes place during childhood and early adulthood."}
+{"idx": 3, "title": "LEARNING Definition & Meaning - Merriam-Webster", "date": "", "ddg_snippet": "Sep 17, 2012 · knowledge, learning , erudition, scholarship mean what is or can be known by an individual or by humankind. knowledge applies to facts or ideas acquired by study, investigation, observation, or experience. learning applies to knowledge acquired especially through formal, often advanced, schooling.", "subpage_snippet": "", "source": "www.merriam-webster.com", "link": "https://www.merriam-webster.com/dictionary/learning", "content": "Sep 17, 2012 · knowledge, learning , erudition, scholarship mean what is or can be known by an individual or by humankind. knowledge applies to facts or ideas acquired by study, investigation, observation, or experience. learning applies to knowledge acquired especially through formal, often advanced, schooling."}
+{"idx": 4, "title": "Learning | Types, Theories & Benefits | Britannica", "date": "", "ddg_snippet": "Sep 10, 2025 · learning , the alteration of behaviour as a result of individual experience. When an organism can perceive and change its behaviour, it is said to learn .", "subpage_snippet": "", "source": "www.britannica.com", "link": "https://www.britannica.com/science/learning", "content": "Sep 10, 2025 · learning , the alteration of behaviour as a result of individual experience. When an organism can perceive and change its behaviour, it is said to learn ."}
+{"idx": 5, "title": "LEARNING | English meaning - Cambridge Dictionary", "date": "", "ddg_snippet": "LEARNING definition: 1. the activity of obtaining knowledge: 2. knowledge or a piece of information obtained by study…. Learn more.", "subpage_snippet": "", "source": "dictionary.cambridge.org", "link": "https://dictionary.cambridge.org/dictionary/english/learning", "content": "LEARNING definition: 1. the activity of obtaining knowledge: 2. knowledge or a piece of information obtained by study…. Learn more."}
+{"idx": 6, "title": "Learning Theories: Theories of Learning in Education | NU", "date": "", "ddg_snippet": "Oct 25, 2022 · Theories of learning provide students with structure & enable educators to teach effectively. Read this piece to learn about the 5 key learning theories.", "subpage_snippet": "", "source": "www.nu.edu", "link": "https://www.nu.edu/blog/theories-of-learning/", "content": "Oct 25, 2022 · Theories of learning provide students with structure & enable educators to teach effectively. Read this piece to learn about the 5 key learning theories."}
+{"idx": 7, "title": "Learning | The Seattle Public Library", "date": "", "ddg_snippet": "We help kids and teens succeed in school and college-bound students prepare for college with live virtual tutoring. Adults and seniors can pursue lifelong learning through free classes and resources. We will even create a custom learning plan for any new skill you want to try.", "subpage_snippet": "", "source": "www.spl.org", "link": "https://www.spl.org/programs-and-services/learning", "content": "We help kids and teens succeed in school and college-bound students prepare for college with live virtual tutoring. Adults and seniors can pursue lifelong learning through free classes and resources. We will even create a custom learning plan for any new skill you want to try."}
+{"idx": 8, "title": "What is Learning ? | SkillsYouNeed", "date": "", "ddg_snippet": "Learn about the processes and principles of learning . How do people learn and what are the key factors that enable effective learning .", "subpage_snippet": "", "source": "www.skillsyouneed.com", "link": "https://www.skillsyouneed.com/learn/learning.html", "content": "Learn about the processes and principles of learning . How do people learn and what are the key factors that enable effective learning ."}
+{"idx": 9, "title": "Free worksheets | K5 Learning", "date": "", "ddg_snippet": "Reading, math and more for kindergarten to grade 5. Thousands of free worksheets in math, reading, science, vocabulary, spelling, grammar and writing.", "subpage_snippet": "", "source": "www.k5learning.com", "link": "https://www.k5learning.com/", "content": "Reading, math and more for kindergarten to grade 5. Thousands of free worksheets in math, reading, science, vocabulary, spelling, grammar and writing."}
diff --git a/data/sampled_jsons/social_science_measurement_framework_criticism_too_much_work_computer_science_history_standards_year_2024.jsonl b/data/sampled_jsons/social_science_measurement_framework_criticism_too_much_work_computer_science_history_standards_year_2024.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..de85d2ebf4aac93613080d2b0b46366a1d13aed9
--- /dev/null
+++ b/data/sampled_jsons/social_science_measurement_framework_criticism_too_much_work_computer_science_history_standards_year_2024.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Algorithmic bias: Social science research integration ...", "date": "", "ddg_snippet": "by K Ukanwa · 2024 · Cited by 8 — This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents ...", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2352250X24000496", "content": "by K Ukanwa · 2024 · Cited by 8 — This paper reviews recent research on algorithmic bias and proposes increased engagement of psychological and social science research to understand antecedents ..."}
+{"idx": 1, "title": "Frameworks and Standards can be limiting and long-lasting", "date": "", "ddg_snippet": "21 Jan 2019 — Frameworks and Standards can be limiting and long-lasting : Alan Kay was right. Through the K-12 CS Framework process (December 2016, see the ...", "subpage_snippet": "", "source": "computinged.wordpress.com", "link": "https://computinged.wordpress.com/2019/01/21/standards-are-limiting-and-long-lasting-alan-kay-was-right/", "content": "21 Jan 2019 — Frameworks and Standards can be limiting and long-lasting : Alan Kay was right. Through the K-12 CS Framework process (December 2016, see the ..."}
+{"idx": 2, "title": "Assessing computational thinking in the social studies", "date": "", "ddg_snippet": "by MMG Manfra · 2022 · Cited by 9 — Our research takes a first step toward documenting student outcomes associated with integrating and assessing computational thinking in the social studies.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/00933104.2021.2003276", "content": "by MMG Manfra · 2022 · Cited by 9 — Our research takes a first step toward documenting student outcomes associated with integrating and assessing computational thinking in the social studies."}
+{"idx": 3, "title": "Machine Learning for Social Science: An Agnostic Approach", "date": "", "ddg_snippet": "by J Grimmer · 2021 · Cited by 395 — For much of its history , empirical work in the social sciences has been defined by scarcity. Data were hard to find, surveys were costly to ...", "subpage_snippet": "", "source": "www.annualreviews.org", "link": "https://www.annualreviews.org/content/journals/10.1146/annurev-polisci-053119-015921?crawler=true&mimetype=application/pdf", "content": "by J Grimmer · 2021 · Cited by 395 — For much of its history , empirical work in the social sciences has been defined by scarcity. Data were hard to find, surveys were costly to ..."}
+{"idx": 4, "title": "The Ethics of Computational Social Science", "date": "", "ddg_snippet": "by D Leslie · 2023 · Cited by 16 — This chapter is concerned with setting up practical guardrails within the research activities and environments of Computational Social ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/chapter/10.1007/978-3-031-16624-2_4", "content": "by D Leslie · 2023 · Cited by 16 — This chapter is concerned with setting up practical guardrails within the research activities and environments of Computational Social ..."}
+{"idx": 5, "title": "The social sciences are useless. So why do we study them ...", "date": "", "ddg_snippet": "12 Mar 2021 — The thing about social science is that it hasn't produced much . We social scientists don't have an inferiority complex; we really are inferior.", "subpage_snippet": "", "source": "statmodeling.stat.columbia.edu", "link": "https://statmodeling.stat.columbia.edu/2021/03/12/the-social-sciences-are-useless-so-why-do-we-study-them-heres-a-good-reason/", "content": "12 Mar 2021 — The thing about social science is that it hasn't produced much . We social scientists don't have an inferiority complex; we really are inferior."}
+{"idx": 6, "title": "Toward a framework for assessing the quality of students ...", "date": "", "ddg_snippet": "by T Klijnstra · 2023 · Cited by 15 — This study aims to describe components and levels of upper secondary social science students' reasoning about social problems.", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/full/10.1080/00933104.2022.2132894", "content": "by T Klijnstra · 2023 · Cited by 15 — This study aims to describe components and levels of upper secondary social science students' reasoning about social problems."}
+{"idx": 7, "title": "Literature Reviews, Theoretical Frameworks, and ...", "date": "", "ddg_snippet": "by JA Luft · 2022 · Cited by 313 — This Research Methods essay is designed to provide an overview of literature reviews , theoretical frameworks , and conceptual frameworks as ...", "subpage_snippet": "", "source": "www.lifescied.org", "link": "https://www.lifescied.org/doi/10.1187/cbe.21-05-0134", "content": "by JA Luft · 2022 · Cited by 313 — This Research Methods essay is designed to provide an overview of literature reviews , theoretical frameworks , and conceptual frameworks as ..."}
+{"idx": 8, "title": "Social prediction: a new research paradigm based on ...", "date": "", "ddg_snippet": "by Y Chen · 2021 · Cited by 47 — This article redefines this concept by introducing why and how machine learning can help prediction in a scientific way.", "subpage_snippet": "", "source": "journalofchinesesociology.springeropen.com", "link": "https://journalofchinesesociology.springeropen.com/articles/10.1186/s40711-021-00152-z", "content": "by Y Chen · 2021 · Cited by 47 — This article redefines this concept by introducing why and how machine learning can help prediction in a scientific way."}
+{"idx": 9, "title": "Explanation in artificial intelligence: Insights from the social ...", "date": "", "ddg_snippet": "by T Miller · 2019 · Cited by 7108 — This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S0004370218305988", "content": "by T Miller · 2019 · Cited by 7108 — This paper argues that the field of explainable artificial intelligence can build on this existing research, and reviews relevant papers."}
