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5107388a-db36-4dc9-9890-e5b713987d1b | attestable-at-semeval-2021-task-9-extending | null | null | https://aclanthology.org/2021.semeval-1.182/ | https://aclanthology.org/2021.semeval-1.182.pdf | AttesTable at SemEval-2021 Task 9: Extending Statement Verification with Tables for Unknown Class, and Semantic Evidence Finding | This paper describes our approach for Task 9 of SemEval 2021: Statement Verification and Evidence Finding with Tables. We participated in both subtasks, namely statement verification and evidence finding. For the subtask of statement verification, we extend the TAPAS model to adapt to the ‘unknown’ class of statements ... | ['Abhishek Rathi', 'Pratik Ratadiya', 'Aadish Jain', 'Harshit Varma'] | 2021-08-01 | null | https://aclanthology.org/2021.semeval-1.182 | https://aclanthology.org/2021.semeval-1.182.pdf | semeval-2021 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 2.43494794e-01 3.19950879e-01 -4.13546652e-01 -3.41800094e-01
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-4.10182387e-01 4.51279551e-01 6.99673533e-01 1.24895714... | [9.536426544189453, 7.701308727264404] |
6f853d5d-ba76-4e06-a94a-e7db0e1dc196 | how-do-decoding-algorithms-distribute | 2303.17006 | null | https://arxiv.org/abs/2303.17006v1 | https://arxiv.org/pdf/2303.17006v1.pdf | How do decoding algorithms distribute information in dialogue responses? | Humans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID principle, and under what conditions adherence to UID might be desirable for dialogue generation. We generate responses using different decod... | ['David Reitter', 'He He', 'Saranya Venkatraman'] | 2023-03-29 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-1.10276587e-01 5.19992828e-01 -1.50387868e-01 -7.50781536e-01
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48baeabf-d60f-4713-9a03-b215dd83dd4d | cl-xabsa-contrastive-learning-for-cross | 2204.00791 | null | https://arxiv.org/abs/2204.00791v5 | https://arxiv.org/pdf/2204.00791v5.pdf | CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis | As an extensive research in the field of natural language processing (NLP), aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment expressed in a text relative to the corresponding aspect. Unfortunately, most languages lack sufficient annotation resources, thus more and more recent researchers f... | ['Shengyi Jiang', 'Aimin Yang', 'Xiaotian Lin', 'Yingwen Fu', 'Nankai Lin'] | 2022-04-02 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.96410462e-01 -4.39838976e-01 -2.52226710e-01 -5.76057076e-01
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6.14443600e-01 4.16067332e-01 -1.65788129e-01 -3.42272848... | [11.369781494140625, 6.81004524230957] |
98699cfd-fef2-4d5c-bf32-60cd200aed3e | sleep-posture-one-shot-learning-framework | 2205.10778 | null | https://arxiv.org/abs/2205.10778v1 | https://arxiv.org/pdf/2205.10778v1.pdf | Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies | Sleep posture is linked to several health conditions such as nocturnal cramps and more serious musculoskeletal issues. However, in-clinic sleep assessments are often limited to vital signs (e.g. brain waves). Wearable sensors with embedded inertial measurement units have been used for sleep posture classification; none... | ['Paolo Paoletti', 'Lyndon Mason', 'Andrew Hopkinson', 'Frans Coenen', 'Omar Elnaggar'] | 2022-05-22 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 3.44505280e-01 3.74863118e-01 -3.14056501e-02 -3.87719452e-01
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b828cc76-6957-4df3-9cd6-fa49278af0ea | causal-analysis-of-the-topcat-trial | 2211.12983 | null | https://arxiv.org/abs/2211.12983v1 | https://arxiv.org/pdf/2211.12983v1.pdf | Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure | We describe the results of applying causal discovery methods on the data from a multi-site clinical trial, on the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT). The trial was inconclusive, with no clear benefits consistently shown for the whole cohort. However, there were... | ['Shlomo Ben-Haim', 'Javed Butler', 'Maksim Sipos', 'Andre Franca', 'Tamara Stemberga', 'Andrew R. Lawrence', 'Hana Chockler', "Tadhg O'Keeffe", 'Francesca E. D. Raimondi'] | 2022-11-23 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 2.18277369e-02 1.69604793e-01 -8.04512739e-01 -3.68926883e-01
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-5.17294466e-01 6.05675280e-01 -1.33412510e-01 4.71503399... | [8.155743598937988, 5.67125129699707] |
49f7fc01-23fc-4e22-bb74-607092a4a64a | residue-density-segmentation-for-monitoring | 2102.04866 | null | https://arxiv.org/abs/2102.04866v1 | https://arxiv.org/pdf/2102.04866v1.pdf | Residue Density Segmentation for Monitoring and Optimizing Tillage Practices | "No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of these approaches depending on numerous factors including a field's soil type(s), topography, and ma... | ['Naira Hovakimyan', 'Ivan Dozier', 'Jennifer Hobbs'] | 2021-02-09 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 4.12913173e-01 -1.16494626e-01 -4.72870946e-01 5.30386344e-02
-1.05541483e-01 -8.45142782e-01 2.27062643e-01 8.29830527e-01
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-3.49633209e-02 2.53770173e-01 -6.03233576e-02 -2.09415674... | [9.297779083251953, -1.5360307693481445] |
3db88695-4d00-41f6-8639-f9590942194f | optimizing-embedding-related-quantum | 2011.00719 | null | https://arxiv.org/abs/2011.00719v2 | https://arxiv.org/pdf/2011.00719v2.pdf | Optimizing embedding-related quantum annealing parameters for reducing hardware bias | Quantum annealers have been designed to propose near-optimal solutions to NP-hard optimization problems. However, the accuracy of current annealers such as the ones of D-Wave Systems, Inc., is limited by environmental noise and hardware biases. One way to deal with these imperfections and to improve the quality of the ... | ['Hristo N. Djidjev', 'Georg Hahn', 'Elijah Pelofske', 'Aaron Barbosa'] | 2020-11-02 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 2.87485152e-01 8.50151330e-02 -3.69039066e-02 -1.59880772e-01
-6.33756697e-01 -6.92237735e-01 3.77377242e-01 4.20743018e-01
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-2.15732306e-01 8.79039645e-01 2.60544389e-01 -4.60031360... | [5.663410663604736, 4.8703508377075195] |
8f161728-1e76-48fb-925d-6e11b56335ef | displacenet-recognising-displaced-people-from | 1905.02025 | null | https://arxiv.org/abs/1905.02025v1 | https://arxiv.org/pdf/1905.02025v1.pdf | DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level | Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the ye... | ['Klaus McDonald-Maier', 'Shoaib Ehsan', 'Grigorios Kalliatakis', 'Maria Fasli'] | 2019-05-03 | null | null | null | null | ['displaced-people-recognition'] | ['computer-vision'] | [ 8.40120241e-02 5.21405280e-01 -3.51321936e-01 -1.58284917e-01
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-1.16113608e-03 4.63339865e-01 -2.18725502e-01 -3.09553027... | [9.454541206359863, -1.2379592657089233] |
10288f33-adef-438f-8fb2-cd57cd2d3620 | on-training-locally-adaptive-cp | 2306.04648 | null | https://arxiv.org/abs/2306.04648v1 | https://arxiv.org/pdf/2306.04648v1.pdf | On training locally adaptive CP | We address the problem of making Conformal Prediction (CP) intervals locally adaptive. Most existing methods focus on approximating the object-conditional validity of the intervals by partitioning or re-weighting the calibration set. Our strategy is new and conceptually different. Instead of re-weighting the calibratio... | ['Nicolo Colombo'] | 2023-06-05 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.67893991e-01 3.74298245e-01 -6.55265450e-01 -7.58717954e-01
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-2.66189665e-01 7.97504842e-01 1.80524647e-01 1.41990617... | [7.981951713562012, 4.282384395599365] |
73797ca1-a77d-4262-8abc-6d0050fecba4 | a-high-precision-pipeline-for-financial | null | null | https://aclanthology.org/2020.coling-main.84 | https://aclanthology.org/2020.coling-main.84.pdf | A High Precision Pipeline for Financial Knowledge Graph Construction | Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents. Knowledge graphs have emerged as a standard for structured knowledge rep... | ['Lanjun Wang', 'Zhefeng Wang', 'Baoxing Huai', 'Michael Simpson', 'Raymond Ng', 'Laks V.S. Lakshmanan', 'Sarah Elhammadi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['data-integration'] | ['knowledge-base'] | [-3.20035815e-01 5.54624736e-01 -4.52451408e-01 -3.20599489e-02
-6.14164650e-01 -1.11961615e+00 7.21704364e-01 1.15902793e+00
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-1.95191339e-01 -1.21086907e+00 -4.85958546e-01 8.92311633e-02
-1.39584035e-01 4.76588845e-01 5.33315539e-01 -2.63316900... | [9.357536315917969, 8.577045440673828] |
4633ca22-912b-4b00-b0da-8dcebdcb8647 | multizoo-multibench-a-standardized-toolkit | 2306.16413 | null | https://arxiv.org/abs/2306.16413v1 | https://arxiv.org/pdf/2306.16413v1.pdf | MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning | Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiZoo, a public toolkit consisting of standardized implementations of > 20 core ... | ['Ruslan Salakhutdinov', 'Louis-Philippe Morency', 'Yun Cheng', 'Arav Agarwal', 'Xiang Fan', 'Yiwei Lyu', 'Paul Pu Liang'] | 2023-06-28 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 3.65260601e-01 -3.38252097e-01 -3.87542307e-01 -2.76125610e-01
-1.40164351e+00 -1.05883324e+00 6.80165946e-01 1.69257596e-01
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-6.15137592e-02 2.65097350e-01 -1.51811570e-01 -3.96897420... | [10.729596138000488, 1.6032147407531738] |
00a31581-c905-4399-b726-2927406b164a | digicall-a-benchmark-for-measuring-the | null | null | https://aclanthology.org/2022.finnlp-1.7/ | https://aclanthology.org/2022.finnlp-1.7.pdf | DigiCall: A Benchmark for Measuring the Maturity of Digital Strategy through Company Earning Calls | Digital transformation reinvents companies, their vision and strategy, organizational structure, processes, capabilities, and culture, and enables the development of new or enhanced products and services delivered to customers more efficiently. Organizations, by formalizing their digital strategy attempt to plan for th... | ['T. Ravichandran', 'Kexuan Sun', 'Hilal Pataci'] | 2022-12-08 | null | null | null | finnlp-emnlp-2022-12 | ['sentence-classification', 'part-of-speech-tagging', 'culture'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 6.86143562e-02 3.30218762e-01 -7.35397756e-01 -1.49924785e-01
-8.62157583e-01 -1.08430564e+00 8.94484162e-01 1.92135394e-01
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6.03794217e-01 6.69791877e-01 -1.41660795e-01 -2.11489871... | [9.51842975616455, 6.6056036949157715] |
8b536bf7-69bf-41ae-8604-9b6e8b6b2b0b | characterizing-an-analogical-concept-memory | 2006.01962 | null | https://arxiv.org/abs/2006.01962v3 | https://arxiv.org/pdf/2006.01962v3.pdf | Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition | Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable... | ['Matthew Shreve', 'Shiwali Mohan', 'Kent Evans', 'Matt Klenk', 'Aaron Ang', 'John Maxwell'] | 2020-06-02 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.48610413e-01 3.31139982e-01 6.46521866e-01 -3.82387996e-01
2.77947664e-01 -8.75263393e-01 1.12101460e+00 5.47005653e-01
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-7.12466896e-01 -1.16149569e+00 -4.73030746e-01 -2.38057181e-01
-4.18393254e-01 8.17546427e-01 4.89462435e-01 -7.97035515... | [4.371372222900391, 1.250159502029419] |
4145c583-7437-4a06-ac9c-8aeb0771b643 | gnn-at-the-edge-cost-efficient-graph-neural | 2210.17281 | null | https://arxiv.org/abs/2210.17281v1 | https://arxiv.org/pdf/2210.17281v1.pdf | GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers | Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks ... | ['Xu Chen', 'Shuai Yu', 'Zhi Zhou', 'Peng Huang', 'Chongyu Yang', 'Liekang Zeng'] | 2022-10-31 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.29759052e-01 2.53359824e-01 -3.99780154e-01 -6.28756639e-03
8.21196940e-03 -5.20816386e-01 1.15284428e-01 9.11877006e-02
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-6.26389146e-01 -1.05665588e+00 -9.62024808e-01 -4.63365018e-01
-4.55341756e-01 4.03028071e-01 -5.64343147e-02 -1.95599258... | [7.01856803894043, 5.577959060668945] |
92d5f8b2-7d0a-4e21-907d-26de597fd531 | unitn-training-deep-convolutional-neural | null | null | https://aclanthology.org/S15-2079 | https://aclanthology.org/S15-2079.pdf | UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification | null | ['ro', 'Aless Moschitti', 'Aliaksei Severyn'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['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... | [-7.282372951507568, 3.702258825302124] |
