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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5263c809-7c17-4695-a311-1bfa71fbb3e6 | xlcost-a-benchmark-dataset-for-cross-lingual | 2206.08474 | null | https://arxiv.org/abs/2206.08474v1 | https://arxiv.org/pdf/2206.08474v1.pdf | XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence | Recent advances in machine learning have significantly improved the understanding of source code data and achieved good performance on a number of downstream tasks. Open source repositories like GitHub enable this process with rich unlabeled code data. However, the lack of high quality labeled data has largely hindered... | ['Chandan K. Reddy', 'Sindhu Tipirneni', 'Roshan Ravindran', 'Karthik Suresh', 'Aneesh Jain', 'Ming Zhu'] | 2022-06-16 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.33644503e-01 -3.18452388e-01 -1.07462907e+00 -4.31942314e-01
-1.32801425e+00 -8.82554173e-01 3.72040421e-01 5.16972721e-01
-1.14260264e-01 2.77410597e-01 2.67192960e-01 -6.83322668e-01
3.87839347e-01 -3.07091445e-01 -7.60159254e-01 3.90765332e-02
-5.82516827e-02 2.17294499e-01 7.00661838e-02 -1.28621221... | [7.638759613037109, 7.978619575500488] |
b02e711f-5aad-4ef1-8708-8c1551bd3f5d | regularized-submodular-maximization-at-scale | 2002.03503 | null | https://arxiv.org/abs/2002.03503v1 | https://arxiv.org/pdf/2002.03503v1.pdf | Regularized Submodular Maximization at Scale | In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$. Indeed, submodularity is inherently related to the notions of diversity, coverage, and representativeness. In p... | ['Shervin Minaee', 'Amin Karbasi', 'Moran Feldman', 'Ehsan Kazemi'] | 2020-02-10 | null | null | null | null | ['product-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [-1.08168773e-01 1.87779084e-01 -2.38587737e-01 -1.80569008e-01
-1.01407003e+00 -1.07396173e+00 -4.99391586e-01 4.74616736e-01
-1.99171409e-01 7.47687280e-01 -5.44629134e-02 -3.44650596e-01
-6.87037587e-01 -1.26191199e+00 -1.02089000e+00 -9.65402663e-01
-5.21153450e-01 5.92484415e-01 -8.49409327e-02 -2.28903428... | [6.5471510887146, 4.837886333465576] |
2bbf48a4-e801-433c-aa88-6f5f0227915a | lifelong-learning-crf-for-supervised-aspect | 1705.00251 | null | http://arxiv.org/abs/1705.00251v1 | http://arxiv.org/pdf/1705.00251v1.pdf | Lifelong Learning CRF for Supervised Aspect Extraction | This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain marked... | ['Bing Liu', 'Hu Xu', 'Lei Shu'] | 2017-04-29 | lifelong-learning-crf-for-supervised-aspect-1 | https://aclanthology.org/P17-2023 | https://aclanthology.org/P17-2023.pdf | acl-2017-7 | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.80498995e-03 7.09971607e-01 -9.45724845e-01 -4.39330846e-01
-6.57803535e-01 -5.87697387e-01 1.04979944e+00 1.78969264e-01
-3.32370549e-01 1.31580663e+00 2.80892342e-01 -2.11522996e-01
1.47311792e-01 -8.97339880e-01 -4.72425491e-01 -1.12822525e-01
-1.80054292e-01 7.36097991e-01 1.73475355e-01 -2.29257807... | [11.236442565917969, 6.959175109863281] |
115a558e-4544-4387-8421-4e0216657865 | affordance-grounding-from-demonstration-video-1 | 2303.14644 | null | https://arxiv.org/abs/2303.14644v1 | https://arxiv.org/pdf/2303.14644v1.pdf | Affordance Grounding from Demonstration Video to Target Image | Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass v... | ['Mike Zheng Shou', 'Kevin Qinghong Lin', 'Difei Gao', 'Joya Chen'] | 2023-03-26 | affordance-grounding-from-demonstration-video | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Affordance_Grounding_From_Demonstration_Video_To_Target_Image_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-to-image-affordance-grounding'] | ['computer-vision'] | [ 2.40883946e-01 7.56019577e-02 -6.69402629e-02 -2.65957475e-01
-5.62658787e-01 -4.34811354e-01 2.45987564e-01 -3.61875266e-01
-2.43139327e-01 5.23008704e-01 2.64829576e-01 -3.60498220e-01
1.32080033e-01 -2.43696988e-01 -1.15535223e+00 -1.97218776e-01
3.93914729e-02 2.62039751e-01 2.93145150e-01 -2.45980650... | [5.083304405212402, 0.018646273761987686] |
1e2c64e3-9956-4db2-a38b-3de0f4e2d695 | integrating-parametric-and-non-parametric | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Shuai_Integrating_Parametric_and_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shuai_Integrating_Parametric_and_2015_CVPR_paper.pdf | Integrating Parametric and Non-Parametric Models For Scene Labeling | We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishable from local context, we alleviate such ambiguity by putting a global scene constraint. We estimate the global potential in... | ['Lifan Zhao', 'Gang Wang', 'Bing Shuai', 'Zhen Zuo', 'Bing Wang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['scene-labeling'] | ['computer-vision'] | [-7.43422434e-02 -2.32765168e-01 -7.15442359e-01 -8.09541464e-01
-9.96455431e-01 -4.12255049e-01 6.07421935e-01 1.13219090e-01
-5.27821898e-01 5.95975399e-01 2.00931445e-01 1.65341139e-01
1.53314099e-01 -9.46622431e-01 -1.00659084e+00 -6.48203731e-01
-1.10023871e-01 5.94981536e-02 5.15108407e-01 2.26116955... | [8.313032150268555, -1.7827990055084229] |
63a93922-d498-4430-9bae-872058b943c4 | sparsifying-transformer-models-with | 2009.05169 | null | https://arxiv.org/abs/2009.05169v4 | https://arxiv.org/pdf/2009.05169v4.pdf | Sparsifying Transformer Models with Trainable Representation Pooling | We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input. A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust train... | ['Łukasz Garncarek', 'Łukasz Borchmann', 'Michał Pietruszka'] | 2020-09-10 | null | https://aclanthology.org/2022.acl-long.590 | https://aclanthology.org/2022.acl-long.590.pdf | acl-2022-5 | ['summarization'] | ['natural-language-processing'] | [ 3.28330457e-01 5.96202791e-01 -1.88318953e-01 -2.24406168e-01
-1.64603555e+00 -4.87478733e-01 4.88211364e-01 3.90649617e-01
-8.33938837e-01 8.81177247e-01 5.65837681e-01 -2.90217161e-01
-6.26730621e-02 -7.93962896e-01 -1.00696146e+00 -6.26085579e-01
-1.08290412e-01 6.04816735e-01 -3.66932563e-02 -7.94077143... | [10.962024688720703, 7.653134822845459] |
5a10c6ae-39e6-4204-ac0a-0098c329dbd3 | generative-moment-matching-network-based | 1902.03389 | null | http://arxiv.org/abs/1902.03389v1 | http://arxiv.org/pdf/1902.03389v1.pdf | Generative Moment Matching Network-based Random Modulation Post-filter for DNN-based Singing Voice Synthesis and Neural Double-tracking | This paper proposes a generative moment matching network (GMMN)-based
post-filter that provides inter-utterance pitch variation for deep neural
network (DNN)-based singing voice synthesis. The natural pitch variation of a
human singing voice leads to a richer musical experience and is used in
double-tracking, a recordi... | ['Hiroki Tamaru', 'Yuki Saito', 'Tomoki Koriyama', 'Shinnosuke Takamichi', 'Hiroshi Saruwatari'] | 2019-02-09 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-5.76188304e-02 -1.80309072e-01 1.04650900e-01 1.17004313e-01
-6.30349576e-01 -6.09310508e-01 1.82675481e-01 -5.59078336e-01
1.55249378e-02 3.34217757e-01 4.52948302e-01 1.16630895e-02
5.37539162e-02 -4.33767617e-01 -5.10894001e-01 -6.55624390e-01
6.60940334e-02 -2.06428692e-01 1.67745516e-01 -5.18227160... | [15.486518859863281, 6.148586750030518] |
819d1183-3a2c-4760-ad03-64ba5b8f0be3 | hybrid-transformer-and-cnn-attention-network | 2305.05177 | null | https://arxiv.org/abs/2305.05177v1 | https://arxiv.org/pdf/2305.05177v1.pdf | Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution | Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, they have not yet shown significant advantages over CNN-based methods in stereo super-resolution tasks. This can be attributed to two key fact... | ['Li Zhang', 'Junlin Li', 'Shijie Zhao', 'Xuhan Sheng', 'Zhenyu Zhang', 'Weiqi Li', 'Xiaopeng Sun', 'Qiufang Ma', 'Haoyu Ma', 'Ming Cheng'] | 2023-05-09 | null | null | null | null | ['image-super-resolution', 'image-enhancement', 'stereo-image-super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.44585013e-01 -6.25900328e-02 7.80579001e-02 -3.62497717e-01
-1.20882595e+00 3.94812897e-02 5.27672231e-01 -4.98320609e-01
-1.97461709e-01 8.93466771e-01 7.74154663e-01 7.42393956e-02
-2.78552738e-03 -6.57523274e-01 -6.79335773e-01 -7.27313280e-01
3.24094653e-01 -8.70416760e-02 4.39153612e-01 -5.65095365... | [10.910097122192383, -2.055483818054199] |
6e009372-bade-419d-b5a3-b7dc381ac0a3 | an-experience-based-direct-generation | 2212.14561 | null | https://arxiv.org/abs/2212.14561v1 | https://arxiv.org/pdf/2212.14561v1.pdf | An Experience-based Direct Generation approach to Automatic Image Cropping | Automatic Image Cropping is a challenging task with many practical downstream applications. The task is often divided into sub-problems - generating cropping candidates, finding the visually important regions, and determining aesthetics to select the most appealing candidate. Prior approaches model one or more of these... | ['Aneesh Vartakavi', 'Casper Christensen'] | 2022-12-30 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 6.51001334e-01 7.16488361e-02 1.05915908e-02 -1.42131880e-01
-8.12578261e-01 -8.70912254e-01 4.32863146e-01 1.25771388e-01
-1.91362530e-01 2.48803064e-01 8.77842307e-02 -4.07050014e-01
2.39330307e-01 -8.36097956e-01 -1.07846999e+00 -3.60327542e-01
2.62114346e-01 7.51065984e-02 3.80385593e-02 -2.46168435... | [11.465424537658691, -0.9805854558944702] |
ea2088aa-c3d6-4bbd-9bf4-8109e1800c7c | gift-a-real-time-and-scalable-3d-shape-search | 1604.01879 | null | http://arxiv.org/abs/1604.01879v2 | http://arxiv.org/pdf/1604.01879v2.pdf | GIFT: A Real-time and Scalable 3D Shape Search Engine | Projective analysis is an important solution for 3D shape retrieval, since
