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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ae1893c0-3787-4b35-984d-8b681b49236f | sequential-local-learning-for-latent | 1703.04082 | null | http://arxiv.org/abs/1703.04082v2 | http://arxiv.org/pdf/1703.04082v2.pdf | Sequential Local Learning for Latent Graphical Models | Learning parameters of latent graphical models (GM) is inherently much harder
than that of no-latent ones since the latent variables make the corresponding
log-likelihood non-concave. Nevertheless, expectation-maximization schemes are
popularly used in practice, but they are typically stuck in local optima. In
the rece... | ['Sejun Park', 'Eunho Yang', 'Jinwoo Shin'] | 2017-03-12 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.73684031e-01 2.85867691e-01 -3.51872742e-01 -2.19481558e-01
-8.12169969e-01 -3.50668997e-01 5.63534021e-01 -2.20496237e-01
-2.06145421e-01 7.35459983e-01 5.67432791e-02 -4.11384016e-01
-4.31235209e-02 -7.99792886e-01 -7.02174485e-01 -1.11378145e+00
-1.00821711e-01 4.22511607e-01 3.12946066e-02 2.57501364... | [7.159030914306641, 3.964144229888916] |
cc265cf1-63dc-432c-acdc-d9bf85996223 | learning-representations-of-bi-level | 2302.02601 | null | https://arxiv.org/abs/2302.02601v3 | https://arxiv.org/pdf/2302.02601v3.pdf | Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction | Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets $T_1$ and $T_2$ where $T_1$ is (Academy_Awards, Nominates, Avatar) ... | ['Joyce Jiyoung Whang', 'Chanyoung Chung'] | 2023-02-06 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.87644833e-01 5.00832915e-01 -3.92342925e-01 -3.32445920e-01
-3.77704352e-01 -4.45982635e-01 3.45020592e-01 5.50128341e-01
-1.22671001e-01 1.00250256e+00 -1.59760807e-02 -5.28083205e-01
-6.93050086e-01 -1.48512101e+00 -1.13297009e+00 -1.99402645e-01
-4.85479146e-01 6.77703917e-01 2.58500844e-01 -3.81476521... | [8.774564743041992, 7.837462425231934] |
0f2b50b3-7a55-41fd-9b12-cde452def348 | predicting-ground-level-scene-layout-from | 1612.02709 | null | http://arxiv.org/abs/1612.02709v1 | http://arxiv.org/pdf/1612.02709v1.pdf | Predicting Ground-Level Scene Layout from Aerial Imagery | We introduce a novel strategy for learning to extract semantically meaningful
features from aerial imagery. Instead of manually labeling the aerial imagery,
we propose to predict (noisy) semantic features automatically extracted from
co-located ground imagery. Our network architecture takes an aerial image as
input, ex... | ['Zachary Bessinger', 'Menghua Zhai', 'Scott Workman', 'Nathan Jacobs'] | 2016-12-08 | predicting-ground-level-scene-layout-from-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.pdf | cvpr-2017-7 | ['cross-view-image-to-image-translation'] | ['computer-vision'] | [ 6.74173534e-01 5.95186949e-01 1.79711923e-01 -6.51662290e-01
-7.62790203e-01 -1.03144419e+00 4.39456999e-01 1.52368829e-01
-4.08839017e-01 2.34680459e-01 6.60612732e-02 -1.73245013e-01
4.11084332e-02 -1.13746929e+00 -9.88372028e-01 -2.75667995e-01
-2.39502221e-01 2.91767538e-01 8.47833753e-02 -1.42232165... | [9.405879974365234, -0.16569675505161285] |
fd56a1ad-5858-4477-9abb-2647391b842b | positive-unlabeled-learning-for-binary-and | 2302.08050 | null | https://arxiv.org/abs/2302.08050v1 | https://arxiv.org/pdf/2302.08050v1.pdf | Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations | Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instanc... | ['Chuyang Ye', 'Zhiwen Liu', 'Yaou Liu', 'Fengqian Pang', 'Zipei Zhao'] | 2023-02-16 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.38561624e-01 7.45252073e-02 -3.99027467e-01 -2.44490609e-01
-6.72533333e-01 -4.95289207e-01 6.70510307e-02 7.17106223e-01
-7.77433753e-01 1.14036930e+00 -5.23315012e-01 -3.21835607e-01
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8.72982666e-02 5.68862379e-01 3.82313848e-01 2.79184312... | [14.900774002075195, -3.0699961185455322] |
8b482749-060e-454b-9f04-859bce827b00 | paraphrase-generation-as-unsupervised-machine | 2109.02950 | null | https://arxiv.org/abs/2109.02950v2 | https://arxiv.org/pdf/2109.02950v2.pdf | Paraphrase Generation as Unsupervised Machine Translation | In this paper, we propose a new paradigm for paraphrase generation by treating the task as unsupervised machine translation (UMT) based on the assumption that there must be pairs of sentences expressing the same meaning in a large-scale unlabeled monolingual corpus. The proposed paradigm first splits a large unlabeled ... | ['Fei Wu', 'Xiaofei Sun', 'Jiwei Li', 'Nanyun Peng', 'Yuxian Meng', 'Yufei Tian', 'Chun Fan'] | 2021-09-07 | null | https://aclanthology.org/2022.coling-1.555 | https://aclanthology.org/2022.coling-1.555.pdf | coling-2022-10 | ['paraphrase-generation', 'unsupervised-machine-translation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 4.16303605e-01 6.01404021e-03 -3.27606052e-01 -6.27356768e-01
-9.45417166e-01 -8.34177256e-01 6.72004998e-01 -1.18788883e-01
-1.33883715e-01 1.01589954e+00 3.17052692e-01 -4.83477116e-01
3.34087372e-01 -6.34437442e-01 -7.00571120e-01 -6.31770730e-01
8.92423093e-01 7.90714145e-01 -2.10596919e-01 -2.70540804... | [11.663334846496582, 9.431385040283203] |
b870fd70-5655-4f29-813c-278c03296456 | causes-of-catastrophic-forgetting-in-class | 2209.08010 | null | https://arxiv.org/abs/2209.08010v1 | https://arxiv.org/pdf/2209.08010v1.pdf | Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation | Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new semantic classes. A major challenge in CiSS is overcoming the effects of catastrophic forgetting, which describes the sudden drop of accura... | ['Jürgen Beyerer', 'Tobias Kalb'] | 2022-09-16 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 7.14708686e-01 4.94656920e-01 1.93332620e-02 -3.71116340e-01
-3.45404744e-01 -4.05616403e-01 5.55549145e-01 3.90361249e-01
-6.32904649e-01 8.90174389e-01 1.09960176e-01 1.37475625e-01
1.04726866e-01 -7.21387029e-01 -1.11775672e+00 -8.05175304e-01
3.19602787e-01 4.40300912e-01 7.30597079e-01 -5.54129593... | [9.37452220916748, 2.419447660446167] |
8873c68f-3a2a-47d1-be36-52768b2da034 | using-simulation-and-domain-adaptation-to | 1709.07857 | null | http://arxiv.org/abs/1709.07857v2 | http://arxiv.org/pdf/1709.07857v2.pdf | Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping | Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, m... | ['Laura Downs', 'Yunfei Bai', 'Matthew Kelcey', 'Vincent Vanhoucke', 'Mrinal Kalakrishnan', 'Alex Irpan', 'Sergey Levine', 'Paul Wohlhart', 'Peter Pastor', 'Kurt Konolige', 'Konstantinos Bousmalis', 'Julian Ibarz'] | 2017-09-22 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 4.86306965e-01 1.42951692e-02 2.91985035e-01 -4.47026938e-01
-7.60884404e-01 -9.83786702e-01 4.21908081e-01 -1.01210497e-01
-5.30580044e-01 9.15142715e-01 -5.49345076e-01 -6.21158350e-03
3.63103971e-02 -8.00294816e-01 -1.40418696e+00 -6.76856101e-01
-3.19503248e-01 9.58703756e-01 3.09235454e-01 -4.67920899... | [5.8810715675354, -0.8258413076400757] |
b2d3c932-8042-42cd-be12-bbcf33433479 | frame-mining-a-free-lunch-for-learning | 2210.07442 | null | https://arxiv.org/abs/2210.07442v1 | https://arxiv.org/pdf/2210.07442v1.pdf | Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds | We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds. There exist a variety of coordinate frame choices to normalize captured robot-object-interaction point clouds. We find that different frames have a profound effect on agent learning performance, and ... | ['Hao Su', 'Yangyan Li', 'Zhan Ling', 'Xuanlin Li', 'Minghua Liu'] | 2022-10-14 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-2.14029938e-01 -3.81587356e-01 -4.59697664e-01 -2.69109488e-01
-4.91208673e-01 -7.42068172e-01 6.19089961e-01 -5.17936312e-02
-6.71355128e-01 3.37172598e-01 -1.67838082e-01 -1.60746932e-01
-1.85090140e-01 -3.99265379e-01 -1.11270761e+00 -5.59194386e-01
-1.69133693e-01 7.19582081e-01 6.09573722e-01 -4.15199906... | [4.821953773498535, 0.4014095664024353] |
b0e30e51-07c6-4032-bbf8-98567fe43a48 | adaensemble-learning-adaptively-sparse | 2301.08353 | null | https://arxiv.org/abs/2301.08353v1 | https://arxiv.org/pdf/2301.08353v1.pdf | AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction | Learning feature interactions is crucial to success for large-scale CTR prediction in recommender systems and Ads ranking. Researchers and practitioners extensively proposed various neural network architectures for searching and modeling feature interactions. However, we observe that different datasets favor different ... | ['Liubo Li', 'YaChen Yan'] | 2023-01-06 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-3.63244891e-01 -5.64713895e-01 -5.61024010e-01 -7.95240223e-01
-3.80474061e-01 -4.54803884e-01 2.72376686e-01 -2.55672246e-01
-1.44325584e-01 2.87392259e-01 2.05530375e-01 -1.70464218e-01
-5.85528374e-01 -7.69126713e-01 -6.96033239e-01 -4.64330107e-01
-4.69150424e-01 8.43320847e-01 3.99514228e-01 -4.24452364... | [10.144776344299316, 5.484459400177002] |
40743fa8-3301-48ea-a999-602d9f62d158 | multi-timeline-summarization-mtls-improving | null | null | https://aclanthology.org/2021.acl-long.32 | https://aclanthology.org/2021.acl-long.32.pdf | Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries | In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for eac... | ['Masatoshi Yoshikawa', 'Kazunari Sugiyama', 'Antoine Doucet', 'Adam Jatowt', 'Yi Yu'] | 2021-08-01 | null | null | null | acl-2021-5 | ['timeline-summarization'] | ['natural-language-processing'] | [ 2.96462744e-01 9.47682261e-02 -6.58264279e-01 -3.74529302e-01
-1.26728368e+00 -5.22481382e-01 6.84974313e-01 6.27805412e-01
-3.08137536e-01 1.04651034e+00 1.02574646e+00 1.38942540e-01
-1.29173055e-01 -5.32111287e-01 -6.58051968e-01 -3.15159947e-01
1.03930958e-01 6.22269154e-01 4.78463233e-01 -1.78463906... | [12.560020446777344, 9.485445022583008] |
4c60af3d-ba6f-453f-ac34-06c5e6a48920 | learning-sparse-auto-encoders-for-green-ai | 2209.04448 | null | https://arxiv.org/abs/2209.04448v1 | https://arxiv.org/pdf/2209.04448v1.pdf | Learning sparse auto-encoders for green AI image coding | Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy computational power.\\ In ... | ['Michel Barlaud', 'Marc Antonini', 'Frédéric Guyard', 'Cyprien Gille'] | 2022-09-09 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.79910645e-01 -1.13048486e-01 -2.29958519e-01 -1.18788801e-01
-5.16609788e-01 2.72385150e-01 -1.13434449e-01 -2.35590767e-02
-6.48955643e-01 7.47821033e-01 -3.27971689e-02 -1.53582990e-01
-2.77483881e-01 -8.05478036e-01 -7.34587848e-01 -7.88914502e-01
-2.08196193e-01 -8.27185586e-02 2.55456328e-01 -3.58334519... | [11.35383415222168, -1.7352826595306396] |
98b7a31b-5f42-4ca9-8c85-481d5e6190c8 | prior-information-based-decomposition-and | 2303.01776 | null | https://arxiv.org/abs/2303.01776v1 | https://arxiv.org/pdf/2303.01776v1.pdf | Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition | Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptiv... | ['Guoying Zhao', 'Yue Xie', 'Jingjie Yan', 'Guanming Lu', 'Haoyu Chen', 'Jinsheng Wei'] | 2023-03-03 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 8.99796039e-02 1.17672369e-01 -4.24055785e-01 -5.82089782e-01
-9.48568359e-02 8.04501399e-02 5.29541016e-01 -4.01241720e-01
2.60988146e-01 -7.15042725e-02 5.57485044e-01 4.22341824e-01
-2.85444885e-01 -8.34412694e-01 -3.43347847e-01 -8.08765233e-01
-1.82868928e-01 -8.18598494e-02 -1.88988566e-01 -6.04570508... | [13.681722640991211, 1.5798745155334473] |
5270d004-2aff-49fa-835d-d45d345dfcea | alternative-telescopic-displacement-an | 2306.16950 | null | https://arxiv.org/abs/2306.16950v1 | https://arxiv.org/pdf/2306.16950v1.pdf | Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method | Feature alignment is the primary means of fusing multimodal data. We propose a feature alignment method that fully fuses multimodal information, which alternately shifts and expands feature information from different modalities to have a consistent representation in a feature space. The proposed method can robustly cap... | ['Xiaojun Zhang', 'Zong Lu', 'Zihong Luo Chengzhi Liu', 'Yitao Xu', 'Jiahao Qin'] | 2023-06-29 | null | null | null | null | ['arrhythmia-detection', 'cross-modal-retrieval', 'time-series-forecasting'] | ['medical', 'miscellaneous', 'time-series'] | [ 1.07432753e-01 -5.63720345e-01 -2.80161828e-01 -5.13118207e-01