diff --git a/data/sampled_jsons/text-to-action_frameworks_GUI_agents_LLM_VLM_integration_2024_2025.jsonl b/data/sampled_jsons/text-to-action_frameworks_GUI_agents_LLM_VLM_integration_2024_2025.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..83253f918d94a96c3e68d82e47eae0aa355508ba
--- /dev/null
+++ b/data/sampled_jsons/text-to-action_frameworks_GUI_agents_LLM_VLM_integration_2024_2025.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - lmgame-org/GamingAgent: LLM / VLM gaming agents and...", "date": "", "ddg_snippet": "LLM / VLM gaming agents and model evaluation through games. License.This repo enables and tests LLM / VLM -based agents in standardized interctive gaming envrionments. It consists of two main features", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/lmgame-org/GamingAgent", "content": "LLM / VLM gaming agents and model evaluation through games. License.This repo enables and tests LLM / VLM -based agents in standardized interctive gaming envrionments. It consists of two main features"}
+{"idx": 1, "title": "KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge...", "date": "", "ddg_snippet": "Despite recent progress, Graphic User Interface ( GUI ) agents powered by Large Language Mod-els (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.00366", "content": "Despite recent progress, Graphic User Interface ( GUI ) agents powered by Large Language Mod-els (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge."}
+{"idx": 2, "title": "Мультимодальные RAG и VLM против OCR + LLM : Как откровенно...", "date": "", "ddg_snippet": "В исследовании Вана ( 2024 ) точность извлечения информации для RAG + VLM составила 89%, а OCR + LLM показали лишь 62%.", "subpage_snippet": "", "source": "blogs.epsilonmetrics.ru", "link": "https://blogs.epsilonmetrics.ru/multimodalnaya-rag-i-vlm-vmesto-ocr-i-llm/", "content": "В исследовании Вана ( 2024 ) точность извлечения информации для RAG + VLM составила 89%, а OCR + LLM показали лишь 62%."}
+{"idx": 3, "title": "VLM -Based Modeling Agents", "date": "", "ddg_snippet": "Explore VLM -based modeling agents that integrate vision-language models for adaptive decision-making in embodied tasks across real and simulated environments.", "subpage_snippet": "", "source": "www.emergentmind.com", "link": "https://www.emergentmind.com/topics/vlm-based-modeling-agents-1632a378-a20c-4475-84fc-fedecfb82923", "content": "Explore VLM -based modeling agents that integrate vision-language models for adaptive decision-making in embodied tasks across real and simulated environments."}
+{"idx": 4, "title": "Vision Language Action Models (VLA) & Policies for Robots", "date": "", "ddg_snippet": "Vision Language Actions Models enables robots to perceive, reason and act over complex tasks and perform precise dexterous movements using VLM & Diffusion Model.", "subpage_snippet": "", "source": "learnopencv.com", "link": "https://learnopencv.com/vision-language-action-models-lerobot-policy/", "content": "Vision Language Actions Models enables robots to perceive, reason and act over complex tasks and perform precise dexterous movements using VLM & Diffusion Model."}
+{"idx": 5, "title": "CogAgent: A Visual Language Model for GUI Agents | Request PDF", "date": "", "ddg_snippet": "To fully exploit GUI -Xplore's unique features, we propose Xplore- Agent , a GUI agent framework that combines Action -aware GUI Modeling with Graph-Guided Environment Reasoning.", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/384235870_CogAgent_A_Visual_Language_Model_for_GUI_Agents", "content": "To fully exploit GUI -Xplore's unique features, we propose Xplore- Agent , a GUI agent framework that combines Action -aware GUI Modeling with Graph-Guided Environment Reasoning."}
+{"idx": 6, "title": "Paper page - VEM: Environment-Free Exploration for Training GUI ...", "date": "", "ddg_snippet": "The proposed environment-free RL framework uses a pretrained Value Environment Model (VEM) for offline value estimation and policy optimization, achieving high performance in GUI automation with minimal interaction costs.", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/papers/2502.18906", "content": "The proposed environment-free RL framework uses a pretrained Value Environment Model (VEM) for offline value estimation and policy optimization, achieving high performance in GUI automation with minimal interaction costs."}
+{"idx": 7, "title": "Exploring the Global Rise of Hybrid VLM and LLM Controllers", "date": "", "ddg_snippet": "Our latest analysis reveals that the hybrid VLM + LLM controller market is expected to witness significant growth in the coming years. The market was valued at USD 6.10 billion in 2024 . It is expected to register a CAGR of 28.6% between 2025 and 2034.", "subpage_snippet": "", "source": "www.polarismarketresearch.com", "link": "https://www.polarismarketresearch.com/blog/why-hybrid-vlm-llm-controllers-are-becoming-increasingly-popular-globally", "content": "Our latest analysis reveals that the hybrid VLM + LLM controller market is expected to witness significant growth in the coming years. The market was valued at USD 6.10 billion in 2024 . It is expected to register a CAGR of 28.6% between 2025 and 2034."}
+{"idx": 8, "title": "Popular GitHub repositories related to Vlm", "date": "", "ddg_snippet": "LLM Agent Framework in ComfyUI includes MCP sever, Omost,GPT-sovits, ChatTTS,GOT-OCR2.0, and FLUX prompt nodes,access to Feishu,discord,and adapts to all llms with similar openai / aisuite interfaces , such as o1,ollama, gemini, grok, qwen, GLM, deepseek, kimi,doubao.", "subpage_snippet": "", "source": "www.aibase.com", "link": "https://www.aibase.com/repos/topic/vlm", "content": "LLM Agent Framework in ComfyUI includes MCP sever, Omost,GPT-sovits, ChatTTS,GOT-OCR2.0, and FLUX prompt nodes,access to Feishu,discord,and adapts to all llms with similar openai / aisuite interfaces , such as o1,ollama, gemini, grok, qwen, GLM, deepseek, kimi,doubao."}
+{"idx": 9, "title": "VLM в Нейро: как мы создавали мультимодальную... / Хабр", "date": "", "ddg_snippet": "Мы сделали вместо LLM ‑рефразера VLM ‑рефразер. Фактически мы стали подавать на вход рефразеру помимо текстового результата визуального поиска ещё и исходное изображение. Добавили в пайплайн отдельную модель VLM ‑captioner.", "subpage_snippet": "", "source": "habr.com", "link": "https://habr.com/ru/companies/yandex/articles/847706/", "content": "Мы сделали вместо LLM ‑рефразера VLM ‑рефразер. Фактически мы стали подавать на вход рефразеру помимо текстового результата визуального поиска ещё и исходное изображение. Добавили в пайплайн отдельную модель VLM ‑captioner."}
diff --git a/data/sampled_jsons/tfemquulED_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Figure_4_Tanh_Tanh-C.jsonl b/data/sampled_jsons/tfemquulED_Training-Free_Diffusion_Model_Alignment_with_Sampling_Demons_Figure_4_Tanh_Tanh-C.jsonl
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index 0000000000000000000000000000000000000000..a46c544762da8a52ab12fa0ffe512a70cedc9902
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - catchfree1225/demonsampling: [ICLR'25] Official ...", "date": "", "ddg_snippet": "Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/catchfree1225/demonsampling", "content": "Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ..."}
+{"idx": 1, "title": "Training-free Diffusion Model Alignment with Sampling Demons Images GitHub - catchfree1225/demonsampling: [ICLR'25] Official ... Published as a conference paper at ICLR 2025 - OpenReview Training-free Diffusion Model Alignment with Sampling Demons Training-free Diffusion Model Alignment with Sampling Demons Training-free Diffusion Model Alignment with Sampling Demons Training-Free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Oct 8, 2024 · To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training . View all Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ... This paper introduces a training-free method to improve diffusion models for text-to-image generation, enhancing aesthetic scores without additional training . The paper introduces \" Sampling Demons ,\" a training-free approach that addresses these limitations by enabling diffusion model alignment with arbitrary, non-differentiable reward functions during inference. Figure 3: An illustration of the Tanh Demon sampling method where K = 4 . (a) A SDE step generates several samples, each determined by sampled noise zk. We use Tanh Demon to classify each noise sample as “low-reward” or “high-reward” w.r.t rβ(xt) based on their respective reward estimates. (b) We flip the sign of the low-reward noise with tanh , thereby transforming it into high-reward ... In Figure 4 , we can observe that the proposed Tanh Demon sampling method, in most cases, outperforms other baseline methods, including our Boltzmann Demon sampling method, Best-of-N, and DOODL , the state-of-the-art inference-time method. Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.05760", "content": "Oct 8, 2024 · To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models . Our method can be easily integrated with existing diffusion models without further training . View all Official implementation of ICLR 2025 \" Sampling Demon \" (arXiv:2410.05760). This repository contains the official implementation of Sampling Demon , an inference-time, backpropagation- free preference alignment method for diffusion models . By aligning the denoising process with user preferences via stochastic optimization, Sampling Demon enables the use of non-differentiable reward signals—such ... This paper introduces a training-free method to improve diffusion models for text-to-image generation, enhancing aesthetic scores without additional training . The paper introduces \" Sampling Demons ,\" a training-free approach that addresses these limitations by enabling diffusion model alignment with arbitrary, non-differentiable reward functions during inference. Figure 3: An illustration of the Tanh Demon sampling method where K = 4 . (a) A SDE step generates several samples, each determined by sampled noise zk. We use Tanh Demon to classify each noise sample as “low-reward” or “high-reward” w.r.t rβ(xt) based on their respective reward estimates. (b) We flip the sign of the low-reward noise with tanh , thereby transforming it into high-reward ... In Figure 4 , we can observe that the proposed Tanh Demon sampling method, in most cases, outperforms other baseline methods, including our Boltzmann Demon sampling method, Best-of-N, and DOODL , the state-of-the-art inference-time method. Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ..."}