human visual perceptions of 3D shapes rely on various 2D observations from
different view points. Although multiple informative and discriminative views
are utilized, most projection-based retrieval systems suffer from heavy
computational cost, ... | ['Zhichao Zhou', 'Song Bai', 'Longin Jan Latecki', 'Zhaoxiang Zhang', 'Xiang Bai'] | 2016-04-07 | gift-a-real-time-and-scalable-3d-shape-search-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Bai_GIFT_A_Real-Time_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Bai_GIFT_A_Real-Time_CVPR_2016_paper.pdf | cvpr-2016-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [-1.34257793e-01 -1.04849744e+00 2.08557211e-02 -1.52837336e-01
-9.11270022e-01 -8.45506489e-01 5.83491027e-01 2.44698137e-01
-1.20525055e-01 -1.72829613e-01 3.41719836e-02 -1.24420516e-01
-3.38084698e-01 -9.02746379e-01 -2.41666973e-01 -7.56531894e-01
3.75699550e-01 7.41110921e-01 5.26872635e-01 -9.73222628... | [8.205363273620605, -3.9162724018096924] |
dd768c54-a164-4ab6-a2ca-5932514adb17 | controllable-person-image-synthesis-with-1 | 2105.14739 | null | https://arxiv.org/abs/2105.14739v3 | https://arxiv.org/pdf/2105.14739v3.pdf | Controllable Person Image Synthesis with Spatially-Adaptive Warped Normalization | Controllable person image generation aims to produce realistic human images with desirable attributes such as a given pose, cloth textures, or hairstyles. However, the large spatial misalignment between source and target images makes the standard image-to-image translation architectures unsuitable for this task. Most s... | ['Humphrey Sh', 'Wei Wang', 'Nicu Sebe', 'Enver Sangineto', 'Hao Tang', 'Aliaksandr Siarohin', 'Jichao Zhang'] | 2021-05-31 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 4.79060948e-01 -3.20481896e-01 2.45947037e-02 -4.09289122e-01
-5.75967491e-01 -3.99870157e-01 6.21051490e-01 -4.82112497e-01
-1.63608149e-01 6.46954715e-01 1.99551210e-01 3.69999856e-01
1.39199704e-01 -6.85422122e-01 -7.33204067e-01 -8.93529713e-01
5.40165961e-01 2.86349118e-01 1.74346104e-01 -3.95994842... | [11.965230941772461, -0.864799439907074] |
f2f920cb-c131-45d2-b8f8-cfbee84740e3 | video-action-understanding-a-tutorial | 2010.06647 | null | https://arxiv.org/abs/2010.06647v2 | https://arxiv.org/pdf/2010.06647v2.pdf | Video Action Understanding | Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, due to the scale and temporal nature of video, the span of video understanding problems and the set of proposed deep learning solutions is arguably wider and more diverse than... | ['Vijay Gadepally', 'Matthew Hutchinson'] | 2020-10-13 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 3.05645049e-01 -1.01857428e-02 -5.60920119e-01 -3.77298146e-01
-3.29178244e-01 -5.81082582e-01 4.67075646e-01 -1.98468208e-01
-1.59641653e-01 3.00807416e-01 4.51521307e-01 -1.75636679e-01
-4.65599269e-01 -3.30311835e-01 -6.53785348e-01 -3.82056028e-01
-3.67136866e-01 5.37243634e-02 4.17022668e-02 -1.07962877... | [8.469354629516602, 0.6305696368217468] |
4b374758-d27f-4c2a-92e7-4b08805b571d | a-survey-on-uncertainty-quantification | 2302.13425 | null | https://arxiv.org/abs/2302.13425v2 | https://arxiv.org/pdf/2302.13425v2.pdf | A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty Source Perspective | Deep neural networks (DNNs) have achieved tremendous success in making accurate predictions for computer vision, natural language processing, as well as science and engineering domains. However, it is also well-recognized that DNNs sometimes make unexpected, incorrect, but overconfident predictions. This can cause seri... | ['Zhe Jiang', 'Wenchong He'] | 2023-02-26 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-1.29110932e-01 4.57437605e-01 -3.75042170e-01 -6.80856049e-01
-6.81779444e-01 -3.48219573e-01 5.66871345e-01 2.79067129e-01
-6.58924103e-01 1.09425581e+00 1.14740163e-01 -3.92814457e-01
-5.42883217e-01 -9.40409899e-01 -6.13453627e-01 -5.16056716e-01
1.07326865e-01 4.61819500e-01 4.30412218e-02 2.81502992... | [7.514026165008545, 3.774718999862671] |
0c1fc147-ab12-4b47-bba3-826c490414bc | geometry-aware-learning-of-maps-for-camera | 1712.03342 | null | http://arxiv.org/abs/1712.03342v3 | http://arxiv.org/pdf/1712.03342v3.pdf | Geometry-Aware Learning of Maps for Camera Localization | Maps are a key component in image-based camera localization and visual SLAM
systems: they are used to establish geometric constraints between images,
correct drift in relative pose estimation, and relocalize cameras after lost
tracking. The exact definitions of maps, however, are often
application-specific and hand-cra... | ['Samarth Brahmbhatt', 'Jan Kautz', 'James Hays', 'Kihwan Kim', 'Jinwei Gu'] | 2017-12-09 | geometry-aware-learning-of-maps-for-camera-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Brahmbhatt_Geometry-Aware_Learning_of_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Brahmbhatt_Geometry-Aware_Learning_of_CVPR_2018_paper.pdf | cvpr-2018-6 | ['camera-localization'] | ['computer-vision'] | [-8.39836746e-02 -1.63651571e-01 -3.08337748e-01 -5.59554338e-01
-5.75273812e-01 -8.33590925e-01 5.53773463e-01 1.08763196e-01
-7.74140179e-01 5.96746325e-01 -6.26040772e-02 -8.36699978e-02
-3.74390185e-03 -6.63314581e-01 -1.32058275e+00 -4.92446840e-01
1.03690013e-01 6.72337830e-01 2.56247193e-01 -8.41012672... | [7.68910026550293, -2.162942409515381] |
58413aa5-c753-4f7b-8fd4-4b397c52cace | context-aware-domain-adaptation-for-time | 2304.07453 | null | https://arxiv.org/abs/2304.07453v1 | https://arxiv.org/pdf/2304.07453v1.pdf | Context-aware Domain Adaptation for Time Series Anomaly Detection | Time series anomaly detection is a challenging task with a wide range of real-world applications. Due to label sparsity, training a deep anomaly detector often relies on unsupervised approaches. Recent efforts have been devoted to time series domain adaptation to leverage knowledge from similar domains. However, existi... | ['Xia Hu', 'Hao Yang', 'Fei Wang', 'Kaixiong Zhou', 'Huiyuan Chen', 'Lan Wang', 'Kwei-Herng Lai'] | 2023-04-15 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 4.69402045e-01 -4.00837213e-01 -6.12479784e-02 -6.66714728e-01
-7.27030933e-01 -6.07212603e-01 5.59472978e-01 3.23082417e-01
-3.19178611e-01 4.75089371e-01 2.26719817e-03 -2.58069485e-01
-1.26164258e-01 -5.43921530e-01 -4.80948180e-01 -5.53088605e-01
-3.38005871e-01 3.99945736e-01 1.42364249e-01 -4.79775295... | [7.651127338409424, 2.610903739929199] |
61f8b28e-d7e5-4f9c-b4b4-a5124f23a586 | bengali-handwritten-digit-recognition-using | 2212.12146 | null | https://arxiv.org/abs/2212.12146v1 | https://arxiv.org/pdf/2212.12146v1.pdf | Bengali Handwritten Digit Recognition using CNN with Explainable AI | Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily understand the content and do not need to read the handwritten document. OCR in the English langua... | ['Md. Golam Rabiul Alam', 'Raihan Tanvir', 'MD Tanvir Rouf Shawon'] | 2022-12-23 | null | null | null | null | ['optical-character-recognition', 'handwritten-digit-recognition'] | ['computer-vision', 'computer-vision'] | [-1.29060075e-01 -4.65165585e-01 -1.09672859e-01 -3.16556454e-01
-2.42987089e-02 -8.48589361e-01 3.53794038e-01 -2.07920685e-01
-2.65159816e-01 6.43401086e-01 -2.60152757e-01 -6.01063192e-01
-2.11758781e-02 -1.06937730e+00 -5.57728469e-01 -6.34450793e-01
4.26110864e-01 5.47150135e-01 4.29624647e-01 -2.56811559... | [11.8240385055542, 2.662203550338745] |
b2d76d43-e70a-44de-ab43-4372a6cc42cd | empirical-study-of-drone-sound-detection-in | 1701.05779 | null | http://arxiv.org/abs/1701.05779v1 | http://arxiv.org/pdf/1701.05779v1.pdf | Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks | This work aims to investigate the use of deep neural network to detect
commercial hobby drones in real-life environments by analyzing their sound
data. The purpose of work is to contribute to a system for detecting drones
used for malicious purposes, such as for terrorism. Specifically, we present a
method capable of d... | ['Hae-Yong Yang', 'Woong-Hee Kim', 'Young-Jun Lee', 'Jong-Woo Shin', 'YoungHyoun Kwon', 'Sungho Jeon'] | 2017-01-20 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 1.34327533e-02 -6.12784445e-01 6.36929929e-01 1.54576182e-01
-6.32574141e-01 -3.37385923e-01 5.00868320e-01 -1.56509787e-01
-4.45752919e-01 3.05210203e-01 -1.36756867e-01 -6.73129186e-02
-5.93859144e-02 -9.82588351e-01 -4.01786983e-01 -7.12810636e-01
-4.81681585e-01 -1.71814188e-01 4.31150228e-01 -4.64011312... | [15.151336669921875, 5.233471393585205] |
54a5f8fe-2796-4e61-994c-77388043c907 | safe-reinforcement-learning-with-contrastive | 2209.09648 | null | https://arxiv.org/abs/2209.09648v1 | https://arxiv.org/pdf/2209.09648v1.pdf | Safe Reinforcement Learning with Contrastive Risk Prediction | As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, w... | ['Yuhong Guo', 'Hanping Zhang'] | 2022-09-10 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.05222717e-01 5.51526725e-01 -6.19499743e-01 -1.84978142e-01
-8.30478370e-01 -3.98798764e-01 7.03354597e-01 -8.44353214e-02
-5.83029032e-01 1.15785742e+00 -4.32444960e-02 -5.72056592e-01
-4.97840345e-01 -6.40175700e-01 -7.66743004e-01 -7.14907050e-01
-8.92618835e-01 1.67263135e-01 3.77945632e-01 -2.69069642... | [4.510588645935059, 2.0737056732177734] |
