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data_without_website/AMR-Evol__Adaptive_Modular_Response_Evolution_Elicits_Better_Knowledge_Distillation_for_Large_Language_Models_in_Code_Generation.json

@@ -0,0 +1,55 @@
+{
+  "id": "2024.emnlp-main.66",
+  "title": "AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation",
+  "track": "main",
+  "author": "Ziyang Luo; Xin Li; Hongzhan Lin; Jing Ma; Lidong Bing",
+  "pdf": "https://aclanthology.org/2024.emnlp-main.66.pdf",
+  "keyword": "",
+  "abstract": "The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation (e.g. Code Evol-Instruct). However, these efforts often neglect the crucial aspect of response quality, relying heavily on teacher models for direct response distillation. This paradigm, especially for complex instructions, can degrade the quality of synthesized data, compromising the knowledge distillation process. To this end, our study introduces the Adaptive Modular Response Evolution (AMR-Evol) framework, which employs a two-stage process to refine response distillation. The first stage, modular decomposition, breaks down the direct response into more manageable sub-modules. The second stage, adaptive response evolution, automatically evolves the response with the related function modules. Our experiments with three popular code benchmarks—HumanEval, MBPP, and EvalPlus—attests to the superiority of the AMR-Evol framework over baseline response distillation methods. By comparing with the open-source Code LLMs trained on a similar scale of data, we observed performance enhancements: more than +3.0 points on HumanEval-Plus and +1.0 points on MBPP-Plus, which underscores the effectiveness of our framework. Our codes are available at https://github.com/ChiYeungLaw/AMR-Evol.",
+  "conference": {
+    "name": "EMNLP",
+    "year": 2024
+  },
+  "Internet_problem": "https://github.com/ChiYeungLaw/",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T13:18:29.933213",
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+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation",
+    "matched_title": "AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation",
+    "citation_count": 0,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "23523733",
+        "name": "Ziyang Luo"
+      },
+      {
+        "authorId": "2323969172",
+        "name": "Xin Li"
+      },
+      {
+        "authorId": "2109380683",
+        "name": "Hongzhan Lin"
+      },
+      {
+        "authorId": "2296745808",
+        "name": "Jing Ma"
+      },
+      {
+        "authorId": "2211459675",
+        "name": "Li Bing"
+      }
+    ]
+  }
+}

data_without_website/A_Simple_but_Effective_Pluggable_Entity_Lookup_Table_for_Pre-trained_Language_Models.json

@@ -0,0 +1,54 @@
+{
+  "id": "2022.acl-short.57",
+  "title": "A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models",
+  "track": "main",
+  "author": "Deming Ye; Yankai Lin; Peng Li; Maosong Sun; Zhiyuan Liu",
+  "pdf": "https://aclanthology.org/2022.acl-short.57.pdf",
+  "keyword": "",
+  "abstract": "Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities. In this paper, we propose to build a simple but effective Pluggable Entity Lookup Table (PELT) on demand by aggregating the entity’s output representations of multiple occurrences in the corpora. PELT can be compatibly plugged as inputs to infuse supplemental entity knowledge into PLMs. Compared to previous knowledge-enhanced PLMs, PELT only requires 0.2%-5% pre-computation with capability of acquiring knowledge from out-of-domain corpora for domain adaptation scenario. The experiments on knowledge-related tasks demonstrate that our method, PELT, can flexibly and effectively transfer entity knowledge from related corpora into PLMs with different architectures. Our code and models are publicly available at https://github.com/thunlp/PELT",
+  "conference": {
+    "name": "ACL",
+    "year": 2022
+  },
+  "template": null,
+  "category": "06. Natural Language Understanding and Semantics",
+  "is_done": true,
+  "timestamp": "2025-08-05T14:42:53.484463",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models",
+    "matched_title": "A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models",
+    "citation_count": 11,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "50816334",
+        "name": "Deming Ye"
+      },
+      {
+        "authorId": "2149202150",
+        "name": "Yankai Lin"
+      },
+      {
+        "authorId": "144326610",
+        "name": "Peng Li"
+      },
+      {
+        "authorId": "1753344",
+        "name": "Maosong Sun"
+      },
+      {
+        "authorId": "2141313179",
+        "name": "Zhiyuan Liu"
+      }
+    ]
+  }
+}

data_without_website/A_Theoretical_Comparison_of_Graph_Neural_Network_Extensions.json

@@ -0,0 +1,42 @@
+{
+  "id": "16097",
+  "title": "A Theoretical Comparison of Graph Neural Network Extensions",
+  "track": "main",
+  "author": "Pál András Papp; Roger Wattenhofer",
+  "pdf": "https://proceedings.mlr.press/v162/papp22a/papp22a.pdf",
+  "keyword": "",
+  "abstract": "We study and compare different Graph Neural Network extensions that increase the expressive power of GNNs beyond the Weisfeiler-Leman test. We focus on (i) GNNs based on higher order WL methods, (ii) GNNs that preprocess small substructures in the graph, (iii) GNNs that preprocess the graph up to a small radius, and (iv) GNNs that slightly perturb the graph to compute an embedding. We begin by presenting a simple improvement for this last extension that strictly increases the expressive power of this GNN variant. Then, as our main result, we compare the expressiveness of these extensions to each other through a series of example constructions that can be distinguished by one of the extensions, but not by another one. We also show negative examples that are particularly challenging for each of the extensions, and we prove several claims about the ability of these extensions to count cliques and cycles in the graph.",
+  "conference": {
+    "name": "ICML",
+    "year": 2022
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T12:28:54.295042",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "A Theoretical Comparison of Graph Neural Network Extensions",
+    "matched_title": "A Theoretical Comparison of Graph Neural Network Extensions",
+    "citation_count": 48,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "150263694",
+        "name": "P. Papp"
+      },
+      {
+        "authorId": "1716440",
+        "name": "Roger Wattenhofer"
+      }
+    ]
+  }
+}

data_without_website/A_step_towards_understanding_why_classification_helps_regression.json

@@ -0,0 +1,50 @@
+{
+  "id": "9c5e84c27f",
+  "title": "A step towards understanding why classification helps regression",
+  "track": "",
+  "author": "Silvia L. Pintea; Yancong Lin; Jouke Dijkstra; Jan C. van Gemert",
+  "pdf": "https://openaccess.thecvf.com/content/ICCV2023/papers/Pintea_A_step_towards_understanding_why_classification_helps_regression_ICCV_2023_paper.pdf",
+  "keyword": "",
+  "abstract": "A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in practice and when it is beneficial. To do so, we start from precisely controlled dataset variations and data samplings and find that the effect of adding a classification loss is the most pronounced for regression with imbalanced data. We explain these empirical findings by formalizing the relation between the balanced and imbalanced regression losses. Finally, we show that our findings hold on two real imbalanced image datasets for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on the problem of imbalanced video progress prediction (Breakfast). Our main takeaway is: for a regression task, if the data sampling is imbalanced, then add a classification loss.",
+  "conference": {
+    "name": "ICCV",
+    "year": 2023
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T15:35:52.754355",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "A step towards understanding why classification helps regression",
+    "matched_title": "A step towards understanding why classification helps regression",
+    "citation_count": 11,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "37041694",
+        "name": "S. Pintea"
+      },
+      {
+        "authorId": "2547795",
+        "name": "Yancong Lin"
+      },
+      {
+        "authorId": "1732035",
+        "name": "J. Dijkstra"
+      },
+      {
+        "authorId": "1738975",
+        "name": "J. V. Gemert"
+      }
+    ]
+  }
+}

data_without_website/Advancing_Social_Intelligence_in_AI_Agents__Technical_Challenges_and_Open_Questions.json

@@ -0,0 +1,46 @@
+{
+  "id": "2024.emnlp-main.1143",
+  "title": "Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions",
+  "track": "main",
+  "author": "Leena Mathur; Paul Pu Liang; Louis-Philippe Morency",
+  "pdf": "https://aclanthology.org/2024.emnlp-main.1143.pdf",
+  "keyword": "",
+  "abstract": "Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal that involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents (human or artificial). Progress towards Social-AI has accelerated in the past decade across several computing communities, including natural language processing, machine learning, robotics, human-machine interaction, computer vision, and speech. Natural language processing, in particular, has been prominent in Social-AI research, as language plays a key role in constructing the social world. In this position paper, we identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI. We anchor our discussion in the context of social intelligence concepts and prior progress in Social-AI research.",
+  "conference": {
+    "name": "EMNLP",
+    "year": 2024
+  },
+  "template": null,
+  "category": "09. Multimodal Learning",
+  "is_done": true,
+  "timestamp": "2025-08-05T12:50:46.278026",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions",
+    "matched_title": "Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions",
+    "citation_count": 11,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2259929826",
+        "name": "Leena Mathur"
+      },
+      {
+        "authorId": "28130078",
+        "name": "Paul Pu Liang"
+      },
+      {
+        "authorId": "49933077",
+        "name": "Louis-philippe Morency"
+      }
+    ]
+  }
+}

data_without_website/Adversarial_Attacks_on_Multi-Agent_Communication.json

@@ -0,0 +1,58 @@
+{
+  "id": "",
+  "title": "Adversarial Attacks on Multi-Agent Communication",
+  "track": "main",
+  "author": "James Tu; Tsunhsuan Wang; Jingkang Wang; Sivabalan Manivasagam; Mengye Ren; Raquel Urtasun",
+  "pdf": "https://openaccess.thecvf.com/content/ICCV2021/papers/Tu_Adversarial_Attacks_on_Multi-Agent_Communication_ICCV_2021_paper.pdf",
+  "keyword": "",
+  "abstract": "Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems. Sharing information and distributing workloads allow autonomous agents to better perform tasks and increase computation efficiency. However, shared information can be modified to execute adversarial attacks on deep learning models that are widely employed in modern systems. Thus, we aim to study the robustness of such systems and focus on exploring adversarial attacks in a novel multi-agent setting where communication is done through sharing learned intermediate representations of neural networks. We observe that an indistinguishable adversarial message can severely degrade performance, but becomes weaker as the number of benign agents increases. Furthermore, we show that black-box transfer attacks are more difficult in this setting when compared to directly perturbing the inputs, as it is necessary to align the distribution of learned representations with domain adaptation. Our work studies robustness at the neural network level to contribute an additional layer of fault tolerance to modern security protocols for more secure multi-agent systems.",
+  "conference": {
+    "name": "ICCV",
+    "year": 2021
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T08:15:24.768298",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Adversarial Attacks on Multi-Agent Communication",
+    "matched_title": "Adversarial Attacks On Multi-Agent Communication",
+    "citation_count": 67,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2021,
+    "authors": [
+      {
+        "authorId": "31813782",
+        "name": "James Tu"
+      },
+      {
+        "authorId": "40897201",
+        "name": "Tsun-Hsuan Wang"
+      },
+      {
+        "authorId": "71563016",
+        "name": "Jingkang Wang"
+      },
+      {
+        "authorId": "39981216",
+        "name": "S. Manivasagam"
+      },
+      {
+        "authorId": "2540599",
+        "name": "Mengye Ren"
+      },
+      {
+        "authorId": "2422559",
+        "name": "R. Urtasun"
+      }
+    ]
+  }
+}

data_without_website/Adversarial_Mask_Explainer_for_Graph_Neural_Networks.json

@@ -0,0 +1,46 @@
+{
+  "id": "DJttojBfnX",
+  "title": "Adversarial Mask Explainer for Graph Neural Networks",
+  "track": "main",
+  "author": "Wei Zhang;XIAOFAN LI;Wolfgang Nejdl",
+  "pdf": "https://openreview.net/pdf?id=DJttojBfnX",
+  "keyword": "",
+  "abstract": "Unpublished working draft.  Not for distribution.  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 Adversarial Mask Explainer for Graph Neural Networks Anonymous Author(s) Affiliation Address xxx@xxx ABSTRACT The Graph Neural Networks (GNNs) model is a powerful tool for integrating node information with graph topology to learn rep- resentations and make predictions",
+  "conference": {
+    "name": "WWW",
+    "year": 2024
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-04T06:15:09.319977",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Adversarial Mask Explainer for Graph Neural Networks",
+    "matched_title": "Adversarial Mask Explainer for Graph Neural Networks",
+    "citation_count": 2,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2300984406",
+        "name": "Wei Zhang"
+      },
+      {
+        "authorId": "2239065651",
+        "name": "Xiaofan Li"
+      },
+      {
+        "authorId": "2237628085",
+        "name": "Wolfgang Nejdl"
+      }
+    ]
+  }
+}