+{"idx": 2, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "The paper introduces \" Sampling Demons ,\" a training-free approach that addresses these limitations by enabling diffusion model alignment with arbitrary, non-differentiable reward functions during inference.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/overview/2410.05760v2", "content": "The paper introduces \" Sampling Demons ,\" a training-free approach that addresses these limitations by enabling diffusion model alignment with arbitrary, non-differentiable reward functions during inference."}
+{"idx": 3, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Figure 3: An illustration of the Tanh Demon sampling method where K = 4 . (a) A SDE step generates several samples, each determined by sampled noise zk. We use Tanh Demon to classify each noise sample as “low-reward” or “high-reward” w.r.t rβ(xt) based on their respective reward estimates. (b) We flip the sign of the low-reward noise with tanh , thereby transforming it into high-reward ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/Training-free-Diffusion-Model-Alignment-with-Demons-Yeh-Lee/25609eb8a8cc9477ba6c654e40b03928f0a5c8af/figure/4", "content": "Figure 3: An illustration of the Tanh Demon sampling method where K = 4 . (a) A SDE step generates several samples, each determined by sampled noise zk. We use Tanh Demon to classify each noise sample as “low-reward” or “high-reward” w.r.t rβ(xt) based on their respective reward estimates. (b) We flip the sign of the low-reward noise with tanh , thereby transforming it into high-reward ..."}
+{"idx": 4, "title": "Training-free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "In Figure 4 , we can observe that the proposed Tanh Demon sampling method, in most cases, outperforms other baseline methods, including our Boltzmann Demon sampling method, Best-of-N, and DOODL , the state-of-the-art inference-time method.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760", "content": "In Figure 4 , we can observe that the proposed Tanh Demon sampling method, in most cases, outperforms other baseline methods, including our Boltzmann Demon sampling method, Best-of-N, and DOODL , the state-of-the-art inference-time method."}
+{"idx": 5, "title": "Training-Free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ...", "subpage_snippet": "", "source": "proceedings.iclr.cc", "link": "https://proceedings.iclr.cc/paper_files/paper/2025/hash/eeab2e00835c71d64458ad1821e05664-Abstract-Conference.html", "content": "Aligning diffusion models with user preferences has been a key challenge.Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.To address these limitations, we propose a stochastic optimization approach, dubbed Demon , to guide the denoising process at inference time without backpropagation through reward functions or model ..."}
+{"idx": 6, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": ". Tanh Demon assigns positive weights to the good noises and negative weights to the bad noises with the. tanh𝑡𝑎𝑛ℎtanhitalic_t italic_a italic_n italic_h. function, based on the reward estimates of the noises (Section 4 .1) Training diffusion models with reinforcement learning.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.05760v1", "content": ". Tanh Demon assigns positive weights to the good noises and negative weights to the bad noises with the. tanh𝑡𝑎𝑛ℎtanhitalic_t italic_a italic_n italic_h. function, based on the reward estimates of the noises (Section 4 .1) Training diffusion models with reinforcement learning."}
+{"idx": 7, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models .", "subpage_snippet": "", "source": "synthical.com", "link": "https://synthical.com/article/Training-free-Diffusion-Model-Alignment-with-Sampling-Demons-7d88de32-4c7d-456a-8886-7029b4030b48", "content": "Aligning diffusion models with user preferences has been a key challenge.To the best of our knowledge, the proposed approach is the first inference-time, backpropagation- free preference alignment method for diffusion models ."}
+{"idx": 8, "title": "Training - free Diffusion Model Alignment with Sampling Demons", "date": "", "ddg_snippet": "Abstract: Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions.", "subpage_snippet": "", "source": "www.alphaxiv.org", "link": "https://www.alphaxiv.org/abs/2410.05760v2", "content": "Abstract: Aligning diffusion models with user preferences has been a key challenge. Existing methods for aligning diffusion models either require retraining or are limited to differentiable reward functions."}
+{"idx": 9, "title": "ICLR Poster Training - Free Diffusion Model Alignment with ...", "date": "", "ddg_snippet": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation.", "subpage_snippet": "", "source": "iclr.cc", "link": "https://iclr.cc/virtual/2025/poster/28034", "content": "alignment method for diffusion models .Our method can be easily integrated with existing diffusion models without further training .Our experiments show that the proposed approach significantly improves the average aesthetics scores for text-to-image generation."}
diff --git a/data/sampled_jsons/types_of_events_studied_in_climate_activism_social_media_research_protests_weather_policy.jsonl b/data/sampled_jsons/types_of_events_studied_in_climate_activism_social_media_research_protests_weather_policy.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b5558830305fb7b826c5f6eaed3fe2778b23f5ee
--- /dev/null
+++ b/data/sampled_jsons/types_of_events_studied_in_climate_activism_social_media_research_protests_weather_policy.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "The impacts of climate activism - Yale Program on Climate Change ...", "date": "", "ddg_snippet": "The review finds strong evidence that climate activism influences public opinion and media coverage, although the specific relationship depends on the kind of actions taken and the way the media covers the events . The evidence shows that protest usually increases support for the movement when protests are peaceful, but not when they are violent.", "subpage_snippet": "", "source": "climatecommunication.yale.edu", "link": "https://climatecommunication.yale.edu/publications/the-impacts-of-climate-activism/", "content": "The review finds strong evidence that climate activism influences public opinion and media coverage, although the specific relationship depends on the kind of actions taken and the way the media covers the events . The evidence shows that protest usually increases support for the movement when protests are peaceful, but not when they are violent."}
+{"idx": 1, "title": "PDF Climate Activism, Social Media and Behavioural Change: A Literature Review", "date": "", "ddg_snippet": "This review paper addresses the impact of youth-led climate protests on online discussions about climate change and solutions, particularly focusing on the influence of social media on shaping societal norms and consumption preferences, notably dietary patterns.", "subpage_snippet": "", "source": "pure.iiasa.ac.at", "link": "https://pure.iiasa.ac.at/id/eprint/19639/1/WP-24-007.pdf", "content": "This review paper addresses the impact of youth-led climate protests on online discussions about climate change and solutions, particularly focusing on the influence of social media on shaping societal norms and consumption preferences, notably dietary patterns."}
+{"idx": 2, "title": "The impacts of climate activism - ScienceDirect", "date": "", "ddg_snippet": "We review 50 studies on the impacts of climate activism . We present the existing evidence in a map of what we know about climate activism and its impacts. There is strong evidence that climate activism shifts public opinion and media coverage in a pro- climate direction, but this varies by context and the tactics employed.", "subpage_snippet": "", "source": "www.sciencedirect.com", "link": "https://www.sciencedirect.com/science/article/pii/S2352154625000178", "content": "We review 50 studies on the impacts of climate activism . We present the existing evidence in a map of what we know about climate activism and its impacts. There is strong evidence that climate activism shifts public opinion and media coverage in a pro- climate direction, but this varies by context and the tactics employed."}
+{"idx": 3, "title": "The Impacts of Climate Activism - The Commons", "date": "", "ddg_snippet": "Climate protests attracted more media coverage than domestic extreme weather events , helping set the public agenda. In a panel study in Germany, researchers found that concern about climate change increased by about 1.2 percentage points following protests .", "subpage_snippet": "", "source": "commonslibrary.org", "link": "https://commonslibrary.org/the-impacts-of-climate-activism/", "content": "Climate protests attracted more media coverage than domestic extreme weather events , helping set the public agenda. In a panel study in Germany, researchers found that concern about climate change increased by about 1.2 percentage points following protests ."}
+{"idx": 4, "title": "Protests and Policies: How Radical Social Movement Activists Engage ...", "date": "", "ddg_snippet": "This study explores how radical social movement activists engage with policy -making, focusing on climate activism and the interplay between protest and policy engagement.", "subpage_snippet": "", "source": "journals.sagepub.com", "link": "https://journals.sagepub.com/doi/pdf/10.1177/0038038520943107", "content": "This study explores how radical social movement activists engage with policy -making, focusing on climate activism and the interplay between protest and policy engagement."}
+{"idx": 5, "title": "PDF Climate activism and its effects - Climate Advocacy Lab", "date": "", "ddg_snippet": "Abstract As activism including climate strikes have become a common occurrence around the world, it is important to consider the growth in climate change-focused activism and participation in social movements as a specific type of civic engagement. Although studies have analyzed climate activism and the climate movement, there is limited research that integrates it into the broader literature ...", "subpage_snippet": "", "source": "climateadvocacylab.org", "link": "https://climateadvocacylab.org/system/files/Fisher+Nasrin+2020+-+Climate+activism+and+its+effects.pdf", "content": "Abstract As activism including climate strikes have become a common occurrence around the world, it is important to consider the growth in climate change-focused activism and participation in social movements as a specific type of civic engagement. Although studies have analyzed climate activism and the climate movement, there is limited research that integrates it into the broader literature ..."}