7060f86a-c0c3-491e-acee-a403eea304f0 | cross-architecture-distillation-for-face | 2306.14662 | null | https://arxiv.org/abs/2306.14662v1 | https://arxiv.org/pdf/2306.14662v1.pdf | Cross Architecture Distillation for Face Recognition | Transformers have emerged as the superior choice for face recognition tasks, but their insufficient platform acceleration hinders their application on mobile devices. In contrast, Convolutional Neural Networks (CNNs) capitalize on hardware-compatible acceleration libraries. Consequently, it has become indispensable to ... | ['Zhen Lei', 'Xiao-Yu Zhang', 'Zhixiang He', 'Xiangyu Zhu', 'Weisong Zhao'] | 2023-06-26 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 5.23382947e-02 -1.36539191e-01 -1.01994328e-01 -4.82498556e-01
-2.62129158e-01 -4.55696702e-01 3.85249197e-01 -3.85072827e-01
-4.93143260e-01 3.11863452e-01 -3.30494821e-01 -5.01402795e-01
-5.05857617e-02 -8.16706419e-01 -6.73228860e-01 -8.17342520e-01
3.80617976e-01 7.84172341e-02 1.65957570e-01 2.22853161... | [13.18393611907959, 0.6351323127746582] |
559265a6-7259-4d07-bfde-429b31bc2812 | ualberta-at-semeval-2021-task-2-determining | null | null | https://aclanthology.org/2021.semeval-1.101 | https://aclanthology.org/2021.semeval-1.101.pdf | UAlberta at SemEval-2021 Task 2: Determining Sense Synonymy via Translations | We describe the University of Alberta systems for the SemEval-2021 Word-in-Context (WiC) disambiguation task. We explore the use of translation information for deciding whether two different tokens of the same word correspond to the same sense of the word. Our focus is on developing principled theoretical approaches wh... | ['Grzegorz Kondrak', 'Arnob Mallik', 'Hongchang Bao', 'Bradley Hauer'] | 2021-08-01 | null | null | null | semeval-2021 | ['explainable-models'] | ['computer-vision'] | [ 2.92830914e-01 1.11522697e-01 -6.42133772e-01 -5.31927645e-01
-9.48062360e-01 -7.88717091e-01 9.61719513e-01 2.35745847e-01
-5.24937510e-01 8.36014390e-01 7.32601941e-01 -9.42996085e-01
1.25036374e-01 -3.76964480e-01 -4.99388427e-01 -1.42508209e-01
1.63439229e-01 5.61486304e-01 -8.29885900e-02 -6.39631510... | [10.825531005859375, 9.647180557250977] |
17f855e5-36f6-4431-b082-e4e388d6e668 | avoiding-catastrophic-forgetting-in | 2004.14366 | null | https://arxiv.org/abs/2004.14366v2 | https://arxiv.org/pdf/2004.14366v2.pdf | Elastic weight consolidation for better bias inoculation | The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. While fine-tuning models on additional data has been used to mitigate them, a common issue is that of catastrophic forgetting of the original tr... | ['James Thorne', 'Andreas Vlachos'] | 2020-04-29 | null | https://aclanthology.org/2021.eacl-main.82 | https://aclanthology.org/2021.eacl-main.82.pdf | eacl-2021-2 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 1.50410280e-01 3.14246744e-01 -3.01621258e-01 -7.26576328e-01
-5.55751860e-01 -4.43552345e-01 5.07532775e-01 4.81081218e-01
-7.56632328e-01 1.03520906e+00 4.19222832e-01 -6.06759369e-01
-1.69150472e-01 -6.12889349e-01 -6.84694231e-01 -2.19840601e-01
1.75703213e-01 3.75695914e-01 2.55363435e-01 -3.15296054... | [10.405486106872559, 8.43018627166748] |
1d7fa2be-3cea-481a-b961-d0475c2b6467 | simple-unsupervised-object-centric-learning | 2205.14065 | null | https://arxiv.org/abs/2205.14065v1 | https://arxiv.org/pdf/2205.14065v1.pdf | Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos | Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional single-vector representations such as poor systematic generalization. Although there have been ma... | ['Sungjin Ahn', 'Yi-Fu Wu', 'Gautam Singh'] | 2022-05-27 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 4.45839435e-01 1.61108091e-01 -2.31154338e-01 -2.82007754e-01
-3.20674300e-01 -9.56302509e-02 1.05473244e+00 -1.87791839e-01
-2.15923816e-01 6.23360515e-01 4.33658451e-01 1.08132772e-01
-3.08277756e-01 -4.96310622e-01 -8.73317242e-01 -7.20647395e-01
-3.06384936e-02 2.73537993e-01 3.06933790e-01 -2.68404871... | [9.063382148742676, 0.8977152109146118] |
fc21fb44-f8d1-4ada-afc1-7c468748fa0a | improving-speaker-discrimination-of-target | 2001.08378 | null | https://arxiv.org/abs/2001.08378v1 | https://arxiv.org/pdf/2001.08378v1.pdf | Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam | Target speech extraction, which extracts a single target source in a mixture given clues about the target speaker, has attracted increasing attention. We have recently proposed SpeakerBeam, which exploits an adaptation utterance of the target speaker to extract his/her voice characteristics that are then used to guide ... | ['Tsubasa Ochiai', 'Shoko Araki', 'Marc Delcroix', 'Naohiro Tawara', 'Keisuke Kinoshita', 'Tomohiro Nakatani', 'Katerina Zmolikova'] | 2020-01-23 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 2.08971724e-01 -9.96697098e-02 -3.36204022e-02 -1.68062285e-01
-1.26827228e+00 -6.33436143e-01 5.27154803e-01 1.02134019e-01
-2.80086279e-01 4.25026327e-01 2.82347858e-01 -2.59877354e-01
-1.50286600e-01 -1.48550570e-01 -1.92319259e-01 -1.10129368e+00
-5.30409776e-02 3.23057443e-01 2.42200539e-01 -5.89360707... | [14.771891593933105, 5.916546821594238] |
8c26e88b-3d10-4fc5-9557-5405c9daaa2d | the-global-information-for-land-cover | 2006.00234 | null | https://arxiv.org/abs/2006.00234v2 | https://arxiv.org/pdf/2006.00234v2.pdf | Integrating global spatial features in CNN based Hyperspectral/SAR imagery classification | The land cover classification has played an important role in remote sensing because it can intelligently identify things in one huge remote sensing image to reduce the work of humans. However, a lot of classification methods are designed based on the pixel feature or limited spatial feature of the remote sensing image... | ['Chen Hu', 'MinChao Yan', 'Fei Ma', 'Jun Ni', 'Fan Zhang'] | 2020-05-30 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.21271360e-01 -6.40789211e-01 3.16742100e-02 -5.91453254e-01
-1.63320497e-01 -2.23843619e-01 2.68168360e-01 -4.45548892e-01
-5.66876054e-01 7.25620270e-01 -3.18982974e-02 -5.79812169e-01
-2.39891753e-01 -1.44000983e+00 -1.36241108e-01 -9.98888373e-01
1.00911781e-01 -3.33598763e-01 -1.47766456e-01 -3.64420116... | [9.80286693572998, -1.570784091949463] |
81c3b234-9ef5-45e8-bdf5-cb094c6dc6ff | on-multiple-intelligences-and-learning-styles | 2008.04793 | null | https://arxiv.org/abs/2008.04793v4 | https://arxiv.org/pdf/2008.04793v4.pdf | Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles | This article discusses some trends and concepts in developing new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence. We describe various aspects of multiple human... | ['Alexander P. Kuleshov', 'Andrzej Cichocki'] | 2020-08-07 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [-2.56650627e-01 4.14807260e-01 2.89915472e-01 1.04526607e-02
7.92387605e-01 -6.57126129e-01 3.73090714e-01 5.56678735e-02
-2.50272572e-01 1.02422452e+00 -2.52080649e-01 1.50750324e-01
-9.18131649e-01 -7.16923594e-01 7.46840909e-02 -5.25114119e-01
-9.32079852e-02 1.23046875e+00 -2.27635145e-01 -7.64056981... | [9.061321258544922, 6.348872184753418] |
2d72fab4-e761-440d-b8f4-bbc7da3705ae | weakly-supervised-action-localization-with | 1908.06552 | null | https://arxiv.org/abs/1908.06552v1 | https://arxiv.org/pdf/1908.06552v1.pdf | Weakly-supervised Action Localization with Background Modeling | We describe a latent approach that learns to detect actions in long sequences given training videos with only whole-video class labels. Our approach makes use of two innovations to attention-modeling in weakly-supervised learning. First, and most notably, our framework uses an attention model to extract both foreground... | ['Charless C. Fowlkes', 'Deva Ramanan', 'Phuc Xuan Nguyen'] | 2019-08-19 | weakly-supervised-action-localization-with-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Nguyen_Weakly-Supervised_Action_Localization_With_Background_Modeling_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Nguyen_Weakly-Supervised_Action_Localization_With_Background_Modeling_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 1.02962583e-01 6.99904263e-02 -7.87274122e-01 -3.79934192e-01
-8.92298937e-01 -5.64692855e-01 7.08926916e-01 -4.64642793e-01
-4.46492791e-01 5.42238414e-01 4.35854614e-01 6.70859739e-02
4.51969326e-01 -3.99401009e-01 -1.07951736e+00 -6.45232201e-01
-4.46221173e-01 1.92585468e-01 6.71881676e-01 9.11752433... | [8.45815372467041, 0.5873402953147888] |
86e6b3af-add5-4ebf-add6-ca30607208fd | specular-and-diffuse-reflection-based-face | 1907.12400 | null | https://arxiv.org/abs/1907.12400v5 | https://arxiv.org/pdf/1907.12400v5.pdf | Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices | In light of the rising demand for biometric-authentication systems, preventing face spoofing attacks is a critical issue for the safe deployment of face recognition systems. Here, we propose an efficient face presentation attack detection (PAD) algorithm that requires minimal hardware and only a small database, making ... | ['Akinori F. Ebihara', 'Kazuyuki Sakurai', 'Hitoshi Imaoka'] | 2019-07-29 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.96205491e-01 -3.84263694e-01 -2.14307439e-02 -2.09714159e-01
-3.88999850e-01 -4.18652594e-01 4.26666290e-01 -3.41222405e-01
-3.11337590e-01 3.44482362e-01 -2.94690907e-01 -5.50861835e-01
2.21116230e-01 -7.30127990e-01 -4.54980522e-01 -1.06364572e+00
8.46059769e-02 -1.53935835e-01 -1.14962444e-01 4.80376408... | [13.050591468811035, 1.1935224533081055] |
