paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
015113fa-1b9e-4d7b-a9d0-e9543f067cbd | neuspell-a-neural-spelling-correction-toolkit | 2010.11085 | null | https://arxiv.org/abs/2010.11085v1 | https://arxiv.org/pdf/2010.11085v1.pdf | NeuSpell: A Neural Spelling Correction Toolkit | We introduce NeuSpell, an open-source toolkit for spelling correction in English. Our toolkit comprises ten different models, and benchmarks them on naturally occurring misspellings from multiple sources. We find that many systems do not adequately leverage the context around the misspelt token. To remedy this, (i) we ... | ['Graham Neubig', 'Danish Pruthi', 'Sai Muralidhar Jayanthi'] | 2020-10-21 | null | https://aclanthology.org/2020.emnlp-demos.21 | https://aclanthology.org/2020.emnlp-demos.21.pdf | emnlp-2020-11 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.60583299e-01 -2.18868732e-01 2.45136060e-02 -5.54823913e-02
-1.37722087e+00 -9.52695906e-01 5.70063889e-01 2.05792516e-01
-5.06661952e-01 7.76647091e-01 5.07286608e-01 -6.46516681e-01
4.34203923e-01 -5.04873991e-01 -9.67409670e-01 -3.14291567e-01
3.90364826e-01 2.21148565e-01 1.41753659e-01 -6.03071988... | [11.030938148498535, 10.367570877075195] |
a5d40e63-f1a7-4ddd-803b-77c326a11da7 | complex-reading-comprehension-through | 2211.03277 | null | https://arxiv.org/abs/2211.03277v1 | https://arxiv.org/pdf/2211.03277v1.pdf | Complex Reading Comprehension Through Question Decomposition | Multi-hop reading comprehension requires not only the ability to reason over raw text but also the ability to combine multiple evidence. We propose a novel learning approach that helps language models better understand difficult multi-hop questions and perform "complex, compositional" reasoning. Our model first learns ... | ['Gholamreza Haffari', 'Yuan-Fang Li', 'Xiao-Yu Guo'] | 2022-11-07 | null | null | null | null | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 4.96408552e-01 4.95212555e-01 1.52302459e-01 -5.01154244e-01
-1.71356642e+00 -1.15248275e+00 2.92281568e-01 4.56970394e-01
-4.02649015e-01 6.69349849e-01 4.28141057e-01 -1.00397873e+00
5.92344180e-02 -8.75910401e-01 -1.16703546e+00 1.55414253e-01
4.54846859e-01 5.92687011e-01 5.02390742e-01 -5.26625335... | [11.140464782714844, 7.9442925453186035] |
5d35a58c-d18d-4cde-8077-7e8b99131454 | boundary-aware-instance-segmentation | 1612.03129 | null | http://arxiv.org/abs/1612.03129v2 | http://arxiv.org/pdf/1612.03129v2.pdf | Boundary-aware Instance Segmentation | We address the problem of instance-level semantic segmentation, which aims at
jointly detecting, segmenting and classifying every individual object in an
image. In this context, existing methods typically propose candidate objects,
usually as bounding boxes, and directly predict a binary mask within each such
proposal.... | ['Zeeshan Hayder', 'Mathieu Salzmann', 'Xuming He'] | 2016-12-09 | boundary-aware-instance-segmentation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Hayder_Boundary-Aware_Instance_Segmentation_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Hayder_Boundary-Aware_Instance_Segmentation_CVPR_2017_paper.pdf | cvpr-2017-7 | ['object-proposal-generation'] | ['computer-vision'] | [ 5.11478305e-01 4.25421298e-01 -8.54879618e-02 -7.35847354e-01
-9.45599258e-01 -4.99414384e-01 4.46029156e-01 -1.83290225e-02
-5.57181001e-01 3.68547350e-01 -2.28973970e-01 -1.80889536e-02
1.09996669e-01 -6.50292516e-01 -1.23033106e+00 -5.04448354e-01
3.54137450e-01 7.42623448e-01 7.32718349e-01 2.34648362... | [9.484508514404297, 0.46579593420028687] |
34fa43ef-b593-4bca-9e0d-5a768244bded | scale-recurrent-network-for-deep-image | 1802.01770 | null | http://arxiv.org/abs/1802.01770v1 | http://arxiv.org/pdf/1802.01770v1.pdf | Scale-recurrent Network for Deep Image Deblurring | In single image deblurring, the "coarse-to-fine" scheme, i.e. gradually
restoring the sharp image on different resolutions in a pyramid, is very
successful in both traditional optimization-based methods and recent
neural-network-based approaches. In this paper, we investigate this strategy
and propose a Scale-recurrent... | ['Xin Tao', 'Xiaoyong Shen', 'Jiaya Jia', 'Jue Wang', 'Yi Wang', 'Hongyun Gao'] | 2018-02-06 | scale-recurrent-network-for-deep-image-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Tao_Scale-Recurrent_Network_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Tao_Scale-Recurrent_Network_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['image-relighting'] | ['computer-vision'] | [ 1.61152050e-01 -6.43882394e-01 -1.37867659e-01 1.52226880e-01
-8.64169896e-01 -1.72065362e-01 3.92911851e-01 -5.21578550e-01
-2.28233427e-01 8.44277382e-01 7.34637082e-01 1.75073985e-02
-4.43179421e-02 -3.96734595e-01 -6.61445618e-01 -7.27158666e-01
1.38939187e-01 -7.49103650e-02 4.46092606e-01 -1.53368592... | [11.510047912597656, -2.5393240451812744] |
776e4b9b-b438-43b8-b1a4-faee3366d96d | an-investigation-of-end-to-end-models-for | 2102.06237 | null | https://arxiv.org/abs/2102.06237v1 | https://arxiv.org/pdf/2102.06237v1.pdf | An Investigation of End-to-End Models for Robust Speech Recognition | End-to-end models for robust automatic speech recognition (ASR) have not been sufficiently well-explored in prior work. With end-to-end models, one could choose to preprocess the input speech using speech enhancement techniques and train the model using enhanced speech. Another alternative is to pass the noisy speech a... | ['Rajbabu Velmurugan', 'Preethi Jyothi', 'Archiki Prasad'] | 2021-02-11 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 5.79912245e-01 2.73043979e-02 3.52295548e-01 -4.67200249e-01
-1.36012435e+00 -4.89001364e-01 6.29452944e-01 -4.00468297e-02
-7.03749359e-01 3.35866421e-01 5.37878573e-01 -7.26952970e-01
3.10718175e-02 -9.28777456e-02 -4.04484659e-01 -7.28862464e-01
1.82698727e-01 1.90934658e-01 9.51993465e-02 -5.06748557... | [14.725558280944824, 6.339413166046143] |
f178940a-2e4c-4ea5-80c2-43b3db5860d4 | multi-view-temporal-alignment-for-non | 2012.15184 | null | https://arxiv.org/abs/2012.15184v1 | https://arxiv.org/pdf/2012.15184v1.pdf | Multi-view Temporal Alignment for Non-parallel Articulatory-to-Acoustic Speech Synthesis | Articulatory-to-acoustic (A2A) synthesis refers to the generation of audible speech from captured movement of the speech articulators. This technique has numerous applications, such as restoring oral communication to people who cannot longer speak due to illness or injury. Most successful techniques so far adopt a supe... | ['Phil D. Green', 'Jose L. Perez-Cordoba', 'Alejandro Gomez-Alanis', 'Miriam Gonzalez-Atienza', 'Jose A. Gonzalez-Lopez'] | 2020-12-30 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 4.42304164e-01 2.90779471e-02 -1.14498168e-01 1.19301446e-01
-8.39160264e-01 -5.98113418e-01 7.10319579e-01 -2.75921464e-01
-1.68543011e-01 4.50552970e-01 5.36495686e-01 -6.09529763e-02
-2.26991221e-01 -3.63694757e-01 -3.56887251e-01 -1.04782963e+00
1.84324473e-01 3.17174554e-01 4.34687734e-02 -3.83539274... | [14.574737548828125, 5.47404146194458] |
34a35dbb-96c6-4aad-b18f-4955c63b707e | computable-phenotypes-to-characterize | 2303.05504 | null | https://arxiv.org/abs/2303.05504v1 | https://arxiv.org/pdf/2303.05504v1.pdf | Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit | In the United States, more than 5 million patients are admitted annually to ICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain dysfunction status, delirium, is often underdiagnosed or undervalued. This study's objective was to develop automated computable phenotypes for acute brain dysfunction ... | ['Tezcan Ozrazgat-Baslanti', 'Azra Bihorac', 'Parisa Rashidi', 'Katharina Busl', 'Carolina B. Maciel', 'Benjamin Shickel', 'Rayon Uddin', 'Ziyuan Guan', 'Tyler J. Loftus', 'Yuanfang Ren'] | 2023-03-09 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-2.35523611e-01 -2.49045789e-01 1.59241065e-01 -4.10268679e-02
-3.51328969e-01 -6.49742246e-01 -1.73214421e-01 7.13225424e-01
-8.34062397e-01 1.13747478e+00 5.03259480e-01 -7.66597509e-01
-4.08649921e-01 -6.56624615e-01 -1.61881279e-02 -3.98178130e-01
-3.01164895e-01 9.36045766e-01 -4.05874014e-01 4.30698305... | [8.009265899658203, 6.136665344238281] |
f9baf5b6-b74e-4fad-9d4a-511bed4e4dc3 | zbs-zero-shot-background-subtraction-via | 2303.14679 | null | https://arxiv.org/abs/2303.14679v1 | https://arxiv.org/pdf/2303.14679v1.pdf | ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection | Background subtraction (BGS) aims to extract all moving objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep learning ... | ['Jinqiao Wang', 'Ming Tang', 'Chaoyang Zhao', 'Haiyun Guo', 'Tao Yu', 'Xu Zhao', 'Yongqi An'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/An_ZBS_Zero-Shot_Background_Subtraction_via_Instance-Level_Background_Modeling_and_Foreground_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/An_ZBS_Zero-Shot_Background_Subtraction_via_Instance-Level_Background_Modeling_and_Foreground_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-object-detection', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.37974495e-01 -4.71735865e-01 4.88981903e-02 -3.29532146e-01
-4.31988984e-01 -1.60409153e-01 4.95730251e-01 -2.17784151e-01
-4.20451343e-01 6.14287615e-01 -3.09358746e-01 -3.18418175e-01
4.64810401e-01 -8.33706617e-01 -6.77326441e-01 -1.17196655e+00
2.01219767e-01 5.60982227e-02 1.11997283e+00 7.77529366... | [9.020655632019043, -0.5850842595100403] |
2f8b3a8d-3246-48df-ab3f-226084a5d950 | hprn-holistic-prior-embedded-relation-network | 2112.14608 | null | https://arxiv.org/abs/2112.14608v2 | https://arxiv.org/pdf/2112.14608v2.pdf | HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution | Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart. Due to the one-to-many nature of the SSR problem, a single RGB image can be reprojected to many HSIs. The key to tackle this ill-posed problem is to plug into multi-source prior information such as the natural spat... | ['Qian Du', 'Yunsong Li', 'Rui Song', 'Jiaojiao Li', 'Chaoxiong Wu'] | 2021-12-29 | null | null | null | null | ['spectral-super-resolution'] | ['computer-vision'] | [ 5.97181261e-01 -4.16268110e-01 1.80527329e-01 -3.53603035e-01
-6.21316671e-01 -2.96959937e-01 2.67946482e-01 -4.02700514e-01
-1.57074258e-01 6.25450373e-01 8.78957435e-02 8.83494597e-03
-6.29877627e-01 -1.14335215e+00 -5.06822169e-01 -1.22682178e+00
3.42073888e-01 -4.89919305e-01 -9.25743505e-02 -4.32721734... | [10.262887001037598, -1.9799680709838867] |
43aee0e9-b2ac-452f-b092-9573d390bc60 | reinforcement-learning-for-survival-a | 2207.08040 | null | https://arxiv.org/abs/2207.08040v2 | https://arxiv.org/pdf/2207.08040v2.pdf | Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients | There has been considerable interest in leveraging RL and stochastic control methods to learn optimal treatment strategies for critically ill patients, directly from observational data. However, there is significant ambiguity on the control objective and on the best reward choice for the standard RL objective. In this ... | ['Thesath Nanayakkara'] | 2022-07-17 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.82172313e-01 1.63920373e-01 -4.42023277e-01 -9.07633528e-02
-6.59644604e-01 -2.96672225e-01 -1.57074139e-01 3.18319231e-01
-5.42577922e-01 1.27635646e+00 3.57021213e-01 -5.37288904e-01
-7.78316617e-01 -3.10659707e-01 -3.69385779e-01 -8.79177153e-01
-4.04673725e-01 6.86333120e-01 -3.64689201e-01 -7.00919032... | [4.007367134094238, 2.7264153957366943] |
8765cac3-18ee-47b2-84da-248900a33504 | miipher-a-robust-speech-restoration-model | 2303.01664 | null | https://arxiv.org/abs/2303.01664v1 | https://arxiv.org/pdf/2303.01664v1.pdf | Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations | Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a new SR application: increasing the amount of high-quality training data for speech generation by converting speech samples collected from the... | ['Michiel Bacchiani', 'Ankur Bapna', 'Wei Han', 'Yu Zhang', 'Nobuyuki Morioka', 'Kohei Yatabe', 'Yifan Ding', 'Shigeki Karita', 'Heiga Zen', 'Yuma Koizumi'] | 2023-03-03 | null | null | null | null | ['speech-enhancement', 'speech-denoising'] | ['speech', 'speech'] | [ 4.28842217e-01 2.29411870e-02 2.06000641e-01 -3.99050087e-01
-1.59914148e+00 -3.84496927e-01 3.42069030e-01 -5.12056053e-01
1.25331417e-01 5.93960643e-01 8.81381035e-01 -5.18437147e-01
1.66727945e-01 -5.30264795e-01 -8.11753988e-01 -5.33526361e-01
2.14736894e-01 -8.95529389e-02 1.00274086e-01 -3.60906273... | [14.882107734680176, 6.118956565856934] |
ab49cc9a-a752-414a-bec6-4c295db564d7 | network-intrusion-detection-system-in-a-light | 2210.03254 | null | https://arxiv.org/abs/2210.03254v1 | https://arxiv.org/pdf/2210.03254v1.pdf | Network Intrusion Detection System in a Light Bulb | Internet of Things (IoT) devices are progressively being utilised in a variety of edge applications to monitor and control home and industry infrastructure. Due to the limited compute and energy resources, active security protections are usually minimal in many IoT devices. This has created a critical security challeng... | ['Marius Portmann', 'Siamak Layeghy', 'Liam Daly Manocchio'] | 2022-10-06 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.82832390e-01 -2.30885878e-01 -4.93536472e-01 6.08801506e-02
5.34759760e-01 -2.85123736e-01 3.28900427e-01 -2.05359221e-01
-1.65283948e-01 5.02338350e-01 -3.86998743e-01 -8.88807952e-01
-2.10584998e-01 -1.00020778e+00 1.67354479e-01 -5.43385029e-01