-1.44878566e+00 -5.70038080e-01 7.79902220e-01 2.33786449e-01
-2.95787573e-01 6.02808535e-01 6.11211598e-01 3.96247923e-01
-1.76983252e-01 -1.44096509e-01 -1.64277643e-01 -8.74099255e-01
9.31162667e-03 2.08466962e-01 -2.19106779e-01 5.78806736... | [13.138978004455566, 4.9800310134887695] |
2a32ef0b-279e-4006-baa5-3cd3df96fffa | rgbd-salient-object-detection-via-deep-fusion | 1607.03333 | null | http://arxiv.org/abs/1607.03333v1 | http://arxiv.org/pdf/1607.03333v1.pdf | RGBD Salient Object Detection via Deep Fusion | Numerous efforts have been made to design different low level saliency cues
for the RGBD saliency detection, such as color or depth contrast features,
background and color compactness priors. However, how these saliency cues
interact with each other and how to incorporate these low level saliency cues
effectively to ge... | ['Shengfeng He', 'Jiandong Tian', 'Yandong Tang', 'Qingxiong Yang', 'Liangqiong Qu', 'Jiawei Zhang'] | 2016-07-12 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.05681223e-01 8.79768282e-03 -2.37507209e-01 -3.32340777e-01
-4.54777628e-01 -1.10517412e-01 3.40518683e-01 1.90676004e-01
-3.22967887e-01 5.24749815e-01 2.77848154e-01 2.21116859e-02
7.27714673e-02 -6.10346735e-01 -5.94089925e-01 -6.01261199e-01
1.80894718e-01 -4.64905351e-01 9.99242544e-01 -2.45749295... | [9.784577369689941, -0.555191159248352] |
6fdbbfdd-3590-4848-8dfb-b50139e3334d | adversarial-robustness-and-feature-impact | 2303.13649 | null | https://arxiv.org/abs/2303.13649v1 | https://arxiv.org/pdf/2303.13649v1.pdf | Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection | Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple e... | ['André Lourenço', 'Isabel Praça', 'Eva Maia', 'Lourenço Rodrigues', 'João Vitorino'] | 2023-03-23 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.49292117e-02 1.40322044e-01 1.31092444e-01 -3.41498137e-01
-2.41466984e-01 -3.69926989e-01 1.94416881e-01 1.61977068e-01
-5.37806213e-01 8.20414543e-01 -7.36843497e-02 -4.56689537e-01
-2.80265480e-01 -5.68009734e-01 -4.10358012e-01 -7.77817190e-01
-2.23012567e-01 -2.76898444e-01 1.36359006e-01 -6.91948295... | [13.499878883361816, 2.8742592334747314] |
e2f4c887-b001-4928-8691-08b09a3a4173 | max-pooling-loss-training-of-long-short-term | 1705.02411 | null | http://arxiv.org/abs/1705.02411v1 | http://arxiv.org/pdf/1705.02411v1.pdf | Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting | We propose a max-pooling based loss function for training Long Short-Term
Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
CPU, memory, and latency requirements. The max-pooling loss training can be
further guided by initializing with a cross-entropy loss trained network. A
posterior smoothin... | ['Geng-Shen Fu', 'Ming Sun', 'Anirudh Raju', 'Nikko Strom', 'George Tucker', 'Arindam Mandal', 'Spyros Matsoukas', 'Shiv Vitaladevuni', 'Sankaran Panchapagesan'] | 2017-05-05 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 1.45306855e-01 -2.51059681e-02 -4.11802560e-01 -4.88822281e-01
-1.01821589e+00 -2.77937222e-02 2.99729556e-01 -1.03119828e-01
-1.07073641e+00 7.15874314e-01 1.12856537e-01 -6.17425025e-01
1.44317016e-01 -5.34374177e-01 -8.68757069e-01 -5.47912657e-01
-1.59566984e-01 -8.15818235e-02 2.98053846e-02 1.25942126... | [14.19853687286377, 6.459183216094971] |
1e1e3f6b-7248-47ea-842d-b4705723c371 | multimodal-end-to-end-group-emotion | 2111.05890 | null | https://arxiv.org/abs/2111.05890v1 | https://arxiv.org/pdf/2111.05890v1.pdf | Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention | Classifying group-level emotions is a challenging task due to complexity of video, in which not only visual, but also audio information should be taken into consideration. Existing works on multimodal emotion recognition are using bulky approach, where pretrained neural networks are used as a feature extractors and the... | ['Lev Evtodienko'] | 2021-11-10 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.74958253e-01 -3.23077254e-02 1.56197757e-01 -4.76396084e-01
-6.30345523e-01 -5.36666572e-01 4.06381309e-01 2.60772742e-02
-7.93274522e-01 5.88223934e-01 2.80509919e-01 2.54293501e-01
1.33488998e-01 -4.34819549e-01 -6.52145267e-01 -6.13707483e-01
-1.51825622e-01 1.27958953e-02 6.49890769e-03 -1.29138932... | [13.266549110412598, 5.1063127517700195] |
46a73860-86fb-4d10-84a4-23a00fe18a4c | fast-and-flexible-protein-design-using-deep | null | null | https://www.cell.com/cell-systems/fulltext/S2405-4712(20)30327-6 | https://www.cell.com/action/showPdf?pii=S2405-4712%2820%2930327-6 | Fast and Flexible Protein Design Using Deep Graph Neural Networks | Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to S... | ['Philip M. Kim', 'Albert Perez-Riba', 'Carles Corbi-Verge', 'David Becerra', 'Alexey Strokach'] | 2020-09-23 | null | null | null | null | ['protein-design'] | ['medical'] | [-9.78067592e-02 1.66688189e-01 -1.51120439e-01 -3.23591471e-01
-2.66862094e-01 -1.03290355e+00 -1.41633630e-01 5.02319150e-02
-1.15583457e-01 1.31104493e+00 1.01902373e-01 -7.46751428e-01
6.59155175e-02 -4.97545958e-01 -1.33930731e+00 -6.71338320e-01
-1.95227191e-01 8.15604448e-01 -1.14416152e-01 -2.76340485... | [4.730828762054443, 5.606196403503418] |
06569eb3-00b5-4998-8d64-22e003535bf4 | relational-boosted-bandits | 2012.09220 | null | https://arxiv.org/abs/2012.09220v1 | https://arxiv.org/pdf/2012.09220v1.pdf | Relational Boosted Bandits | Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for real-world domains like social networks are inherently relational. We propose Relational Boosted Bandi... | ['Balaraman Ravindran', 'Sriraam Natarajan', 'Ashutosh Kakadiya'] | 2020-12-16 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 5.06969579e-02 6.24503374e-01 -1.34399462e+00 -6.81800008e-01
-2.99691200e-01 -2.44728982e-01 4.93348360e-01 1.31284997e-01
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-7.82732308e-01 -1.11186516e+00 -8.71184707e-01 -2.25926638e-01
-2.37535685e-01 7.99399495e-01 4.87211496e-02 -3.54107171... | [8.888039588928223, 7.651078224182129] |
e0ce67f9-ab58-4ba8-b2ae-668e5ba51a40 | evolutionary-hierarchical-dirichlet-process | null | null | https://aclanthology.org/P13-2099 | https://aclanthology.org/P13-2099.pdf | Evolutionary Hierarchical Dirichlet Process for Timeline Summarization | null | ['Jiwei Li', 'Sujian Li'] | 2013-08-01 | null | null | null | acl-2013-8 | ['timeline-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.35828971862793, 3.7603485584259033] |
7637d6cf-6c08-4f3a-81b9-9bf403be52fc | video-based-frame-level-facial-analysis-of | null | null | https://openaccess.thecvf.com/content/CVPR2022W/ABAW/html/Savchenko_Video-Based_Frame-Level_Facial_Analysis_of_Affective_Behavior_on_Mobile_Devices_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/ABAW/papers/Savchenko_Video-Based_Frame-Level_Facial_Analysis_of_Affective_Behavior_on_Mobile_Devices_CVPRW_2022_paper.pdf | Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets | In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNe... | ['Savchenko A.V.'] | 2022-06-10 | null | null | null | cvpr-workshop-2022-6 | ['facial-expression-recognition', 'action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.28242591e-01 -1.87755432e-02 7.62001649e-02 -7.72823095e-01
-7.66865969e-01 -2.45775297e-01 4.51530188e-01 3.13562937e-02
-5.83633840e-01 5.19147277e-01 9.56002697e-02 6.07451379e-01
3.70827913e-01 -9.49034914e-02 -3.72689396e-01 -7.81389654e-01
-4.32289034e-01 -1.19334422e-01 -3.81213218e-01 -3.76383275... | [13.578600883483887, 2.0753636360168457] |
012cadd0-f152-4b06-bae1-deb88e4f0352 | three-dimensional-blind-image-deconvolution | 1904.09974 | null | http://arxiv.org/abs/1904.09974v1 | http://arxiv.org/pdf/1904.09974v1.pdf | Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks | Due to image blurring image deconvolution is often used for studying
biological structures in fluorescence microscopy. Fluorescence microscopy image
volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted
by various types of noise which exacerbate image quality at deeper tissue
depth. Therefore,... | ['Shuo Han', 'Edward J. Delp', 'Paul Salama', 'Soonam Lee', 'Kenneth W. Dunn'] | 2019-04-19 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.03108269e-01 -6.45393848e-01 7.73830116e-01 -1.78439140e-01
-4.70677733e-01 -8.27966869e-01 -9.16981697e-03 -4.34740067e-01
-8.00283670e-01 1.28226626e+00 1.05816759e-02 -4.88236845e-02
-5.38988505e-03 -2.27702763e-02 -3.87423933e-01 -1.39730072e+00
3.21723878e-01 1.98918320e-02 -3.73949222e-02 3.41600955... | [12.62283992767334, -2.665886402130127] |
ac30f5b1-0a3c-48f8-bf21-ccc1a52f0f3d | semi-supervised-soil-moisture-prediction | 2012.03506 | null | https://arxiv.org/abs/2012.03506v2 | https://arxiv.org/pdf/2012.03506v2.pdf | Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture | Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable... | ['Sambaran Bandyopadhyay', 'Anoushka Vyas'] | 2020-12-07 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.70013362e-01 -8.02743137e-02 -3.09668362e-01 -2.14339137e-01
3.35345715e-01 -4.32055175e-01 1.82385251e-01 7.86364734e-01
1.07213557e-02 8.50510299e-01 -3.15455645e-01 -9.76531863e-01
-3.89830112e-01 -1.74162531e+00 -5.89395165e-01 -6.94901943e-01
-5.34762621e-01 1.05181627e-01 5.65880656e-01 -5.96714675... | [9.380097389221191, -1.5409661531448364] |
3b164b15-c1dc-4165-a12a-d432f5539f49 | table-detection-for-visually-rich-document | 2305.19181 | null | https://arxiv.org/abs/2305.19181v1 | https://arxiv.org/pdf/2305.19181v1.pdf | Table Detection for Visually Rich Document Images | Table Detection (TD) is a fundamental task towards visually rich document understanding. Current studies usually formulate the TD problem as an object detection problem, then leverage Intersection over Union (IoU) based metrics to evaluate the model performance and IoU-based loss functions to optimize the model. TD app... | ['Ala Abu Alkheir', 'Burak Kantarci', 'Murat Simsek', 'Bin Xiao'] | 2023-05-30 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-7.84197822e-02 -1.71376541e-01 -2.85720229e-01 -2.53307223e-01
-8.04868102e-01 -3.60658646e-01 3.92658085e-01 3.09906095e-01
-1.09005466e-01 5.81296325e-01 3.60015512e-01 -1.17467448e-01
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2.56509721e-01 2.99460977e-01 4.56588835e-01 1.61920875... | [11.486440658569336, 2.305534601211548] |
dea3bb65-eed4-4a73-9770-7a8002b82aa7 | pythia-a-suite-for-analyzing-large-language | 2304.01373 | null | https://arxiv.org/abs/2304.01373v2 | https://arxiv.org/pdf/2304.01373v2.pdf | Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling | How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide... | ['Oskar van der Wal', 'Lintang Sutawika', 'Aviya Skowron', 'Edward Raff', 'USVSN Sai Prashanth', 'Shivanshu Purohit', 'Mohammad Aflah Khan', 'Eric Hallahan', "Kyle O'Brien", 'Herbie Bradley', 'Quentin Anthony', 'Hailey Schoelkopf', 'Stella Biderman'] | 2023-04-03 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.08689934e-01 -2.57384181e-01 -3.97861421e-01 -4.35543776e-01
-8.84861708e-01 -7.20521271e-01 6.93723261e-01 8.43022093e-02
-5.33954799e-01 6.10684812e-01 2.88065635e-02 -6.66076958e-01
6.87341094e-02 -2.96904624e-01 -8.57383907e-01 -4.67508763e-01
-2.09320247e-01 6.05766177e-01 1.31258428e-01 -1.67988092... | [10.646623611450195, 8.310639381408691] |
da969a55-f3ac-4724-9153-4c01ecbef544 | abcnet-v2-adaptive-bezier-curve-network-for | 2105.03620 | null | https://arxiv.org/abs/2105.03620v3 | https://arxiv.org/pdf/2105.03620v3.pdf | ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting | End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentary tasks. It remains an open problem especially when processing arbitrarily-shaped text instances. Previous methods can be roughly categorize... | ['Hao Chen', 'Chongyu Liu', 'Peng Chen', 'Tong He', 'Lianwen Jin', 'Chunhua Shen', 'Yuliang Liu'] | 2021-05-08 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.21264261e-01 -5.42643249e-01 1.47010639e-01 -2.41218165e-01
