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ee08c54c-7f3c-4bca-a03f-4689594e7bd7 | towards-a-fair-comparison-and-realistic | 2205.12569 | null | https://arxiv.org/abs/2205.12569v2 | https://arxiv.org/pdf/2205.12569v2.pdf | Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning | As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date, often reporting impresive detection performances. However, the lack of reproduci... | ['Jose Miguel-Alonso', 'Alexander Mendiburu', 'Usue Mori', 'Borja Molina-Coronado'] | 2022-05-25 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.41376236e-01 -2.67057657e-01 -5.43922782e-01 -3.75055261e-02
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-1.52304545e-01 5.00982031e-02 3.40363830e-01 2.05464736... | [14.45086669921875, 9.71253776550293] |
ac823ae1-8db4-43e8-8c32-4e2ebc4df46d | chinese-temporal-tagging-with-heideltime | null | null | https://aclanthology.org/E14-4026 | https://aclanthology.org/E14-4026.pdf | Chinese Temporal Tagging with HeidelTime | null | ['Jannik Str{\\"o}tgen', 'Michael Gertz', 'Hui Li', 'Julian Zell'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.181768894195557, 3.6178677082061768] |
dec797ba-bc8b-479a-96f3-7f05386d0350 | solo-t-dirl-socially-aware-dynamic-local | 2209.07996 | null | https://arxiv.org/abs/2209.07996v1 | https://arxiv.org/pdf/2209.07996v1.pdf | SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learning | This work proposes a new framework for a socially-aware dynamic local planner in crowded environments by building on the recently proposed Trajectory-ranked Maximum Entropy Deep Inverse Reinforcement Learning (T-MEDIRL). To address the social navigation problem, our multi-modal learning planner explicitly considers soc... | ['Maani Ghaffari', 'Tribhi Kathuria', 'Theodor Chakhachiro', 'Yifan Xu'] | 2022-09-16 | null | null | null | null | ['social-navigation'] | ['robots'] | [-3.94642413e-01 4.47326183e-01 1.59950256e-01 -1.46167606e-01
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-5.08513868e-01 6.16523385e-01 6.84129953e-01 -7.54295230... | [4.761853218078613, 0.986661970615387] |
c5c8d89b-f77a-4e69-bfdd-0b349feb6ca8 | think-positive-towards-twitter-sentiment | null | null | https://aclanthology.org/S14-2115 | https://aclanthology.org/S14-2115.pdf | Think Positive: Towards Twitter Sentiment Analysis from Scratch | null | ["C{\\'\\i}cero dos Santos"] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.368536472320557, 3.700489044189453] |
d983b636-0e9e-4ae4-bf66-290f583660b5 | compact-transformer-tracker-with-correlative | 2301.10938 | null | https://arxiv.org/abs/2301.10938v1 | https://arxiv.org/pdf/2301.10938v1.pdf | Compact Transformer Tracker with Correlative Masked Modeling | Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism. Most recent advances focus on exploring attention mechanism variants for better information aggregation. ... | ['Wei Yang', 'Yi-Ping Phoebe Chen', 'Junqing Yu', 'Run Luo', 'Zikai Song'] | 2023-01-26 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-1.81020141e-01 -1.78241864e-01 -3.14739883e-01 9.31216218e-03
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1.80661008e-01 1.75962925e-01 6.95439696e-01 -6.87195435... | [6.295228004455566, -2.124281167984009] |
a1801c19-ba2a-4653-a948-4f93b21e1a17 | analyzing-the-impact-of-sars-cov-2-variants | 2206.12309 | null | https://arxiv.org/abs/2206.12309v1 | https://arxiv.org/pdf/2206.12309v1.pdf | Analyzing the impact of SARS-CoV-2 variants on respiratory sound signals | The COVID-19 outbreak resulted in multiple waves of infections that have been associated with different SARS-CoV-2 variants. Studies have reported differential impact of the variants on respiratory health of patients. We explore whether acoustic signals, collected from COVID-19 subjects, show computationally distinguis... | ['Murali Alagesan', 'Sadhana Gonuguntla', 'Suhail K K', 'Sahiti Nori', 'Chandrakiran C', 'Sriram Ganapathy', 'Pravin Mote', 'Srikanth Raj Chetupalli', 'Neeraj Kumar Sharma', 'Debottam Dutta', 'Debarpan Bhattacharya'] | 2022-06-24 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.16130927e-01 -6.65273547e-01 4.70822513e-01 9.65962037e-02
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-4.54874367e-01 6.96908653e-01 1.87023893e-01 1.30650203... | [14.443401336669922, 3.8904645442962646] |
907d21cb-dec1-4452-9a75-f09b3fa84db9 | first-steps-towards-the-semi-automatic | null | null | https://aclanthology.org/L12-1037 | https://aclanthology.org/L12-1037.pdf | First Steps towards the Semi-automatic Development of a Wordformation-based Lexicon of Latin | Although lexicography of Latin has a long tradition dating back to ancient grammarians, and almost all Latin grammars devote to wordformation at least one part of the section(s) concerning morphology, none of the today available lexical resources and NLP tools of Latin feature a wordformation-based organization of the ... | ['Marco Passarotti', 'Francesco Mambrini'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.25790192e-02 2.40773678e-01 -3.35922569e-01 -2.85028249e-01
-3.76087964e-01 -1.03773642e+00 4.94379699e-01 8.05461764e-01
-7.84767389e-01 8.38809431e-01 3.23901594e-01 -8.16475093e-01
-3.43777388e-01 -7.21590579e-01 6.07526563e-02 -3.74043018e-01
2.07354739e-01 8.64233673e-01 3.12577069e-01 -2.98765033... | [10.388833045959473, 10.16643238067627] |
8c29ff3c-5b8f-4c5c-91a4-cd40fa4e211c | buffet-benchmarking-large-language-models-for | 2305.14857 | null | https://arxiv.org/abs/2305.14857v1 | https://arxiv.org/pdf/2305.14857v1.pdf | BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer | Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English. To facilitate research on few-shot cross-lingual transfer, we introduce a new benchmark, called BUFFET, which unifies 15 diverse tasks across 54 languages in a sequenc... | ['Hannaneh Hajishirzi', 'Sebastian Ruder', 'Yulia Tsvetkov', 'Machel Reid', 'Hila Gonen', 'Terra Blevins', 'Xinyan Velocity Yu', 'Sneha Kudugunta', 'Akari Asai'] | 2023-05-24 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 3.64736468e-02 -3.97214651e-01 -5.50868928e-01 -7.09003985e-01
-1.49094892e+00 -7.91991591e-01 6.84361577e-01 -2.82249004e-02
-9.49601889e-01 7.23853827e-01 4.49917883e-01 -7.27462649e-01
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6.85353205e-02 6.99701786e-01 2.00951263e-01 -7.22975671... | [11.061476707458496, 9.732359886169434] |
03c4c9cc-4c9c-46e2-a55b-08c8aa42c1a0 | computationally-efficient-approach-for | 2211.1125 | null | https://arxiv.org/abs/2211.11250v1 | https://arxiv.org/pdf/2211.11250v1.pdf | Computationally Efficient Approach for Preheating of Battery Electric Vehicles before Fast Charging in Cold Climates | This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management (TM) system has been considered, including a high-voltage coolant heater (HVCH) for the battery a... | ['Jonas Fredriksson', 'Viktor Larsson', 'Nikolce Murgovski', 'Jimmy Forsman', 'Ahad Hamednia'] | 2022-11-21 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.01594828e-01 3.65538329e-01 -1.09290406e-01 -6.72933161e-02
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3.87304336e-01 3.67186576e-01 -1.35604590e-01 6.73530176... | [5.621906280517578, 2.208503484725952] |
7e3bf49f-0a44-4b91-914f-15e09fb79d8e | a-multi-task-model-for-sentiment-aided-stance | 2211.03533 | null | https://arxiv.org/abs/2211.03533v1 | https://arxiv.org/pdf/2211.03533v1.pdf | A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets | Climate change has become one of the biggest challenges of our time. Social media platforms such as Twitter play an important role in raising public awareness and spreading knowledge about the dangers of the current climate crisis. With the increasing number of campaigns and communication about climate change through s... | ['Wolfgang Nejdl', 'Marco Fisichella', 'Apoorva Upadhyaya'] | 2022-11-07 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.19965300e-01 -4.21392843e-02 -2.33186558e-01 -5.73806524e-01
-6.41982734e-01 -5.77142775e-01 8.25315118e-01 6.71163380e-01
-3.96725088e-01 6.29460573e-01 6.40152633e-01 -5.14593959e-01
4.04030353e-01 -1.00996315e+00 -4.90393788e-01 -1.02368474e+00
4.67821807e-01 6.32344410e-02 -1.02146029e-01 -6.15042865... | [11.1021728515625, 6.926227569580078] |
6793900d-03ae-4775-b735-12a51f39e59b | a-preliminary-analysis-on-the-code-generation | 2305.09402 | null | https://arxiv.org/abs/2305.09402v1 | https://arxiv.org/pdf/2305.09402v1.pdf | A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functions | This paper evaluates the capability of two state-of-the-art artificial intelligence (AI) models, GPT-3.5 and Bard, in generating Java code given a function description. We sourced the descriptions from CodingBat.com, a popular online platform that provides practice problems to learn programming. We compared the Java co... | ['Marco Ortu', 'Silvia Bartolucci', 'Giuseppe Destefanis'] | 2023-05-16 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-1.36430591e-01 5.66016436e-01 -1.04887895e-01 -3.60417038e-01
-3.39691937e-01 -7.97416091e-01 5.89652658e-01 2.52505183e-01
2.25599110e-01 4.59123433e-01 2.18573287e-02 -8.05054486e-01
-2.37811074e-01 -9.47718084e-01 -7.72004426e-01 -3.84588279e-02
-1.36169463e-01 4.21480358e-01 -1.13129243e-01 -4.76839542... | [8.019492149353027, 7.497570514678955] |
65c239da-0edc-4602-9853-2416811d658e | discovering-hierarchical-achievements-in | 2307.03486 | null | https://arxiv.org/abs/2307.03486v1 | https://arxiv.org/pdf/2307.03486v1.pdf | Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning | Discovering achievements with a hierarchical structure on procedurally generated environments poses a significant challenge. This requires agents to possess a broad range of abilities, including generalization and long-term reasoning. Many prior methods are built upon model-based or hierarchical approaches, with the be... | ['Hyun Oh Song', 'Bumsoo Park', 'Junyoung Yeom', 'Seungyong Moon'] | 2023-07-07 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [-5.65977395e-02 3.00100833e-01 -3.35149109e-01 5.55469096e-03
-8.12699139e-01 -4.48709816e-01 9.28772628e-01 1.10768624e-01
-4.82126564e-01 9.93562758e-01 3.86440068e-01 -3.34709078e-01
-5.72265506e-01 -8.20888638e-01 -9.00527179e-01 -6.58064961e-01
-2.85755277e-01 8.61059189e-01 3.53042603e-01 -4.43270355... | [4.1005706787109375, 1.542660117149353] |
e0b5be9a-2bb2-4026-a73a-a8023461fc8d | a-unified-weight-learning-and-low-rank | 2005.04619 | null | https://arxiv.org/abs/2005.04619v4 | https://arxiv.org/pdf/2005.04619v4.pdf | A Unified Weight Learning and Low-Rank Regression Model for Robust Complex Error Modeling | One of the most important problems in regression-based error model is modeling the complex representation error caused by various corruptions and environment changes in images. For example, in robust face recognition, images are often affected by varying types and levels of corruptions, such as random pixel corruptions... | ['Jun Zhou', 'Yongsheng Gao', 'Miaohua Zhang'] | 2020-05-10 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 7.96366781e-02 -7.71582544e-01 1.72319397e-01 -4.12709951e-01
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4.72037308e-02 6.63509145e-02 6.07099608e-02 5.35426177... | [12.533388137817383, 0.38198062777519226] |
ca23a848-f2c4-4334-96ab-bbd3981e8713 | joint-alignment-of-multi-task-feature-and | 2209.04112 | null | https://arxiv.org/abs/2209.04112v1 | https://arxiv.org/pdf/2209.04112v1.pdf | Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction | Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MT... | ['Donghong Ji', 'Fei Li', 'Hao Fei', 'Shengqiong Wu', 'Jingye Li', 'Xiaochuan Shi', 'Shunjie Chen'] | 2022-09-09 | null | https://aclanthology.org/2022.coling-1.606 | https://aclanthology.org/2022.coling-1.606.pdf | coling-2022-10 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 1.58673882e-01 -1.47327349e-01 -1.92796186e-01 -6.58228755e-01
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-1.04685999e-01 5.14879785e-02 -1.72401994e-01 -1.98122382... | [12.63770866394043, 6.212916851043701] |