+{"idx": 6, "title": "Sentiment and Social Signals in the Climate Crisis: A Survey on ...", "date": "", "ddg_snippet": "Abstract. Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable insights into public perception, inform policy decisions, and enhance emergency responses. Although sentiment analysis has been widely studied in ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.18837v2", "content": "Abstract. Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment expressed in these online discussions can offer valuable insights into public perception, inform policy decisions, and enhance emergency responses. Although sentiment analysis has been widely studied in ..."}
+{"idx": 7, "title": "(PDF) Climate activism and its effects - ResearchGate", "date": "", "ddg_snippet": "Although studies have analyzed climate activism and the climate movement, there is limited research that integrates it into the broader literature on civic engagement and which considers how these ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/345455893_Climate_activism_and_its_effects", "content": "Although studies have analyzed climate activism and the climate movement, there is limited research that integrates it into the broader literature on civic engagement and which considers how these ..."}
+{"idx": 8, "title": "How effective are climate protests at swaying policy - Nature", "date": "", "ddg_snippet": "To answer that in the case of climate activism requires further research in three key areas: what motivates some people to join protests while others do not? What are the pros and cons of the ...", "subpage_snippet": "", "source": "www.nature.com", "link": "https://www.nature.com/articles/d41586-023-03721-z", "content": "To answer that in the case of climate activism requires further research in three key areas: what motivates some people to join protests while others do not? What are the pros and cons of the ..."}
+{"idx": 9, "title": "Tracing the Emergent Field of Digital Environmental and Climate ...", "date": "", "ddg_snippet": "vation of a dearth of research on climate activism (Agin & Karlsson, 2021) may no longer be true. Because the proliferation of digital media has shifted the theories (e.g. Bennett & Segerberg, 2011, 2013) as well as methods and data (cf. Neumayer & Rossi, 2016) used to investigate social movements, and the way these movements constitute themselves, digital environmental/ climate activism , speci ...", "subpage_snippet": "", "source": "www.tandfonline.com", "link": "https://www.tandfonline.com/doi/pdf/10.1080/17524032.2023.2212137", "content": "vation of a dearth of research on climate activism (Agin & Karlsson, 2021) may no longer be true. Because the proliferation of digital media has shifted the theories (e.g. Bennett & Segerberg, 2011, 2013) as well as methods and data (cf. Neumayer & Rossi, 2016) used to investigate social movements, and the way these movements constitute themselves, digital environmental/ climate activism , speci ..."}
diff --git a/data/sampled_jsons/understanding_safety_finetuning_minGPT_8_layers_synthetic_experiments.jsonl b/data/sampled_jsons/understanding_safety_finetuning_minGPT_8_layers_synthetic_experiments.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..746138d27e20b9bc727c2646813f2bec939895e6
--- /dev/null
+++ b/data/sampled_jsons/understanding_safety_finetuning_minGPT_8_layers_synthetic_experiments.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "GitHub - fiveai/understanding_safety_finetuning: Official ...", "date": "", "ddg_snippet": "The official implementation of \"What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ...", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/fiveai/understanding_safety_finetuning", "content": "The official implementation of \"What Makes and Breaks Safety Fine-tuning ? A Mechanistic Study\". This work is accepted to NeurIPS 2024. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient ..."}
+{"idx": 1, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks.", "subpage_snippet": "", "source": "proceedings.neurips.cc", "link": "https://proceedings.neurips.cc/paper_files/paper/2024/file/a9bef53eb7b0e5950d4f2d9c74a16006-Paper-Conference.pdf", "content": "Systematic setup to study safety fine-tuning and jailbreaks. We introduce a novel synthetic data generation framework that allows controlled generation of data for safety fine-tuning , jailbreaks, and adversarial attacks."}
+{"idx": 2, "title": "What Makes and Breaks Safety Fine-tuning? A Mechanistic Study", "date": "", "ddg_snippet": "Sep 25, 2024 · This work designs a synthetic data generation framework with the purpose of understanding safety fine-tuning . It investigates (1) Supervised safety fine-tuning ; (2) Direct preference optimization; and (3) Unlearning.", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=JEflV4nRlH", "content": "Sep 25, 2024 · This work designs a synthetic data generation framework with the purpose of understanding safety fine-tuning . It investigates (1) Supervised safety fine-tuning ; (2) Direct preference optimization; and (3) Unlearning."}
+{"idx": 3, "title": "Safety-Aware Fine-Tuning of Large Language Models What makes and breaks safety fine-tuning? a mechanistic study LLM-Tuning-Safety/LLMs-Finetuning-Safety - GitHub LLM- Tuning - Safety /LLMs- Finetuning - Safety - GitHub Safety -Aware Fine-Tuning of Large Language Models Official Code for What Makes and Breaks Safety Fine-tuning? A LLM- Tuning - Safety /LLMs- Finetuning - Safety - GitHub Safety -Aware Fine-Tuning of Large Language Models Safety -Aware Fine-Tuning of Large Language Models Mechanistically analyzing the effects of fine-tuning on ...", "date": "", "ddg_snippet": "Oct 13, 2024 · Fine-tuning Large Language Models (LLMs) has emerged as a common practice for tailoring models to individual needs and preferences. The choice of datasets for fine-tuning can be diverse, introducing safety concerns regarding the potential inclusion of harmful data samples. Manually filtering or avoiding such samples, however, can be labor-intensive and subjective. To address these difficulties ... Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ... Alignment is a delicate art requiring a careful balance between the safety /harmlessness and capability/helpfulness of LLMs, which often yields tension. Reckless fine-tuning could disrupt this balance, e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective. What happens if a model is fine-tuned? Reckless fine-tuning could disrupt this balance, e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective. Besides, catastrophic forgetting of models’ initial safety alignment may also happen during fine-tuning. What is fine-tuning large language models? Fine-tuning Large Language Models (LLMs) has emerged as a common practice for tailoring models to individual needs and preferences . The choice of datasets for fine-tuning can be diverse, introducing safety concerns regarding the potential inclusion of harmful data samples. How to make the safety fine-tuning datasets run? To make the safety fine-tuning datasets run: For more details check run.sh. It generates the datasets for pre-training and fine-tuning. To encourage the learning of the PCFG grammar rules before the bijective functions, we train the model primarily on text generated from PCFGs first, and later train it on bijective outputs as well. Is fine-tuning a good idea for aligning LLMs? Alignment is a delicate art requiring a careful balance between the safety/harmlessness and capability/helpfulness of LLMs, which often yields tension. Reckless fine-tuning could disrupt this balance , e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective. Is fine-tuning data safe? The choice of datasets for fine-tuning can be diverse, introducing safety concerns regarding the potential inclusion of harmful data samples . Manually filtering or avoiding such samples, however, can be labor-intensive and subjective. What is safety-aware fine-tuning (Saft)? To address these difficulties, we propose a novel Safety-Aware Fine-Tuning (SAFT) framework designed to automatically detect and remove potentially harmful data , by leveraging a scoring function that exploits the subspace information of harmful and benign samples. Model details: We use the minGPT model by Karpathy (2020) for all experiments on the synthetically generated PCFG dataset, similar to Allen-Zhu & Li (2023c). The model has close to 3 million parameters and consists of 6 blocks each made up of multihead self attention with 6 heads and two layers of MLP layers with an embedding dimension of 192.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2410.10014", "content": "Oct 13, 2024 · Fine-tuning Large Language Models (LLMs) has emerged as a common practice for tailoring models to individual needs and preferences. The choice of datasets for fine-tuning can be diverse, introducing safety concerns regarding the potential inclusion of harmful data samples. Manually filtering or avoiding such samples, however, can be labor-intensive and subjective. To address these difficulties ... Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ... Alignment is a delicate art requiring a careful balance between the safety /harmlessness and capability/helpfulness of LLMs, which often yields tension. Reckless fine-tuning could disrupt this balance, e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective. What happens if a model is fine-tuned? Reckless fine-tuning could disrupt this balance, e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective. Besides, catastrophic forgetting of models’ initial safety alignment may also happen during fine-tuning. What is fine-tuning large language models? Fine-tuning Large Language Models (LLMs) has emerged as a common practice for tailoring models to individual needs and preferences . The choice of datasets for fine-tuning can be diverse, introducing safety concerns regarding the potential inclusion of harmful data samples. How to make the safety fine-tuning datasets run? To make the safety fine-tuning datasets run: For more details check run.sh. It generates the datasets for pre-training and fine-tuning. To encourage the learning of the PCFG grammar rules before the bijective functions, we train the model primarily on text generated from PCFGs first, and later train it on bijective outputs as well. Is fine-tuning a good idea for aligning LLMs? Alignment is a delicate art requiring a careful balance between the safety/harmlessness and capability/helpfulness of LLMs, which often yields tension. Reckless fine-tuning could disrupt this balance , e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective. Is fine-tuning data safe? The choice of datasets for fine-tuning can be diverse, introducing safety concerns regarding the potential inclusion of harmful data samples . Manually filtering or avoiding such samples, however, can be labor-intensive and subjective. What is safety-aware fine-tuning (Saft)? To address these difficulties, we propose a novel Safety-Aware Fine-Tuning (SAFT) framework designed to automatically detect and remove potentially harmful data , by leveraging a scoring function that exploits the subspace information of harmful and benign samples. Model details: We use the minGPT model by Karpathy (2020) for all experiments on the synthetically generated PCFG dataset, similar to Allen-Zhu & Li (2023c). The model has close to 3 million parameters and consists of 6 blocks each made up of multihead self attention with 6 heads and two layers of MLP layers with an embedding dimension of 192."}