data_without_website/All-Day_Multi-Camera_Multi-Target_Tracking.json

@@ -0,0 +1,59 @@
+{
+  "id": "35125",
+  "title": "All-Day Multi-Camera Multi-Target Tracking",
+  "track": "main",
+  "author": "Huijie Fan; Yu Qiao; Yihao Zhen; Tinghui Zhao; Baojie Fan; Qiang Wang",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_All-Day_Multi-Camera_Multi-Target_Tracking_CVPR_2025_paper.pdf",
+  "keyword": "",
+  "abstract": "The capability of tracking objects in low-light environments like nighttime is crucial for numerous real-world applications such as crowd behavior analysis and traffic scene understanding. However, previous Multi-Camera Multi-Target(MCMT) tracking methods are primarily focused on tracking during daytime with favorable lighting, shying away from low-light environments. The main difficulty of tracking under low light condition is lack of detailed visible appearance features. To address this issue, we incorporate the infrared modality into MCMT tracking framework to provide more useful information. We constructed the first Multi-modality(RGBT) Multi-camera Multi-target tracking dataset named M3Track, which contains sequences captured in low-light environments, laying a solid foundation for all-day multi-camera tracking. Based on the proposed dataset, we propose All-Day Multi-Camera Multi-Target tracking network, termed as ADMCMT. Specifically, we propose an All-Day Mamba Fusion(ADMF) model to adaptively fuse information from different modalities. Within ADMF, the Lighting Guidance Model(IGM) extracts lighting relevant information to guide the fusion process. Furthermore, the Nearby Target Collection(NTC) strategy is designed to enhance tracking accuracy by leveraging information derived from surrounding objects of target. Experiments conducted on M3Track demonstrate that ADMCMT exhibits strong generalization across different lighting conditions. The code will be released soon at https://github.com/QTRACKY/ADMCMT.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2025
+  },
+  "Internet_problem": "https://github.com/QTRACKY/",
+  "template": null,
+  "category": "09. Multimodal Learning",
+  "is_done": true,
+  "timestamp": "2025-08-04T02:39:36.349659",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "All-Day Multi-Camera Multi-Target Tracking",
+    "matched_title": "All-Day Multi-Camera Multi-Target Tracking",
+    "citation_count": 0,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2025,
+    "authors": [
+      {
+        "authorId": "48439910",
+        "name": "Huijie Fan"
+      },
+      {
+        "authorId": "2327935728",
+        "name": "Yu Qiao"
+      },
+      {
+        "authorId": "2372411347",
+        "name": "Yihao Zhen"
+      },
+      {
+        "authorId": "2310398028",
+        "name": "Tinghui Zhao"
+      },
+      {
+        "authorId": "2241662695",
+        "name": "Baojie Fan"
+      },
+      {
+        "authorId": "2153018830",
+        "name": "Qiang Wang"
+      }
+    ]
+  }
+}

data_without_website/Altruism_in_Coalition_Formation_Games.json

@@ -0,0 +1,42 @@
+{
+  "id": "paper49",
+  "title": "Altruism in Coalition Formation Games",
+  "track": "main",
+  "author": "Anna Maria Kerkmann; Jörg Rothe",
+  "pdf": "https://www.ijcai.org/proceedings/2020/0049.pdf",
+  "keyword": "",
+  "abstract": "Nguyen et al. [2016] introduced altruistic hedonic games in which agents’ utilities depend not only on their own preferences but also on those of their friends in the same coalition. We propose to extend their model to coalition formation games in general, considering also the friends in other coalitions. Comparing the two models, we argue that excluding some friends from the altruistic behavior of an agent is a major disadvantage that comes with the restriction to hedonic games. After introducing our model, we additionally study some common stability notions and provide a computational analysis of the associated verification and existence problems.",
+  "conference": {
+    "name": "IJCAI",
+    "year": 2020
+  },
+  "template": null,
+  "category": "03. ML Theory and Optimization",
+  "is_done": true,
+  "timestamp": "2025-08-05T13:54:19.387265",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Altruism in Coalition Formation Games",
+    "matched_title": "Altruism in Coalition Formation Games",
+    "citation_count": 14,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2020,
+    "authors": [
+      {
+        "authorId": "122366249",
+        "name": "Anna Maria Kerkmann"
+      },
+      {
+        "authorId": "145200357",
+        "name": "J. Rothe"
+      }
+    ]
+  }
+}

data_without_website/An_Exact_Solver_for_Satisfiability_Modulo_Counting_with_Probabilistic_Circuits.json

@@ -0,0 +1,27 @@
+{
+  "id": "AmEN51cTKW",
+  "title": "An Exact Solver for Satisfiability Modulo Counting with Probabilistic Circuits",
+  "track": "main",
+  "author": "Jinzhao Li;Nan Jiang;Yexiang Xue",
+  "pdf": "https://openreview.net/pdf?id=AmEN51cTKW",
+  "keyword": "",
+  "abstract": "Satisfiability Modulo Counting (SMC) is a general language to reason about problems integrating statistical and symbolic artificial intelligence. An SMC formula is an SAT formula in which the truth values of a few Boolean predicates are determined by model counting, or equivalently, probabilistic inference. Existing solvers optimize surrogate objectives and hence provide no formal guarantee. Hence, an exact solver is desperately in need. However, the direct integration of satisfiability and probabilistic inference solvers results in slow SMC solving because of many back-and-forth invocations of both solvers. We develop KOCO-SMC, a fast exact SMC solver, exploiting the fact that many similar probabilistic inferences are needed throughout SMC solving. We compile the probabilistic inference part of SMC solving into probabilistic circuits, supporting efficient lower and upper-bound computation. Experiment results in several real-world applications demonstrate that our approach provides exact solutions, much better than those from approximate solvers, while is more efficient than direct integration with the current exact solvers.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "04. Probabilistic Methods and Causal Inference",
+  "is_done": true,
+  "timestamp": "2025-08-05T17:15:57.664097",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "citation_count": 0
+  }
+}

data_without_website/An_LLM_Compiler_for_Parallel_Function_Calling.json

@@ -0,0 +1,61 @@
+{
+  "id": "uQ2FUoFjnF",
+  "title": "An LLM Compiler for Parallel Function Calling",
+  "track": "main",
+  "author": "Sehoon Kim;Suhong Moon;Ryan Tabrizi;Nicholas Lee;Michael W. Mahoney;Kurt Keutzer;Amir Gholami",
+  "pdf": "https://openreview.net/pdf?id=uQ2FUoFjnF",
+  "keyword": "",
+  "abstract": "The reasoning capabilities of the recent LLMs enable them to execute external function calls to overcome their inherent limitations, such as knowledge cutoffs, poor arithmetic skills, or lack of access to private data. This development has allowed LLMs to select and coordinate multiple functions based on the context to tackle more complex problems. However, current methods for function calling often require sequential reasoning and acting for each function which can result in high latency, cost, and sometimes inaccurate behavior. To address this, we introduce LLMCompiler, which executes functions in parallel to efficiently orchestrate multiple function calls. Drawing inspiration from the principles of classical compilers, LLMCompiler enables parallel function calling with three components: (i) a Function Calling Planner, formulating execution plans for function calling; (ii) a Task Fetching Unit, dispatching function calling tasks; and (iii) an Executor, executing these tasks in parallel. LLMCompiler automatically generates an optimized orchestration for the function calls and can be used with both open-source and closed-source models. We have benchmarked LLMCompiler on a range of tasks with different patterns of function calling. We observe consistent latency speedup of up to $3.7 \\times$, cost savings of up to $6.7 \\times$, and accuracy improvement of up to $\\sim 9 \\%$ compared to ReAct.Our code is available at https://github.com/SqueezeAILab/LLMCompiler.",
+  "conference": {
+    "name": "ICML",
+    "year": 2024
+  },
+  "github_base": "https://github.com/SqueezeAILab/LLMCompiler",
+  "template": null,
+  "category": "12. ML Systems and Infrastructure",
+  "is_done": true,
+  "timestamp": "2025-08-07T16:38:14.680638",
+  "log": {
+    "timestamp": "2025-08-07T16:38:14.680638",
+    "stage": "special situation",
+    "note": "论文没有项目主页但找到了GitHub相关信息"
+  },
+  "citation_data": {
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+    "matched_title": "An LLM Compiler for Parallel Function Calling",
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+    "year": 2023,
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+        "authorId": "2262511276",
+        "name": "Sehoon Kim"
+      },
+      {
+        "authorId": "2237424458",
+        "name": "Suhong Moon"
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+      {
+        "authorId": "2270943053",
+        "name": "Ryan Tabrizi"
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+      {
+        "authorId": "2167739385",
+        "name": "Nicholas Lee"
+      },
+      {
+        "authorId": "2271923589",
+        "name": "Michael W. Mahoney"
+      },
+      {
+        "authorId": "2242659602",
+        "name": "Kurt Keutzer"
+      },
+      {
+        "authorId": "10419477",
+        "name": "A. Gholami"
+      }
+    ]
+  }
+}

data_without_website/An_amortized_approach_to_non-linear_mixed-effects_modeling_based_on_neural_posterior_estimation.json

@@ -0,0 +1,58 @@
+{
+  "id": "uCdcXRuHnC",
+  "title": "An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation",
+  "track": "main",
+  "author": "Jonas Arruda;Yannik Schälte;Clemens Peiter;Olga Teplytska;Ulrich Jaehde;Jan Hasenauer",
+  "pdf": "https://openreview.net/pdf?id=uCdcXRuHnC",
+  "keyword": "",
+  "abstract": "Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in various fields, including biology, medicine, economics, and engineering. Here, the aim is to find a distribution over the parameters that describe the whole population using a model that can generate simulations for an individual of that population. However, fitting these distributions to data is computationally challenging if the description of individuals is complex and the population is large. To address this issue, we propose a novel machine learning-based approach: We exploit neural density estimation based on conditional normalizing flows to approximate individual-specific posterior distributions in an amortized fashion, thereby allowing for efficient inference of population parameters. Applying this approach to problems from cell biology and pharmacology, we demonstrate its unseen flexibility and scalability to large data sets compared to established methods.",
+  "conference": {
+    "name": "ICML",
+    "year": 2024
+  },
+  "template": null,
+  "category": "04. Probabilistic Methods and Causal Inference",
+  "is_done": true,
+  "timestamp": "2025-08-05T06:48:03.901427",
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+  "html_fail": null,
+  "citation_data": {
+    "original_title": "An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation",
+    "matched_title": "An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation",
+    "citation_count": 4,
+    "similarity": 1.0,
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+        "name": "Jonas Arruda"
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+      {
+        "authorId": "81794993",
+        "name": "Yannik Schälte"
+      },
+      {
+        "authorId": "2233781623",
+        "name": "Clemens Peiter"
+      },
+      {
+        "authorId": "2145656906",
+        "name": "Olga Teplytska"
+      },
+      {
+        "authorId": "6990936",
+        "name": "U. Jaehde"
+      },
+      {
+        "authorId": "2112581",
+        "name": "J. Hasenauer"
+      }
+    ]
+  }
+}

data_without_website/Are_Pretrained_Convolutions_Better_than_Pretrained_Transformers_.json

@@ -0,0 +1,62 @@
+{
+  "id": "2021.acl-long.335",
+  "title": "Are Pretrained Convolutions Better than Pretrained Transformers?",
+  "track": "main",
+  "author": "Yi Tay; Mostafa Dehghani; Jai Prakash Gupta; Vamsi Aribandi; Dara Bahri; Zhen Qin; Donald Metzler",
+  "pdf": "https://aclanthology.org/2021.acl-long.335.pdf",
+  "keyword": "",
+  "abstract": "In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, architectures, they have not been explored using the pre-train-fine-tune paradigm. In the context of language models, are convolutional models competitive to Transformers when pre-trained? This paper investigates this research question and presents several interesting findings. Across an extensive set of experiments on 8 datasets/tasks, we find that CNN-based pre-trained models are competitive and outperform their Transformer counterpart in certain scenarios, albeit with caveats. Overall, the findings outlined in this paper suggest that conflating pre-training and architectural advances is misguided and that both advances should be considered independently. We believe our research paves the way for a healthy amount of optimism in alternative architectures.",
+  "conference": {
+    "name": "ACL",
+    "year": 2021
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T15:42:38.431518",
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+  "llm_believed_url": null,
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+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Are Pretrained Convolutions Better than Pretrained Transformers?",
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+    "source": "semantic_scholar",
+    "year": 2021,
+    "authors": [
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+        "name": "Yi Tay"
+      },
+      {
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+        "name": "Mostafa Dehghani"
+      },
+      {
+        "authorId": "1995602019",
+        "name": "J. Gupta"
+      },
+      {
+        "authorId": "11774695",
+        "name": "Dara Bahri"
+      },
+      {
+        "authorId": "1481714624",
+        "name": "V. Aribandi"
+      },
+      {
+        "authorId": "145144957",
+        "name": "Zhen Qin"
+      },
+      {
+        "authorId": "1680617",
+        "name": "Donald Metzler"
+      }
+    ]
+  }
+}

data_without_website/Ask_Again,_Then_Fail__Large_Language_Models_Vacillations_in_Judgement.json

@@ -0,0 +1,50 @@
+{
+  "id": "9ceadCJY4B",
+  "title": "Ask Again, Then Fail: Large Language Models’ Vacillations in Judgement",
+  "track": "main",
+  "author": "Qiming Xie;Zengzhi Wang;Yi Feng;Rui Xia",
+  "pdf": "https://openreview.net/pdf?id=9ceadCJY4B",
+  "keyword": "",
+  "abstract": "With the emergence of generative conversational large language models (LLMs) like ChatGPT, serving as virtual assistants in various fields, the stability and reliability of their responses have become crucial. However, during usage, it has been observed that these models tend to waver in their judgements when confronted with follow-up questions from users expressing skepticism or disagreement. In this work, we draw inspiration from questioning strategies in education and propose a \\textsc{Follow-up Questioning Mechanism} along with two evaluation metrics to assess the judgement consistency of LLMs before and after exposure to disturbances. We evaluate the judgement consistency of ChatGPT, PaLM2-Bison, and Vicuna-13B under this mechanism across eight reasoning benchmarks. Empirical results show that even when the initial answers are correct, judgement consistency sharply decreases when LLMs face disturbances such as questioning, negation, or misleading. Additionally, we study these models' judgement consistency under various settings (sampling temperature and prompts) to validate this issue further, observing the impact of prompt tone and conducting an in-depth error analysis for deeper behavioral insights. Furthermore, we also explore several prompting methods to mitigate this issue and demonstrate their effectiveness.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2024
+  },
+  "template": null,
+  "category": "06. Natural Language Understanding and Semantics",
+  "is_done": true,
+  "timestamp": "2025-08-04T04:41:00.696264",
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+  "github_base": null,
+  "llm_believed_url": null,
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+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Ask Again, Then Fail: Large Language Models’ Vacillations in Judgement",
+    "matched_title": "Ask Again, Then Fail: Large Language Models' Vacillations in Judgement",
+    "citation_count": 10,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "2213862793",
+        "name": "Qiming Xie"
+      },
+      {
+        "authorId": "3196852",
+        "name": "Zengzhi Wang"
+      },
+      {
+        "authorId": "2213903745",
+        "name": "Yi Feng"
+      },
+      {
+        "authorId": "2253471115",
+        "name": "Rui Xia"
+      }
+    ]
+  }
+}

data_without_website/Auditing_and_Robustifying_COVID-19_Misinformation_Datasets_via_Anticontent_Sampling.json