fa3325e6-7e00-495d-803b-4d65946dce48 | semi-supervised-formality-style-transfer-with | 2203.1362 | null | https://arxiv.org/abs/2203.13620v1 | https://arxiv.org/pdf/2203.13620v1.pdf | Semi-Supervised Formality Style Transfer with Consistency Training | Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. To address the data-scarcity problem of existing parallel datasets, previous studies tend to adopt a cycle-reconstruction scheme to utilize additional unlabeled data, where the FST mo... | ['Naoaki Okazaki', 'An Wang', 'Ao Liu'] | 2022-03-25 | null | https://aclanthology.org/2022.acl-long.321 | https://aclanthology.org/2022.acl-long.321.pdf | acl-2022-5 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 5.66969812e-01 9.59143713e-02 -2.00626910e-01 -5.19202530e-01
-1.10663497e+00 -6.80396616e-01 4.68834698e-01 -6.36319667e-02
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5.85537314e-01 2.32954830e-01 6.21245243e-02 -3.66088122... | [11.572830200195312, 9.376734733581543] |
63487603-a11e-452d-baa5-8971ff870bb9 | representing-and-extracting-knowledge-from | 2304.13084 | null | https://arxiv.org/abs/2304.13084v1 | https://arxiv.org/pdf/2304.13084v1.pdf | Representing and extracting knowledge from single cell data | Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper th... | ['Johan Henriksson', 'Sarang Chafle', 'Ionut Sebastian Mihai'] | 2023-04-25 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 4.20140177e-01 -1.71884477e-01 -3.94429527e-02 -2.75004357e-02
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-5.14652908e-01 -8.23327243e-01 -2.75130361e-01 -1.14727855e+00
-2.20113099e-01 7.47400820e-01 6.12934045e-02 -8.10439438... | [13.770212173461914, -3.0809483528137207] |
aefb8ca5-0625-4a1a-a9f2-abfd76432188 | privacy-preserving-collaborative-learning-1 | 2212.06322 | null | https://arxiv.org/abs/2212.06322v1 | https://arxiv.org/pdf/2212.06322v1.pdf | Privacy-Preserving Collaborative Learning through Feature Extraction | We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific meth... | ['Farshad Khorrami', 'Siddharth Garg', 'Prashanth Krishnamurthy', 'Hao Fu', 'Alireza Sarmadi'] | 2022-12-13 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-2.23646253e-01 1.16957434e-01 -6.36511594e-02 -5.59280992e-01
-8.45534444e-01 -1.05783749e+00 4.46995169e-01 4.53723758e-01
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-9.65053663e-02 -1.21774352e+00 -9.54130828e-01 -7.22531497e-01
-3.60726267e-01 1.24108844e-01 -1.26255870e-01 1.44357998... | [5.788498878479004, 6.682905673980713] |
d71e0acf-77a3-413a-a422-67d25ed7d199 | off-policy-action-anticipation-in-multi-agent | 2304.01447 | null | https://arxiv.org/abs/2304.01447v1 | https://arxiv.org/pdf/2304.01447v1.pdf | Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning | Learning anticipation in Multi-Agent Reinforcement Learning (MARL) is a reasoning paradigm where agents anticipate the learning steps of other agents to improve cooperation among themselves. As MARL uses gradient-based optimization, learning anticipation requires using Higher-Order Gradients (HOG), with so-called HOG m... | ['Gijs Dubbelman', 'Pavol Jancura', 'Daan de Geus', 'Ariyan Bighashdel'] | 2023-04-04 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [-2.11984843e-01 8.81644487e-02 -1.99289441e-01 2.89551228e-01
-4.08975899e-01 -4.34814334e-01 7.02313364e-01 1.60399884e-01
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-2.81464577e-01 3.07777554e-01 5.21686316e-01 -4.67549443... | [3.8535425662994385, 2.0086669921875] |
10281804-e59d-4e64-b342-395019e630e6 | multilingual-epidemic-event-extraction-from | null | null | https://aclanthology.org/2021.ranlp-main.138 | https://aclanthology.org/2021.ranlp-main.138.pdf | Multilingual Epidemic Event Extraction : From Simple Classification Methods to Open Information Extraction (OIE) and Ontology | There is an incredible amount of information available in the form of textual documents due to the growth of information sources. In order to get the information into an actionable way, it is common to use information extraction and more specifically the event extraction, it became crucial in various domains even in pu... | ['Gaël Lejeune', 'Sihem Sahnoun'] | null | null | https://aclanthology.org/2021.ranlp-1.138 | https://aclanthology.org/2021.ranlp-1.138.pdf | ranlp-2021-9 | ['epidemiology', 'open-information-extraction'] | ['medical', 'natural-language-processing'] | [ 1.48518430e-02 4.50977325e-01 1.96340576e-01 5.09107262e-02
-2.26943254e-01 -2.59576499e-01 7.96207845e-01 1.03654456e+00
-1.10438526e+00 9.22769129e-01 3.75166565e-01 -4.44977582e-01
-5.79252958e-01 -1.04816329e+00 -1.40808135e-01 -3.10402811e-01
-6.59191683e-02 1.02659559e+00 6.19092882e-01 -4.66263056... | [9.309024810791016, 8.702888488769531] |
7632c079-f6f0-495b-8d9a-6f6dec48a140 | anomalybert-self-supervised-transformer-for | 2305.04468 | null | https://arxiv.org/abs/2305.04468v1 | https://arxiv.org/pdf/2305.04468v1.pdf | AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme | Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalities in such data, it is crucial to understand the temporal context and interrelation between variables simultaneously. The anomaly detection t... | ['Myungjoo Kang', 'Imseong Park', 'Jung Hyun Ryu', 'Eunseok Yang', 'Yungi Jeong'] | 2023-05-08 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 2.67013371e-01 -3.49620074e-01 2.89508492e-01 -3.85782182e-01
-4.38502759e-01 -4.25713599e-01 3.57358843e-01 3.50697726e-01
-3.60565484e-02 3.46717119e-01 1.40026525e-01 -2.77182490e-01
-3.09481621e-02 -5.75881660e-01 -8.93139422e-01 -7.35638201e-01
-3.53370130e-01 1.03112221e-01 1.94943279e-01 -2.77253717... | [7.403461456298828, 2.6041641235351562] |
63003f97-a233-4c61-a996-117e73d93bf7 | depth-sims-semi-parametric-image-and-depth | 2203.03405 | null | https://arxiv.org/abs/2203.03405v2 | https://arxiv.org/pdf/2203.03405v2.pdf | Depth-SIMS: Semi-Parametric Image and Depth Synthesis | In this paper we present a compositing image synthesis method that generates RGB canvases with well aligned segmentation maps and sparse depth maps, coupled with an in-painting network that transforms the RGB canvases into high quality RGB images and the sparse depth maps into pixel-wise dense depth maps. We benchmark ... | ['Paul Newman', 'Matthew Gadd', 'Daniele De Martini', 'Valentina Musat'] | 2022-03-07 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 7.56124854e-01 4.20581311e-01 2.47605070e-01 -2.45000392e-01
-7.70289838e-01 -5.21307349e-01 6.31274521e-01 -1.95930809e-01
-1.84704706e-01 7.23819852e-01 1.12975299e-01 3.40217128e-02
1.18904300e-01 -1.31878972e+00 -7.36372352e-01 -4.89699841e-01
4.02040035e-01 5.74511945e-01 5.24086297e-01 -2.37675920... | [9.214765548706055, -2.9481325149536133] |
ac689baf-d199-4c58-a028-4872e3694778 | fast-rotation-search-with-stereographic | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Bustos_Fast_Rotation_Search_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Bustos_Fast_Rotation_Search_2014_CVPR_paper.pdf | Fast Rotation Search with Stereographic Projections for 3D Registration | Recently there has been a surge of interest to use branch-and-bound (bnb) optimisation for 3D point cloud registration. While bnb guarantees globally optimal solutions, it is usually too slow to be practical. A fundamental source of difficulty is the search for the rotation parameters in the 3D rigid transform. In this... | ['Tat-Jun Chin', 'David Suter', 'Alvaro Parra Bustos'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-feature-matching', 'geometric-matching'] | ['computer-vision', 'computer-vision'] | [ 1.39525130e-01 -4.33825284e-01 9.20242742e-02 -1.13267362e-01
-8.05917501e-01 -8.08453798e-01 5.20282865e-01 2.25431114e-01
-2.33981699e-01 2.41702244e-01 -2.95360833e-01 -5.03869772e-01
-2.84907818e-01 -6.74042344e-01 -5.91479480e-01 -6.15564048e-01
1.16913565e-01 8.05252671e-01 3.10752362e-01 -1.90955877... | [7.8015828132629395, -2.714897394180298] |
8621f14e-6fae-4ce1-8200-128f957e3bd4 | image-augmentation-for-satellite-images | 2207.1458 | null | https://arxiv.org/abs/2207.14580v1 | https://arxiv.org/pdf/2207.14580v1.pdf | Image Augmentation for Satellite Images | This study proposes the use of generative models (GANs) for augmenting the EuroSAT dataset for the Land Use and Land Cover (LULC) Classification task. We used DCGAN and WGAN-GP to generate images for each class in the dataset. We then explored the effect of augmenting the original dataset by about 10% in each case on m... | ['Olayiwola Arowolo', 'Opeyemi Ajayi', 'Peter Owoade', 'Oluwadara Adedeji'] | 2022-07-29 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.23449433e-01 5.79787433e-01 3.71304899e-02 -4.02989447e-01
-7.44483232e-01 -5.66275239e-01 1.11334276e+00 -5.32725394e-01
-2.33978510e-01 1.07913339e+00 3.32607836e-01 -6.83307767e-01
3.76884162e-01 -1.38598025e+00 -8.66688967e-01 -1.02855158e+00
1.90832950e-02 4.23615575e-01 -4.22650874e-01 -4.08021808... | [9.501020431518555, -1.4687211513519287] |
c21fe5e8-6d90-44c1-8477-21a94a32a4bc | ered-enhanced-text-representations-with | 2208.08954 | null | https://arxiv.org/abs/2208.08954v1 | https://arxiv.org/pdf/2208.08954v1.pdf | Ered: Enhanced Text Representations with Entities and Descriptions | External knowledge,e.g., entities and entity descriptions, can help humans understand texts. Many works have been explored to include external knowledge in the pre-trained models. These methods, generally, design pre-training tasks and implicitly introduce knowledge by updating model weights, alternatively, use it stra... | ['Yuxuan Lei', 'Shuai Ma', 'Qinghua Zhao'] | 2022-08-18 | null | null | null | null | ['entity-embeddings'] | ['methodology'] | [-4.06073369e-02 3.49490106e-01 -2.36627415e-01 -3.82467359e-01
-1.25672922e-01 -4.37867343e-01 4.66420442e-01 3.74433219e-01
-9.15429056e-01 7.00859547e-01 3.47033292e-01 -1.42760919e-02
1.47117853e-01 -8.57240319e-01 -6.07565582e-01 -4.87930536e-01
3.15848023e-01 3.04478973e-01 3.92449439e-01 -2.27576420... | [9.705371856689453, 9.066308975219727] |
d6deb77e-c920-4f0b-9beb-7dedc68ec23d | attention-neural-model-for-temporal-relation | null | null | https://aclanthology.org/W19-1917 | https://aclanthology.org/W19-1917.pdf | Attention Neural Model for Temporal Relation Extraction | Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art... | ['Li-Wei Wang', 'Vipin Chaudhary', 'Sijia Liu', 'Hongfang Liu'] | 2019-06-01 | null | null | null | ws-2019-6 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.79401264e-01 9.11918104e-01 -5.05118310e-01 -3.24309111e-01
-6.05690241e-01 1.87853009e-01 4.95870143e-01 5.21408796e-01
-7.11315453e-01 9.36804175e-01 9.03656304e-01 -4.51608330e-01
-5.21439135e-01 -4.61632520e-01 -9.15262401e-02 -1.32505715e-01
-5.73025405e-01 5.84854960e-01 1.22795343e-01 -6.18501365... | [8.485808372497559, 8.996788024902344] |
20d24555-d9c8-4198-897a-5d06d04bdb69 | assessment-framework-for-deepfake-detection | 2304.06125 | null | https://arxiv.org/abs/2304.06125v1 | https://arxiv.org/pdf/2304.06125v1.pdf | Assessment Framework for Deepfake Detection in Real-world Situations | Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and have exhibited remarkable performance. However, the performance... | ['Touradj Ebrahimi', 'Yuhang Lu'] | 2023-04-12 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-2.99717616e-02 -2.56566882e-01 -2.47167535e-02 -2.03544319e-01
-3.81227881e-01 -4.10320997e-01 9.33561087e-01 -8.61519501e-02
-2.37715349e-01 3.00642639e-01 -2.80982673e-01 3.80905927e-03
-4.31757160e-02 -5.19585550e-01 -6.67301476e-01 -8.79352868e-01
-2.49463126e-01 -2.92993575e-01 1.23629794e-01 -7.12372363... | [12.645977973937988, 1.0946866273880005] |
3b511172-4ed2-42b5-b162-fec2ee3d3ef5 | a-comparative-study-of-graph-matching | 2207.00291 | null | https://arxiv.org/abs/2207.00291v2 | https://arxiv.org/pdf/2207.00291v2.pdf | A Comparative Study of Graph Matching Algorithms in Computer Vision | The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their perform... | ['Bogdan Savchynskyy', 'Paul Swoboda', 'Dagmar Kainmüller', 'Carsten Rother', 'Florian Bernard', 'Lisa Hutschenreiter', 'Lorenz Feineis', 'Stefan Haller'] | 2022-07-01 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.62915426e-01 1.22974426e-01 -2.06432909e-01 -2.20166773e-01
-6.57889307e-01 -6.78162992e-01 5.66384256e-01 3.98847073e-01