-6.40538871e-01 -6.30686820e-01 6.64292157e-01 1.71513334e-01
-6.60748482e-01 2.03746930e-01 -1.12114184e-01 -4.92838621e-01
1.83079496e-01 -7.19374835e-01 -6.33661628e-01 -5.26775360e-01
5.16796172e-01 3.08617324e-01 3.23601902e-01 -2.31570065... | [12.035871505737305, 2.2355618476867676] |
15ff7eec-4272-4631-88e7-e49f119816a1 | teacher-student-network-for-3d-point-cloud | 2210.17258 | null | https://arxiv.org/abs/2210.17258v2 | https://arxiv.org/pdf/2210.17258v2.pdf | Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples | Anomaly detection, which is a critical and popular topic in computer vision, aims to detect anomalous samples that are different from the normal (i.e., non-anomalous) ones. The current mainstream methods focus on anomaly detection for images, whereas little attention has been paid to 3D point cloud. In this paper, draw... | ['Chao Zhang', 'Jun Yu', 'Chunzhi Gu', 'Jianjian Qin'] | 2022-10-31 | null | null | null | null | ['3d-anomaly-detection'] | ['methodology'] | [-3.60702202e-02 -1.21040586e-02 3.09140533e-01 -2.61108130e-01
-4.84459162e-01 -2.19577551e-01 5.54809809e-01 4.16150540e-01
-1.75779030e-01 -2.00205043e-01 -4.11527067e-01 -2.54885048e-01
6.46485314e-02 -5.13207734e-01 -6.55209303e-01 -8.31778288e-01
-1.19534083e-01 3.51747304e-01 4.64815766e-01 -1.30307779... | [7.633332252502441, 2.166276693344116] |
2b7e088b-ce3a-4c4d-89ee-85253b238fab | evaluating-pre-trained-language-models-on | null | null | https://aclanthology.org/2022.sdp-1.22 | https://aclanthology.org/2022.sdp-1.22.pdf | Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews | Systematic literature reviews in the biomedical space are often expensive to conduct. Automation through machine learning and large language models could improve the accuracy and research outcomes from such reviews. In this study, we evaluate a pre-trained LongT5 model on the MSLR22: Multi-Document Summarization for Li... | ['Benjamin Yu'] | null | null | null | null | sdp-coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 7.72917792e-02 1.77119106e-01 -6.92016423e-01 -2.60415226e-01
-1.69420266e+00 -4.72208142e-01 5.24410963e-01 8.15796971e-01
-3.86303216e-01 1.16907132e+00 1.07790899e+00 -6.37126982e-01
-2.85786480e-01 -1.00967944e-01 -6.08554006e-01 -4.64332709e-03
2.35038653e-01 4.41709578e-01 -3.12471837e-01 2.14033216... | [12.36037540435791, 9.604561805725098] |
a8e9ee19-005f-4874-a407-a26b42caca13 | measuring-faithful-and-plausible-visual | 2305.15015 | null | https://arxiv.org/abs/2305.15015v1 | https://arxiv.org/pdf/2305.15015v1.pdf | Measuring Faithful and Plausible Visual Grounding in VQA | Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been a common problem among state-of-the-art VQA systems and can manifest in over-reliance on irrelevant... | ['Tanja Schultz', 'Felix Putze', 'Daniel Reich'] | 2023-05-24 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [-4.69224714e-03 1.75214857e-01 1.37511846e-02 -4.50256407e-01
-7.26876318e-01 -8.27153921e-01 7.83099592e-01 3.68239433e-01
1.39909685e-01 4.08544332e-01 1.95692927e-01 -5.46754897e-01
-2.48802930e-01 -7.71354914e-01 -5.17599463e-01 -3.02044272e-01
5.78563869e-01 3.23431104e-01 3.90463918e-01 -4.79260713... | [10.92969036102295, 1.8096896409988403] |
c9ac957a-6df8-4e36-b102-e77eb72b6230 | self-eval-self-supervised-fine-grained | 2208.08094 | null | https://arxiv.org/abs/2208.08094v5 | https://arxiv.org/pdf/2208.08094v5.pdf | SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation | This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrar... | ['Ting Liu', 'Mingda Li', 'Weinan Zhang', 'Ziyu Zhuang', 'Longxuan Ma'] | 2022-08-17 | null | https://aclanthology.org/2022.coling-1.39 | https://aclanthology.org/2022.coling-1.39.pdf | coling-2022-10 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.67018521e-01 3.16921115e-01 -1.31778613e-01 -8.97847295e-01
-1.09996414e+00 -5.67643762e-01 8.02576125e-01 2.10952356e-01
-3.92597467e-01 1.09260511e+00 7.59357989e-01 4.44031432e-02
-1.56385019e-01 -6.73358858e-01 1.24529107e-02 -1.82310581e-01
1.88179791e-01 9.74890947e-01 2.99746990e-01 -9.20916677... | [12.767324447631836, 8.131373405456543] |
2fb437b7-b6fd-4cd3-b9a1-20cd422c0c97 | personalized-federated-recommender-systems | 2212.08779 | null | https://arxiv.org/abs/2212.08779v1 | https://arxiv.org/pdf/2212.08779v1.pdf | Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders | Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: t... | ['Jie Ding', 'Vahid Tarokh', 'Ali Anwar', 'Xinran Wang', 'Enmao Diao', 'Qi Le'] | 2022-12-17 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-3.24880093e-01 5.50397597e-02 -3.93867016e-01 -7.64534116e-01
-7.85575986e-01 -4.14675176e-01 1.56213045e-01 -1.61458671e-01
-2.36448571e-01 4.78426248e-01 5.75956523e-01 -8.14811587e-02
-2.17831701e-01 -8.92632961e-01 -8.82882535e-01 -7.02062726e-01
-1.01706184e-01 4.09652531e-01 -2.33693078e-01 -2.16925547... | [5.9230546951293945, 6.293081760406494] |
db797666-3100-46c6-b797-5547648da1b1 | deep-representation-learning-for | null | null | https://www.sciencedirect.com/science/article/pii/S1532046419302229 | https://www.sciencedirect.com/science/article/pii/S1532046419302229/pdfft?md5=b6a2b5ddc61984744f599ba097379d32&pid=1-s2.0-S1532046419302229-main.pdf | Deep representation learning for individualized treatment effect estimation using electronic health records | Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased estimators of the treatment effects. Although valuable, learning a ba... | ['Zhengxing Huang', 'Kunlun He', 'Uzay Kaymak', 'Xudong', 'Wei Dong', 'Peipei Chen'] | 2019-12-01 | null | null | null | journal-of-biomedical-informatics-2019-12 | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.15343535e-01 3.63729708e-02 -8.77167046e-01 -5.44418395e-01
-1.09354186e+00 -1.10033907e-01 2.10497111e-01 2.32200980e-01
-2.66146868e-01 1.27520072e+00 6.80780530e-01 -2.78666973e-01
-6.85873508e-01 -5.78867972e-01 -6.25746191e-01 -7.90297866e-01
-3.41920525e-01 5.81243455e-01 -5.46365798e-01 4.88777131... | [7.998507976531982, 5.6124982833862305] |
537818c9-3da0-4f33-89a7-aab940cff139 | reflection-removal-using-low-rank-matrix | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Han_Reflection_Removal_Using_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Han_Reflection_Removal_Using_CVPR_2017_paper.pdf | Reflection Removal Using Low-Rank Matrix Completion | The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose a low-rank matrix completion algorithm to remove reflection artifacts automatically from multiple glass images taken at slightly different camera locations. We assume that the transmi... | ['Jae-Young Sim', 'Byeong-Ju Han'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['reflection-removal'] | ['computer-vision'] | [ 0.66825014 -0.19038658 0.5519239 -0.20232275 -0.72354466 -0.10995226
-0.18761599 -0.7207479 -0.12585324 0.2822958 0.41321465 0.4610328
-0.2375103 -0.43803048 -0.6551302 -1.2409654 0.24336089 -0.17198001
0.20397963 -0.11654833 0.39437443 0.01245691 -1.2974501 0.48753113
0.7448243 0.78007966 0.6... | [10.370481491088867, -2.7857789993286133] |
f2f4c8ca-2d76-41db-a63c-968b0a768a3f | real-or-fake-text-investigating-human-ability | 2212.12672 | null | https://arxiv.org/abs/2212.12672v1 | https://arxiv.org/pdf/2212.12672v1.pdf | Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text | As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire p... | ['Chris Callison-Burch', 'Sherry Shi', 'Arun Kirubarajan', 'Daphne Ippolito', 'Liam Dugan'] | 2022-12-24 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 2.96167403e-01 1.43018499e-01 9.85059440e-02 -2.58507848e-01
-1.02403283e+00 -8.23955655e-01 6.80593073e-01 7.33829260e-01
-8.25043678e-01 5.55854559e-01 4.84600037e-01 -4.30869192e-01
3.89816701e-01 -3.31335694e-01 -5.83634377e-01 -3.58205549e-02
5.14442861e-01 5.49555957e-01 7.46638253e-02 -1.93199903... | [11.708534240722656, 8.938375473022461] |
2eeaf98b-13b9-442f-8451-678326c345e9 | a-new-approach-for-pedestrian-density | 1811.05006 | null | https://arxiv.org/abs/1811.05006v2 | https://arxiv.org/pdf/1811.05006v2.pdf | A new approach for pedestrian density estimation using moving sensors and computer vision | An understanding of pedestrian dynamics is indispensable for numerous urban applications including the design of transportation networks and planing for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods d... | ['Roberto M. Cesar-Jr.', 'Gabriel B. A. Ferreira', 'Eric K. Tokuda', 'David Boyle', 'Yitzchak Lockerman', 'Claudio T. Silva', 'Ethan Sorrelgreen'] | 2018-11-12 | null | null | null | null | ['pedestrian-density-estimation'] | ['computer-vision'] | [-1.45080581e-01 -3.05344194e-01 7.01652616e-02 -1.72669411e-01
-5.08204877e-01 -3.15071017e-01 6.27047658e-01 1.87573627e-01
-6.86163127e-01 8.77452791e-01 -6.00249879e-02 -5.61008096e-01
1.05998717e-01 -1.17573357e+00 -4.78088737e-01 -5.59136689e-01
-8.75348300e-02 6.68120027e-01 4.86701429e-01 7.10818693... | [8.22743034362793, -0.43251267075538635] |
3e651286-8776-428c-999d-f31da33f664b | focusing-on-what-to-decode-and-what-to-train | 2307.02291 | null | https://arxiv.org/abs/2307.02291v1 | https://arxiv.org/pdf/2307.02291v1.pdf | Focusing on what to decode and what to train: Efficient Training with HOI Split Decoders and Specific Target Guided DeNoising | Recent one-stage transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOI) task by leveraging the detection of DETR. However, the current methods redirect the detection target of the object decoder, and the box target is not explicitly separated from the query embeddings, which lea... | ['Keiji Yanai', 'Yingcheng Wang', 'Junwen Chen'] | 2023-07-05 | null | null | null | null | ['human-object-interaction-detection', 'object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.52688372e-01 2.79192090e-01 -2.10769743e-01 -4.31470007e-01
-9.16515052e-01 -3.01797926e-01 3.33494753e-01 -1.20325349e-01
-4.77938831e-01 2.52716064e-01 1.58050403e-01 2.07358282e-02
2.31633395e-01 -4.81608719e-01 -9.24070537e-01 -5.37899375e-01
5.73438764e-01 5.67651808e-01 4.76882279e-01 1.52774706... | [9.624897003173828, 1.3614614009857178] |
d6886628-fbcf-47a9-bb15-e9f9422024bd | dynamic-spatial-propagation-network-for-depth | 2202.09769 | null | https://arxiv.org/abs/2202.09769v1 | https://arxiv.org/pdf/2202.09769v1.pdf | Dynamic Spatial Propagation Network for Depth Completion | Image-guided depth completion aims to generate dense depth maps with sparse depth measurements and corresponding RGB images. Currently, spatial propagation networks (SPNs) are the most popular affinity-based methods in depth completion, but they still suffer from the representation limitation of the fixed affinity and ... | ['Hua Yang', 'Wending Zhou', 'Qi Zhong', 'Tao Cheng', 'Yuankai Lin'] | 2022-02-20 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 7.77468681e-02 2.33120441e-01 9.43265706e-02 -2.85068363e-01
-5.12431204e-01 5.54339541e-03 6.01331413e-01 2.48803392e-01
-8.48802030e-01 6.22735202e-01 3.69970113e-01 1.48982629e-01
-2.60561913e-01 -1.06899917e+00 -7.16944337e-01 -7.49979615e-01
8.65292549e-02 7.72732794e-01 7.27737546e-01 -2.28443250... | [8.846720695495605, -2.3612003326416016] |
f96eea17-a43c-4f90-a194-016f8af882bb | balancing-effect-of-training-dataset | 2305.15582 | null | https://arxiv.org/abs/2305.15582v1 | https://arxiv.org/pdf/2305.15582v1.pdf | Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer | Text style transfer is an exciting task within the field of natural language generation that is often plagued by the need for high-quality paired datasets. Furthermore, training a model for multi-attribute text style transfer requires datasets with sufficient support across all combinations of the considered stylistic ... | ['Dongyeop Kang', 'David Ma', 'Debarati Das'] | 2023-05-24 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 5.47194004e-01 -1.24694400e-01 -1.24411546e-01 -5.33540010e-01
-7.31706083e-01 -8.88724506e-01 7.49181509e-01 -1.02751860e-02
-4.61434782e-01 9.64516461e-01 4.70832497e-01 -2.64774173e-01
8.84776637e-02 -8.77701223e-01 -7.83268213e-01 -4.45750684e-01
5.02063274e-01 6.92168236e-01 -1.05622225e-01 -3.82600129... | [11.52079963684082, 9.725409507751465] |