637726dd-ed7d-4662-a114-56866dcdc338 | structured-state-space-models-for-in-context | 2303.03982 | null | https://arxiv.org/abs/2303.03982v2 | https://arxiv.org/pdf/2303.03982v2.pdf | Structured State Space Models for In-Context Reinforcement Learning | Structured state space sequence (S4) models have recently achieved state-of-the-art performance on long-range sequence modeling tasks. These models also have fast inference speeds and parallelisable training, making them potentially useful in many reinforcement learning settings. We propose a modification to a variant ... | ['Feryal Behbahani', 'Satinder Singh', 'Jakob Foerster', 'Emilio Parisotto', 'Albert Gu', 'Yannick Schroecker', 'Chris Lu'] | 2023-03-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 1.48582935e-01 -3.19482312e-02 -3.85480136e-01 -1.86736852e-01
-5.32882750e-01 -5.23459375e-01 9.71030176e-01 -1.42074227e-01
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-4.10480738e-01 6.80405438e-01 4.49197471e-01 -5.80858767... | [4.144777297973633, 1.7551339864730835] |
8ba27f33-263f-43fb-9a32-fa553b6e8f80 | deep-learning-based-gait-recognition-using | 1811.00338 | null | https://arxiv.org/abs/1811.00338v3 | https://arxiv.org/pdf/1811.00338v3.pdf | Deep Learning-Based Gait Recognition Using Smartphones in the Wild | Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes ... | ['Yi Zhao', 'Yanling Wang', 'Qin Zou', 'Qingquan Li', 'Qian Wang'] | 2018-11-01 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-2.26618107e-02 -6.49190247e-01 -2.50540584e-01 -2.58487016e-01
-1.05807170e-01 9.99584571e-02 3.51913311e-02 -7.76865939e-03
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2.27830365e-01 -8.94368470e-01 -2.96001762e-01 -8.43058348e-01
-4.07397486e-02 -9.88910347e-02 -2.30457380e-01 2.48021800... | [14.136154174804688, 1.4529379606246948] |
14178148-442a-4e35-97c1-d1346d17b79a | how-does-value-distribution-in-distributional | 2209.14513 | null | https://arxiv.org/abs/2209.14513v1 | https://arxiv.org/pdf/2209.14513v1.pdf | How Does Value Distribution in Distributional Reinforcement Learning Help Optimization? | We consider the problem of learning a set of probability distributions from the Bellman dynamics in distributional reinforcement learning~(RL) that learns the whole return distribution compared with only its expectation in classical RL. Despite its success to obtain superior performance, we still have a poor understand... | ['Linglong Kong', 'Bei Jiang', 'Ke Sun'] | 2022-09-29 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.25331718e-01 8.87529477e-02 -3.72160882e-01 -3.65621418e-01
-5.97057700e-01 -5.65765262e-01 3.35015893e-01 1.70038342e-01
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-5.16643047e-01 -7.37183213e-01 -8.54590774e-01 -1.00983894e+00
-2.44968146e-01 2.58722544e-01 -4.46919173e-01 -4.20919150... | [4.0963969230651855, 2.580162763595581] |
e47ed1ec-2e96-4f11-9634-5cc29fb04da1 | class-conditional-alignment-for-partial | 2003.06722 | null | https://arxiv.org/abs/2003.06722v1 | https://arxiv.org/pdf/2003.06722v1.pdf | Class Conditional Alignment for Partial Domain Adaptation | Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the sour... | ['Farhad Kamangar', 'Mohsen Kheirandishfard', 'Fariba Zohrizadeh'] | 2020-03-14 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.93124151e-01 8.06970745e-02 -2.81222165e-01 -5.46191812e-01
-1.04634809e+00 -7.86343753e-01 5.89917302e-01 -1.05861671e-01
-2.94095457e-01 1.08425617e+00 1.14112794e-02 5.91063090e-02
-1.26151964e-01 -8.19607377e-01 -8.44716311e-01 -8.27730775e-01
3.49063784e-01 5.37159681e-01 4.03312482e-02 -2.17416540... | [10.307760238647461, 3.1639013290405273] |
4e8c5d2f-24e9-4a4d-97e2-c293c0ef7e42 | dialogue-act-recognition-for-text-based | null | null | https://aclanthology.org/W15-5952 | https://aclanthology.org/W15-5952.pdf | Dialogue Act Recognition for Text-based Sinhala | null | ['Surangika Ranathunga', 'B', 'Sudheera Palihakkara', 'Sahab', 'Chamika ara', 'Ahsan Shamsudeen', 'Dammina u'] | 2015-12-01 | null | null | null | ws-2015-12 | ['meeting-summarization'] | ['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
-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... | [-7.312225818634033, 3.7235584259033203] |
9db46eba-a32f-4b2a-8d8e-6c96cba786f4 | q-attention-enabling-efficient-learning-for | 2105.14829 | null | https://arxiv.org/abs/2105.14829v2 | https://arxiv.org/pdf/2105.14829v2.pdf | Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation | Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks. This is partly due to the fact that reinforcement learning algorithms are notoriously difficult and time consuming to train, which is exacerbated when train... | ['Andrew J. Davison', 'Stephen James'] | 2021-05-31 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 5.00824094e-01 1.38660744e-01 -3.51535194e-02 -1.07203007e-01
-6.91807747e-01 -5.92177212e-01 5.52737653e-01 -1.33832306e-01
-5.27680516e-01 6.79134011e-01 -6.84258416e-02 -1.59236714e-01
-4.15120691e-01 -3.82142067e-01 -7.85490632e-01 -6.27225101e-01
-1.73110098e-01 7.86985278e-01 2.74073601e-01 -3.26035738... | [4.644885063171387, 0.7178045511245728] |
394e79e1-8c19-4194-82ed-246830b0dafe | optimization-algorithms-in-smart-grids-a | 2301.07512 | null | https://arxiv.org/abs/2301.07512v1 | https://arxiv.org/pdf/2301.07512v1.pdf | Optimization Algorithms in Smart Grids: A Systematic Literature Review | Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, a... | ['Ali Bou Nassif', 'Ala Altaweel', 'Sidra Aslam'] | 2023-01-16 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.31341445e-01 -4.67389256e-01 -1.13314003e-01 5.76074049e-02
2.41635427e-01 -4.71138775e-01 2.58959234e-01 2.80176908e-01
2.41692245e-01 1.13433659e+00 -9.11820754e-02 -9.00511965e-02
-5.07140517e-01 -1.06258380e+00 2.16945097e-01 -1.37020743e+00
-2.33574688e-01 2.12754235e-01 -2.41135702e-01 -2.77042776... | [5.756521701812744, 2.6291842460632324] |
ed70e332-808e-4a47-913f-7f5297a8915d | endmember-guided-unmixing-network-egu-net-a | 2105.10194 | null | https://arxiv.org/abs/2105.10194v1 | https://arxiv.org/pdf/2105.10194v1.pdf | Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing | Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in d... | ['Bing Zhang', 'Uta Heiden', 'Jocelyn Chanussot', 'Naoto Yokoya', 'Jing Yao', 'Lianru Gao', 'Danfeng Hong'] | 2021-05-21 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.24196011e-01 -4.95760202e-01 8.21390189e-03 -2.33793035e-01
-4.63837147e-01 -5.40416420e-01 2.88039178e-01 -2.85043478e-01
1.54091592e-03 7.70125747e-01 1.12496503e-01 -3.80974770e-01
-4.34479952e-01 -8.67423832e-01 -7.77218223e-01 -1.13038373e+00
-9.71056297e-02 3.65820438e-01 -6.59085095e-01 -2.50471294... | [10.085330963134766, -2.0092220306396484] |
6640b634-9726-48d9-951b-73bccdcccd54 | how-to-teach-dnns-to-pay-attention-to-the | 2004.08250 | null | https://arxiv.org/abs/2004.08250v1 | https://arxiv.org/pdf/2004.08250v1.pdf | How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition | Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship ... | ['George Sterpu', 'Naomi Harte', 'Christian Saam'] | 2020-04-17 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 5.40425301e-01 5.58487512e-02 -6.65513128e-02 -5.42682670e-02
-8.87203038e-01 -4.53556567e-01 9.04454052e-01 -2.80021548e-01
-3.00826252e-01 2.74695486e-01 6.21181548e-01 -1.99983090e-01
-7.63708632e-03 1.26006499e-01 -5.84649324e-01 -9.07757819e-01
2.40856186e-01 9.43092704e-02 5.97852096e-03 -2.67412096... | [14.355118751525879, 5.082784652709961] |
8dec8a2e-37d6-480e-8e00-44e62b219955 | active-gradual-machine-learning-for-entity | null | null | https://openreview.net/forum?id=xaWIeItQ7zb | https://openreview.net/pdf?id=xaWIeItQ7zb | Active Gradual Machine Learning for Entity Resolution | Recent work has shown that the task of entity resolution (ER) can be effectively performed by gradual machine learning (GML). GML begins with some easy instances, which can be automatically labeled by the machine with high accuracy, and then gradually labels more challenging instances by iterative knowledge conveyance ... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['entity-resolution'] | ['natural-language-processing'] | [ 4.18961048e-01 6.13364458e-01 -5.47253013e-01 -3.93388659e-01
-9.63267446e-01 -4.52378064e-01 3.84890348e-01 3.48426342e-01
-4.74770665e-01 1.09247828e+00 -4.76753525e-02 -2.11633950e-01
-5.51086664e-01 -1.03775287e+00 -1.05469954e+00 -6.99373662e-01
-5.87514639e-02 6.54172182e-01 2.39629060e-01 -1.20824901... | [9.137910842895508, 8.448630332946777] |
a7b3f551-c4d6-44e0-8883-c7fc70a8cb74 | consistency-of-spectral-hypergraph | 1505.01582 | null | http://arxiv.org/abs/1505.01582v2 | http://arxiv.org/pdf/1505.01582v2.pdf | Consistency of Spectral Hypergraph Partitioning under Planted Partition Model | Hypergraph partitioning lies at the heart of a number of problems in machine
learning and network sciences. Many algorithms for hypergraph partitioning have
been proposed that extend standard approaches for graph partitioning to the
case of hypergraphs. However, theoretical aspects of such methods have seldom
received ... | ['Debarghya Ghoshdastidar', 'Ambedkar Dukkipati'] | 2015-05-07 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 2.94530720e-01 5.70765555e-01 -5.71868896e-01 -1.10098168e-01
-2.71477878e-01 -7.53544807e-01 -9.83192772e-02 2.58146495e-01
2.21910581e-01 7.99857438e-01 -2.36575007e-01 -4.64605451e-01
-8.43938470e-01 -9.69293952e-01 -5.18446982e-01 -8.15268755e-01
-3.63191485e-01 9.91063118e-01 3.42873394e-01 1.57296613... | [7.024355411529541, 5.185583114624023] |
652ed44c-4393-4acb-9c4e-1dc5a3246bbc | receiver-bandwidth-extension-beyond-nyquist | 2210.07821 | null | https://arxiv.org/abs/2210.07821v2 | https://arxiv.org/pdf/2210.07821v2.pdf | Receiver Bandwidth Extension Beyond Nyquist Using Channel Bonding | Current and upcoming communication and sensing technologies require ever larger bandwidths. Channel bonding can be utilized to extend a receiver's instantaneous bandwidth beyond a single converter's Nyquist limit. Two potential joint front-end and converter design approaches are theoretically introduced, realized and e... | ['Alexander Ihlow', 'Maximilian Engelhardt', 'Michael Schubert', 'Carsten Andrich', 'Sebastian Giehl'] | 2022-10-14 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 4.34994340e-01 1.75020605e-01 1.60235111e-02 -2.40317568e-01
-7.47966945e-01 -7.48409331e-01 3.70708942e-01 -6.57483339e-02
-2.73678273e-01 8.12399805e-01 2.11064168e-03 -5.15059054e-01
-3.57830167e-01 -7.02636719e-01 9.63664651e-02 -4.11721259e-01
-2.63088524e-01 1.84594050e-01 -9.70023870e-02 2.88513392... | [6.492501258850098, 1.2455313205718994] |
a5e085e1-4c8c-4d82-bf8c-7c6915352a99 | decipherment-of-substitution-ciphers-with | null | null | https://aclanthology.org/d18-1102 | https://aclanthology.org/d18-1102.pdf | Decipherment of Substitution Ciphers with Neural Language Models | null | ['Anahita Mansouri Bigvand', 'Nishant Kambhatla', 'Anoop Sarkar'] | 2018-10-01 | null | https://aclanthology.org/D18-1102 | https://aclanthology.org/D18-1102.pdf | emnlp-2018-10 | ['decipherment'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.53917396068573, 15.869172096252441] |
cbe9ba2e-4f82-4d2a-892b-e9c61cf03fa6 | investigating-capsule-networks-with-dynamic | 1804.00538 | null | http://arxiv.org/abs/1804.00538v4 | http://arxiv.org/pdf/1804.00538v4.pdf | Investigating Capsule Networks with Dynamic Routing for Text Classification | In this study, we explore capsule networks with dynamic routing for text
classification. We propose three strategies to stabilize the dynamic routing
process to alleviate the disturbance of some noise capsules which may contain
"background" information or have not been successfully trained. A series of
experiments are ... | ['Jianbo Ye', 'Zhou Zhao', 'Zeyang Lei', 'Min Yang', 'Wei Zhao', 'Suofei Zhang'] | 2018-03-29 | investigating-capsule-networks-with-dynamic-1 | https://aclanthology.org/D18-1350 | https://aclanthology.org/D18-1350.pdf | emnlp-2018-10 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-3.54809500e-02 9.05770659e-02 -5.82833171e-01 -4.70997155e-01
-4.88049597e-01 -7.19240844e-01 5.13542593e-01 3.56326193e-01
-5.88420592e-02 3.89752746e-01 7.59744585e-01 -2.50536680e-01
-1.14073372e-02 -5.21200120e-01 -7.62628734e-01 -4.50888366e-01
-2.85421818e-01 4.20824975e-01 -5.41731156e-02 2.21922934... | [14.771929740905762, -2.6424903869628906] |
9bbbee83-da2a-4458-badb-bff0f27aef25 | when-more-data-hurts-a-troubling-quirk-in | null | null | https://openreview.net/forum?id=53F2mLQQj8J | https://openreview.net/pdf?id=53F2mLQQj8J | When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems | In natural language understanding (NLU) production systems, the end users' evolving needs necessitate the addition of new abilities, indexed by discrete symbols, requiring additional training data and resulting in dynamic, ever-growing datasets.