-2.76196413e-02 1.11709125e-01 6.84746325e-01 -6.97192922... | [5.178332805633545, 7.154537677764893] |
685ea557-9dba-4789-9ca4-127a9287feca | dds3d-dense-pseudo-labels-with-dynamic | 2303.05079 | null | https://arxiv.org/abs/2303.05079v2 | https://arxiv.org/pdf/2303.05079v2.pdf | DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection | In this paper, we present a simple yet effective semi-supervised 3D object detector named DDS3D. Our main contributions have two-fold. On the one hand, different from previous works using Non-Maximal Suppression (NMS) or its variants for obtaining the sparse pseudo labels, we propose a dense pseudo-label generation str... | ['Jinghua Hou', 'Zhe Liu', 'Jingyu Li', 'Dingkang Liang'] | 2023-03-09 | null | null | null | null | ['pseudo-label'] | ['miscellaneous'] | [ 2.04939824e-02 1.71769366e-01 -2.12522373e-01 -4.45546210e-01
-6.59624696e-01 -3.87545884e-01 4.52539057e-01 -9.32026953e-02
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3.63204211e-01 -5.07486343e-01 -6.02102339e-01 -8.21933031e-01
1.06673345e-01 3.64120871e-01 7.37089157e-01 9.52826217... | [9.14746379852295, 1.2379863262176514] |
6345f278-a782-441c-8375-10eefdcdc0bf | frequency-enhanced-hybrid-attention-network | 2304.09184 | null | https://arxiv.org/abs/2304.09184v3 | https://arxiv.org/pdf/2304.09184v3.pdf | Frequency Enhanced Hybrid Attention Network for Sequential Recommendation | The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture hi... | ['Victor S. Sheng', 'Fuzhen Zhuang', 'Jianfeng Qu', 'Guanfeng Liu', 'Pengpeng Zhao', 'Huanhuan Yuan', 'Xinyu Du'] | 2023-04-18 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-1.72236994e-01 -6.53810501e-01 -3.97406787e-01 -2.51394242e-01
-1.41562864e-01 -2.44931877e-01 4.27862763e-01 -9.45776850e-02
2.01480854e-02 2.47701243e-01 6.08272433e-01 1.38801515e-01
-4.65057909e-01 -6.97363555e-01 -7.17067540e-01 -6.84373260e-01
2.44208537e-02 -2.57139474e-01 4.11039650e-01 -3.44869763... | [10.137913703918457, 5.552881240844727] |
ee902da4-f3ec-4c52-ad8f-96e979794a1c | s2d2net-an-improved-approach-for-robust-steel | null | null | https://ieeexplore.ieee.org/document/9506405 | https://ieeexplore.ieee.org/document/9506405 | S2D2Net: An Improved Approach For Robust Steel Surface Defects Diagnosis With Small Sample Learning | Surface defect recognition of products is a necessary process to guarantee the quality of industrial production. This paper proposes a hybrid model, S2D2Net (Steel Surface Defect Diagnosis Network), for an efficient and robust inspection of the steel surface during the manufacturing process. The S2D2Net uses a pretrain... | ['Chiranjoy Chattopadhyay', 'Vikanksh Nath'] | 2021-08-23 | null | null | null | ieee-international-conference-on-image-3 | ['small-data'] | ['computer-vision'] | [ 3.02442044e-01 4.72871840e-01 3.62253606e-01 -2.08059132e-01
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-5.97940125e-02 -8.90968680e-01 -4.81380105e-01 -8.24734509e-01
8.54260176e-02 5.47486722e-01 4.72263753e-01 -5.15250087... | [7.382396221160889, 1.8610985279083252] |
1b5c1e33-7bd9-4019-bdd1-9e771081878f | cbeaf-adapting-enhanced-continual-pretraining | 2211.11363 | null | https://arxiv.org/abs/2211.11363v1 | https://arxiv.org/pdf/2211.11363v1.pdf | CBEAF-Adapting: Enhanced Continual Pretraining for Building Chinese Biomedical Language Model | Continual pretraining is a standard way of building a domain-specific pretrained language model from a general-domain language model. However, sequential task training may cause catastrophic forgetting, which affects the model performance in downstream tasks. In this paper, we propose a continual pretraining method for... | ['Tong Ruan', 'Qi Ye', 'Kui Xue', 'Yongyu Yan'] | 2022-11-21 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 7.41995946e-02 3.15479130e-01 5.58626577e-02 -4.24290985e-01
-6.98905349e-01 5.15854619e-02 3.11057687e-01 4.09887219e-03
-9.55102205e-01 1.03813601e+00 3.13461035e-01 -4.44843948e-01
3.82180601e-01 -4.12806004e-01 -7.61134505e-01 -4.34274495e-01
1.99109346e-01 5.05204082e-01 3.24424595e-01 -3.95497382... | [10.412894248962402, 8.104899406433105] |
70315324-b75a-43bd-a0b5-514d4347d27f | prediction-guided-distillation-for-dense | 2203.05469 | null | https://arxiv.org/abs/2203.05469v2 | https://arxiv.org/pdf/2203.05469v2.pdf | Prediction-Guided Distillation for Dense Object Detection | Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful information from a larger teacher model. However, a key challenge is identifying the most informative features produced by the teacher for distillati... | ['Elliot J. Crowley', 'Amos Storkey', 'Mateusz Ochal', 'Chenhongyi Yang'] | 2022-03-10 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [-3.53412956e-01 2.21942008e-01 -3.75592560e-01 -1.33354723e-01
-9.74296987e-01 -3.94798696e-01 7.55116761e-01 2.05370978e-01
-6.74923480e-01 4.97388661e-01 9.72292498e-02 -4.49738652e-02
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9.87702981e-02 6.58091009e-01 1.00089633e+00 -1.16019301... | [9.2761869430542, 1.3079094886779785] |
4822f522-2ca5-406e-b19c-b81c8e8942c4 | a-novel-deep-reinforcement-learning-based | 2107.00931 | null | https://arxiv.org/abs/2107.00931v1 | https://arxiv.org/pdf/2107.00931v1.pdf | A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments | Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the direction predictio... | ['Zeynep Hilal Kilimci', 'Anil Berk Altuner'] | 2021-07-02 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-8.90677750e-01 -1.56748921e-01 2.14262772e-02 -1.04370125e-01
6.76636351e-03 -5.97709835e-01 4.20615554e-01 -5.07534668e-02
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-1.83741793e-01 -1.25662720e+00 -6.60911500e-01 -4.75754440e-01
-2.65855044e-01 1.73936486e-01 3.20109516e-01 -8.85694444... | [4.373959541320801, 4.253649711608887] |
4e9c645e-89dd-4b0b-aa9f-e72754d2facb | exploring-global-and-local-information-for | 2306.02025 | null | https://arxiv.org/abs/2306.02025v1 | https://arxiv.org/pdf/2306.02025v1.pdf | Exploring Global and Local Information for Anomaly Detection with Normal Samples | Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, containing crucial information, and are suitable for application scenes like intrusion detection, fraud detection, fault diagnosis, e-commerce ... | ['Xibin Zhao', 'Nan Wang', 'Fan Xu'] | 2023-06-03 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [ 1.22565567e-01 -4.23793554e-01 -2.14871820e-02 -3.02909851e-01
-3.05470884e-01 8.90398324e-02 2.78438956e-01 4.62393463e-01
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-1.22247949e-01 1.59339324e-01 4.74133402e-01 1.00329049... | [7.55881404876709, 2.3992648124694824] |
a4c3cd18-71f8-4acb-926e-88b57f021eb2 | adaptive-wavelet-transformer-network-for-3d | null | null | https://openreview.net/forum?id=5MLb3cLCJY | https://openreview.net/pdf?id=5MLb3cLCJY | Adaptive Wavelet Transformer Network for 3D Shape Representation Learning | We present a novel method for 3D shape representation learning using multi-scale wavelet decomposition. Distinct from previous works that either decompose 3D shapes into complimentary components at a single scale, or naively adopt up-/down-sampling to build hierarchies and treat all points or local regions equally, we ... | ['Yi Fang', 'Hao Huang'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['3d-shape-retrieval', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 8.32062773e-03 -2.93196682e-02 -1.67784676e-01 -2.53131449e-01
-9.88172412e-01 -5.38452327e-01 1.26969799e-01 5.05874604e-02
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-7.53085539e-02 -1.19489133e+00 -5.61700881e-01 -7.44453728e-01
-2.98475593e-01 6.03269696e-01 4.48817968e-01 -1.73592091... | [8.048065185546875, -3.627959728240967] |
14ca2ea8-20d4-4c9a-8756-e01e609b64f0 | visual-localization-using-imperfect-3d-models | 2304.05947 | null | https://arxiv.org/abs/2304.05947v1 | https://arxiv.org/pdf/2304.05947v1.pdf | Visual Localization using Imperfect 3D Models from the Internet | Visual localization is a core component in many applications, including augmented reality (AR). Localization algorithms compute the camera pose of a query image w.r.t. a scene representation, which is typically built from images. This often requires capturing and storing large amounts of data, followed by running Struc... | ['Torsten Sattler', 'Zuzana Kukelova', 'Vojtech Panek'] | 2023-04-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Panek_Visual_Localization_Using_Imperfect_3D_Models_From_the_Internet_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Panek_Visual_Localization_Using_Imperfect_3D_Models_From_the_Internet_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [ 5.10381535e-02 -4.69418287e-01 2.09403206e-02 -3.22556645e-01
-1.04914308e+00 -1.03433144e+00 4.21870291e-01 6.52138563e-03
-1.35616541e-01 4.02549297e-01 8.98105726e-02 -3.39207202e-01
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8.89010578e-02 5.32998919e-01 2.58524746e-01 -4.54229154... | [7.6259446144104, -2.265016555786133] |
daea76ad-099f-4aec-991a-345971ffc290 | modeling-semantic-composition-with-syntactic | 2205.06530 | null | https://arxiv.org/abs/2205.06530v1 | https://arxiv.org/pdf/2205.06530v1.pdf | Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering | A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects. Existing methods mostly seek to align the word representations with the video regions. However, word representations are often not able to convey a complete descrip... | ['Fuwei Zhang', 'Zijing Ou', 'Qinliang Su', 'Wanjun Zhong', 'Zenan Xu'] | 2022-05-13 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.64325088e-01 -6.83122650e-02 -2.63978839e-01 -4.12624300e-01
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3.71543080e-01 3.09713602e-01 3.71143878e-01 -2.84716040... | [10.463918685913086, 1.2403932809829712] |
a28a10b4-192f-4513-8870-6968c24782b1 | coordinated-frequency-constrained-stochastic | 2305.11440 | null | https://arxiv.org/abs/2305.11440v1 | https://arxiv.org/pdf/2305.11440v1.pdf | Coordinated Frequency-Constrained Stochastic Economic Dispatch for Integrated Transmission and Distribution System via Distributed Optimization | When large-scale uncertain centralized and distributed renewable energy sources are connected to a power system, separate dispatching of the transmission power system (TPS) and the active distribution network (ADN) will lower the network security and frequency security of the system. To address these problems, this pap... | ['Zhengshuo Li', 'Ye Tian'] | 2023-05-19 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-5.20802498e-01 -6.87446892e-02 -5.73841870e-01 3.29218119e-01
4.95323539e-02 -1.07207918e+00 8.25830549e-02 -1.46440461e-01
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-6.80983424e-01 -1.19875288e+00 1.20847858e-01 -1.10800529e+00
-1.72834992e-01 7.30504766e-02 -3.10748279e-01 -5.19945145... | [5.658140182495117, 2.5312445163726807] |
d68eddb0-f471-4eba-8afb-81ea39b61e24 | matchformer-interleaving-attention-in | 2203.09645 | null | https://arxiv.org/abs/2203.09645v3 | https://arxiv.org/pdf/2203.09645v3.pdf | MatchFormer: Interleaving Attention in Transformers for Feature Matching | Local feature matching is a computationally intensive task at the subpixel level. While detector-based methods coupled with feature descriptors struggle in low-texture scenes, CNN-based methods with a sequential extract-to-match pipeline, fail to make use of the matching capacity of the encoder and tend to overburden t... | ['Rainer Stiefelhagen', 'Kunyu Peng', 'Kailun Yang', 'Jiaming Zhang', 'Qing Wang'] | 2022-03-17 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 9.48884413e-02 -2.07641706e-01 -7.87223130e-02 -2.47957587e-01
-1.12769747e+00 -5.77942014e-01 4.95544642e-01 2.34541714e-01
-6.84598804e-01 2.97875404e-01 1.02538370e-01 -1.75273493e-02
1.58303812e-01 -9.59184110e-01 -1.11704755e+00 -3.59069347e-01
1.82735845e-01 2.52339333e-01 5.40426254e-01 -8.76856595... | [7.850198745727539, -2.101478338241577] |
739a9991-c5e8-477b-8cf1-2a1a0cf13156 | little-motion-big-results-using-motion | 2008.04946 | null | https://arxiv.org/abs/2008.04946v1 | https://arxiv.org/pdf/2008.04946v1.pdf | Little Motion, Big Results: Using Motion Magnification to Reveal Subtle Tremors in Infants | Detecting tremors is challenging for both humans and machines. Infants exposed to opioids during pregnancy often show signs and symptoms of withdrawal after birth, which are easy to miss with the human eye. The constellation of clinical features, termed as Neonatal Abstinence Syndrome (NAS), include tremors, seizures, ... | ['Ish K. Gulati', 'Girik Malik'] | 2020-08-01 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 2.60681123e-01 -1.14462674e-01 -2.68472165e-01 -1.73000559e-01
-4.14995819e-01 -8.26502502e-01 -1.46071106e-01 6.48586228e-02
-5.27224541e-01 5.13250530e-01 2.82919168e-01 -1.74119473e-01
-1.88980937e-01 -3.11826348e-01 -5.58678925e-01 -7.05423832e-01
-1.50262892e-01 2.85843074e-01 7.76665956e-02 -1.09295622... | [14.029666900634766, -2.2706637382507324] |
e66120c6-0be5-4297-99e0-93704c7c5019 | performer-a-novel-ppg-to-ecg-reconstruction | 2204.11795 | null | https://arxiv.org/abs/2204.11795v3 | https://arxiv.org/pdf/2204.11795v3.pdf | Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection | Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-c... | ['Ella Lan'] | 2022-04-25 | null | null | null | null | ['photoplethysmography-ppg', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.65043694e-01 -2.71832049e-01 1.16421878e-01 -2.70039141e-01