@@ -0,0 +1,42 @@
+{
+  "id": "article-26780",
+  "title": "Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling",
+  "track": "aaai special track",
+  "author": "Clay H. Yoo; Ashiqur R. KhudaBukhsh",
+  "pdf": "https://ojs.aaai.org/index.php/AAAI/article/view/26780/26552",
+  "keyword": "",
+  "abstract": "This paper makes two key contributions. First, it argues that highly specialized rare content classifiers trained on small data typically have limited exposure to the richness and topical diversity of the negative class (dubbed anticontent) as observed in the wild. As a result, these classifiers' strong performance observed on the test set may not translate into real-world settings. In the context of COVID-19 misinformation detection, we conduct an in-the-wild audit of multiple datasets and demonstrate that models trained with several prominently cited recent datasets are vulnerable to anticontent when evaluated in the wild. Second, we present a novel active learning pipeline that requires zero manual annotation and iteratively augments the training data with challenging anticontent, robustifying these classifiers.",
+  "conference": {
+    "name": "AAAI",
+    "year": 2023
+  },
+  "template": null,
+  "category": "10. Trustworthy and Ethical AI",
+  "is_done": true,
+  "timestamp": "2025-08-05T07:45:42.847592",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
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+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling",
+    "matched_title": "Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling",
+    "citation_count": 5,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "2114724628",
+        "name": "Clay H. Yoo"
+      },
+      {
+        "authorId": "2856003",
+        "name": "Ashiqur R. KhudaBukhsh"
+      }
+    ]
+  }
+}

data_without_website/Automatic_Combination_of_Sample_Selection_Strategies_for_Few-Shot_Learning.json

@@ -0,0 +1,50 @@
+{
+  "id": "p8qhVIo980",
+  "title": "Automatic Combination of Sample Selection Strategies for Few-Shot Learning",
+  "track": "main",
+  "author": "Branislav Pecher;Ivan Srba;Maria Bielikova;Joaquin Vanschoren",
+  "pdf": "https://openreview.net/pdf?id=p8qhVIo980",
+  "keyword": "",
+  "abstract": "In few-shot learning, such as meta-learning, few-shot fine-tuning or in-context learning, the selection of samples has a significant impact on the performance of the trained model. Although many sample selection strategies are employed and evaluated in typical supervised settings, their impact on the performance of few-shot learning is largely unknown. In this paper, we investigate the impact of 20 sample selection strategies on the performance of 5 representative few-shot learning approaches over 8 image and 6 text datasets. We propose a new method for Automatic Combination of SamplE Selection Strategies (ACSESS), to leverage the strengths and complementarity of the individual strategies in order to select more impactful samples. The experimental results show that our method consistently outperforms all individual selection strategies. We also show that the majority of existing strategies strongly depend on modality, dataset characteristics and few-shot learning approach, while improving performance especially on imbalanced and noisy datasets. Lastly, we show that sample selection strategies work well even on smaller datasets and provide larger benefit when selecting a lower number of shots, while frequently regressing to random selection with higher numbers of shots.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T17:15:35.290904",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Automatic Combination of Sample Selection Strategies for Few-Shot Learning",
+    "matched_title": "Automatic Combination of Sample Selection Strategies for Few-Shot Learning",
+    "citation_count": 2,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2051747952",
+        "name": "Branislav Pecher"
+      },
+      {
+        "authorId": "2129782",
+        "name": "Ivan Srba"
+      },
+      {
+        "authorId": "1726847",
+        "name": "M. Bieliková"
+      },
+      {
+        "authorId": "1717534",
+        "name": "J. Vanschoren"
+      }
+    ]
+  }
+}

data_without_website/Automatic_Text_Evaluation_through_the_Lens_of_Wasserstein_Barycenters.json

@@ -0,0 +1,50 @@
+{
+  "id": "2021.emnlp-main.817",
+  "title": "Automatic Text Evaluation through the Lens of Wasserstein Barycenters",
+  "track": "main",
+  "author": "Pierre Colombo; Guillaume Staerman; Chloé Clavel; Pablo Piantanida",
+  "pdf": "https://aclanthology.org/2021.emnlp-main.817.pdf",
+  "keyword": "",
+  "abstract": "A new metric BaryScore to evaluate text generation based on deep contextualized embeddings (e.g., BERT, Roberta, ELMo) is introduced. This metric is motivated by a new framework relying on optimal transport tools, i.e., Wasserstein distance and barycenter. By modelling the layer output of deep contextualized embeddings as a probability distribution rather than by a vector embedding; this framework provides a natural way to aggregate the different outputs through the Wasserstein space topology. In addition, it provides theoretical grounds to our metric and offers an alternative to available solutions (e.g., MoverScore and BertScore). Numerical evaluation is performed on four different tasks: machine translation, summarization, data2text generation and image captioning. Our results show that BaryScore outperforms other BERT based metrics and exhibits more consistent behaviour in particular for text summarization.",
+  "conference": {
+    "name": "EMNLP",
+    "year": 2021
+  },
+  "template": null,
+  "category": "06. Natural Language Understanding and Semantics",
+  "is_done": true,
+  "timestamp": "2025-08-04T09:41:58.882295",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Automatic Text Evaluation through the Lens of Wasserstein Barycenters",
+    "matched_title": "Automatic Text Evaluation through the Lens of Wasserstein Barycenters",
+    "citation_count": 41,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2021,
+    "authors": [
+      {
+        "authorId": "46985469",
+        "name": "Pierre Colombo"
+      },
+      {
+        "authorId": "108056624",
+        "name": "Guillaume Staerman"
+      },
+      {
+        "authorId": "2049106",
+        "name": "C. Clavel"
+      },
+      {
+        "authorId": "1743922",
+        "name": "P. Piantanida"
+      }
+    ]
+  }
+}

data_without_website/BASiS__Batch_Aligned_Spectral_Embedding_Space.json

@@ -0,0 +1,46 @@
+{
+  "id": "22211",
+  "title": "BASiS: Batch Aligned Spectral Embedding Space",
+  "track": "main",
+  "author": "Or Streicher; Ido Cohen; Guy Gilboa",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2023/papers/Streicher_BASiS_Batch_Aligned_Spectral_Embedding_Space_CVPR_2023_paper.pdf",
+  "keyword": "",
+  "abstract": "Graph is a highly generic and diverse representation, suitable for almost any data processing problem. Spectral graph theory has been shown to provide powerful algorithms, backed by solid linear algebra theory. It thus can be extremely instrumental to design deep network building blocks with spectral graph characteristics. For instance, such a network allows the design of optimal graphs for certain tasks or obtaining a canonical orthogonal low-dimensional embedding of the data. Recent attempts to solve this problem were based on minimizing Rayleigh-quotient type losses. We propose a different approach of directly learning the graph's eigensapce. A severe problem of the direct approach, applied in batch-learning, is the inconsistent mapping of features to eigenspace coordinates in different batches. We analyze the degrees of freedom of learning this task using batches and propose a stable alignment mechanism that can work both with batch changes and with graph-metric changes. We show that our learnt spectral embedding is better in terms of NMI, ACC, Grassman distnace, orthogonality and classification accuracy, compared to SOTA. In addition, the learning is more stable.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2023
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T05:24:27.102503",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "BASiS: Batch Aligned Spectral Embedding Space",
+    "matched_title": "BASiS: Batch Aligned Spectral Embedding Space",
+    "citation_count": 2,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "120354516",
+        "name": "O. Streicher"
+      },
+      {
+        "authorId": "38400371",
+        "name": "I. Cohen"
+      },
+      {
+        "authorId": "2368011",
+        "name": "Guy Gilboa"
+      }
+    ]
+  }
+}

data_without_website/Better_AMR-To-Text_Generation_with_Graph_Structure_Reconstruction.json

@@ -0,0 +1,47 @@
+{
+  "id": "paper542",
+  "title": "Better AMR-To-Text Generation with Graph Structure Reconstruction",
+  "track": "main",
+  "author": "Tianming Wang; Xiaojun Wan; Shaowei Yao",
+  "pdf": "https://www.ijcai.org/proceedings/2020/0542.pdf",
+  "keyword": "",
+  "abstract": "AMR-to-text generation is a challenging task of generating texts from graph-based semantic representations. Recent studies formalize this task a graph-to-sequence learning problem and use various graph neural networks to model graph structure. In this paper, we propose a novel approach that generates texts from AMR graphs while reconstructing the input graph structures. Our model employs graph attention mechanism to aggregate information for encoding the inputs. Moreover, better node representations are learned by optimizing two simple but effective auxiliary reconstruction objectives: link prediction objective which requires predicting the semantic relationship between nodes, and distance prediction objective which requires predicting the distance between nodes. Experimental results on two benchmark datasets show that our proposed model improves considerably over strong baselines and achieves new state-of-the-art.",
+  "conference": {
+    "name": "IJCAI",
+    "year": 2020
+  },
+  "Internet_problem": "https://github.com/sodawater/",
+  "template": null,
+  "category": "06. Natural Language Understanding and Semantics",
+  "is_done": true,
+  "timestamp": "2025-08-05T14:07:21.631417",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Better AMR-To-Text Generation with Graph Structure Reconstruction",
+    "matched_title": "Better AMR-To-Text Generation with Graph Structure Reconstruction",
+    "citation_count": 68,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2020,
+    "authors": [
+      {
+        "authorId": "2355121707",
+        "name": "Tianming Wang"
+      },
+      {
+        "authorId": "2286531120",
+        "name": "Xiaojun Wan"
+      },
+      {
+        "authorId": "47188971",
+        "name": "Shaowei Yao"
+      }
+    ]
+  }
+}

data_without_website/Beyond_DAGs__A_Latent_Partial_Causal_Model_for_Multimodal_Learning.json

@@ -0,0 +1,66 @@
+{
+  "id": "HtvZCGiATs",
+  "title": "Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning",
+  "track": "main",
+  "author": "Yuhang Liu;Zhen Zhang;Dong Gong;Biwei Huang;Mingming Gong;Anton van den Hengel;Kun Zhang;Javen Qinfeng Shi",
+  "pdf": "https://openreview.net/pdf?id=HtvZCGiATs",
+  "keyword": "",
+  "abstract": "Directed acyclic graphs (DAGs) are often assumed in causal discovery, however, accurately identifying these DAGs necessitates various assumptions, particularly in latent causal models, which can be challenging to validate in real-world applications. This raises a critical question: Are DAG assumptions truly necessary for certain applications? In this work, we introduce a novel latent partial causal model for multimodal data, which features two latent coupled variables, connected by an undirected edge, effectively representing transferable knowledge across different modalities. We focus on a prominent learning framework, e.g., multimodal contrastive learning, and demonstrate that, with certain statistical assumptions, multimodal contrastive learning successfully identifies the latent coupled variables up to trivial transformation. This finding enhances our understanding of the mechanisms driving the success of multimodal contrastive learning. Furthermore, this finding reveals a unique potential for disentanglement in multimodal contrastive representation learning, improving the utility of pre-trained models like CLIP that are trained using this approach. Through experiments with synthetic data, we demonstrate the robustness of our findings, even in the presence of violated assumptions. In addition, we validate the disentanglement capabilities of pre-trained CLIP in learning disentangled representations, facilitating few-shot learning and improving domain generalization across a diverse range of real-world datasets.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "04. Probabilistic Methods and Causal Inference",
+  "is_done": true,
+  "timestamp": "2025-08-05T17:11:31.327617",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning",
+    "matched_title": "Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning",
+    "citation_count": 5,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2144422920",
+        "name": "Yuhang Liu"
+      },
+      {
+        "authorId": "2261395519",
+        "name": "Zhen Zhang"
+      },
+      {
+        "authorId": "2251401897",
+        "name": "Dong Gong"
+      },
+      {
+        "authorId": "1938684",
+        "name": "Biwei Huang"
+      },
+      {
+        "authorId": "2261392477",
+        "name": "Mingming Gong"
+      },
+      {
+        "authorId": "5546141",
+        "name": "A. Hengel"
+      },
+      {
+        "authorId": "2283790289",
+        "name": "Kun Zhang"
+      },
+      {
+        "authorId": "3177281",
+        "name": "Javen Qinfeng Shi"
+      }
+    ]
+  }
+}

data_without_website/BiLoRA__Almost-Orthogonal_Parameter_Spaces_for_Continual_Learning.json