Dataset growth introduces new challenges: we find that when learning to ma... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['intent-recognition'] | ['natural-language-processing'] | [ 6.09781742e-01 3.91868144e-01 -3.99768174e-01 -5.45224845e-01
-7.08758771e-01 -8.78121912e-01 4.67683375e-01 2.95084387e-01
-5.10386050e-01 6.76967978e-01 5.10239124e-01 -5.50917268e-01
1.78860515e-01 -6.08595133e-01 -1.06318164e+00 8.29630271e-02
1.57911301e-01 4.63332146e-01 1.45007342e-01 -1.92883849... | [10.644779205322266, 8.480096817016602] |
2a36b5e5-a039-498d-9f08-f8b90078cab3 | continuous-mixtures-of-tractable | 2209.10584 | null | https://arxiv.org/abs/2209.10584v3 | https://arxiv.org/pdf/2209.10584v3.pdf | Continuous Mixtures of Tractable Probabilistic Models | Probabilistic models based on continuous latent spaces, such as variational autoencoders, can be understood as uncountable mixture models where components depend continuously on the latent code. They have proven to be expressive tools for generative and probabilistic modelling, but are at odds with tractable probabilis... | ['Robert Peharz', 'Cassio de Campos', 'Erik Quaeghebeur', 'Gennaro Gala', 'Alvaro H. C. Correia'] | 2022-09-21 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.37173259e-01 2.96231538e-01 -9.54862982e-02 6.60521984e-02
-1.14832044e+00 -8.59412432e-01 1.03529561e+00 -2.03085348e-01
-6.03188463e-02 8.56180012e-01 -5.89110814e-02 -3.27935308e-01
-2.84456074e-01 -1.01562262e+00 -1.04836535e+00 -1.19468677e+00
-3.95881906e-02 1.14457154e+00 -1.62801482e-02 2.67232656... | [6.955729007720947, 3.980893135070801] |
72278a00-1d7c-487d-a73c-262ebdd99237 | monolingual-and-cross-lingual-acceptability | 2109.12053 | null | https://arxiv.org/abs/2109.12053v1 | https://arxiv.org/pdf/2109.12053v1.pdf | Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus | The development of automated approaches to linguistic acceptability has been greatly fostered by the availability of the English CoLA corpus, which has also been included in the widely used GLUE benchmark. However, this kind of research for languages other than English, as well as the analysis of cross-lingual approach... | ['Sara Tonelli', 'Elisa Leonardelli', 'Raffaele Guarasci', 'Daniela Trotta'] | 2021-09-24 | null | https://aclanthology.org/2021.findings-emnlp.250 | https://aclanthology.org/2021.findings-emnlp.250.pdf | findings-emnlp-2021-11 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-2.69627810e-01 1.51774973e-01 -7.44401244e-03 -6.20598495e-01
-1.00441360e+00 -8.03833008e-01 8.67059946e-01 6.76123381e-01
-7.54572809e-01 8.70554864e-01 3.80002677e-01 -3.92487854e-01
-1.15181088e-01 -6.20208263e-01 -3.35264385e-01 -3.61966640e-01
1.08950920e-01 7.72209466e-01 3.21738213e-01 -5.96131206... | [10.576825141906738, 9.935096740722656] |
2b7c1b77-2c6d-4595-9730-e4df328d5012 | macro-micro-adversarial-network-for-human | 1807.08260 | null | http://arxiv.org/abs/1807.08260v2 | http://arxiv.org/pdf/1807.08260v2.pdf | Macro-Micro Adversarial Network for Human Parsing | In human parsing, the pixel-wise classification loss has drawbacks in its
low-level local inconsistency and high-level semantic inconsistency. The
introduction of the adversarial network tackles the two problems using a single
discriminator. However, the two types of parsing inconsistency are generated by
distinct mech... | ['Junqing Yu', 'Liang Zheng', 'Zhedong Zheng', 'Yawei Luo', 'Yi Yang', 'Tao Guan'] | 2018-07-22 | macro-micro-adversarial-network-for-human-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yawei_Luo_Macro-Micro_Adversarial_Network_ECCV_2018_paper.pdf | eccv-2018-9 | ['human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 2.95291901e-01 3.66507322e-01 8.76573008e-03 -2.74137169e-01
-1.02442777e+00 -5.98260522e-01 1.61262631e-01 -1.29717007e-01
-3.31299126e-01 8.18454266e-01 -6.02427945e-02 -1.48132533e-01
3.65235656e-01 -9.02792573e-01 -8.47373605e-01 -6.72429740e-01
4.47119385e-01 1.95180371e-01 4.89656746e-01 -3.38111296... | [9.217816352844238, 0.6362105011940002] |
bf2be6b4-3e8f-4839-91db-217a847a6c80 | bo-icp-initialization-of-iterative-closest | 2304.13114 | null | https://arxiv.org/abs/2304.13114v1 | https://arxiv.org/pdf/2304.13114v1.pdf | BO-ICP: Initialization of Iterative Closest Point Based on Bayesian Optimization | Typical algorithms for point cloud registration such as Iterative Closest Point (ICP) require a favorable initial transform estimate between two point clouds in order to perform a successful registration. State-of-the-art methods for choosing this starting condition rely on stochastic sampling or global optimization te... | ['Christoffer Heckman', 'Andrew Beathard', 'Harel Biggie'] | 2023-04-25 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 1.84501141e-01 -2.36639827e-01 1.87843874e-01 -4.17211145e-01
-1.24471605e+00 -4.95777339e-01 6.97883010e-01 4.56613243e-01
-5.90231657e-01 6.66951716e-01 -3.19949687e-01 -2.44036913e-01
-3.03459942e-01 -9.12743807e-01 -8.76639485e-01 -6.05167925e-01
-1.04324192e-01 1.32672954e+00 7.42342710e-01 -1.80850178... | [7.801695346832275, -2.756293296813965] |
940a80c4-b718-42b9-8677-9f4c6889b62a | bytecover2-towards-dimensionality-reduction | null | null | https://ieeexplore.ieee.org/abstract/document/9747630 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9747630 | BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION | Convolutional neural network (CNN)-based methods have
dominated the recent research of cover song identification
(CSI). A typical example is the ByteCover system we proposed, which has achieved state-of-the-art results on all the
mainstream datasets of CSI. In this paper, we propose an upgraded version of ByteCover,... | ['Zejun Ma', 'Bilei Zhu', 'Zijie Wang', 'Ke Chen', 'Xingjian Du'] | 2022-04-27 | null | null | null | icassp-2022-4 | ['cover-song-identification'] | ['music'] | [ 2.59097647e-02 -2.75728911e-01 -2.15884633e-02 1.61008865e-01
-7.44356155e-01 -5.45105934e-01 3.65232676e-01 -5.97070120e-02
-4.15472120e-01 2.49347612e-01 3.55003357e-01 4.86518852e-02
-2.39088193e-01 -7.65907943e-01 -5.29489040e-01 -7.43326187e-01
-2.84214616e-01 2.97034800e-01 -9.33499485e-02 1.36417121... | [15.64250373840332, 5.220884799957275] |
7edf3c3f-837e-4de7-bb32-58880ce5d4f3 | addressing-cold-start-problem-for-end-to-end | 2306.14310 | null | https://arxiv.org/abs/2306.14310v1 | https://arxiv.org/pdf/2306.14310v1.pdf | Addressing Cold Start Problem for End-to-end Automatic Speech Scoring | Integrating automatic speech scoring/assessment systems has become a critical aspect of second-language speaking education. With self-supervised learning advancements, end-to-end speech scoring approaches have exhibited promising results. However, this study highlights the significant decrease in the performance of spe... | ['Seungtaek Choi', 'Jungbae Park'] | 2023-06-25 | null | null | null | null | ['self-supervised-learning'] | ['computer-vision'] | [-1.85235232e-01 -4.27106135e-02 7.15428367e-02 -5.57555676e-01
-1.59317100e+00 -6.60907388e-01 3.57982993e-01 1.45203367e-01
-7.39420712e-01 4.64103609e-01 7.56599784e-01 -3.76035750e-01
-1.23706654e-01 -2.71342248e-01 -2.48026967e-01 -4.71125275e-01
4.27262187e-01 3.94064784e-01 4.10907149e-01 -4.66933697... | [14.451778411865234, 6.70559024810791] |
d534ac6f-104d-4510-b24a-d08f9c483529 | truedeep-a-systematic-approach-of-crack | 2305.19088 | null | https://arxiv.org/abs/2305.19088v1 | https://arxiv.org/pdf/2305.19088v1.pdf | TrueDeep: A systematic approach of crack detection with less data | Supervised and semi-supervised semantic segmentation algorithms require significant amount of annotated data to achieve a good performance. In many situations, the data is either not available or the annotation is expensive. The objective of this work is to show that by incorporating domain knowledge along with deep le... | ['Akshit Achara', 'Ram Krishna Pandey'] | 2023-05-30 | null | null | null | null | ['crack-segmentation', 'semi-supervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 9.26847905e-02 1.03873432e-01 -2.11697027e-01 -6.15988791e-01
-1.28119063e+00 -7.29564011e-01 -4.16240133e-02 -8.46306328e-03
-8.39444101e-01 6.24280870e-01 -9.13924575e-02 -1.68673843e-01
6.48245662e-02 -7.63528168e-01 -1.02627921e+00 -5.46004832e-01
2.09750786e-01 7.87178934e-01 6.50599539e-01 1.71564609... | [9.540806770324707, 0.6309944987297058] |
5f15b481-fa6c-4273-8e93-b270dee83b44 | target-detection-in-synthetic-aperture-radar | 1804.04719 | null | http://arxiv.org/abs/1804.04719v1 | http://arxiv.org/pdf/1804.04719v1.pdf | Target detection in synthetic aperture radar imagery: a state-of-the-art survey | Target detection is the front-end stage in any automatic target recognition
system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of
the detector directly impacts the succeeding stages in the SAR-ATR processing
chain. There are numerous methods reported in the literature for implementing
the detecto... | [] | 2018-04-12 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.07584953e+00 -5.68542957e-01 1.64984190e-03 -4.05523092e-01
-1.04862297e+00 -6.53729618e-01 4.85725820e-01 -2.42394373e-01
-1.72479719e-01 4.93807524e-01 -1.07308097e-01 -6.12946451e-01
-6.58703446e-01 -4.95590150e-01 1.98776037e-01 -1.15003896e+00
-5.24686992e-01 -1.16913445e-01 -2.88327280e-02 -3.17239225... | [6.834278106689453, 1.0998727083206177] |
c942856a-f84a-4adc-b5ca-34dc06d84e22 | local-temporal-bilinear-pooling-for-fine | 1812.01922 | null | https://arxiv.org/abs/1812.01922v3 | https://arxiv.org/pdf/1812.01922v3.pdf | Local Temporal Bilinear Pooling for Fine-grained Action Parsing | Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate laye... | ['Siyu Tang', 'Christian Jarvers', 'Yan Zhang', 'Heiko Neumann', 'Krikamol Muandet'] | 2018-12-05 | local-temporal-bilinear-pooling-for-fine-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Local_Temporal_Bilinear_Pooling_for_Fine-Grained_Action_Parsing_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Local_Temporal_Bilinear_Pooling_for_Fine-Grained_Action_Parsing_CVPR_2019_paper.pdf | cvpr-2019-6 | ['action-parsing'] | ['natural-language-processing'] | [ 2.66930789e-01 6.69130683e-02 -5.29084802e-01 -3.45539272e-01
-7.81337261e-01 -3.67998660e-01 3.67969811e-01 2.01635227e-01
-7.52834439e-01 7.83220708e-01 5.74458897e-01 -1.78163916e-01
-1.76910266e-01 -5.90070128e-01 -7.15695143e-01 -7.53889263e-01
-2.92533576e-01 -2.21987709e-01 6.72947466e-01 -3.11353467... | [14.389604568481445, -2.9417929649353027] |
cba654f4-eb0c-4162-8939-a358fb229f4f | review-of-visual-saliency-detection-with | 1803.03391 | null | http://arxiv.org/abs/1803.03391v2 | http://arxiv.org/pdf/1803.03391v2.pdf | Review of Visual Saliency Detection with Comprehensive Information | Visual saliency detection model simulates the human visual system to perceive
the scene, and has been widely used in many vision tasks. With the acquisition
technology development, more comprehensive information, such as depth cue,
inter-image correspondence, or temporal relationship, is available to extend
image salie... | ['Ming-Ming Cheng', 'Jianjun Lei', 'Huazhu Fu', 'Runmin Cong', 'Qingming Huang', 'Weisi Lin'] | 2018-03-09 | null | null | null | null | ['co-saliency-detection', 'video-saliency-detection'] | ['computer-vision', 'computer-vision'] | [ 4.44061935e-01 -4.04037327e-01 -3.96680266e-01 -4.33227271e-02
-2.15876788e-01 -9.18415189e-02 1.81062818e-01 2.62919348e-02
-1.51559502e-01 3.86216283e-01 2.37274170e-01 1.48542583e-01
1.03255540e-01 -2.62281895e-01 -3.31992537e-01 -7.04923928e-01
2.19585016e-01 -5.80537379e-01 1.26155758e+00 -2.07448930... | [9.791982650756836, -0.49380579590797424] |
e410b9cb-1cb9-47c7-ac59-19699ae05c7c | exploring-adaptor-grammars-for-native | null | null | https://aclanthology.org/D12-1064 | https://aclanthology.org/D12-1064.pdf | Exploring Adaptor Grammars for Native Language Identification | null | ['Sze-Meng Jojo Wong', 'Mark Johnson', 'Mark Dras'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['native-language-identification'] | ['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