-9.16303098e-01 -3.34575087e-01 -1.11271136e-01 -1.06903493e-01
1.13554284e-01 6.72966838e-01 1.57240167e-01 -3.74652892e-01
-9.09579843e-02 -5.26283145e-01 -5.36607325e-01 -6.54311895e-01
-3.44352335e-01 -1.37098599e-02 -6.22381032e-01 2.28271961... | [14.239614486694336, 3.210813283920288] |
a0063075-0def-4483-bd47-965a77ff2cee | self-supervised-learning-in-remote-sensing-a | 2206.13188 | null | https://arxiv.org/abs/2206.13188v2 | https://arxiv.org/pdf/2206.13188v2.pdf | Self-supervised Learning in Remote Sensing: A Review | In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation remains locked. In this paper,... | ['Xiao Xiang Zhu', 'Lichao Mou', 'Nassim Ait Ali Braham', 'Conrad M Albrecht', 'Yi Wang'] | 2022-06-27 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 3.77594978e-01 -5.03872894e-02 -9.69129279e-02 -6.12954617e-01
-3.47283423e-01 -4.49769497e-01 7.67319202e-01 1.28030330e-01
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-3.57427120e-01 -1.03359401e+00 -5.01298964e-01 -9.41384494e-01
-5.56042671e-01 -5.84362708e-02 -1.78378865e-01 -4.12043631... | [9.568696022033691, -1.4469492435455322] |
298c2963-5180-41a4-a1e4-15304f99d2e1 | crime-prediction-using-machine-learning-with | 2211.01551 | null | https://arxiv.org/abs/2211.01551v1 | https://arxiv.org/pdf/2211.01551v1.pdf | Crime Prediction using Machine Learning with a Novel Crime Dataset | Crime is an unlawful act that carries legal repercussions. Bangladesh has a high crime rate due to poverty, population growth, and many other socio-economic issues. For law enforcement agencies, understanding crime patterns is essential for preventing future criminal activity. For this purpose, these agencies need stru... | ['Mohammad Shafiul Alam', 'Muhammad Ibrahim', 'Abu Ubaida Akash', 'Faisal Tareque Shohan'] | 2022-11-03 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-7.91089684e-02 -5.68203866e-01 -1.82164565e-01 -5.82331419e-01
-6.68352187e-01 -3.81560922e-01 4.46374774e-01 9.07588601e-01
-7.26383150e-01 9.34478283e-01 9.16622341e-01 -4.62240934e-01
-1.93977222e-01 -1.42192411e+00 -1.11495636e-01 -6.79722309e-01
-4.06082310e-02 1.61510974e-01 -1.66037127e-01 -2.79534131... | [6.763400554656982, 1.941611886024475] |
ec7ffb65-0d93-4a55-b8e0-ae588eb76dfb | dfa-nerf-personalized-talking-head-generation | 2201.00791 | null | https://arxiv.org/abs/2201.00791v1 | https://arxiv.org/pdf/2201.00791v1.pdf | DFA-NeRF: Personalized Talking Head Generation via Disentangled Face Attributes Neural Rendering | While recent advances in deep neural networks have made it possible to render high-quality images, generating photo-realistic and personalized talking head remains challenging. With given audio, the key to tackling this task is synchronizing lip movement and simultaneously generating personalized attributes like head m... | ['Xiaokang Yang', 'Guangtao Zhai', 'Yichao Yan', 'RuiZhe Zhong', 'Shunyu Yao'] | 2022-01-03 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 7.06389695e-02 1.17461927e-01 -7.87122548e-02 -3.62571508e-01
-1.15894246e+00 -1.96247354e-01 6.22667611e-01 -5.59906781e-01
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3.84226590e-02 -5.36411524e-01 -9.29048717e-01 -1.14514172e+00
5.37214339e-01 2.15931460e-02 -2.58325666e-01 5.43993153... | [13.240913391113281, -0.41317978501319885] |
f5ac9bfc-e2b1-4283-853c-79e80c50d08b | towards-understanding-the-generalization-of | 2303.12898 | null | https://arxiv.org/abs/2303.12898v1 | https://arxiv.org/pdf/2303.12898v1.pdf | Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets | Electronic medical records (EMRs) are stored in relational databases. It can be challenging to access the required information if the user is unfamiliar with the database schema or general database fundamentals. Hence, researchers have explored text-to-SQL generation methods that provide healthcare professionals direct... | ['Anthony Rios', 'Glenn Dietrich', 'Kim-Kwang Raymond Choo', 'Richard Tarbell'] | 2023-03-22 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 2.39054963e-01 4.69310552e-01 -1.80181354e-01 -7.39724219e-01
-1.41574597e+00 -3.81061733e-01 2.90434867e-01 7.72684872e-01
-3.77111256e-01 5.90212762e-01 3.10309172e-01 -7.53758192e-01
-1.11660525e-01 -8.67711961e-01 -5.96132457e-01 -5.90219796e-02
8.18648860e-02 8.88719201e-01 -6.78832531e-02 -4.70164359... | [8.533320426940918, 8.571063041687012] |
f08c0087-d888-4a99-8983-68f1094f161e | good-for-misconceived-reasons-revisiting | null | null | https://openreview.net/forum?id=Q9U_H8lQ4yV | https://openreview.net/pdf?id=Q9U_H8lQ4yV | Good for Misconceived Reasons: Revisiting Neural Multimodal Machine Translation | A neural multimodal machine translation (MMT) system is one that aims to perform better translation by extending conventional text-only translation models with multimodal information. Many recent studies report improvements when equipping their models with the multimodal module, despite the controversy whether such imp... | ['Ben Kao', 'Lingpeng Kong', 'Zhiyong Wu'] | 2021-01-01 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.40621758e-01 4.61040348e-01 -5.25497854e-01 -3.94923419e-01
-1.26379347e+00 -6.34897053e-01 1.00082135e+00 -1.10833198e-01
-3.32622975e-01 8.55141222e-01 4.51160640e-01 -6.46056294e-01
2.06513796e-02 -2.67415553e-01 -9.42496717e-01 -6.17667139e-01
4.68664616e-01 1.09673381e+00 -6.55421674e-01 -6.78887308... | [11.466684341430664, 1.5101838111877441] |
77140edc-e5a3-4c27-8064-3b3b01a83253 | cells-a-parallel-corpus-for-biomedical-lay | 2211.03818 | null | https://arxiv.org/abs/2211.03818v1 | https://arxiv.org/pdf/2211.03818v1.pdf | CELLS: A Parallel Corpus for Biomedical Lay Language Generation | Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-... | ['Trevor Cohen', 'Sheng Wang', 'Gondy Leroy', 'Wei Qiu', 'Yue Guo'] | 2022-11-07 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 2.99987406e-01 9.22692418e-01 -7.22509086e-01 1.81854963e-02
-1.39136565e+00 -5.52887678e-01 6.85909808e-01 5.99886656e-01
-1.95643172e-01 1.21227229e+00 1.03600013e+00 -5.67612886e-01
-2.08107874e-01 -6.08652771e-01 -8.30737472e-01 -1.02455042e-01
5.27001381e-01 5.57968080e-01 -3.23919803e-01 -3.62826139... | [11.986271858215332, 9.209197044372559] |
5644f6df-4c99-4f8b-a798-6ace57d6f289 | vilas-integrating-vision-and-language-into | 2305.19972 | null | https://arxiv.org/abs/2305.19972v1 | https://arxiv.org/pdf/2305.19972v1.pdf | ViLaS: Integrating Vision and Language into Automatic Speech Recognition | Employing additional multimodal information to improve automatic speech recognition (ASR) performance has been proven effective in previous works. However, many of these works focus only on the utilization of visual cues from human lip motion. In fact, context-dependent visual and linguistic cues can also be used to im... | ['Bo Xu', 'Shuang Xu', 'Jing Shi', 'Linghui Meng', 'Ziyi Ni', 'Feilong Chen', 'Minglun Han'] | 2023-05-31 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 1.91377461e-01 -3.28035742e-01 -2.64706820e-01 -3.82938117e-01
-1.37079775e+00 -7.46631205e-01 9.03276920e-01 -1.82604060e-01
-4.09669012e-01 2.17161864e-01 5.69270015e-01 -3.36301863e-01
1.39345080e-01 -9.24663320e-02 -3.14185649e-01 -6.78343832e-01
4.99803126e-01 8.50193575e-02 1.53974518e-01 -3.48502815... | [14.2049560546875, 5.090799331665039] |
a7c01e81-021c-4555-bafb-419cbe2e2c27 | metaphorical-polysemy-detection-conventional-1 | 2212.08395 | null | https://arxiv.org/abs/2212.08395v1 | https://arxiv.org/pdf/2212.08395v1.pdf | Metaphorical Polysemy Detection: Conventional Metaphor meets Word Sense Disambiguation | Linguists distinguish between novel and conventional metaphor, a distinction which the metaphor detection task in NLP does not take into account. Instead, metaphoricity is formulated as a property of a token in a sentence, regardless of metaphor type. In this paper, we investigate the limitations of treating convention... | ['Simone Teufel', 'Rowan Hall Maudslay'] | 2022-12-16 | metaphorical-polysemy-detection-conventional | https://aclanthology.org/2022.coling-1.7 | https://aclanthology.org/2022.coling-1.7.pdf | coling-2022-10 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.79178340e-02 -4.47262302e-02 -1.97208747e-01 -6.80096401e-03
-3.81104708e-01 -1.02993441e+00 1.10799015e+00 5.35551310e-01
-8.33986521e-01 3.88497859e-01 5.04413009e-01 -7.48474598e-01
-9.74046141e-02 -7.34834433e-01 -1.10041887e-01 -4.01054800e-01
3.21255207e-01 4.64719713e-01 -4.03343476e-02 -7.00091124... | [10.575827598571777, 9.133906364440918] |
e82ffbac-918e-434a-b0f8-22ece13dfb9b | cabinet-scaling-neural-collision-detection | 2304.09302 | null | https://arxiv.org/abs/2304.09302v1 | https://arxiv.org/pdf/2304.09302v1.pdf | CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation | We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes - orders of magnitude more than prior work - in diverse everyday environments, such as cabinets and shelves. We render synthetic partial point clouds from th... | ['Dieter Fox', 'Adam Fishman', 'Clemens Eppner', 'Arsalan Mousavian', 'Adithyavairavan Murali'] | 2023-04-18 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [-6.1702300e-03 1.8090616e-01 4.7355068e-01 -2.2081095e-01
-7.3198307e-01 -9.3174911e-01 5.4156905e-01 2.1452406e-01
-5.1400518e-01 6.0149139e-01 -1.5025745e-02 -3.9016291e-01
-2.6929036e-02 -9.6234196e-01 -1.3191881e+00 -2.3075841e-01
-2.6701003e-01 1.3610562e+00 5.2744251e-01 -5.2619416e-01
3.8044375e-01... | [4.77144193649292, 0.6511719822883606] |
306aa651-aab0-48d9-a1e3-458c16794b14 | rethinking-semi-supervised-medical-image | 2302.01735 | null | https://arxiv.org/abs/2302.01735v4 | https://arxiv.org/pdf/2302.01735v4.pdf | Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective | For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anato... | ['James S Duncan', 'Lawrence Hamilton Staib', 'S Kevin Zhou', 'David A. Clifton', 'Fenglin Liu', 'Yifei Min', 'Weicheng Dai', 'Chenyu You'] | 2023-02-03 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 7.12290823e-01 4.33108151e-01 -5.92059076e-01 -4.83143479e-01
-1.11162543e+00 -4.07205462e-01 4.18249846e-01 4.64277267e-01
-3.61690074e-01 6.78682983e-01 -6.63519949e-02 -2.10193306e-01
-2.03812987e-01 -4.26942736e-01 -6.44828081e-01 -9.31368768e-01
-1.38592049e-01 6.74611390e-01 2.40144342e-01 5.51170520... | [14.690028190612793, -2.152475118637085] |
ebcbd0d1-3f08-401a-a1a8-fe561c0fe5c7 | spatio-temporal-transformer-for-dynamic | 2205.04749 | null | https://arxiv.org/abs/2205.04749v1 | https://arxiv.org/pdf/2205.04749v1.pdf | Spatio-Temporal Transformer for Dynamic Facial Expression Recognition in the Wild | Previous methods for dynamic facial expression in the wild are mainly based on Convolutional Neural Networks (CNNs), whose local operations ignore the long-range dependencies in videos. To solve this problem, we propose the spatio-temporal Transformer (STT) to capture discriminative features within each frame and model... | ['Shutao Li', 'Bin Sun', 'Fuyan Ma'] | 2022-05-10 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.11219451e-01 -4.23577428e-01 -2.93052107e-01 -8.98382366e-01
-3.13573241e-01 -2.78275013e-01 3.76079917e-01 -4.21878219e-01
-4.86244231e-01 4.54877853e-01 2.29719922e-01 1.02636479e-01
7.60799600e-03 -4.54628319e-01 -7.03436911e-01 -9.31986749e-01
-2.43822187e-01 -4.25308168e-01 -1.16361361e-02 -9.47391763... | [13.627209663391113, 1.7204114198684692] |
b72a652e-6a25-4df4-be41-1a040e7d2bcd | structure-aware-and-class-balanced-3d-object | 2205.12519 | null | https://arxiv.org/abs/2205.12519v2 | https://arxiv.org/pdf/2205.12519v2.pdf | Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset | 3-D object detection is pivotal for autonomous driving. Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information. NuTonomy's nuScenes dataset greatly extends commonly used datasets such as KITTI in size, sensor modalities, categories, and annotation ... | ['Mohan Trivedi', 'Akshay Rangesh', 'Savitha Srinivasan', 'Asfiya Baig', 'Sushruth Nagesh'] | 2022-05-25 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.53238913e-02 7.79772028e-02 -3.21423709e-01 -4.76679116e-01
-5.76153040e-01 -5.67844987e-01 5.76586902e-01 1.32791117e-01
-5.07460237e-01 4.35833663e-01 -4.47011858e-01 -2.60752380e-01
-9.59165103e-04 -9.42353487e-01 -8.36349428e-01 -8.21276307e-01
1.31112486e-01 5.08609474e-01 8.10681224e-01 5.19186594... | [7.920213222503662, -2.5704421997070312] |
1ef152cc-b3b7-4327-add6-62e163c70bec | reflection-and-rotation-symmetry-detection | 2203.16787 | null | https://arxiv.org/abs/2203.16787v1 | https://arxiv.org/pdf/2203.16787v1.pdf | Reflection and Rotation Symmetry Detection via Equivariant Learning | The inherent challenge of detecting symmetries stems from arbitrary orientations of symmetry patterns; a reflection symmetry mirrors itself against an axis with a specific orientation while a rotation symmetry matches its rotated copy with a specific orientation. Discovering such symmetry patterns from an image thus be... | ['Minsu Cho', 'Suha Kwak', 'Byungjin Kim', 'Ahyun Seo'] | 2022-03-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Seo_Reflection_and_Rotation_Symmetry_Detection_via_Equivariant_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Seo_Reflection_and_Rotation_Symmetry_Detection_via_Equivariant_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['symmetry-detection'] | ['computer-vision'] | [ 3.35775167e-01 3.37771922e-02 -1.69904768e-01 -5.85281312e-01