-3.23646009e-01 4.72307891e-01 -2.62605667e-01 -1.73751920e-01
-3.60391676e-01 -7.34451234e-01 -5.90337336e-01 -5.59594929e-01
-2.05238059e-01 6.73204541e-01 6.11740589e-01 -2.49655783... | [8.236377716064453, -1.8700807094573975] |
e4b177f8-2f96-4ed4-9d4a-a0b1479b2ecd | level-line-guided-edge-drawing-for-robust | 2305.05883 | null | https://arxiv.org/abs/2305.05883v1 | https://arxiv.org/pdf/2305.05883v1.pdf | Level-line Guided Edge Drawing for Robust Line Segment Detection | Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequat... | ['Ce Zhu', 'Yipeng Liu', 'Yingjie Zhou', 'Xinyu Lin'] | 2023-05-10 | null | null | null | null | ['line-segment-detection'] | ['computer-vision'] | [-1.09879106e-01 -5.45320213e-01 -1.41100436e-01 2.59679668e-02
-1.00954853e-01 -1.65931344e-01 2.23699436e-01 4.71359134e-01
-4.30452347e-01 4.30163473e-01 -2.55089372e-01 -3.70923012e-01
-1.41786069e-01 -7.37943888e-01 -2.47710541e-01 -6.40706301e-01
1.57467648e-01 -8.18989575e-02 5.84770501e-01 -2.03389108... | [8.425735473632812, -1.566275954246521] |
a250fd39-361b-4a5b-9dea-79dabd2c89bd | grammatical-error-detection-in-transcriptions | null | null | https://aclanthology.org/2020.coling-main.195 | https://aclanthology.org/2020.coling-main.195.pdf | Grammatical error detection in transcriptions of spoken English | We describe the collection of transcription corrections and grammatical error annotations for the CrowdED Corpus of spoken English monologues on business topics. The corpus recordings were crowdsourced from native speakers of English and learners of English with German as their first language. The new transcriptions an... | ['Paula Buttery', 'Marek Rei', 'Kate Knill', 'Christian Bentz', 'Andrew Caines'] | 2020-12-01 | null | null | null | coling-2020-8 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 5.19240350e-02 5.44434428e-01 1.42080680e-01 -7.60942757e-01
-1.28739297e+00 -6.05040133e-01 4.84972358e-01 6.78983867e-01
-9.82688367e-01 8.56852233e-01 9.10104275e-01 -2.95650244e-01
3.92322540e-01 -1.04206495e-01 -7.32584476e-01 -6.62220642e-03
3.70438814e-01 7.69719422e-01 2.83038497e-01 -4.60340232... | [11.104985237121582, 10.652153015136719] |
5cf344cb-4107-47c0-8e0d-ddc613802165 | joint-cluster-head-selection-and-trajectory | 2112.00333 | null | https://arxiv.org/abs/2112.00333v1 | https://arxiv.org/pdf/2112.00333v1.pdf | Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model | Employing unmanned aerial vehicles (UAVs) has attracted growing interests and emerged as the state-of-the-art technology for data collection in Internet-of-Things (IoT) networks. In this paper, with the objective of minimizing the total energy consumption of the UAV-IoT system, we formulate the problem of jointly desig... | ['Jerome Henry', 'Robert Barton', 'Ha H. Nguyen', 'Ebrahim Bedeer', 'Botao Zhu'] | 2021-12-01 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.41803712e-01 -1.65860161e-01 -2.07688734e-01 -2.09736526e-01
-1.91374421e-01 -7.19065726e-01 1.26013547e-01 -1.66899353e-01
-4.53666002e-01 6.64950311e-01 -5.07585406e-01 -5.90117633e-01
-6.15515530e-01 -8.99757922e-01 -6.96692050e-01 -1.00959623e+00
-3.45724285e-01 2.59127885e-01 -5.01565933e-02 -3.75649966... | [5.803004264831543, 1.6156504154205322] |
7cdb2743-0a40-4abf-9411-9a00fb9a5d9f | manet-improving-video-denoising-with-a-multi | 2202.09704 | null | https://arxiv.org/abs/2202.09704v2 | https://arxiv.org/pdf/2202.09704v2.pdf | MANet: Improving Video Denoising with a Multi-Alignment Network | In video denoising, the adjacent frames often provide very useful information, but accurate alignment is needed before such information can be harnassed. In this work, we present a multi-alignment network, which generates multiple flow proposals followed by attention-based averaging. It serves to mimic the non-local me... | ['Edmund Y. Lam', 'Jiebo Luo', 'Zhongrui Wang', 'Haitian Zheng', 'Yaping Zhao'] | 2022-02-20 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 8.38687047e-02 -2.40343004e-01 -1.83027625e-01 -2.54972756e-01
-9.23547387e-01 -3.94510478e-01 4.15076405e-01 -2.90336043e-01
-3.49092811e-01 6.66461170e-01 7.07069576e-01 1.53276790e-02
3.99341494e-01 -5.23639143e-01 -6.22355819e-01 -7.67287791e-01
-1.84084535e-01 -1.20006867e-01 3.94279599e-01 -2.66468734... | [11.167195320129395, -1.8482370376586914] |
750a0cb9-e1ca-4a85-a364-b838a34c1faf | blazepose-on-device-real-time-body-pose | 2006.10204 | null | https://arxiv.org/abs/2006.10204v1 | https://arxiv.org/pdf/2006.10204v1.pdf | BlazePose: On-device Real-time Body Pose tracking | We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices. During inference, the network produces 33 body keypoints for a single person and runs at over 30 frames per second on a Pixel 2 phone. This makes it particu... | ['Valentin Bazarevsky', 'Matthias Grundmann', 'Tyler Zhu', 'Karthik Raveendran', 'Ivan Grishchenko', 'Fan Zhang'] | 2020-06-17 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 2.02119008e-01 2.60831147e-01 -3.95372838e-01 -1.97887912e-01
-4.26233739e-01 -6.21236339e-02 1.42136201e-01 -4.84770507e-01
-5.98060608e-01 4.55922157e-01 2.02025965e-01 6.15481520e-03
2.21045181e-01 -7.15781093e-01 -7.43548989e-01 -1.32046640e-01
-3.21219951e-01 6.39302850e-01 1.92986801e-01 -2.36091211... | [7.027639865875244, -0.6259347200393677] |
50eb9b31-4e92-4dd5-a6e3-abf488af3231 | boun-tabi-smm4h22-text-to-text-adverse-drug | null | null | https://aclanthology.org/2022.smm4h-1.9 | https://aclanthology.org/2022.smm4h-1.9.pdf | BOUN-TABI@SMM4H’22: Text-to-Text Adverse Drug Event Extraction with Data Balancing and Prompting | This paper describes models developed for the Social Media Mining for Health 2022 Shared Task. We participated in two subtasks: classification of English tweets reporting adverse drug events (ADE) (Task 1a) and extraction of ADE spans in such tweets (Task 1b). We developed two separate systems based on the T5 model, vi... | ['Zeynep Yirmibeşoğlu', 'Gökçe Uludoğan'] | null | null | null | null | smm4h-coling-2022-10 | ['event-extraction'] | ['natural-language-processing'] | [ 4.61098880e-01 3.16424787e-01 -4.02881712e-01 -5.30894756e-01
-1.35422683e+00 -3.18848222e-01 8.42088580e-01 7.95739710e-01
-6.45814240e-01 9.84909952e-01 3.90466332e-01 -5.27474940e-01
-2.43914425e-01 -5.99135280e-01 -4.57188338e-01 -3.71762335e-01
-2.33194619e-01 3.73766512e-01 -2.60658786e-02 -2.26154868... | [8.451443672180176, 8.999263763427734] |
0c43271a-77f7-4a4d-8568-4138d626d066 | attention-based-second-order-pooling-network | null | null | https://ieeexplore.ieee.org/document/9325094 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9325094 | Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification | Deep learning (DL) has exhibited huge potentials for hyperspectral image (HSI) classification due to its powerful nonlinear modeling and end-to-end optimization characteristics. Although the superior performance of DL-based methods has
been witnessed, some limitations can still be found. On the one hand, existing DL f... | ['Peijun Du', 'Yifeng Liu', 'Mengxue Zhang', 'Zhaohui Xue'] | 2021-01-14 | null | null | null | ieee-transactions-on-geoscience-and-remote-5 | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 0.23371309 -0.4821272 -0.11973314 -0.45752448 -0.61448485 -0.04842366
0.27054897 0.01768482 -0.4528035 0.6346602 -0.2766098 -0.23571855
-0.5504859 -0.8271247 -0.5168369 -1.2266566 -0.05061243 -0.22779238
0.05018538 0.04015935 0.04831131 0.6216801 -1.4903725 -0.05851248
1.3965209 1.5889904 0.... | [10.020346641540527, -1.5952484607696533] |
cdd74422-bff8-470a-9cb4-559653a0d197 | fuzzy-finite-element-model-updating-using | 1701.00833 | null | http://arxiv.org/abs/1701.00833v1 | http://arxiv.org/pdf/1701.00833v1.pdf | Fuzzy finite element model updating using metaheuristic optimization algorithms | In this paper, a non-probabilistic method based on fuzzy logic is used to
update finite element models (FEMs). Model updating techniques use the measured
data to improve the accuracy of numerical models of structures. However, the
measured data are contaminated with experimental noise and the models are
inaccurate due ... | ['H. Haddad Khodaparast', 'I. Boulkaibet', 'T. Marwala', 'S. Adhikari', 'M. I. Friswell'] | 2017-01-03 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-6.17641546e-02 -1.89148217e-01 2.65427709e-01 -1.40285060e-01
-1.81669548e-01 -1.29883885e-01 1.84091311e-02 2.10497051e-01
-3.94358456e-01 1.11917424e+00 -4.60654438e-01 3.07299998e-02
-1.13853431e+00 -1.21352255e+00 -4.01519507e-01 -9.44196701e-01
3.86177629e-01 7.65292823e-01 3.70399684e-01 -1.84819266... | [6.031670570373535, 3.4971730709075928] |
a7af0e25-1266-4fd2-b365-e6c51602b431 | enhancing-neural-mathematical-reasoning-by | 2203.14487 | null | https://arxiv.org/abs/2203.14487v1 | https://arxiv.org/pdf/2203.14487v1.pdf | Enhancing Neural Mathematical Reasoning by Abductive Combination with Symbolic Library | Mathematical reasoning recently has been shown as a hard challenge for neural systems. Abilities including expression translation, logical reasoning, and mathematics knowledge acquiring appear to be essential to overcome the challenge. This paper demonstrates that some abilities can be achieved through abductive combin... | ['Yang Yu', 'Yangyang Hu'] | 2022-03-28 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-8.32125098e-02 5.67509413e-01 -9.14101079e-02 -3.25478733e-01
-4.87275720e-01 -3.73256624e-01 8.08967888e-01 -2.45143846e-01
-1.36638731e-01 8.38207245e-01 -1.90123126e-01 -7.08238304e-01
-4.51043367e-01 -1.10213614e+00 -1.23647547e+00 1.39714396e-02
-1.75132796e-01 6.78248644e-01 2.84099448e-02 -7.56392539... | [9.300549507141113, 7.220861434936523] |
43d97432-b46c-4d1f-aca8-938a02e1d0b6 | a-novel-explainable-artificial-intelligence | 2307.04137 | null | https://arxiv.org/abs/2307.04137v1 | https://arxiv.org/pdf/2307.04137v1.pdf | A Novel Explainable Artificial Intelligence Model in Image Classification problem | In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the... | ['Xuan Phong Nguyen', 'Vo Thanh Khang Nguyen', 'Truong Thanh Hung Nguyen', 'Quoc Hung Cao'] | 2023-07-09 | null | null | null | null | ['explainable-artificial-intelligence', 'object-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.62737334e-01 3.54732387e-02 -1.44716218e-01 -4.20246840e-01
2.19460666e-01 -3.50189149e-01 5.40000081e-01 -1.22435987e-01
-4.09721375e-01 6.62292361e-01 -1.41317129e-01 -7.16972828e-01
-1.61238149e-01 -7.36890733e-01 -4.61047411e-01 -3.76769274e-01
2.35131979e-01 2.34196752e-01 4.52006966e-01 -3.92102629... | [9.872481346130371, 2.0246684551239014] |
925e0abc-f31d-433f-a030-d2281eac1d2e | thematic-recommendations-on-knowledge-graphs | 2105.05733 | null | https://arxiv.org/abs/2105.05733v1 | https://arxiv.org/pdf/2105.05733v1.pdf | Thematic recommendations on knowledge graphs using multilayer networks | We present a framework to generate and evaluate thematic recommendations based on multilayer network representations of knowledge graphs (KGs). In this representation, each layer encodes a different type of relationship in the KG, and directed interlayer couplings connect the same entity in different roles. The relativ... | ['Till Hoffmann', 'Dimitrios Korkinof', 'Mariano Beguerisse-Díaz'] | 2021-05-12 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.22546048e-02 3.82698804e-01 -5.44802010e-01 -4.53331113e-01
-1.31023511e-01 -8.21393192e-01 6.40415847e-01 4.28163677e-01
-3.71928275e-01 4.25768167e-01 8.55358481e-01 -1.72100589e-01
-9.46061134e-01 -1.13081098e+00 -5.09681106e-01 -6.55080080e-02
-2.85390496e-01 4.64112610e-01 3.67966980e-01 -6.53420448... | [10.019332885742188, 5.7780375480651855] |
598eb0b3-4a14-46d6-95ce-a8751029b5ab | deep-neural-network-based-i-vector-mapping | 1810.07309 | null | http://arxiv.org/abs/1810.07309v1 | http://arxiv.org/pdf/1810.07309v1.pdf | Deep neural network based i-vector mapping for speaker verification using short utterances | Text-independent speaker recognition using short utterances is a highly
challenging task due to the large variation and content mismatch between short
utterances. I-vector based systems have become the standard in speaker
verification applications, but they are less effective with short utterances.