-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... | [-7.245744228363037, 3.729912757873535] |
abe6b37a-31fc-48db-babf-d0ab68030588 | on-the-need-and-applicability-of-causality | 2207.04053 | null | https://arxiv.org/abs/2207.04053v1 | https://arxiv.org/pdf/2207.04053v1.pdf | On the Need and Applicability of Causality for Fair Machine Learning | Causal reasoning has an indispensable role in how humans make sense of the world and come to decisions in everyday life. While $20th$ century science was reserved from making causal claims as too strong and not achievable, the $21st$ century is marked by the return of causality encouraged by the mathematization of caus... | ['Sami Zhioua', 'Rūta Binkytė'] | 2022-07-08 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 2.76784033e-01 5.58807194e-01 -4.85085934e-01 -4.12472010e-01
-6.44959137e-02 -5.55069804e-01 9.58550990e-01 6.71124458e-01
-8.05540383e-01 1.15052831e+00 5.91675520e-01 -9.54428136e-01
-6.98310256e-01 -6.78493917e-01 -4.47903365e-01 -4.16012615e-01
1.32671908e-01 2.16993749e-01 -2.40159079e-01 -1.04542531... | [8.717499732971191, 5.621066570281982] |
dbe1e274-aaa2-4f6d-bd2e-85f48e801200 | error-compensation-framework-for-flow-guided | 2207.10391 | null | https://arxiv.org/abs/2207.10391v1 | https://arxiv.org/pdf/2207.10391v1.pdf | Error Compensation Framework for Flow-Guided Video Inpainting | The key to video inpainting is to use correlation information from as many reference frames as possible. Existing flow-based propagation methods split the video synthesis process into multiple steps: flow completion -> pixel propagation -> synthesis. However, there is a significant drawback that the errors in each step... | ['Seon Joo Kim', 'Seoung Wug Oh', 'Jaeyeon Kang'] | 2022-07-21 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 2.00634584e-01 -3.72361958e-01 -1.43240228e-01 -4.62116338e-02
-2.47427195e-01 -1.25184044e-01 2.92530686e-01 -3.74834895e-01
-2.05890343e-01 9.95599747e-01 3.07723820e-01 -1.71319358e-02
-4.92751673e-02 -6.48087502e-01 -4.44924086e-01 -5.33755779e-01
-7.09889084e-02 -2.67690390e-01 6.09901607e-01 -4.06756364... | [10.740038871765137, -1.4881021976470947] |
9bef92a8-39e8-4bd0-a209-243f463503d4 | the-impact-of-preprocessing-on-deep | 1808.10032 | null | http://arxiv.org/abs/1808.10032v1 | http://arxiv.org/pdf/1808.10032v1.pdf | The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments | The use of iris as a biometric trait is widely used because of its high level
of distinction and uniqueness. Nowadays, one of the major research challenges
relies on the recognition of iris images obtained in visible spectrum under
unconstrained environments. In this scenario, the acquired iris are affected by
capture ... | ['Alceu S. Britto Jr.', 'Rayson Laroca', 'Luiz S. Oliveira', 'Luiz A. Zanlorensi', 'Eduardo Luz', 'David Menotti'] | 2018-08-29 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 4.03182775e-01 -9.71823707e-02 -6.81134290e-04 -2.40381628e-01
-1.72045186e-01 -4.06398535e-01 5.01782894e-01 -2.49364004e-02
-6.56731904e-01 5.16232371e-01 -1.61947886e-04 -7.76390508e-02
-3.59394073e-01 -4.53284889e-01 -4.90267515e-01 -9.32545066e-01
1.79942608e-01 2.83926189e-01 -6.00269437e-01 -1.85199585... | [3.7304298877716064, -3.646768808364868] |
3c649f67-5f41-490a-a1c0-be60a7f0bbd7 | multimodal-machine-learning-integrating | null | null | https://aclanthology.org/P17-5002 | https://aclanthology.org/P17-5002.pdf | Multimodal Machine Learning: Integrating Language, Vision and Speech | Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition... | ['Tadas Baltru{\\v{s}}aitis', 'Louis-Philippe Morency'] | 2017-07-01 | null | null | null | acl-2017-7 | ['audio-visual-speech-recognition'] | ['speech'] | [ 5.33521175e-01 -1.47051126e-01 -3.42044592e-01 -2.97253698e-01
-1.30915356e+00 -5.36810398e-01 8.24628890e-01 1.71059906e-01
-3.71786326e-01 5.90963185e-01 4.46264982e-01 -3.27469051e-01
-1.22931581e-02 2.92400103e-02 -6.84342861e-01 -4.18958932e-01
3.54566425e-02 5.18813431e-01 -3.66609514e-01 -4.70504910... | [11.02455997467041, 1.7407044172286987] |
c8e5fc05-3a31-4724-810b-564b3253a9dc | learning-to-zoom-a-saliency-based-sampling | 1809.03355 | null | http://arxiv.org/abs/1809.03355v1 | http://arxiv.org/pdf/1809.03355v1.pdf | Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks | We introduce a saliency-based distortion layer for convolutional neural
networks that helps to improve the spatial sampling of input data for a given
task. Our differentiable layer can be added as a preprocessing block to
existing task networks and trained altogether in an end-to-end fashion. The
effect of the layer is... | ['Antonio Torralba', 'Simon Stent', 'Adrià Recasens', 'Wojciech Matusik', 'Petr Kellnhofer'] | 2018-09-10 | learning-to-zoom-a-saliency-based-sampling-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Adria_Recasens_Learning_to_Zoom_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Adria_Recasens_Learning_to_Zoom_ECCV_2018_paper.pdf | eccv-2018-9 | ['caricature'] | ['computer-vision'] | [ 5.05833805e-01 3.16876352e-01 -4.87993397e-02 -6.16150141e-01
-6.04644537e-01 -2.50848114e-01 3.91028643e-01 -8.59157816e-02
-5.72961986e-01 7.34909773e-01 3.17025989e-01 3.12968111e-03
1.27188519e-01 -6.03380203e-01 -1.15473402e+00 -6.77266240e-01
4.12199885e-01 2.62667567e-01 3.84368509e-01 -1.74143404... | [9.959418296813965, 0.09280504286289215] |
498fe39e-991b-48b6-ae35-f685e7c75e20 | efficient-and-multiply-robust-risk-estimation | 2306.16406 | null | https://arxiv.org/abs/2306.16406v2 | https://arxiv.org/pdf/2306.16406v2.pdf | Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift | Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source populations, which share some conditional distributions or are linked in other ways with the target domain. Techniques leveraging such \emph{datas... | ['Edgar Dobriban', 'Eric Tchetgen Tchetgen', 'Hongxiang Qiu'] | 2023-06-28 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 4.58383441e-01 1.22310661e-01 -6.66601002e-01 -5.49725473e-01
-1.07316756e+00 -5.58730006e-01 3.94834757e-01 1.31548867e-01
-4.15944368e-01 1.11052024e+00 5.44572771e-02 -2.34091431e-01
-6.67121351e-01 -5.89704871e-01 -8.72589529e-01 -7.49925256e-01
1.82758197e-02 4.85719889e-01 -1.74878970e-01 2.89960116... | [8.248895645141602, 4.681735038757324] |
7ebd9dfc-8799-4644-b361-de7302baa2db | foundationtts-text-to-speech-for-asr | 2303.02939 | null | https://arxiv.org/abs/2303.02939v3 | https://arxiv.org/pdf/2303.02939v3.pdf | FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model | Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectrograms or self-extracted speech frames as the intermediate representations to bridge acoustic model and vocoder, which suffers from two limit... | ['Sheng Zhao', 'Edward Lin', 'Linquan Liu', 'Xu Tan', 'Lei He', 'Yanqing Liu', 'Ruiqing Xue'] | 2023-03-06 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.03983805e-01 -6.61916211e-02 1.35597505e-03 -2.69020826e-01
-1.29820204e+00 -5.08251667e-01 1.96587622e-01 -3.16533148e-01
-1.39793679e-01 4.21179086e-01 4.29675937e-01 -5.46211421e-01
7.30573952e-01 -5.14690578e-01 -7.74808288e-01 -5.73973060e-01
1.71156287e-01 2.41049882e-02 6.81535751e-02 -3.25180411... | [15.033737182617188, 6.47881555557251] |
70371237-4e2f-4c72-839f-5dde2ea5e8bc | implicit-discourse-relation-identification | 1907.03975 | null | https://arxiv.org/abs/1907.03975v1 | https://arxiv.org/pdf/1907.03975v1.pdf | Implicit Discourse Relation Identification for Open-domain Dialogues | Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dial... | ['Kevin K. Bowden', 'Marilyn Walker', 'Wen Cui', 'Mingyu Derek Ma', 'Jiaqi Wu'] | 2019-07-09 | implicit-discourse-relation-identification-1 | https://aclanthology.org/P19-1065 | https://aclanthology.org/P19-1065.pdf | acl-2019-7 | ['implicit-discourse-relation-classification', 'implicit-relations'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.42214489e-01 9.51170325e-01 -6.90727979e-02 -1.66326940e-01
-6.56237364e-01 -9.86935556e-01 1.29022837e+00 3.73957843e-01
-2.05768749e-01 9.19399559e-01 7.26516843e-01 -5.46598554e-01
-1.88387766e-01 -5.61808467e-01 -6.49188319e-03 -2.32650831e-01
3.71872298e-02 8.60490859e-01 3.74699444e-01 -8.22168529... | [12.348992347717285, 8.128059387207031] |
6994810d-8b63-49fd-b9a9-52f6bed28b47 | content-preserving-image-stitching-with-1 | null | null | https://www.webofscience.com/wos/alldb/full-record/WOS:000655924400008 | https://sci-hub.se/10.1109/TVCG.2020.2965097 | Content-Preserving Image Stitching with Piecewise Rectangular Boundary Constraints | Abstract—This paper proposes an approach to content-
preserving image stitching with regular boundary constraints,
which aims to stitch multiple images to generate a panoramic
image with a piecewise rectangular boundary. Existing methods
treat image stitching and rectangling as two separate steps,
which may result... | ['and Fang-Lue Zhang', 'IEEE', 'Member', 'Yu-Kun Lai', 'Yun Zhang'] | 2021-07-01 | null | null | null | ieee-transactions-on-visualization-and-5 | ['image-stitching'] | ['computer-vision'] | [ 8.83660555e-01 -9.79525521e-02 -3.50341722e-02 1.47026151e-01
-3.36092561e-01 -7.67400086e-01 4.73142356e-01 -1.92506224e-01
-1.11698806e-01 4.90987659e-01 2.31466144e-01 -8.25901479e-02
6.60433993e-02 -7.78833210e-01 -6.94116235e-01 -8.25898767e-01
2.26857856e-01 1.22022800e-01 3.25560212e-01 -1.20084867... | [9.395583152770996, -2.3526129722595215] |
51835522-2c0b-462c-bbaf-6b6de9a4d5e6 | a-novel-framework-based-on-medical-concept | null | null | https://openreview.net/forum?id=syvKAodJ-Gj | https://openreview.net/pdf?id=syvKAodJ-Gj | A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge | Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction. Recent studies employ deep neural networks and the external knowledge to tackle it. However, such appro... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['medical-code-prediction'] | ['medical'] | [ 1.21991411e-01 4.15697157e-01 -2.65199006e-01 -4.38840538e-01
-8.17894220e-01 2.92750285e-03 -1.90929864e-02 6.75821066e-01
1.18189901e-02 6.13494754e-01 6.59908950e-01 -3.28577191e-01
-3.29912394e-01 -6.26712859e-01 -4.88319397e-01 -5.46793103e-01
-4.45491387e-05 5.87282956e-01 -1.98035195e-01 1.21911503... | [7.979848384857178, 6.789289951324463] |
8fd283cc-3534-403a-a9ee-5ce1c3420018 | texture-enhanced-light-field-super-resolution | 2111.04069 | null | https://arxiv.org/abs/2111.04069v2 | https://arxiv.org/pdf/2111.04069v2.pdf | Texture-enhanced Light Field Super-resolution with Spatio-Angular Decomposition Kernels | Despite the recent progress in light field super-resolution (LFSR) achieved by convolutional neural networks, the correlation information of light field (LF) images has not been sufficiently studied and exploited due to the complexity of 4D LF data. To cope with such high-dimensional LF data, most of the existing LFSR ... | ['Zhibo Chen', 'Yuk Ying Chung', 'Henry Wing Fung Yeung', 'Xiaoming Chen', 'Zexi Hu'] | 2021-11-07 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 4.21398461e-01 -4.25033510e-01 8.23938996e-02 -5.03564738e-02
-3.59316766e-01 -1.20895855e-01 4.84684855e-01 -2.97441751e-01
-1.83376372e-01 8.58513892e-01 4.78233732e-02 -1.39410168e-01
-5.28977633e-01 -1.00472736e+00 -4.36261415e-01 -9.86149848e-01
-2.02462468e-02 -4.55387145e-01 2.63687223e-01 -7.53386319... | [10.780512809753418, -2.4347057342529297] |
b8a19a8d-697e-4bf2-a6e4-da92cdb27079 | can-differentiable-decision-trees-learn | 2306.13004 | null | https://arxiv.org/abs/2306.13004v2 | https://arxiv.org/pdf/2306.13004v2.pdf | Can Differentiable Decision Trees Learn Interpretable Reward Functions? | There is an increasing interest in learning reward functions that model human intent and human preferences. However, many frameworks use blackbox learning methods that, while expressive, are difficult to interpret. We propose and evaluate a novel approach for learning expressive and interpretable reward functions from ... | ['Daniel S. Brown', 'Akansha Kalra'] | 2023-06-22 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 4.35093381e-02 4.87002343e-01 -4.29088622e-01 -8.23011637e-01