284e9333-4682-483b-9e64-33e05f26b640 | csg0-continual-urban-scene-generation-with | 2112.03252 | null | https://arxiv.org/abs/2112.03252v2 | https://arxiv.org/pdf/2112.03252v2.pdf | CSG0: Continual Urban Scene Generation with Zero Forgetting | With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; id... | ['Matthieu Cord', 'Patrick Pérez', 'Tuan-Hung Vu', 'Himalaya Jain'] | 2021-12-06 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 5.35594583e-01 3.50525022e-01 2.05029234e-01 8.80882051e-03
-6.14212275e-01 -5.87326765e-01 9.26789522e-01 -1.56893060e-01
-3.31341028e-01 1.18226540e+00 -3.43688689e-02 -1.53192237e-01
1.24482132e-01 -1.14122999e+00 -1.12261057e+00 -7.52138674e-01
3.35779160e-01 6.10008657e-01 2.64827996e-01 -2.63094425... | [11.679048538208008, -0.403053879737854] |
33223977-a844-4a58-8942-44825b23f4c8 | dfdl-discriminative-feature-oriented | 1502.01032 | null | http://arxiv.org/abs/1502.01032v1 | http://arxiv.org/pdf/1502.01032v1.pdf | DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification | In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-... | ['Hojjat S. Mousavi', 'Vishal Monga', 'UK Arvind Rao', 'Ganesh Rao', 'Tiep H. Vu'] | 2015-02-03 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 1.75713316e-01 -3.20465773e-01 -1.80503696e-01 -2.01953158e-01
-9.18628633e-01 -4.06580895e-01 4.62368309e-01 7.61272490e-01
-4.03167844e-01 5.93104482e-01 9.27989110e-02 -2.31252149e-01
-5.08362651e-01 -6.19135499e-01 -9.75039080e-02 -1.34547007e+00
-4.56252009e-01 6.60040200e-01 2.91767806e-01 -2.57311221... | [15.057138442993164, -2.881216049194336] |
9ed25fbe-ac36-46d8-a2fa-54012b63c0f2 | load-local-orientation-adaptive-descriptor | 1504.05809 | null | http://arxiv.org/abs/1504.05809v1 | http://arxiv.org/pdf/1504.05809v1.pdf | LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification | In this paper, we propose a novel local feature, called Local Orientation
Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD,
we proposed to define point description on an Adaptive Coordinate System (ACS),
adopt a binary sequence descriptor to capture relationships between one point
and its ne... | ['Xianbiao Qi', 'Qingquan Li', 'Linlin Shen', 'Matti Pietikainen', 'Guoying Zhao'] | 2015-04-22 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.59546936e-01 -6.66133344e-01 -2.07578093e-01 -2.98594624e-01
-6.15737319e-01 -3.13404381e-01 6.61381662e-01 5.27741946e-02
-4.87058252e-01 4.32239711e-01 -9.89510417e-02 2.62462407e-01
-4.21977222e-01 -9.04350102e-01 -5.47198176e-01 -1.06938100e+00
-6.36531487e-02 -1.01524033e-01 4.02896672e-01 -3.69139344... | [10.382233619689941, -0.3696097433567047] |
a8cb55a1-5473-4992-8acd-cbe5e8a7e025 | faster-tad-towards-temporal-action-detection | 2204.02674 | null | https://arxiv.org/abs/2204.02674v1 | https://arxiv.org/pdf/2204.02674v1.pdf | Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network | Temporal action detection (TAD) aims to detect the semantic labels and boundaries of action instances in untrimmed videos. Current mainstream approaches are multi-step solutions, which fall short in efficiency and flexibility. In this paper, we propose a unified network for TAD, termed Faster-TAD, by re-purposing a Fas... | ['Yandong Guo', 'Xunqiang Tao', 'Wei Li', 'Chen Chen', 'Shimin Chen'] | 2022-04-06 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 2.63949394e-01 -9.81742069e-02 -4.92651194e-01 -1.31268755e-01
-5.64289808e-01 -3.65916997e-01 5.51163197e-01 -4.46267188e-01
-5.39411187e-01 4.89811063e-01 5.25860965e-01 5.93279526e-02
1.31336600e-01 -5.13836503e-01 -4.63609248e-01 -5.79016745e-01
-1.02015778e-01 -2.45520864e-02 1.05875027e+00 -2.21543938... | [8.383606910705566, 0.5133983492851257] |
ddb63b45-c1af-4540-98a3-32b958ba668d | sources-of-uncertainty-in-machine-learning-a | 2305.16703 | null | https://arxiv.org/abs/2305.16703v1 | https://arxiv.org/pdf/2305.16703v1.pdf | Sources of Uncertainty in Machine Learning -- A Statisticians' View | Machine Learning and Deep Learning have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction, which is the primary strength of most supervised machine learning algorithms, the quantification... | ['Göran Kauermann', 'Frauke Kreuter', 'Malte Schierholz', 'Patrick Oliver Schenk', 'Cornelia Gruber'] | 2023-05-26 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.03512073e-01 4.18959051e-01 -1.45653710e-01 -6.09567702e-01
-7.67640889e-01 -5.35734236e-01 1.02618873e+00 5.03686368e-01
-5.08146286e-01 9.87626672e-01 2.30995014e-01 -4.74385560e-01
-7.86131322e-01 -9.37094808e-01 -6.07983589e-01 -7.87678540e-01
-4.95787710e-02 4.96260673e-01 -1.66535154e-01 1.61689714... | [7.588131904602051, 4.09501838684082] |
862ceeba-d9fd-4474-90e3-2bac1c4c8bd2 | learning-summary-worthy-visual-representation | 2305.04824 | null | https://arxiv.org/abs/2305.04824v1 | https://arxiv.org/pdf/2305.04824v1.pdf | Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video | Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of the large-scale generative pre-trained language model (GPLM) in gener... | ['Qun Liu', 'Xin Jiang', 'Zexuan Qiu', 'Qinliang Su', 'Yasheng Wang', 'Xiaojun Meng', 'Zenan Xu'] | 2023-05-08 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.06497717e-01 -2.70736264e-03 -2.72978306e-01 -2.11447701e-01
-1.39641154e+00 -6.05136096e-01 7.07105994e-01 1.09627936e-02
-1.64086193e-01 7.40912259e-01 8.80246580e-01 9.15913209e-02
4.85878557e-01 -3.48869890e-01 -9.50329840e-01 -6.71594381e-01
2.35548675e-01 -8.85755867e-02 -6.24409690e-02 -5.19902147... | [10.666755676269531, 0.6948280930519104] |
420ba012-c8f8-4dc1-a2ac-ccbc73d1e686 | a-deep-dive-into-dataset-imbalance-and-bias | 2203.08235 | null | https://arxiv.org/abs/2203.08235v1 | https://arxiv.org/pdf/2203.08235v1.pdf | A Deep Dive into Dataset Imbalance and Bias in Face Identification | As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals often center imbalance as the main source of bias, i.e., that FR models perform worse on images of non-white people or women because these d... | ['Tom Goldstein', 'Micah Goldblum', 'Hossein Souri', 'Samuel Dooley', 'Steven Reich', 'Valeriia Cherepanova'] | 2022-03-15 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 0.29770455 -0.13551442 -0.30469325 -0.5444574 -0.30794957 -0.57206786
0.31736684 0.09171268 -0.36740765 0.53002465 0.33392057 -0.44135243
-0.02198026 -0.62931406 -0.44256976 -0.63981014 0.30663994 0.27402744
-0.53328943 -0.03414468 0.40655977 0.589834 -1.6167222 0.07096032
0.5521152 0.7903514 -0.... | [12.984983444213867, 1.2172410488128662] |
a95f2cca-a31a-47f8-b057-48a8a009b0b9 | enhancing-visual-domain-adaptation-with | 2306.10142 | null | https://arxiv.org/abs/2306.10142v1 | https://arxiv.org/pdf/2306.10142v1.pdf | Enhancing Visual Domain Adaptation with Source Preparation | Robotic Perception in diverse domains such as low-light scenarios, where new modalities like thermal imaging and specialized night-vision sensors are increasingly employed, remains a challenge. Largely, this is due to the limited availability of labeled data. Existing Domain Adaptation (DA) techniques, while promising ... | ['Jeff Schneider', 'Christoph Mertz', 'Anurag Ghosh', 'Anirudha Ramesh'] | 2023-06-16 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 7.02680647e-01 -2.62471437e-01 -2.00573444e-01 -5.71600854e-01
-9.94585037e-01 -9.96069312e-01 5.85524559e-01 -3.71461987e-01
-6.16745830e-01 5.89038372e-01 -2.98021510e-02 1.31443009e-01
2.87313983e-02 -3.31007987e-01 -7.75189877e-01 -1.00036287e+00
5.19479275e-01 5.31956792e-01 5.44028044e-01 -1.83212042... | [8.701216697692871, -1.993804931640625] |
8646372d-b2cc-4bd1-a3d9-f1e34f314921 | superpixel-perception-graph-neural-network | 2210.07539 | null | https://arxiv.org/abs/2210.07539v1 | https://arxiv.org/pdf/2210.07539v1.pdf | Superpixel Perception Graph Neural Network for Intelligent Defect Detection | Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. How... | ['Ruqiang Yan', 'Xuefeng Chen', 'Chuang Sun', 'Qixiu Yang', 'Hongbing Shang'] | 2022-10-14 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.85483059e-02 -7.73321614e-02 2.89936215e-01 1.38711348e-01
1.21028975e-01 -2.93816060e-01 6.00661784e-02 -2.47855857e-01
1.30373031e-01 2.36100271e-01 -3.32036525e-01 -3.60957384e-01
-6.31094053e-02 -1.14985943e+00 -4.62490678e-01 -8.67211044e-01
-1.62761565e-02 -9.13203508e-02 4.43145663e-01 -3.11961979... | [9.603386878967285, -0.8162592053413391] |
ebd9836d-fd97-4ed7-a42c-d173b31742fa | understanding-user-behavior-in-carousel | 2307.01866 | null | https://arxiv.org/abs/2307.01866v1 | https://arxiv.org/pdf/2307.01866v1.pdf | Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank | Carousels (also-known as multilists) have become the standard user interface for e-commerce platforms replacing the ranked list, the previous standard for recommender systems. While the research community has begun to focus on carousels, there are many unanswered questions and undeveloped areas when compared to the lit... | ['Santiago de Leon-Martinez'] | 2023-07-04 | null | null | null | null | ['learning-to-rank', 'learning-to-rank', 'movie-recommendation', 'information-retrieval'] | ['graphs', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-4.77145314e-02 -1.18615001e-01 -6.10293508e-01 -4.48285282e-01
-7.41634220e-02 -8.58483374e-01 4.46220040e-01 1.33454455e-02
-4.54999596e-01 1.08442418e-01 2.50684589e-01 -9.11332846e-01
-7.79919386e-01 -4.39598411e-01 -2.57525206e-01 -3.15233655e-02
-4.95421030e-02 2.02991113e-01 4.80032533e-01 -4.24286276... | [10.044998168945312, 5.806351661682129] |
d7adf715-2d1f-4aaf-a761-d805e71cf728 | keyword-aware-influential-community-search-in | 1912.02114 | null | https://arxiv.org/abs/1912.02114v1 | https://arxiv.org/pdf/1912.02114v1.pdf | Keyword Aware Influential Community Search in Large Attributed Graphs | We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching... | ['Farhana M. Choudhury', 'Yong-Bin Kang', 'Md. Saiful Islam', 'Mohammed Eunus Ali', 'Timos Sellis'] | 2019-12-04 | null | null | null | null | ['community-search'] | ['graphs'] | [-7.43508488e-02 9.61382464e-02 -2.38834172e-01 5.06709963e-02
-2.23205194e-01 -7.21978188e-01 7.64720559e-01 7.69468188e-01
-4.37715679e-01 2.87992835e-01 4.33965355e-01 -6.56128749e-02
-7.23981917e-01 -1.25406039e+00 -2.90433258e-01 -4.76370186e-01
-5.46753287e-01 6.95768833e-01 7.30264783e-01 -2.55844355... | [7.304025173187256, 5.878608226776123] |
c3ee841f-94a5-40b7-b2d4-af4effc4b538 | towards-dense-volumetric-pancreas | 1711.06439 | null | http://arxiv.org/abs/1711.06439v2 | http://arxiv.org/pdf/1711.06439v2.pdf | Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks | Pancreas segmentation in computed tomography imaging has been historically
difficult for automated methods because of the large shape and size variations
between patients. In this work, we describe a custom-build 3D fully
convolutional network (FCN) that can process a 3D image including the whole
pancreas and produce a... | ['Kensaku MORI', 'Kazunari Misawa', 'Hirohisa ODA', 'Yuichiro Hayashi', 'Takayuki Kitasaka', 'Michitaka Fujiwara', 'Holger Roth', 'Masahiro Oda', 'Natsuki Shimizu'] | 2017-11-17 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 5.96094877e-02 2.15445504e-01 6.85702786e-02 -4.29654151e-01
-5.66547751e-01 -6.69684470e-01 1.89050540e-01 2.53853053e-01
-4.95060146e-01 3.58247042e-01 2.28324607e-01 -6.84286118e-01
1.34864952e-02 -3.92881066e-01 -6.95517957e-01 -6.23936594e-01
-6.90145433e-01 9.04730439e-01 9.61918384e-02 2.97175109... | [14.494793891906738, -2.610658645629883] |
961b169e-01e2-42df-aed4-c3ba71739846 | inversion-of-1d-frequency-and-time-domain | 1912.00612 | null | https://arxiv.org/abs/1912.00612v1 | https://arxiv.org/pdf/1912.00612v1.pdf | Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks | Inversion of electromagnetic data finds applications in many areas of geophysics. The inverse problem is commonly solved with either deterministic optimization methods (such as the nonlinear conjugate gradient or Gauss-Newton) which are prone to getting trapped in a local minimum, or probabilistic methods which are ver... | ['Andrei Swidinsky', 'Vladimir Puzyrev'] | 2019-12-02 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 8.72159600e-02 -2.11218253e-01 4.87744242e-01 -4.98720378e-01