8e45244b-1959-4133-a746-fef56a5ace5e | cerebrum-7t-fast-and-fully-volumetric-brain | null | null | https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25636 | https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.25636 | CEREBRUM‐7T: Fast and Fully Volumetric Brain Segmentation of 7 Tesla MR Volumes | Ultra high-field MRI enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structure... | ['L', 'Muckli', 'D.', 'Bontempi', 'S.', 'Benini', 'M.', 'Svanera'] | 2021-01-28 | null | null | null | human-brain-mapping-2021-1 | ['brain-segmentation'] | ['medical'] | [ 8.17570239e-02 1.43765509e-01 5.03872752e-01 -4.66231436e-01
-4.71493989e-01 -4.59070116e-01 2.67687052e-01 2.30170731e-02
-8.66077006e-01 6.75924003e-01 -2.78054476e-01 -3.74881238e-01
-2.38299266e-01 -3.48699152e-01 -6.56016231e-01 -6.78942919e-01
-6.43917799e-01 8.35491896e-01 3.68276834e-01 1.75031558... | [14.148470878601074, -2.29386305809021] |
a8fde303-274c-4147-a96a-0db15867ed32 | unsupervised-acoustic-unit-discovery-for | 1904.07556 | null | https://arxiv.org/abs/1904.07556v2 | https://arxiv.org/pdf/1904.07556v2.pdf | Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks | For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsupervised discrete subword modelling could be useful for studies of phonetic category learning in infants or in low-resource speech technology... | ['Ewald van der Westhuizen', 'Elan van Biljon', 'Avashna Govender', 'André Nortje', 'Lisa van Staden', 'Leanne Nortje', 'Arnu Pretorius', 'Ryan Eloff', 'Herman Kamper', 'Benjamin van Niekerk'] | 2019-04-16 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 4.12716776e-01 5.81987202e-01 1.61939431e-02 -3.93205345e-01
-1.00644314e+00 -6.05057478e-01 5.30400932e-01 -2.97241986e-01
-2.90106684e-01 4.67628360e-01 5.72423816e-01 -4.59309965e-01
4.70608711e-01 -4.87909645e-01 -1.10011458e+00 -8.01233470e-01
1.12035535e-01 8.06475341e-01 -3.47150594e-01 2.29521811... | [14.80983829498291, 6.594585418701172] |
64c0530b-d239-49a9-b640-8a305f0431da | tackling-low-resourced-sign-language | 2212.01140 | null | https://arxiv.org/abs/2212.01140v1 | https://arxiv.org/pdf/2212.01140v1.pdf | Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22 | This paper describes the system developed at the Universitat Polit\`ecnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the voca... | ['Jordi Torres', 'Xavier Giró-i-Nieto', 'Gerard I. Gàllego', 'Laia Tarrés'] | 2022-12-02 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.46000117e-01 1.49078041e-01 -1.70531705e-01 -3.04997534e-01
-1.32907546e+00 -6.02317393e-01 7.56554782e-01 -7.81340718e-01
-8.65332901e-01 8.07968199e-01 6.24966621e-01 -2.39498585e-01
5.01860917e-01 -2.10302263e-01 -7.11464345e-01 -6.72557354e-01
1.73332378e-01 8.84899795e-01 -8.37011915e-03 -3.91080499... | [9.24260425567627, -6.570901870727539] |
d5e0cb4f-c2b2-4dc0-9bf4-f9d5962d419a | explainable-ai-and-visual-reasoning-insights | 2304.03318 | null | https://arxiv.org/abs/2304.03318v1 | https://arxiv.org/pdf/2304.03318v1.pdf | Explainable AI And Visual Reasoning: Insights From Radiology | Why do explainable AI (XAI) explanations in radiology, despite their promise of transparency, still fail to gain human trust? Current XAI approaches provide justification for predictions, however, these do not meet practitioners' needs. These XAI explanations lack intuitive coverage of the evidentiary basis for a given... | ['David Kirsh', 'Robert Kaufman'] | 2023-04-06 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.84883738e-01 1.44256175e+00 -7.37561464e-01 -7.31901169e-01
-4.39409882e-01 -4.07586455e-01 2.58422375e-01 6.25021100e-01
1.45421252e-01 8.16883147e-01 9.44437563e-01 -1.32210934e+00
-7.28732467e-01 -5.63233681e-02 -5.55757523e-01 -1.72358409e-01
1.39992192e-01 3.05653334e-01 -3.77700716e-01 2.27418616... | [8.647876739501953, 5.817203521728516] |
8e681ec8-9133-4577-b575-b486d2e52aa1 | scorpiano-a-system-for-automatic-music | 2108.10689 | null | https://arxiv.org/abs/2108.10689v1 | https://arxiv.org/pdf/2108.10689v1.pdf | Scorpiano -- A System for Automatic Music Transcription for Monophonic Piano Music | Music transcription is the process of transcribing music audio into music notation. It is a field in which the machines still cannot beat human performance. The main motivation for automatic music transcription is to make it possible for anyone playing a musical instrument, to be able to generate the music notes for a ... | ['Branislav Gerazov', 'Bojan Sofronievski'] | 2021-08-24 | null | null | null | null | ['music-transcription'] | ['music'] | [ 5.62804639e-01 -1.59816578e-01 3.22032571e-01 1.98279753e-01
-8.62464726e-01 -9.43451524e-01 4.13905531e-02 7.01816455e-02
-1.02894135e-01 3.86884272e-01 1.89728051e-01 -2.57511109e-01
-3.52410823e-01 -5.17036378e-01 2.79344451e-02 -5.17627656e-01
5.09096719e-02 5.39557755e-01 1.53822094e-01 -4.25983727... | [15.932680130004883, 5.328808307647705] |
44a715a9-c2c1-43b0-9269-ad852b16ab87 | learning-optimized-risk-scores | 1610.00168 | null | https://arxiv.org/abs/1610.00168v5 | https://arxiv.org/pdf/1610.00168v5.pdf | Learning Optimized Risk Scores | Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey ... | ['Berk Ustun', 'Cynthia Rudin'] | 2016-10-01 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.55816928e-01 1.76643610e-01 -6.17247581e-01 -4.79753673e-01
-1.37166059e+00 -5.22980988e-01 -3.34615946e-01 6.22154951e-01
-4.74845827e-01 8.81975234e-01 5.47387786e-02 -5.62742054e-01
-8.34816098e-01 -4.99530584e-01 -3.11523765e-01 -5.05404592e-01
-4.42493856e-01 8.62600803e-01 -1.63526043e-01 6.85717613... | [7.372278690338135, 4.571709632873535] |
96089798-2fb0-4831-be22-4efb55b27288 | cgc-contrastive-graph-clustering-for | 2204.08504 | null | https://arxiv.org/abs/2204.08504v4 | https://arxiv.org/pdf/2204.08504v4.pdf | CGC: Contrastive Graph Clustering for Community Detection and Tracking | Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clustering perspective. Recently, state-of-the-art clustering performance in various domains has ... | ['Christos Faloutsos', 'Nesreen Ahmed', 'Fan Du', 'Sungchul Kim', 'Iftikhar Ahamath Burhanuddin', 'Eunyee Koh', 'Ryan Rossi', 'Namyong Park'] | 2022-04-05 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-5.10601699e-01 -1.00190304e-01 -7.38935322e-02 -2.69625813e-01
5.49114794e-02 -6.50131702e-01 6.57828510e-01 6.48247838e-01
-1.89837396e-01 1.82545915e-01 1.91530690e-01 3.30144074e-03
-3.21070462e-01 -1.06854069e+00 -5.95402658e-01 -7.22347260e-01
-7.69624949e-01 7.95787871e-01 8.80387425e-02 -1.12045661... | [7.23563289642334, 5.992980003356934] |
e425c2a6-76ad-4ebd-9493-6f4533c49797 | learn-to-explain-multimodal-reasoning-via | 2209.09513 | null | https://arxiv.org/abs/2209.09513v2 | https://arxiv.org/pdf/2209.09513v2.pdf | Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering | When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used ... | ['Ashwin Kalyan', 'Peter Clark', 'Oyvind Tafjord', 'Song-Chun Zhu', 'Kai-Wei Chang', 'Liang Qiu', 'Tony Xia', 'Swaroop Mishra', 'Pan Lu'] | 2022-09-20 | null | null | null | null | ['science-question-answering', 'visual-commonsense-reasoning'] | ['miscellaneous', 'reasoning'] | [ 1.97965484e-02 5.05385876e-01 -7.74357989e-02 -5.30391812e-01
-1.30202198e+00 -9.04959202e-01 6.82815135e-01 2.03847647e-01
-3.81075479e-02 5.87237477e-01 4.24846768e-01 -7.32727349e-01
-2.11289749e-01 -8.18131804e-01 -9.19335604e-01 -1.40128762e-01
5.77538908e-01 8.29744816e-01 2.06826165e-01 -5.43365836... | [11.102359771728516, 7.928632736206055] |
c528887a-7c42-4bc1-b299-ecf44eeaed5a | self-supervised-video-representation-learning | 1811.09795 | null | http://arxiv.org/abs/1811.09795v1 | http://arxiv.org/pdf/1811.09795v1.pdf | Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles | Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have
been utilized for visual representation learning for still images, when the
number of labeled images is limited or absent at all. Recently, this worthwhile
stream of study extends to video domain where the cost of human labeling is
even more ... | ['Dahun Kim', 'In So Kweon', 'Donghyeon Cho'] | 2018-11-24 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 6.15732670e-02 -3.87810498e-01 -3.80675256e-01 -1.18475787e-01
-4.39693183e-01 -6.53882563e-01 3.49734634e-01 -4.88566607e-01
-4.19331342e-01 6.88589215e-01 5.67346513e-02 -3.19277763e-01
8.87378231e-02 -4.48761195e-01 -1.19421220e+00 -7.48538554e-01
-2.64063209e-01 3.86222214e-01 4.52665955e-01 -1.21291451... | [8.740528106689453, 0.41492748260498047] |
cd9ca85c-7d4b-4a7c-9b15-85574dec67f2 | longitudinal-analysis-of-mask-and-no-mask-on | 2111.00121 | null | https://arxiv.org/abs/2111.00121v5 | https://arxiv.org/pdf/2111.00121v5.pdf | Longitudinal Analysis of Mask and No-Mask on Child Face Recognition | Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verifi... | ['Neeta Nain', 'Zahid Akhtar', 'Praveen Kumar Chandaliya'] | 2021-10-29 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 4.85535972e-02 -1.76179588e-01 1.24710403e-01 -7.17170715e-01
-2.50534862e-01 -4.72978204e-01 3.84213179e-01 -3.39942962e-01
-2.48328105e-01 6.55941069e-01 -1.29274562e-01 1.11044779e-01
7.76992589e-02 -4.37317878e-01 -6.01433218e-01 -6.65641427e-01
-1.23108193e-01 1.09777570e-01 -4.56249833e-01 1.72597349... | [13.144583702087402, 0.9061009287834167] |
81c8a918-588a-44e7-9a77-fa0c70e42554 | tov-the-original-vision-model-for-optical | 2204.04716 | null | https://arxiv.org/abs/2204.04716v1 | https://arxiv.org/pdf/2204.04716v1.pdf | TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning | Do we on the right way for remote sensing image understanding (RSIU) by training models via supervised data-dependent and task-dependent way, instead of human vision in a label-free and task-independent way? We argue that a more desirable RSIU model should be trained with intrinsic structure from data rather that extri... | ['Haifeng Li', 'Weipeng Lu', 'Qing Zhu', 'Guo Zhang', 'Ji Qia', 'Chao Tao'] | 2022-04-10 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 5.10004222e-01 8.32697377e-02 -2.81190574e-01 -6.43568993e-01