-5.53502262e-01 -7.22480774e-01 6.38405800e-01 -1.83512896e-01
-6.06860340e-01 9.74514484e-01 6.18521333e-01 -4.35300618e-01
-3.50145847e-01 -4.64853108e-01 -5.06248534e-01 -4.09011513e-01
-3.43389839e-01 6.53139532e-01 -3.03334713e-01 -3.56457621... | [4.02855920791626, 1.7116193771362305] |
edb59d14-3118-49c8-89cd-cbe96688a0a0 | orthographic-feature-transform-for-monocular | 1811.08188 | null | http://arxiv.org/abs/1811.08188v1 | http://arxiv.org/pdf/1811.08188v1.pdf | Orthographic Feature Transform for Monocular 3D Object Detection | 3D object detection from monocular images has proven to be an enormously
challenging task, with the performance of leading systems not yet achieving
even 10\% of that of LiDAR-based counterparts. One explanation for this
performance gap is that existing systems are entirely at the mercy of the
perspective image-based r... | ['Alex Kendall', 'Thomas Roddick', 'Roberto Cipolla'] | 2018-11-20 | null | null | null | null | ['3d-object-detection-from-monocular-images'] | ['computer-vision'] | [ 1.41067207e-01 -2.66565889e-01 1.86336577e-01 -6.01508200e-01
-3.41030359e-01 -8.86860132e-01 7.17389345e-01 -3.91268618e-02
-5.11669815e-01 2.27420747e-01 -1.63619578e-01 -3.60515952e-01
4.16794196e-02 -6.40223503e-01 -7.88918853e-01 -4.81390268e-01
1.09967224e-01 6.24315858e-01 4.37545419e-01 -8.25424194... | [7.831857204437256, -2.7388062477111816] |
28bf5fc4-dbf3-4e92-b686-01b4d7d047f6 | differentiable-multi-agent-actor-critic-for | 2203.08257 | null | https://arxiv.org/abs/2203.08257v2 | https://arxiv.org/pdf/2203.08257v2.pdf | Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization | The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A cascade of tasks are required to automatically generate an abstractive summary of the typical informa... | ['Oladimeji Farri', 'Hinrich Schuetze', 'Ning Liu', 'Sanjeev Kumar Karn'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.109 | https://aclanthology.org/2022.acl-long.109.pdf | acl-2022-5 | ['extractive-summarization'] | ['natural-language-processing'] | [ 7.87696779e-01 8.95748615e-01 -7.42496029e-02 -6.05430007e-01
-1.95189464e+00 -5.11392295e-01 4.44444180e-01 1.09102237e+00
-2.27339238e-01 8.31353188e-01 1.26630831e+00 -4.55994189e-01
-1.07138611e-01 -1.12641349e-01 -4.70495522e-01 -2.45420560e-01
1.33601213e-02 3.87285858e-01 1.13056842e-02 2.49667555... | [15.031944274902344, -1.3411144018173218] |
f2ea10e6-9122-48db-b1ad-7e2d76a60e25 | data-augmentation-using-random-image-cropping | 1811.09030 | null | https://arxiv.org/abs/1811.09030v2 | https://arxiv.org/pdf/1811.09030v2.pdf | Data Augmentation using Random Image Cropping and Patching for Deep CNNs | Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are... | ['Ryo Takahashi', 'Takashi Matsubara', 'Kuniaki Uehara'] | 2018-11-22 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 1.78322762e-01 2.24799514e-01 -6.12927526e-02 -6.12274110e-01
-4.28730875e-01 -1.54458612e-01 5.34585297e-01 7.52152828e-03
-9.81032312e-01 6.58689916e-01 -1.77590683e-01 -2.85061777e-01
2.87817091e-01 -7.58418441e-01 -9.65051174e-01 -6.71974063e-01
1.03962637e-01 5.52493148e-02 -5.53396996e-03 -3.76267955... | [9.332764625549316, 2.1617109775543213] |
bf15c52d-e283-42e0-95ed-b8e39129537c | deep-unsupervised-active-learning-on | 2111.04286 | null | https://arxiv.org/abs/2111.04286v1 | https://arxiv.org/pdf/2111.04286v1.pdf | Deep Unsupervised Active Learning on Learnable Graphs | Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving huge room to design more effective representation learning mechanisms for unsupervised active learnin... | ['Guoren Wang', 'Ye Yuan', 'Xinchu Shi', 'Changsheng Li', 'Handong Ma'] | 2021-11-08 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.94549784e-01 7.57631540e-01 -7.10456967e-01 -5.73869765e-01
-5.37472427e-01 -2.45908603e-01 4.14101928e-01 3.05714786e-01
-2.36769080e-01 6.03357732e-01 2.31657952e-01 1.06519209e-02
-2.79368788e-01 -1.14510381e+00 -7.49436021e-01 -7.23254919e-01
-3.11090201e-01 2.12476015e-01 1.51311919e-01 1.29053041... | [7.277472972869873, 6.231772422790527] |
f647cad8-09d2-4460-a618-0bfc1343a787 | exploring-wasserstein-distance-across-concept | 2207.11324 | null | https://arxiv.org/abs/2207.11324v2 | https://arxiv.org/pdf/2207.11324v2.pdf | Exploring Wasserstein Distance across Concept Embeddings for Ontology Matching | Measuring the distance between ontological elements is fundamental for ontology matching. String-based distance metrics are notorious for shallow syntactic matching. In this exploratory study, we investigate Wasserstein distance targeting continuous space that can incorporate various types of information. We use a pre-... | ['Jane Greenberg', 'Alex Kalinowski', 'Yuan An'] | 2022-07-22 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 1.27285033e-01 1.26451373e-01 -1.03519820e-01 -5.55263221e-01
-7.48140097e-01 -4.74619627e-01 3.54676068e-01 7.17028558e-01
-7.70434082e-01 1.00900702e-01 6.52927041e-01 -4.10553306e-01
-7.37785637e-01 -1.09623945e+00 -2.72182792e-01 3.30951288e-02
-5.09910703e-01 6.18903697e-01 1.90208539e-01 -4.17328954... | [9.295034408569336, 8.189722061157227] |
f3bf3fdf-76be-48aa-affd-7db0a38099ee | despeckling-sentinel-1-grd-images-by-deep | 2102.00692 | null | https://arxiv.org/abs/2102.00692v1 | https://arxiv.org/pdf/2102.00692v1.pdf | Despeckling Sentinel-1 GRD images by deep learning and application to narrow river segmentation | This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy. Training the deep neural network on collections of Sentinel 1 GRD images leads to a despeckling algorithm that is robust to space-variant spatial correlations of sp... | ['Florence Tupin', 'Loïc Denis', 'Emanuele Dalsasso', 'Nicolas Gasnier'] | 2021-02-01 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 1.89598277e-01 -6.12231977e-02 4.70009148e-01 -4.90357101e-01
-4.75237995e-01 -7.06605136e-01 8.31363559e-01 -4.28479135e-01
-6.41176879e-01 4.00226206e-01 3.33343685e-01 -3.04761618e-01
-4.31340069e-01 -1.06335747e+00 -6.18991613e-01 -8.84600699e-01
-4.37530965e-01 1.25822574e-01 1.05516084e-01 -7.06005633... | [10.34183406829834, -2.18440580368042] |
8acf052f-d8b4-4a05-bf27-4f10e5d7de9d | on-the-usefulness-of-synthetic-tabular-data | 2306.15636 | null | https://arxiv.org/abs/2306.15636v1 | https://arxiv.org/pdf/2306.15636v1.pdf | On the Usefulness of Synthetic Tabular Data Generation | Despite recent advances in synthetic data generation, the scientific community still lacks a unified consensus on its usefulness. It is commonly believed that synthetic data can be used for both data exchange and boosting machine learning (ML) training. Privacy-preserving synthetic data generation can accelerate data e... | ['Sergül Aydöre', 'Dionysis Manousakas'] | 2023-06-27 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'data-summarization'] | ['medical', 'miscellaneous', 'miscellaneous'] | [ 1.85177550e-01 3.80270869e-01 -8.58662367e-01 -7.01213002e-01
-9.74101961e-01 -6.07014477e-01 8.37706804e-01 1.20647275e+00
-6.88295841e-01 1.51843488e+00 3.78638208e-01 -9.78274286e-01
2.33263806e-01 -8.52601171e-01 -9.96775985e-01 -6.19620740e-01
3.72220546e-01 5.61991572e-01 -2.57932305e-01 -6.98185563... | [6.329204082489014, 6.724236488342285] |
317d8012-6d12-419f-992e-338bf4cf2483 | prediction-then-correction-an-abductive | 2304.14050 | null | https://arxiv.org/abs/2304.14050v1 | https://arxiv.org/pdf/2304.14050v1.pdf | Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation | Sequential recommender models typically generate predictions in a single step during testing, without considering additional prediction correction to enhance performance as humans would. To improve the accuracy of these models, some researchers have attempted to simulate human analogical reasoning to correct prediction... | ['Fuli Feng', 'Chenxu Wang', 'Qifan Wang', 'Yang Zhang', 'Yulong Huang'] | 2023-04-27 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 2.71863550e-01 3.47289920e-01 -2.75745749e-01 -8.55709612e-01
-9.40264575e-03 -5.24024189e-01 4.40856099e-01 -6.81133270e-02
-2.17126697e-01 8.06970000e-01 2.00848535e-01 -7.78315187e-01
-4.49345440e-01 -9.18282866e-01 -1.04161870e+00 -4.57958784e-04
3.21725577e-01 7.07752347e-01 2.64858961e-01 -4.48314905... | [9.885007858276367, 5.7705583572387695] |
7c05f8eb-b82e-42bb-8c12-ec7a7f9368ff | aitlas-artificial-intelligence-toolbox-for | 2201.08789 | null | https://arxiv.org/abs/2201.08789v1 | https://arxiv.org/pdf/2201.08789v1.pdf | AiTLAS: Artificial Intelligence Toolbox for Earth Observation | The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks,... | ['Dragi Kocev', 'Nikola Simidjievski', 'Panče Panov', 'Ivan Kitanovski', 'Ivica Dimitrovski'] | 2022-01-21 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-2.45892555e-02 -7.80960321e-02 -2.06106722e-01 -2.28010640e-01
8.84544924e-02 -6.93543792e-01 5.61300218e-01 4.36084479e-01
-1.94146380e-01 4.00849551e-01 -2.85115719e-01 -8.20982039e-01
-4.36210930e-01 -1.15411854e+00 -2.45426834e-01 -8.15796793e-01
-6.14944339e-01 6.21386468e-01 -1.77406166e-02 -5.76029956... | [9.492644309997559, -1.477249026298523] |
78063232-15e0-4423-86cc-03182b73a15f | attribute-controllable-beautiful-caucasian | 2208.04517 | null | https://arxiv.org/abs/2208.04517v1 | https://arxiv.org/pdf/2208.04517v1.pdf | Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning | In recent years, image generation has made great strides in improving the quality of images, producing high-fidelity ones. Also, quite recently, there are architecture designs, which enable GAN to unsupervisedly learn the semantic attributes represented in different layers. However, there is still a lack of research on... | ['Chaoen Xiao', 'Qiang Deng', 'Xin Zhao', 'Le Zhang', 'Shu Zhao', 'Xin Jin'] | 2022-08-09 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [ 1.83328297e-02 5.34663260e-01 1.52734652e-01 -5.20825565e-01
-5.58470376e-02 -1.23103485e-01 5.13334811e-01 -4.69714701e-01
2.40534227e-02 8.21837246e-01 3.12609911e-01 3.43253553e-01
-2.60882564e-02 -1.09518230e+00 -4.90117401e-01 -6.90520644e-01
8.28711092e-02 1.69565842e-01 -6.00096703e-01 -5.23536384... | [12.589089393615723, 0.03825101628899574] |
80f9d09e-3c3a-4781-ab20-33a025d1cc8c | efficient-multi-task-auxiliary-learning | null | null | https://aclanthology.org/2021.emnlp-main.34 | https://aclanthology.org/2021.emnlp-main.34.pdf | Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity | Multi-task auxiliary learning utilizes a set of relevant auxiliary tasks to improve the performance of a primary task. A common usage is to manually select multiple auxiliary tasks for multi-task learning on all data, which raises two issues: (1) selecting beneficial auxiliary tasks for a primary task is nontrivial; (2... | ['Yun-Nung Chen', 'Tse-Hsuan Yang', 'Yi-Cheng Chen', 'Sheng-Siang Yin', 'Po-Nien Kung'] | null | null | null | null | emnlp-2021-11 | ['auxiliary-learning'] | ['methodology'] | [ 2.92407662e-01 -2.83422265e-02 -4.03452575e-01 -5.70362285e-02
-1.36545098e+00 -4.63268131e-01 5.12746274e-01 -1.65679231e-01
-8.57243419e-01 1.27899718e+00 3.12222868e-01 -3.55383337e-01
1.74541578e-01 -2.70775169e-01 -4.88275886e-01 -9.83500302e-01
4.93950754e-01 8.04694235e-01 3.59652340e-01 -3.09355378... | [9.348692893981934, 3.9144175052642822] |
723c9be9-3753-401a-8e19-3b7d471460d8 | reghec-hand-eye-calibration-via-simultaneous | 2304.14092 | null | https://arxiv.org/abs/2304.14092v1 | https://arxiv.org/pdf/2304.14092v1.pdf | RegHEC: Hand-Eye Calibration via Simultaneous Multi-view Point Clouds Registration of Arbitrary Object | RegHEC is a registration-based hand-eye calibration technique with no need for accurate calibration rig but arbitrary available objects, applicable for both eye-in-hand and eye-to-hand cases. It tries to find the hand-eye relation which brings multi-view point clouds of arbitrary scene into simultaneous registration un... | ['Min Tan', 'Fengshui Jing', 'Shiyu Xing'] | 2023-04-27 | null | null | null | null | ['point-cloud-registration', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [-3.63383591e-01 -2.12265715e-01 2.12386534e-01 -2.06985772e-01