-3.22741479e-01 -8.60198975e-01 9.82645273e-01 -6.64716244e-01
2.46009752e-01 -2.08773669e-02 7.34589875e-01 -5.74934259e-02
-2.72415668e-01 -6.34495318e-01 -7.76528895e-01 -4.32525307e-01
-1.01459481e-01 5.69024444e-01 -1.40589103e-01 -4.14073318... | [8.557003021240234, -2.1471669673919678] |
c359295d-8490-42eb-9bcf-1a903ed63779 | domain-adaptive-person-search | 2207.11898 | null | https://arxiv.org/abs/2207.11898v1 | https://arxiv.org/pdf/2207.11898v1.pdf | Domain Adaptive Person Search | Person search is a challenging task which aims to achieve joint pedestrian detection and person re-identification (ReID). Previous works have made significant advances under fully and weakly supervised settings. However, existing methods ignore the generalization ability of the person search models. In this paper, we t... | ['Shouhong Ding', 'Qiong Jia', 'Fufu Yu', 'Guanshuo Wang', 'Yichao Yan', 'Junjie Li'] | 2022-07-25 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.22253811e-02 -2.20054090e-01 -2.32892796e-01 -4.98918325e-01
-9.24724221e-01 -6.54691041e-01 6.99866772e-01 -3.15755785e-01
-7.48227119e-01 7.75213242e-01 2.67099470e-01 5.63220270e-02
2.18655378e-01 -4.66772974e-01 -5.71522832e-01 -6.73092961e-01
4.05558497e-01 7.67252803e-01 5.17956853e-01 -4.92239464... | [14.83962631225586, 0.8527619242668152] |
37e25c15-edb2-471c-a5cf-36d2ed6beb81 | audio-super-resolution-using-neural-networks | 1708.00853 | null | http://arxiv.org/abs/1708.00853v1 | http://arxiv.org/pdf/1708.00853v1.pdf | Audio Super Resolution using Neural Networks | We introduce a new audio processing technique that increases the sampling
rate of signals such as speech or music using deep convolutional neural
networks. Our model is trained on pairs of low and high-quality audio examples;
at test-time, it predicts missing samples within a low-resolution signal in an
interpolation p... | ['Volodymyr Kuleshov', 'S. Zayd Enam', 'Stefano Ermon'] | 2017-08-02 | null | null | null | null | ['audio-generation', 'audio-super-resolution', 'audio-super-resolution'] | ['audio', 'audio', 'music'] | [ 6.01294398e-01 -1.64501145e-02 -1.52722552e-01 -2.62571305e-01
-1.55457878e+00 -2.47926727e-01 5.81496596e-01 -3.24392110e-01
-4.18466888e-02 6.55527294e-01 7.29536474e-01 -2.38161415e-01
1.00558758e-01 -7.86005735e-01 -9.79173005e-01 -2.09436297e-01
-2.92059988e-01 3.27475935e-01 -5.16364239e-02 -2.21536860... | [15.550663948059082, 5.8164520263671875] |
bcce9bd4-6eb7-44da-b89d-0efb6b91a51c | inductive-detection-of-influence-operations | 2305.16544 | null | https://arxiv.org/abs/2305.16544v1 | https://arxiv.org/pdf/2305.16544v1.pdf | Inductive detection of Influence Operations via Graph Learning | Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations which evade current detection methods and influence public discourse on social media with great... | ['Neil F. Johnson', 'David A. Broniatowski', 'Nicholas A. Gabriel'] | 2023-05-26 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 7.39979267e-01 4.73730683e-01 -6.38696909e-01 4.14022863e-01
-5.15949130e-01 -8.06402743e-01 1.10193801e+00 7.25758731e-01
4.30216119e-02 5.03372788e-01 9.88799334e-01 -7.37095833e-01
-5.09027958e-01 -1.08187664e+00 -6.10635221e-01 -3.74482155e-01
-5.58949232e-01 2.47894123e-01 -1.34311497e-01 -6.97761297... | [8.509660720825195, 10.054767608642578] |
b67ac0fc-61ec-452b-83f1-0c1a8685ad0f | unifacegan-a-unified-framework-for-temporally | 2108.05650 | null | https://arxiv.org/abs/2108.05650v1 | https://arxiv.org/pdf/2108.05650v1.pdf | UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing | Recent research has witnessed advances in facial image editing tasks including face swapping and face reenactment. However, these methods are confined to dealing with one specific task at a time. In addition, for video facial editing, previous methods either simply apply transformations frame by frame or utilize multip... | ['Jiebo Luo', 'Zhifeng Li', 'Linchao Bao', 'Sheng Wang', 'Li Shen', 'Xuan Wang', 'Hao Wang', 'HaoZhi Huang', 'Meng Cao'] | 2021-08-12 | null | null | null | null | ['face-reenactment', 'facial-editing'] | ['computer-vision', 'computer-vision'] | [ 4.67312306e-01 -2.27166042e-01 -9.58976597e-02 -5.56996524e-01
-3.48557234e-01 -3.36803317e-01 6.72768176e-01 -6.67493522e-01
-1.98509961e-01 6.87051058e-01 6.37091249e-02 2.11505234e-01
2.14543924e-01 -6.57476962e-01 -6.98485494e-01 -7.14380085e-01
5.44303417e-01 -1.30200580e-01 9.31997672e-02 -2.20423177... | [12.709600448608398, -0.23991349339485168] |
dcbf13ad-85de-4268-8ffb-d1c2eed03cec | learning-joint-intensity-in-a-multivariate | null | null | https://openreview.net/forum?id=VvpRCm5aWYr | https://openreview.net/pdf?id=VvpRCm5aWYr | Learning Joint Intensity in a Multivariate Poisson Process on Statistical Manifolds | We show that generalized additive models (GAMs) can be treated via the log-linear model on a structured sample space, which has a well established information geometric background. Connecting GAMs with multivariate stochastic processes, we present the additive Poisson process (APP), a novel framework that can model the... | ['Mahito Sugiyama', 'Lamiae Azizi', 'Feng Zhou', 'Simon Luo'] | 2020-10-19 | null | null | null | neurips-workshop-dl-ig-2020-12 | ['additive-models'] | ['methodology'] | [ 1.80857882e-01 1.65260136e-01 2.26939201e-01 -3.33871633e-01
-5.42382598e-01 -3.91019762e-01 1.03866434e+00 -1.97989911e-01
-2.07380995e-01 4.91813451e-01 2.69444376e-01 -2.50653573e-03
-5.36890626e-01 -9.30173755e-01 -9.39156592e-01 -1.07793880e+00
-3.37783784e-01 8.34150255e-01 -2.17582777e-01 3.69026124... | [6.941216945648193, 3.8454360961914062] |
8beb8432-0453-44ef-b01b-f2816c6fe1bb | end-to-end-modeling-via-information-tree-for | 2203.08013 | null | https://arxiv.org/abs/2203.08013v2 | https://arxiv.org/pdf/2203.08013v2.pdf | End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding | Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing methods rely on dense video frame annotations, which require a tremendous amount of human effort. To achieve effective grounding under a lim... | ['Fei Wu', 'ShiLiang Pu', 'Peng Wang', 'Jin Wang', 'Wenming Tan', 'Wenqiao Zhang', 'Jiaxu Miao', 'Zhou Zhao', 'Shengyu Zhang', 'Haoyu Zhang', 'Tianbao Wang', 'Mengze Li'] | 2022-03-15 | null | https://aclanthology.org/2022.acl-long.596 | https://aclanthology.org/2022.acl-long.596.pdf | acl-2022-5 | ['video-grounding'] | ['computer-vision'] | [ 2.69329369e-01 1.71361580e-01 -4.63779658e-01 -2.80032665e-01
-8.27553213e-01 -1.41480714e-01 7.16756657e-02 7.97439516e-02
-3.11637908e-01 5.89965165e-01 2.40977228e-01 1.03925932e-02
1.74849451e-01 -6.31317437e-01 -8.46110821e-01 -4.50216055e-01
5.00001311e-02 -1.06555112e-01 8.69985819e-01 -9.36077982... | [9.71955394744873, 0.6336004734039307] |
06e864b2-3524-4ae8-bb5a-f772b0c549b5 | joint-entity-and-relation-extraction-with-set | 2011.01675 | null | https://arxiv.org/abs/2011.01675v2 | https://arxiv.org/pdf/2011.01675v2.pdf | Joint Entity and Relation Extraction with Set Prediction Networks | The joint entity and relation extraction task aims to extract all relational triples from a sentence. In essence, the relational triples contained in a sentence are unordered. However, previous seq2seq based models require to convert the set of triples into a sequence in the training phase. To break this bottleneck, we... | ['Shengping Liu', 'Xiangrong Zeng', 'Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Dianbo Sui'] | 2020-11-03 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 2.74656981e-01 5.05603611e-01 -2.78808147e-01 -5.94310880e-01
-8.50141466e-01 -4.86180753e-01 3.58796597e-01 2.53455132e-01
-3.19340616e-01 9.44818556e-01 3.28206390e-01 -2.48641342e-01
1.40565291e-01 -1.17927182e+00 -1.24474585e+00 -4.18318808e-01
9.31455866e-02 5.88987231e-01 3.95305678e-02 -3.84221673... | [9.42734146118164, 8.667145729064941] |
7e7df375-1fee-4e55-8a2e-6947725a7d0a | vifs-an-end-to-end-variational-inference-for | 2306.05004 | null | https://arxiv.org/abs/2306.05004v1 | https://arxiv.org/pdf/2306.05004v1.pdf | VIFS: An End-to-End Variational Inference for Foley Sound Synthesis | The goal of DCASE 2023 Challenge Task 7 is to generate various sound clips for Foley sound synthesis (FSS) by "category-to-sound" approach. "Category" is expressed by a single index while corresponding "sound" covers diverse and different sound examples. To generate diverse sounds for a given category, we adopt VITS, a... | ['Yong-Hwa Park', 'Hyeonuk Nam', 'Junhyeok Lee'] | 2023-06-08 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 9.26905647e-02 4.06896211e-02 2.15128869e-01 -2.83577681e-01
-1.33763874e+00 -7.36400545e-01 7.19555974e-01 -6.10442102e-01
2.00056791e-01 6.86359823e-01 7.76233435e-01 -1.31783551e-02
1.93352640e-01 -4.59584564e-01 -7.99224436e-01 -6.69409871e-01
5.87453544e-01 1.52802661e-01 1.22096278e-01 -8.25011432... | [15.321371078491211, 6.321898937225342] |
33b52548-6070-4bf8-80f5-583d3689418d | improving-multiple-documents-grounded-goal | null | null | https://aclanthology.org/2022.dialdoc-1.15 | https://aclanthology.org/2022.dialdoc-1.15.pdf | Improving Multiple Documents Grounded Goal-Oriented Dialog Systems via Diverse Knowledge Enhanced Pretrained Language Model | In this paper, we mainly discuss about our submission to MultiDoc2Dial task, which aims to model the goal-oriented dialogues grounded in multiple documents. The proposed task is split into grounding span prediction and agent response generation. The baseline for the task is the retrieval augmented generation model, whi... | ['Kyomin Jung', 'Hyunkyung Bae', 'Hwanhee Lee', 'Taegwan Kang', 'Hyung Joo Park', 'Dongryeol Lee', 'Yunah Jang'] | null | null | null | null | dialdoc-acl-2022-5 | ['goal-oriented-dialog', 'passage-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.28486276e-02 4.95641500e-01 1.64901078e-01 -2.36003682e-01
-1.41636479e+00 -4.96420175e-01 1.06737137e+00 7.97635410e-04
-3.60051066e-01 1.12686038e+00 7.50720263e-01 -1.15003902e-02
2.40714014e-01 -7.89648712e-01 -6.37834549e-01 -2.97606885e-01
2.25840822e-01 1.00177705e+00 3.42477649e-01 -8.90006661... | [12.392857551574707, 8.207060813903809] |
1bc3f79e-d166-4cb8-b24b-e1368b79324e | query-efficient-physical-hard-label-attacks | 2002.07088 | null | https://arxiv.org/abs/2002.07088v6 | https://arxiv.org/pdf/2002.07088v6.pdf | GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems | This paper investigates an adversary's ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness, i.e.,... | ['Atul Prakash', 'Somesh Jha', 'Earlence Fernandes', 'Jiefeng Chen', 'Neal Mangaokar', 'Ryan Feng'] | 2020-02-17 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 5.03907084e-01 -1.27527580e-01 2.29038402e-01 -6.12334646e-02
-1.33964455e+00 -1.39912319e+00 3.12594026e-01 -1.60115704e-01
-4.71882969e-01 4.40535694e-01 -4.29895729e-01 -9.44380939e-01
-2.47418769e-02 -9.73548830e-01 -8.88865948e-01 -6.79308116e-01
-2.72923440e-01 1.86301604e-01 3.94515306e-01 -3.79652619... | [5.550463676452637, 7.907053470611572] |
a773869b-2b42-4e7c-a59b-238b7a54afaf | density-ratio-based-personalised-ranking-from | 2101.07481 | null | https://arxiv.org/abs/2101.07481v1 | https://arxiv.org/pdf/2101.07481v1.pdf | Density-Ratio Based Personalised Ranking from Implicit Feedback | Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopting a pairwise ranking approach with negative sampling. However, the pairwise ranking approach has a severe disadvantage in the convergence t... | ["Shin'ichi Satoh", 'Mayu Otani', 'Masahiro Kato', 'Riku Togashi'] | 2021-01-19 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.39668211e-01 -2.08579659e-01 -3.23865652e-01 -2.97496259e-01
-1.10425842e+00 -5.22642374e-01 3.87290776e-01 3.22677195e-01
-6.42729163e-01 1.04524016e+00 -8.52062628e-02 -2.12394118e-01
-5.92277765e-01 -8.03704202e-01 -5.40666759e-01 -8.04296315e-01
-9.11202207e-02 5.69553494e-01 -1.73788741e-02 -2.21148893... | [9.95772933959961, 5.521844863891602] |
84271eff-c988-49fa-b285-75c000aaabcb | when-cnn-meet-with-vit-towards-semi | 2208.06449 | null | https://arxiv.org/abs/2208.06449v1 | https://arxiv.org/pdf/2208.06449v1.pdf | When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation | Due to the lack of quality annotation in medical imaging community, semi-supervised learning methods are highly valued in image semantic segmentation tasks. In this paper, an advanced consistency-aware pseudo-label-based self-ensembling approach is presented to fully utilize the power of Vision Transformer(ViT) and Con... | ['Baoru Huang', 'Jian-Qing Zheng', 'Tianze Li', 'Ziyang Wang'] | 2022-08-12 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.19699228e-01 4.02934104e-01 -4.49525297e-01 -5.25987267e-01
-5.58649361e-01 -2.49255344e-01 3.33205193e-01 -2.24613637e-01
-1.60954103e-01 5.56764543e-01 2.23868892e-01 -4.45745178e-02
-2.48404875e-01 -5.20251691e-01 -6.63325787e-01 -9.79383051e-01
7.65451938e-02 2.87536800e-01 6.13707066e-01 -7.58374482... | [14.733248710632324, -2.1076555252075195] |
032708b0-4d9a-4c97-87da-d351951c3d09 | multilinear-class-specific-discriminant | 1710.10695 | null | http://arxiv.org/abs/1710.10695v1 | http://arxiv.org/pdf/1710.10695v1.pdf | Multilinear Class-Specific Discriminant Analysis | There has been a great effort to transfer linear discriminant techniques that