-2.60687977e-01 -4.65902418e-01 5.55559933e-01 -1.53217271e-01
-5.55278838e-01 5.28143883e-01 -1.07890002e-01 -4.59527194e-01
-2.68616080e-01 -9.17337358e-01 -8.82045448e-01 -4.72211480e-01
2.50999540e-01 5.21341145e-01 2.33731449e-01 -2.06098214... | [9.632099151611328, -1.2771785259246826] |
34a66a23-415a-45cd-a9da-6ccfaa3dc6ea | thermodynamics-of-protein-folding | 2307.02175 | null | https://arxiv.org/abs/2307.02175v2 | https://arxiv.org/pdf/2307.02175v2.pdf | Thermodynamics of Protein Folding | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['K. Anton Feenstra', 'Sanne Abeln', 'Arthur Goetzee', 'Isabel Houtkamp', 'Maurits Dijkstra', 'Erik van Dijk', 'Halima Mouhib', 'Juami H. M. van Gils'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction', 'protein-folding'] | ['miscellaneous', 'natural-language-processing'] | [ 2.38445729e-01 -1.19647525e-01 -1.42193958e-01 -3.85668665e-01
-1.25437349e-01 -5.38052380e-01 -3.40559222e-02 4.77284014e-01
-1.86239362e-01 1.18015754e+00 -1.53514042e-01 -7.31226385e-01
1.02454431e-01 -4.75934684e-01 -6.56943738e-01 -1.28229845e+00
-2.00365886e-01 2.20421210e-01 6.00253083e-02 -4.93584275... | [4.7784528732299805, 5.246938228607178] |
2589b6fe-1013-41d1-b0d7-1299c5db8939 | learning-to-regulate-3d-head-shape-by | 2208.12078 | null | https://arxiv.org/abs/2208.12078v1 | https://arxiv.org/pdf/2208.12078v1.pdf | Learning to regulate 3D head shape by removing occluding hair from in-the-wild images | Recent 3D face reconstruction methods reconstruct the entire head compared to earlier approaches which only model the face. Although these methods accurately reconstruct facial features, they do not explicitly regulate the upper part of the head. Extracting information about this part of the head is challenging due to ... | ['Cai Yiyu', 'Varsha Saravanabavan', 'Sohan Anisetty'] | 2022-08-25 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.82552490e-02 5.29525280e-01 1.90623969e-01 -4.42330956e-01
-6.52028501e-01 -3.92394811e-01 5.61361074e-01 -2.67012686e-01
-6.85888082e-02 2.00719520e-01 2.77437806e-01 2.39155188e-01
6.00671053e-01 -5.74655056e-01 -9.28858101e-01 -7.13478565e-01
1.53997503e-02 6.66162610e-01 1.21051572e-01 -5.46652004... | [13.083429336547852, -0.062016479671001434] |
eec72c04-4e91-4c8b-949c-add9f9525a56 | a-hybrid-end-to-end-spatio-temporal-attention | 2307.03068 | null | https://arxiv.org/abs/2307.03068v1 | https://arxiv.org/pdf/2307.03068v1.pdf | A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition | Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tas... | ['Mujdat Cetin', 'Mastaneh Torkamani-Azar', 'Shadi Sartipi'] | 2023-07-06 | null | null | null | null | ['emotion-recognition', 'emotion-classification', 'transfer-learning', 'eeg-emotion-recognition', 'emotion-classification'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [ 1.72010995e-02 -1.39359146e-01 4.64179337e-01 -7.13650346e-01
-3.95984590e-01 -2.65803933e-01 2.64695406e-01 1.27743915e-01
-6.12647295e-01 7.38970637e-01 1.39942214e-01 5.40276170e-02
-2.35800251e-01 -6.48212016e-01 -6.83449805e-01 -5.89303792e-01
-4.59729463e-01 3.68219730e-03 -4.71739054e-01 -2.96906859... | [13.137338638305664, 3.476245164871216] |
bbb585f2-954e-48fe-9f3d-2809336eb1a7 | sketch-and-refine-towards-faithful-and | 2105.14778 | null | https://arxiv.org/abs/2105.14778v1 | https://arxiv.org/pdf/2105.14778v1.pdf | Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation | Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from low coverage and poor faithfulness problems. To mitigate these problems, we propose a novel Skeleton-based two-stage method that combines b... | ['Hongxia Yang', 'Jingren Zhou', 'Yichang Zhang', 'Chang Zhou', 'An Yang', 'Junyang Lin', 'Peng Wang'] | 2021-05-31 | null | https://aclanthology.org/2021.findings-acl.427 | https://aclanthology.org/2021.findings-acl.427.pdf | findings-acl-2021-8 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.96602130e-01 6.55889511e-01 -1.91267818e-01 -2.68508404e-01
-1.33495355e+00 -3.82765591e-01 8.15768838e-01 2.30490506e-01
-1.31669909e-01 1.03351128e+00 6.26078844e-01 -5.26985265e-02
1.11378327e-01 -1.21402121e+00 -7.13891089e-01 -1.78573579e-01
4.37855840e-01 1.00029862e+00 1.83202267e-01 -4.31708574... | [11.769485473632812, 8.889394760131836] |
36dafb2f-4062-47dd-a54f-594c38149e60 | scene-text-image-super-resolution-via-content | 2210.06924 | null | https://arxiv.org/abs/2210.06924v1 | https://arxiv.org/pdf/2210.06924v1.pdf | Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks | Text image super-resolution is a unique and important task to enhance readability of text images to humans. It is widely used as pre-processing in scene text recognition. However, due to the complex degradation in natural scenes, recovering high-resolution texts from the low-resolution inputs is ambiguous and challengi... | ['Yu-Wing Tai', 'Bin Wang', 'Rui Qin'] | 2022-10-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 8.06429982e-01 -6.34715796e-01 -9.36233178e-02 -4.75696295e-01
-8.25713754e-01 -1.67718291e-01 8.26449096e-01 -4.10165727e-01
-2.52596319e-01 5.25154829e-01 5.61553359e-01 2.22053781e-01
1.01398751e-01 -8.80303621e-01 -8.85664642e-01 -8.26905966e-01
9.55289125e-01 2.76927978e-01 2.73122877e-01 -4.10560489... | [11.361883163452148, -1.756841778755188] |
ce3325b3-8789-4109-867e-5f2a9069ffd5 | a-lightweight-music-texture-transfer-system-1 | 1810.01248 | null | https://arxiv.org/abs/1810.01248v3 | https://arxiv.org/pdf/1810.01248v3.pdf | A Lightweight Music Texture Transfer System | Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiate a novel system for transferring the texture of music, and release it as an open... | ['Yidan Liu', 'Faqiang Shi', 'Zhi Cai', 'JianXin Li', 'Chen Li', 'Xutan Peng'] | 2018-09-27 | a-lightweight-music-texture-transfer-system | https://arxiv.org/abs/1810.01248 | https://arxiv.org/pdf/1810.01248 | arxiv-preprint-2018-9 | ['music-texture-transfer'] | ['music'] | [ 2.63294101e-01 -6.25586092e-01 2.29157597e-01 -6.19163699e-02
-6.66348457e-01 -3.85374695e-01 3.95122677e-01 -9.39182639e-01
-4.25913595e-02 4.03836906e-01 1.59047499e-01 -1.68941338e-02
1.49310846e-02 -1.01876938e+00 -7.43429601e-01 -8.44536662e-01
4.77806509e-01 4.59716655e-02 2.18115836e-01 -1.07417688... | [15.777923583984375, 5.381175994873047] |
6e5fa786-26de-4747-83ea-0dd70cf3f9ff | evaluating-gpt-3-5-and-gpt-4-on-grammatical | 2306.15788 | null | https://arxiv.org/abs/2306.15788v1 | https://arxiv.org/pdf/2306.15788v1.pdf | Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese | We investigate the effectiveness of GPT-3.5 and GPT-4, two large language models, as Grammatical Error Correction (GEC) tools for Brazilian Portuguese and compare their performance against Microsoft Word and Google Docs. We introduce a GEC dataset for Brazilian Portuguese with four categories: Grammar, Spelling, Intern... | ['Fábio Perez', 'Maria Carolina Penteado'] | 2023-06-27 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-4.92593437e-01 4.38452736e-02 -7.48907849e-02 3.63128670e-02
-7.86474586e-01 -4.20695901e-01 2.79317528e-01 9.55604076e-01
-8.16051602e-01 9.01185095e-01 1.33431792e-01 -1.17958629e+00
-4.01980318e-02 -6.06415033e-01 -7.78069675e-01 2.73447037e-01
2.04397738e-01 4.35608596e-01 4.17089492e-01 -4.27928388... | [11.080853462219238, 10.675994873046875] |
32e1ad44-2770-4871-8561-78dc36225d87 | towards-more-suitable-personalization-in | 2305.15157 | null | https://arxiv.org/abs/2305.15157v1 | https://arxiv.org/pdf/2305.15157v1.pdf | Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training | Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems. However, almost all existing works have to face large communication burdens and the risk of disruption if the central server fails. Only limited... | ['DaCheng Tao', 'Xueqian Wang', 'Li Shen', 'Zihao Lin', 'Yan Sun', 'Yingqi Liu', 'Yifan Shi'] | 2023-05-24 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-6.23668969e-01 -1.10297829e-01 -3.13914925e-01 -5.98121107e-01
-9.62754369e-01 -4.26072896e-01 2.17627585e-01 -4.57482815e-01
-3.36405672e-02 7.72925913e-01 2.20842019e-01 -1.33395276e-03
-4.00230199e-01 -4.29217607e-01 -9.25404012e-01 -1.20450616e+00
1.62547268e-02 7.67885149e-01 -1.20476983e-01 6.72075897... | [5.835999011993408, 6.2428059577941895] |
47593dde-3ed4-496d-9352-37509f280ba2 | unseen-object-instance-segmentation-with | 2204.09847 | null | https://arxiv.org/abs/2204.09847v2 | https://arxiv.org/pdf/2204.09847v2.pdf | Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation | Segmenting unseen objects is a crucial ability for the robot since it may encounter new environments during the operation. Recently, a popular solution is leveraging RGB-D features of large-scale synthetic data and directly applying the model to unseen real-world scenarios. However, the domain shift caused by the sim2r... | ['Zhiyong Liu', 'Hong Qiao', 'Xu Yang', 'Siqi Zhang', 'Lu Zhang'] | 2022-04-21 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 3.80200386e-01 3.57171834e-01 1.47279650e-01 -5.29615879e-01
-9.61374164e-01 -4.59698886e-01 2.78525442e-01 -4.70657609e-02
-7.99329281e-01 7.07399905e-01 -4.46869880e-01 1.88109372e-02
4.29351479e-02 -6.61517739e-01 -9.95653331e-01 -8.36128294e-01
1.95596263e-01 6.13412619e-01 5.01345694e-01 -4.33219858... | [9.530826568603516, 1.2456157207489014] |