-6.39043510e-01 -6.24724209e-01 5.36678970e-01 -3.80182087e-01
-3.41396034e-01 2.71104842e-01 -3.61095458e-01 9.13028605e-03
-1.80996239e-01 -4.34589624e-01 -5.46452641e-01 -8.27700198e-01
7.13641942e-01 1.24048054e+00 4.62086022e-01 -3.18919659... | [7.787588596343994, -2.7068700790405273] |
80bc8588-8da2-4702-a9c8-7550cc5894f9 | self-supervised-learning-for-panoptic | 2209.04618 | null | https://arxiv.org/abs/2209.04618v1 | https://arxiv.org/pdf/2209.04618v1.pdf | Self-supervised Learning for Panoptic Segmentation of Multiple Fruit Flower Species | Convolutional neural networks trained using manually generated labels are commonly used for semantic or instance segmentation. In precision agriculture, automated flower detection methods use supervised models and post-processing techniques that may not perform consistently as the appearance of the flowers and the data... | ['Henry Medeiros', 'Amy Tabb', 'Abubakar Siddique'] | 2022-09-10 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 6.34664416e-01 -4.02515978e-02 -1.58418253e-01 -7.93956518e-01
-1.97627634e-01 -1.14937043e+00 5.83309591e-01 4.23720390e-01
-5.52254692e-02 2.32547522e-01 -5.86325347e-01 -2.49459282e-01
1.89183488e-01 -8.64831388e-01 -7.51655757e-01 -3.50738347e-01
7.57198706e-02 5.99402726e-01 4.31126058e-01 -7.34845474... | [9.069375038146973, -1.4891462326049805] |
a24ff8bf-3d09-4a21-b6f7-007e351aebaf | visually-grounded-continual-learning-of | 2005.00785 | null | https://arxiv.org/abs/2005.00785v5 | https://arxiv.org/pdf/2005.00785v5.pdf | Visually Grounded Continual Learning of Compositional Phrases | Humans acquire language continually with much more limited access to data samples at a time, as compared to contemporary NLP systems. To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases ... | ['Xisen Jin', 'Arka Sadhu', 'Xiang Ren', 'Ram Nevatia', 'Junyi Du'] | 2020-05-02 | null | https://aclanthology.org/2020.emnlp-main.158 | https://aclanthology.org/2020.emnlp-main.158.pdf | emnlp-2020-11 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 5.35149872e-01 -7.38632157e-02 -1.06111050e-01 -3.72955203e-01
-9.71623540e-01 -9.50836182e-01 7.92543352e-01 3.27991605e-01
-3.15728694e-01 5.18330097e-01 1.48789585e-01 -4.93968934e-01
3.81051868e-01 -5.27145505e-01 -1.23040354e+00 -6.12161636e-01
-3.62092376e-01 6.74528480e-01 1.33574739e-01 -1.58084169... | [10.79304313659668, 1.8316060304641724] |
97fc5ce1-b157-4f0e-a0de-181b6a99efe1 | unifying-and-personalizing-weakly-supervised | 2304.05635 | null | https://arxiv.org/abs/2304.05635v1 | https://arxiv.org/pdf/2304.05635v1.pdf | Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation | Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. The statistical heterogeneity (e.g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model. Weakly supervised s... | ['Xiaoying Tang', 'Kenneth K. Y. Wong', 'Yixiang Liu', 'Jiewei Wu', 'Li Lin'] | 2023-04-12 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 2.51420349e-01 -4.26303595e-02 -6.47692680e-01 -5.82415342e-01
-1.25169277e+00 -5.89779675e-01 1.42225713e-01 1.88014761e-01
-4.03087974e-01 5.98000407e-01 2.41058961e-01 -2.21209869e-01
5.91041967e-02 -5.81509888e-01 -6.81842029e-01 -1.11401665e+00
2.85706818e-01 3.63418043e-01 8.43649879e-02 2.57152528... | [6.01348352432251, 6.440356254577637] |
cdb45b44-b6fe-4a14-9a6f-e295e68fbe24 | a-deep-learning-approach-to-predict-blood | 2108.00099 | null | https://arxiv.org/abs/2108.00099v1 | https://arxiv.org/pdf/2108.00099v1.pdf | A Deep Learning Approach to Predict Blood Pressure from PPG Signals | Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuou... | ['Marco Levorato', 'Ali Tazarv'] | 2021-07-30 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 8.52929279e-02 5.16019575e-02 4.60244082e-02 -6.19257033e-01
-1.83349773e-01 1.34523943e-01 -1.48228168e-01 -4.91855852e-02
-2.00153887e-01 1.05938530e+00 4.62570876e-01 -3.83469820e-01
-2.85067171e-01 -9.17959750e-01 -1.64855495e-01 -6.34627879e-01
-5.65863371e-01 1.80285256e-02 -1.13095738e-01 -1.30692527... | [14.068920135498047, 2.9638867378234863] |
a431a143-fb00-4895-a948-fe22ca73baa1 | clartts-an-open-source-classical-arabic-text | 2303.00069 | null | https://arxiv.org/abs/2303.00069v1 | https://arxiv.org/pdf/2303.00069v1.pdf | ClArTTS: An Open-Source Classical Arabic Text-to-Speech Corpus | At present, Text-to-speech (TTS) systems that are trained with high-quality transcribed speech data using end-to-end neural models can generate speech that is intelligible, natural, and closely resembles human speech. These models are trained with relatively large single-speaker professionally recorded audio, typically... | ['Hanan Aldarmaki', 'Sara Abedalmonem Mohammad Shatnawi', 'Atharva Kulkarni', 'Ajinkya Kulkarni'] | 2023-02-28 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [-1.59406904e-02 2.54318774e-01 3.85519683e-01 -5.41720331e-01
-1.31234312e+00 -7.31324315e-01 4.65888262e-01 -2.28528194e-02
-1.97122186e-01 4.46515620e-01 4.56477940e-01 -5.90615511e-01
2.18000203e-01 -1.47999614e-01 -3.54858935e-01 -5.14320076e-01
2.45867902e-03 6.48580432e-01 -7.45471101e-03 -6.99322164... | [14.520038604736328, 6.735870361328125] |
40c9653c-b575-4bc0-87c2-45913976265a | degae-a-new-pretraining-paradigm-for-low | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_DegAE_A_New_Pretraining_Paradigm_for_Low-Level_Vision_CVPR_2023_paper.pdf | DegAE: A New Pretraining Paradigm for Low-Level Vision | Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these ess... | ['Chao Dong', 'Yu Qiao', 'Xiangtao Kong', 'Jinjin Gu', 'Jingwen He', 'Yihao Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['philosophy'] | ['miscellaneous'] | [ 3.90602767e-01 2.49292869e-02 -2.79333871e-02 -3.45816255e-01
-4.16101515e-01 -1.99211001e-01 5.64089656e-01 -1.61325395e-01
-7.29005158e-01 4.19989049e-01 2.13610336e-01 -2.15704963e-01
-1.02695636e-03 -6.52028441e-01 -8.12828660e-01 -7.66916633e-01
1.70635790e-01 -2.47748211e-01 2.95982450e-01 -3.77741665... | [11.18035888671875, -2.2343060970306396] |
2ac27c7a-3ecb-4e8a-b214-a3575357ac7f | 190206443 | 1902.06443 | null | http://arxiv.org/abs/1902.06443v1 | http://arxiv.org/pdf/1902.06443v1.pdf | Sparse residual tree and forest | Sparse residual tree (SRT) is an adaptive exploration method for multivariate
scattered data approximation. It leads to sparse and stable approximations in
areas where the data is sufficient or redundant, and points out the possible
local regions where data refinement is needed. Sparse residual forest (SRF) is
a combin... | ['Xin Xu', 'Xiaopeng Luo'] | 2019-02-18 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-5.46244644e-02 7.82495290e-02 -1.06811926e-01 -1.34592608e-01
-6.43548429e-01 1.80336282e-01 -3.06344241e-01 4.17655200e-01
-7.70969167e-02 1.02870739e+00 -1.41316533e-01 -2.91983843e-01
-6.01718783e-01 -1.09721959e+00 -4.88370061e-01 -9.36497033e-01
-6.10009670e-01 5.98220229e-01 2.51029819e-01 -2.39381135... | [6.664926052093506, 4.539480686187744] |
fbf50a39-0a78-4045-a114-c01cc8f59da1 | hyperparameter-free-losses-for-model-based | 1908.09001 | null | https://arxiv.org/abs/1908.09001v1 | https://arxiv.org/pdf/1908.09001v1.pdf | Hyperparameter-Free Losses for Model-Based Monocular Reconstruction | This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are jointly optimized in a sole term expression. This simplification re... | ['Xavier Giró-i-Nieto', 'Guillermo Ruiz', 'Eduard Ramon', 'Thomas Batard'] | 2019-08-16 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 6.42588269e-03 3.01920384e-01 1.57054082e-01 -2.35292435e-01
-6.49193347e-01 -6.13275707e-01 3.89747888e-01 -9.13306102e-02
-3.59030962e-01 4.60779816e-01 -1.62466578e-02 -6.08932823e-02
-9.50751305e-02 -5.93598962e-01 -9.15740132e-01 -5.52831173e-01
3.59043539e-01 6.51596606e-01 7.76945427e-02 -8.19002688... | [8.655072212219238, -3.015953302383423] |
bdc5dff9-ff70-4aa3-918b-a6c1fd8120b2 | unbalanced-optimal-transport-for-unbalanced | 2306.04116 | null | https://arxiv.org/abs/2306.04116v1 | https://arxiv.org/pdf/2306.04116v1.pdf | Unbalanced Optimal Transport for Unbalanced Word Alignment | Monolingual word alignment is crucial to model semantic interactions between sentences. In particular, null alignment, a phenomenon in which words have no corresponding counterparts, is pervasive and critical in handling semantically divergent sentences. Identification of null alignment is useful on its own to reason a... | ['Sho Yokoi', 'Han Bao', 'Yuki Arase'] | 2023-06-07 | null | null | null | null | ['word-alignment', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.77820432e-01 1.51852608e-01 -5.02147913e-01 -4.35520530e-01
-6.20421588e-01 -7.98683524e-01 5.72344124e-01 4.16734964e-01
-4.38488126e-01 6.34254873e-01 5.22656381e-01 -4.39263284e-01
-1.88614950e-01 -3.83892566e-01 -3.62473667e-01 -4.78205562e-01
3.52038771e-01 8.00125241e-01 3.04116700e-02 -7.79772103... | [10.996826171875, 9.384066581726074] |
f8904bdf-7b39-4c42-be40-d0c6fd8db963 | dir-as-decoupling-individual-identification | 2304.02110 | null | https://arxiv.org/abs/2304.02110v1 | https://arxiv.org/pdf/2304.02110v1.pdf | DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation | Fully supervised action segmentation works on frame-wise action recognition with dense annotations and often suffers from the over-segmentation issue. Existing works have proposed a variety of solutions such as boundary-aware networks, multi-stage refinement, and temporal smoothness losses. However, most of them take a... | ['Haibin Ling', 'Peiyao Wang'] | 2023-04-04 | null | null | null | null | ['action-recognition-in-videos', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.05582690e-01 1.05199054e-01 -4.25736248e-01 -3.14394981e-01
-8.73222888e-01 -1.74303085e-01 2.97723621e-01 -6.24038577e-02
-4.91178155e-01 5.68658173e-01 3.83758932e-01 1.25920773e-01
-5.97019568e-02 -4.25741583e-01 -4.78775293e-01 -7.39645720e-01
1.36343732e-01 3.70498486e-02 6.25361323e-01 -1.62992589... | [8.45718765258789, 0.4453001618385315] |
437fb2ba-72c5-4b39-9d66-206b283fd084 | video-salient-object-detection-via-fully | 1702.00871 | null | http://arxiv.org/abs/1702.00871v3 | http://arxiv.org/pdf/1702.00871v3.pdf | Video Salient Object Detection via Fully Convolutional Networks | This paper proposes a deep learning model to efficiently detect salient
regions in videos. It addresses two important issues: (1) deep video saliency
model training with the absence of sufficiently large and pixel-wise annotated
video data, and (2) fast video saliency training and detection. The proposed
deep video sal... | ['Wenguan Wang', 'Jianbing Shen', 'Ling Shao'] | 2017-02-02 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.79766911e-01 -1.38204753e-01 -3.73661876e-01 -4.72295694e-02
-6.04949236e-01 -1.90704674e-01 3.87864470e-01 -2.81759650e-01
-3.29290628e-01 7.84948647e-01 2.60395974e-01 -1.47443816e-01
1.68039933e-01 -4.24380362e-01 -1.07405210e+00 -4.35432911e-01
-3.80444825e-01 -3.55134368e-01 1.06256878e+00 -1.03765339... | [9.702887535095215, -0.3137211799621582] |
1714fd98-0d37-4072-9685-a6c59a2c74fb | controllable-list-wise-ranking-for-universal | 1911.10566 | null | https://arxiv.org/abs/1911.10566v2 | https://arxiv.org/pdf/1911.10566v2.pdf | Controllable List-wise Ranking for Universal No-reference Image Quality Assessment | No-reference image quality assessment (NR-IQA) has received increasing attention in the IQA community since reference image is not always available. Real-world images generally suffer from various types of distortion. Unfortunately, existing NR-IQA methods do not work with all types of distortion. It is a challenging t... | ['Guopu Zhu', 'Yuan-Gen Wang', 'Fu-Zhao Ou', 'Sam Kwong', 'Jin Li'] | 2019-11-24 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.76230508e-01 -4.48117733e-01 8.32652301e-02 -3.85297954e-01
-1.19022405e+00 -4.89583880e-01 3.32423657e-01 -2.60628521e-01