+{"idx": 4, "title": "What makes and breaks safety fine-tuning? a mechanistic study", "date": "", "ddg_snippet": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ...", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.5555/3737916.3740879", "content": "Safety fine-tuning helps align Large Language Models (LLMs) with human preferences for their safe deployment. To better understand the underlying factors that make models safe via safety fine-tuning , we design a synthetic data generation framework that captures salient aspects of an unsafe input by modeling the interaction between the task the model is asked to perform (e.g., \"design\") versus ..."}
+{"idx": 5, "title": "LLM-Tuning-Safety/LLMs-Finetuning-Safety - GitHub", "date": "", "ddg_snippet": "Alignment is a delicate art requiring a careful balance between the safety /harmlessness and capability/helpfulness of LLMs, which often yields tension. Reckless fine-tuning could disrupt this balance, e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective.", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/LLM-Tuning-Safety/LLMs-Finetuning-Safety", "content": "Alignment is a delicate art requiring a careful balance between the safety /harmlessness and capability/helpfulness of LLMs, which often yields tension. Reckless fine-tuning could disrupt this balance, e.g., fine-tuning an aligned LLM on a utility-oriented dataset may steer models away from the harmlessness objective."}
+{"idx": 6, "title": "Mechanistically analyzing the effects of fine-tuning on ...", "date": "", "ddg_snippet": "Model details: We use the minGPT model by Karpathy (2020) for all experiments on the synthetically generated PCFG dataset, similar to Allen-Zhu & Li (2023c). The model has close to 3 million parameters and consists of 6 blocks each made up of multihead self attention with 6 heads and two layers of MLP layers with an embedding dimension of 192.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2311.12786v2", "content": "Model details: We use the minGPT model by Karpathy (2020) for all experiments on the synthetically generated PCFG dataset, similar to Allen-Zhu & Li (2023c). The model has close to 3 million parameters and consists of 6 blocks each made up of multihead self attention with 6 heads and two layers of MLP layers with an embedding dimension of 192."}
+{"idx": 7, "title": "The Principles of Deep Learning Theory | Hacker News", "date": "", "ddg_snippet": "I highly recommended reading the papers “How to Train a 10,000 Layer Neural Network” [1], and “ReZero is All You Need: Fast Convergence at ...", "subpage_snippet": "", "source": "news.ycombinator.com", "link": "https://news.ycombinator.com/item?id=31051540", "content": "I highly recommended reading the papers “How to Train a 10,000 Layer Neural Network” [1], and “ReZero is All You Need: Fast Convergence at ..."}
+{"idx": 8, "title": "Cjwbw - Find Top AI Models on Hugging Face", "date": "", "ddg_snippet": "You can also experiment with using the model s output as input to other computer vision tasks, like image segmentation or object tracking, to see how ...", "subpage_snippet": "", "source": "www.aimodels.fyi", "link": "https://www.aimodels.fyi/creators/huggingFace/cjwbw", "content": "You can also experiment with using the model s output as input to other computer vision tasks, like image segmentation or object tracking, to see how ..."}
+{"idx": 9, "title": "GitHub - jiep/offensive-ai-compilation: A curated list of", "date": "", "ddg_snippet": "A curated list of useful resources that cover Offensive AI. - jiep/offensive-ai-compilation", "subpage_snippet": "", "source": "github.com", "link": "https://github.com/jiep/offensive-ai-compilation", "content": "A curated list of useful resources that cover Offensive AI. - jiep/offensive-ai-compilation"}
diff --git a/data/sampled_jsons/understanding_safety_finetuning_n_layer_default_configuration_synthetic_experiments.jsonl b/data/sampled_jsons/understanding_safety_finetuning_n_layer_default_configuration_synthetic_experiments.jsonl
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index 0000000000000000000000000000000000000000..42f5e229d543818ac42656f299f2dbb9f3eeb786
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@@ -0,0 +1,10 @@
+{"idx": 0, "title": "RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting", "date": "", "ddg_snippet": "Threat Model In this work, we mainly focus on privacy attacks where malicious attackers aim to infer users’ sensitive information, including not ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.19286v1", "content": "Threat Model In this work, we mainly focus on privacy attacks where malicious attackers aim to infer users’ sensitive information, including not ..."}
+{"idx": 1, "title": "", "date": "", "ddg_snippet": "", "subpage_snippet": "", "source": "", "link": "", "content": ""}
+{"idx": 2, "title": "Unlocking Chemical Insights: Superior Molecular Representations", "date": "", "ddg_snippet": "... finetuned up to the corresponding intermediate layers for the same set of tasks, again observing that in over 71% of cases, intermediate layers lead ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.06443v1", "content": "... finetuned up to the corresponding intermediate layers for the same set of tasks, again observing that in over 71% of cases, intermediate layers lead ..."}
+{"idx": 3, "title": "Moshi: a speech-text foundation model for real-time dialogue", "date": "", "ddg_snippet": "Second, as language understanding and generation happens in the textual domain, any non-written information is ignored by the model.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2410.00037v2", "content": "Second, as language understanding and generation happens in the textual domain, any non-written information is ignored by the model."}
+{"idx": 4, "title": "ACE and Diverse Generalization via Selective Disagreement", "date": "", "ddg_snippet": "... advocate a bottom up approach, attempting to rigorously understand and shape the learning process and representations of deep networks.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2509.07955v1", "content": "... advocate a bottom up approach, attempting to rigorously understand and shape the learning process and representations of deep networks."}
+{"idx": 5, "title": "LaSM: Layer-wise Scaling Mechanism for Defending Pop-up Attack", "date": "", "ddg_snippet": "... layer -wise saliency analysis of four mainstream MLLM backbones, we uncover a consistent attention divergence pattern —correct and incorrect ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.10610v1", "content": "... layer -wise saliency analysis of four mainstream MLLM backbones, we uncover a consistent attention divergence pattern —correct and incorrect ..."}
+{"idx": 6, "title": "GenAI Handbook", "date": "", "ddg_snippet": "I’m creating this document not as a “generative AI expert”, but rather as someone who’s recently had the experience of ramping up on many of ...", "subpage_snippet": "", "source": "genai-handbook.github.io", "link": "https://genai-handbook.github.io/", "content": "I’m creating this document not as a “generative AI expert”, but rather as someone who’s recently had the experience of ramping up on many of ..."}
+{"idx": 7, "title": "Mechanistic Interpretability for AI Safety — A Review |", "date": "", "ddg_snippet": "... an approach to reverse engineering neural networks into human-understandable algorithms and concepts, focusing on its relevance to AI safety ...", "subpage_snippet": "", "source": "leonardbereska.github.io", "link": "https://leonardbereska.github.io/blog/2024/mechinterpreview/", "content": "... an approach to reverse engineering neural networks into human-understandable algorithms and concepts, focusing on its relevance to AI safety ..."}
+{"idx": 8, "title": "OpenVINO™ Blog | Efficient Inference and Quantization of CGD", "date": "", "ddg_snippet": "For INT8 quantization, we found some useful tricks to mitigate accuracy issue caused by accuracy sensitive layers , e.g., YOLOv8 OpenVINO Notebook ...", "subpage_snippet": "", "source": "blog.openvino.ai", "link": "https://blog.openvino.ai/blog-posts/efficient-inference-and-quantization-of-cgd-for-image-retrieval-with-openvinotm-and-nncf", "content": "For INT8 quantization, we found some useful tricks to mitigate accuracy issue caused by accuracy sensitive layers , e.g., YOLOv8 OpenVINO Notebook ..."}
+{"idx": 9, "title": "Han Zhao's homepage", "date": "", "ddg_snippet": "Notably, moment alignment provides a unifying understanding of Invariant Risk Minimization, gradient matching, and Hessian matching, three previously ...", "subpage_snippet": "", "source": "hanzhaoml.github.io", "link": "https://hanzhaoml.github.io/", "content": "Notably, moment alignment provides a unifying understanding of Invariant Risk Minimization, gradient matching, and Hessian matching, three previously ..."}
diff --git a/data/sampled_jsons/unstable_training_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_abstract_year_2021.jsonl b/data/sampled_jsons/unstable_training_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_abstract_year_2021.jsonl
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index 0000000000000000000000000000000000000000..7a7b08f6d91e07eb239d95d234c1f97a9b2db900
--- /dev/null
+++ b/data/sampled_jsons/unstable_training_Score-Based_Generative_Modeling_through_Stochastic_Differential_Equations_abstract_year_2021.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Generative adversarial network - Wikipedia", "date": "", "ddg_snippet": "... based on the \"indirect\" training through the discriminator, another neural network that can tell how \"realistic\" the input seems, which itself is also ...", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Generative_adversarial_network", "content": "... based on the \"indirect\" training through the discriminator, another neural network that can tell how \"realistic\" the input seems, which itself is also ..."}