92007bc1-ff8d-407e-a9ad-0f802ccd2217 | keyword-extraction-in-scientific-documents | 2207.01888 | null | https://arxiv.org/abs/2207.01888v2 | https://arxiv.org/pdf/2207.01888v2.pdf | Keyword Extraction in Scientific Documents | The scientific publication output grows exponentially. Therefore, it is increasingly challenging to keep track of trends and changes. Understanding scientific documents is an important step in downstream tasks such as knowledge graph building, text mining, and discipline classification. In this workshop, we provide a b... | ['Ce Zhang', 'Peter Egger', 'Vanya Brucker', 'Michael Wechner', 'Sandra Mitrović', 'Emmanuel de Salis', 'Sara Nasirian', 'Parijat Ghoshal', 'Piriyakorn Piriyatamwong', 'Susie Xi Rao'] | 2022-07-05 | null | null | null | null | ['keyword-extraction', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-9.82662514e-02 -2.47457609e-01 -4.92543906e-01 1.71373561e-01
-4.16454762e-01 -1.00705624e+00 7.80545056e-01 1.30798447e+00
-4.16998744e-01 1.02674890e+00 2.72890747e-01 -8.77609253e-01
-3.97990793e-01 -9.75805461e-01 -4.11971986e-01 -2.81158566e-01
-5.07430956e-02 3.72065276e-01 1.43204138e-01 1.89421728... | [12.08536434173584, 8.930730819702148] |
0808ec01-32ca-4bf0-849a-01467ca92db9 | multi-view-multi-label-anomaly-network | 2210.16719 | null | https://arxiv.org/abs/2210.16719v2 | https://arxiv.org/pdf/2210.16719v2.pdf | Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network | Network traffic classification is the basis of many network security applications and has attracted enough attention in the field of cyberspace security. Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global informatio... | ['Xinbo Gao', 'Chao Yang', 'Chunlei Peng', 'Zhangxuan Dang', 'Yu Zheng'] | 2022-10-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.89289071e-03 -7.88692713e-01 -6.14145458e-01 -5.18473566e-01
2.31507748e-01 -5.72936594e-01 3.59176427e-01 -1.84950203e-01
-1.49702966e-01 2.69366443e-01 2.06062198e-02 -9.24284101e-01
-5.89129701e-02 -1.06555760e+00 -2.29061827e-01 -6.80549979e-01
3.19985390e-01 -8.77253264e-02 4.35609818e-01 -1.48878723... | [5.0692243576049805, 7.233149528503418] |
57a82ba3-8dd7-4a9a-a342-1d6ac70d5bf0 | data-augmentation-for-low-resource-neural | 1705.00440 | null | http://arxiv.org/abs/1705.00440v1 | http://arxiv.org/pdf/1705.00440v1.pdf | Data Augmentation for Low-Resource Neural Machine Translation | The quality of a Neural Machine Translation system depends substantially on
the availability of sizable parallel corpora. For low-resource language pairs
this is not the case, resulting in poor translation quality. Inspired by work
in computer vision, we propose a novel data augmentation approach that targets
low-frequ... | ['Arianna Bisazza', 'Marzieh Fadaee', 'Christof Monz'] | 2017-05-01 | data-augmentation-for-low-resource-neural-1 | https://aclanthology.org/P17-2090 | https://aclanthology.org/P17-2090.pdf | acl-2017-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.97198111e-01 -1.29152574e-02 -2.10146084e-01 -2.54756421e-01
-1.35180938e+00 -8.04049313e-01 8.96178126e-01 1.16941438e-03
-6.18306637e-01 1.26249397e+00 3.60763609e-01 -4.79429990e-01
6.65021539e-01 -5.62045932e-01 -1.07789350e+00 -3.82641375e-01
3.63911033e-01 6.90200984e-01 -3.79806727e-01 -6.25043631... | [11.611393928527832, 10.182670593261719] |
26652383-adeb-46b5-ae4e-84e9d0ab1918 | norm-in-norm-loss-with-faster-convergence-and | 2008.03889 | null | https://arxiv.org/abs/2008.03889v1 | https://arxiv.org/pdf/2008.03889v1.pdf | Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment | Currently, most image quality assessment (IQA) models are supervised by the MAE or MSE loss with empirically slow convergence. It is well-known that normalization can facilitate fast convergence. Therefore, we explore normalization in the design of loss functions for IQA. Specifically, we first normalize the predicted ... | ['Dingquan Li', 'Tingting Jiang', 'Ming Jiang'] | 2020-08-10 | null | null | null | null | ['blind-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.06395148e-02 -2.95008123e-01 -1.47086218e-01 -4.26205099e-01
-8.84489655e-01 -1.15397789e-01 3.37844230e-02 1.90935031e-01
-3.78846616e-01 5.40506244e-01 -4.84003387e-02 -1.16778269e-01
-2.98182786e-01 -6.47447765e-01 -4.91666287e-01 -8.02430332e-01
-1.02787264e-01 -2.57372409e-01 1.04080454e-01 2.12996081... | [11.739311218261719, -1.9129209518432617] |
a919a1d9-c360-4122-986f-b9bd4c501517 | a-dual-branch-network-for-infrared-and | 2101.09643 | null | https://arxiv.org/abs/2101.09643v1 | https://arxiv.org/pdf/2101.09643v1.pdf | A Dual-branch Network for Infrared and Visible Image Fusion | Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance ... | ['Xiao-Jun Wu', 'Yu Fu'] | 2021-01-24 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 2.84292251e-01 -2.07455590e-01 2.38041282e-01 -2.98877746e-01
-6.68761551e-01 -3.35866243e-01 5.04123390e-01 -6.05775833e-01
-2.63796002e-01 9.32499230e-01 1.58984229e-01 -1.04430884e-01
3.19827706e-01 -9.99138594e-01 -8.68013382e-01 -1.12010181e+00
4.95006472e-01 -4.04724121e-01 -1.01884745e-01 -3.97632211... | [10.589693069458008, -1.9071447849273682] |
7b9e4c74-d7a4-4d44-8b4a-6c37ac17c5ff | a-bayesian-approach-to-uncertainty-in-word | 2306.09066 | null | https://arxiv.org/abs/2306.09066v1 | https://arxiv.org/pdf/2306.09066v1.pdf | A Bayesian approach to uncertainty in word embedding bias estimation | Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and employing bootstrapping techniques... | ['Rafal Urbaniak', 'Alicja Dobrzeniecka'] | 2023-06-15 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-2.23861430e-02 -4.88902479e-02 -4.66216534e-01 -2.61084110e-01
-6.53618991e-01 -8.47827137e-01 8.95669281e-01 7.25620747e-01
-8.76201808e-01 6.62961721e-01 5.81436217e-01 -6.34173214e-01
-1.65282637e-01 -8.36202145e-01 -4.11308259e-01 -4.82627869e-01
1.01774141e-01 6.30953461e-02 4.01763283e-02 -2.53715757... | [9.316153526306152, 10.10273265838623] |
8cd0b376-f3af-4041-9676-9262350cc7fc | scalenet-a-shallow-architecture-for-scale | 2112.04846 | null | https://arxiv.org/abs/2112.04846v3 | https://arxiv.org/pdf/2112.04846v3.pdf | ScaleNet: A Shallow Architecture for Scale Estimation | In this paper, we address the problem of estimating scale factors between images. We formulate the scale estimation problem as a prediction of a probability distribution over scale factors. We design a new architecture, ScaleNet, that exploits dilated convolutions as well as self and cross-correlation layers to predict... | ['Krystian Mikolajczyk', 'Yurun Tian', 'Axel Barroso-Laguna'] | 2021-12-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Barroso-Laguna_ScaleNet_A_Shallow_Architecture_for_Scale_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['geometric-matching'] | ['computer-vision'] | [-7.90492371e-02 -1.06493779e-01 -4.96694557e-02 -6.94201350e-01
-6.81657255e-01 -7.41012871e-01 5.19200385e-01 -2.68375635e-01
-3.83895934e-01 1.60176754e-01 3.05499524e-01 6.87732846e-02
5.75870611e-02 -6.75125301e-01 -1.12433672e+00 -1.40941873e-01
-3.29465568e-02 4.51858968e-01 3.82638991e-01 2.79040225... | [8.262670516967773, -2.148998975753784] |
da09b666-b400-48ff-9a14-5072eee51b49 | scaneru-interactive-3d-visual-grounding-based | 2303.13186 | null | https://arxiv.org/abs/2303.13186v1 | https://arxiv.org/pdf/2303.13186v1.pdf | ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding | Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effe... | ['Heng Tao Shen', 'Zheng Wang', 'Yang Yang', 'Guoqing Wang', 'Yunqiang Pei', 'Ziyang Lu'] | 2023-03-23 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 2.20270172e-01 2.46316418e-01 -1.76838368e-01 -2.70151287e-01
-2.75054812e-01 -1.30716428e-01 6.67169392e-01 -5.52238785e-02
-1.00147694e-01 4.67277586e-01 2.02220981e-03 2.78377179e-02
-5.16372584e-02 -5.60524762e-01 -7.67953336e-01 -2.60664970e-01
1.41655669e-01 4.28622097e-01 9.86145437e-02 -2.98458815... | [5.168176651000977, 0.1244179978966713] |
cbc85854-164c-4fe7-9f86-3f1a98e98f2c | rotationnet-joint-object-categorization-and | 1603.06208 | null | http://arxiv.org/abs/1603.06208v4 | http://arxiv.org/pdf/1603.06208v4.pdf | RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints | We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which ar... | ['Yoshifumi Nishida', 'Yasuyuki Matsushita', 'Asako Kanezaki'] | 2016-03-20 | rotationnet-joint-object-categorization-and-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kanezaki_RotationNet_Joint_Object_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kanezaki_RotationNet_Joint_Object_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-object-classification', 'object-categorization'] | ['computer-vision', 'computer-vision'] | [-3.17469805e-01 -1.82281524e-01 -4.20149624e-01 -6.58445060e-01
-6.23032868e-01 -8.44142020e-01 6.07251763e-01 -2.47621581e-01
-1.29837126e-01 6.38347641e-02 -1.47694692e-01 9.51086432e-02
1.87541042e-02 -6.53755546e-01 -9.84600306e-01 -6.65558398e-01
2.35168979e-01 9.26422894e-01 1.17783405e-01 2.81519562... | [7.764144420623779, -2.7963435649871826] |