-1.96381718e-01 6.10848844e-01 1.39569134e-01 -2.50545144e-01
-2.67620653e-01 -8.02679777e-01 -6.82388008e-01 -9.01377022e-01
9.41828713e-02 -2.09850110e-02 8.07588100e-02 -1.26429632... | [11.779006958007812, -1.946273922920227] |
613d92f9-bf47-4618-85b2-c4fd0cf49196 | pac-bayesian-like-error-bound-for-a-class-of | 2212.14838 | null | https://arxiv.org/abs/2212.14838v1 | https://arxiv.org/pdf/2212.14838v1.pdf | PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models | In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent... | ['Mihaly Petreczky', 'Rafal Wisniewski', 'Zheng-Hua Tan', 'John Leth', 'Deividas Eringis'] | 2022-12-30 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 1.08973324e-01 7.61539564e-02 -2.57342100e-01 -2.01103479e-01
-5.89993000e-01 -3.75450909e-01 8.31548989e-01 -3.24053228e-01
-2.45524988e-01 1.04018319e+00 -8.35177451e-02 -5.65103710e-01
-6.29624605e-01 -5.26198566e-01 -6.03113532e-01 -1.18241763e+00
-1.72693748e-02 5.08263171e-01 2.71302700e-01 1.53076231... | [6.672574996948242, 3.5704729557037354] |
03fd6f16-3c03-4e4b-ad10-7584ef956a12 | time-masking-leveraging-temporal-information | 1907.11315 | null | https://arxiv.org/abs/1907.11315v1 | https://arxiv.org/pdf/1907.11315v1.pdf | Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems | In a spoken dialogue system, dialogue state tracker (DST) components track the state of the conversation by updating a distribution of values associated with each of the slots being tracked for the current user turn, using the interactions until then. Much of the previous work has relied on modeling the natural order o... | ['Rylan Conway', 'Lambert Mathias'] | 2019-07-25 | time-masking-leveraging-temporal-information-1 | https://aclanthology.org/W19-5907 | https://aclanthology.org/W19-5907.pdf | ws-2019-9 | ['video-salient-object-detection'] | ['computer-vision'] | [-8.54182094e-02 3.57125610e-01 -3.27595472e-01 -7.41802394e-01
-3.43642622e-01 -9.91721749e-01 1.19277692e+00 3.50243896e-01
-5.88797450e-01 6.17970526e-01 8.91351342e-01 -4.26042616e-01
-6.50931001e-02 -5.42753816e-01 1.25981063e-01 -2.11199135e-01
-2.29466990e-01 9.25826252e-01 4.26386267e-01 -7.46046424... | [12.837806701660156, 7.918231964111328] |
aa8dcc41-d320-49c2-9606-5bb77b2919a6 | structured-reordering-for-modeling-latent | 2106.03257 | null | https://arxiv.org/abs/2106.03257v3 | https://arxiv.org/pdf/2106.03257v3.pdf | Structured Reordering for Modeling Latent Alignments in Sequence Transduction | Despite success in many domains, neural models struggle in settings where train and test examples are drawn from different distributions. In particular, in contrast to humans, conventional sequence-to-sequence (seq2seq) models fail to generalize systematically, i.e., interpret sentences representing novel combinations ... | ['Ivan Titov', 'Mirella Lapata', 'Bailin Wang'] | 2021-06-06 | null | http://proceedings.neurips.cc/paper/2021/hash/6f46dd176364ccec308c2760189a4605-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/6f46dd176364ccec308c2760189a4605-Paper.pdf | neurips-2021-12 | ['systematic-generalization'] | ['reasoning'] | [ 1.02039015e+00 5.00044823e-01 -1.06944978e-01 -6.45809531e-01
-1.09598827e+00 -1.17163491e+00 4.93410707e-01 -1.09835356e-01
-2.35303193e-01 9.58434105e-01 4.18076152e-03 -7.84491420e-01
-4.34482768e-02 -8.00555527e-01 -1.28617680e+00 -6.23391807e-01
8.79653394e-02 9.37168121e-01 -2.07960203e-01 1.17553331... | [10.810187339782715, 8.976712226867676] |
a2ffc7b4-fa45-4cf1-97e4-6cb6772eed71 | robust-person-following-under-severe-indoor | null | null | https://ieeexplore.ieee.org/abstract/document/9649857 | https://ieeexplore.ieee.org/abstract/document/9649857 | Robust Person Following Under Severe Indoor Illumination Changes for Mobile Robots: Online Color-Based Identification Update | Tracking a specific person in environments with non-uniform illumination is a difficult task for mobile robots. Image information such as color is essential to identify a target person. However, the information is not reliable under severe illumination changes unless the system can accommodate these changes over time. ... | ['Redhwan Algabri'] | 2021-12-28 | null | null | null | conference-2021-12 | ['person-identification'] | ['computer-vision'] | [ 1.08149178e-01 -8.13529432e-01 4.12299663e-01 -3.50427836e-01
-6.45846054e-02 -6.14318907e-01 2.94958681e-01 -4.49204803e-01
-8.65972698e-01 8.45262170e-01 -2.80632645e-01 3.16076785e-01
2.84187198e-01 -2.98103541e-01 -7.23204017e-01 -9.26874161e-01
1.06465966e-01 2.29044095e-01 2.21012264e-01 3.95609513... | [6.760674476623535, -1.6836971044540405] |
26f3b7b6-919d-45dd-a082-c842a5b37753 | trans4trans-efficient-transformer-for | 2107.03172 | null | https://arxiv.org/abs/2107.03172v2 | https://arxiv.org/pdf/2107.03172v2.pdf | Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World | Common fully glazed facades and transparent objects present architectural barriers and impede the mobility of people with low vision or blindness, for instance, a path detected behind a glass door is inaccessible unless it is correctly perceived and reacted. However, segmenting these safety-critical objects is rarely c... | ['Rainer Stiefelhagen', 'Karin Müller', 'Kunyu Peng', 'Angela Constantinescu', 'Kailun Yang', 'Jiaming Zhang'] | 2021-07-07 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.80327687e-02 4.97809164e-02 1.25294074e-01 -2.49414638e-01
-5.17933369e-01 -2.48804346e-01 1.70310497e-01 -4.26220149e-01
-4.36164916e-01 6.88919663e-01 1.95843458e-01 -6.78976357e-01
1.50512502e-01 -8.60414863e-01 -6.23675883e-01 -4.08145756e-01
1.21683754e-01 2.06349753e-02 5.04662156e-01 -2.63356000... | [7.900519371032715, -1.470446228981018] |
aeec4c6d-c5c6-4700-b8df-4bb3ab73f017 | audio-defect-detection-in-music-with-deep | 2202.05718 | null | https://arxiv.org/abs/2202.05718v1 | https://arxiv.org/pdf/2202.05718v1.pdf | Audio Defect Detection in Music with Deep Networks | With increasing amounts of music being digitally transferred from production to distribution, automatic means of determining media quality are needed. Protection mechanisms in digital audio processing tools have not eliminated the need of production entities located downstream the distribution chain to assess audio qua... | ['Axel Roebel', 'Rémi Mignot', 'Daniel Wolff'] | 2022-02-11 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 5.98991334e-01 -3.62119079e-01 5.45037270e-01 -1.63349628e-01
-1.36118841e+00 -6.62329257e-01 1.44821197e-01 6.53887391e-01
-4.03700590e-01 2.00622231e-01 6.05535150e-01 1.55884102e-02
-3.60811293e-01 -5.45047998e-01 -6.83181226e-01 -5.09850718e-02
-5.17811477e-01 4.74066660e-02 4.35053408e-01 -1.26766086... | [15.600123405456543, 5.653520584106445] |
b01dd9b3-4474-496a-be49-6e703e22dfbb | promptda-label-guided-data-augmentation-for | 2205.09229 | null | https://arxiv.org/abs/2205.09229v3 | https://arxiv.org/pdf/2205.09229v3.pdf | PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners | Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly fine-tuning PLMs heavily relies on sufficient labeled training instances, which are usually hard to obtain. Prompt-based tuning on PLMs has... | ['Kai Shu', 'Canyu Chen'] | 2022-05-18 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 5.15939534e-01 1.22259796e-01 -7.37277925e-01 -6.29085600e-01
-7.22985744e-01 -2.03923613e-01 6.77798331e-01 3.32221597e-01
-5.83436787e-01 7.12129653e-01 4.88116026e-01 -1.86372951e-01
1.41637608e-01 -8.06560457e-01 -3.65495682e-01 -5.61937213e-01
6.41140103e-01 5.88245928e-01 -1.51401013e-01 -5.22534668... | [10.750792503356934, 7.923755168914795] |
039e1900-03e3-4b04-bff6-4315bb572159 | shapley-variable-importance-cloud-for-machine | 2212.08370 | null | https://arxiv.org/abs/2212.08370v1 | https://arxiv.org/pdf/2212.08370v1.pdf | Shapley variable importance cloud for machine learning models | Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e.g., by using the Shapley additive explanations (SHAP) method. The recently developed Shapley variable importance cloud (ShapleyVIC) extends the current practice to a group of "nearly optimal models" to pr... | ['Nan Liu', 'Mingxuan Liu', 'Yilin Ning'] | 2022-12-16 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.71524012e-01 7.68723369e-01 -8.29574585e-01 -4.36619788e-01
-6.52082086e-01 -4.59279805e-01 6.54633462e-01 1.42634660e-01
1.49507374e-01 1.34108901e+00 1.41235247e-01 -8.45834792e-01
-4.84030455e-01 -4.75582689e-01 -6.44201875e-01 -5.02158403e-01
1.17433488e-01 7.25676775e-01 -4.65530872e-01 -8.78601335... | [8.7015380859375, 5.60586404800415] |
509f8244-bda8-4fe9-be94-8a27f09554b3 | bone-marrow-cell-recognition-training-deep | 2110.12647 | null | https://arxiv.org/abs/2110.12647v1 | https://arxiv.org/pdf/2110.12647v1.pdf | Bone Marrow Cell Recognition: Training Deep Object Detection with A New Loss Function | For a long time, bone marrow cell morphology examination has been an essential tool for diagnosing blood diseases. However, it is still mainly dependent on the subjective diagnosis of experienced doctors, and there is no objective quantitative standard. Therefore, it is crucial to study a robust bone marrow cell detect... | ['Jie Li', 'Qiongxiong Ma', 'Zhihao Su', 'Rui Fan', 'Jintao Cheng', 'Dehao Huang'] | 2021-10-25 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-1.75489813e-01 -3.96961421e-01 -1.40675694e-01 -1.84341550e-01
-4.98154968e-01 5.56013621e-02 2.09223330e-01 8.14827383e-01
-5.92868209e-01 7.68505096e-01 -3.08482319e-01 1.19062260e-01
1.97482258e-01 -1.10654211e+00 3.04495782e-01 -1.18251240e+00
2.79177368e-01 9.42398965e-01 6.22148693e-01 9.50263813... | [14.95155143737793, -3.089763879776001] |
c86aad72-a7c6-4c97-824b-8019c92c02ab | spatiotemporal-deep-learning-model-for | 1911.12919 | null | https://arxiv.org/abs/1911.12919v1 | https://arxiv.org/pdf/1911.12919v1.pdf | Spatiotemporal deep learning model for citywide air pollution interpolation and prediction | Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollut... | ['Sang Kyun Cha', 'Tien-Cuong Bui', 'Van-Duc Le'] | 2019-11-29 | null | null | null | null | ['air-pollution-prediction'] | ['miscellaneous'] | [-3.11889350e-01 -9.61072624e-01 -5.92327751e-02 -1.91857219e-01
-7.05710649e-01 -2.22081065e-01 2.93793321e-01 1.75275300e-02
-4.68280524e-01 8.69320214e-01 1.29523352e-01 -6.59017920e-01
-5.48118830e-01 -1.61394310e+00 -6.41991317e-01 -8.27520251e-01
1.95084512e-01 8.31505936e-03 8.40764269e-02 -1.64962769... | [6.263516902923584, 2.473785161972046] |
17d3b4cd-26f8-42ff-93d0-28381ad218a8 | probabilistic-forecasting-methods-for-system | 2210.09399 | null | https://arxiv.org/abs/2210.09399v1 | https://arxiv.org/pdf/2210.09399v1.pdf | Probabilistic Forecasting Methods for System-Level Electricity Load Forecasting | Load forecasts have become an integral part of energy security. Due to the various influencing factors that can be considered in such a forecast, there is also a wide range of models that attempt to integrate these parameters into a system in various ways. Due to the growing importance of probabilistic load forecast mo... | ['Philipp Giese'] | 2022-10-17 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-4.03607160e-01 -1.10970467e-01 -4.59485352e-01 -2.18172058e-01
-1.17597863e-01 -6.66984200e-01 9.65868533e-01 1.50179490e-01
9.01835337e-02 9.24958825e-01 2.72266120e-01 -5.66256464e-01
-4.33809429e-01 -1.11637723e+00 1.34181648e-01 -1.03191018e+00
-3.89345852e-03 4.82543677e-01 5.00738733e-02 -3.17524582... | [6.056568622589111, 2.8236916065216064] |
2dcdd5d3-fe67-4cbc-a082-8a276fb5e02e | deep-reinforcement-learning-for-contact-rich | 2008.13223 | null | https://arxiv.org/abs/2008.13223v2 | https://arxiv.org/pdf/2008.13223v2.pdf | Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives | In recent years, industrial robots have been installed in various industries to handle advanced manufacturing and high precision tasks. However, further integration of industrial robots is hampered by their limited flexibility, adaptability and decision making skills compared to human operators. Assembly tasks are espe... | ['Oren Spector', 'Miriam Zacksenhouse'] | 2020-08-30 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 2.10551187e-01 3.44768226e-01 -2.87124574e-01 1.04194671e-01