-7.97832549e-01 -4.84897941e-01 6.01703107e-01 1.05257519e-01
-6.85223579e-01 1.12369812e+00 -3.06298733e-01 -7.77985632e-01
-5.47234893e-01 -1.07248640e+00 -7.71982193e-01 -9.62566912e-01
-2.99166620e-01 7.24525273e-01 -5.83264083e-02 -5.78354657... | [6.709049701690674, 2.8370156288146973] |
686205ba-3d0b-4166-a65a-7673213a0f1e | pc2-projection-conditioned-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.pdf | PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction | Reconstructing the 3D shape of an object from a single RGB image is a long-standing problem in computer vision. In this paper, we propose a novel method for single-image 3D reconstruction which generates a sparse point cloud via a conditional denoising diffusion process. Our method takes as input a single RGB image... | ['Andrea Vedaldi', 'Christian Rupprecht', 'Luke Melas-Kyriazi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 5.05649745e-01 -1.30674437e-01 4.52406794e-01 -1.48859158e-01
-9.86514926e-01 -7.29742467e-01 7.32040763e-01 -1.04122661e-01
-1.97279438e-01 2.59316921e-01 -6.12502545e-02 1.06141761e-01
5.21981232e-02 -1.09553075e+00 -1.13875961e+00 -8.98568809e-01
6.69801474e-01 1.14991236e+00 1.70447335e-01 1.82001591... | [8.7964448928833, -3.3324708938598633] |
f1c15332-1217-4b16-822b-cb987fa114cb | ji-yu-duo-tou-zhu-yi-li-he-bilstmgai-jin | null | null | https://aclanthology.org/2020.ccl-1.30 | https://aclanthology.org/2020.ccl-1.30.pdf | 基于多头注意力和BiLSTM改进DAM模型的中文问答匹配方法(Chinese question answering method based on multi-head attention and BiLSTM improved DAM model) | 针对目前检索式多轮对话深度注意力机制模型DAM(Deep Attention Matching Network)候选回复细节不匹配和语义混淆的问题,本文提出基于多头注意力和双向长短时记忆网络(BiLSTM)改进DAM模型的中文问答匹配方法,该方法采用多头注意力机制,使模型有能力建模较长的多轮对话,更好的处理目标回复与上下文的匹配关系。此外,本文在特征融合过程中采用BiLSTM模型,通过捕获多轮对话中的序列依赖关系,进一步提升选择目标候选回复的准确率。本文在豆瓣和电商两个开放数据集上进行实验,实验性能均优于DAM基线模型,R10@1指标在含有词向量增强的情况下提升了1.5%。 | ['Xia Zhao', 'Weijie Jiang', 'Chongchong Yu', 'Hanzhong Qin'] | null | null | null | null | ccl-2020-10 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-0.81465846 -0.53620666 0.57658917 0.63949645 -0.03455365 -0.08120593
-0.06266747 1.298765 -0.3264723 0.166293 0.8044529 0.11080449
0.04242913 -1.2629861 -0.3989925 -1.2384853 -0.7696726 1.6158489
1.2553394 -0.91242003 0.29202372 0.9180892 -0.8747693 0.36349005
0.8449636 0.7818563 0.8... | [-3.3160908222198486, 6.907607555389404] |
3e694808-1caf-4722-ba68-c9012edbad2c | improving-interpretability-via-regularization | 2211.08686 | null | https://arxiv.org/abs/2211.08686v1 | https://arxiv.org/pdf/2211.08686v1.pdf | Improving Interpretability via Regularization of Neural Activation Sensitivity | State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about the security and general... | ['Asaf Shabtai', 'Ron Bitton', 'Gil Fidel', 'Ofir Moshe'] | 2022-11-16 | null | null | null | null | ['explanation-fidelity-evaluation', 'interpretability-techniques-for-deep-learning'] | ['methodology', 'miscellaneous'] | [ 7.05161318e-02 3.87040645e-01 4.53269869e-01 -3.75833392e-01
-2.22631305e-01 -8.91219914e-01 7.11884260e-01 -2.08330646e-01
-6.34029329e-01 6.48503363e-01 8.30142796e-02 -6.80915833e-01
-7.90506676e-02 -6.20366156e-01 -9.53537166e-01 -5.20610213e-01
-1.09770276e-01 1.26297280e-01 1.49183556e-01 -6.03806734... | [5.678408622741699, 7.841391086578369] |
b5066723-fbbd-4cb2-b05b-06bdb3a819a2 | sub-pixel-face-landmarks-using-heatmaps-and-a | 2103.03059 | null | https://arxiv.org/abs/2103.03059v2 | https://arxiv.org/pdf/2103.03059v2.pdf | Sub-pixel face landmarks using heatmaps and a bag of tricks | Accurate face landmark localization is an essential part of face recognition, reconstruction and morphing. To accurately localize face landmarks, we present our heatmap regression approach. Each model consists of a MobileNetV2 backbone followed by several upscaling layers, with different tricks to optimize both perform... | ['Siwa Boonpunmongkol', 'Pavit Noinongyao', 'Sanjana Jain', 'Aubin Samacoits', 'Samuel W. F. Earp'] | 2021-03-04 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-2.67340094e-01 2.04422791e-02 -4.11386713e-02 -7.15173304e-01
-1.04410684e+00 -6.25511289e-01 6.42669678e-01 -2.72775412e-01
-5.56761444e-01 2.92355478e-01 2.76216511e-02 2.84405444e-02
3.16709548e-01 -3.45944136e-01 -1.07616591e+00 -5.05403399e-01
-3.74643505e-01 7.02858090e-01 4.63965870e-02 8.89592469... | [13.493762969970703, 0.33908575773239136] |
9627b965-49d6-4345-8cd4-b1be94166e55 | deep-semantics-aware-photo-adjustment | 1706.08260 | null | http://arxiv.org/abs/1706.08260v1 | http://arxiv.org/pdf/1706.08260v1.pdf | Deep Semantics-Aware Photo Adjustment | Automatic photo adjustment is to mimic the photo retouching style of
professional photographers and automatically adjust photos to the learned
style. There have been many attempts to model the tone and the color adjustment
globally with low-level color statistics. Also, spatially varying photo
adjustment methods have b... | ['Seonghyeon Nam', 'Seon Joo Kim'] | 2017-06-26 | null | null | null | null | ['photo-retouching'] | ['computer-vision'] | [ 2.83648700e-01 -2.00563222e-01 -4.97634038e-02 -8.35198045e-01
-4.06123430e-01 -4.28220361e-01 4.40784216e-01 1.08446933e-01
-4.62966919e-01 1.69191927e-01 2.80062079e-01 2.70548254e-01
-3.90410013e-02 -7.92158604e-01 -7.10263252e-01 -5.61223090e-01
6.37862384e-01 -9.82572064e-02 3.47484201e-01 -4.44742978... | [11.366817474365234, -0.9952687621116638] |
305968ef-a7c3-45b7-b194-91a2267641db | latentkeypointgan-controlling-gans-via-latent | 2103.15812 | null | https://arxiv.org/abs/2103.15812v4 | https://arxiv.org/pdf/2103.15812v4.pdf | LatentKeypointGAN: Controlling GANs via Latent Keypoints | Generative adversarial networks (GANs) have attained photo-realistic quality in image generation. However, how to best control the image content remains an open challenge. We introduce LatentKeypointGAN, a two-stage GAN which is trained end-to-end on the classical GAN objective with internal conditioning on a set of sp... | ['Helge Rhodin', 'Bastian Wandt', 'Xingzhe He'] | 2021-03-29 | latentkeypointgan-controlling-gans-via-latent-1 | https://openreview.net/forum?id=y_tIL5vki1l | https://openreview.net/pdf?id=y_tIL5vki1l | null | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 4.69359875e-01 3.54855478e-01 1.31624117e-02 -7.98916742e-02
-7.36323118e-01 -9.45706844e-01 9.56222594e-01 -4.93284851e-01
-2.21640263e-02 5.44860363e-01 1.12909645e-01 1.09797217e-01
4.85846773e-02 -8.55522513e-01 -9.54476953e-01 -9.84938085e-01
4.16543245e-01 3.97458404e-01 -3.26637119e-01 -1.15001708... | [11.724371910095215, -0.48492559790611267] |
88a1c00b-5a1a-4845-a398-b325e40c2fa2 | deepfake-network-architecture-attribution | 2202.13843 | null | https://arxiv.org/abs/2202.13843v2 | https://arxiv.org/pdf/2202.13843v2.pdf | Deepfake Network Architecture Attribution | With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted t... | ['Xirong Li', 'Lei LI', 'Juan Cao', 'Ziyao Huang', 'Tianyun Yang'] | 2022-02-28 | null | null | null | null | ['fake-image-attribution'] | ['computer-vision'] | [ 4.78482664e-01 1.61816761e-01 -6.02790639e-02 -4.17049855e-01
-6.77661836e-01 -8.35907638e-01 6.98623776e-01 -5.23068130e-01
6.93793371e-02 7.97997653e-01 -2.36666456e-01 -3.65214646e-01
3.64603698e-01 -7.25652635e-01 -1.06126308e+00 -6.60302639e-01
3.12764943e-01 4.83424157e-01 5.02768643e-02 -2.09674329... | [12.520745277404785, 1.0452675819396973] |
2684c6b7-4968-4bda-8d4d-062e5cfb802c | omdet-language-aware-object-detection-with | 2209.05946 | null | https://arxiv.org/abs/2209.05946v1 | https://arxiv.org/pdf/2209.05946v1.pdf | OmDet: Language-Aware Object Detection with Large-scale Vision-Language Multi-dataset Pre-training | Advancing object detection to open-vocabulary and few-shot transfer has long been a challenge for computer vision research. This work explores a continual learning approach that enables a detector to expand its zero/few-shot capabilities via multi-dataset vision-language pre-training. Using natural language as knowledg... | ['Kyusong Lee', 'Xiaopeng Lu', 'Peng Liu', 'Tiancheng Zhao'] | 2022-09-10 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 8.61147940e-02 -1.05756432e-01 -1.83674812e-01 -1.68295071e-01
-1.05648375e+00 -4.15734798e-01 7.57321417e-01 -1.72387674e-01
-9.49168503e-01 4.19665962e-01 -7.58544952e-02 -9.78412107e-02
2.95380265e-01 -4.15807545e-01 -7.08311558e-01 -4.61140484e-01
3.77350986e-01 4.92286593e-01 8.20143461e-01 -2.93580055... | [9.596418380737305, 1.492775321006775] |
ba42f51a-2de1-44b0-a208-29d29e976a4d | a-survey-on-causal-discovery-methods-for | 2303.15027 | null | https://arxiv.org/abs/2303.15027v2 | https://arxiv.org/pdf/2303.15027v2.pdf | A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data | Causal Discovery (CD) is the process of identifying the cause-effect relationships among the variables of a system from data. Over the years, several methods have been developed primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study, we present an extensive discu... | ['Md Osman Gani', 'Emam Hossain', 'Uzma Hasan'] | 2023-03-27 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.26714069e-01 -1.09718762e-01 -4.11913067e-01 -3.34495604e-01
-3.67819279e-01 -5.58303475e-01 8.69299531e-01 3.07168990e-01
4.71869648e-01 1.05820572e+00 4.85175729e-01 -6.34731710e-01
-1.06320369e+00 -1.02500105e+00 -5.24354517e-01 -6.28625512e-01
-1.13565922e+00 3.84588748e-01 1.20154366e-01 2.45328844... | [7.835007667541504, 5.359640121459961] |
b7eb60d1-73fb-4995-9260-eeeb989762c1 | adaptive-knowledge-sharing-in-multi-task | null | null | https://aclanthology.org/P18-2104 | https://aclanthology.org/P18-2104.pdf | Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation | Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction,... | ['Poorya Zaremoodi', 'Gholamreza Haffari', 'Wray Buntine'] | 2018-07-01 | null | null | null | acl-2018-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.43385667e-01 -1.49698019e-01 -3.67037654e-01 -4.28758085e-01
-1.42345977e+00 -8.91872466e-01 7.90892601e-01 -2.85107642e-01
-6.46617115e-01 1.03246808e+00 6.03570700e-01 -8.39666069e-01
3.11699450e-01 -2.20610470e-01 -9.61669624e-01 -4.47614551e-01
3.33509833e-01 8.35646808e-01 -1.07792318e-01 -4.06641096... | [11.604574203491211, 10.188304901123047] |
35ed3768-7c3b-42b6-8bc9-f92b430650ca | learning-to-measure-the-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.pdf | Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space | For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects a... | ['Yong liu', 'Chengjie Wang', 'Mingang Chen', 'Zhenyu Zhang', 'Ying Tai', 'Jiangning Zhang', 'Zhonggan Ding', 'Tianxin Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 3.71617936e-02 -6.02239966e-02 1.93011731e-01 -4.21879351e-01
-7.36421347e-01 -3.54094863e-01 3.84532958e-01 -8.66338387e-02
-2.13088036e-01 4.01345313e-01 -2.48491302e-01 8.12393799e-02
-2.27338523e-01 -1.21403074e+00 -1.07414186e+00 -6.18026137e-01
1.69313908e-01 5.57156622e-01 3.52274060e-01 -3.09400201... | [8.379057884216309, -3.4644813537597656] |
5d6f450a-3f0b-4148-937e-964d4a1311d7 | multi-target-regression-via-output-space | 2003.09896 | null | https://arxiv.org/abs/2003.09896v1 | https://arxiv.org/pdf/2003.09896v1.pdf | Multi-target regression via output space quantization | Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors. Two key challenges in multi-target regression are: (a) modelling target dependencies and (b) scalability to large output spaces. In this paper, a new multi-target regression method is propo... | ['Ioannis Vlahavas', 'Konstantinos Sechidis', 'Eleftherios Spyromitros-Xioufis'] | 2020-03-22 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.98896807e-01 -1.71526670e-01 -4.50012565e-01 -4.92259324e-01
-1.39773023e+00 -2.99529463e-01 6.46045446e-01 2.84586579e-01
-1.21160783e-01 7.19763696e-01 -1.76423401e-01 -7.68340379e-02
-4.00938839e-01 -7.94403613e-01 -5.21916509e-01 -9.08499658e-01
1.70374848e-02 7.35286534e-01 6.87370673e-02 -2.10111976... | [9.08290958404541, 4.229432582855225] |