In this paper, we fi... | ['Ying-Nian Wu', 'Yang Shi', 'Jinxi Guo', 'Abeer Alwan', 'Ning Xu', 'Kaiyuan Xu', 'Kailun Qian'] | 2018-10-16 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 9.77993533e-02 -3.18804681e-01 -2.62275822e-02 -6.12208545e-01
-9.99015927e-01 -3.88331115e-01 5.54019868e-01 -4.82890308e-01
-4.02613044e-01 3.66039038e-01 5.05829215e-01 -5.08916140e-01
4.01145667e-01 -2.53474228e-02 -4.20219064e-01 -9.54534829e-01
3.09846729e-01 9.92142409e-02 -8.04073140e-02 -2.10278422... | [14.362841606140137, 6.088962078094482] |
a8ad19fb-b573-4999-9414-c287e8b57f34 | entity-enhancement-for-implicit-discourse | null | null | https://aclanthology.org/2021.acl-short.116 | https://aclanthology.org/2021.acl-short.116.pdf | Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain | Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB; Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biom... | ['Vera Demberg', 'Wei Shi'] | 2021-08-01 | null | null | null | acl-2021-5 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 6.37415588e-01 1.28108096e+00 -4.83191282e-01 -2.02463001e-01
-1.04651582e+00 -5.75888753e-01 1.03761947e+00 7.77883351e-01
-5.79364896e-01 1.33276916e+00 6.93655610e-01 -4.71107632e-01
-1.74170345e-01 -5.99096060e-01 -6.09929860e-01 -5.37939012e-01
-9.49665438e-03 7.65868187e-01 4.79310274e-01 -5.28214991... | [10.767465591430664, 9.24195671081543] |
2fa1a317-a467-4df4-9a21-fddcb49c97ec | incorporating-semantic-attention-in-video | null | null | https://aclanthology.org/L18-1477 | https://aclanthology.org/L18-1477.pdf | Incorporating Semantic Attention in Video Description Generation | null | ['Hideki Nakayama', 'Naoaki Okazaki', 'Natsuda Laokulrat'] | 2018-05-01 | incorporating-semantic-attention-in-video-1 | https://aclanthology.org/L18-1477 | https://aclanthology.org/L18-1477.pdf | lrec-2018-5 | ['video-description'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.370655536651611, 3.872107982635498] |
ec51139f-3d93-4a45-942a-f8797f447b34 | conversational-search-for-learning | 2001.02912 | null | https://arxiv.org/abs/2001.02912v1 | https://arxiv.org/pdf/2001.02912v1.pdf | Conversational Search for Learning Technologies | Conversational search is based on a user-system cooperation with the objective to solve an information-seeking task. In this report, we discuss the implication of such cooperation with the learning perspective from both user and system side. We also focus on the stimulation of learning through a key component of conver... | ['Laure Soulier', 'Sharon Oviatt'] | 2020-01-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.77049190e-01 7.72282660e-01 -5.12295067e-01 -2.62035541e-02
-5.38130939e-01 -5.02684891e-01 8.65629673e-01 -6.41824454e-02
-4.24625188e-01 6.65674210e-01 4.28390980e-01 -3.36660385e-01
-5.57637870e-01 -4.88691151e-01 4.34052013e-02 -6.90085948e-01
1.75390631e-01 3.17712009e-01 -3.15225810e-01 -6.43619895... | [12.264481544494629, 7.788639068603516] |
3a53543a-252f-418b-b0a4-b5d6fb392ff3 | improving-adversarial-text-generation-with-n | null | null | https://aclanthology.org/2021.paclic-1.69 | https://aclanthology.org/2021.paclic-1.69.pdf | Improving Adversarial Text Generation with n-Gram Matching | null | ['Massimo Piccardi', 'Shijie Li'] | null | null | null | null | paclic-2021-11 | ['adversarial-text'] | ['adversarial'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.249998569488525, 3.669483184814453] |
8a64470f-7e73-43af-97cd-7c4a4e6124ef | temporal-pattern-attention-for-multivariate | 1809.04206 | null | https://arxiv.org/abs/1809.04206v3 | https://arxiv.org/pdf/1809.04206v3.pdf | Temporal Pattern Attention for Multivariate Time Series Forecasting | Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is cru... | ['Hung-Yi Lee', 'Fan-Keng Sun', 'Shun-Yao Shih'] | 2018-09-12 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 2.45774269e-01 -6.06459975e-01 -7.80494213e-02 -2.90682793e-01
-3.41742933e-01 -4.60947365e-01 5.84259152e-01 -3.27423140e-02
-7.19202086e-02 6.91260397e-01 4.72235590e-01 -3.98167044e-01
-4.09533441e-01 -8.27614307e-01 -5.31925559e-01 -7.44478047e-01
-1.37362510e-01 -2.37911582e-01 -4.49276417e-02 -2.57807851... | [6.857569694519043, 2.9370033740997314] |
7b5f28eb-4a0a-443e-b93d-00f954fc872e | authorship-verification-an-approach-based-on | 1607.08885 | null | http://arxiv.org/abs/1607.08885v1 | http://arxiv.org/pdf/1607.08885v1.pdf | Authorship Verification - An Approach based on Random Forest | Authorship attribution, being an important problem in many areas in-cluding
information retrieval, computational linguistics, law and journalism etc., has
been identified as a subject of increasingly research interest in the re-cent
years. In case of Author Identification task in PAN at CLEF 2015, the main
focus was gi... | ['Promita Maitra', 'Souvick Ghosh', 'Dipankar Das'] | 2016-07-29 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-2.58110706e-02 -2.30939120e-01 -2.82021761e-01 -1.25379115e-01
-4.64759111e-01 -7.49132454e-01 1.15904903e+00 7.56578207e-01
-5.93112051e-01 1.01594627e+00 2.56921142e-01 -3.68570626e-01
-4.16665912e-01 -1.83380678e-01 -7.70941749e-02 -3.17327112e-01
2.18580633e-01 5.72070599e-01 -1.80034265e-01 1.96656883... | [9.58424186706543, 10.602775573730469] |
f9e567af-3040-4bbf-b365-91615bf803ab | one-shot-neural-band-selection-for-spectral | 2305.09236 | null | https://arxiv.org/abs/2305.09236v1 | https://arxiv.org/pdf/2305.09236v1.pdf | One-shot neural band selection for spectral recovery | Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands. These methods are either very slow due to the computational cost of ... | ['Ajin Meng', 'Zhilin Han', 'Liu Liu', 'YiTao Zhang', 'You Song', 'Wenshuai Xu', 'Zhenbo Xu', 'Hai-Miao Hu'] | 2023-05-16 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.44645810e-01 -5.99760532e-01 -4.48802322e-01 -1.05811015e-01
-1.24387777e+00 -4.16459173e-01 1.86289996e-01 -3.39985758e-01
-3.29795331e-01 8.31295371e-01 3.24998081e-01 -8.11694562e-02
-5.41935802e-01 -6.43679857e-01 -6.37796760e-01 -8.45192015e-01
1.47100426e-02 7.48847649e-02 7.93290734e-02 -2.31731907... | [10.481234550476074, -2.1490633487701416] |
80800465-5cd8-420c-9512-cd17da8ae0a9 | speaker-independent-neural-formant-synthesis | 2306.01957 | null | https://arxiv.org/abs/2306.01957v1 | https://arxiv.org/pdf/2306.01957v1.pdf | Speaker-independent neural formant synthesis | We describe speaker-independent speech synthesis driven by a small set of phonetically meaningful speech parameters such as formant frequencies. The intention is to leverage deep-learning advances to provide a highly realistic signal generator that includes control affordances required for stimulus creation in the spee... | ['Lauri Juvela', 'Gustav Eje Henter', 'Zofia Malisz', 'Pablo Pérez Zarazaga'] | 2023-06-02 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 3.91908437e-01 3.39118361e-01 -8.62864405e-02 -6.76536262e-02
-1.35189629e+00 -5.95925629e-01 7.91584313e-01 -4.73254740e-01
1.92612052e-01 7.53877997e-01 7.38333702e-01 -3.73359710e-01
1.40803486e-01 -5.46069562e-01 -7.06712246e-01 -6.91884041e-01
2.80850194e-02 2.68601701e-02 -2.13187754e-01 -5.01947820... | [15.303849220275879, 6.257985591888428] |
86f87ac3-8da5-4dc7-bf11-242080ae0885 | bert-for-joint-multichannel-speech | 2010.10892 | null | https://arxiv.org/abs/2010.10892v2 | https://arxiv.org/pdf/2010.10892v2.pdf | BERT for Joint Multichannel Speech Dereverberation with Spatial-aware Tasks | We propose a method for joint multichannel speech dereverberation with two spatial-aware tasks: direction-of-arrival (DOA) estimation and speech separation. The proposed method addresses involved tasks as a sequence to sequence mapping problem, which is general enough for a variety of front-end speech enhancement tasks... | ['Yang Jiao'] | 2020-10-21 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.84162217e-01 -3.88165236e-01 -2.24149730e-02 -2.38116160e-01
-1.02709484e+00 -4.70616579e-01 4.34293270e-01 -3.84696841e-01
-3.13377887e-01 7.69405067e-01 6.03192329e-01 -6.51465118e-01
-1.25763863e-01 -1.83788195e-01 -4.92010772e-01 -8.80983829e-01
-1.49282977e-01 -4.79551971e-01 -4.98800874e-02 -2.38001898... | [14.798408508300781, 5.978110313415527] |
e047312d-4db5-48de-8c5b-2bd7c2ad55b2 | remix-regret-minimization-for-monotonic-value | 2302.05593 | null | https://arxiv.org/abs/2302.05593v1 | https://arxiv.org/pdf/2302.05593v1.pdf | ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning | Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution paradigm. By factorizing the optimal joint action-value function using a monotonic mixing function of agents' utilities, these algorithms ensure... | ['Tian Lan', 'Hanhan Zhou', 'Yongsheng Mei'] | 2023-02-11 | null | null | null | null | ['starcraft'] | ['playing-games'] | [-2.73500025e-01 1.29597843e-01 -5.90354502e-01 -8.90354663e-02
-7.19030201e-01 -7.82663226e-01 3.86692613e-01 -1.12711526e-01
-7.57847250e-01 1.21347356e+00 2.44174466e-01 -3.78426313e-01
-5.09454668e-01 -6.22175574e-01 -4.82844055e-01 -1.02569592e+00
-3.69843513e-01 5.33710063e-01 -5.81799336e-02 -5.26480019... | [3.9030048847198486, 2.239994764328003] |
2f4d1c7b-43eb-4ed3-bb30-48d345d63912 | high-speed-human-action-recognition-using-a | 2305.15283 | null | https://arxiv.org/abs/2305.15283v2 | https://arxiv.org/pdf/2305.15283v2.pdf | High Speed Human Action Recognition using a Photonic Reservoir Computer | The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classification algorithm. Here we address recognition of human actions using the ... | ['Serge Massar', 'Piotr Antonik', 'Enrico Picco'] | 2023-05-24 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.91223776e-01 -2.60863185e-01 2.46656626e-01 2.89835203e-02
-1.67722985e-01 -3.55255663e-01 8.52766156e-01 1.15077011e-01
-8.65667284e-01 6.07961595e-01 -3.98226470e-01 -8.73149484e-02
3.49109098e-02 -7.93433607e-01 -5.07056952e-01 -1.12940335e+00
-3.09745878e-01 4.33211207e-01 5.43634593e-01 1.95277594... | [8.641164779663086, -1.3180357217788696] |
b66584b2-cb67-4864-8f9e-0c46f875ed73 | allowing-for-weak-identification-when-testing | 2210.11398 | null | https://arxiv.org/abs/2210.11398v1 | https://arxiv.org/pdf/2210.11398v1.pdf | Allowing for weak identification when testing GARCH-X type models | In this paper, we use the results in Andrews and Cheng (2012), extended to allow for parameters to be near or at the boundary of the parameter space, to derive the asymptotic distributions of the two test statistics that are used in the two-step (testing) procedure proposed by Pedersen and Rahbek (2019). The latter aim... | ['Philipp Ketz'] | 2022-10-20 | null | null | null | null | ['type'] | ['speech'] | [-1.36602506e-01 2.84494728e-01 3.75324488e-02 -6.69830218e-02
-5.30865252e-01 -7.22811520e-01 5.36844969e-01 2.32467338e-01
-4.18347180e-01 7.54674911e-01 -1.52884603e-01 -1.05986011e+00
-3.36270958e-01 -9.02370214e-01 -5.97432971e-01 -7.54915297e-01
-1.56317502e-01 9.71377492e-02 1.69178993e-01 2.55108446... | [6.6250715255737305, 4.312077045440674] |
6d9a6a2d-9e6c-4dff-9c01-d7ce432f984e | knowledge-distillation-meets-few-shot | null | null | https://aclanthology.org/2022.nlp4convai-1.10 | https://aclanthology.org/2022.nlp4convai-1.10.pdf | Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains | Large Transformer-based natural language understanding models have achieved state-of-the-art performance in dialogue systems. However, scarce labeled data for training, the large model size, and low inference speed hinder their deployment in low-resource scenarios. Few-shot learning and knowledge distillation technique... | ['Fabian Küch', 'Shima Asaadi', 'Anna Sauer'] | null | null | null | null | nlp4convai-acl-2022-5 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 1.36619415e-02 5.56988120e-01 -5.05745530e-01 -5.75256586e-01
-8.93524706e-01 -5.15761375e-01 7.36149728e-01 -3.86337866e-04
-5.61697483e-01 8.62778664e-01 2.89649546e-01 -2.48556852e-01
1.25176758e-01 -9.29278851e-01 -2.47626737e-01 -1.93287045e-01
3.15390617e-01 9.85455513e-01 4.23426300e-01 -5.59597075... | [11.870461463928223, 7.690049648284912] |