91fc00d8-a93b-4d82-9c9b-4520ec4579f6 | speech-to-speech-translation-for-a-real-world | null | null | https://research.facebook.com/publications/hokkien-direct-speech-to-speech-translation/ | https://research.facebook.com/micro_site/url/?click_from_context_menu=true&country=US&destination=https%3A%2F%2Fresearch.facebook.com%2Ffile%2F799432337944526%2FSpeech-to-speech-translation-for-a-real-world-unwritten-language.pdf&event_type=click&last_nav_impression_id=0ZiwMSAu7fV8ZZvIz&max_percent_page_viewed=40&max_v... | Speech-to-speech translation for a real-world unwritten language | We study speech-to-speech translation (S2ST) that translates speech from one language into another language and focuses on building systems to support languages without standard text writing systems. We use English-Taiwanese Hokkien as a case study, and present an end-to-end solution from training data collection, mode... | ['Ann Lee', 'Wei-Ning Hsu', 'Juan Pino', 'Changhan Wang', 'Sravya Popuri', 'Hirofumi Inaguma', 'Hongyu Gong', 'Holger Schwenk', 'Paul-Ambroise Duquenne', 'Paden Tomasello', 'Yu-An Chung', 'Justine Kao', 'Jingfei Du', 'Yilin Yang', 'Kevin Tran', 'Peng-Jen Chen'] | 2022-10-19 | null | null | null | arxiv-2022-10 | ['speech-to-speech-translation'] | ['speech'] | [ 4.88468200e-01 5.54951847e-01 -5.80787063e-01 -7.74839878e-01
-1.44499624e+00 -6.30182505e-01 7.32773423e-01 -4.87022787e-01
-1.24976330e-01 6.94182038e-01 5.75888574e-01 -8.56350601e-01
4.98329341e-01 -1.31024420e-01 -8.07370842e-01 -5.23992330e-02
3.72650057e-01 9.40520704e-01 -1.17335357e-01 -2.85665601... | [14.490272521972656, 7.177356243133545] |
33f3debb-2e09-4388-ada0-62a4d349d7c5 | program-repair-with-repeated-learning | null | null | https://openreview.net/forum?id=l5NavnRLD0A | https://openreview.net/pdf?id=l5NavnRLD0A | Program Repair with Repeated Learning | A key challenge in generate-and-validate automated program repair is directing the search for fixes so that it can efficiently find those that are more likely to be correct. To this end, several techniques use machine learning to capture the features of programmer-written fixes. In existing approaches, fitting the mode... | ['Anonymous'] | 2021-04-24 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.60832539e-01 -1.68007314e-01 -5.05125523e-01 -2.01207861e-01
-8.30608845e-01 -7.14776099e-01 1.36671677e-01 7.11254060e-01
8.52382928e-02 5.50206065e-01 -3.48333865e-02 -5.12608945e-01
-3.83327194e-02 -9.30086851e-01 -1.12016475e+00 -3.61703455e-01
-1.45319998e-01 2.58107960e-01 2.91982085e-01 -3.36009651... | [7.637397766113281, 7.718563556671143] |
80e7fde5-f5a5-4019-94ca-2b6a1d85c0fa | end-to-end-environmental-sound-classification | 1904.08990 | null | http://arxiv.org/abs/1904.08990v1 | http://arxiv.org/pdf/1904.08990v1.pdf | End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network | In this paper, we present an end-to-end approach for environmental sound
classification based on a 1D Convolution Neural Network (CNN) that learns a
representation directly from the audio signal. Several convolutional layers are
used to capture the signal's fine time structure and learn diverse filters that
are relevan... | ['Sajjad Abdoli', 'Patrick Cardinal', 'Alessandro Lameiras Koerich'] | 2019-04-18 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.04186520e-01 -2.60208607e-01 6.70436919e-01 -3.80966514e-01
-5.30789495e-01 -3.25001925e-01 2.20437095e-01 1.15051761e-01
-7.34794915e-01 3.60070497e-01 3.54678668e-02 -1.73729375e-01
-3.11874062e-01 -5.96797824e-01 -6.16286695e-01 -6.57553315e-01
-3.69116515e-01 -2.87637591e-01 4.12062109e-01 -9.50733945... | [15.219493865966797, 5.323307991027832] |
f46dbf42-b5a5-450c-b115-5042b9f8b31b | context-aware-semantic-similarity-measurement | 2305.03520 | null | https://arxiv.org/abs/2305.03520v1 | https://arxiv.org/pdf/2305.03520v1.pdf | Context-Aware Semantic Similarity Measurement for Unsupervised Word Sense Disambiguation | The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotate... | ['Jorge Martinez-Gil'] | 2023-05-05 | null | null | null | null | ['word-sense-disambiguation', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.65264916e-01 -1.41121492e-01 -9.53858346e-02 -3.49363446e-01
-5.17111957e-01 -6.44746482e-01 7.01820374e-01 8.04695010e-01
-1.00032604e+00 6.19067729e-01 4.15686578e-01 -7.41500929e-02
-2.78473705e-01 -6.75104499e-01 2.45441034e-01 -4.93084997e-01
3.30547392e-01 4.46042240e-01 3.08170706e-01 -7.23882318... | [10.2015962600708, 9.008199691772461] |
02afc2f8-2ea8-4137-af83-c438ae357604 | a-comparative-study-of-pretrained-language-1 | 2301.11847 | null | https://arxiv.org/abs/2301.11847v1 | https://arxiv.org/pdf/2301.11847v1.pdf | A Comparative Study of Pretrained Language Models for Long Clinical Text | Objective: Clinical knowledge enriched transformer models (e.g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the p... | ['Yuan Luo', 'Hanyin Wang', 'Faraz S. Ahmad', 'Ramsey M. Wehbe', 'Yikuan Li'] | 2023-01-27 | null | null | null | null | ['clinical-knowledge', 'document-classification'] | ['miscellaneous', 'natural-language-processing'] | [-8.53132159e-02 1.81750983e-01 -3.26733083e-01 -2.17286080e-01
-1.31181705e+00 -5.15197098e-01 2.38860369e-01 2.67761648e-01
-6.24366760e-01 8.52926731e-01 7.40002394e-01 -7.32484877e-01
-2.27343246e-01 -5.93498111e-01 -4.80899602e-01 -4.93072212e-01
-2.07356051e-01 9.18251395e-01 -7.81563297e-02 -2.22430408... | [8.560450553894043, 8.698201179504395] |
f5325eab-60aa-464b-a49c-d6454c3f2791 | replay-and-synthetic-speech-detection-with | 2010.15006 | null | https://arxiv.org/abs/2010.15006v3 | https://arxiv.org/pdf/2010.15006v3.pdf | Replay and Synthetic Speech Detection with Res2net Architecture | Existing approaches for replay and synthetic speech detection still lack generalizability to unseen spoofing attacks. This work proposes to leverage a novel model structure, so-called Res2Net, to improve the anti-spoofing countermeasure's generalizability. Res2Net mainly modifies the ResNet block to enable multiple fea... | ['Helen Meng', 'Dong Yu', 'Dan Su', 'Xunying Liu', 'Chao Weng', 'Na Li', 'Xu Li'] | 2020-10-28 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 3.71264368e-02 -4.04587746e-01 -2.39826247e-01 2.68488854e-01
-5.78139663e-01 -6.54967010e-01 4.63479728e-01 -1.62190065e-01
-2.92502195e-01 2.03699842e-01 2.72660166e-01 -7.94335544e-01
2.29786411e-01 -6.54297352e-01 -6.85938239e-01 -5.74676931e-01
-3.28538537e-01 -5.70650876e-01 8.05217505e-01 -4.69776273... | [14.068016052246094, 5.86100435256958] |
171f14fb-42d9-429a-b125-36fe219ceae0 | a-novel-discourse-parser-based-on-support | null | null | https://aclanthology.org/P09-1075 | https://aclanthology.org/P09-1075.pdf | A Novel Discourse Parser Based on Support Vector Machine Classification | This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statistical machine learning (multivariate capabilities of Support Vector Machines) and a rich feature space. RST offers a formal framework for hiera... | ['Helmut Prendinger', 'David duVerle'] | 2009-08-02 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.12360394e-01 1.16386390e+00 -2.82603562e-01 -3.48148435e-01
-9.45222497e-01 -8.07184637e-01 9.22505975e-01 7.78404176e-01
-4.04204041e-01 8.21166158e-01 7.71007359e-01 -6.46493495e-01
-4.08806950e-02 -6.30256236e-01 -2.59867251e-01 -3.90440792e-01
-2.23593995e-01 7.62107968e-01 4.98016685e-01 -6.77843094... | [10.779594421386719, 9.368782043457031] |
a4015666-acf0-43db-b6a1-830a4010e5e1 | anytime-sampling-for-autoregressive-models-1 | 2102.11495 | null | https://arxiv.org/abs/2102.11495v1 | https://arxiv.org/pdf/2102.11495v1.pdf | Anytime Sampling for Autoregressive Models via Ordered Autoencoding | Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to real-time computational resources. This challenge impedes the deployment of powerful autoregressive models, which involve a slow sampling pr... | ['Stefano Ermon', 'Aditya Grover', 'Rui Shu', 'Linyuan Gong', 'Sahaj Garg', 'Yang song', 'Yilun Xu'] | 2021-02-23 | anytime-sampling-for-autoregressive-models | https://openreview.net/forum?id=TSRTzJnuEBS | https://openreview.net/pdf?id=TSRTzJnuEBS | iclr-2021-1 | ['audio-generation'] | ['audio'] | [ 1.85659260e-01 5.42336283e-03 1.55596346e-01 -1.72358274e-01
-9.44084227e-01 -4.88858908e-01 6.08754456e-01 -3.85850877e-01
-2.96064019e-01 3.96184176e-01 3.58766913e-01 -1.79612160e-01
-1.00046851e-01 -6.16056561e-01 -5.16525924e-01 -7.71265745e-01
-8.85795727e-02 3.25362831e-01 -1.17539361e-01 1.36886120... | [15.444231033325195, 5.736680030822754] |
b5c77fc8-d2a4-439a-82a6-7e6a12662b57 | information-recovery-driven-deep-incomplete | 2304.00429 | null | https://arxiv.org/abs/2304.00429v3 | https://arxiv.org/pdf/2304.00429v3.pdf | Information Recovery-Driven Deep Incomplete Multiview Clustering Network | Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is c... | ['Yong Xu', 'Chao Huang', 'Xiaoling Luo', 'Zhihao Wu', 'Jie Wen', 'Chengliang Liu'] | 2023-04-02 | null | null | null | null | ['incomplete-multi-view-clustering', 'graph-reconstruction'] | ['computer-vision', 'graphs'] | [-9.11063850e-02 4.25929390e-02 -1.62424222e-01 -4.23258245e-01
-5.01040459e-01 -2.79129118e-01 3.95366609e-01 -3.81181210e-01
1.47922084e-01 2.68174112e-01 8.50572765e-01 2.12567672e-01
-2.99941957e-01 -6.27968490e-01 -6.62545741e-01 -7.67877758e-01
4.90176350e-01 2.63863474e-01 -5.97991683e-02 -4.09559794... | [8.374944686889648, 4.6024980545043945] |
02aee6be-f101-4c81-b070-edc3f922b91d | named-entity-inclusion-in-abstractive-text-1 | 2307.02570 | null | https://arxiv.org/abs/2307.02570v1 | https://arxiv.org/pdf/2307.02570v1.pdf | Named Entity Inclusion in Abstractive Text Summarization | We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model's attention on the named entities in a text. At first, the named entity recognition model RoBERTa is trained to determine named entities in the text. After tha... | ['Tatiana Batura', 'Sergey Berezin'] | 2023-07-05 | named-entity-inclusion-in-abstractive-text | https://aclanthology.org/2022.sdp-1.17 | https://aclanthology.org/2022.sdp-1.17.pdf | sdp-coling-2022-10 | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.96158513e-01 6.94148719e-01 -4.12985772e-01 -4.22739714e-01