@@ -0,0 +1,50 @@
+{
+  "id": "32506",
+  "title": "BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning",
+  "track": "main",
+  "author": "Hao Zhu; Yifei Zhang; Junhao Dong; Piotr Koniusz",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_BiLoRA_Almost-Orthogonal_Parameter_Spaces_for_Continual_Learning_CVPR_2025_paper.pdf",
+  "keyword": "",
+  "abstract": "Continual learning requires models to learn tasks sequentially while maintaining a delicate balance between stability (retaining knowledge of previous tasks) and plasticity (adapting to new tasks). A key challenge is preventing interference between tasks - where learning new tasks degrades performance on previously learned ones. Recent approaches have leveraged parameter-efficient fine-tuning (PEFT) methods, which adapt pre-trained models by injecting a small number of learnable parameters. However, existing PEFT-based continual learning methods like InfLoRA face fundamental limitations: they rely on complex optimization procedures to learn orthogonal task-specific spaces, and finding such spaces becomes increasingly difficult as tasks accumulate. We propose a novel bilinear reformulation that fundamentally reimagines task separation through fixed orthogonal bases. Our key insight is that by expanding the parameter space quadratically through two fixed bases, we can achieve \"almost orthogonal\" task subspaces probabilistically, eliminating the need for explicit interference elimination procedures. We provide theoretical guarantees that this approach reduces the probability of task interference from \\bigO((k/d)^2) to \\bigO((k/d^2)^2), ensuring reliable task separation without complex optimization. Through extensive experiments on ImageNet-R, CIFAR100, and DomainNet, we validate our theoretical bounds and demonstrate state-of-the-art performance with reduced parameter count.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2025
+  },
+  "Internet_problem": "https://github.com/yifeiacc/BiLoRA",
+  "github_base": "https://github.com/yifeiacc",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-07T12:45:54.603170",
+  "log": {
+    "timestamp": "2025-08-07T12:45:54.603170",
+    "stage": "special situation",
+    "note": "论文没有项目主页但找到了GitHub相关信息"
+  },
+  "citation_data": {
+    "original_title": "BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning",
+    "matched_title": "BiLoRA: Almost-Orthogonal Parameter Spaces for Continual Learning",
+    "citation_count": 0,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2025,
+    "authors": [
+      {
+        "authorId": "2373014062",
+        "name": "Hao Zhu"
+      },
+      {
+        "authorId": "2372426117",
+        "name": "Yifei Zhang"
+      },
+      {
+        "authorId": "2329328324",
+        "name": "Junhao Dong"
+      },
+      {
+        "authorId": "2288276495",
+        "name": "Piotr Koniusz"
+      }
+    ]
+  }
+}

data_without_website/Boosting_Adversarial_Training_via_Fisher-Rao_Norm-based_Regularization_1.json

@@ -0,0 +1,26 @@
+{
+  "id": "29323",
+  "title": "Boosting Adversarial Training via Fisher-Rao Norm-based Regularization",
+  "track": "main",
+  "author": "Xiangyu Yin; Wenjie Ruan",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2024/papers/Yin_Boosting_Adversarial_Training_via_Fisher-Rao_Norm-based_Regularization_CVPR_2024_paper.pdf",
+  "keyword": "",
+  "abstract": "Adversarial training is extensively utilized to improve the adversarial robustness of deep neural networks. Yet mitigating the degradation of standard generalization performance in adversarial-trained models remains an open problem. This paper attempts to resolve this issue through the lens of model complexity. First We leverage the Fisher-Rao norm a geometrically invariant metric for model complexity to establish the non-trivial bounds of the Cross-Entropy Loss-based Rademacher complexity for a ReLU-activated Multi-Layer Perceptron. Building upon this observation we propose a novel regularization framework called Logit-Oriented Adversarial Training (LOAT) which can mitigate the trade-off between robustness and accuracy while imposing only a negligible increase in computational overhead. Our extensive experiments demonstrate that the proposed regularization strategy can boost the performance of the prevalent adversarial training algorithms including PGD-AT TRADES TRADES (LSE) MART and DM-AT across various network architectures. Our code will be available at https://github.com/TrustAI/LOAT.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2024
+  },
+  "github_base": "https://github.com/TrustAI/LOAT",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-07T01:54:01.012260",
+  "log": {
+    "timestamp": "2025-08-07T01:54:01.012260",
+    "stage": "special situation",
+    "note": "论文没有项目主页但找到了GitHub相关信息"
+  },
+  "citation_data": {
+    "citation_count": 0
+  }
+}

data_without_website/Bridging_the_Gap_Between_Model_Explanations_in_Partially_Annotated_Multi-Label_Classification.json

@@ -0,0 +1,59 @@
+{
+  "id": "21240",
+  "title": "Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification",
+  "track": "main",
+  "author": "Youngwook Kim; Jae Myung Kim; Jieun Jeong; Cordelia Schmid; Zeynep Akata; Jungwoo Lee",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Bridging_the_Gap_Between_Model_Explanations_in_Partially_Annotated_Multi-Label_CVPR_2023_paper.pdf",
+  "keyword": "",
+  "abstract": "Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https://github.com/youngwk/BridgeGapExplanationPAMC.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2023
+  },
+  "rule_final_url": "https://github.com/youngwk/BridgeGapExplanationPAMC",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T05:20:54.414089",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification",
+    "matched_title": "Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification",
+    "citation_count": 11,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": null,
+        "name": "Youngwook Kim"
+      },
+      {
+        "authorId": "47963899",
+        "name": "Jae Myung Kim"
+      },
+      {
+        "authorId": "2115678237",
+        "name": "Ji-Eun Jeong"
+      },
+      {
+        "authorId": "2462253",
+        "name": "C. Schmid"
+      },
+      {
+        "authorId": "2893664",
+        "name": "Zeynep Akata"
+      },
+      {
+        "authorId": "49684692",
+        "name": "Jungwook Lee"
+      }
+    ]
+  }
+}

data_without_website/CAD-Editor__Text-based_CAD_Editing_through_Adapting_Large_Language_Models_with_Synthetic_Data.json

@@ -0,0 +1,42 @@
+{
+  "id": "Jrb9yXZJKG",
+  "title": "CAD-Editor: Text-based CAD Editing through Adapting Large Language Models with Synthetic Data",
+  "track": "main",
+  "author": "Yu Yuan;Shizhao Sun;Qi Liu;Jiang Bian",
+  "pdf": "https://openreview.net/pdf?id=Jrb9yXZJKG",
+  "keyword": "",
+  "abstract": "Computer Aided Design (CAD) is indispensable across various industries. \n\\emph{Text-based CAD editing}, which automatically modifies CAD models following textual instructions, is important yet not extensively studied. \nExisting work explores design variation generation, which randomly alters specific parts of a CAD model, offering no control over the final appearance.\nThis work introduces \\emph{CAD-Editor} for text-based editing.\nWe leverage Large Language Models (LLMs) as the backbone to take the concatenation of textual instruction and original CAD sequence as input and predict the edited CAD sequence, where the sequence representation of a CAD model is designed for easier processing by LLMs.\nMoreover, we propose fine-tuning LLMs by using a synthetic dataset followed by a selective dataset.\nThe synthetic data is produced by leveraging powerful existing models, including design variation generation models for producing paired CAD models and multi-modal models for capturing textual differences between these pairs.\nThe selective data is created by choosing top examples from outputs of the initially fine-tuned LLMs based on human feedback or metrics.\nIn this way, a large-scale synthetic dataset offers basic capability while a selective dataset that is less noisy and better aligned with human intentions boosts performance further.\nExtensive experiments demonstrate the advantage of CAD-Editor both quantitatively and qualitatively.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "07. Generative Model",
+  "is_done": true,
+  "timestamp": "2025-08-05T02:01:47.363072",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "CAD-Editor: Text-based CAD Editing through Adapting Large Language Models with Synthetic Data",
+    "matched_title": "Computational science and its applications - ICCSA 2007 : International Conference Kuala Lumpur, Malaysia, August 26-29, 2007 : proceedings",
+    "citation_count": 0,
+    "similarity": 0.07207207207207207,
+    "source": "semantic_scholar",
+    "year": 2007,
+    "authors": [
+      {
+        "authorId": "1684675",
+        "name": "O. Gervasi"
+      },
+      {
+        "authorId": "1679427",
+        "name": "M. Gavrilova"
+      }
+    ]
+  }
+}

data_without_website/CLadder__Assessing_Causal_Reasoning_in_Language_Models.json

@@ -0,0 +1,73 @@
+{
+  "id": "e2wtjx0Yqu",
+  "title": "CLadder: Assessing Causal Reasoning in Language Models",
+  "track": "main",
+  "author": "Zhijing Jin;Yuen Chen;Felix Leeb;Luigi Gresele;Ojasv Kamal;Zhiheng LYU;Kevin Blin;Fernando Gonzalez Adauto;Max Kleiman-Weiner;Mrinmaya Sachan;Bernhard Schölkopf",
+  "pdf": "https://openreview.net/pdf?id=e2wtjx0Yqu",
+  "keyword": "",
+  "abstract": "The ability to perform causal reasoning is widely considered a core feature of intelligence. In this work, we investigate whether large language models (LLMs) can coherently reason about causality. Much of the existing work in natural language processing (NLP) focuses on evaluating _commonsense_ causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined _formal rules_. To address this, we propose a new NLP task, _causal inference in natural language_, inspired by the _\"causal inference engine\"_ postulated by Judea Pearl et al. We compose a large dataset, CLadder, with 10K samples: based on a collection of causal graphs and queries (associational, interventional, and counterfactual), we obtain symbolic questions and ground-truth answers, through an oracle causal inference engine. These are then translated into natural language. We evaluate multiple LLMs on our dataset, and we introduce and evaluate a bespoke chain-of-thought prompting strategy, CausalCoT. We show that our task is highly challenging for LLMs, and we conduct an in-depth analysis to gain deeper insight into the causal reasoning abilities of LLMs. Our data is open-sourced at https://huggingface.co/datasets/causalNLP/cladder, and our code can be found at https://github.com/causalNLP/cladder.",
+  "conference": {
+    "name": "NIPS",
+    "year": 2023
+  },
+  "github_base": "https://github.com/causalNLP/cladder",
+  "llm_believed_url": "https://huggingface.co/datasets/causalnlp/CLadder",
+  "template": null,
+  "category": "04. Probabilistic Methods and Causal Inference",
+  "is_done": true,
+  "timestamp": "2025-08-06T08:56:28.832140",
+  "citation_data": {
+    "original_title": "CLadder: Assessing Causal Reasoning in Language Models",
+    "matched_title": "CLadder: Assessing Causal Reasoning in Language Models",
+    "citation_count": 86,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "2111472502",
+        "name": "Zhijing Jin"
+      },
+      {
+        "authorId": "2265625277",
+        "name": "Yuen Chen"
+      },
+      {
+        "authorId": "27569366",
+        "name": "Felix Leeb"
+      },
+      {
+        "authorId": "31821560",
+        "name": "Luigi Gresele"
+      },
+      {
+        "authorId": "2046878528",
+        "name": "Ojasv Kamal"
+      },
+      {
+        "authorId": "2114227440",
+        "name": "Zhiheng Lyu"
+      },
+      {
+        "authorId": "2272686866",
+        "name": "Kevin Blin"
+      },
+      {
+        "authorId": "2272838249",
+        "name": "Fernando Gonzalez Adauto"
+      },
+      {
+        "authorId": "2272856326",
+        "name": "Max Kleiman-Weiner"
+      },
+      {
+        "authorId": "2790926",
+        "name": "Mrinmaya Sachan"
+      },
+      {
+        "authorId": "2261392474",
+        "name": "Bernhard Scholkopf"
+      }
+    ]
+  }
+}

data_without_website/COLD__A_Benchmark_for_Chinese_Offensive_Language_Detection.json

@@ -0,0 +1,58 @@
+{
+  "id": "2022.emnlp-main.796",
+  "title": "COLD: A Benchmark for Chinese Offensive Language Detection",
+  "track": "main",
+  "author": "Jiawen Deng; Jingyan Zhou; Hao Sun; Chujie Zheng; Fei Mi; Helen Meng; Minlie Huang",
+  "pdf": "https://aclanthology.org/2022.emnlp-main.796.pdf",
+  "keyword": "",
+  "abstract": "Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the scarcity of reliable datasets. To this end, we propose a benchmark –COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset –COLDATASET and a baseline detector –COLDETECTOR which is trained on the dataset. We show that the COLD benchmark contributes to Chinese offensive language detection which is challenging for existing resources. We then deploy the COLDETECTOR and conduct detailed analyses on popular Chinese pre-trained language models. We first analyze the offensiveness of existing generative models and show that these models inevitably expose varying degrees of offensive issues. Furthermore, we investigate the factors that influence the offensive generations, and we find that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier.",
+  "conference": {
+    "name": "EMNLP",
+    "year": 2022
+  },
+  "template": null,
+  "category": "06. Natural Language Understanding and Semantics",
+  "is_done": true,
+  "timestamp": "2025-08-04T09:15:28.710910",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "COLD: A Benchmark for Chinese Offensive Language Detection",
+    "matched_title": "COLD: A Benchmark for Chinese Offensive Language Detection",
+    "citation_count": 111,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "150076569",
+        "name": "Deng Jiawen"
+      },
+      {
+        "authorId": "30887444",
+        "name": "Jingyan Zhou"
+      },
+      {
+        "authorId": "144990601",
+        "name": "Hao Sun"
+      },
+      {
+        "authorId": "146452866",
+        "name": "Chujie Zheng"
+      },
+      {
+        "authorId": "33727421",
+        "name": "Fei Mi"
+      },
+      {
+        "authorId": "1730108",
+        "name": "Minlie Huang"
+      }
+    ]
+  }
+}

data_without_website/CORE__Common_Random_Reconstruction_for_Distributed_Optimization_with_Provable_Low_Communication_Complexity.json