4fa5a5f7-b4a6-443b-b610-68fd3cbff72f | model-free-context-aware-word-composition | null | null | https://aclanthology.org/C18-1240 | https://aclanthology.org/C18-1240.pdf | Model-Free Context-Aware Word Composition | Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and c... | ['Xianpei Han', 'Bo An', 'Le Sun'] | 2018-08-01 | model-free-context-aware-word-composition-1 | https://aclanthology.org/C18-1240 | https://aclanthology.org/C18-1240.pdf | coling-2018-8 | ['learning-word-embeddings'] | ['methodology'] | [ 3.90810162e-01 -1.75505713e-01 -6.37515366e-01 -3.10060173e-01
-5.77737749e-01 -5.36502182e-01 6.25967383e-01 6.38830304e-01
-4.00446415e-01 7.17861533e-01 9.03793216e-01 -3.18538249e-01
-3.83073129e-02 -8.89420450e-01 -2.68401593e-01 -5.40320814e-01
2.90549308e-01 2.14422718e-01 2.18320131e-01 -4.00873125... | [10.361298561096191, 8.962565422058105] |
59d0b139-61f6-4a96-b36b-a12909f700b0 | exploring-metaphoric-paraphrase-generation | null | null | https://aclanthology.org/2021.conll-1.26 | https://aclanthology.org/2021.conll-1.26.pdf | Exploring Metaphoric Paraphrase Generation | Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models. We focus here on the specific task of metaphoric paraphrase generation, in which we provide a literal sentence and generate a metaphoric sentence which paraphrases that input. We compare naive, “free”... | ['Iryna Gurevych', 'Nils Beck', 'Kevin Stowe'] | null | null | null | null | conll-emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.44307837e-01 1.49951637e-01 1.02027856e-01 -2.95941919e-01
-7.17749536e-01 -1.12026131e+00 1.25429761e+00 2.17664495e-01
-2.94289291e-01 7.40739346e-01 9.56974626e-01 -3.78458560e-01
2.60596752e-01 -7.63717830e-01 -3.82874727e-01 -2.66918808e-01
2.88022131e-01 7.08524942e-01 -2.79859930e-01 -8.48559976... | [11.248568534851074, 9.134411811828613] |
c04b20d7-d9ad-452f-875d-4dfd0f772450 | resource-efficient-mountainous-skyline | 2107.10997 | null | https://arxiv.org/abs/2107.10997v1 | https://arxiv.org/pdf/2107.10997v1.pdf | Resource Efficient Mountainous Skyline Extraction using Shallow Learning | Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented reality applications. We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edge... | ['George Bebis', 'Martin Čadík', 'Ebrahim Emami', 'Touqeer Ahmad'] | 2021-07-23 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [-3.13104615e-02 -2.73109853e-01 -1.80252641e-01 -3.39632064e-01
-5.94182372e-01 -7.35174954e-01 4.52422172e-01 -1.48047760e-01
-4.37044919e-01 4.88529295e-01 -2.65989989e-01 -4.58753288e-01
-1.44123673e-01 -1.06095123e+00 -8.09194803e-01 -6.18764758e-01
-1.01722792e-01 3.05464894e-01 5.80231905e-01 -2.02243090... | [8.605481147766113, -1.6376255750656128] |
ad7b80db-82d0-4a4d-a095-2445373bd3d1 | auxiliary-learning-by-implicit | 2007.02693 | null | https://arxiv.org/abs/2007.02693v3 | https://arxiv.org/pdf/2007.02693v3.pdf | Auxiliary Learning by Implicit Differentiation | Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss. Here, we propose a novel frame... | ['Idan Achituve', 'Haggai Maron', 'Gal Chechik', 'Ethan Fetaya', 'Aviv Navon'] | 2020-06-22 | null | https://openreview.net/forum?id=n7wIfYPdVet | https://openreview.net/pdf?id=n7wIfYPdVet | iclr-2021-1 | ['small-data', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 7.32690156e-01 1.98311321e-02 -1.57744616e-01 -3.61069173e-01
-1.33725834e+00 -4.61346269e-01 5.89179754e-01 -1.60762537e-02
-8.30078125e-01 1.01603830e+00 1.48795009e-01 3.26931826e-04
-2.29998887e-01 -1.85507089e-01 -9.08699155e-01 -9.78151679e-01
3.04227591e-01 5.93575537e-01 -1.52415959e-02 -1.47164240... | [9.360533714294434, 3.719163417816162] |
b8b78c15-0dcc-432c-a28c-201dd6cb392c | user-loss-a-forced-choice-inspired-approach | 1807.09303 | null | http://arxiv.org/abs/1807.09303v2 | http://arxiv.org/pdf/1807.09303v2.pdf | User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction | In this paper, we investigate whether is it possible to train a neural
network directly from user inputs. We consider this approach to be highly
relevant for applications in which the point of optimality is not well-defined
and user-dependent. Our application is medical image denoising which is
essential in fluoroscopy... | ['Andreas Maier', 'Bernhard Stimpel', 'Shahab Zarei', 'Christopher Syben'] | 2018-07-24 | null | null | null | null | ['medical-image-denoising'] | ['computer-vision'] | [ 5.33922911e-01 2.01868489e-01 3.50962162e-01 -5.02771854e-01
-5.52975357e-01 -5.12358725e-01 1.65833876e-01 2.36469269e-01
-8.60006213e-01 6.19149148e-01 -3.23519737e-01 -4.46590364e-01
-4.95787203e-01 -6.32716656e-01 -7.01534986e-01 -7.83929825e-01
1.04828805e-01 3.95160705e-01 3.11015129e-01 -3.05718273... | [11.520303726196289, -2.232736825942993] |
77fb8242-9b75-4462-8d93-36b8e04c072e | rete-retrieval-enhanced-temporal-event | 2202.06129 | null | https://arxiv.org/abs/2202.06129v1 | https://arxiv.org/pdf/2202.06129v1.pdf | RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph | With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. Recently, prior works for user behavior prediction mainly focus on the interactions with product-side information. However, the interactions ... | ['Tarek Abdelzaher', 'Bing Yin', 'Tong Zhao', 'Qingyu Yin', 'Danqing Zhang', 'Zheng Li', 'Ruijie Wang'] | 2022-02-12 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-2.99244672e-02 -3.59968752e-01 -5.34860969e-01 -2.78344363e-01
-3.80740152e-04 -3.87171865e-01 4.47898626e-01 3.80731791e-01
-6.06768299e-03 2.00453132e-01 3.65883648e-01 -9.87191051e-02
-6.09731913e-01 -1.13542175e+00 -4.97000605e-01 -4.93824244e-01
-2.11902678e-01 3.71927559e-01 4.36968803e-01 -6.18643284... | [10.210827827453613, 5.657740116119385] |
1c91a0f0-1baf-47b2-a043-9f7c05eceb8e | unidu-towards-a-unified-generative-dialogue | 2204.04637 | null | https://arxiv.org/abs/2204.04637v2 | https://arxiv.org/pdf/2204.04637v2.pdf | UniDU: Towards A Unified Generative Dialogue Understanding Framework | With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent models for each distinct DU task without considering shared knowledge across different DU tasks. In this paper, we propose a unified genera... | ['Kai Yu', 'Jian-Guang Lou', 'Su Zhu', 'Yuncong Liu', 'Libo Qin', 'Bei Chen', 'Lu Chen', 'Zhi Chen'] | 2022-04-10 | null | https://aclanthology.org/2022.sigdial-1.43 | https://aclanthology.org/2022.sigdial-1.43.pdf | sigdial-acl-2022-9 | ['dialogue-understanding', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.57870313e-02 1.96671918e-01 -6.09510168e-02 -3.53349239e-01
-8.45961928e-01 -6.08419120e-01 1.00544524e+00 -2.61458069e-01
-2.01509088e-01 9.90294755e-01 4.47982818e-01 1.85398310e-02
-6.41844571e-02 -6.27162039e-01 -3.34910601e-01 -6.32171929e-01
5.58736563e-01 8.65498543e-01 5.44595718e-02 -6.72421157... | [12.471324920654297, 8.03454303741455] |
5b4108fe-ef99-41c1-9513-b803fc2f0130 | applications-of-machine-learning-for-the | 2212.03114 | null | https://arxiv.org/abs/2212.03114v3 | https://arxiv.org/pdf/2212.03114v3.pdf | Applications of Machine Learning for the Ratemaking in Agricultural Insurances | This paper evaluates Machine Learning (ML) in establishing ratemaking for new insurance schemes. To make the evaluation feasible, we established expected indemnities as premiums. Then, we use ML to forecast indemnities using a minimum set of variables. The analysis simulates the introduction of an income insurance sche... | ['Luigi Biagini'] | 2022-12-06 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.43386398e-02 3.61880630e-01 -8.27583492e-01 -2.39757106e-01
-3.72599125e-01 -4.05459106e-01 3.36089790e-01 4.85275894e-01
-3.38668197e-01 9.44292843e-01 -5.68969585e-02 -9.38238144e-01
-5.77798188e-01 -1.02310944e+00 -9.93609250e-01 -6.23868108e-01
-1.31440014e-01 4.04661894e-01 -5.20555556e-01 -3.53056878... | [7.719493865966797, 4.95490837097168] |
d4ac1669-dc9b-42cf-97b5-9da85a551230 | syntax-guided-neural-module-distillation-to | 2301.08998 | null | https://arxiv.org/abs/2301.08998v2 | https://arxiv.org/pdf/2301.08998v2.pdf | Syntax-guided Neural Module Distillation to Probe Compositionality in Sentence Embeddings | Past work probing compositionality in sentence embedding models faces issues determining the causal impact of implicit syntax representations. Given a sentence, we construct a neural module net based on its syntax parse and train it end-to-end to approximate the sentence's embedding generated by a transformer model. Th... | ['Rohan Pandey'] | 2023-01-21 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-composition'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.04844072e-01 7.79723287e-01 -2.62277782e-01 -5.74480057e-01
-1.99603260e-01 -5.90546846e-01 9.53789711e-01 2.42325857e-01
-3.82576376e-01 2.22966850e-01 1.28626680e+00 -9.06990945e-01
5.09167463e-02 -1.09356177e+00 -6.76721394e-01 -1.87364385e-01
-9.95766255e-04 3.67672354e-01 -2.37643234e-02 -4.91750926... | [10.622530937194824, 9.032967567443848] |
e5afa870-da03-46bb-bf88-c5d44a7c4a3e | eecbs-a-bounded-suboptimal-search-for-multi | 2010.01367 | null | https://arxiv.org/abs/2010.01367v2 | https://arxiv.org/pdf/2010.01367v2.pdf | EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding | Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, is important for many applications where small runtimes are necessary, including the kind of automated warehouses operated by Amazon. CBS is a leading two-level search algorithm for solving MAPF optimally. ECBS is a bounded-subopti... | ['Sven Koenig', 'Wheeler Ruml', 'Jiaoyang Li'] | 2020-10-03 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.97954315e-01 3.05593371e-01 -4.83818531e-01 -7.10526556e-02
-9.40598011e-01 -6.97673619e-01 -1.59202352e-01 3.94274145e-01
-2.45764926e-01 1.11352301e+00 -4.44838762e-01 -6.45407438e-01
-6.63921297e-01 -1.04286480e+00 -1.12884653e+00 -7.04076409e-01
-7.38579035e-01 1.17728436e+00 8.23874533e-01 -2.94662833... | [4.966983795166016, 1.8780362606048584] |
dbb08fde-735a-4dde-b6ec-4b4121c0a471 | learning-landmarks-motion-from-speech-for | 2306.01415 | null | https://arxiv.org/abs/2306.01415v1 | https://arxiv.org/pdf/2306.01415v1.pdf | Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation | This paper presents a novel approach for generating 3D talking heads from raw audio inputs. Our method grounds on the idea that speech related movements can be comprehensively and efficiently described by the motion of a few control points located on the movable parts of the face, i.e., landmarks. The underlying muscul... | ['Stefano Berretti', 'Claudio Ferrari', 'Federico Nocentini'] | 2023-06-02 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [-3.58266197e-02 6.32508636e-01 -2.05787085e-02 -3.44134629e-01
-6.97285891e-01 -3.65244269e-01 6.49334967e-01 -4.00276154e-01
-1.18756019e-01 5.47931015e-01 2.13454604e-01 3.88793796e-01
2.62986213e-01 -5.05646586e-01 -7.23725200e-01 -8.61808181e-01
1.38913430e-02 5.44765294e-01 -3.29478942e-02 -2.85447329... | [13.146198272705078, -0.3594132363796234] |
bf355f65-07b8-44bc-b3e3-6fa0f1ad54e6 | systematic-inequalities-in-language-1 | null | null | https://aclanthology.org/2022.acl-long.376 | https://aclanthology.org/2022.acl-long.376.pdf | Systematic Inequalities in Language Technology Performance across the World’s Languages | Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule ... | ['Graham Neubig', 'Antonios Anastasopoulos', 'Damian Blasi'] | null | null | null | null | acl-2022-5 | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.49406844e-02 3.96515995e-01 -5.55214107e-01 -2.43760914e-01
-1.15997267e+00 -1.06367731e+00 7.71576405e-01 7.70577967e-01
-4.01000708e-01 7.50233531e-01 7.03823328e-01 -9.38873410e-01
4.68184575e-02 -5.71763992e-01 -3.77677709e-01 -8.44118148e-02
4.59137768e-01 4.21790957e-01 -1.31137058e-01 -2.21216395... | [10.772211074829102, 9.03565502166748] |