operate on vector data to high-order data, generally referred to as Multilinear
Discriminant Analysis (MDA) techniques. Many existing works focus on maximizing
the inter-class variances to intra-class variances defined on tensor data
represen... | ['Moncef Gabbouj', 'Alexandros Iosifidis', 'Dat Thanh Tran'] | 2017-10-29 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-2.89954394e-01 -6.81711257e-01 -1.85119182e-01 -4.76546347e-01
-4.46153224e-01 -6.33043706e-01 4.13743794e-01 -4.03008938e-01
-1.31645858e-01 2.57008344e-01 -1.33357763e-01 -2.11222708e-01
-5.20272374e-01 -3.47976178e-01 2.58679036e-02 -8.16426516e-01
-1.03134409e-01 3.79109114e-01 -1.36655927e-01 -8.90026614... | [7.844665050506592, 4.219435214996338] |
d4283a9f-1edd-4e5a-b762-bcb1aa08fd99 | discriminative-local-sparse-representations | 1111.01947 | null | http://arxiv.org/abs/1111.1947v1 | http://arxiv.org/pdf/1111.1947v1.pdf | Discriminative Local Sparse Representations for Robust Face Recognition | A key recent advance in face recognition models a test face image as a sparse
linear combination of a set of training face images. The resulting sparse
representations have been shown to possess robustness against a variety of
distortions like random pixel corruption, occlusion and disguise. This approach
however makes... | ['Trac. D. Tran', 'Vishal Monga', 'Umamahesh Srinivas', 'Yi Chen', 'Thong T. Do'] | 2011-11-08 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 6.15834773e-01 -2.84246147e-01 -2.93917984e-01 -7.50098884e-01
-4.91765827e-01 -3.83625180e-01 8.00697744e-01 -1.29904166e-01
8.05653557e-02 5.07919133e-01 2.55769700e-01 2.42718965e-01
-4.01805937e-01 -6.15460396e-01 -7.72140384e-01 -9.62931037e-01
-2.02385381e-01 2.16996491e-01 -1.93839461e-01 2.43079096... | [12.728736877441406, 0.38442298769950867] |
4b475e70-91ee-4860-b0ec-6bb6ec3eeb99 | lap-net-adaptive-features-sampling-via | 2011.07915 | null | https://arxiv.org/abs/2011.07915v1 | https://arxiv.org/pdf/2011.07915v1.pdf | LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection | Online action detection is a task with the aim of identifying ongoing actions from streaming videos without any side information or access to future frames. Recent methods proposed to aggregate fixed temporal ranges of invisible but anticipated future frames representations as supplementary features and achieved promis... | ['Alois Knoll', 'Fan Lu', 'Jinhu Dong', 'Dan Xu', 'Guang Chen', 'Sanqing Qu'] | 2020-11-16 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.87648994e-01 -3.59616637e-01 -4.30183709e-01 -3.17459196e-01
-4.52947229e-01 -2.49423310e-01 7.31462061e-01 -9.07188728e-02
-6.56816185e-01 5.67272604e-01 3.94675583e-01 1.05734520e-01
-3.03776175e-01 -5.40659010e-01 -3.38427752e-01 -6.88588202e-01
-4.13940817e-01 -8.81987289e-02 8.18878055e-01 1.40882041... | [8.424476623535156, 0.5343735218048096] |
15aaaa0f-98c7-4e01-bfea-7be6b52c09e4 | a-food-photography-app-with-image-recognition | null | null | https://ieeexplore.ieee.org/document/8523925 | https://www.researchgate.net/publication/328834305_A_Food_Photography_App_with_Image_Recognition_for_Thai_Food | A Food Photography App with Image Recognition for Thai Food | In this paper, we present a food photography application for smart phones, which can recognise 13 types of Thai food from photos. With this feature, the application can easily help users calculate their calories and make some suggestion, just by keep taking a photo of food they are eating. Our application uses React Na... | ['Vacharapat Mettanant', 'Peerapon Chunpongthong', 'Ukrit Tiankaew'] | 2018-07-01 | null | null | null | ict-ispc-2018-7 | ['image-recognition'] | ['computer-vision'] | [-2.73729622e-01 7.77539611e-02 -4.60936397e-01 -5.63121080e-01
-3.95612180e-01 -3.81913006e-01 1.21425264e-01 1.64240763e-01
-5.41907728e-01 2.16168970e-01 2.29690418e-01 -2.26004586e-01
5.92988133e-01 -1.22678173e+00 -1.14123201e+00 -4.35710549e-01
1.54899769e-02 -8.21967125e-02 5.83043380e-04 -1.38138805... | [11.573399543762207, 4.404551982879639] |
a4895dda-614d-4191-9a85-2f17ffdd8271 | heterogeneous-graph-neural-network-for-1 | 2205.11343 | null | https://arxiv.org/abs/2205.11343v3 | https://arxiv.org/pdf/2205.11343v3.pdf | Heterogeneous Graph Neural Network for Personalized Session-Based Recommendation with User-Session Constraints | The recommendation system provides users with an appropriate limit of recent online large amounts of information. Session-based recommendation, a sub-area of recommender systems, attempts to recommend items by interpreting sessions that consist of sequences of items. Recently, research to include user information in th... | ['Minjae Park'] | 2022-05-23 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [ 1.83847040e-01 1.68555573e-01 -5.59179604e-01 -7.37717569e-01
7.27200881e-02 -3.61970603e-01 4.69436914e-01 9.54830274e-02
-1.26446818e-03 6.30009830e-01 8.09701920e-01 -2.30849266e-01
-6.33678377e-01 -8.56852770e-01 -3.79350871e-01 -2.49988317e-01
-2.83943117e-01 4.81026232e-01 9.01423395e-02 -6.37763083... | [10.17075252532959, 5.617922782897949] |
819fbdef-aa77-42be-b4d7-51ec64afea6a | effective-sequential-classifier-training-for | 1706.04719 | null | http://arxiv.org/abs/1706.04719v2 | http://arxiv.org/pdf/1706.04719v2.pdf | Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification | The explosive availability of remote sensing images has challenged supervised
classification algorithms such as Support Vector Machines (SVM), as training
samples tend to be highly limited due to the expensive and laborious task of
ground truthing. The temporal correlation and spectral similarity between
multitemporal ... | ['David Paull', 'Yiqing Guo', 'Xiuping Jia'] | 2017-06-15 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 8.90579581e-01 -3.19807947e-01 -1.51826069e-01 -5.01404643e-01
-5.74493527e-01 -4.81155604e-01 4.80332494e-01 2.52199560e-01
-4.80907708e-01 9.52730000e-01 -5.21526337e-01 -5.24412215e-01
-3.96863043e-01 -8.52714241e-01 -2.68454850e-01 -9.91516709e-01
-2.20795006e-01 1.65560290e-01 2.84211874e-01 -4.02840346... | [9.628179550170898, -1.5228917598724365] |
e7446314-e882-48d1-9f55-72f86bbd83a8 | a-dynamic-spatial-temporal-attention-network | 2106.10197 | null | https://arxiv.org/abs/2106.10197v2 | https://arxiv.org/pdf/2106.10197v2.pdf | A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents | The rapid advancement of sensor technologies and artificial intelligence are creating new opportunities for traffic safety enhancement. Dashboard cameras (dashcams) have been widely deployed on both human driving vehicles and automated driving vehicles. A computational intelligence model that can accurately and promptl... | ['Zhaozheng Yin', 'Ruwen Qin', 'Yu Li', 'Muhammad Monjurul Karim'] | 2021-06-18 | null | null | null | null | ['action-anticipation', 'accident-anticipation'] | ['computer-vision', 'computer-vision'] | [ 2.16864109e-01 -1.20131835e-01 -2.02073395e-01 -2.45002389e-01
-5.72636008e-01 1.70402214e-01 6.58139706e-01 -3.90537441e-01
-5.15480936e-01 2.36999914e-01 3.76639247e-01 -4.03240740e-01
-8.23618472e-02 -5.12436628e-01 -5.12885809e-01 -7.64045179e-01
-2.22916584e-02 -2.55775452e-01 6.74079239e-01 -3.16378385... | [7.524539470672607, 0.1062919944524765] |
4ab912f3-b638-4877-be39-3e527652ed3c | the-first-cross-lingual-challenge-on | null | null | https://aclanthology.org/W17-1412 | https://aclanthology.org/W17-1412.pdf | The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages | This paper describes the outcomes of the first challenge on multilingual named entity recognition that aimed at recognizing mentions of named entities in web documents in Slavic languages, their normalization/lemmatization, and cross-language matching. It was organised in the context of the 6th Balto-Slavic Natural Lan... | ['Jan {\\v{S}}najder', 'Lidia Pivovarova', 'Jakub Piskorski', 'Roman Yangarber', 'Josef Steinberger'] | 2017-04-01 | null | null | null | ws-2017-4 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-9.26475972e-02 8.35381746e-02 4.99909483e-02 -5.12552977e-01
-8.92362297e-01 -1.05585742e+00 9.62132454e-01 5.73238850e-01
-9.68913257e-01 7.57156193e-01 5.19740462e-01 -4.90827322e-01
6.20508045e-02 -3.47785383e-01 -2.66864419e-01 -2.42427979e-02
5.45201860e-02 6.53933048e-01 1.03111438e-01 -3.22757244... | [9.792044639587402, 9.708505630493164] |
77cf9595-b3e1-4b02-9d2d-bee4a6cf57ea | interpretable-automatic-fine-grained | 2305.14548 | null | https://arxiv.org/abs/2305.14548v1 | https://arxiv.org/pdf/2305.14548v1.pdf | Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization | Existing factual consistency evaluation approaches for text summarization provide binary predictions and limited insights into the weakness of summarization systems. Therefore, we propose the task of fine-grained inconsistency detection, the goal of which is to predict the fine-grained types of factual errors in a summ... | ['Heng Ji', 'Qi Zeng', 'Hou Pong Chan'] | 2023-05-23 | null | null | null | null | ['semantic-role-labeling', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.03850478e-01 7.46948898e-01 -8.05500925e-01 -5.69274187e-01
-1.10188162e+00 -5.51771581e-01 8.70084107e-01 9.09748316e-01
1.70095697e-01 1.15657544e+00 1.37078834e+00 -2.06243340e-02
-1.32101804e-01 -4.82077867e-01 -7.45205104e-01 7.15274662e-02
2.83525199e-01 4.84154880e-01 2.25988925e-01 -1.74393594... | [12.21902084350586, 9.357685089111328] |
cb1cc23b-3474-4c1c-9ff5-81a05e4505ac | multi-hypothesis-pose-networks-rethinking-top | 2101.11223 | null | https://arxiv.org/abs/2101.11223v3 | https://arxiv.org/pdf/2101.11223v3.pdf | Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation | A key assumption of top-down human pose estimation approaches is their expectation of having a single person/instance present in the input bounding box. This often leads to failures in crowded scenes with occlusions. We propose a novel solution to overcome the limitations of this fundamental assumption. Our Multi-Insta... | ['Ambrish Tyagi', 'Amit Agrawal', 'Visesh Chari', 'Rawal Khirodkar'] | 2021-01-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Khirodkar_Multi-Instance_Pose_Networks_Rethinking_Top-Down_Pose_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Khirodkar_Multi-Instance_Pose_Networks_Rethinking_Top-Down_Pose_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.05360352e-01 2.24793509e-01 2.07598671e-01 -2.82908976e-01
-9.59002078e-01 -3.82532269e-01 3.69955271e-01 -9.50407311e-02
-6.22101188e-01 1.03695822e+00 1.50719713e-02 2.59095103e-01
8.60167146e-02 -6.79810703e-01 -1.10436702e+00 -3.73133421e-01
-1.96900874e-01 7.83650339e-01 5.61637819e-01 -2.31324494... | [7.1827497482299805, -0.7394098043441772] |
1b07cd74-2944-4989-934a-ffa2d83f2cd7 | aet-efn-a-versatile-design-for-static-and | 2103.11645 | null | https://arxiv.org/abs/2103.11645v1 | https://arxiv.org/pdf/2103.11645v1.pdf | AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision | The neuromorphic event cameras, which capture the optical changes of a scene, have drawn increasing attention due to their high speed and low power consumption. However, the event data are noisy, sparse, and nonuniform in the spatial-temporal domain with an extremely high temporal resolution, making it challenging to d... | ['Ngai Wong', 'Edmund Lam', 'Xiaojuan Qi', 'Chang Liu'] | 2021-03-22 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.91910821e-01 -9.47756767e-01 2.35784039e-01 -3.21223736e-01
-7.83477202e-02 -2.66341418e-01 6.75489247e-01 9.68023017e-02
-5.95631659e-01 7.54692852e-01 -5.23308069e-02 3.33929509e-01
-3.30393195e-01 -8.49870920e-01 -8.41077209e-01 -8.30809414e-01
7.67496824e-02 4.29684594e-02 8.86735797e-01 3.49688649... | [8.679582595825195, -1.1901817321777344] |
1f852c4c-0302-4a34-b3b4-a7f03da58e51 | randomized-conditional-flow-matching-for | 2211.14575 | null | https://arxiv.org/abs/2211.14575v1 | https://arxiv.org/pdf/2211.14575v1.pdf | Randomized Conditional Flow Matching for Video Prediction | We introduce a novel generative model for video prediction based on latent flow matching, an efficient alternative to diffusion-based models. In contrast to prior work that either incurs a high training cost by modeling the past through a memory state, as in recurrent neural networks, or limits the computational load b... | ['Paolo Favaro', 'Sepehr Sameni', 'Aram Davtyan'] | 2022-11-26 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.84915298e-01 8.59105811e-02 -2.18623579e-01 3.96690052e-03
-5.21301985e-01 -3.51495564e-01 7.16151237e-01 -1.26884148e-01
-4.20846105e-01 6.25038087e-01 4.48107660e-01 -2.70291001e-01
2.77898163e-01 -1.06797743e+00 -9.38131928e-01 -5.94704092e-01
6.01442484e-03 4.01365846e-01 3.13153118e-01 3.90433013... | [10.682145118713379, -0.7093892097473145] |
64d2ce42-3b1c-4d23-8df4-557487d61d06 | interactive-ontology-debugging-two-query | 1107.4303 | null | http://arxiv.org/abs/1107.4303v2 | http://arxiv.org/pdf/1107.4303v2.pdf | Interactive ontology debugging: two query strategies for efficient fault localization | Effective debugging of ontologies is an important prerequisite for their
broad application, especially in areas that rely on everyday users to create
and maintain knowledge bases, such as the Semantic Web. In such systems
ontologies capture formalized vocabularies of terms shared by its users.