6ca444ab-a1bf-428f-ba84-ee9a346eae3c | improving-word-mover-s-distance-by-leveraging | 2211.06229 | null | https://arxiv.org/abs/2211.06229v1 | https://arxiv.org/pdf/2211.06229v1.pdf | Improving word mover's distance by leveraging self-attention matrix | Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it difficult to distinguish sentences with large overlaps of similar... | ['Hidetoshi Shimodaira', 'Sho Yokoi', 'Hiroaki Yamagiwa'] | 2022-11-11 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [-8.05305615e-02 -3.07793587e-01 -1.51411101e-01 -3.56937975e-01
-6.53401673e-01 -4.51834142e-01 3.66032481e-01 6.16546988e-01
-6.55991614e-01 1.76974013e-01 6.57143176e-01 -3.08580548e-01
-3.69741052e-01 -7.15599597e-01 -3.64417881e-02 -5.64868748e-01
3.52633506e-01 7.67403319e-02 2.24772573e-01 -2.83892632... | [10.770050048828125, 8.722293853759766] |
39c0bf9e-2eb7-4d16-9694-4b1fd0809c12 | kgpool-dynamic-knowledge-graph-context | 2106.00459 | null | https://arxiv.org/abs/2106.00459v2 | https://arxiv.org/pdf/2106.00459v2.pdf | KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction | We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in this presumed sentential RE setting, the context of a single sentence is often sparse. This paper introduces the KGPool method to addres... | ['Vijay Saraswat', 'Saeedeh Shekarpour', 'Johannes Hoffart', "Isaiah Onando Mulang'", 'Kuldeep Singh', 'Anson Bastos', 'Abhishek Nadgeri'] | 2021-06-01 | null | https://aclanthology.org/2021.findings-acl.48 | https://aclanthology.org/2021.findings-acl.48.pdf | findings-acl-2021-8 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 1.77121371e-01 8.67431700e-01 -4.77859735e-01 -4.22272444e-01
-4.25775349e-01 -4.05313075e-01 6.13765240e-01 9.18271482e-01
-6.44090772e-01 1.20596492e+00 4.66424108e-01 -3.19070071e-01
-1.35726333e-01 -1.24942684e+00 -1.07203126e+00 -1.81591120e-02
-3.61560404e-01 5.84217250e-01 3.17703158e-01 -1.66325539... | [9.316832542419434, 8.414727210998535] |
fb96dc9b-c3be-4718-9fd5-56621f37a784 | towards-end-to-end-prosody-transfer-for | 1803.09047 | null | http://arxiv.org/abs/1803.09047v1 | http://arxiv.org/pdf/1803.09047v1.pdf | Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron | We present an extension to the Tacotron speech synthesis architecture that
learns a latent embedding space of prosody, derived from a reference acoustic
representation containing the desired prosody. We show that conditioning
Tacotron on this learned embedding space results in synthesized audio that
matches the prosody... | ['Ron J. Weiss', 'RJ Skerry-Ryan', 'Ying Xiao', 'Daisy Stanton', 'Yuxuan Wang', 'Rif A. Saurous', 'Eric Battenberg', 'Rob Clark', 'Joel Shor'] | 2018-03-24 | towards-end-to-end-prosody-transfer-for-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2281 | http://proceedings.mlr.press/v80/skerry-ryan18a/skerry-ryan18a.pdf | icml-2018-7 | ['expressive-speech-synthesis'] | ['speech'] | [ 1.24578506e-01 2.25087881e-01 -2.46865168e-01 -1.83730751e-01
-1.18494463e+00 -7.36543298e-01 4.74581689e-01 -4.89310831e-01
1.53507501e-01 5.23573875e-01 1.13131523e+00 4.22189683e-02
1.82736024e-01 -4.63260323e-01 -6.03574455e-01 -7.36233711e-01
1.08888231e-01 2.95537889e-01 -1.83885068e-01 -3.16061020... | [15.011167526245117, 6.555191516876221] |
0d1550b4-89c4-40b1-9e91-212e2fd0242d | specializing-multilingual-language-models-an | 2106.09063 | null | https://arxiv.org/abs/2106.09063v4 | https://arxiv.org/pdf/2106.09063v4.pdf | Specializing Multilingual Language Models: An Empirical Study | Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary augmentation and script transliteration. Our evaluations on par... | ['Noah A. Smith', 'Ethan C. Chau'] | 2021-06-16 | null | https://aclanthology.org/2021.mrl-1.5 | https://aclanthology.org/2021.mrl-1.5.pdf | emnlp-mrl-2021-11 | ['transliteration', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-2.25396112e-01 -9.64756534e-02 -6.45547032e-01 -4.87667352e-01
-9.40041363e-01 -1.15318215e+00 5.07628500e-01 7.53718102e-03
-8.25778544e-01 1.04883623e+00 4.76344913e-01 -8.21033418e-01
4.33461219e-01 -3.47104907e-01 -6.21004283e-01 -3.67693906e-03
6.79419041e-02 6.65103734e-01 1.51104003e-01 -2.61838019... | [10.458941459655762, 9.943202018737793] |
f29fcb12-cc22-48c7-b0ec-d8ff0ebf8b5c | mmea-entity-alignment-for-multi-modal | null | null | https://link.springer.com/chapter/10.1007/978-3-030-55130-8_12 | http://home.ustc.edu.cn/~liyichen/assets/files/LiyiChen_KSEM20.pdf | MMEA: Entity Alignment for Multi-Modal Knowledge Graphs | Entity alignment plays an essential role in the knowledge graph (KG) integration. Though large efforts have been made on exploring the association of relational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multimodal knowledge in the real application scenario. T... | ['Enhong Chen', 'Zhefeng Wang', 'Tong Xu', 'Yijun Wang', 'Zhi Li', 'Liyi Chen'] | 2020-08-20 | null | null | null | null | ['multi-modal-entity-alignment'] | ['knowledge-base'] | [-3.38131756e-01 4.15427610e-02 -3.41483831e-01 -1.21537752e-01
-6.88671291e-01 -5.38126767e-01 5.56838095e-01 4.35973227e-01
-6.57595620e-02 3.03635597e-01 4.67706710e-01 -7.34659582e-02
-3.78332973e-01 -1.01382923e+00 -4.96138543e-01 -5.71990252e-01
2.17366174e-01 1.37071371e-01 -2.77176183e-02 -3.20030123... | [8.674450874328613, 7.728212833404541] |
a0dc17cf-01d0-4974-9b53-8ccb1e691861 | on-projection-methods-for-functional-time | 2105.04399 | null | https://arxiv.org/abs/2105.04399v1 | https://arxiv.org/pdf/2105.04399v1.pdf | On projection methods for functional time series forecasting | Two nonparametric methods are presented for forecasting functional time series (FTS). The FTS we observe is a curve at a discrete-time point. We address both one-step-ahead forecasting and dynamic updating. Dynamic updating is a forward prediction of the unobserved segment of the most recent curve. Among the two propos... | ['Hanlin Shang', 'Raúl Jiménez', 'Antonio Elías'] | 2021-05-10 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 1.49121985e-01 -1.62883028e-01 -1.71298981e-02 -3.86288673e-01
-3.94614607e-01 -5.49908638e-01 6.28492057e-01 1.00590251e-01
-4.71415780e-02 9.94338691e-01 1.30670354e-01 -3.68946642e-01
-5.41636407e-01 -9.41785812e-01 -7.51999617e-01 -1.10700715e+00
-3.69923115e-01 3.58655274e-01 9.60531458e-02 -3.05689186... | [6.852479934692383, 3.2490885257720947] |
9234517b-bf4a-46cd-8513-edfefc2feb55 | the-many-faces-of-1-lipschitz-neural-networks | 2104.05097 | null | https://arxiv.org/abs/2104.05097v6 | https://arxiv.org/pdf/2104.05097v6.pdf | Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks | Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of certifiably robust classifiers. However they remain commonly considered as less accurate, and their properties in learning are still not fully u... | ['Alberto González-Sanz', 'Corentin Friedrich', 'Mathieu Serrurier', 'Thibaut Boissin', 'Franck Mamalet', 'Louis Béthune'] | 2021-04-11 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 7.70585537e-02 1.38199791e-01 -4.50092226e-01 -6.76023483e-01
-7.36938477e-01 -7.13703334e-01 3.59394848e-01 1.33889422e-01
-7.35213935e-01 9.52081323e-01 -2.16880292e-01 -1.18497908e-01
-5.65019965e-01 -6.19974732e-01 -9.29455698e-01 -1.03267324e+00
-2.20896795e-01 2.49677479e-01 7.28262216e-02 -6.46862984... | [7.878880500793457, 3.8396413326263428] |
c4ac8ee3-eeee-4ed0-bca1-0e70dc9038fe | cliff-carrying-location-information-in-full | 2208.00571 | null | https://arxiv.org/abs/2208.00571v2 | https://arxiv.org/pdf/2208.00571v2.pdf | CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation | Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem. However, cropping, their first step, discards the location information from the very beginning, which makes themselves unable to accurately predi... | ['Youliang Yan', 'Songcen Xu', 'Zhensong Zhang', 'Jianzhuang Liu', 'Zhihao LI'] | 2022-08-01 | null | null | null | null | ['3d-human-pose-and-shape-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.46507198e-01 -3.52490656e-02 -1.55847654e-01 -2.29330122e-01
-7.32218802e-01 -5.28808177e-01 3.27825725e-01 -3.22726816e-01
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3.49144220e-01 7.37637877e-01 3.25886421e-02 -2.96806782... | [7.06966495513916, -0.889286458492279] |
80ca3600-f4a9-46e2-9b8a-1a3672691259 | classi-fly-inferring-aircraft-categories-from | 1908.01061 | null | https://arxiv.org/abs/1908.01061v2 | https://arxiv.org/pdf/1908.01061v2.pdf | Classi-Fly: Inferring Aircraft Categories from Open Data using Machine Learning | In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of information about military and government movements. Ty... | ['Ivan Martinovic', 'Vincent Lenders', 'Matthew Smith', 'Martin Strohmeier'] | 2019-07-30 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-1.77699938e-01 -2.97163457e-01 -5.66946208e-01 -2.77008504e-01
-5.49366653e-01 -1.00029194e+00 6.51998162e-01 5.96668720e-01
-4.56976593e-01 7.53373921e-01 3.32476497e-01 -5.20841062e-01
-3.46343786e-01 -1.10490906e+00 -5.18282473e-01 -2.41809472e-01
-3.60544980e-01 5.98265886e-01 3.19172412e-01 -6.50777593... | [7.409072399139404, 3.1328537464141846] |
50a76c64-8091-4872-838c-3fc0019b2c51 | music-composition-with-deep-learning-a-review | 2108.12290 | null | https://arxiv.org/abs/2108.12290v2 | https://arxiv.org/pdf/2108.12290v2.pdf | Music Composition with Deep Learning: A Review | Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical language. Music generation have been faced with Algorithmic methods and recently, with Deep Learning models that are being used in other fi... | ['Jose R. Beltran', 'Carlos Hernandez-Olivan'] | 2021-08-27 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.90684408e-01 1.50727943e-01 3.13873082e-01 2.91556239e-01
1.65504396e-01 -8.02003205e-01 9.30964351e-01 -3.39329481e-01
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-6.12067044e-01 -9.68731105e-01 -3.47628295e-01 -5.88671327e-01
8.50318223e-02 8.40636075e-01 -2.92203903e-01 -7.72048473... | [16.08775520324707, 5.516983985900879] |
67fb8b98-4a67-472e-8a9b-b1f0722c3001 | lightweight-multi-drone-detection-and-3d | 2202.09097 | null | https://arxiv.org/abs/2202.09097v1 | https://arxiv.org/pdf/2202.09097v1.pdf | Lightweight Multi-Drone Detection and 3D-Localization via YOLO | In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithm... | ['Mangal Kothari', 'Nitik Jain', 'Aryan Sharma'] | 2022-02-18 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-8.83405954e-02 -4.00702417e-01 7.09321856e-01 -1.72675192e-01
-3.88048559e-01 -1.03364503e+00 3.59054476e-01 3.51012908e-02
-7.44156599e-01 2.83981532e-01 -7.17079937e-01 -2.42301390e-01
2.71651536e-01 -8.49168837e-01 -5.99917948e-01 -2.68991023e-01
-5.94718099e-01 5.61352015e-01 9.36506987e-01 -3.55737865... | [8.27503776550293, -1.2591378688812256] |
bf041729-5535-4e98-8380-68df420023d7 | improving-iterative-text-revision-by-learning | 2212.01350 | null | https://arxiv.org/abs/2212.01350v1 | https://arxiv.org/pdf/2212.01350v1.pdf | Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks | Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document. Most recent research has focused on understanding and classifying different types of edits in the iterative revision pro... | ['Dongyeop Kang', 'Dhruv Kumar', 'Vipul Raheja', 'Wanyu Du', 'Zae Myung Kim'] | 2022-12-02 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 8.12640429e-01 5.93751848e-01 -1.40434466e-02 -6.36870027e-01
-7.78570592e-01 -4.64362562e-01 5.55235505e-01 8.13886583e-01
-4.14051682e-01 6.96613848e-01 7.73164034e-01 -4.67761129e-01
-2.00041570e-02 -3.81917655e-01 -6.10408843e-01 5.32561481e-01
7.12021351e-01 5.90230346e-01 -1.82853535e-01 -6.62890911... | [11.902214050292969, 9.33910846710205] |
2b67b8f8-04b3-4bcb-b33b-1a18f89baa79 | deep-scattering-spectrum-germaneness-to-fault | 2210.09837 | null | https://arxiv.org/abs/2210.09837v3 | https://arxiv.org/pdf/2210.09837v3.pdf | Deep Scattering Spectrum germaneness to Fault Detection and Diagnosis for Component-level Prognostics and Health Management (PHM) | In fault detection and diagnosis of prognostics and health management (PHM) systems, most of the methodologies utilize machine learning (ML) or deep learning (DL) through which either some features are extracted beforehand (in the case of ML) or filters are used to extract features autonomously (in case of DL) to perfo... | ['Ali Rohan'] | 2022-10-18 | null | null | null | null | ['fault-detection', 'industrial-robots'] | ['miscellaneous', 'robots'] | [ 8.18889886e-02 -1.81817397e-01 4.97970849e-01 -1.18518462e-02