0a146ab4-fa28-4d64-be94-7142c37cc3f9 | improving-language-model-negotiation-with | 2305.10142 | null | https://arxiv.org/abs/2305.10142v1 | https://arxiv.org/pdf/2305.10142v1.pdf | Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback | We study whether multiple large language models (LLMs) can autonomously improve each other in a negotiation game by playing, reflecting, and criticizing. We are interested in this question because if LLMs were able to improve each other, it would imply the possibility of creating strong AI agents with minimal human int... | ['Mirella Lapata', 'Tushar Khot', 'Hao Peng', 'Yao Fu'] | 2023-05-17 | null | null | null | null | ['unrolling'] | ['computer-vision'] | [-4.35259342e-02 7.38755107e-01 -9.58102942e-02 -8.41804072e-02
-8.85297716e-01 -9.44102287e-01 6.83151841e-01 3.77946813e-03
-8.23322058e-01 8.78282249e-01 1.19685397e-01 -3.30066860e-01
7.99091831e-02 -8.81194890e-01 -4.66979355e-01 -3.99524868e-01
-1.05727375e-01 1.25900280e+00 4.41100091e-01 -8.79240870... | [3.6675798892974854, 1.73600435256958] |
977753c2-94e7-4cf0-99a5-574c89bc4dbd | cooperative-multi-objective-reinforcement | 2306.09662 | null | https://arxiv.org/abs/2306.09662v1 | https://arxiv.org/pdf/2306.09662v1.pdf | Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction | Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture called Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG), which estimates multip... | ['Shin You Teng', 'Jun Wei Hsieh', 'Cheng Ruei Tang'] | 2023-06-16 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-2.49931186e-01 -8.13163817e-02 -3.99273723e-01 -6.73557669e-02
-8.22133005e-01 -2.80422807e-01 3.79778832e-01 -1.36372194e-01
-6.52364671e-01 1.37619400e+00 -1.15899101e-01 -5.36461949e-01
-2.57120460e-01 -7.93684721e-01 -5.71540415e-01 -7.55220830e-01
1.69483013e-02 8.34625781e-01 4.10353988e-01 -2.45380774... | [5.30814266204834, 1.522692322731018] |
3791b560-402e-4f05-aeef-4d6550c8352c | a-constrained-weighted-l1-minimization | 1709.0409 | null | http://arxiv.org/abs/1709.04090v2 | http://arxiv.org/pdf/1709.04090v2.pdf | A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs | Determining functional brain connectivity is crucial to understanding the
brain and neural differences underlying disorders such as autism. Recent
studies have used Gaussian graphical models to learn brain connectivity via
statistical dependencies across brain regions from neuroimaging. However,
previous studies often ... | ['Yanjun Qi', 'Chandan Singh', 'Beilun Wang'] | 2017-09-13 | a-constrained-weighted-l1-minimization-1 | null | null | arxiv-2017-9 | ['connectivity-estimation'] | ['graphs'] | [-1.63436625e-02 6.32742792e-02 -8.02248158e-03 -4.57191765e-01
-2.15901002e-01 -1.54647619e-01 2.67100006e-01 1.93147406e-01
-5.47216296e-01 6.40361428e-01 1.32777855e-01 -4.43472922e-01
-7.32786834e-01 -4.31557268e-01 -3.75526756e-01 -3.05677354e-01
-6.50734782e-01 3.66673976e-01 5.76208979e-02 2.93817759... | [12.443341255187988, 3.375438928604126] |
b96e9fc6-52ba-4510-bd20-7f1dcdd9bad8 | warpinggan-warping-multiple-uniform-priors | 2203.12917 | null | https://arxiv.org/abs/2203.12917v1 | https://arxiv.org/pdf/2203.12917v1.pdf | WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation | We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, Warping-GAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., ... | ['Xuefei Zhe', 'Junhui Hou', 'Yiming Zeng', 'Qijian Zhang', 'Yue Qian', 'Yingzhi Tang'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-generation'] | ['computer-vision'] | [ 9.48642045e-02 7.09396750e-02 -5.09540364e-02 2.29263548e-02
-9.22525227e-01 -8.36068511e-01 7.26791739e-01 -5.91735363e-01
2.44128719e-01 5.91906548e-01 1.00420795e-01 -1.30533725e-01
1.79345012e-01 -1.16421711e+00 -1.05514371e+00 -8.38019490e-01
2.30313897e-01 6.57071471e-01 -1.55282751e-01 -6.32413551... | [8.909085273742676, -3.6707160472869873] |
674a622a-26eb-44b8-ba64-8ee40ee105f0 | gimme-signals-discriminative-signal-encoding | 2003.06156 | null | https://arxiv.org/abs/2003.06156v2 | https://arxiv.org/pdf/2003.06156v2.pdf | Gimme Signals: Discriminative signal encoding for multimodal activity recognition | We present a simple, yet effective and flexible method for action recognition supporting multiple sensor modalities. Multivariate signal sequences are encoded in an image and are then classified using a recently proposed EfficientNet CNN architecture. Our focus was to find an approach that generalizes well across diffe... | ['Raphael Memmesheimer', 'Nick Theisen', 'Dietrich Paulus'] | 2020-03-13 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 8.87539029e-01 -1.25154123e-01 -5.51961660e-01 -2.96718270e-01
-1.14894640e+00 -2.63036221e-01 5.53557754e-01 -4.72365797e-01
-6.38032556e-01 5.16832411e-01 6.96158826e-01 2.01023012e-01
-3.31475973e-01 -5.86263716e-01 -8.12758029e-01 -5.65687954e-01
-3.22658598e-01 -7.55573958e-02 1.72712579e-01 -6.17182814... | [7.807640552520752, 0.5164549946784973] |
1a36f895-51b6-4db5-bfa6-cdb33fd39c0b | camera-relocalization-by-computing-pairwise | 1707.09733 | null | http://arxiv.org/abs/1707.09733v2 | http://arxiv.org/pdf/1707.09733v2.pdf | Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network | We propose a new deep learning based approach for camera relocalization. Our
approach localizes a given query image by using a convolutional neural network
(CNN) for first retrieving similar database images and then predicting the
relative pose between the query and the database images, whose poses are known.
The camer... | ['Zakaria Laskar', 'Surya Kalia', 'Juho Kannala', 'Iaroslav Melekhov'] | 2017-07-31 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 1.76489264e-01 -3.21382374e-01 -8.17841142e-02 -5.01803339e-01
-8.99330318e-01 -8.84388626e-01 6.84182763e-01 1.46382198e-01
-9.52675879e-01 3.61712664e-01 -5.43098189e-02 9.39526185e-02
-3.55840800e-03 -5.37291348e-01 -1.16410637e+00 -5.99977791e-01
2.52908021e-01 6.70158327e-01 3.82916898e-01 9.62811057... | [7.658998489379883, -2.1562235355377197] |
e8ba63fa-4b6d-49ec-89ff-6767a5e869ce | towards-more-robust-and-accurate-sequential | 2304.05492 | null | https://arxiv.org/abs/2304.05492v1 | https://arxiv.org/pdf/2304.05492v1.pdf | Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training | Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of sequential recommendation models, their robustness has recently come into question. Two properties unique to the nature of sequential recom... | ['Huan Wang', 'Yongfeng Zhang', 'Yongjun Chen', 'Zhiwei Liu', 'Shelby Heinecke', 'Juntao Tan'] | 2023-04-11 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.01157078e-01 -2.42223442e-01 -3.37725967e-01 1.63430739e-02
-1.40341744e-01 -1.03829622e+00 1.05807483e+00 -3.30293387e-01
-1.90390944e-01 4.40330833e-01 5.61281323e-01 -6.71341419e-01
-1.41781896e-01 -7.20086992e-01 -9.80758488e-01 -1.80927277e-01
-1.95329025e-01 3.65820557e-01 1.61223009e-01 -6.25971913... | [10.114396095275879, 5.812971591949463] |
70934a6a-398a-41fc-8775-52f93d1b16e2 | conformal-regression-in-calorie-prediction | 2304.03778 | null | https://arxiv.org/abs/2304.03778v2 | https://arxiv.org/pdf/2304.03778v2.pdf | Conformal Regression in Calorie Prediction for Team Jumbo-Visma | UCI WorldTour races, the premier men's elite road cycling tour, are grueling events that put physical fitness and endurance of riders to the test. The coaches of Team Jumbo-Visma have long been responsible for predicting the energy needs of each rider of the Dutch team for every race on the calendar. Those must be esti... | ['Christof Seiler', 'Mark Dirksen', 'Kristian van Kuijk'] | 2023-04-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.32041761e-01 -1.77641600e-01 -7.48080075e-01 -2.38046080e-01
-6.78781509e-01 -5.41017234e-01 3.66903841e-02 1.92555979e-01
-3.78629863e-01 1.11487269e+00 1.42658561e-01 -3.74875724e-01
-6.25013113e-01 -1.04495883e+00 -5.14811993e-01 -3.03270787e-01
3.45922522e-02 4.85786647e-01 3.59215409e-01 -2.85925448... | [7.617336750030518, 4.072122097015381] |
0c857af7-e737-4d6c-9f7e-e37d634636a3 | stackelberg-decision-transformer-for | 2305.07856 | null | https://arxiv.org/abs/2305.07856v1 | https://arxiv.org/pdf/2305.07856v1.pdf | Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems | Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely constrained by network structures or environmental limitations... | ['Guoliang Fan', 'Rui Zhao', 'Dapeng Li', 'Zhiwei Xu', 'Lijuan Li', 'Hangyu Mao', 'Bin Zhang'] | 2023-05-13 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-2.08371341e-01 -2.01748803e-01 4.03247997e-02 2.30107158e-01
-2.94725716e-01 -6.43727541e-01 6.40585363e-01 1.23999445e-02
-4.16545719e-01 9.92347240e-01 7.96608999e-03 -2.05334887e-01
-6.60031259e-01 -7.28177488e-01 -9.73703340e-02 -9.18229699e-01
-4.75619525e-01 5.01734436e-01 5.60468316e-01 -6.60556793... | [3.7709386348724365, 2.0036556720733643] |
a815e407-1bc4-4399-9e15-f73f3452ee9a | latent-variable-generative-models-for-data | 1910.00382 | null | https://arxiv.org/abs/1910.00382v1 | https://arxiv.org/pdf/1910.00382v1.pdf | Latent-Variable Generative Models for Data-Efficient Text Classification | Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan,2002; Yogatama et al., 2017; Lewis and Fan,2019). In this paper, we improve generative text classifie... | ['Xiaoan Ding', 'Kevin Gimpel'] | 2019-10-01 | latent-variable-generative-models-for-data-1 | https://aclanthology.org/D19-1048 | https://aclanthology.org/D19-1048.pdf | ijcnlp-2019-11 | ['small-data'] | ['computer-vision'] | [ 2.68333942e-01 1.61466494e-01 -3.53108197e-01 -3.85425717e-01
-9.71682072e-01 -6.79019153e-01 1.19155955e+00 -4.22868133e-02
-3.94088268e-01 7.98115730e-01 5.20950079e-01 -3.21791500e-01
-5.05592581e-03 -9.02422547e-01 -5.91771007e-01 -7.70473480e-01
3.16642344e-01 6.37932897e-01 -2.67126054e-01 1.13628611... | [11.47783088684082, 8.845158576965332] |
646982de-52c9-46e1-87b4-4a9585ea15b0 | controllable-attention-for-structured-layered-1 | 1910.11306 | null | https://arxiv.org/abs/1910.11306v1 | https://arxiv.org/pdf/1910.11306v1.pdf | Controllable Attention for Structured Layered Video Decomposition | The objective of this paper is to be able to separate a video into its natural layers, and to control which of the separated layers to attend to. For example, to be able to separate reflections, transparency or object motion. We make the following three contributions: (i) we introduce a new structured neural network ar... | ['Relja Arandjelović', 'João Carreira', 'Jean-Baptiste Alayrac', 'Andrew Zisserman'] | 2019-10-24 | controllable-attention-for-structured-layered | http://openaccess.thecvf.com/content_ICCV_2019/html/Alayrac_Controllable_Attention_for_Structured_Layered_Video_Decomposition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Alayrac_Controllable_Attention_for_Structured_Layered_Video_Decomposition_ICCV_2019_paper.pdf | iccv-2019-10 | ['reflection-removal'] | ['computer-vision'] | [ 7.16139197e-01 1.75653502e-01 1.92700788e-01 -1.91152081e-01
-4.67110604e-01 -5.13211012e-01 6.00715935e-01 -4.33724791e-01
-1.82190686e-01 3.07392806e-01 4.30697292e-01 -2.64326274e-01
-1.40415758e-01 -3.48892838e-01 -6.99118137e-01 -5.01990199e-01
-4.21713680e-01 -1.08857512e-01 4.72150713e-01 -2.01159015... | [10.732179641723633, -1.3278669118881226] |
5a39daaa-c9ac-477d-9c74-6ea21874119c | social-sparsity-brain-decoders-faster-spatial | 1606.06439 | null | http://arxiv.org/abs/1606.06439v1 | http://arxiv.org/pdf/1606.06439v1.pdf | Social-sparsity brain decoders: faster spatial sparsity | Spatially-sparse predictors are good models for brain decoding: they give
accurate predictions and their weight maps are interpretable as they focus on a
small number of regions. However, the state of the art, based on total
variation or graph-net, is computationally costly. Here we introduce sparsity
in the local neig... | ['Matthieu Kowalski', 'Gaël Varoquaux', 'Bertrand Thirion'] | 2016-06-21 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.59612507e-01 4.67968494e-01 -5.14498293e-01 -6.64773464e-01
-4.33543384e-01 -1.06548623e-03 4.04856741e-01 1.67199560e-02
-1.20188400e-01 7.70978987e-01 8.82541537e-01 -1.42984137e-01
-3.36521387e-01 -3.52462411e-01 -8.24231327e-01 -5.98487318e-01