+{"idx": 1, "title": "Solving dynamic portfolio selection problems via score-based", "date": "", "ddg_snippet": "Traditionally, a score ‑ based diffusion model is defined through a forward stochastic differential equation (SDE) whose initial condition is the ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.09916v3", "content": "Traditionally, a score ‑ based diffusion model is defined through a forward stochastic differential equation (SDE) whose initial condition is the ..."}
+{"idx": 2, "title": "Physics-Constrained Flow Matching: Sampling Generative Models", "date": "", "ddg_snippet": "Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2506.04171v1", "content": "Deep generative models have recently been applied to physical systems governed by partial differential equations (PDEs), offering scalable simulation ..."}
+{"idx": 3, "title": "Physics-Constrained Fine-Tuning of Flow-Matching Models for", "date": "", "ddg_snippet": "... based fine-tuning as a stochastic control problem, extending flow-matching models to generate latent parameters alongside states, enabling inverse ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.09156v1", "content": "... based fine-tuning as a stochastic control problem, extending flow-matching models to generate latent parameters alongside states, enabling inverse ..."}
+{"idx": 4, "title": "Improving Compositional Generation with Diffusion Models Using", "date": "", "ddg_snippet": "... success of diffusion models in generating high-quality images (Sohl-Dickstein et al ., 2015 ; Ho et al ., 2020 ; Nichol & Dhariwal, 2021 ; Song ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2505.13740v1", "content": "... success of diffusion models in generating high-quality images (Sohl-Dickstein et al ., 2015 ; Ho et al ., 2020 ; Nichol & Dhariwal, 2021 ; Song ..."}
+{"idx": 5, "title": "An introduction to Diffusion Probabilistic Models – Ayan Das", "date": "", "ddg_snippet": "... likelihood computation, restrictive architecture, unstable training dynamics etc.) have led to the developement of a new class of generative models ...", "subpage_snippet": "", "source": "ayandas.me", "link": "https://ayandas.me/blogs/2021-12-04-diffusion-prob-models.html", "content": "... likelihood computation, restrictive architecture, unstable training dynamics etc.) have led to the developement of a new class of generative models ..."}
+{"idx": 6, "title": "Vinija's Notes • Primers • Diffusion Models", "date": "", "ddg_snippet": "... models (also simply called diffusion models ) are generative models , meaning that they are used to generate data similar to the data on which they are ...", "subpage_snippet": "", "source": "vinija.ai", "link": "https://vinija.ai/models/diffusion-models/", "content": "... models (also simply called diffusion models ) are generative models , meaning that they are used to generate data similar to the data on which they are ..."}
+{"idx": 7, "title": "Threats, attacks and defenses to federated learning: issues,", "date": "", "ddg_snippet": "... clients work together to train a model under the coordination of a central server, while the training data remains stored locally (Kairouz et al ...", "subpage_snippet": "", "source": "cybersecurity.springeropen.com", "link": "https://cybersecurity.springeropen.com/articles/10.1186/s42400-021-00105-6", "content": "... clients work together to train a model under the coordination of a central server, while the training data remains stored locally (Kairouz et al ..."}
+{"idx": 8, "title": "POSE: Phased One-Step Adversarial Equilibrium for Video", "date": "", "ddg_snippet": "... methods on VBench-I2V by average 7.15% in semantic alignment, temporal conference and frame quality, reducing the latency of the pre-trained model by ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.21019v1", "content": "... methods on VBench-I2V by average 7.15% in semantic alignment, temporal conference and frame quality, reducing the latency of the pre-trained model by ..."}
+{"idx": 9, "title": "Predictive learning rules generate a cortical-like replay of", "date": "", "ddg_snippet": "... model trained on a perceptual decision-making task can replicate both unbiased and biased decision behaviors of monkeys without fine-tuning of ...", "subpage_snippet": "", "source": "elifesciences.org", "link": "https://elifesciences.org/articles/92712", "content": "... model trained on a perceptual decision-making task can replicate both unbiased and biased decision behaviors of monkeys without fine-tuning of ..."}
diff --git a/data/sampled_jsons/von_Luxburg_2007_A_tutorial_on_spectral_clustering_abstract.jsonl b/data/sampled_jsons/von_Luxburg_2007_A_tutorial_on_spectral_clustering_abstract.jsonl
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index 0000000000000000000000000000000000000000..d91fe3f8ecf87d4af65b20883bf27d1ffd3d4097
--- /dev/null
+++ b/data/sampled_jsons/von_Luxburg_2007_A_tutorial_on_spectral_clustering_abstract.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "PDF A tutorial on spectral clustering", "date": "", "ddg_snippet": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms.", "subpage_snippet": "", "source": "www.cs.cornell.edu", "link": "https://www.cs.cornell.edu/courses/cs6241/2020sp/readings/vonLuxburg-2007-spectral.pdf", "content": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007 Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms."}
+{"idx": 1, "title": "[0711.0189] A Tutorial on Spectral Clustering - arXiv.org", "date": "", "ddg_snippet": "View a PDF of the paper titled A Tutorial on Spectral Clustering , by Ulrike von Luxburg", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/0711.0189", "content": "View a PDF of the paper titled A Tutorial on Spectral Clustering , by Ulrike von Luxburg"}
+{"idx": 2, "title": "A Tutorial on Spectral Clustering - ADS", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "ui.adsabs.harvard.edu", "link": "https://ui.adsabs.harvard.edu/abs/2007arXiv0711.0189V/abstract", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 3, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm.", "subpage_snippet": "", "source": "dl.acm.org", "link": "https://dl.acm.org/doi/10.1007/s11222-007-9033-z", "content": "Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k -means algorithm."}
+{"idx": 4, "title": "[PDF] A tutorial on spectral clustering | Semantic Scholar", "date": "", "ddg_snippet": "This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ...", "subpage_snippet": "", "source": "www.semanticscholar.org", "link": "https://www.semanticscholar.org/paper/A-tutorial-on-spectral-clustering-Luxburg/eda90bd43f4256986688e525b45b833a3addab97", "content": "This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Abstract In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear ..."}
+{"idx": 5, "title": "von Luxburg, U. (2007) A Tutorial on Spectral Clustering. Statistics ...", "date": "", "ddg_snippet": "The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering ; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering .", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=2317610", "content": "The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering ; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering ."}
+{"idx": 6, "title": "A Tutorial on Spectral Clustering - ResearchGate", "date": "", "ddg_snippet": "We implement this by using a spectral clustering algorithm ( von Luxburg , 2007 ), which examines individuals per ancestor group and assigns them to new taxon groups based on the clustering of their ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/234801250_A_Tutorial_on_Spectral_Clustering", "content": "We implement this by using a spectral clustering algorithm ( von Luxburg , 2007 ), which examines individuals per ancestor group and assigns them to new taxon groups based on the clustering of their ..."}
+{"idx": 7, "title": "A tutorial on spectral clustering | Statistics and Computing", "date": "", "ddg_snippet": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ...", "subpage_snippet": "", "source": "link.springer.com", "link": "https://link.springer.com/article/10.1007/s11222-007-9033-z", "content": "In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works ..."}
+{"idx": 8, "title": "A Tutorial on Spectral Clustering | Modern Magnetic Systems - Max ...", "date": "", "ddg_snippet": "@article{4488, title = { A Tutorial on Spectral Clustering }, journal = {Statistics and Computing}, abstract = {In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On ...", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/mms/publications/4488", "content": "@article{4488, title = { A Tutorial on Spectral Clustering }, journal = {Statistics and Computing}, abstract = {In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On ..."}
+{"idx": 9, "title": "PDF A tutorial on spectral clustering", "date": "", "ddg_snippet": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007", "subpage_snippet": "", "source": "www.cs.bilkent.edu.tr", "link": "https://www.cs.bilkent.edu.tr/~saksoy/courses/cs551-Spring2009/papers/luxburg07_spectral_clustering_tutorial.pdf", "content": "A tutorial on spectral clustering Ulrike von Luxburg Received: 15 August 2006 / Accepted: 7 July 2007 / Published online: 22 August 2007 Springer Science+Business Media, LLC 2007"}
diff --git a/data/sampled_jsons/von_Luxburg_2007_A_tutorial_on_spectral_clustering_abstract_year_2007.jsonl b/data/sampled_jsons/von_Luxburg_2007_A_tutorial_on_spectral_clustering_abstract_year_2007.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..2b9c2b65212d308178f970dc7ae1557e32be3e98
--- /dev/null
+++ b/data/sampled_jsons/von_Luxburg_2007_A_tutorial_on_spectral_clustering_abstract_year_2007.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "A Tutorial on Spectral Clustering", "date": "", "ddg_snippet": "A Tutorial on Spectral Clustering . Ulrike von Luxburg Max Planck Institute for Biological Cybernetics.", "subpage_snippet": "", "source": "engr.case.edu", "link": "http://engr.case.edu/ray_soumya/mlrg/Luxburg07_tutorial_spectral_clustering.pdf", "content": "A Tutorial on Spectral Clustering . Ulrike von Luxburg Max Planck Institute for Biological Cybernetics."}
+{"idx": 1, "title": "A Tutorial on Spectral Clustering", "date": "", "ddg_snippet": "A Tutorial on Spectral Clustering . Ulrike von Luxburg Max Planck Institute for Biological Cybernetics.", "subpage_snippet": "", "source": "people.csail.mit.edu", "link": "https://people.csail.mit.edu/dsontag/courses/ml12/notes/Luxburg07_tutorial_spectral_clustering.pdf", "content": "A Tutorial on Spectral Clustering . Ulrike von Luxburg Max Planck Institute for Biological Cybernetics."}