@@ -0,0 +1,62 @@
+{
+  "id": "ER1VDuwWvB",
+  "title": "CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity",
+  "track": "main",
+  "author": "Pengyun Yue;Hanzhen Zhao;Cong Fang;Di He;Liwei Wang;Zhouchen Lin;Song-Chun Zhu",
+  "pdf": "https://openreview.net/pdf?id=ER1VDuwWvB",
+  "keyword": "",
+  "abstract": "With distributed machine learning being a prominent technique for large-scale machine learning tasks, communication complexity has become a major bottleneck for speeding up training and scaling up machine numbers. In this paper, we propose a new technique named Common randOm REconstruction(CORE), which can be used to compress the information transmitted between machines in order to reduce communication complexity without other strict conditions. Especially, our technique CORE projects the vector-valued information to a low-dimensional one through common random vectors and reconstructs the information with the same random noises after communication. We apply CORE to two distributed tasks, respectively convex optimization on linear models and generic non-convex optimization, and design new distributed algorithms, which achieve provably lower communication complexities.  For example, we show for linear models CORE-based algorithm can encode the gradient vector to $\\mathcal{O}(1)$-bits (against $\\mathcal{O}(d)$), with the convergence rate not worse, preceding the existing results.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2024
+  },
+  "template": null,
+  "category": "12. ML Systems and Infrastructure",
+  "is_done": true,
+  "timestamp": "2025-08-05T10:42:57.732905",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity",
+    "matched_title": "CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity",
+    "citation_count": 1,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "2198489600",
+        "name": "Pengyun Yue"
+      },
+      {
+        "authorId": "10156671",
+        "name": "Hanzheng Zhao"
+      },
+      {
+        "authorId": "47967033",
+        "name": "Cong Fang"
+      },
+      {
+        "authorId": "1391126980",
+        "name": "Di He"
+      },
+      {
+        "authorId": "39060743",
+        "name": "Liwei Wang"
+      },
+      {
+        "authorId": "2146393925",
+        "name": "Zhouchen Lin"
+      },
+      {
+        "authorId": "145380991",
+        "name": "Song-Chun Zhu"
+      }
+    ]
+  }
+}

data_without_website/Cascaded_Contrastive_Medical_Language-Image_Pretraining_on_Radiology_Images.json

@@ -0,0 +1,70 @@
+{
+  "id": "BRTyPCq4wL",
+  "title": "Cascaded Contrastive Medical Language-Image Pretraining on Radiology Images",
+  "track": "main",
+  "author": "Chengsheng Mao;Hanyin Wang;Yuan Luo",
+  "pdf": "https://openreview.net/pdf?id=BRTyPCq4wL",
+  "keyword": "",
+  "abstract": "Due to the concise design and the wonderful generalization performance, contrastive language-image pre-training (CLIP) has been investigated in the medical domain for medical image understanding. However, few studies have been done on CLIP for multilevel medical information alignment. In this paper, we proposed cascaded CLIP (casCLIP) where contrastive alignment is performed on multilevel information. In addition, we propose aligning the report with the entire image series and employ a multi-layer transformer to integrate the image embeddings from a study into a single embedding of image series. Moreover, we introduce support alignment opposition de-alignment method to enhance higher-level alignment. In this study, casCLIP was pre-trained on a dataset of chest X-ray images with reports and the high level disease information extracted from the reports. Experimental results on multiple public benchmarks demonstrate the effectiveness of our model for zero-shot classification.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2024
+  },
+  "template": null,
+  "category": "09. Multimodal Learning",
+  "is_done": true,
+  "timestamp": "2025-08-04T04:32:25.694122",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Cascaded Contrastive Medical Language-Image Pretraining on Radiology Images",
+    "matched_title": "SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI",
+    "citation_count": 0,
+    "similarity": 0.5802469135802469,
+    "source": "semantic_scholar",
+    "year": 2025,
+    "authors": [
+      {
+        "authorId": "2109231338",
+        "name": "Zhiyang Liu"
+      },
+      {
+        "authorId": "2295509074",
+        "name": "Dong Yang"
+      },
+      {
+        "authorId": "2295483977",
+        "name": "Minghao Zhang"
+      },
+      {
+        "authorId": "2353066720",
+        "name": "Hanyu Sun"
+      },
+      {
+        "authorId": "2352123564",
+        "name": "Hong Wu"
+      },
+      {
+        "authorId": "2130310227",
+        "name": "Huiying Wang"
+      },
+      {
+        "authorId": "2240334933",
+        "name": "Wen Shen"
+      },
+      {
+        "authorId": "49719142",
+        "name": "Chao Chai"
+      },
+      {
+        "authorId": "2237578106",
+        "name": "Shuang Xia"
+      }
+    ]
+  }
+}

data_without_website/Class_Incremental_Learning_via_Likelihood_Ratio_Based_Task_Prediction.json

@@ -0,0 +1,60 @@
+{
+  "id": "8QfK9Dq4q0",
+  "title": "Class Incremental Learning via Likelihood Ratio Based Task Prediction",
+  "track": "main",
+  "author": "Haowei Lin;Yijia Shao;Weinan Qian;Ningxin Pan;Yiduo Guo;Bing Liu",
+  "pdf": "https://openreview.net/pdf?id=8QfK9Dq4q0",
+  "keyword": "",
+  "abstract": "Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key feature of CIL is that no task identifier (or task-id) is provided at test time. Predicting the task-id for each test sample is a challenging problem. An emerging theory-guided approach (called TIL+OOD) is to train a task-specific model for each task in a shared network for all tasks based on a task-incremental learning (TIL) method to deal with catastrophic forgetting. The model for each task is an out-of-distribution (OOD) detector rather than a conventional classifier. The OOD detector can perform both within-task (in-distribution (IND)) class prediction and OOD detection. The OOD detection capability is the key to task-id prediction during inference. However, this paper argues that using a traditional OOD detector for task-id prediction is sub-optimal because additional information (e.g., the replay data and the learned tasks) available in CIL can be exploited to design a better and principled method for task-id prediction. We call the new method TPL (Task-id Prediction based on Likelihood Ratio). TPL markedly outperforms strong CIL baselines and has negligible catastrophic forgetting. The code of TPL is publicly available at https://github.com/linhaowei1/TPL.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2024
+  },
+  "project_url": "https://linhaowei1.github.io/",
+  "project_url1": "https://shaoyijia.github.io/",
+  "github_base": "https://github.com/linhaowei1/TPL",
+  "rule_base_possible_url": "https://linhaowei1.github.io/",
+  "rule_base_possible_url1": "https://shaoyijia.github.io/",
+  "rule_base_possible_url2": "https://linhaowei1.github.io",
+  "rule_base_possible_url3": "https://shaoyijia.github.io",
+  "llm_believed_url": "https://linhaowei1.github.io/",
+  "llm_believed_url1": "https://shaoyijia.github.io/",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T10:56:46.069300",
+  "citation_data": {
+    "original_title": "Class Incremental Learning via Likelihood Ratio Based Task Prediction",
+    "matched_title": "Class Incremental Learning via Likelihood Ratio Based Task Prediction",
+    "citation_count": 14,
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+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "2257447835",
+        "name": "Haowei Lin"
+      },
+      {
+        "authorId": "74175857",
+        "name": "Yijia Shao"
+      },
+      {
+        "authorId": "103913684",
+        "name": "W. Qian"
+      },
+      {
+        "authorId": "2104459181",
+        "name": "Ningxin Pan"
+      },
+      {
+        "authorId": "2214448244",
+        "name": "Yiduo Guo"
+      },
+      {
+        "authorId": "143830417",
+        "name": "Bing Liu"
+      }
+    ]
+  }
+}

data_without_website/Closely_Interactive_Human_Reconstruction_with_Proxemics_and_Physics-Guided_Adaption_1.json

@@ -0,0 +1,59 @@
+{
+  "id": "29454",
+  "title": "Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption",
+  "track": "main",
+  "author": "Buzhen Huang; Chen Li; Chongyang Xu; Liang Pan; Yangang Wang; Gim Hee Lee",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Closely_Interactive_Human_Reconstruction_with_Proxemics_and_Physics-Guided_Adaption_CVPR_2024_paper.pdf",
+  "keyword": "",
+  "abstract": "Existing multi-person human reconstruction approaches mainly focus on recovering accurate poses or avoiding penetration but overlook the modeling of close interactions. In this work we tackle the task of reconstructing closely interactive humans from a monocular video. The main challenge of this task comes from insufficient visual information caused by depth ambiguity and severe inter-person occlusion. In view of this we propose to leverage knowledge from proxemic behavior and physics to compensate the lack of visual information. This is based on the observation that human interaction has specific patterns following the social proxemics. Specifically we first design a latent representation based on Vector Quantised-Variational AutoEncoder (VQ-VAE) to model human interaction. A proxemics and physics guided diffusion model is then introduced to denoise the initial distribution. We design the diffusion model as dual branch with each branch representing one individual such that the interaction can be modeled via cross attention. With the learned priors of VQ-VAE and physical constraint as the additional information our proposed approach is capable of estimating accurate poses that are also proxemics and physics plausible. Experimental results on Hi4D 3DPW and CHI3D demonstrate that our method outperforms existing approaches. The code is available at https://github.com/boycehbz/HumanInteraction.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2024
+  },
+  "Internet_problem": "https://github.com/boycehbz/",
+  "template": null,
+  "category": "05. 3D Vision and Computational Graphics",
+  "is_done": true,
+  "timestamp": "2025-08-07T05:52:30.511554",
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+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption",
+    "matched_title": "Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption",
+    "citation_count": 8,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
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+        "authorId": "51295961",
+        "name": "Buzhen Huang"
+      },
+      {
+        "authorId": "2256784813",
+        "name": "Chen Li"
+      },
+      {
+        "authorId": "2297146351",
+        "name": "Chongyang Xu"
+      },
+      {
+        "authorId": "2297133591",
+        "name": "Liang Pan"
+      },
+      {
+        "authorId": "2256978081",
+        "name": "Yangang Wang"
+      },
+      {
+        "authorId": "2261454965",
+        "name": "Gim Hee Lee"
+      }
+    ]
+  }
+}

data_without_website/Co-Scale_Conv-Attentional_Image_Transformers.json

@@ -0,0 +1,52 @@
+{
+  "id": "",
+  "title": "Co-Scale Conv-Attentional Image Transformers",
+  "track": "main",
+  "author": "Weijian Xu; Yifan Xu; Tyler Chang; Zhuowen Tu",
+  "pdf": "https://openaccess.thecvf.com/content/ICCV2021/papers/Xu_Co-Scale_Conv-Attentional_Image_Transformers_ICCV_2021_paper.pdf",
+  "keyword": "",
+  "abstract": "In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers' encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other; we design a series of serial and parallel blocks to realize the co-scale mechanism. Second, we devise a conv-attentional mechanism by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. On ImageNet, relatively small CoaT models attain superior classification results compared with similar-sized convolutional neural networks and image/vision Transformers. The effectiveness of CoaT's backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to downstream computer vision tasks.",
+  "conference": {
+    "name": "ICCV",
+    "year": 2021
+  },
+  "project_url": "https://tylerachang.github.io/",
+  "project_url1": "INTERNET_PROBLEM",
+  "github_base": "https://github.com/mlpc-ucsd/CoaT",
+  "rule_base_possible_url": "https://tylerachang.github.io/",
+  "rule_base_possible_url1": "https://tylerachang.github.io",
+  "llm_believed_url": "https://weijianxu.com/",
+  "llm_believed_url1": "https://yfxu.com/",
+  "llm_believed_url2": "https://tylerachang.github.io/",
+  "llm_believed_url3": "https://pages.ucsd.edu/~ztu/",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-06T20:33:40.103091",
+  "citation_data": {
+    "original_title": "Co-Scale Conv-Attentional Image Transformers",
+    "matched_title": "Co-Scale Conv-Attentional Image Transformers",
+    "citation_count": 382,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2021,
+    "authors": [
+      {
+        "authorId": "2110546250",
+        "name": "Weijian Xu"
+      },
+      {
+        "authorId": "2125063007",
+        "name": "Yifan Xu"
+      },
+      {
+        "authorId": "2087001989",
+        "name": "Tyler A. Chang"
+      },
+      {
+        "authorId": "144035504",
+        "name": "Z. Tu"
+      }
+    ]
+  }
+}

data_without_website/Co-training_for_Low_Resource_Scientific_Natural_Language_Inference.json

@@ -0,0 +1,42 @@
+{
+  "id": "2024.acl-long.139",
+  "title": "Co-training for Low Resource Scientific Natural Language Inference",
+  "track": "main",
+  "author": "Mobashir Sadat; Cornelia Caragea",
+  "pdf": "https://aclanthology.org/2024.acl-long.139.pdf",
+  "keyword": "",
+  "abstract": "Scientific Natural Language Inference (NLI) is the task of predicting the semantic relation between a pair of sentences extracted from research articles. The automatic annotation method based on distant supervision for the training set of SciNLI, the first and most popular dataset for this task, results in label noise which inevitably degenerates the performance of classifiers. In this paper, we propose a novel co-training method that assigns weights based on the training dynamics of the classifiers to the distantly supervised labels, reflective of the manner they are used in the subsequent training epochs. That is, unlike the existing semi-supervised learning (SSL) approaches, we consider the historical behavior of the classifiers to evaluate the quality of the automatically annotated labels. Furthermore, by assigning importance weights instead of filtering out examples based on an arbitrary threshold on the predicted confidence, we maximize the usage of automatically labeled data, while ensuring that the noisy labels have a minimal impact on model training. The proposed method obtains an improvement of 1.5% in Macro F1 over the distant supervision baseline, and substantial improvements over several other strong SSL baselines. We make our code and data available on Github.",
+  "conference": {
+    "name": "ACL",
+    "year": 2024
+  },
+  "Internet_problem": "https://github.com/msadat3/weighted_",
+  "github_base": "https://github.com/msadat3",
+  "template": null,
+  "category": "06. Natural Language Understanding and Semantics",
+  "is_done": true,
+  "timestamp": "2025-08-07T17:03:07.359853",
+  "log": {
+    "timestamp": "2025-08-07T17:03:07.359853",
+    "stage": "special situation",
+    "note": "论文没有项目主页但找到了GitHub相关信息"
+  },
+  "citation_data": {
+    "original_title": "Co-training for Low Resource Scientific Natural Language Inference",
+    "matched_title": "Co-training for Low Resource Scientific Natural Language Inference",
+    "citation_count": 0,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2140791766",
+        "name": "Mobashir Sadat"
+      },
+      {
+        "authorId": "2296598482",
+        "name": "Cornelia Caragea"
+      }
+    ]
+  }
+}

data_without_website/CoCA__Fusing_Position_Embedding_with_Collinear_Constrained_Attention_in_Transformers_for_Long_Context_Window_Extending.json