-4.10536557e-01 4.25307035e-01 -3.06305774e-02 1.07545098... | [12.370177268981934, 3.3698763847351074] |
8f8edf34-3dc0-4615-92d8-6a0c8d10e36f | scenesqueezer-learning-to-compress-scene-for | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_SceneSqueezer_Learning_To_Compress_Scene_for_Camera_Relocalization_CVPR_2022_paper.pdf | SceneSqueezer: Learning To Compress Scene for Camera Relocalization | Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image. Storing these pre-built 3D scene models can be prohibitively expensive for large-scale environments, especially on mobile devices with limited storage and com... | ['Ping Tan', 'Zhaopeng Cui', 'Guofeng Zhang', 'Shuaicheng Liu', 'Wenbo Li', 'Rakesh Shrestha', 'Luwei Yang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['camera-relocalization'] | ['computer-vision'] | [ 1.34689257e-01 -5.07922053e-01 -2.28666112e-01 -4.10401493e-01
-1.03576541e+00 -6.41615272e-01 3.36886138e-01 5.87442458e-01
-6.69786394e-01 2.49211162e-01 -1.75278619e-01 1.20912818e-02
-2.74403580e-02 -7.94733107e-01 -8.77840221e-01 -4.25471425e-01
-6.05290830e-02 5.94702125e-01 5.84061861e-01 3.09046388... | [7.587852954864502, -2.1956541538238525] |
7b714f35-41ca-468b-b2db-2a2757018a62 | towards-a-foundation-model-for-neural-network | 2303.09949 | null | https://arxiv.org/abs/2303.09949v1 | https://arxiv.org/pdf/2303.09949v1.pdf | Towards a Foundation Model for Neural Network Wavefunctions | Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schr\"odinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A signifi... | ['Philipp Grohs', 'Leon Gerard', 'Michael Scherbela'] | 2023-03-17 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.42637268e-01 -3.92742187e-01 -6.76711500e-02 -4.82911170e-01
-1.14523768e+00 -3.40098500e-01 1.85926214e-01 2.00767770e-01
-5.89388251e-01 1.36033034e+00 -1.32693738e-01 -4.42311972e-01
-1.68605193e-01 -8.91020358e-01 -8.23424935e-01 -1.03714025e+00
-5.36737405e-03 6.65987551e-01 -3.58955771e-01 -2.76904047... | [5.339962005615234, 5.215750694274902] |
499f0e57-b02b-4c67-87ba-250621ce7a8b | a-neural-network-solves-and-generates | 2112.15594 | null | https://arxiv.org/abs/2112.15594v4 | https://arxiv.org/pdf/2112.15594v4.pdf | A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level | We demonstrate that a neural network pre-trained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates new questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 8... | ['Linda Chen', 'Kevin Liu', 'Albert Lu', 'Reece Shuttleworth', 'Sarah Zhang', 'Gilbert Strang', 'Eugene Wu', 'Nakul Verma', 'Avi Shporer', 'Jayson Lynch', 'Taylor L. Patti', 'Nikhil Singh', 'Elizabeth Ke', 'Leonard Tang', 'Newman Cheng', 'Roman Wang', 'Sunny Tran', 'Iddo Drori'] | 2021-12-31 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-3.73719856e-02 2.58621335e-01 5.73776895e-03 -6.91292575e-03
-9.47574914e-01 -9.76678252e-01 4.25053477e-01 5.55468082e-01
-2.37197895e-02 5.48640370e-01 4.08227630e-02 -1.15525687e+00
-4.12278920e-01 -1.70833492e+00 -9.56969500e-01 1.86719328e-01
8.80919620e-02 4.65375215e-01 1.05379425e-01 -6.78314209... | [9.762528419494629, 7.388423442840576] |
f89103f6-2d63-4c0e-a837-cb8da1e8ba61 | early-predictions-for-medical-crowdfunding-a | 1911.05702 | null | https://arxiv.org/abs/1911.05702v1 | https://arxiv.org/pdf/1911.05702v1.pdf | Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs | Medical crowdfunding is a popular channel for people needing financial help paying medical bills to collect donations from large numbers of people. However, large heterogeneity exists in donations across cases, and fundraisers face significant uncertainty in whether their crowdfunding campaigns can meet fundraising goa... | ['Hu', 'Yu', 'Tong Wang', 'Fujie Jin', 'Yuan Cheng'] | 2019-11-09 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.88349652e-01 -1.77184790e-01 -5.73992431e-01 -5.78033745e-01
-4.81552392e-01 -5.07050276e-01 5.37342072e-01 5.94327986e-01
-2.85365939e-01 5.61600447e-01 7.93140292e-01 -4.49599952e-01
-2.60905772e-01 -9.20291126e-01 -1.96881980e-01 -4.76373374e-01
-1.12735577e-01 4.53678399e-01 -2.52090424e-01 -2.79090464... | [7.605957984924316, 5.9245829582214355] |
200aa7b5-8a83-4e89-8c14-a4844c3e3b0c | prototype-based-interpretable-graph-neural | null | null | https://ieeexplore.ieee.org/document/9953541 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9953541&tag=1 | Prototype-based Interpretable Graph Neural Networks | Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works propose post-hoc meth... | ['Roberto Capobianco', 'Biagio La Rosa', 'Alessio Ragno'] | 2022-11-16 | null | null | null | ieee-transactions-on-artificial-intelligence | ['explainable-models'] | ['computer-vision'] | [ 4.22051549e-01 1.35744953e+00 -2.26609543e-01 -4.32946861e-01
6.26535714e-01 -3.49659979e-01 8.39128733e-01 3.97345722e-01
3.01356971e-01 6.37076318e-01 -1.66297629e-02 -6.35783970e-01
-7.28909314e-01 -8.87978494e-01 -7.57303298e-01 -2.32778683e-01
-3.58918250e-01 9.74525034e-01 2.05139428e-01 -3.86631519... | [7.680084228515625, 6.2200493812561035] |
c60843f7-73df-42cc-b7d0-1625875a4998 | tarc-tunisian-arabish-corpus-first-complete | 2207.04796 | null | https://arxiv.org/abs/2207.04796v1 | https://arxiv.org/pdf/2207.04796v1.pdf | TArC: Tunisian Arabish Corpus First complete release | In this paper we present the final result of a project on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the creation of two integrated and independent resources: a corpus and a NLP tool created to annotate the former with various levels of linguistic in... | ['Marco Dinarelli', 'Elisa Gugliotta'] | 2022-07-11 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-8.08972418e-02 1.79717138e-01 -1.66419491e-01 -4.83733535e-01
-3.52339834e-01 -9.28718626e-01 7.76811421e-01 1.12426028e-01
-6.05597615e-01 1.20782495e+00 5.22783935e-01 -5.81241906e-01
1.76705532e-02 -6.21645689e-01 1.39322981e-01 -3.92267913e-01
9.77779329e-02 8.73523235e-01 -1.89108267e-01 -7.09923446... | [10.370837211608887, 10.308610916137695] |
4148fdc1-2a83-41fd-8d44-2251b3362e7e | uncertainty-based-traffic-accident | 2008.00334 | null | https://arxiv.org/abs/2008.00334v1 | https://arxiv.org/pdf/2008.00334v1.pdf | Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning | Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic scenes and limited visual cues, it is of great challenge to predict how long there will be an accident from early observed frames. Most ex... | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2020-08-01 | null | null | null | null | ['accident-anticipation', 'activity-prediction', 'time-to-event-prediction', 'activity-prediction'] | ['computer-vision', 'computer-vision', 'time-series', 'time-series'] | [-1.50204478e-02 3.06325685e-02 -2.67962784e-01 -5.09524047e-01
-7.37997293e-01 2.00200547e-02 6.64261758e-01 -2.78943088e-02
-9.93949454e-03 3.42470586e-01 6.39174581e-01 -2.57081062e-01
-4.84342545e-01 -6.96855068e-01 -9.04839098e-01 -5.28504193e-01
-1.57252952e-01 1.25260949e-01 3.32868636e-01 -1.55384064... | [7.581345558166504, 0.39110898971557617] |
5df1e8d0-04b6-4499-8bba-4b167ece23a2 | seatrans-learning-segmentation-assisted | 2206.05763 | null | https://arxiv.org/abs/2206.05763v2 | https://arxiv.org/pdf/2206.05763v2.pdf | SeATrans: Learning Segmentation-Assisted diagnosis model via Transformer | Clinically, the accurate annotation of lesions/tissues can significantly facilitate the disease diagnosis. For example, the segmentation of optic disc/cup (OD/OC) on fundus image would facilitate the glaucoma diagnosis, the segmentation of skin lesions on dermoscopic images is helpful to the melanoma diagnosis, etc. Wi... | ['Yanwu Xu', 'Yehui Yang', 'Jing Gao', 'Zhaowei Wang', 'Dalu Yang', 'Fangxin Shang', 'Huihui Fang', 'Junde Wu'] | 2022-06-12 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 1.87670290e-01 2.85626948e-02 -2.93474257e-01 -2.67642885e-01
-5.00869036e-01 -4.01670637e-04 2.33549446e-01 -2.16068059e-01
-1.84868358e-03 4.87598836e-01 4.26061824e-02 -4.25351486e-02
-3.76599252e-01 -6.55673862e-01 -2.72054374e-02 -1.15141332e+00
2.70613313e-01 4.25513327e-01 3.35914373e-01 7.58365020... | [15.580938339233398, -2.9688103199005127] |
57ad705a-7d39-4cf0-99d6-4443d10e0262 | evaluating-feature-attribution-methods-for | 2211.12702 | null | https://arxiv.org/abs/2211.12702v1 | https://arxiv.org/pdf/2211.12702v1.pdf | Evaluating Feature Attribution Methods for Electrocardiogram | The performance of cardiac arrhythmia detection with electrocardiograms(ECGs) has been considerably improved since the introduction of deep learning models. In practice, the high performance alone is not sufficient and a proper explanation is also required. Recently, researchers have started adopting feature attributio... | ['Wonjong Rhee', 'Euna Jung', 'Jimyeong Kim', 'Jangwon Suh'] | 2022-11-23 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [-1.00895219e-01 -4.01574403e-01 4.03914154e-02 -2.99473822e-01
-6.30842865e-01 -5.36108732e-01 1.21915817e-01 4.02542025e-01
-8.57302696e-02 6.23071730e-01 -7.35595599e-02 -3.96713555e-01
-4.45527405e-01 -2.20689818e-01 -1.03981018e-01 -5.13094366e-01
-8.56229439e-02 1.34964988e-01 -4.20322120e-02 -1.56489000... | [14.31857681274414, 3.294062614440918] |
7e9e2d92-ad4f-471d-bfc3-4e64305bfe3d | joint-path-planning-and-power-allocation-of-a | 2306.10071 | null | https://arxiv.org/abs/2306.10071v1 | https://arxiv.org/pdf/2306.10071v1.pdf | Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning | This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV's goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quali... | ['Ismail Guvenc', 'Fatemeh Afghah', 'Sajad Mousavi', 'Fatemeh Lotfi', 'Alireza Shamsoshoara'] | 2023-06-15 | null | null | null | null | ['q-learning'] | ['methodology'] | [-6.46454468e-03 2.86284417e-01 -2.52952874e-01 3.73902112e-01
-5.93941174e-02 -4.08479244e-01 4.94211689e-02 -2.83016175e-01
-3.36384296e-01 1.39860451e+00 -5.76577246e-01 -5.53434789e-01
-7.57847965e-01 -9.60343421e-01 -5.48285723e-01 -1.15321803e+00
-3.66374671e-01 7.09100887e-02 -1.86658964e-01 -2.65572935... | [5.819453239440918, 1.6012474298477173] |
23e68a2d-6e37-4a33-9ec8-32dee1dc43f0 | fisheyemodnet-moving-object-detection-on | 1908.11789 | null | https://arxiv.org/abs/1908.11789v1 | https://arxiv.org/pdf/1908.11789v1.pdf | FisheyeMODNet: Moving Object detection on Surround-view Cameras for Autonomous Driving | Moving Object Detection (MOD) is an important task for achieving robust autonomous driving. An autonomous vehicle has to estimate collision risk with other interacting objects in the environment and calculate an optional trajectory. Collision risk is typically higher for moving objects than static ones due to the need ... | ['Senthil Yogamani', 'Varun Ravi Kumar', 'Marie Yahiaoui', 'Letizia Mariotti', 'Ian Clancy', 'Hazem Rashed', 'Lucie Yahiaoui', 'Ganesh Sistu'] | 2019-08-30 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [-2.55449444e-01 -4.50013578e-02 2.43076999e-02 -5.88877678e-01
-1.35222882e-01 -5.05675793e-01 2.53256053e-01 -3.03357095e-01
-1.00687873e+00 2.25040242e-01 -4.49098349e-01 -6.15302503e-01
1.87788025e-01 -6.40430450e-01 -1.07544053e+00 -3.59127551e-01
-4.08024549e-01 1.09807320e-01 9.49262381e-01 -3.46890092... | [8.0181245803833, -1.309174656867981] |
be8426aa-9225-45ce-a729-7f2907058516 | improving-robustness-of-semantic-segmentation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.pdf | Improving Robustness of Semantic Segmentation to Motion-Blur Using Class-Centric Augmentation | Semantic segmentation involves classifying each pixel into one of a pre-defined set of object/stuff classes. Such a fine-grained detection and localization of objects in the scene is challenging by itself. The complexity increases manifold in the presence of blur. With cameras becoming increasingly light-weight and... | ['A. N. Rajagopalan', 'Aakanksha'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 6.14043653e-01 -3.11540812e-01 3.06586117e-01 -2.66691715e-01
-4.53603655e-01 -8.45545053e-01 5.58719516e-01 -3.87456596e-01
-5.93156397e-01 6.87098205e-01 8.83178711e-02 5.75998873e-02