-1.85686007e-01 -4.13573861e-01 3.72428089e-01 2.17804126e-02
-4.39607382e-01 1.01580048e+00 -5.49393237e-01 -1.96713120e-01
-8.01812410e-01 -6.81189418e-01 -7.50398040e-01 -8.31903696e-01
-3.52429867e-01 6.52731240e-01 3.02010953e-01 -4.11259860... | [4.760690212249756, 1.423081636428833] |
0fda7fda-9a22-4bed-90cf-7f0c6adbd894 | universal-domain-adaptation-through-self | 2002.07953 | null | https://arxiv.org/abs/2002.07953v3 | https://arxiv.org/pdf/2002.07953v3.pdf | Universal Domain Adaptation through Self Supervision | Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the partic... | ['Kate Saenko', 'Kuniaki Saito', 'Donghyun Kim', 'Stan Sclaroff'] | 2020-02-19 | null | http://proceedings.neurips.cc/paper/2020/hash/bb7946e7d85c81a9e69fee1cea4a087c-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/bb7946e7d85c81a9e69fee1cea4a087c-Paper.pdf | neurips-2020-12 | ['universal-domain-adaptation', 'partial-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 6.54592961e-02 -9.95083377e-02 -4.20426309e-01 -6.23318613e-01
-6.75791860e-01 -1.11740994e+00 6.27485573e-01 -1.05867662e-01
-4.31383431e-01 6.95168972e-01 2.89648831e-01 1.11254372e-01
-8.51104385e-04 -4.60183203e-01 -5.50783992e-01 -8.01132321e-01
3.09620291e-01 8.31469297e-01 4.27164197e-01 -1.34980068... | [10.276707649230957, 3.0462701320648193] |
e47a5917-b01a-40c1-94fb-17b61efac3af | multi-vqg-generating-engaging-questions-for | 2211.07441 | null | https://arxiv.org/abs/2211.07441v2 | https://arxiv.org/pdf/2211.07441v2.pdf | Multi-VQG: Generating Engaging Questions for Multiple Images | Generating engaging content has drawn much recent attention in the NLP community. Asking questions is a natural way to respond to photos and promote awareness. However, most answers to questions in traditional question-answering (QA) datasets are factoids, which reduce individuals' willingness to answer. Furthermore, t... | ["Ting-Hao 'Kenneth' Haung", 'Lun-Wei Ku', 'Vicent Chen', 'Min-Hsuan Yeh'] | 2022-11-14 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.25129205e-01 6.42410159e-01 2.29084000e-01 -4.61255580e-01
-8.63564789e-01 -8.90362144e-01 1.04629374e+00 1.89692676e-01
-1.20557338e-01 6.80356979e-01 9.55937266e-01 -1.48432106e-01
3.48357439e-01 -9.63802516e-01 -6.23235583e-01 -9.16761085e-02
3.65750015e-01 1.71935722e-01 -1.23963401e-01 -2.60794759... | [11.055864334106445, 1.343553900718689] |
ddeb909c-d187-4f49-89bf-810f792c4fc7 | crowd-counting-with-sparse-annotation | 2304.06021 | null | https://arxiv.org/abs/2304.06021v1 | https://arxiv.org/pdf/2304.06021v1.pdf | Crowd Counting with Sparse Annotation | This paper presents a new annotation method called Sparse Annotation (SA) for crowd counting, which reduces human labeling efforts by sparsely labeling individuals in an image. We argue that sparse labeling can reduce the redundancy of full annotation and capture more diverse information from distant individuals that i... | ['Tong Zhang', 'Wei Ke', 'Fei Wang', 'Qing Liu', 'Zhengzheng Wang', 'Shiwei Zhang'] | 2023-04-12 | null | null | null | null | ['crowd-counting'] | ['computer-vision'] | [-4.18948010e-02 1.67975873e-01 -1.52297569e-02 -1.72405913e-01
-5.19033372e-01 -2.14646608e-01 4.40751433e-01 1.65367797e-01
-6.05468273e-01 8.06150675e-01 3.47857475e-01 5.05724609e-01
5.00449419e-01 -7.06817448e-01 -5.83922684e-01 -4.71232712e-01
1.90284103e-01 1.05011225e+00 9.90012348e-01 -2.01452062... | [8.280688285827637, -0.3880387544631958] |
b9a5bdae-ac75-4d3a-87f0-316c8c85ed4a | computational-protein-design-using-andor | 1412.3138 | null | http://arxiv.org/abs/1412.3138v2 | http://arxiv.org/pdf/1412.3138v2.pdf | Computational Protein Design Using AND/OR Branch-and-Bound Search | The computation of the global minimum energy conformation (GMEC) is an
important and challenging topic in structure-based computational protein
design. In this paper, we propose a new protein design algorithm based on the
AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional
branch-and-bound sear... | ['Jianyang Zeng', 'Yuexin Wu', 'Yichao Zhou'] | 2014-12-08 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.54676372e-01 1.47363037e-01 -1.00216590e-01 -6.27836511e-02
-4.36428368e-01 -8.04734528e-01 -1.89377844e-01 5.87217212e-01
-3.20875347e-01 1.22999084e+00 -3.18530262e-01 -9.00901139e-01
-2.36815572e-01 -7.70101964e-01 -9.57668126e-01 -8.91940832e-01
-9.26054493e-02 6.88974082e-01 3.76307070e-01 -3.86409730... | [4.853063106536865, 5.514769554138184] |
4f40dda9-2ab3-439d-9720-4b85d988d849 | towards-zero-shot-sign-language-recognition | 2201.05914 | null | https://arxiv.org/abs/2201.05914v1 | https://arxiv.org/pdf/2201.05914v1.pdf | Towards Zero-shot Sign Language Recognition | This paper tackles the problem of zero-shot sign language recognition (ZSSLR), where the goal is to leverage models learned over the seen sign classes to recognize the instances of unseen sign classes. In this context, readily available textual sign descriptions and attributes collected from sign language dictionaries ... | ['Nazli Ikizler-Cinbis', 'Ramazan Gokberk Cinbis', 'Yunus Can Bilge'] | 2022-01-15 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 3.20892453e-01 -8.69087800e-02 -6.85229361e-01 -7.35196769e-01
-8.02577376e-01 -4.24902231e-01 8.09116542e-01 -3.77301812e-01
-4.13705915e-01 3.69139850e-01 6.56074464e-01 4.61510643e-02
-2.53036320e-01 -6.04337394e-01 -4.12098080e-01 -6.91087961e-01
-5.73668489e-03 3.05121899e-01 2.59498626e-01 -1.42077416... | [9.20519733428955, -6.430078506469727] |
c247d892-da76-40a9-a428-36876c7506e0 | vihealthbert-pre-trained-language-models-for-1 | null | null | https://aclanthology.org/2022.lrec-1.35 | https://aclanthology.org/2022.lrec-1.35.pdf | ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining | Pre-trained language models have become crucial to achieving competitive results across many Natural Language Processing (NLP) problems. For monolingual pre-trained models in low-resource languages, the quantity has been significantly increased. However, most of them relate to the general domain, and there are limited ... | ['Steven Quoc Hung Truong', 'Trung Huu Bui', 'Huy Duc Ta', 'Vu Hoang', 'Vu Hoang Tran', 'Nguyen Minh'] | null | null | null | null | lrec-2022-6 | ['vietnamese-datasets'] | ['natural-language-processing'] | [-2.80334167e-02 2.58296579e-01 -6.20736063e-01 -3.98892760e-01
-1.51420999e+00 -5.53543925e-01 4.79501367e-01 7.27102757e-01
-7.84190178e-01 1.04286671e+00 1.04359066e+00 -3.61332864e-01
3.33696827e-02 -4.41854686e-01 -2.34728694e-01 -2.72427142e-01
2.23417327e-01 9.45396721e-01 -3.17459464e-01 -5.80925584... | [8.789753913879395, 8.934115409851074] |
7ac0f3f2-b201-4197-ac68-049ea8ad1a49 | i2l-meshnet-image-to-lixel-prediction-network-1 | 2008.03713 | null | https://arxiv.org/abs/2008.03713v2 | https://arxiv.org/pdf/2008.03713v2.pdf | I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image | Most of the previous image-based 3D human pose and mesh estimation methods estimate parameters of the human mesh model from an input image. However, directly regressing the parameters from the input image is a highly non-linear mapping because it breaks the spatial relationship between pixels in the input image. In add... | ['Kyoung Mu Lee', 'Gyeongsik Moon'] | 2020-08-09 | i2l-meshnet-image-to-lixel-prediction-network | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/397_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520732.pdf | eccv-2020-8 | ['3d-human-reconstruction'] | ['computer-vision'] | [-5.72467409e-02 5.10316014e-01 -2.53189534e-01 -3.94028723e-01
-7.06708252e-01 -1.58446297e-01 1.05579846e-01 -1.55955657e-01
-2.35328466e-01 6.09393656e-01 8.25758353e-02 2.12962367e-02
6.56289840e-03 -8.03488255e-01 -1.31751764e+00 -2.41467074e-01
2.14758009e-01 7.84005284e-01 4.79249626e-01 1.62144974... | [7.101963996887207, -1.2622084617614746] |
65c2947c-3ff5-4d1e-937a-4e8cc2e33bd7 | tfusion-transformer-based-n-to-one-multimodal | 2208.12776 | null | https://arxiv.org/abs/2208.12776v2 | https://arxiv.org/pdf/2208.12776v2.pdf | SFusion: Self-attention based N-to-One Multimodal Fusion Block | People perceive the world with different senses, such as sight, hearing, smell, and touch. Processing and fusing information from multiple modalities enables Artificial Intelligence to understand the world around us more easily. However, when there are missing modalities, the number of available modalities is different... | ['Jianlong Zhou', 'Rui Li', 'Jia Wei', 'Zecheng Liu'] | 2022-08-26 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.67105067e-01 -4.81576882e-02 -2.79435843e-01 -2.86274999e-01
-6.31673634e-01 -2.19981164e-01 5.37236571e-01 3.37531269e-02
-4.27233875e-01 5.25766671e-01 4.71337974e-01 -8.33576173e-02
3.34648341e-02 -6.62223697e-01 -3.01716447e-01 -9.44197655e-01
5.90146065e-01 -8.48079938e-03 -4.37066369e-02 -6.32362142... | [13.102859497070312, 4.97509765625] |
3e4ce589-6607-4250-a457-2be76f1c46c0 | an-empirical-model-of-large-batch-training | 1812.06162 | null | http://arxiv.org/abs/1812.06162v1 | http://arxiv.org/pdf/1812.06162v1.pdf | An Empirical Model of Large-Batch Training | In an increasing number of domains it has been demonstrated that deep
learning models can be trained using relatively large batch sizes without
sacrificing data efficiency. However the limits of this massive data
parallelism seem to differ from domain to domain, ranging from batches of tens
of thousands in ImageNet to ... | ['OpenAI Dota Team', 'Jared Kaplan', 'Dario Amodei', 'Sam McCandlish'] | 2018-12-14 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-3.64919662e-01 -4.44959477e-02 7.47516155e-02 -5.68241060e-01
-4.38021362e-01 -4.33526546e-01 8.01807344e-01 -1.49931267e-01
-8.62078011e-01 8.76151800e-01 -2.05672514e-02 -3.87167007e-01
-1.33324489e-01 -6.93283021e-01 -6.62934244e-01 -7.47469187e-01
-2.16219783e-01 8.87614489e-01 3.29461992e-01 -2.41852269... | [8.23194694519043, 3.3859894275665283] |
57f7e735-9829-452e-b244-5b2ed2c71c90 | explaining-the-black-box-smoothly-a | 2101.04230 | null | https://arxiv.org/abs/2101.04230v3 | https://arxiv.org/pdf/2101.04230v3.pdf | Explaining the Black-box Smoothly- A Counterfactual Approach | We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain "how" imaging features in important anatomical regions are relevant to the classification decision. Our frame... | ['Kayhan Batmanghelich', 'Brian Pollack', 'Motahhare Eslami', 'Stephen Wallace', 'Sumedha Singla'] | 2021-01-11 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.42918944e-01 1.08804262e+00 -3.56100321e-01 -4.57854003e-01
-5.12074053e-01 -4.60400373e-01 4.39305961e-01 3.33885819e-01
-2.23305792e-01 8.19147706e-01 5.43910205e-01 -7.06980228e-01
-8.29486996e-02 -4.48664933e-01 -8.08735311e-01 -5.83541572e-01
1.63663402e-01 4.00825918e-01 -5.36582209e-02 6.96968362... | [8.74123764038086, 5.497229099273682] |
790fa410-d731-4c48-ac3c-d0271611c525 | deep-learning-for-video-based-person-re | 2303.11332 | null | https://arxiv.org/abs/2303.11332v1 | https://arxiv.org/pdf/2303.11332v1.pdf | Deep Learning for Video-based Person Re-Identification: A Survey | Video-based person re-identification (video re-ID) has lately fascinated growing attention due to its broad practical applications in various areas, such as surveillance, smart city, and public safety. Nevertheless, video re-ID is quite difficult and is an ongoing stage due to numerous uncertain challenges such as view... | ['Khawar Islam'] | 2023-03-21 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-1.22243427e-01 -7.46058166e-01 -1.45593509e-01 -4.15122002e-01
-5.91444016e-01 -2.35621214e-01 5.46924531e-01 -3.07709187e-01
-3.87871236e-01 6.60898864e-01 4.76733983e-01 2.91478008e-01
3.26612256e-02 -2.11861014e-01 -4.32798713e-01 -5.13529658e-01
-5.94105665e-03 3.09238672e-01 -8.54425598e-03 -1.36106268... | [14.682838439941406, 0.975313663482666] |
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