@@ -0,0 +1,56 @@
+{
+  "id": "2024.acl-long.233",
+  "title": "CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending",
+  "track": "main",
+  "author": "Shiyi Zhu; Jing Ye; Wei Jiang; Siqiao Xue; Qi Zhang; Yifan Wu; Jianguo Li",
+  "pdf": "https://aclanthology.org/2024.acl-long.233.pdf",
+  "keyword": "",
+  "abstract": "Self-attention and position embedding are two crucial modules in transformer-based Large Language Models (LLMs). However, the potential relationship between them is far from well studied, especially for long context window extending. In fact, anomalous behaviors that hinder long context extrapolation exist between Rotary Position Embedding (RoPE) and vanilla self-attention.Incorrect initial angles between Q and K can cause misestimation in modeling rotary position embedding of the closest tokens.To address this issue, we propose Collinear Constrained Attention mechanism, namely CoCA. Specifically, we enforce a collinear constraint between Q and K to seamlessly integrate RoPE and self-attention.While only adding minimal computational and spatial complexity, this integration significantly enhances long context window extrapolation ability. We provide an optimized implementation, making it a drop-in replacement for any existing transformer-based models.Extensive experiments demonstrate that CoCA excels in extending context windows. A CoCA-based GPT model, trained with a context length of 512, can extend the context window up to 32K (60×) without any fine-tuning.Additionally, incorporating CoCA into LLaMA-7B achieves extrapolation up to 32K within a training length of only 2K.Our code is publicly available at: https://github.com/codefuse-ai/Collinear-Constrained-Attention",
+  "conference": {
+    "name": "ACL",
+    "year": 2024
+  },
+  "llm_believed_url": "https://github.com/codefuse-ai/Collinear-Constrained-Attention",
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T14:49:50.539599",
+  "citation_data": {
+    "original_title": "CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending",
+    "matched_title": "CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending",
+    "citation_count": 5,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "2243808364",
+        "name": "Shiyi Zhu"
+      },
+      {
+        "authorId": "2260831276",
+        "name": "Jingting Ye"
+      },
+      {
+        "authorId": "2243775310",
+        "name": "Wei Jiang"
+      },
+      {
+        "authorId": "2287922278",
+        "name": "Siqiao Xue"
+      },
+      {
+        "authorId": "2256972664",
+        "name": "Qi Zhang"
+      },
+      {
+        "authorId": "2243304520",
+        "name": "Yifan Wu"
+      },
+      {
+        "authorId": "2223080664",
+        "name": "Jianguo Li"
+      }
+    ]
+  }
+}

data_without_website/Combating_Disinformation_on_Social_Media_and_Its_Challenges__A_Computational_Perspective.json

@@ -0,0 +1,38 @@
+{
+  "id": "article-26821",
+  "title": "Combating Disinformation on Social Media and Its Challenges: A Computational Perspective",
+  "track": "new faculty highlights",
+  "author": "Kai Shu",
+  "pdf": "https://ojs.aaai.org/index.php/AAAI/article/view/26821/26593",
+  "keyword": "",
+  "abstract": "The use of social media has accelerated information sharing and instantaneous communications. The low barrier to entering social media enables more users to participate and keeps them engaged longer, incentivizing individuals with a hidden agenda to spread disinformation online to manipulate information and sway opinion. Disinformation, such as fake news, hoaxes, and conspiracy theories, has increasingly become a hindrance to the functioning of online social media as an effective channel for trustworthy information. Therefore, it is imperative to understand disinformation and systematically investigate how to improve resistance against it. This article highlights relevant theories and recent advancements of detecting disinformation from a computational perspective, and urges the need for future interdisciplinary research.",
+  "conference": {
+    "name": "AAAI",
+    "year": 2023
+  },
+  "template": null,
+  "category": "10. Trustworthy and Ethical AI",
+  "is_done": true,
+  "timestamp": "2025-08-05T15:37:24.054141",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Combating Disinformation on Social Media and Its Challenges: A Computational Perspective",
+    "matched_title": "Combating Disinformation on Social Media and Its Challenges: A Computational Perspective",
+    "citation_count": 3,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "145800151",
+        "name": "Kai Shu"
+      }
+    ]
+  }
+}

data_without_website/Comparator-Adaptive_Convex_Bandits.json

@@ -0,0 +1,46 @@
+{
+  "id": "18756",
+  "title": "Comparator-Adaptive Convex Bandits",
+  "track": "main",
+  "author": "Dirk van der Hoeven; Ashok Cutkosky; Haipeng Luo",
+  "pdf": "https://papers.nips.cc/paper_files/paper/2020/file/e4f37b9ed429c1fe5ce61860d9902521-Paper.pdf",
+  "keyword": "",
+  "abstract": "We study bandit convex optimization methods that adapt to the norm of the comparator, a topic that has only been studied before for its full-information counterpart. Specifically, we develop convex bandit algorithms with regret bounds that are small whenever the norm of the comparator is small. \nWe first use techniques from the full-information setting to develop comparator-adaptive algorithms for linear bandits. Then, we extend the ideas to convex bandits with Lipschitz or smooth loss functions, using a new single-point gradient estimator and carefully designed surrogate losses.",
+  "conference": {
+    "name": "NIPS",
+    "year": 2020
+  },
+  "template": null,
+  "category": "02. Reinforcement Learning and Control",
+  "is_done": true,
+  "timestamp": "2025-08-05T14:18:21.076523",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Comparator-Adaptive Convex Bandits",
+    "matched_title": "Comparator-adaptive Convex Bandits",
+    "citation_count": 7,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2020,
+    "authors": [
+      {
+        "authorId": "35751056",
+        "name": "Dirk van der Hoeven"
+      },
+      {
+        "authorId": "9407860",
+        "name": "Ashok Cutkosky"
+      },
+      {
+        "authorId": "2131127",
+        "name": "Haipeng Luo"
+      }
+    ]
+  }
+}

data_without_website/Complementary_Patch_for_Weakly_Supervised_Semantic_Segmentation.json

@@ -0,0 +1,50 @@
+{
+  "id": "",
+  "title": "Complementary Patch for Weakly Supervised Semantic Segmentation",
+  "track": "main",
+  "author": "Fei Zhang; Chaochen Gu; Chenyue Zhang; Yuchao Dai",
+  "pdf": "https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Complementary_Patch_for_Weakly_Supervised_Semantic_Segmentation_ICCV_2021_paper.pdf",
+  "keyword": "",
+  "abstract": "Weakly Supervised Semantic Segmentation (WSSS) based on image-level labels has been greatly advanced by exploiting the outputs of Class Activation Map (CAM) to generate the pseudo labels for semantic segmentation. However, CAM merely discovers seeds from a small number of regions, which may be insufficient to serve as pseudo masks for semantic segmentation. In this paper, we formulate the expansion of object regions in CAM as an increase in information. From the perspective of information theory, we propose a novel Complementary Patch (CP) Representation and prove that the information of the sum of the CAMs by a pair of input images with complementary hidden (patched) parts, namely CP Pair, is greater than or equal to the information of the baseline CAM. Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM. We propose a CP Network (CPN) implemented by a triplet network and three regularization functions. To further improve the quality of the CAMs, we propose a Pixel-Region Correlation Module (PRCM) to augment the contextual information by using object-region relations between the feature maps and the CAMs. Experimental results on the PASCAL VOC 2012 datasets show that our proposed method achieves a new state-of-the-art in WSSS, validating the effectiveness of our CP Representation and CPN.",
+  "conference": {
+    "name": "ICCV",
+    "year": 2021
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T17:52:31.597227",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Complementary Patch for Weakly Supervised Semantic Segmentation",
+    "matched_title": "Complementary Patch for Weakly Supervised Semantic Segmentation",
+    "citation_count": 141,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2021,
+    "authors": [
+      {
+        "authorId": "2144025834",
+        "name": "Fei Zhang"
+      },
+      {
+        "authorId": "5494837",
+        "name": "Chaochen Gu"
+      },
+      {
+        "authorId": "2124944878",
+        "name": "Chenyue Zhang"
+      },
+      {
+        "authorId": "2116916030",
+        "name": "Yuchao Dai"
+      }
+    ]
+  }
+}

data_without_website/Concept_Weaver__Enabling_Multi-Concept_Fusion_in_Text-to-Image_Models.json

@@ -0,0 +1,58 @@
+{
+  "id": "29678",
+  "title": "Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models",
+  "track": "main",
+  "author": "Gihyun Kwon; Simon Jenni; Dingzeyu Li; Joon-Young Lee; Jong Chul Ye; Fabian Caba Heilbron",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2024/papers/Kwon_Concept_Weaver_Enabling_Multi-Concept_Fusion_in_Text-to-Image_Models_CVPR_2024_paper.pdf",
+  "keyword": "",
+  "abstract": "While there has been significant progress in customizing text-to-image generation models generating images that combine multiple personalized concepts remains challenging. In this work we introduce Concept Weaver a method for composing customized text-to-image diffusion models at inference time. Specifically the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2024
+  },
+  "template": null,
+  "category": "07. Generative Model",
+  "is_done": true,
+  "timestamp": "2025-08-04T07:46:26.280985",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models",
+    "matched_title": "Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models",
+    "citation_count": 14,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "116153377",
+        "name": "Gihyun Kwon"
+      },
+      {
+        "authorId": "2295510604",
+        "name": "Simon Jenni"
+      },
+      {
+        "authorId": "2295595049",
+        "name": "Dingzeyu Li"
+      },
+      {
+        "authorId": "2283090591",
+        "name": "Joon-Young Lee"
+      },
+      {
+        "authorId": "2254155689",
+        "name": "Jong Chul Ye"
+      },
+      {
+        "authorId": "3175258",
+        "name": "Fabian Caba Heilbron"
+      }
+    ]
+  }
+}

data_without_website/Cross-domain_Object_Detection_through_Coarse-to-Fine_Feature_Adaptation.json

@@ -0,0 +1,50 @@
+{
+  "id": "",
+  "title": "Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation",
+  "track": "main",
+  "author": "Yangtao Zheng;  Di Huang;  Songtao Liu;  Yunhong Wang",
+  "pdf": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Cross-domain_Object_Detection_through_Coarse-to-Fine_Feature_Adaptation_CVPR_2020_paper.pdf",
+  "keyword": "",
+  "abstract": "Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2020
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-04T09:36:03.221173",
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+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation",
+    "matched_title": "Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation",
+    "citation_count": 205,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2020,
+    "authors": [
+      {
+        "authorId": "152524379",
+        "name": "Yangtao Zheng"
+      },
+      {
+        "authorId": "40119164",
+        "name": "Di Huang"
+      },
+      {
+        "authorId": "2144363412",
+        "name": "Songtao Liu"
+      },
+      {
+        "authorId": "2108702972",
+        "name": "Yunhong Wang"
+      }
+    ]
+  }
+}

data_without_website/DAMM-Diffusion__Learning_Divergence-Aware_Multi-Modal_Diffusion_Model_for_Nanoparticles_Distribution_Prediction.json

@@ -0,0 +1,57 @@
+{
+  "id": "32774",
+  "title": "DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction",
+  "track": "main",
+  "author": "Junjie Zhou; Shouju Wang; Yuxia Tang; Qi Zhu; Daoqiang Zhang; Wei Shao",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_DAMM-Diffusion_Learning_Divergence-Aware_Multi-Modal_Diffusion_Model_for_Nanoparticles_Distribution_Prediction_CVPR_2025_paper.pdf",
+  "keyword": "",
+  "abstract": "The prediction of nanoparticles (NPs) distribution is crucial for the diagnosis and treatment of tumors. Recent studies indicate that the heterogeneity of tumor microenvironment (TME) highly affects the distribution of NPs across tumors. Hence, it has become a research hotspot to generate the NPs distribution by the aid of multi-modal TME components. However, the distribution divergence among multi-modal TME components may cause side effects i.e., the best uni-modal model may outperform the joint generative model. To address the above issues, we propose a \\Divergence-Aware Multi-Modal Diffusion model (i.e., DAMM-Diffusion) to adaptively generate the prediction results from uni-modal and multi-modal branches in a unified network. In detail, the uni-modal branch is composed of the U-Net architecture while the multi-modal branch extends it by introducing two novel fusion modules i.e., Multi-Modal Fusion Module (MMFM) and Uncertainty-Aware Fusion Module (UAFM). Specifically, the MMFM is proposed to fuse features from multiple modalities, while the UAFM module is introduced to learn the uncertainty map for cross-attention computation. Following the individual prediction results from each branch, the Divergence-Aware Multi-Modal Predictor (DAMMP) module is proposed to assess the consistency of multi-modal data with the uncertainty map, which determines whether the final prediction results come from  multi-modal or uni-modal predictions. We predict the NPs distribution given the TME components of tumor vessels and cell nuclei, and the experimental results show that DAMM-Diffusion can generate the distribution of NPs with higher accuracy than the comparing methods. Additional results on the multi-modal brain image synthesis task further validate the effectiveness of the proposed method.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2025
+  },
+  "github_base": "https://github.com/JJ-ZHOU-Code/DAMM-Diffusion",
+  "template": null,
+  "category": "07. Generative Model",
+  "is_done": true,
+  "timestamp": "2025-08-07T10:55:57.320156",
+  "log": {
+    "timestamp": "2025-08-07T10:55:57.320156",
+    "stage": "special situation",
+    "note": "论文没有项目主页但找到了GitHub相关信息"
+  },
+  "citation_data": {
+    "original_title": "DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction",
+    "matched_title": "DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction",
+    "citation_count": 1,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2025,
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+        "authorId": "2321709525",
+        "name": "Junjie Zhou"
+      },
+      {
+        "authorId": "2301074039",
+        "name": "Shouju Wang"
+      },
+      {
+        "authorId": "2238945905",
+        "name": "Yuxia Tang"
+      },
+      {
+        "authorId": "2307992599",
+        "name": "Qi Zhu"
+      },
+      {
+        "authorId": "2280179032",
+        "name": "Daoqiang Zhang"
+      },
+      {
+        "authorId": "100769021",
+        "name": "Wei Shao"
+      }
+    ]
+  }
+}

data_without_website/DPU__Dynamic_Prototype_Updating_for_Multimodal_Out-of-Distribution_Detection.json