-3.14656556e-01 2.55320203e-02 3.39652359e-01 2.65855163e-01
1.07314341e-01 6.37192011e-01 -4.26609904e-01 -1.60783917e-01
-7.88607478e-01 -6.90176189e-01 -4.07053143e-01 -9.89019513e-01
-4.19256270e-01 3.76222014e-01 1.57313675e-01 -2.59147733... | [6.753728866577148, 2.3765451908111572] |
18d97aa1-9f40-4cc4-878b-89827b3c1de4 | temporally-guided-articulated-hand-pose | 2101.04281 | null | https://arxiv.org/abs/2101.04281v2 | https://arxiv.org/pdf/2101.04281v2.pdf | Temporally Guided Articulated Hand Pose Tracking in Surgical Videos | Articulated hand pose tracking is an under-explored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robust and accurate tracking system on in-vivo surgical videos, the motion dynamics and movement patterns of the hands can be captured and analy... | ['Jason J. Corso', 'Donald S. Likosky', 'Francis D. Pagani', 'Milisa Manojlovich', 'Roger D. Dias', 'Steven J. Yule', 'Luowei Zhou', 'Nathan Louis'] | 2021-01-12 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-1.15217730e-01 -1.71367124e-01 -6.32996023e-01 1.96436018e-01
-1.12630785e+00 -8.48918855e-01 1.94248885e-01 -1.23833247e-01
-5.72080076e-01 5.35635948e-01 4.99896914e-01 -9.47160646e-02
-3.03093970e-01 1.64085656e-01 -5.30143440e-01 -8.18119824e-01
-3.95449698e-01 5.75775802e-01 2.58053571e-01 4.03193757... | [14.02945613861084, -3.3355019092559814] |
d27a280a-bb92-4891-8601-cae1f9ee8b46 | multi-view-deep-subspace-clustering-networks | 1908.01978 | null | https://arxiv.org/abs/1908.01978v1 | https://arxiv.org/pdf/1908.01978v1.pdf | Multi-view Deep Subspace Clustering Networks | Multi-view subspace clustering aims to discover the inherent structure by fusing multi-view complementary information. Most existing methods first extract multiple types of hand-crafted features and then learn a joint affinity matrix for clustering. The disadvantage lies in two aspects: 1) Multi-view relations are not ... | ['QinGhua Hu', 'Dawei Du', 'Longyin Wen', 'Binyuan Hui', 'Pengfei Zhu', 'Changqing Zhang'] | 2019-08-06 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.26243967e-01 -3.62722129e-01 -2.14323863e-01 -5.01459301e-01
-5.50728977e-01 -7.29648948e-01 5.10803819e-01 -5.61631143e-01
-2.68639643e-02 1.11840159e-01 4.39725608e-01 3.10073078e-01
-3.26031834e-01 -4.66695905e-01 -4.91248429e-01 -9.66041088e-01
2.29408890e-01 4.75848794e-01 -2.61250973e-01 -1.63872056... | [8.374654769897461, 4.542566776275635] |
6853f3d9-891f-47c0-ad18-2e4faa5c2d50 | exploring-the-impact-of-noise-and | 2211.07445 | null | https://arxiv.org/abs/2211.07445v1 | https://arxiv.org/pdf/2211.07445v1.pdf | Exploring the Impact of Noise and Degradations on Heart Sound Classification Models | The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signa... | ['Susan Mckeever', 'Andrew Hines', 'Davoud Shariat Panah'] | 2022-11-14 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.89031267e-01 -2.27223188e-01 4.35303718e-01 -9.55911279e-02
-4.90850121e-01 -4.71796453e-01 8.03212076e-02 1.97474137e-01
8.08195770e-02 3.48521978e-01 4.88205254e-01 -3.40736389e-01
-5.22941887e-01 -6.59847260e-01 -2.03619182e-01 -8.18654895e-01
4.67391172e-03 -1.06058039e-01 1.81542695e-01 -1.70912564... | [14.290766716003418, 3.312272071838379] |
fda42197-ce7f-41ec-82ef-7798e213924f | reference-based-video-super-resolution-using | 2203.14537 | null | https://arxiv.org/abs/2203.14537v1 | https://arxiv.org/pdf/2203.14537v1.pdf | Reference-based Video Super-Resolution Using Multi-Camera Video Triplets | We propose the first reference-based video super-resolution (RefVSR) approach that utilizes reference videos for high-fidelity results. We focus on RefVSR in a triple-camera setting, where we aim at super-resolving a low-resolution ultra-wide video utilizing wide-angle and telephoto videos. We introduce the first RefVS... | ['Seungyong Lee', 'Sunghyun Cho', 'Myeonghee Lee', 'Junyong Lee'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_Reference-Based_Video_Super-Resolution_Using_Multi-Camera_Video_Triplets_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_Reference-Based_Video_Super-Resolution_Using_Multi-Camera_Video_Triplets_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution', 'reference-based-video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.69200110e-01 -6.16742730e-01 -1.53124154e-01 -2.06186324e-01
-1.33524692e+00 -2.55323142e-01 5.77122271e-01 -8.61065388e-01
-2.65621215e-01 8.52156401e-01 4.98294502e-01 2.36621082e-01
-3.56786132e-01 -4.05113548e-01 -9.24669862e-01 -4.67574418e-01
-1.31704271e-01 -2.51884997e-01 5.50588071e-01 -2.96542972... | [11.004864692687988, -1.9430314302444458] |
dcb8eac9-fc12-4b5c-b89a-dc97d5dcea39 | a-new-information-theory-of-certainty-for | 2304.12833 | null | https://arxiv.org/abs/2304.12833v1 | https://arxiv.org/pdf/2304.12833v1.pdf | A New Information Theory of Certainty for Machine Learning | Claude Shannon coined entropy to quantify the uncertainty of a random distribution for communication coding theory. We observe that the uncertainty nature of entropy also limits its direct usage in mathematical modeling. Therefore we propose a new concept troenpy,as the canonical dual of entropy, to quantify the certai... | ['Arthur Jun Zhang'] | 2023-04-25 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 2.25513846e-01 1.74454868e-01 -1.33082017e-01 -1.60380512e-01
-5.11577010e-01 -6.29410625e-01 9.03914034e-01 2.70058513e-01
-5.03676355e-01 8.77439559e-01 1.48442954e-01 -5.46189129e-01
-2.48214141e-01 -9.24871027e-01 -2.66508609e-01 -9.60357785e-01
-2.72466332e-01 2.70855278e-01 -3.11408788e-01 -2.66794205... | [5.635475158691406, 4.939809799194336] |
6ea3dac7-e3dd-4ddf-9cad-4b8207763a65 | audio-to-intent-using-acoustic-textual | 2210.12134 | null | https://arxiv.org/abs/2210.12134v1 | https://arxiv.org/pdf/2210.12134v1.pdf | Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR | Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) di... | ['Ahmed Tewfik', 'Xiaochuan Niu', 'Erik Marchi', 'Oggi Rudovic', 'Prateeth Nayak', 'Pranay Dighe'] | 2022-10-21 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 4.91247118e-01 1.69856861e-01 -1.33353949e-01 -2.86601245e-01
-1.40921342e+00 -6.68698251e-01 3.36222947e-01 5.14352368e-03
-1.14644237e-01 1.73127815e-01 8.22729886e-01 -4.61841613e-01
1.28220990e-01 -2.43341163e-01 -5.49387395e-01 -4.45087016e-01
4.66019809e-02 1.48998141e-01 -2.66768754e-01 -8.43399689... | [14.631667137145996, 6.2125115394592285] |
fb029d96-2b8f-4968-9abe-b2414bc28a18 | edge-cloud-collaborative-learning-with | 2304.05871 | null | https://arxiv.org/abs/2304.05871v1 | https://arxiv.org/pdf/2304.05871v1.pdf | Edge-cloud Collaborative Learning with Federated and Centralized Features | Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server has the ability to store his... | ['Chao Wu', 'Guannan Zhang', 'Wenliang Zhong', 'Yi Zhou', 'Qunwei Li', 'Zexi Li'] | 2023-04-12 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-6.04812086e-01 -1.72813669e-01 -5.32856405e-01 -2.61580408e-01
-3.31452250e-01 -6.29191518e-01 2.16942519e-01 -9.82500762e-02
-1.51687622e-01 6.20615423e-01 -3.99648063e-02 -5.38599432e-01
-2.85003573e-01 -8.58645976e-01 -5.31951249e-01 -5.07302463e-01
-2.10068733e-01 5.67159466e-02 -9.65883806e-02 2.03801230... | [5.897154808044434, 6.231959819793701] |
13550ee7-84fb-4fd5-abac-26a067ae98a0 | audio-visual-efficient-conformer-for-robust | 2301.01456 | null | https://arxiv.org/abs/2301.01456v1 | https://arxiv.org/pdf/2301.01456v1.pdf | Audio-Visual Efficient Conformer for Robust Speech Recognition | End-to-end Automatic Speech Recognition (ASR) systems based on neural networks have seen large improvements in recent years. The availability of large scale hand-labeled datasets and sufficient computing resources made it possible to train powerful deep neural networks, reaching very low Word Error Rate (WER) on academ... | ['Radu Timofte', 'Maxime Burchi'] | 2023-01-04 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.36843789e-01 1.37701035e-01 -1.21488914e-01 -3.36847007e-01
-1.45400739e+00 -1.37636885e-01 5.46199262e-01 -1.63103744e-01
-5.88434041e-01 4.21706289e-01 6.07993186e-01 -4.52604502e-01
3.83902639e-01 -1.45731807e-01 -8.03395212e-01 -4.99460429e-01
4.87650722e-01 1.01628065e-01 2.93724149e-01 1.75939761... | [14.36329174041748, 5.125904560089111] |
f64680e0-bd68-40ed-bf81-d14cb0db39c0 | parkinson-gait-modelling-from-an-anomaly-deep | 2301.11418 | null | https://arxiv.org/abs/2301.11418v1 | https://arxiv.org/pdf/2301.11418v1.pdf | Parkinson gait modelling from an anomaly deep representation | Parkinson's Disease is associated with gait movement disorders, such as postural instability, stiffness, and tremors. Today, some approaches implemented learning representations to quantify kinematic patterns during locomotion, supporting clinical procedures such as diagnosis and treatment planning. These approaches as... | ['Fabio Martinez', 'Edgar Rangel'] | 2023-01-26 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 1.78113252e-01 4.56397504e-01 -4.15985733e-01 -3.83319199e-01
-4.50516671e-01 -9.61987227e-02 3.03397268e-01 -9.29157436e-02
-5.09259701e-01 1.03954947e+00 4.32824045e-01 2.71307558e-01
-3.50900531e-01 -6.06523156e-01 -3.23285133e-01 -7.62506902e-01
-6.94818974e-01 7.98560262e-01 1.70847103e-01 -1.00970373... | [7.1380205154418945, 0.3367210924625397] |
1eae87c0-7fa0-4f4d-a982-d8c98575c1b2 | constructing-a-multi-hop-qa-dataset-for | 2011.01060 | null | https://arxiv.org/abs/2011.01060v2 | https://arxiv.org/pdf/2011.01060v2.pdf | Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps | A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies reveal... | ['Akiko Aizawa', 'Saku Sugawara', 'Anh-Khoa Duong Nguyen', 'Xanh Ho'] | 2020-11-02 | null | https://aclanthology.org/2020.coling-main.580 | https://aclanthology.org/2020.coling-main.580.pdf | coling-2020-8 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-5.18569350e-02 8.53326201e-01 9.72804800e-02 -7.01576710e-01
-1.00367427e+00 -8.05751681e-01 4.92996544e-01 2.19759807e-01
2.26756245e-01 7.61010706e-01 2.87910104e-01 -9.36734200e-01
-4.89645243e-01 -1.21793294e+00 -9.47527230e-01 4.89779860e-01
4.76480454e-01 7.96216011e-01 7.84759283e-01 -5.37305415... | [10.992527961730957, 7.908002853393555] |
5887fac9-2784-4066-8562-4623bc2bdede | learning-speaker-representation-with-semi | 2110.13653 | null | https://arxiv.org/abs/2110.13653v1 | https://arxiv.org/pdf/2110.13653v1.pdf | Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling | Speaker profiling, which aims to estimate speaker characteristics such as age and height, has a wide range of applications inforensics, recommendation systems, etc. In this work, we propose a semisupervised learning approach to mitigate the issue of low training data for speaker profiling. This is done by utilizing ext... | ['Chng Eng Siong', 'Pham Van Tung', 'Shangeth Rajaa'] | 2021-10-24 | null | null | null | null | ['age-estimation', 'age-estimation', 'speaker-profiling'] | ['computer-vision', 'miscellaneous', 'speech'] | [ 1.29875541e-01 4.66123611e-01 -1.46042362e-01 -7.43001640e-01
-8.84254813e-01 -1.34062499e-01 6.88729644e-01 3.97519886e-01
-2.78671503e-01 7.02968955e-01 3.52056235e-01 5.64930476e-02
-2.61165828e-01 -4.68307763e-01 -2.45415792e-01 -8.33708644e-01
2.72578862e-03 5.74605644e-01 4.86884937e-02 8.23585242... | [14.213830947875977, 6.097212791442871] |
4fb2b67c-c06a-4021-945b-654a69db09af | dual-domain-self-supervised-learning-for | 2302.09244 | null | https://arxiv.org/abs/2302.09244v1 | https://arxiv.org/pdf/2302.09244v1.pdf | Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction | While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are limited to Cartesian sampling patterns. These factors limit their practical adoption as fully-sampled MRI is prohibitively time-consuming to... | ['Michal Sofka', 'James S. Duncan', 'Chi Liu', 'Kevin Sheth', 'Seyed Sadegh Mohseni Salehi', 'Neel Dey', 'Jo Schlemper', 'Bo Zhou'] | 2023-02-18 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.21177673e-01 1.35072872e-01 -3.53251338e-01 -6.22726738e-01