-2.81674061e-02 -4.80844051e-01 -9.25122201e-01 -7.12991118e-01
4.39310431e-01 1.49216920e-01 5.30037522e-01 2.25244164... | [11.57508659362793, -2.6245079040527344] |
f5bc0098-8e12-4a03-966c-9e200e10ea8f | polyphone-disambiguition-in-mandarin-chinese | 2102.00621 | null | https://arxiv.org/abs/2102.00621v2 | https://arxiv.org/pdf/2102.00621v2.pdf | Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning | The majority of Chinese characters are monophonic, while a special group of characters, called polyphonic characters, have multiple pronunciations. As a prerequisite of performing speech-related generative tasks, the correct pronunciation must be identified among several candidates. This process is called Polyphone Dis... | ['Bin Wang', 'Yu Chen', 'Congyi Wang', 'Yi Shi'] | 2021-02-01 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 2.53499299e-01 -2.22009078e-01 -4.69026476e-01 -2.72461265e-01
-1.11105931e+00 -5.28465390e-01 5.96375465e-01 -5.30604199e-02
-3.57142448e-01 9.23365533e-01 4.02158380e-01 -2.79647976e-01
4.29384440e-01 -6.42880499e-01 -4.06999469e-01 -7.85999477e-01
2.51970202e-01 1.90442264e-01 1.56486891e-02 1.65100530... | [14.460860252380371, 6.91352653503418] |
8a3e33f6-8575-4d9d-a2b4-a2d0f35f2ed4 | where-a-strong-backbone-meets-strong-features | 2211.09074 | null | https://arxiv.org/abs/2211.09074v1 | https://arxiv.org/pdf/2211.09074v1.pdf | Where a Strong Backbone Meets Strong Features -- ActionFormer for Ego4D Moment Queries Challenge | This report describes our submission to the Ego4D Moment Queries Challenge 2022. Our submission builds on ActionFormer, the state-of-the-art backbone for temporal action localization, and a trio of strong video features from SlowFast, Omnivore and EgoVLP. Our solution is ranked 2nd on the public leaderboard with 21.76%... | ['Yin Li', 'Gillian Wang', 'Sicheng Mo', 'Fangzhou Mu'] | 2022-11-16 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [-5.18986106e-01 -1.59878612e-01 -7.68295169e-01 -1.05069153e-01
-1.03204870e+00 -7.95904517e-01 7.06455767e-01 -3.85527313e-01
-5.70175171e-01 7.90442586e-01 1.04709065e+00 2.68351436e-01
3.99239846e-02 -2.64812529e-01 -6.65975571e-01 -3.98134232e-01
-5.69923639e-01 2.82296360e-01 5.72613478e-01 2.99879629... | [8.36678695678711, 0.41829895973205566] |
a8047406-a692-4426-8937-7ebfb1ac8a73 | do-we-need-to-penalize-variance-of-losses-for | 2201.12739 | null | https://arxiv.org/abs/2201.12739v1 | https://arxiv.org/pdf/2201.12739v1.pdf | Do We Need to Penalize Variance of Losses for Learning with Label Noise? | Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of losses will improve the stability and generalization of the algorithms. Interestingly, we found that the variance should be increased for t... | ['Tongliang Liu', 'Mingming Gong', 'Bo Han', 'Jun Yu', 'Yuxuan Du', 'Yu Yao', 'Yexiong Lin'] | 2022-01-30 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.16452137e-01 3.20450403e-02 -3.03294390e-01 -6.44796848e-01
-7.87132263e-01 -4.12681192e-01 2.49550432e-01 2.73546278e-01
-7.38768876e-01 7.73842514e-01 -2.79253465e-03 -5.42235859e-02
-7.58201033e-02 -6.93227112e-01 -7.20905542e-01 -8.33668530e-01
1.11030139e-01 1.29188403e-01 2.63952971e-01 8.65761191... | [9.241592407226562, 3.9856975078582764] |
647eda30-9461-460d-a386-f91b530301a0 | consciousness-is-learning-predictive | 2301.07016 | null | https://arxiv.org/abs/2301.07016v2 | https://arxiv.org/pdf/2301.07016v2.pdf | Consciousness is learning: predictive processing systems that learn by binding may perceive themselves as conscious | Machine learning algorithms have achieved superhuman performance in specific complex domains. Yet learning online from few examples and efficiently generalizing across domains remains elusive. In humans such learning proceeds via declarative memory formation and is closely associated with consciousness. Predictive proc... | ['V. A. Aksyuk'] | 2023-01-17 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 2.89291978e-01 1.72713697e-01 2.84118533e-01 -3.15489829e-01
2.84845917e-03 -5.15251338e-01 1.08931625e+00 1.82754844e-01
-4.39825356e-01 7.97309756e-01 5.50586760e-01 -2.30435356e-01
-4.97400999e-01 -7.36982405e-01 -5.76862931e-01 -8.49753380e-01
-3.66674453e-01 4.16755408e-01 3.44426543e-01 -1.34453669... | [8.705809593200684, 6.1819353103637695] |
9422bd8d-2790-458d-8b87-6af58006821e | pvp-pre-trained-visual-parameter-efficient | 2304.13639 | null | https://arxiv.org/abs/2304.13639v1 | https://arxiv.org/pdf/2304.13639v1.pdf | PVP: Pre-trained Visual Parameter-Efficient Tuning | Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. Recently, Parameter-Efficient Tuning (PETuning) techniques, e.g., Vi... | ['Qingyong Hu', 'Peng Qiao', 'Junjie Zhu', 'Naiyang Guan', 'Ke Yang', 'Zhao Song'] | 2023-04-26 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.33125743e-02 -3.27675283e-01 -1.85423374e-01 -2.40887225e-01
-8.43698382e-01 -7.62899697e-01 7.21479475e-01 -2.92744607e-01
-4.70245779e-01 3.89814109e-01 1.26655087e-01 -4.50527579e-01
-5.74785434e-02 -6.23255312e-01 -7.31578231e-01 -8.31414878e-01
3.16564262e-01 6.59642398e-01 4.15664673e-01 -1.88185468... | [10.191439628601074, 1.9944404363632202] |
55d55862-6830-4a31-849e-8d2edbdf8a2b | global-structure-knowledge-guided-relation | 2305.1385 | null | https://arxiv.org/abs/2305.13850v1 | https://arxiv.org/pdf/2305.13850v1.pdf | Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document | Visual relation extraction (VRE) aims to extract relations between entities from visuallyrich documents. Existing methods usually predict relations for each entity pair independently based on entity features but ignore the global structure information, i.e., dependencies between entity pairs. The absence of global stru... | ['Siliang Tang', 'Xiaozhong Liu', 'Jun Lin', 'Qian Xiao', 'Duo Dong', 'Juncheng Li', 'Xiangnan Chen'] | 2023-05-23 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [-1.08712256e-01 4.16886449e-01 -5.95220566e-01 -4.00334358e-01
-7.84360886e-01 -6.29425764e-01 7.05653727e-01 4.13584918e-01
-6.39782026e-02 6.83744431e-01 5.28786242e-01 -4.03171986e-01
-2.30125800e-01 -9.39730644e-01 -6.02771640e-01 -4.43621874e-01
-2.16372311e-01 7.82262862e-01 1.21501975e-01 -9.43389609... | [9.14686393737793, 8.292787551879883] |
f93359b1-027c-4f9d-8485-2586493c459f | non-contrastive-self-supervised-learning-for | 2208.05445 | null | https://arxiv.org/abs/2208.05445v1 | https://arxiv.org/pdf/2208.05445v1.pdf | Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech | In recent studies, self-supervised pre-trained models tend to outperform supervised pre-trained models in transfer learning. In particular, self-supervised learning (SSL) of utterance-level speech representation can be used in speech applications that require discriminative representation of consistent attributes withi... | ['Najim Dehak', 'Laureano Moro-Velazquez', "Jes'us Villalba", 'Jaejin Cho'] | 2022-08-10 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 2.33240187e-01 2.64639050e-01 -1.77083790e-01 -7.96267092e-01
-7.75074303e-01 -2.31088251e-01 4.95192766e-01 4.19428170e-01
-6.50230229e-01 8.01939547e-01 2.87005037e-01 -2.87042230e-01
3.45280141e-01 -6.24465168e-01 -4.94053513e-01 -6.81219757e-01
-1.54862478e-01 2.91085094e-01 1.82066292e-01 -2.26451606... | [14.076260566711426, 6.152613639831543] |
a3e2e822-bd8b-4754-bda2-eb8d59be5d3a | shuffle-transformer-with-feature-alignment | 2106.0865 | null | https://arxiv.org/abs/2106.08650v1 | https://arxiv.org/pdf/2106.08650v1.pdf | Shuffle Transformer with Feature Alignment for Video Face Parsing | This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing rep... | ['Bin Fu', 'Gang Yu', 'Guozhong Luo', 'Pei Cheng', 'Zilong Huang', 'Yang Han', 'Rui Zhang'] | 2021-06-16 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 1.44007534e-01 1.72063380e-01 1.74122006e-01 -6.66381717e-01
-9.93465662e-01 -5.02950132e-01 9.12812427e-02 -5.89981973e-01
-8.21357593e-02 3.39583516e-01 3.79779220e-01 1.21588651e-02
6.14889860e-02 -5.13391256e-01 -6.27791584e-01 -3.89003724e-01
8.26378912e-02 2.25674152e-01 3.54064137e-01 -1.35918453... | [13.381505012512207, 0.6090220212936401] |
875c5b6e-9857-4cb3-b2cb-044350b80109 | rng-kbqa-generation-augmented-iterative-1 | null | null | https://openreview.net/forum?id=xEWyJkPuZlI | https://openreview.net/pdf?id=xEWyJkPuZlI | RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering | Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior ranking-based approaches have shown some success in generalization, but suffer from the coverage issue. We present RnG-KBQA, a Rank-and-Generate approac... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.61560193e-01 1.65286317e-01 -2.91039258e-01 -4.31031048e-01
-1.58388317e+00 -8.63329589e-01 3.29475284e-01 4.59482163e-01
-3.58131915e-01 1.09574306e+00 1.46184281e-01 -1.25123784e-01
-7.09939778e-01 -1.16784012e+00 -1.02046442e+00 -3.88278723e-01
1.24171913e-01 1.18906748e+00 8.02987397e-01 -8.52407515... | [10.507923126220703, 7.932332515716553] |
75b5018f-857e-4c8f-9315-419fd03eac65 | webly-supervised-concept-expansion-for | 2202.02317 | null | https://arxiv.org/abs/2202.02317v2 | https://arxiv.org/pdf/2202.02317v2.pdf | Webly Supervised Concept Expansion for General Purpose Vision Models | General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully supervised datasets. Scaling GPVs to tens of thousands of concepts by acquiring data to learn each concep... | ['Aniruddha Kembhavi', 'Derek Hoiem', 'Eric Kolve', 'Tanmay Gupta', 'Christopher Clark', 'Amita Kamath'] | 2022-02-04 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.20825146e-02 -2.24168584e-01 -7.44092464e-02 -3.17938536e-01
-5.89927197e-01 -1.01334953e+00 7.48258770e-01 1.86947405e-01
-4.94479358e-01 3.92244905e-01 6.87675271e-03 -4.93759573e-01
8.97577032e-03 -6.17200851e-01 -1.03999579e+00 -2.07135051e-01
-2.17015073e-02 6.74172640e-01 4.37050015e-01 -3.31095517... | [10.407437324523926, 1.6873009204864502] |
30245b85-4997-4bba-a0ff-18cde9c27fea | embodied-question-answering | 1711.11543 | null | http://arxiv.org/abs/1711.11543v2 | http://arxiv.org/pdf/1711.11543v2.pdf | Embodied Question Answering | We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person ... | ['Stefan Lee', 'Samyak Datta', 'Dhruv Batra', 'Georgia Gkioxari', 'Devi Parikh', 'Abhishek Das'] | 2017-11-30 | embodied-question-answering-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Das_Embodied_Question_Answering_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Das_Embodied_Question_Answering_CVPR_2018_paper.pdf | cvpr-2018-6 | ['embodied-question-answering'] | ['computer-vision'] | [ 2.79724032e-01 4.82133687e-01 6.12340391e-01 -4.17896658e-01
-6.84898198e-01 -9.10886645e-01 5.04597425e-01 -5.23278564e-02
-4.94726151e-01 6.84258223e-01 8.34292695e-02 -5.86641014e-01
-5.95903061e-02 -9.72621739e-01 -7.10419655e-01 -4.32455152e-01
-1.28867641e-01 7.15408206e-01 1.37317836e-01 -6.76537573... | [4.358462333679199, 0.649852991104126] |
11905af4-357a-4c8c-b65d-8c9e56526e77 | a-deep-tree-structured-fusion-model-for | 1811.08632 | null | http://arxiv.org/abs/1811.08632v1 | http://arxiv.org/pdf/1811.08632v1.pdf | A Deep Tree-Structured Fusion Model for Single Image Deraining | We propose a simple yet effective deep tree-structured fusion model based on
feature aggregation for the deraining problem. We argue that by effectively
aggregating features, a relatively simple network can still handle tough image
deraining problems well. First, to capture the spatial structure of rain we use
dilated ... | ['John Paisley', 'Feng Wu', 'Xinghao Ding', 'Xueyang Fu', 'Qi Qi', 'Yue Huang'] | 2018-11-21 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.17119735e-02 -8.09070021e-02 1.80062205e-01 -4.57748741e-01
-3.97130907e-01 -1.79032505e-01 2.91845620e-01 -8.29280093e-02
-1.91348284e-01 7.20823169e-01 2.65595198e-01 -2.08885655e-01
-2.37075165e-01 -9.60224807e-01 -6.33768499e-01 -9.78041589e-01
-2.45448694e-01 -4.43355411e-01 2.64510542e-01 -4.41341937... | [10.933924674987793, -2.988527536392212] |
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