@@ -0,0 +1,58 @@
+{
+  "id": "33698",
+  "title": "DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection",
+  "track": "main",
+  "author": "Shawn Li; Huixian Gong; Hao Dong; Tiankai Yang; Zhengzhong Tu; Yue Zhao",
+  "pdf": "https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DPU_Dynamic_Prototype_Updating_for_Multimodal_Out-of-Distribution_Detection_CVPR_2025_paper.pdf",
+  "keyword": "",
+  "abstract": "Out-of-distribution (OOD) detection is crucial for ensuring the robustness of machine learning models by identifying samples that deviate from the training distribution. While traditional OOD detection has predominantly focused on single-modality inputs, such as images, recent advancements in multimodal models have shown the potential of utilizing multiple modalities (e.g., video, optical flow, audio) to improve detection performance. However, existing approaches often neglect intra-class variability within in-distribution (ID) data, assuming that samples of the same class are perfectly cohesive and consistent. This assumption can lead to performance degradation, especially when prediction discrepancies are indiscriminately amplified across all samples. To address this issue, we propose Dynamic Prototype Updating (DPU), a novel plug-and-play framework for multimodal OOD detection that accounts for intra-class variations. Our method dynamically updates class center representations for each class by measuring the variance of similar samples within each batch, enabling tailored adjustments. This approach allows us to intensify prediction discrepancies based on the updated class centers, thereby enhancing the model's robustness and generalization across different modalities. Extensive experiments on two tasks, five datasets, and nine base OOD algorithms demonstrate that DPU significantly improves OOD detection performances, setting a new state-of-the-art in multimodal OOD detection, including improvements up to 80% in Far-OOD detection.To improve accessibility and reproducibility, our code is released anonymously at https://anonymous.4open.science/r/CVPR-9177.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "09. Multimodal Learning",
+  "is_done": true,
+  "timestamp": "2025-08-04T02:45:25.056635",
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+  "html_fail": null,
+  "citation_data": {
+    "original_title": "DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection",
+    "matched_title": "DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection",
+    "citation_count": 10,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2156060734",
+        "name": "Li Li"
+      },
+      {
+        "authorId": "2330398673",
+        "name": "Huixian Gong"
+      },
+      {
+        "authorId": "2330540738",
+        "name": "Hao Dong"
+      },
+      {
+        "authorId": "2330647341",
+        "name": "Tiankai Yang"
+      },
+      {
+        "authorId": "2330397762",
+        "name": "Zhengzhong Tu"
+      },
+      {
+        "authorId": "2267429303",
+        "name": "Yue Zhao"
+      }
+    ]
+  }
+}

data_without_website/DUEL__Duplicate_Elimination_on_Active_Memory_for_Self-Supervised_Class-Imbalanced_Learning.json

@@ -0,0 +1,58 @@
+{
+  "id": "article-29040",
+  "title": "DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning",
+  "track": "main",
+  "author": "Won-Seok Choi; Hyundo Lee; Dong-Sig Han; Junseok Park; Heeyeon Koo; Byoung-Tak Zhang",
+  "pdf": "https://ojs.aaai.org/index.php/AAAI/article/view/29040/29969",
+  "keyword": "",
+  "abstract": "Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw data often leads to overfitting towards frequently occurring class information. To address class imbalances cost-efficiently, we propose an active data filtering process during self-supervised pre-training in our novel framework, Duplicate Elimination (DUEL). This framework integrates an active memory inspired by human working memory and introduces distinctiveness information, which measures the diversity of the data in the memory, to optimize both the feature extractor and the memory. The DUEL policy, which replaces the most duplicated data with new samples, aims to enhance the distinctiveness information in the memory and thereby mitigate class imbalances. We validate the effectiveness of the DUEL framework in class-imbalanced environments, demonstrating its robustness and providing reliable results in downstream tasks. We also analyze the role of the DUEL policy in the training process through various metrics and visualizations.",
+  "conference": {
+    "name": "AAAI",
+    "year": 2024
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-05T07:55:28.216137",
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+  "html_fail": null,
+  "citation_data": {
+    "original_title": "DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning",
+    "matched_title": "DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning",
+    "citation_count": 1,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
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+        "authorId": "2284397990",
+        "name": "Won-Seok Choi"
+      },
+      {
+        "authorId": "2109160804",
+        "name": "Hyun-Dong Lee"
+      },
+      {
+        "authorId": "47894241",
+        "name": "Dong-Sig Han"
+      },
+      {
+        "authorId": "2109220128",
+        "name": "Junseok Park"
+      },
+      {
+        "authorId": "2284066101",
+        "name": "Heeyeon Koo"
+      },
+      {
+        "authorId": "2285628750",
+        "name": "Byoung-Tak Zhang"
+      }
+    ]
+  }
+}

data_without_website/Deep_Optics_for_Single-Shot_High-Dynamic-Range_Imaging.json

@@ -0,0 +1,50 @@
+{
+  "id": "",
+  "title": "Deep Optics for Single-Shot High-Dynamic-Range Imaging",
+  "track": "main",
+  "author": "Christopher A. Metzler;  Hayato Ikoma;  Yifan Peng;  Gordon Wetzstein",
+  "pdf": "https://openaccess.thecvf.com/content_CVPR_2020/papers/Metzler_Deep_Optics_for_Single-Shot_High-Dynamic-Range_Imaging_CVPR_2020_paper.pdf",
+  "keyword": "",
+  "abstract": "High-dynamic-range (HDR) imaging is crucial for many applications. Yet, acquiring HDR images with a single shot remains a challenging problem. Whereas modern deep learning approaches are successful at hallucinating plausible HDR content from a single low-dynamic-range (LDR) image, saturated scene details often cannot be faithfully recovered. Inspired by recent deep optical imaging approaches, we interpret this problem as jointly training an optical encoder and electronic decoder where the encoder is parameterized by the point spread function (PSF) of the lens, the bottleneck is the sensor with a limited dynamic range, and the decoder is a convolutional neural network (CNN). The lens surface is then jointly optimized with the CNN in a training phase; we fabricate this optimized optical element and attach it as a hardware add-on to a conventional camera during inference. In extensive simulations and with a physical prototype, we demonstrate that this end-to-end deep optical imaging approach to single-shot HDR imaging outperforms both purely CNN-based approaches and other PSF engineering approaches.",
+  "conference": {
+    "name": "CVPR",
+    "year": 2020
+  },
+  "template": null,
+  "category": "05. 3D Vision and Computational Graphics",
+  "is_done": true,
+  "timestamp": "2025-08-04T09:58:53.469161",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
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+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Deep Optics for Single-Shot High-Dynamic-Range Imaging",
+    "matched_title": "Deep Optics for Single-Shot High-Dynamic-Range Imaging",
+    "citation_count": 163,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2019,
+    "authors": [
+      {
+        "authorId": "49496926",
+        "name": "Christopher A. Metzler"
+      },
+      {
+        "authorId": "9375150",
+        "name": "H. Ikoma"
+      },
+      {
+        "authorId": "2111014438",
+        "name": "Yifan Peng"
+      },
+      {
+        "authorId": "1731170",
+        "name": "Gordon Wetzstein"
+      }
+    ]
+  }
+}

data_without_website/Deep_Regression_Unlearning.json

@@ -0,0 +1,51 @@
+{
+  "id": "oJANAXYc18",
+  "title": "Deep Regression Unlearning",
+  "track": "main",
+  "author": "Ayush Kumar Tarun;Vikram Singh Chundawat;Murari Mandal;Mohan Kankanhalli",
+  "pdf": "https://openreview.net/pdf?id=oJANAXYc18",
+  "keyword": "",
+  "abstract": "With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models. In this work, we explore unlearning for the regression problem, particularly in deep learning models. Unlearning in classification and simple linear regression has been considerably investigated. However, unlearning in deep regression models largely remains an untouched problem till now. In this work, we introduce deep regression unlearning methods that generalize well and are robust to privacy attacks. We propose the Blindspot unlearning method which uses a novel weight optimization process. A randomly initialized model, partially exposed to the retain samples and a copy of the original model are used together to selectively imprint knowledge about the data that we wish to keep and scrub off the information of the data we wish to forget. We also propose a Gaussian fine tuning method for regression unlearning. The existing unlearning metrics for classification are not directly applicable to regression unlearning. Therefore, we adapt these metrics for the regression setting. We conduct regression unlearning experiments for computer vision, natural language processing and forecasting applications. Our methods show excellent performance for all these datasets across all the metrics. Source code: https://github.com/ayu987/deep-regression-unlearning",
+  "conference": {
+    "name": "ICML",
+    "year": 2023
+  },
+  "Internet_problem": "https://github.com/ayu987/",
+  "template": null,
+  "category": "10. Trustworthy and Ethical AI",
+  "is_done": true,
+  "timestamp": "2025-08-05T08:00:58.056734",
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+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Deep Regression Unlearning",
+    "matched_title": "Deep Regression Unlearning",
+    "citation_count": 38,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "2141034263",
+        "name": "Ayush K Tarun"
+      },
+      {
+        "authorId": "2141033650",
+        "name": "Vikram S Chundawat"
+      },
+      {
+        "authorId": "2113781247",
+        "name": "Murari Mandal"
+      },
+      {
+        "authorId": "1744045",
+        "name": "Mohan Kankanhalli"
+      }
+    ]
+  }
+}

data_without_website/Defining_and_Measuring_Disentanglement_for_non-Independent_Factors_of_Variation.json

@@ -0,0 +1,54 @@
+{
+  "id": "3Mq1tY75nv",
+  "title": "Defining and Measuring Disentanglement for non-Independent Factors of Variation",
+  "track": "main",
+  "author": "Antonio Almudévar;Alfonso Ortega;Luis Vicente;Antonio Miguel;Eduardo Lleida",
+  "pdf": "https://openreview.net/pdf?id=3Mq1tY75nv",
+  "keyword": "",
+  "abstract": "Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it separates the different factors  of variation in a way that is understandable to humans. Definitions of disentanglement and metrics to measure it usually assume that the factors  of variation are independent of each other. However, this is generally false in the real world, which limits the use of these definitions and metrics to very specific and unrealistic scenarios. In this paper we give a definition of disentanglement based on information theory that is also valid when the factors are not independent. Furthermore, we demonstrate that this definition is equivalent to having a representation composed of minimal and sufficient variables. Finally, we propose a method to measure the degree of disentanglement from the given definition that works when the factors are not independent. We show through different experiments that the method proposed in this paper correctly measures disentanglement with independent and non-independent factors, while other methods fail in the latter scenario.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "03. ML Theory and Optimization",
+  "is_done": true,
+  "timestamp": "2025-08-05T01:51:48.771189",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Defining and Measuring Disentanglement for non-Independent Factors of Variation",
+    "matched_title": "Defining and Measuring Disentanglement for non-Independent Factors of Variation",
+    "citation_count": 1,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2024,
+    "authors": [
+      {
+        "authorId": "2307466951",
+        "name": "Antonio Almud'evar"
+      },
+      {
+        "authorId": "2183523082",
+        "name": "Alfonso Ortega"
+      },
+      {
+        "authorId": "2256553090",
+        "name": "Luis Vicente"
+      },
+      {
+        "authorId": "145157834",
+        "name": "A. Miguel"
+      },
+      {
+        "authorId": "1783191",
+        "name": "EDUARDO LLEIDA SOLANO"
+      }
+    ]
+  }
+}

data_without_website/Demystifying_Limited_Adversarial_Transferability_in_Automatic_Speech_Recognition_Systems.json

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+{
+  "id": "l5aSHXi8jG5",
+  "title": "Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems",
+  "track": "main",
+  "author": "Hadi Abdullah;Aditya Karlekar;Vincent Bindschaedler;Patrick Traynor",
+  "pdf": "https://openreview.net/pdf?id=l5aSHXi8jG5",
+  "keyword": "",
+  "abstract": "The targeted transferability of adversarial samples enables attackers to exploit black-box models in the real-world. The most popular method to produce these adversarial samples is optimization attacks, which have been shown to achieve a high level of transferability in some domains. However, recent research has demonstrated that these attack samples fail to transfer when applied to Automatic Speech Recognition Systems (ASRs). In this paper, we investigate factors preventing this transferability via exhaustive experimentation. To do so, we perform an ablation study on each stage of the ASR pipeline. We discover and quantify six factors (i.e., input type, MFCC, RNN, output type, and vocabulary and sequence sizes) that impact the targeted transferability of optimization attacks against ASRs. Future research can leverage our findings to build ASRs that are more robust to other transferable attack types (e.g., signal processing attacks), or to modify architectures in other domains to reduce their exposure to targeted transferability of optimization attacks.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2022
+  },
+  "template": null,
+  "category": "08. Speech and Audio Processing",
+  "is_done": true,
+  "timestamp": "2025-08-04T03:34:45.864346",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems",
+    "matched_title": "Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems",
+    "citation_count": 8,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "47994360",
+        "name": "H. Abdullah"
+      },
+      {
+        "authorId": "1412664252",
+        "name": "Aditya Karlekar"
+      },
+      {
+        "authorId": "3094927",
+        "name": "Vincent Bindschaedler"
+      },
+      {
+        "authorId": "145744451",
+        "name": "Patrick Traynor"
+      }
+    ]
+  }
+}

data_without_website/Dissecting_Gradient_Masking_and_Denoising_in_Diffusion_Models_for_Adversarial_Purification.json