+{"idx": 2, "title": "A Tutorial on Spectral Clustering | Empirical Inference – Max Planck...", "date": "", "ddg_snippet": "Ulrike von Luxburg .On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions.", "subpage_snippet": "", "source": "is.mpg.de", "link": "https://is.mpg.de/ei/publications/4488", "content": "Ulrike von Luxburg .On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions."}
+{"idx": 3, "title": "A Tutorial on Spectral Clustering : Ulrike von Luxburg : Free...", "date": "", "ddg_snippet": "A Tutorial on Spectral Clustering . Bookreader Item Preview.by. Ulrike von Luxburg . Publication date. 2007 -11-01.", "subpage_snippet": "", "source": "archive.org", "link": "https://archive.org/details/arxiv-0711.0189", "content": "A Tutorial on Spectral Clustering . Bookreader Item Preview.by. Ulrike von Luxburg . Publication date. 2007 -11-01."}
+{"idx": 4, "title": "A Tutorial on Spectral Clustering", "date": "", "ddg_snippet": "Show abstract . ... Spectral clustering is a clustering method based on similarity measures between samples and graph theory ( Von Luxburg 2007 ).", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/234801250_A_Tutorial_on_Spectral_Clustering", "content": "Show abstract . ... Spectral clustering is a clustering method based on similarity measures between samples and graph theory ( Von Luxburg 2007 )."}
+{"idx": 5, "title": "von Luxburg , U. ( 2007 ) A Tutorial on Spectral Clustering .", "date": "", "ddg_snippet": "ABSTRACT : A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions (). The data set is first reduced to a smaller set of partitions (multi-dimensional bins).", "subpage_snippet": "", "source": "www.scirp.org", "link": "https://www.scirp.org/reference/referencespapers?referenceid=2317610", "content": "ABSTRACT : A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions (). The data set is first reduced to a smaller set of partitions (multi-dimensional bins)."}
+{"idx": 6, "title": "A tutorial on spectral clustering | BibSonomy", "date": "", "ddg_snippet": "U. von Luxburg .17. file. :Article/ Luxburg 2007 - A Tutorial on Spectral Clustering .pdf:PDF.", "subpage_snippet": "", "source": "www.bibsonomy.org", "link": "https://www.bibsonomy.org/bibtex/2d759ceac69febb34335adb24ca379801/tobydriscoll", "content": "U. von Luxburg .17. file. :Article/ Luxburg 2007 - A Tutorial on Spectral Clustering .pdf:PDF."}
+{"idx": 7, "title": "Spectral Clustering with Graph Neural Networks for Graph Pooling", "date": "", "ddg_snippet": "Spectral clustering (SC) is a popular clustering technique to nd strongly connected communi-ties on a graph. Spectral clustering (SC) obtains the cluster assignments by applying k-means to the rows of Q∗, which are node em-beddings in the Laplacian eigenspace ( Von Luxburg , 2007 ).", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/1907.00481", "content": "Spectral clustering (SC) is a popular clustering technique to nd strongly connected communi-ties on a graph. Spectral clustering (SC) obtains the cluster assignments by applying k-means to the rows of Q∗, which are node em-beddings in the Laplacian eigenspace ( Von Luxburg , 2007 )."}
+{"idx": 8, "title": "Distinguishing between spectral clustering and cluster analysis of...", "date": "", "ddg_snippet": "Abstract : The term “ spectral clustering ” is sometimes used to refer to the clustering of mass spectrometry data. On spectral clustering : Analysis and an algorithm. In Advances in neural information processing systems (pp. 849-856). 14 Von Luxburg , U. ( 2007 ).", "subpage_snippet": "", "source": "hal.science", "link": "https://hal.science/hal-02082928/document", "content": "Abstract : The term “ spectral clustering ” is sometimes used to refer to the clustering of mass spectrometry data. On spectral clustering : Analysis and an algorithm. In Advances in neural information processing systems (pp. 849-856). 14 Von Luxburg , U. ( 2007 )."}
+{"idx": 9, "title": "Understanding the Generalization Performance of Spectral Clustering ...", "date": "", "ddg_snippet": "Abstract . The theoretical analysis of spectral clustering is mainly de-voted to consistency, while there is little research on its gen-eralization performance. Von Luxburg , U. 2007 . A tutorial on spectral clustering . Statistics and computing, 17(4): 395–416.", "subpage_snippet": "", "source": "ojs.aaai.org", "link": "https://ojs.aaai.org/index.php/AAAI/article/download/26037/25809", "content": "Abstract . The theoretical analysis of spectral clustering is mainly de-voted to consistency, while there is little research on its gen-eralization performance. Von Luxburg , U. 2007 . A tutorial on spectral clustering . Statistics and computing, 17(4): 395–416."}
diff --git a/data/sampled_jsons/we_collected_images_from_AND_VRSBench.jsonl b/data/sampled_jsons/we_collected_images_from_AND_VRSBench.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..11084fa420200d2040dd9ac0640f8678e6941e81
--- /dev/null
+++ b/data/sampled_jsons/we_collected_images_from_AND_VRSBench.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "22218 PDFs | Review articles in IMAGE UNDERSTANDING", "date": "", "ddg_snippet": "These fake colorized images , often generated from grayscale sources using GANs and other deep networks, pose serious challenges to media integrity ...", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/topic/Image-Understanding/publications", "content": "These fake colorized images , often generated from grayscale sources using GANs and other deep networks, pose serious challenges to media integrity ..."}
+{"idx": 1, "title": "Landsat30-AU: A Vision-Language Dataset for Australian Landsat", "date": "", "ddg_snippet": "We address this gap with Landsat30-AU, a large-scale vision-language dataset built from 30-meter resolution imagery collected by four Landsat ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.03127v1", "content": "We address this gap with Landsat30-AU, a large-scale vision-language dataset built from 30-meter resolution imagery collected by four Landsat ..."}
+{"idx": 2, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "9 Dec 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ...", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/virtual/2024/poster/97530", "content": "9 Dec 2024 — This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer ..."}
+{"idx": 3, "title": "VRSBench", "date": "", "ddg_snippet": "This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. It ...", "subpage_snippet": "", "source": "vrsbench.github.io", "link": "https://vrsbench.github.io/", "content": "This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. It ..."}
+{"idx": 4, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. It ...", "subpage_snippet": "", "source": "openreview.net", "link": "https://openreview.net/forum?id=kwDOxOmGE0&referrer=[the+profile+of+Jian+Ding](/profile?id=~Jian_Ding3)", "content": "This benchmark comprises 29,614 images , with 29,614 human-verified detailed captions, 52,472 object references, and 123,221 question-answer pairs. It ..."}
+{"idx": 5, "title": "VRSBench", "date": "", "ddg_snippet": "by X Li · 2024 · Cited by 49 — Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2406.12384?", "content": "by X Li · 2024 · Cited by 49 — Based on the semi-automatic data collection pipeline, we collect VRSBench dataset that provides detailed image captioning, visual grounding ..."}
+{"idx": 6, "title": "Supplementary of VRSBench: A Versatile Benchmark for ...", "date": "", "ddg_snippet": "by XLJDM Elhoseiny — VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs.", "subpage_snippet": "", "source": "papers.nips.cc", "link": "https://papers.nips.cc/paper_files/paper/2024/file/05b7f821234f66b78f99e7803fffa78a-Supplemental-Datasets_and_Benchmarks_Track.pdf", "content": "by XLJDM Elhoseiny — VRSBench consists of 29,614 remote sensing images with detailed captions, 52,472 object refers,. 123,221 visual question-answer pairs."}
+{"idx": 7, "title": "xiang709/VRSBench · Datasets at Hugging Face", "date": "", "ddg_snippet": "The image , sourced from GoogleEarth, provides an aerial view of a developed area with structures and vehicles. There are several vehicles, notably in the bottom ...", "subpage_snippet": "", "source": "huggingface.co", "link": "https://huggingface.co/datasets/xiang709/VRSBench", "content": "The image , sourced from GoogleEarth, provides an aerial view of a developed area with structures and vehicles. There are several vehicles, notably in the bottom ..."}
+{"idx": 8, "title": "VRSBench: A Versatile Vision-Language Benchmark ...", "date": "", "ddg_snippet": "by X Li · Cited by 49 — We split the datasets according to official splits of DOTA and DIOR datasets, where their training images are used to build the training set of VRSBench and.", "subpage_snippet": "", "source": "neurips.cc", "link": "https://neurips.cc/media/neurips-2024/Slides/97530.pdf", "content": "by X Li · Cited by 49 — We split the datasets according to official splits of DOTA and DIOR datasets, where their training images are used to build the training set of VRSBench and."}
+{"idx": 9, "title": "AeroLite: Tag-Guided Lightweight Generation of Aerial Image", "date": "", "ddg_snippet": "Accurate and automated captioning of aerial imagery is crucial for applications like environmental monitoring, urban planning, and disaster ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2504.09528v1", "content": "Accurate and automated captioning of aerial imagery is crucial for applications like environmental monitoring, urban planning, and disaster ..."}
diff --git a/data/sampled_jsons/what_is_flow_matching_in_machine_learning.jsonl b/data/sampled_jsons/what_is_flow_matching_in_machine_learning.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..4c184277b7c1a6902be0288be823c606c0704160
--- /dev/null
+++ b/data/sampled_jsons/what_is_flow_matching_in_machine_learning.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Flow Matching at Scale: A Machine Learning Framework for", "date": "", "ddg_snippet": "We propose a machine learning framework based on Flow Matching to overcome the scaling limitations of Markov Chain Monte Carlo (MCMC) methods.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.15318v1", "content": "We propose a machine learning framework based on Flow Matching to overcome the scaling limitations of Markov Chain Monte Carlo (MCMC) methods."}
+{"idx": 1, "title": "Source-Guided Flow Matching", "date": "", "ddg_snippet": "In this view, the flow matching problem is to find a vector field u t u_{t} that induces a probability flow p t p_{t} such that p 0 = q 0 p_{0}=q_{0 ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.14807v1", "content": "In this view, the flow matching problem is to find a vector field u t u_{t} that induces a probability flow p t p_{t} such that p 0 = q 0 p_{0}=q_{0 ..."}