However in many cases use... | ['Kostyantyn Shchekotykhin', 'Gerhard Friedrich', 'Philipp Fleiss', 'Patrick Rodler'] | 2011-07-20 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-4.62718569e-02 3.16705912e-01 1.30557254e-01 -3.03289443e-01
-5.53520620e-01 -5.31206608e-01 1.42067358e-01 6.44178391e-01
-2.64959574e-01 7.20006406e-01 -3.65971744e-01 -3.16688687e-01
-4.37035918e-01 -9.36129034e-01 -5.03394008e-01 -3.33507150e-01
1.18814548e-02 7.55725384e-01 6.61524415e-01 -1.72493890... | [5.473856449127197, 2.8131134510040283] |
a61089d8-7715-4a2c-aeb6-2fd84ca3d7d8 | synthetic-medical-images-from-dual-generative | 1709.01872 | null | http://arxiv.org/abs/1709.01872v3 | http://arxiv.org/pdf/1709.01872v3.pdf | Synthetic Medical Images from Dual Generative Adversarial Networks | Currently there is strong interest in data-driven approaches to medical image
classification. However, medical imaging data is scarce, expensive, and fraught
with legal concerns regarding patient privacy. Typical consent forms only allow
for patient data to be used in medical journals or education, meaning the
majority... | ['Tejpal S. Virdi', 'Peter S. Li', 'John T. Guibas'] | 2017-09-06 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 4.89488125e-01 6.03057683e-01 2.08398774e-01 -6.61356807e-01
-1.08083975e+00 -7.00860441e-01 4.00638759e-01 1.11084342e-01
-3.22871029e-01 7.76137888e-01 2.65328437e-01 -7.34765053e-01
3.33275259e-01 -7.18691230e-01 -6.35985255e-01 -4.43219841e-01
2.25370899e-01 4.14139986e-01 -1.15843542e-01 -4.55213450... | [14.302605628967285, -1.862199306488037] |
29a083d4-97fb-4f76-a07e-134b63739520 | deep-learning-approaches-in-food-recognition | 2004.03357 | null | https://arxiv.org/abs/2004.03357v2 | https://arxiv.org/pdf/2004.03357v2.pdf | Deep learning approaches in food recognition | Automatic image-based food recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches enabled the identification of food types and their ingredients. The contents of food dishes are typically deformable ob... | ['Stella Markantonatou', 'George Pavlidis', 'Chairi Kiourt'] | 2020-04-04 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 2.08076596e-01 -2.25179732e-01 -5.10256886e-01 -4.17942405e-01
-2.37023145e-01 -7.88396537e-01 4.46838677e-01 7.22085297e-01
-3.59578133e-01 2.25286305e-01 8.40442404e-02 1.22510396e-01
-2.38133878e-01 -8.46367121e-01 -7.18491852e-01 -1.05743659e+00
-3.61492068e-01 2.50986844e-01 -2.37173632e-01 -2.35740870... | [11.562455177307129, 4.3987603187561035] |
b1a6cce8-ad00-4df4-a2bd-46d8f9fe927b | aspect-sentiment-classification-with-aspect | 2010.02696 | null | https://arxiv.org/abs/2010.02696v2 | https://arxiv.org/pdf/2010.02696v2.pdf | Aspect Based Sentiment Analysis with Aspect-Specific Opinion Spans | Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able ... | ['Fei Huang', 'Wei Lu', 'Lidong Bing', 'Lu Xu'] | 2020-10-06 | aspect-sentiment-classification-with-aspect-1 | https://aclanthology.org/2020.emnlp-main.288 | https://aclanthology.org/2020.emnlp-main.288.pdf | emnlp-2020-11 | ['extract-aspect'] | ['natural-language-processing'] | [-2.75894776e-02 2.12065428e-01 -7.14036644e-01 -7.09250987e-01
-6.26276612e-01 -6.93408549e-01 4.84042019e-01 2.49248102e-01
-9.38952863e-02 6.65914059e-01 6.14218771e-01 -3.04134637e-01
2.59706020e-01 -9.27628636e-01 -2.27280498e-01 -3.66039395e-01
1.34077922e-01 3.65811914e-01 -8.24404508e-02 -2.82357872... | [11.431374549865723, 6.683012008666992] |
3b73e4db-11d4-4475-a7ec-c5b295736bd9 | tecno-surgical-phase-recognition-with-multi | 2003.10751 | null | https://arxiv.org/abs/2003.10751v1 | https://arxiv.org/pdf/2003.10751v1.pdf | TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks | Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that perf... | ['Nassir Navab', 'Matthias Keicher', 'Walter Simson', 'Magdalini Paschali', 'Seong Tae Kim', 'Tobias Czempiel', 'Hubertus Feussner'] | 2020-03-24 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 5.84700167e-01 4.85858619e-01 -6.04425132e-01 -3.61672521e-01
-3.38291943e-01 -4.79923874e-01 7.09758043e-01 4.41858947e-01
-6.42250597e-01 3.23515952e-01 4.55795497e-01 -7.08382726e-01
-6.92834198e-01 -2.64780581e-01 -5.56857467e-01 -5.81135511e-01
-5.18912554e-01 3.95689636e-01 1.02802873e-01 1.27059266... | [14.072769165039062, -3.364346504211426] |
1e45ee16-362e-4221-8b23-7b7fc1f2f8cc | nefii-inverse-rendering-for-reflectance | 2303.16617 | null | https://arxiv.org/abs/2303.16617v1 | https://arxiv.org/pdf/2303.16617v1.pdf | NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination | Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images. In order to achieve better decomposition, recent approaches attempt to model indirect illuminations reflected from different materials via Spherical Gaussians (SG), which, however, tends to blur the high-frequency... | ['Xin Yu', 'Changjie Fan', 'Yongqiang Zhang', 'Lincheng Li', 'Zhipeng Hu', 'Haoqian Wu'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.pdf | cvpr-2023-1 | ['inverse-rendering'] | ['computer-vision'] | [ 3.38043630e-01 -4.91812974e-01 7.58607864e-01 -3.81558657e-01
-6.25810206e-01 -4.32512581e-01 6.22320890e-01 -4.13117886e-01
-2.17928812e-02 5.97594202e-01 3.09678733e-01 -9.45081338e-02
6.37435541e-02 -1.08849585e+00 -7.66193330e-01 -9.70910847e-01
5.08689940e-01 2.80086100e-01 -8.40576179e-03 8.41040537... | [9.704903602600098, -3.1075592041015625] |
0797a58f-9385-400b-ad37-0d9656feb6a6 | augmenting-pre-trained-language-models-with | 2204.04581 | null | https://arxiv.org/abs/2204.04581v3 | https://arxiv.org/pdf/2204.04581v3.pdf | Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering | Retrieval augmented language models have recently become the standard for knowledge intensive tasks. Rather than relying purely on latent semantics within the parameters of large neural models, these methods enlist a semi-parametric memory to encode an index of knowledge for the model to retrieve over. Most prior work ... | ['William Cohen', 'John Wieting', 'Michiel de Jong', 'Pat Verga', 'Wenhu Chen'] | 2022-04-10 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.79245234e-01 4.76311117e-01 -3.82640749e-01 -5.04681319e-02
-1.31181812e+00 -6.37032270e-01 8.25335801e-01 3.50683570e-01
-5.38564086e-01 9.01248336e-01 5.93526781e-01 -3.60096037e-01
-4.15159464e-01 -9.16197777e-01 -9.46545601e-01 -3.71510535e-01
2.21795380e-01 8.83243799e-01 4.42223758e-01 -6.73287332... | [11.076493263244629, 8.038080215454102] |
4e137d55-c19f-4ab4-b1b0-9e0905869739 | key-mention-pairs-guided-document-level | null | null | https://aclanthology.org/2022.coling-1.165 | https://aclanthology.org/2022.coling-1.165.pdf | Key Mention Pairs Guided Document-Level Relation Extraction | Document-level Relation Extraction (DocRE) aims at extracting relations between entities in a given document. Since different mention pairs may express different relations or even no relation, it is crucial to identify key mention pairs responsible for the entity-level relation labels. However, most recent studies trea... | ['Shengda Fan', 'Shasha Mo', 'Jianwei Niu', 'Feng Jiang'] | null | null | null | null | coling-2022-10 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.66272689e-02 5.87087929e-01 -5.40555418e-01 -3.31486672e-01
-8.43923390e-01 -5.31264305e-01 5.78315556e-01 5.65352619e-01
-3.37047428e-01 8.32346201e-01 3.18352249e-03 -2.45247290e-01
-3.27544898e-01 -1.11457682e+00 -3.48314047e-01 -3.71683121e-01
-9.71077904e-02 8.23935032e-01 3.03844243e-01 -1.05056524... | [9.26065444946289, 8.632157325744629] |
0017d481-e63c-4d56-bc52-f699c55eb64a | enhancing-neural-rendering-methods-with-image | 2306.08904 | null | https://arxiv.org/abs/2306.08904v1 | https://arxiv.org/pdf/2306.08904v1.pdf | Enhancing Neural Rendering Methods with Image Augmentations | Faithfully reconstructing 3D geometry and generating novel views of scenes are critical tasks in 3D computer vision. Despite the widespread use of image augmentations across computer vision applications, their potential remains underexplored when learning neural rendering methods (NRMs) for 3D scenes. This paper presen... | ['Bernard Ghanem', 'Jesus Zarzar', 'Sara Rojas', 'Juan C. Pérez'] | 2023-06-15 | null | null | null | null | ['neural-rendering', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.20760858e-01 3.06809068e-01 4.78475779e-01 -3.49589854e-01
-4.86218184e-01 -6.04110897e-01 6.65448189e-01 -1.50664732e-01
-1.12373568e-01 2.40763113e-01 2.61677414e-01 -1.42459080e-01
2.08204135e-01 -6.56939566e-01 -1.23709249e+00 -5.73366582e-01
3.91924903e-02 7.47581050e-02 -6.30896119e-03 -2.85989463... | [9.313700675964355, -3.0905582904815674] |
3b1b1247-13f5-4443-b4c8-98dd303598b1 | wavlm-large-scale-self-supervised-pre | 2110.13900 | null | https://arxiv.org/abs/2110.13900v5 | https://arxiv.org/pdf/2110.13900v5.pdf | WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing | Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all s... | ['Jian Wu', 'Xiangzhan Yu', 'Michael Zeng', 'Furu Wei', 'Yao Qian', 'Yanmin Qian', 'Shuo Ren', 'Long Zhou', 'Xiong Xiao', 'Takuya Yoshioka', 'Naoyuki Kanda', 'Jinyu Li', 'Zhuo Chen', 'Shujie Liu', 'Yu Wu', 'Zhengyang Chen', 'Chengyi Wang', 'Sanyuan Chen'] | 2021-10-26 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 1.82988331e-01 1.55969873e-01 -1.84975818e-01 -6.76531792e-01
-1.15925300e+00 -4.25817639e-01 5.25624812e-01 -1.61949888e-01
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-8.19413457e-03 3.52050513e-01 6.82042316e-02 -3.35940659... | [14.540565490722656, 6.429957389831543] |
72aa417c-0dfc-4293-a703-e02fbb7144b8 | csagn-conversational-structure-aware-graph | 2109.11541 | null | https://arxiv.org/abs/2109.11541v2 | https://arxiv.org/pdf/2109.11541v2.pdf | CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling | Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding. However, it remains a major challenge for existing CSRL parser to handle conversational structural information. In this paper, we present a simple and effective architecture for CSRL which aims to address this ... | ['Linqi Song', 'Kun Xu', 'Han Wu'] | 2021-09-23 | null | https://aclanthology.org/2021.emnlp-main.177 | https://aclanthology.org/2021.emnlp-main.177.pdf | emnlp-2021-11 | ['semantic-role-labeling', 'dialogue-understanding'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.34811890e-01 6.07139587e-01 -2.02945963e-01 -8.05231035e-01
-8.88938963e-01 -6.86074495e-01 9.56072271e-01 -3.47211100e-02
-2.21784100e-01 7.73742557e-01 1.03234339e+00 -3.34436327e-01
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3.55385207e-02 4.95275080e-01 4.20135051e-01 -9.58664656... | [12.430427551269531, 8.051023483276367] |
524f3d8f-feb9-46c1-bc2c-2873ef81ff9e | extending-the-forward-forward-algorithm | 2307.04205 | null | https://arxiv.org/abs/2307.04205v1 | https://arxiv.org/pdf/2307.04205v1.pdf | Extending the Forward Forward Algorithm | The Forward Forward algorithm, proposed by Geoffrey Hinton in November 2022, is a novel method for training neural networks as an alternative to backpropagation. In this project, we replicate Hinton's experiments on the MNIST dataset, and subsequently extend the scope of the method with two significant contributions. F... | ['Advaith Sridhar', 'Jonah Kornberg', 'Ritu Gala', 'Saumya Gandhi'] | 2023-07-09 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-3.53236916e-03 2.49594480e-01 8.12986866e-02 -8.47450197e-01
-2.66193300e-01 -3.25614959e-01 7.31134236e-01 7.07544461e-02
-8.67030442e-01 4.17746097e-01 3.30746233e-01 -4.66493607e-01
-3.66309226e-01 -3.02444816e-01 -7.29971707e-01 -5.28014243e-01
-2.39993677e-01 1.66746214e-01 2.09988624e-01 -4.21389669... | [8.1849946975708, 3.4648995399475098] |
c9acbaa2-f901-434b-90b3-c65be125c710 | dawn-of-the-transformer-era-in-speech-emotion | 2203.07378 | null | https://arxiv.org/abs/2203.07378v2 | https://arxiv.org/pdf/2203.07378v2.pdf | Dawn of the transformer era in speech emotion recognition: closing the valence gap | Recent advances in transformer-based architectures which are pre-trained in self-supervised manner have shown great promise in several machine learning tasks. In the audio domain, such architectures have also been successfully utilised in the field of speech emotion recognition (SER). However, existing works have not e... | ['Björn W. Schuller', 'Felix Burkhardt', 'Florian Eyben', 'Maximilian Schmitt', 'Hagen Wierstorf', 'Andreas Triantafyllopoulos', 'Johannes Wagner'] | 2022-03-14 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 5.71139976e-02 2.62987822e-01 2.57369488e-01 -6.08988464e-01
-5.37939966e-01 -5.09126663e-01 6.97535157e-01 4.42054898e-01
-6.68298423e-01 6.04072988e-01 3.64062935e-01 -5.46236662e-03
-1.56984061e-01 -4.16017115e-01 -4.49123949e-01 -6.17321134e-01