@@ -0,0 +1,27 @@
+{
+  "id": "Lxc4nBkJuq",
+  "title": "Dissecting Gradient Masking and Denoising in Diffusion Models for Adversarial Purification",
+  "track": "main",
+  "author": "Liu Yuezhang;Xue-Xin Wei",
+  "pdf": "https://openreview.net/pdf?id=Lxc4nBkJuq",
+  "keyword": "",
+  "abstract": "Diffusion models exhibit remarkable empirical robustness in adversarial purification. The mechanisms underlying such improvements remain unclear. It is possible that diffusion models effectively purify the adversarial examples via the learned stimuli prior. Alternatively, the substantial randomness added in the diffusion models may cause gradient masking that contaminates the empirical estimate of adversarial robustness. Here, we seek to dissect the contribution of these two potential factors. Theoretically, we illustrate how a purification system with randomness can cause gradient masking, which can not be addressed by the standard expectation-over-time (EOT) method. Inspired by this, we propose and justify that a simple procedure, randomness replay, can provide a better robustness estimate when randomness is involved. Experimentally, we verify that gradient masking indeed happens under previous evaluations of diffusion models. After properly controlling the effect of randomness, the reverse-only diffusion model (RevPure) provides a better robustness improvement than the previous DiffPure framework, suggesting that the robustness improvement is solely attributed to the reverse process. Furthermore, our analyses reveal that robustness improvement is caused by a sequential denoising mechanism that transforms the stimulus to a direction orthogonal to the original adversarial perturbation, rather than reducing the $\\ell_2$ distance between the transformed and clean stimuli. Our results shed new light on the mechanisms underlying the empirical robustness from diffusion models, and shall inform future development of more efficient adversarial purification systems.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2024
+  },
+  "template": null,
+  "category": "07. Generative Model",
+  "is_done": true,
+  "timestamp": "2025-08-04T06:22:50.579051",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "citation_count": 0
+  }
+}

data_without_website/Dual-level_Prototypes_Guidance_for_Single-frame_Temporal_Action_Localization.json

@@ -0,0 +1,27 @@
+{
+  "id": "PageLgQlXz",
+  "title": "Dual-level Prototypes Guidance for Single-frame Temporal Action Localization",
+  "track": "main",
+  "author": "Jinrong Sheng;Wenshi Li;Ao Li;Yongxin Ge",
+  "pdf": "https://openreview.net/pdf?id=PageLgQlXz",
+  "keyword": "",
+  "abstract": "In recent, single-frame temporal action localization (STAL) has captured the attention of the computer vision community. Due to the sparse single-frame annotations, current STAL methods generally employ pseudo-labels strategies to bridge the gap between weakly-supervised methods and fully-supervised methods. However, these methods derive pseudo-labels from single-frame of the corresponding instances, yet the intra-class affinity from the current single-frame to other action snippets remains neglected. To capitalize on this affinity, we design a dual-level prototypes guidance (DPG) method with the graph matching random walk (Gm-Rw) algorithm to achieve instance-level and video-level prototype guidance for pseudo-labels refinement. For instance-level guidance, the Gm-Rw exploits the high affinity prototype among instances of the current video to build intra-class associations. For video-level guidance, an online memory bank is constructed to iteratively summarize more discriminative prototype. After Gm-Rw builds affinity among intra-class videos, an exponential moving average (EMA) mechanism is designed to achieve dual-level prototypes guidance for pseudo-labels refinement. Notably, the dual-level guidance is mutually reinforcing, prompting us to propose a novel adaptive collaborative strategy (ACS) for dynamic optimization. Extensive experiments on THUMOS14, GTEA, BEOID, and ActivityNet1.3 reveal that our method significantly outperforms state-of-the-art methods.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2025
+  },
+  "template": null,
+  "category": "05. 3D Vision and Computational Graphics",
+  "is_done": true,
+  "timestamp": "2025-08-05T16:45:14.128054",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "citation_count": 0
+  }
+}

data_without_website/DynamicVAE__Decoupling_Reconstruction_Error_and_Disentangled_Representation_Learning.json

@@ -0,0 +1,58 @@
+{
+  "id": "6htjOqus6C3",
+  "title": "DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning",
+  "track": "main",
+  "author": "Huajie Shao;Haohong Lin;Qinmin Yang;Shuochao Yao;Han Zhao;Tarek Abdelzaher",
+  "pdf": "https://openreview.net/pdf?id=6htjOqus6C3",
+  "keyword": "",
+  "abstract": "This paper challenges the common assumption that the weight $\\beta$, in $\\beta$-VAE, should be larger than $1$ in order to effectively disentangle latent factors. We demonstrate that $\\beta$-VAE, with $\\beta < 1$, can not only attain good disentanglement but also significantly improve reconstruction accuracy via dynamic control. The paper \\textit{removes the inherent trade-off} between reconstruction accuracy and disentanglement for $\\beta$-VAE. Existing methods, such as $\\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence term in the objective function, leading to high reconstruction errors for the sake of better disentanglement. To mitigate this problem, a ControlVAE has recently been developed that dynamically tunes the KL-divergence weight in an attempt to \\textit{control the trade-off} to more a favorable point. However, ControlVAE fails to eliminate the conflict between the need for a large $\\beta$ (for disentanglement) and the need for a small $\\beta$ (for smaller reconstruction error). Instead, we propose DynamicVAE that maintains a different $\\beta$ at different stages of training, thereby \\textit{decoupling disentanglement and reconstruction accuracy}. In order to evolve the weight, $\\beta$, along a trajectory that enables such decoupling, DynamicVAE leverages a modified incremental PI (proportional-integral) controller, a variant of proportional-integral-derivative controller (PID) algorithm, and employs a moving average as well as a hybrid annealing method to evolve the value of KL-divergence smoothly in a tightly controlled fashion. We theoretically prove the stability of the proposed approach. Evaluation results on three benchmark datasets demonstrate that DynamicVAE significantly improves the reconstruction accuracy while achieving disentanglement comparable to the best of existing methods. The results verify that our method can separate disentangled representation learning and reconstruction, removing the inherent tension between the two. ",
+  "conference": {
+    "name": "ICLR",
+    "year": 2021
+  },
+  "template": null,
+  "category": "07. Generative Model",
+  "is_done": true,
+  "timestamp": "2025-08-04T08:48:44.976498",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning",
+    "matched_title": "DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning",
+    "citation_count": 6,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2020,
+    "authors": [
+      {
+        "authorId": "3395273",
+        "name": "Huajie Shao"
+      },
+      {
+        "authorId": "2258702",
+        "name": "Haohong Lin"
+      },
+      {
+        "authorId": "2149534610",
+        "name": "Qinmin Yang"
+      },
+      {
+        "authorId": "2325031",
+        "name": "Shuochao Yao"
+      },
+      {
+        "authorId": "2146233072",
+        "name": "Han Zhao"
+      },
+      {
+        "authorId": "1730531",
+        "name": "T. Abdelzaher"
+      }
+    ]
+  }
+}

data_without_website/Efficient_ConvBN_Blocks_for_Transfer_Learning_and_Beyond.json

@@ -0,0 +1,62 @@
+{
+  "id": "lHZm9vNm5H",
+  "title": "Efficient ConvBN Blocks for Transfer Learning and Beyond",
+  "track": "main",
+  "author": "Kaichao You;Guo Qin;Anchang Bao;Meng Cao;Ping Huang;Jiulong Shan;Mingsheng Long",
+  "pdf": "https://openreview.net/pdf?id=lHZm9vNm5H",
+  "keyword": "",
+  "abstract": "Convolution-BatchNorm (ConvBN) blocks are integral components in various computer vision tasks and other domains. A ConvBN block can operate in three modes: Train, Eval, and Deploy. While the Train mode is indispensable for training models from scratch, the Eval mode is suitable for transfer learning and beyond, and the Deploy mode is designed for the deployment of models. This paper focuses on the trade-off between stability and efficiency in ConvBN blocks: Deploy mode is efficient but suffers from training instability; Eval mode is widely used in transfer learning but lacks efficiency. To solve the dilemma, we theoretically reveal the reason behind the diminished training stability observed in the Deploy mode. Subsequently, we propose a novel Tune mode to bridge the gap between Eval mode and Deploy mode. The proposed Tune mode is as stable as Eval mode for transfer learning, and its computational efficiency closely matches that of the Deploy mode. Through extensive experiments in object detection, classification, and adversarial example generation across $5$ datasets and $12$ model architectures, we demonstrate that the proposed Tune mode retains the performance while significantly reducing GPU memory footprint and training time, thereby contributing efficient ConvBN blocks for transfer learning and beyond. Our method has been integrated into both PyTorch (general machine learning framework) and MMCV/MMEngine (computer vision framework). Practitioners just need one line of code to enjoy our efficient ConvBN blocks thanks to PyTorch's builtin machine learning compilers.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2024
+  },
+  "template": null,
+  "category": "12. ML Systems and Infrastructure",
+  "is_done": true,
+  "timestamp": "2025-08-05T10:32:52.961723",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Efficient ConvBN Blocks for Transfer Learning and Beyond",
+    "matched_title": "Efficient ConvBN Blocks for Transfer Learning and Beyond",
+    "citation_count": 1,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2023,
+    "authors": [
+      {
+        "authorId": "89470893",
+        "name": "Kaichao You"
+      },
+      {
+        "authorId": "2217957790",
+        "name": "Guo Qin"
+      },
+      {
+        "authorId": "2217954215",
+        "name": "Anchang Bao"
+      },
+      {
+        "authorId": "2057073717",
+        "name": "Mengsi Cao"
+      },
+      {
+        "authorId": "1704261333",
+        "name": "Ping-Chia Huang"
+      },
+      {
+        "authorId": "2091600962",
+        "name": "Jiulong Shan"
+      },
+      {
+        "authorId": "2054275000",
+        "name": "Mingsheng Long"
+      }
+    ]
+  }
+}

data_without_website/Efficient_Ensembles_of_Graph_Neural_Networks.json

@@ -0,0 +1,46 @@
+{
+  "id": "lTiW8Jet8t",
+  "title": "Efficient Ensembles of Graph Neural Networks",
+  "track": "main",
+  "author": "Amrit Nagarajan;Jacob R. Stevens;Anand Raghunathan",
+  "pdf": "https://openreview.net/pdf?id=lTiW8Jet8t",
+  "keyword": "",
+  "abstract": "Graph Neural Networks (GNNs) have enabled the power of deep learning to be applied to inputs beyond the Euclidean domain, with applications ranging from social networks and product recommendation engines to the life sciences. GNNs, like other classes of machine learning models, benefit from ensemble learning, wherein multiple models are combined to provide higher accuracy and robustness than single models. However, ensembles suffer from significantly higher inference processing and storage requirements, limiting their use in practical applications. In this work, we leverage the unique characteristics of GNNs to overcome these overheads, creating efficient ensemble GNNs that are faster than even single models at inference time. We observe that during message passing, nodes that are incorrectly classified (error nodes) also end up adversely affecting the representations of other nodes in their neighborhood. This error propagation also makes GNNs more difficult to approximate (e.g., through pruning) for efficient inference. We propose a technique to create ensembles of diverse models, and further propose Error Node Isolation (ENI), which prevents error nodes from sending messages to (and thereby influencing) other nodes. In addition to improving accuracy, ENI also leads to a significant reduction in the memory footprint and the number of arithmetic operations required to evaluate the computational graphs of all neighbors of error nodes. Remarkably, these savings outweigh even the overheads of using multiple models in the ensemble. A second key benefit of ENI is that it  enhances the resilience of GNNs to approximations. Consequently, we propose Edge Pruning and Network Pruning techniques that target both the input graph and the neural networks used to process the graph. Our experiments on GNNs for transductive and inductive node classification demonstrate that ensembles with ENI are simultaneously more accurate (by up to 4.6% and 3.8%) and  faster (by up to 2.8$\\times$ and 5.7$\\times$) when compared to the best-performing single models and ensembles without ENI, respectively. In addition, GNN ensembles with ENI are consistently more accurate than single models and ensembles without ENI when subject to pruning, leading to additional speedups of up to 5$\\times$ with no loss in accuracy.",
+  "conference": {
+    "name": "ICLR",
+    "year": 2022
+  },
+  "template": null,
+  "category": "01. Deep Learning Architectures and Methods",
+  "is_done": true,
+  "timestamp": "2025-08-04T03:37:10.914449",
+  "rule_paper_possible_url": null,
+  "github_base": null,
+  "llm_believed_url": null,
+  "rule_base_possible_url": null,
+  "confirmed_url": null,
+  "Internet_fail": null,
+  "html_fail": null,
+  "citation_data": {
+    "original_title": "Efficient Ensembles of Graph Neural Networks",
+    "matched_title": "Efficient ensembles of graph neural networks",
+    "citation_count": 6,
+    "similarity": 1.0,
+    "source": "semantic_scholar",
+    "year": 2022,
+    "authors": [
+      {
+        "authorId": "1992685082",
+        "name": "Amrit Nagarajan"
+      },
+      {
+        "authorId": "38791934",
+        "name": "Jacob R. Stevens"
+      },
+      {
+        "authorId": "145291370",
+        "name": "A. Raghunathan"
+      }
+    ]
+  }
+}