+{"idx": 2, "title": "[2412.06264] Flow Matching Guide and Code", "date": "", "ddg_snippet": "... Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/abs/2412.06264", "content": "... Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including ..."}
+{"idx": 3, "title": "Flow Matching Meets Biology and Life Science: A Survey", "date": "", "ddg_snippet": "Figure 2: Trend of published papers on flow matching (FM) and its applications in biology and life sciences across major ML conferences from 2023 to ...", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2507.17731v1", "content": "Figure 2: Trend of published papers on flow matching (FM) and its applications in biology and life sciences across major ML conferences from 2023 to ..."}
+{"idx": 4, "title": "Flow Matching at Scale: A Machine Learning Framework for", "date": "", "ddg_snippet": "We propose a machine learning framework based on Flow Matching to overcomethe scaling limitations of Markov Chain Monte Carlo (MCMC) methods.", "subpage_snippet": "", "source": "deeplearn.org", "link": "https://deeplearn.org/arxiv/634061/flow-matching-at-scale:-a-machine-learning-framework-for-efficient-large-size-sampling-of-many-body-systems", "content": "We propose a machine learning framework based on Flow Matching to overcomethe scaling limitations of Markov Chain Monte Carlo (MCMC) methods."}
+{"idx": 5, "title": "Flow Matching Guide and Code | Research - AI at Meta", "date": "", "ddg_snippet": "Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including ...", "subpage_snippet": "", "source": "ai.meta.com", "link": "https://ai.meta.com/research/publications/flow-matching-guide-and-code/", "content": "Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including ..."}
+{"idx": 6, "title": "Discrete flow matching | Research - AI at Meta", "date": "", "ddg_snippet": "In this work, we present Discrete Flow Matching , a novel discrete flow paradigm designed specifically for generating discrete data.", "subpage_snippet": "", "source": "ai.meta.com", "link": "https://ai.meta.com/research/publications/discrete-flow-matching/", "content": "In this work, we present Discrete Flow Matching , a novel discrete flow paradigm designed specifically for generating discrete data."}
+{"idx": 7, "title": "Neural flow matching models — The Dan MacKinlay stable of", "date": "", "ddg_snippet": "Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including ...", "subpage_snippet": "", "source": "danmackinlay.name", "link": "https://danmackinlay.name/notebook/nn_flow_matching", "content": "Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including ..."}
+{"idx": 8, "title": "An introduction to Flow Matching · Cambridge MLG Blog", "date": "", "ddg_snippet": "Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilistic ML community.", "subpage_snippet": "", "source": "mlg.eng.cam.ac.uk", "link": "https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html", "content": "Flow matching (FM) is a recent generative modelling paradigm which has rapidly been gaining popularity in the deep probabilistic ML community."}
+{"idx": 9, "title": "Flow Matching for Probabilistic Learning of Dynamical Systems", "date": "", "ddg_snippet": "In this case, our goal is to learn the function f f italic_f given the observations 𝐲 ( t ) {\\bf y}(t) bold_y ( italic_t ) .", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/html/2508.01101v1", "content": "In this case, our goal is to learn the function f f italic_f given the observations 𝐲 ( t ) {\\bf y}(t) bold_y ( italic_t ) ."}
diff --git a/data/sampled_jsons/why_LiDAR_improves_pedestrian_motion_reconstruction_performance_multi-modal.jsonl b/data/sampled_jsons/why_LiDAR_improves_pedestrian_motion_reconstruction_performance_multi-modal.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..b97b4f926d0b19c7f1ebe17923b6bdb8b5ca341e
--- /dev/null
+++ b/data/sampled_jsons/why_LiDAR_improves_pedestrian_motion_reconstruction_performance_multi-modal.jsonl
@@ -0,0 +1,10 @@
+{"idx": 0, "title": "Simultaneous localization and mapping - Wikipedia", "date": "", "ddg_snippet": "Active SLAM is generally performed by approximating the entropy of the map under hypothetical actions. \" Multi agent SLAM\" extends this problem to the case of multiple robots coordinating themselves to explore optimally.", "subpage_snippet": "", "source": "en.wikipedia.org", "link": "https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping", "content": "Active SLAM is generally performed by approximating the entropy of the map under hypothetical actions. \" Multi agent SLAM\" extends this problem to the case of multiple robots coordinating themselves to explore optimally."}
+{"idx": 1, "title": "Modelling Diverse Interactions and Multimodality for Pedestrian ...", "date": "", "ddg_snippet": "Moreover, pedestrian future trajectories are inherently multimodal . Some existing methods [13], [24], [25] introduced Gaussian noise to predict trajectory multimodality.", "subpage_snippet": "", "source": "www.ieee-jas.net", "link": "https://www.ieee-jas.net/en/article/doi/10.1109/JAS.2025.125363", "content": "Moreover, pedestrian future trajectories are inherently multimodal . Some existing methods [13], [24], [25] introduced Gaussian noise to predict trajectory multimodality."}
+{"idx": 2, "title": "(PDF) Multi - Modal Camera-Based Detection of Vulnerable Road Users", "date": "", "ddg_snippet": "tial of multimodal detection to improve . VRU safety at intersections. improves performance : they fine-tuned YOLOv3/v4 on. the BDD100K dataset and attained mAP scores of 47—-. 64% for person, bicycle and motorcycle classes, signifi", "subpage_snippet": "", "source": "www.researchgate.net", "link": "https://www.researchgate.net/publication/395355253_Multi-Modal_Camera-Based_Detection_of_Vulnerable_Road_Users", "content": "tial of multimodal detection to improve . VRU safety at intersections. improves performance : they fine-tuned YOLOv3/v4 on. the BDD100K dataset and attained mAP scores of 47—-. 64% for person, bicycle and motorcycle classes, signifi"}
+{"idx": 3, "title": "Apple LiDAR Scanner Guide: How to Capture Stunning 3D Models on...", "date": "", "ddg_snippet": "Learn how to use the Apple LiDAR Scanner on your iPhone to measure spaces, scan rooms, and enhance AR experiences with precision and ease.", "subpage_snippet": "", "source": "challix.com", "link": "https://challix.com/blogs/apple-questions/how-to-use-the-apple-lidar-scanner", "content": "Learn how to use the Apple LiDAR Scanner on your iPhone to measure spaces, scan rooms, and enhance AR experiences with precision and ease."}
+{"idx": 4, "title": "Exploring multimodal implicit behavior learning for vehicle navigation...", "date": "", "ddg_snippet": "Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. We explore Implicit Behavioral Cloning (IBC) with Energy-Based Models (EBMs) to better capture this multimodality.", "subpage_snippet": "", "source": "arxiv.org", "link": "https://arxiv.org/pdf/2509.15400", "content": "Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. We explore Implicit Behavioral Cloning (IBC) with Energy-Based Models (EBMs) to better capture this multimodality."}
+{"idx": 5, "title": "Enhancing Safety with AI-Based Pedestrian Detection | John Buttery", "date": "", "ddg_snippet": "Discover how AI-driven pedestrian detection is redefining safety protocols across industrial environments.", "subpage_snippet": "", "source": "www.linkedin.com", "link": "https://www.linkedin.com/pulse/enhancing-safety-ai-based-pedestrian-detection-john-buttery-ggtyc", "content": "Discover how AI-driven pedestrian detection is redefining safety protocols across industrial environments."}
+{"idx": 6, "title": "Evaluating a Multi - Modal Large Language Model for... | Preprints.org", "date": "", "ddg_snippet": "Conclusions: The multimodal pipeline improved diagnostic alignment in ophthalmology triage. Image inputs enhanced accuracy, and CoT reasoning reduced errors from ambiguous features, supporting its potential as an accurate tool for ophthalmology triage.", "subpage_snippet": "", "source": "www.preprints.org", "link": "https://www.preprints.org/manuscript/202509.1349/v1", "content": "Conclusions: The multimodal pipeline improved diagnostic alignment in ophthalmology triage. Image inputs enhanced accuracy, and CoT reasoning reduced errors from ambiguous features, supporting its potential as an accurate tool for ophthalmology triage."}
+{"idx": 7, "title": "AI in Transportation: 9 Disruptive Use Cases [2024 Update]", "date": "", "ddg_snippet": "Pedestrian Detection . Traffic Flow Analysis. Computer Vision-Powered Parking Management.To detect pedestrians , different types of features have been used, including motion -based features, texture-based features, shape-based features, and gradient-based features.", "subpage_snippet": "", "source": "www.v7labs.com", "link": "https://www.v7labs.com/blog/ai-in-transportation", "content": "Pedestrian Detection . Traffic Flow Analysis. Computer Vision-Powered Parking Management.To detect pedestrians , different types of features have been used, including motion -based features, texture-based features, shape-based features, and gradient-based features."}
+{"idx": 8, "title": "Computer Vision in Autonomous Vehicles - Use Cases and Benefits", "date": "", "ddg_snippet": "2. LiDAR ( Light Detection and Ranging ). LiDAR in computer vision in the automotive industry creates a detailed 3D map of the vehicle’s surroundings.", "subpage_snippet": "", "source": "www.a3logics.com", "link": "https://www.a3logics.com/blog/computer-vision-in-autonomous-vehicles/", "content": "2. LiDAR ( Light Detection and Ranging ). LiDAR in computer vision in the automotive industry creates a detailed 3D map of the vehicle’s surroundings."}
+{"idx": 9, "title": "Progression of Industrial 3D Scanning Technologies (2009–2025)", "date": "", "ddg_snippet": "This report examines the trends in industrial 3D scanning modalities (structured light , laser/ LiDAR , and photogrammetry) over this period.Many options available by 2025; improved performance at lower cost, partly due to competition. Metrology-grade scanning arm + laser head.", "subpage_snippet": "", "source": "rapid3d.co.za", "link": "https://rapid3d.co.za/progression-of-industrial-3d-scanning-technologies/", "content": "This report examines the trends in industrial 3D scanning modalities (structured light , laser/ LiDAR , and photogrammetry) over this period.Many options available by 2025; improved performance at lower cost, partly due to competition. Metrology-grade scanning arm + laser head."}