-3.40306669e-01 2.96614587e-01 -2.03261629e-01 -6.98144436... | [13.66866683959961, 5.85616397857666] |
51404f40-deb7-4967-bfb8-dcf4f03d1eef | gesturegan-for-hand-gesture-to-gesture | 1808.04859 | null | https://arxiv.org/abs/1808.04859v2 | https://arxiv.org/pdf/1808.04859v2.pdf | GestureGAN for Hand Gesture-to-Gesture Translation in the Wild | Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gesture and the output target gesture. To tackle this problem, we pro... | ['Yan Yan', 'Hao Tang', 'Wei Wang', 'Nicu Sebe', 'Dan Xu'] | 2018-08-14 | null | null | null | null | ['gesture-to-gesture-translation'] | ['computer-vision'] | [ 3.11845928e-01 -3.23789358e-01 -1.67692468e-01 -2.89544910e-01
-7.95947671e-01 -5.82086444e-01 5.31173468e-01 -9.91553962e-01
-3.54712546e-01 5.45885265e-01 1.96616836e-02 -1.38636634e-01
4.22915131e-01 -7.29757547e-01 -9.28664863e-01 -1.01418424e+00
2.73191899e-01 3.16370934e-01 3.87470014e-02 1.14897288... | [11.702908515930176, -0.7249549031257629] |
452c1025-3c1b-4be7-a621-f605864438bc | learning-discrete-state-abstractions-with | 2003.04300 | null | https://arxiv.org/abs/2003.04300v3 | https://arxiv.org/pdf/2003.04300v3.pdf | Learning Discrete State Abstractions With Deep Variational Inference | Abstraction is crucial for effective sequential decision making in domains with large state spaces. In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction. We use a deep neural encoder to map states onto continuous embeddings. We map these embedding... | ['Jan-Willem van de Meent', 'Lawson L. S. Wong', 'Robert Platt', 'Ondrej Biza'] | 2020-03-09 | null | https://openreview.net/forum?id=oU7dRDX8SNA | https://openreview.net/pdf?id=oU7dRDX8SNA | pproximateinference-aabi-symposium-2021-1 | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-7.71408379e-02 4.23085600e-01 -2.34982207e-01 -2.38781035e-01
-6.81603968e-01 -4.14624572e-01 8.50616455e-01 2.53682822e-01
-5.03582656e-01 7.23754108e-01 4.77609545e-01 -1.61004424e-01
-4.57690582e-02 -8.29055071e-01 -1.00361085e+00 -3.78240913e-01
-5.14446437e-01 1.13289821e+00 7.00032488e-02 -2.76691377... | [4.412838935852051, 1.0712003707885742] |
cc58acd9-b150-42cd-9a9e-d106f1c058bb | identification-of-energy-management | 2306.08318 | null | https://arxiv.org/abs/2306.08318v1 | https://arxiv.org/pdf/2306.08318v1.pdf | Identification of Energy Management Configuration Concepts from a Set of Pareto-optimal Solutions | Optimizing building configurations for an efficient use of energy is increasingly receiving attention by current research and several methods have been developed to address this task. Selecting a suitable configuration based on multiple conflicting objectives, such as initial investment cost, recurring cost, robustness... | ['Sebastian Schmitt', 'Yaochu Jin', 'Qiqi Liu', 'Felix Lanfermann'] | 2023-06-14 | null | null | null | null | ['management', 'energy-management'] | ['miscellaneous', 'time-series'] | [ 9.73312482e-02 -5.31957805e-01 1.41115859e-01 -4.03640807e-01
-3.54338050e-01 -6.95662916e-01 1.89998358e-01 9.00264561e-01
-3.17483902e-01 7.15028703e-01 1.60933748e-01 -2.22970590e-01
-9.60792422e-01 -9.35396850e-01 2.00297479e-02 -7.91572452e-01
-4.43974845e-02 5.62025487e-01 -2.20046505e-01 -7.62800425... | [5.8413004875183105, 3.515735149383545] |
131dca57-0cc2-4a64-bca5-4c38c7cdcaaf | birds-of-a-feather-flock-together-category | 2204.02111 | null | https://arxiv.org/abs/2204.02111v1 | https://arxiv.org/pdf/2204.02111v1.pdf | Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation | Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. Present UDA models focus on alleviating the domain shift by minimizing the feature discrepancy between the source domain and the target domain but usually ignore the class confu... | ['Changhu Wang', 'Zehuan Yuan', 'Shuai Shao', 'Danpei Zhao', 'Bo Yuan'] | 2022-04-05 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.17931700e-01 -8.10873881e-02 -1.64550647e-01 -7.42087901e-01
-7.50170588e-01 -6.54132128e-01 4.33571666e-01 2.51780957e-01
-3.89845580e-01 4.69050646e-01 -3.96272503e-02 7.31077641e-02
-4.27697182e-01 -1.20027113e+00 -4.79595363e-01 -8.06985140e-01
4.30578560e-01 4.88102704e-01 4.95025724e-01 -3.61678861... | [9.689699172973633, 1.412293791770935] |
a4a33756-3d96-409e-9a84-9cfb26875123 | registration-free-hybrid-learning-empowers | 2307.03425 | null | https://arxiv.org/abs/2307.03425v1 | https://arxiv.org/pdf/2307.03425v1.pdf | Registration-Free Hybrid Learning Empowers Simple Multimodal Imaging System for High-quality Fusion Detection | Multimodal fusion detection always places high demands on the imaging system and image pre-processing, while either a high-quality pre-registration system or image registration processing is costly. Unfortunately, the existing fusion methods are designed for registered source images, and the fusion of inhomogeneous fea... | ['Yuanjie Gu', 'Shouyu Wang', 'Zekuan Yu', 'Haoran Dai', 'Yinghan Guan'] | 2023-07-07 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [ 3.69834721e-01 -8.73297453e-01 2.42123559e-01 -2.16032416e-01
-8.47746313e-01 -3.00921530e-01 5.71334481e-01 -6.17201440e-02
-5.29420137e-01 9.80178863e-02 1.34260012e-02 -4.13871370e-02
-3.55596691e-01 -1.12458324e+00 -2.36533388e-01 -1.11057258e+00
2.35531896e-01 -1.39862686e-01 2.08508506e-01 -3.18766475... | [10.230500221252441, -1.7408684492111206] |
7c3fe395-6bb6-441b-8398-ee1b17cfb466 | occlusion-robust-face-alignment-using-a | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Occlusion-Robust_Face_Alignment_Using_a_Viewpoint-Invariant_Hierarchical_Network_Architecture_CVPR_2022_paper.pdf | Occlusion-Robust Face Alignment Using a Viewpoint-Invariant Hierarchical Network Architecture | The occlusion problem heavily degrades the localization performance of face alignment. Most current solutions for this problem focus on annotating new occlusion data, introducing boundary estimation, and stacking deeper models to improve the robustness of neural networks. However, the performance degradation of mod... | ['Yinzheng Gu', 'Xiaoqiang Li', 'Shaorong Xie', 'Xintong Wan', 'Congcong Zhu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['robust-face-alignment', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-2.18837142e-01 4.29842085e-01 -3.20443898e-01 -6.94488823e-01
-1.64349955e-02 -1.75124317e-01 3.91332537e-01 -5.50375402e-01
2.12169960e-01 2.23861724e-01 3.58355910e-01 8.06469992e-02
-5.60465381e-02 -6.66627586e-01 -6.47194564e-01 -6.50948346e-01
2.26753652e-01 3.52287948e-01 5.56551442e-02 -4.38480675... | [13.420672416687012, 0.46458008885383606] |
aee85ce9-1c5b-4e4f-9815-c3f072d0abee | meta-rangeseg-lidar-sequence-semantic | 2202.13377 | null | https://arxiv.org/abs/2202.13377v3 | https://arxiv.org/pdf/2202.13377v3.pdf | Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation | LiDAR sensor is essential to the perception system in autonomous vehicles and intelligent robots. To fulfill the real-time requirements in real-world applications, it is necessary to efficiently segment the LiDAR scans. Most of previous approaches directly project 3D point cloud onto the 2D spherical range image so tha... | ['Ruixiang Zhang', 'Jianke Zhu', 'Song Wang'] | 2022-02-27 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.12549716e-01 -3.57426435e-01 6.56442642e-02 -6.97505832e-01
-4.78641093e-01 -2.18416914e-01 4.65541095e-01 -1.34165883e-01
-6.46575451e-01 2.79536068e-01 -4.63393420e-01 -2.44673491e-01
-2.07262069e-01 -1.29814434e+00 -8.43759596e-01 -6.30508184e-01
2.50065833e-01 5.14379263e-01 7.24358737e-01 -3.45343560... | [8.23693561553955, -2.60425066947937] |
d41fe8a6-cbed-4825-8783-69d84118cca2 | orthogonal-coding-based-feature-generation | 2207.05957 | null | https://arxiv.org/abs/2207.05957v1 | https://arxiv.org/pdf/2207.05957v1.pdf | Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling | Open-set recognition (OSR) aims to simultaneously detect unknown-class samples and classify known-class samples. Most of the existing OSR methods are inductive methods, which generally suffer from the domain shift problem that the learned model from the known-class domain might be unsuitable for the unknown-class domai... | ['Qiulei Dong', 'Jiayin Sun'] | 2022-07-13 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.52505970e-01 2.40958594e-02 -3.55545700e-01 -1.49075389e-01
-1.20314062e+00 -4.43000138e-01 4.86387640e-01 -4.24045652e-01
1.12230778e-01 8.28224838e-01 -6.43125176e-02 6.89702779e-02
-1.06816635e-01 -7.32863128e-01 -6.26083970e-01 -9.80279088e-01
3.73497456e-01 6.32240176e-01 1.39654309e-01 -5.42204976... | [10.071480751037598, 2.9308316707611084] |
1c54a37b-d352-4b05-9507-765e5beb84eb | how-to-solve-fair-k-center-in-massive-data | 2002.07682 | null | https://arxiv.org/abs/2002.07682v2 | https://arxiv.org/pdf/2002.07682v2.pdf | How to Solve Fair $k$-Center in Massive Data Models | Fueled by massive data, important decision making is being automated with the help of algorithms, therefore, fairness in algorithms has become an especially important research topic. In this work, we design new streaming and distributed algorithms for the fair $k$-center problem that models fair data summarization. The... | ['Sivaramakrishnan Natarajan Ramamoorthy', 'Sagar Kale', 'Ashish Chiplunkar'] | 2020-02-18 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-3.22423764e-02 3.71868664e-04 -3.22307110e-01 -3.93436551e-01
-8.69346142e-01 -4.46505040e-01 1.55929774e-01 8.37715328e-01
-5.62503219e-01 8.41986001e-01 1.68104693e-01 -2.15026751e-01
-5.85693300e-01 -1.13511944e+00 -5.95554233e-01 -7.10175335e-01
-4.93924707e-01 9.43735957e-01 5.54890752e-01 -1.69280186... | [6.613731384277344, 4.905677795410156] |
f4e629ad-206d-472e-85f8-9b4bc105037b | self-ensembling-contrastive-learning-for-semi | 2105.12924 | null | https://arxiv.org/abs/2105.12924v2 | https://arxiv.org/pdf/2105.12924v2.pdf | Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation | Deep learning has demonstrated significant improvements in medical image segmentation using a sufficiently large amount of training data with manual labels. Acquiring well-representative labels requires expert knowledge and exhaustive labors. In this paper, we aim to boost the performance of semi-supervised learning fo... | ['Shaoting Zhang', 'Qing Xia', 'Wenji Wang', 'Zhuowei Li', 'Jinxi Xiang'] | 2021-05-27 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.63527763e-01 4.71182674e-01 -3.98085177e-01 -7.84205317e-01
-1.12227178e+00 -2.44503468e-01 1.75213113e-01 1.20933294e-01
-7.18312144e-01 6.90023124e-01 -1.99878439e-01 -1.50439620e-01
-2.26216074e-02 -6.01212561e-01 -7.52280712e-01 -9.41509664e-01
8.77431110e-02 5.28989255e-01 1.97756529e-01 3.40476662... | [14.689047813415527, -2.105760335922241] |
f5dfa716-1b40-4ed0-a7dc-018906c7bda2 | chosen-methods-of-improving-object | 2208.13591 | null | https://arxiv.org/abs/2208.13591v2 | https://arxiv.org/pdf/2208.13591v2.pdf | Chosen methods of improving small object recognition with weak recognizable features | Many object detection models struggle with several problematic aspects of small object detection including the low number of samples, lack of diversity and low features representation. Taking into account that GANs belong to generative models class, their initial objective is to learn to mimic any data distribution. Us... | ['Marcin Pietroń', 'Magdalena Stachoń'] | 2022-08-29 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.78251731e-01 1.71630695e-01 1.49684295e-01 -2.28151262e-01
-3.28402221e-01 -3.95062417e-01 6.79919958e-01 -4.20396566e-01
-2.05545679e-01 9.49583232e-01 -4.55604792e-02 1.37192428e-01
1.64056391e-01 -1.02643383e+00 -7.46447146e-01 -7.58145928e-01
4.86963838e-01 5.85702479e-01 3.28855097e-01 -5.88145927... | [8.542191505432129, -0.6365504264831543] |
5d8e32a8-a851-4217-a6ce-51bf7e714dc8 | all-in-one-multi-task-prompting-for-graph | 2307.01504 | null | https://arxiv.org/abs/2307.01504v1 | https://arxiv.org/pdf/2307.01504v1.pdf | All in One: Multi-task Prompting for Graph Neural Networks | Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application. However, graph tasks with node level, edge level, and graph level are far diversified, making the pre-training... | ['Jihong Guan', 'Bo Liu', 'Jia Li', 'Hong Cheng', 'Xiangguo Sun'] | 2023-07-04 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 2.70777255e-01 2.58627743e-01 -2.60326117e-01 -3.07653874e-01
-4.21518773e-01 -5.60202897e-01 5.87525249e-01 3.65360260e-01
-1.66507110e-01 2.85087109e-01 2.34327868e-01 -6.14810348e-01
-8.19262192e-02 -8.74109209e-01 -6.04376078e-01 -4.63269532e-01
7.29198307e-02 1.84836075e-01 3.11338395e-01 -2.20241651... | [8.728549003601074, 7.515232563018799] |