-1.11639178e+00 -3.09419394e-01 2.12704867e-01 -1.67046353e-01
-3.54508519e-01 6.91689968e-01 3.17151099e-01 -3.56990010e-01
-3.26435417e-01 -3.65092367e-01 -8.06175768e-01 -7.99993634e-01
-5.02864540e-01 5.67878842e-01 1.55944929e-01 2.20796198... | [13.543734550476074, -2.410111427307129] |
da9507eb-2580-4df8-b7c2-083abaee5210 | learning-better-representation-for-tables-by | 2010.07606 | null | https://arxiv.org/abs/2010.07606v3 | https://arxiv.org/pdf/2010.07606v3.pdf | Learning Better Representation for Tables by Self-Supervised Tasks | Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems still overlooked. The first is that the values recorded in many tables are mostly nu... | ['Dayong Hu', 'Linjun Shou', 'Yinliang Yue', 'Can Ma', 'Liang Li'] | 2020-10-15 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.32768446e-01 4.20055777e-01 -5.15319109e-01 -3.21196944e-01
-6.98629916e-01 -3.81253988e-01 5.75499535e-01 5.25168121e-01
-2.55723327e-01 1.29563820e+00 7.65134513e-01 -2.34513074e-01
1.71399862e-01 -1.22111988e+00 -8.38700533e-01 -4.23817307e-01
1.25990778e-01 8.34180892e-01 3.18805158e-01 -8.23585868... | [11.677376747131348, 8.820026397705078] |
dc6b8da7-54eb-4c8b-982b-ccfbce354561 | revisiting-domain-generalized-stereo-matching | 2203.10887 | null | https://arxiv.org/abs/2203.10887v1 | https://arxiv.org/pdf/2203.10887v1.pdf | Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective | Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between matching pixels is a vital factor for promoting the generalization capability of stereo m... | ['Edwin R. Hancock', 'Tatsuya Harada', 'Jun Zhou', 'Lin Gu', 'Yimin Chen', 'Lei Huang', 'Chen Wang', 'Xiao Bai', 'Xiang Wang', 'Jiawei Zhang'] | 2022-03-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 3.74608099e-01 -1.31729692e-01 -1.30549476e-01 -5.90716660e-01
-4.79387224e-01 -5.25023282e-01 7.51233697e-01 -1.75362766e-01
-4.05260414e-01 7.24068642e-01 2.45883539e-01 8.72865170e-02
1.49295563e-02 -7.49154687e-01 -8.79267037e-01 -7.45626330e-01
3.14952582e-01 1.53358296e-01 6.06362939e-01 -3.07443053... | [8.735492706298828, -2.299800395965576] |
9f30915d-9ec2-445a-9b60-acd125931fb5 | zegot-zero-shot-segmentation-through-optimal | 2301.12171 | null | https://arxiv.org/abs/2301.12171v2 | https://arxiv.org/pdf/2301.12171v2.pdf | ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts | Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) has led to great promise in zero-shot semantic segmentation by transferring image-text aligned knowledge to pixel-level classification. However, existing methods usually require an additional image encoder or retraining/tuning the CLIP module.... | ['Jong Chul Ye', 'Yujin Oh', 'Kwanyoung Kim'] | 2023-01-28 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 7.37217963e-01 1.15678795e-01 -4.26245451e-01 -5.92692912e-01
-1.30950356e+00 -2.95072049e-01 3.86466473e-01 -2.64769495e-01
-5.31345606e-01 2.48435155e-01 -1.68687999e-01 -1.06453687e-01
2.43008524e-01 -5.23393929e-01 -1.13358104e+00 -5.34098506e-01
5.73557794e-01 6.09070957e-01 4.84328032e-01 7.56009221... | [9.7290678024292, 0.8257309198379517] |
893b210d-21a1-4545-8a37-3f75b2f9061c | on-the-relation-between-sharpness-aware | 2305.05392 | null | https://arxiv.org/abs/2305.05392v2 | https://arxiv.org/pdf/2305.05392v2.pdf | Sharpness-Aware Minimization Alone can Improve Adversarial Robustness | Sharpness-Aware Minimization (SAM) is an effective method for improving generalization ability by regularizing loss sharpness. In this paper, we explore SAM in the context of adversarial robustness. We find that using only SAM can achieve superior adversarial robustness without sacrificing clean accuracy compared to st... | ['Yihao Zhang', 'Jingyu Zhu', 'Zeming Wei'] | 2023-05-09 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 1.29404619e-01 -1.46704791e-02 -9.34248939e-02 -1.53182030e-01
-1.06980836e+00 -1.06615245e+00 3.66605699e-01 -7.63026671e-03
-3.09564590e-01 7.81290472e-01 3.44229639e-01 -5.06901860e-01
-8.91777724e-02 -6.63382530e-01 -9.53029275e-01 -7.28767872e-01
5.52335009e-02 -3.43115211e-01 -9.85088423e-02 -3.20128053... | [5.655416488647461, 7.829474925994873] |
60f63592-dda8-4ae8-86aa-21b57e58c22a | brundlefly-at-semeval-2016-task-12-recurrent-1 | null | null | https://aclanthology.org/S16-1198 | https://aclanthology.org/S16-1198.pdf | Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction | null | ['Jason Fries'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.221503734588623, 3.6570255756378174] |
c278567b-39a0-4f79-b35f-85779da2cb50 | robust-multimodal-fusion-for-human-activity | 2303.04636 | null | https://arxiv.org/abs/2303.04636v1 | https://arxiv.org/pdf/2303.04636v1.pdf | Robust Multimodal Fusion for Human Activity Recognition | The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple sensors with different modalities. While there are promising deep neural network architectures for multimodal fusion, their performance falls apa... | ['Omid Ardakanian', 'Xin Yang', 'Sanju Xaviar'] | 2023-03-08 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [-4.81292494e-02 -3.08614463e-01 3.34018141e-01 -2.67602324e-01
-1.09578383e+00 -1.43254921e-01 6.14204645e-01 2.01769710e-01
-5.74667037e-01 6.99284434e-01 8.63931596e-01 2.42388353e-01
-2.93228626e-02 -5.51765382e-01 -1.02289343e+00 -7.01938987e-01
-1.95732657e-02 5.23458160e-02 -3.78245950e-01 -5.17313540... | [7.638155937194824, 0.8345587849617004] |
c082e7c7-d0ce-4c00-8925-d4d8e4f8752a | fast-exploration-and-learning-of-latent | 2303.07397 | null | https://arxiv.org/abs/2303.07397v3 | https://arxiv.org/pdf/2303.07397v3.pdf | Fast exploration and learning of latent graphs with aliased observations | We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic. Observations are gathered by an agent traversing the graph. Aliasing means that multiple nodes emit the same observation, so the agent can not know in which node it is located. Th... | ['Dileep George', 'Meet Dave', 'Sivaramakrishnan Swaminathan', 'Ishan Deshpande', 'Miguel Lazaro-Gredilla'] | 2023-03-13 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 3.94444972e-01 9.21959937e-01 -4.96351302e-01 1.43830806e-01
-8.93792629e-01 -1.01595879e+00 6.13768280e-01 4.42805678e-01
-3.61323714e-01 7.35701978e-01 2.61968911e-01 -3.18246543e-01
-2.12598935e-01 -7.98225522e-01 -6.85694933e-01 -9.09324169e-01
-7.91790783e-01 1.57242393e+00 2.40402505e-01 5.45792639... | [4.308643817901611, 2.1260082721710205] |
8e21e55b-eeb5-45d3-b4a1-a2d9ccd9db01 | goferbot-a-visual-guided-human-robot | 2304.08840 | null | https://arxiv.org/abs/2304.08840v2 | https://arxiv.org/pdf/2304.08840v2.pdf | GoferBot: A Visual Guided Human-Robot Collaborative Assembly System | The current transformation towards smart manufacturing has led to a growing demand for human-robot collaboration (HRC) in the manufacturing process. Perceiving and understanding the human co-worker's behaviour introduces challenges for collaborative robots to efficiently and effectively perform tasks in unstructured an... | ['Robert Mahony', 'Stephen Gould', 'Jiahao Zhang', 'Yizhak Ben-Shabat', 'Zheyu Zhuang'] | 2023-04-18 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.74740607e-01 3.67087126e-01 4.16123033e-01 -4.17630374e-01
7.33719319e-02 -5.94276726e-01 5.80661416e-01 3.02270025e-01
-1.66480869e-01 2.26297036e-01 -1.41426831e-01 4.38141562e-02
-3.23258936e-01 -3.34354103e-01 -3.94954056e-01 -3.16131741e-01
1.04211941e-01 8.05498064e-01 4.83429134e-01 -7.28143215... | [4.907749652862549, 0.7604445219039917] |
3962e3ac-447c-4b3f-97e7-9c0b1e0a5418 | category-specific-semantic-coherency-learning | null | null | https://dl.acm.org/doi/pdf/10.1145/3394171.3413871 | https://dl.acm.org/doi/pdf/10.1145/3394171.3413871 | Category-specific Semantic Coherency Learning for Fine-grained Image Recognition | Existing deep learning based weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative
regions from the high-level feature (HLF) maps directly. However, as HLF maps are derived based on spatial aggregation of convolution which is basically a pattern matching process that appl... | ['Wanli Ouyang', 'Haojie Li', 'Zhihui Wang', 'Shijie Wang'] | 2020-10-12 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 8.38491321e-03 -2.86625713e-01 -2.00669944e-01 -7.83749700e-01
-5.92373312e-01 -6.84434772e-01 6.81740820e-01 -1.02134556e-01
-1.02238856e-01 2.52867669e-01 3.79837215e-01 9.45309084e-03
-4.38880265e-01 -8.00154686e-01 -7.17182159e-01 -9.02020037e-01
4.29007001e-02 3.33622098e-01 2.24305987e-01 -5.28587028... | [9.715191841125488, 2.037099599838257] |
0ba6eddd-2b93-4a77-9712-8d9372ff0d60 | explaining-rl-decisions-with-trajectories | 2305.04073 | null | https://arxiv.org/abs/2305.04073v1 | https://arxiv.org/pdf/2305.04073v1.pdf | Explaining RL Decisions with Trajectories | Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this work, we propose a complementary approach to these explanations, parti... | ['Jayakumar Subramanian', 'Georgios Theocharous', 'Chirag Agarwal', 'Nan Jiang', 'Balaji Krishnamurthy', 'Arpan Dasgupta', 'Shripad Vilasrao Deshmukh'] | 2023-05-06 | null | null | null | null | ['offline-rl', 'continuous-control'] | ['playing-games', 'playing-games'] | [ 2.12509051e-01 4.39310044e-01 -4.70670730e-01 -1.97056472e-01
-2.95092136e-01 -5.58740437e-01 7.88022459e-01 2.81394571e-01
-4.10174757e-01 1.01519454e+00 3.28116119e-01 -3.58865321e-01
-2.30783418e-01 -4.71000046e-01 -9.38276112e-01 -5.15390933e-01
-2.90052176e-01 5.28160334e-01 9.81160030e-02 -2.88455248... | [4.188821315765381, 1.701714038848877] |
6ce07795-6e9f-4182-b218-219f261c723b | the-limitations-of-large-width-in-neural | 2106.06529 | null | https://arxiv.org/abs/2106.06529v2 | https://arxiv.org/pdf/2106.06529v2.pdf | The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective | Large width limits have been a recent focus of deep learning research: modulo computational practicalities, do wider networks outperform narrower ones? Answering this question has been challenging, as conventional networks gain representational power with width, potentially masking any negative effects. Our analysis in... | ['John P. Cunningham', 'Geoff Pleiss'] | 2021-06-11 | null | http://proceedings.neurips.cc/paper/2021/hash/1b9f38268c50805669fd8caf8f3cc84a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/1b9f38268c50805669fd8caf8f3cc84a-Paper.pdf | neurips-2021-12 | ['l2-regularization'] | ['methodology'] | [ 6.06155284e-02 4.69369709e-01 -1.18982047e-01 -1.06969262e-02
-3.77430499e-01 -7.14005709e-01 5.69222033e-01 1.29982056e-02
-5.22088349e-01 7.09815204e-01 1.97148532e-01 -5.54042578e-01
-3.98667663e-01 -8.77039194e-01 -9.10708666e-01 -1.15739346e+00
-1.92487001e-01 4.70463514e-01 4.59274828e-01 1.42772332... | [7.820926666259766, 3.6821727752685547] |
286e8258-a7b9-4b73-97b0-1e0a326a4fe3 | matt-a-multiple-instance-attention-mechanism | 2209.04109 | null | https://arxiv.org/abs/2209.04109v1 | https://arxiv.org/pdf/2209.04109v1.pdf | MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification | Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field for identifying the long-tail, data-poor genre based on the related music audio segments, which is very prevalent in real-world scenarios. Most of the existing models are designed for class-balanced music datasets, re... | ['Menghua Zhang', 'Xiaokai Liu'] | 2022-09-09 | null | null | null | null | ['genre-classification', 'music-information-retrieval'] | ['computer-vision', 'music'] | [ 1.48319885e-01 -5.70496380e-01 -4.37535912e-01 -1.30174294e-01
-1.32658005e+00 -4.70007032e-01 8.72564875e-03 1.11686006e-01
-4.23326008e-02 2.58832425e-01 4.66160029e-01 2.09790856e-01
-5.16759098e-01 -5.42990506e-01 -5.73329151e-01 -7.20111787e-01
7.37395212e-02 6.04740977e-01 -3.05119038e-01 -1.56294823... | [15.759607315063477, 5.2430572509765625] |
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