8528bbf5-a3e0-425c-999c-1a9c238fd4c1 | 360-circ-high-resolution-depth-estimation-via | 2304.07967 | null | https://arxiv.org/abs/2304.07967v1 | https://arxiv.org/pdf/2304.07967v1.pdf | 360$^\circ$ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer | Recently, omnidirectional images (ODIs) have become increasingly popular; however, their angular resolution tends to be lower than that of perspective images.This leads to degraded structural details such as edges, causing difficulty in learning 3D scene understanding tasks, especially monocular depth estimation. Exist... | ['Lin Wang', 'Hao Ai', 'Zidong Cao'] | 2023-04-17 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 3.61466736e-01 1.75011605e-01 -2.05073729e-01 -5.05190969e-01
-8.58034492e-01 -2.72973031e-01 4.00227278e-01 -4.40890342e-01
-2.80472100e-01 8.11001837e-01 2.72179246e-01 -7.60355890e-02
-6.60663918e-02 -9.51174140e-01 -8.33868444e-01 -7.77333856e-01
3.70595843e-01 7.71541670e-02 4.92664315e-02 9.91993546... | [8.939605712890625, -2.7149229049682617] |
dee579e1-eeca-4fea-8869-09be40dce852 | dialogue-strategy-adaptation-to-new-action | 2204.07082 | null | https://arxiv.org/abs/2204.07082v1 | https://arxiv.org/pdf/2204.07082v1.pdf | Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling | A major bottleneck for building statistical spoken dialogue systems for new domains and applications is the need for large amounts of training data. To address this problem, we adopt the multi-dimensional approach to dialogue management and evaluate its potential for transfer learning. Specifically, we exploit pre-trai... | ['Rama Doddipatla', 'Svetlana Stoyanchev', 'Norbert Braunschweiler', 'Simon Keizer'] | 2022-04-14 | null | null | null | null | ['dialogue-management', 'spoken-dialogue-systems'] | ['natural-language-processing', 'speech'] | [ 1.22144178e-01 6.58126473e-01 1.16320066e-01 -4.77854103e-01
-1.00123990e+00 -6.79281175e-01 9.88577306e-01 -2.27521695e-02
-1.05565012e+00 1.21510005e+00 7.33892083e-01 -4.02327180e-01
2.42680937e-01 -1.81089401e-01 -7.23524690e-02 -5.73877156e-01
-6.21541440e-02 1.07814658e+00 4.04121637e-01 -7.41547823... | [13.02657413482666, 8.017746925354004] |
4a20de99-af44-491e-9127-6a05db32d4f0 | learning-large-scale-topological-maps-using | 1706.03416 | null | http://arxiv.org/abs/1706.03416v2 | http://arxiv.org/pdf/1706.03416v2.pdf | Learning Large-Scale Topological Maps Using Sum-Product Networks | In order to perform complex actions in human environments, an autonomous
robot needs the ability to understand the environment, that is, to gather and
maintain spatial knowledge. Topological map is commonly used for representing
large scale, global maps such as floor plans. Although much work has been done
in topologic... | ['Kaiyu Zheng'] | 2017-06-11 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-5.45293503e-02 4.51678663e-01 1.75238743e-01 -6.76428497e-01
3.12015209e-02 -5.57947040e-01 7.81953096e-01 5.41775346e-01
-1.20171100e-01 9.10916209e-01 4.50920969e-01 -1.42319441e-01
-8.15522373e-01 -1.42126894e+00 -9.98139203e-01 -4.42251801e-01
-7.41807818e-01 1.19599891e+00 6.11476004e-01 -3.93122584... | [4.910096645355225, 0.41539883613586426] |
cd392296-345b-4426-a25d-3c1ef36dcbb9 | counterpoint-by-convolution | 1903.07227 | null | http://arxiv.org/abs/1903.07227v1 | http://arxiv.org/pdf/1903.07227v1.pdf | Counterpoint by Convolution | Machine learning models of music typically break up the task of composition
into a chronological process, composing a piece of music in a single pass from
beginning to end. On the contrary, human composers write music in a nonlinear
fashion, scribbling motifs here and there, often revisiting choices previously
made. In... | ['Cheng-Zhi Anna Huang', 'Tim Cooijmans', 'Adam Roberts', 'Douglas Eck', 'Aaron Courville'] | 2019-03-18 | null | null | null | null | ['music-modeling'] | ['music'] | [ 3.97687674e-01 2.58133650e-01 2.61938348e-02 -1.35382771e-01
-6.95532620e-01 -9.12446260e-01 9.17533696e-01 -1.94205835e-01
-2.23974273e-01 7.19892502e-01 4.66929018e-01 -9.21991095e-02
-4.70609926e-02 -9.44874704e-01 -9.89758611e-01 -4.45933193e-01
-2.80861650e-02 8.66650105e-01 -1.67354524e-01 -2.18029603... | [15.896590232849121, 5.562900066375732] |
01a2e095-63c8-46fd-847f-60e223718bfa | tapex-table-pre-training-via-learning-a | 2107.07653 | null | https://arxiv.org/abs/2107.07653v3 | https://arxiv.org/pdf/2107.07653v3.pdf | TAPEX: Table Pre-training via Learning a Neural SQL Executor | Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data. However, it is still a challenge to apply pre-training on structured tabular data due to the absence of large-scale high-quality tabular data. In this paper, we propose TAPEX to show that ta... | ['Weizhu Chen', 'Morteza Ziyadi', 'Jian-Guang Lou', 'Zeqi Lin', 'Jiaqi Guo', 'Bei Chen', 'Qian Liu'] | 2021-07-16 | tapex-table-pre-training-via-learning-a-1 | https://openreview.net/forum?id=O50443AsCP | https://openreview.net/pdf?id=O50443AsCP | iclr-2022-4 | ['table-based-fact-verification'] | ['natural-language-processing'] | [ 4.82842922e-02 2.55169779e-01 -5.27637720e-01 -5.25215089e-01
-1.47603846e+00 -6.56662941e-01 5.30988812e-01 2.91572124e-01
-2.35133469e-01 8.29192460e-01 1.12118125e-01 -6.72509611e-01
1.57589927e-01 -9.65113938e-01 -1.25234127e+00 9.70418900e-02
-1.37735337e-01 9.90278482e-01 -2.76906379e-02 -2.83183992... | [9.896820068359375, 7.862509727478027] |
5589fe70-efb2-4f86-9ebf-4c35365a8b67 | interpreting-bert-architecture-predictions | 2111.07137 | null | https://arxiv.org/abs/2111.07137v1 | https://arxiv.org/pdf/2111.07137v1.pdf | Interpreting BERT architecture predictions for peptide presentation by MHC class I proteins | The major histocompatibility complex (MHC) class-I pathway supports the detection of cancer and viruses by the immune system. It presents parts of proteins (peptides) from inside a cell on its membrane surface enabling visiting immune cells that detect non-self peptides to terminate the cell. The ability to predict whe... | ['Ajitha Rajan', 'Javier Alfaro', 'David Goodlett', 'Bo Ren', 'Georges Bedran', 'Hans-Christof Gasser'] | 2021-11-13 | null | null | null | null | ['mhc-presentation-prediction'] | ['medical'] | [ 4.12664711e-01 3.82457636e-02 -3.98848683e-01 -2.45992154e-01
-3.73910926e-02 -8.57053280e-01 2.08674535e-01 7.97326148e-01
-2.03270584e-01 1.01543880e+00 1.97780758e-01 -5.81046045e-01
-1.28534600e-01 -7.40900517e-01 -4.34818119e-01 -9.62407529e-01
-4.92315888e-01 6.66822314e-01 2.57297754e-01 -4.59093601... | [4.74350643157959, 5.409682750701904] |
b5e93ccf-b1cb-457f-998a-035f40b7442a | the-truth-and-nothing-but-the-truth | 1903.04484 | null | http://arxiv.org/abs/1903.04484v1 | http://arxiv.org/pdf/1903.04484v1.pdf | The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection | We propose a data-driven method for automatic deception detection in
real-life trial data using visual and verbal cues. Using OpenFace with facial
action unit recognition, we analyze the movement of facial features of the
witness when posed with questions and the acoustic patterns using OpenSmile. We
then perform a lex... | ['Sairam Tabibu', 'Rajiv Bajpai', 'Mimansa Jaiswal'] | 2019-03-11 | null | null | null | null | ['facial-action-unit-detection', 'deception-detection', 'lexical-analysis'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 9.51646641e-02 -4.78806235e-02 2.66887303e-02 -9.36055183e-01
-9.20628071e-01 -5.18480718e-01 4.78590488e-01 7.41139054e-02
-3.35844189e-01 2.43542299e-01 4.25231963e-01 -1.38213737e-02
1.92666307e-01 -7.99002200e-02 -2.28377879e-01 -4.54594761e-01
-1.71027556e-01 -2.37225682e-01 -1.89996734e-01 -1.90662786... | [13.336318016052246, 2.1722841262817383] |
49e8efc0-4b6e-4ccb-8d14-fdc3f732cf2b | seed-guided-topic-discovery-with-out-of | 2205.01845 | null | https://arxiv.org/abs/2205.01845v1 | https://arxiv.org/pdf/2205.01845v1.pdf | Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds | Discovering latent topics from text corpora has been studied for decades. Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance. Although there exist seed-guided topic discovery approache... | ['Jiawei Han', 'Sheng Wang', 'Xuan Wang', 'Yu Meng', 'Yu Zhang'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.21 | https://aclanthology.org/2022.naacl-main.21.pdf | naacl-2022-7 | ['general-knowledge', 'topic-models'] | ['miscellaneous', 'natural-language-processing'] | [-6.90441430e-02 2.52306968e-01 -7.22398400e-01 -3.70457500e-01
-8.24064553e-01 -5.41697860e-01 9.63475049e-01 3.38860363e-01
-6.74558133e-02 6.66002631e-01 5.13037264e-01 -3.12770382e-02
6.21757507e-02 -8.43618810e-01 -4.16568279e-01 -5.70779562e-01
-1.25789136e-01 5.49504757e-01 6.99332654e-01 -1.89178571... | [10.404695510864258, 6.926445484161377] |
f4d4ab82-4de0-468e-a173-078139d313dc | investigating-evaluation-of-open-domain | 1907.10568 | null | https://arxiv.org/abs/1907.10568v2 | https://arxiv.org/pdf/1907.10568v2.pdf | Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple References | The aim of this paper is to mitigate the shortcomings of automatic evaluation of open-domain dialog systems through multi-reference evaluation. Existing metrics have been shown to correlate poorly with human judgement, particularly in open-domain dialog. One alternative is to collect human annotations for evaluation, w... | ['Prakhar Gupta', 'Amy Pavel', 'Tiancheng Zhao', 'Shikib Mehri', 'Maxine Eskenazi', 'Jeffrey P. Bigham'] | 2019-07-24 | investigating-evaluation-of-open-domain-1 | https://aclanthology.org/W19-5944 | https://aclanthology.org/W19-5944.pdf | ws-2019-9 | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-2.80027658e-01 2.52071321e-01 2.90702820e-01 -6.56785429e-01
-9.90134537e-01 -1.12747169e+00 8.89581800e-01 2.63342261e-01
-7.19382107e-01 1.10500085e+00 3.91116917e-01 -1.62433669e-01
-6.73952848e-02 -1.98598549e-01 3.13571483e-01 -1.06901929e-01
3.35677296e-01 7.87253439e-01 4.60554570e-01 -7.09723890... | [12.894831657409668, 8.035649299621582] |
c786bc1a-f17b-4b14-96d6-a1d7240d4ff8 | memsac-memory-augmented-sample-consistency | 2207.12389 | null | https://arxiv.org/abs/2207.12389v1 | https://arxiv.org/pdf/2207.12389v1.pdf | MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation | Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well. In this work we propose MemSAC, which exploits sample level similarity... | ['Manmohan Chandraker', 'Astuti Sharma', 'Tarun Kalluri'] | 2022-07-25 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 3.14824522e-01 -2.15564221e-01 -2.81384587e-01 -7.77698040e-01
-8.68644118e-01 -1.01965892e+00 6.36131883e-01 1.48088112e-01
-6.08932197e-01 8.21149290e-01 3.66572365e-02 1.36931226e-01
-3.21985006e-01 -3.39941174e-01 -6.67726994e-01 -5.79808891e-01
-1.35387212e-01 9.10073936e-01 4.26290929e-01 -1.75207496... | [10.235920906066895, 2.878852605819702] |
dc4fd0f4-b60a-4a77-8589-a47665a4f26c | behavior-contrastive-learning-for | 2305.04477 | null | https://arxiv.org/abs/2305.04477v1 | https://arxiv.org/pdf/2305.04477v1.pdf | Behavior Contrastive Learning for Unsupervised Skill Discovery | In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual information (MI) between states and skills. However, such an MI objective tends to learn simple and static skills and may hinder exploration. In this ... | ['Xuelong Li', 'Peng Liu', 'Zhen Wang', 'Bin Zhao', 'Siyuan Li', 'Hongyi Guo', 'Chenjia Bai', 'Rushuai Yang'] | 2023-05-08 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.39038944e-01 2.38293037e-01 -3.15683424e-01 -1.01583060e-02
-3.61891866e-01 -5.86836696e-01 3.11990678e-01 1.04631737e-01
-6.63258791e-01 1.22812223e+00 -2.77941935e-02 -1.76659048e-01
-4.72612113e-01 -6.89034939e-01 -6.61527693e-01 -8.05898368e-01
-2.43342176e-01 3.76241416e-01 3.34060431e-01 -3.22664350... | [4.051044940948486, 1.779664158821106] |
bcb9c68e-f2ae-467b-bb8d-1d486a2e6c43 | multi-xscience-a-large-scale-dataset-for | 2010.14235 | null | https://arxiv.org/abs/2010.14235v1 | https://arxiv.org/pdf/2010.14235v1.pdf | Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles | Multi-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work secti... | ['Laurent Charlin', 'Yue Dong', 'Yao Lu'] | 2020-10-27 | null | https://aclanthology.org/2020.emnlp-main.648 | https://aclanthology.org/2020.emnlp-main.648.pdf | emnlp-2020-11 | ['extreme-summarization'] | ['natural-language-processing'] | [ 3.50358844e-01 1.89355791e-01 -7.43393242e-01 6.38091490e-02
-1.34283125e+00 -4.59525526e-01 8.12375724e-01 8.57693613e-01
-3.69384885e-02 1.15521657e+00 1.13152611e+00 -1.46026956e-03
-3.29341114e-01 -3.88636738e-01 -8.27719688e-01 -2.41330341e-01
2.44281366e-01 4.29020077e-01 -1.67803526e-01 -2.78502971... | [12.435452461242676, 9.523080825805664] |
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