-9.34324324e-01 -4.04491305e-01 4.55907464e-01 3.20096642e-01
-6.23851717e-01 9.86102581e-01 9.59309876e-01 -1.12496279e-01
3.15081656e-01 -5.91795683e-01 -7.21300781e-01 3.98983546e-02
1.72440380e-01 6.18861377e-01 1.57639533e-01 -1.25952139... | [12.505828857421875, 9.477412223815918] |
6dbd3e2c-3c84-460f-8fbe-64f9804d5a62 | image-set-querying-based-localization | 1509.06016 | null | http://arxiv.org/abs/1509.06016v1 | http://arxiv.org/pdf/1509.06016v1.pdf | Image Set Querying Based Localization | Conventional single image based localization methods usually fail to localize
a querying image when there exist large variations between the querying image
and the pre-built scene. To address this, we propose an image-set querying
based localization approach. When the localization by a single image fails to
work, the s... | ['Jie zhou', 'Baohua Chen', 'Yueqi Duan', 'Siyuan Huang', 'Lei Deng'] | 2015-09-20 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-3.67073677e-02 -3.70187342e-01 -1.04645588e-01 -4.33812678e-01
-1.12159514e+00 -8.11948001e-01 3.05338413e-01 6.35370240e-02
-4.19664741e-01 2.48110875e-01 -4.06911045e-01 -2.25856751e-02
-1.63877741e-01 -5.56728184e-01 -8.08189988e-01 -5.55118024e-01
2.31310189e-01 5.02562761e-01 6.65662467e-01 4.60067950... | [7.582637310028076, -2.1835479736328125] |
987629a7-e63f-47d5-aff3-daa3d57069ac | on-the-effectiveness-of-neural-text | 2006.05129 | null | https://arxiv.org/abs/2006.05129v1 | https://arxiv.org/pdf/2006.05129v1.pdf | On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech | Advanced neural network models have penetrated Automatic Speech Recognition (ASR) in recent years, however, in language modeling many systems still rely on traditional Back-off N-gram Language Models (BNLM) partly or entirely. The reason for this are the high cost and complexity of training and using neural language mo... | ['Péter Mihajlik', 'Tibor Fegyó', 'György Szaszák', 'Balázs Tarján'] | 2020-06-09 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 5.18807054e-01 6.10411525e-01 1.47165731e-01 -2.04325005e-01
-7.41414905e-01 -4.26379234e-01 6.88813388e-01 -1.23537742e-01
-6.71833694e-01 7.90651679e-01 5.06874204e-01 -9.13777053e-01
3.83590519e-01 -3.64526123e-01 -7.24138796e-01 -4.38971162e-01
3.02970827e-01 7.21233785e-01 -6.41507730e-02 -6.07552588... | [14.370655059814453, 6.843779563903809] |
aed67dbb-70dc-4317-a02e-0d64eef908cc | glm-dialog-noise-tolerant-pre-training-for | 2302.14401 | null | https://arxiv.org/abs/2302.14401v1 | https://arxiv.org/pdf/2302.14401v1.pdf | GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation | We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowl... | ['Jie Tang', 'Juanzi Li', 'Sunrui Lu', 'Nianyi Lin', 'Xiaohan Zhang', 'Haohua Wang', 'Yiqi Xu', 'Zeyao Ma', 'Zijun Yao', 'Jifan Yu', 'Daniel Zhang-li', 'Xiaokang Zhang', 'Jing Zhang'] | 2023-02-28 | null | null | null | null | ['dialogue-evaluation', 'dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [-6.07834697e-01 5.22842407e-01 6.25113817e-03 -2.91519076e-01
-9.32773650e-01 -1.02347803e+00 7.96079814e-01 -1.17099971e-01
-5.51158786e-01 9.48470592e-01 4.95037943e-01 -4.08221513e-01
-9.15433839e-02 -3.73978257e-01 1.18157744e-01 -5.93560450e-02
1.16891101e-01 1.20213807e+00 4.90150601e-01 -8.00721169... | [12.746399879455566, 7.947054862976074] |
99f95b7c-a3a7-4a02-aab2-4d9cf69d8625 | dch-2-a-parallel-customer-helpdesk-dialogue | 2104.08755 | null | https://arxiv.org/abs/2104.08755v2 | https://arxiv.org/pdf/2104.08755v2.pdf | DCH-2: A Parallel Customer-Helpdesk Dialogue Corpus with Distributions of Annotators' Labels | We introduce a data set called DCH-2, which contains 4,390 real customer-helpdesk dialogues in Chinese and their English translations. DCH-2 also contains dialogue-level annotations and turn-level annotations obtained independently from either 19 or 20 annotators. The data set was built through our effort as organisers... | ['Tetsuya Sakai', 'Zhaohao Zeng'] | 2021-04-18 | null | null | null | null | ['dialogue-evaluation', 'short-text-conversation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.93635631e-02 6.01556301e-01 7.34668896e-02 -5.20191610e-01
-1.35821354e+00 -9.81396616e-01 1.00859547e+00 5.64262152e-01
-7.50701785e-01 1.08279026e+00 8.20560217e-01 -6.12408221e-01
2.83627033e-01 -2.12394059e-01 2.27508023e-01 -2.05344304e-01
1.91194504e-01 1.22311664e+00 1.94366291e-01 -9.25011754... | [12.768919944763184, 8.024236679077148] |
e987e5db-9d1f-4ba4-b682-8b82d173bdb7 | fast-rir-fast-neural-diffuse-room-impulse | 2110.04057 | null | https://arxiv.org/abs/2110.04057v2 | https://arxiv.org/pdf/2110.04057v2.pdf | FAST-RIR: Fast neural diffuse room impulse response generator | We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RIR takes rectangular room dimensions, listener and speaker positions, and reverberation time as inputs and generates specular and diffuse ref... | ['Dong Yu', 'Dinesh Manocha', 'Zhenyu Tang', 'Meng Yu', 'Shi-Xiong Zhang', 'Anton Ratnarajah'] | 2021-10-07 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [-5.21794520e-02 -4.88971889e-01 1.32064021e+00 -2.66497970e-01
-1.67902768e+00 -5.46581924e-01 3.59918296e-01 -4.28415120e-01
-2.61158645e-01 3.98228019e-01 5.67361414e-01 -8.33875299e-01
3.85206729e-01 -9.04258311e-01 -7.38079607e-01 -8.87390196e-01
-7.16666952e-02 1.48621067e-01 5.03836088e-02 -5.22732317... | [15.163655281066895, 5.8220624923706055] |
92529a1d-8cae-4003-a760-a018d3c560f5 | micro-expression-recognition-based-on | 2205.14643 | null | https://arxiv.org/abs/2205.14643v1 | https://arxiv.org/pdf/2205.14643v1.pdf | Micro-Expression Recognition Based on Attribute Information Embedding and Cross-modal Contrastive Learning | Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions. Therefore, recognizing micro-expressions is a chal... | ['Jing Xiao', 'Zhangcheng Huang', 'Tianbo Wu', 'Jianzong Wang', 'Yanxin Song'] | 2022-05-29 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 4.00851145e-02 -5.74718535e-01 -2.28492066e-01 -5.30397654e-01
-2.48273283e-01 -2.62973551e-03 2.98133552e-01 -6.21660888e-01
-4.50829893e-01 5.04072905e-01 1.20698296e-01 2.92806983e-01
2.59869486e-01 -6.69779778e-01 -2.42680773e-01 -1.09503412e+00
9.15062055e-02 -4.15926456e-01 -3.47392231e-01 -1.64348125... | [13.6216402053833, 1.7398133277893066] |
a2bb4996-d637-4073-a2f6-19d85b82acf2 | signal-novelty-detection-as-an-intrinsic | null | null | https://www.mdpi.com/1424-8220/23/8/3985 | https://www.mdpi.com/1424-8220/23/8/3985/pdf | Signal Novelty Detection as an Intrinsic Reward for Robotics | In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot’s environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task’s completion or failure, leading to slow converge... | ['Jiří Pospíchal', 'Iveta Dirgová Luptáková', 'Martin Kubovčík'] | 2023-04-14 | null | null | null | mdpi-sensors-2023-4 | ['acrobot'] | ['playing-games'] | [-1.12372555e-01 2.06864342e-01 4.04418167e-03 -1.63090184e-01
-1.16655484e-01 -2.32886747e-01 5.12317121e-01 1.64850548e-01
-9.78576303e-01 9.60045218e-01 -1.59881815e-01 2.66507834e-01
-2.18851238e-01 -8.04230988e-01 -8.69657636e-01 -8.24645996e-01
-4.92504507e-01 4.06186998e-01 1.80522397e-01 -8.21697176... | [4.424523830413818, 1.6167484521865845] |
83803577-2353-4f04-9b36-c8513c26842d | analysis-of-different-losses-for-deep | 2204.02980 | null | https://arxiv.org/abs/2204.02980v3 | https://arxiv.org/pdf/2204.02980v3.pdf | Analysis of Different Losses for Deep Learning Image Colorization | Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically colorize an image, one can define well-suited objective functions related to the desired color output. Some of them are based on a specific ty... | ['Patricia Vitoria', 'Lara Raad', 'Rémi Giraud', 'Michaël Clément', 'Hernan Carrillo', 'Aurélie Bugeau', 'Coloma Ballester'] | 2022-04-06 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.02227077e-01 4.18420695e-02 1.40241235e-01 -2.63765037e-01
-7.54308164e-01 -5.55220544e-01 4.26532507e-01 8.51959363e-02
-5.73831141e-01 8.45551372e-01 -1.87073737e-01 -2.98449192e-02
1.36320451e-02 -8.29064608e-01 -6.63182259e-01 -8.59210551e-01
1.70429856e-01 1.49697170e-01 3.56410854e-02 -2.15278253... | [11.235074996948242, -1.3697936534881592] |
a3ccec13-a63d-4807-83fc-2d7eb579468c | audio-denoising-with-deep-network-priors | 1904.07612 | null | https://arxiv.org/abs/1904.07612v3 | https://arxiv.org/pdf/1904.07612v3.pdf | Speech Denoising by Accumulating Per-Frequency Modeling Fluctuations | We present a method for audio denoising that combines processing done in both the time domain and the time-frequency domain. Given a noisy audio clip, the method trains a deep neural network to fit this signal. Since the fitting is only partly successful and is able to better capture the underlying clean signal than th... | ['Michael Michelashvili', 'Lior Wolf'] | 2019-04-16 | null | null | null | null | ['audio-denoising', 'speech-denoising'] | ['audio', 'speech'] | [ 3.10017258e-01 -1.97349161e-01 4.20521259e-01 -1.93587273e-01
-1.25494802e+00 -4.66386735e-01 8.02396461e-02 2.54695356e-01
-2.65596271e-01 4.02184844e-01 3.80604565e-01 1.42566890e-01
-2.32109338e-01 -5.91884375e-01 -5.24721563e-01 -8.93658280e-01
-3.02801341e-01 -4.03785296e-02 5.73883727e-02 -1.02206022... | [15.282376289367676, 5.701899528503418] |
8e0ce48a-dfb4-4543-bc55-bf9dff108d43 | accelerated-training-for-matrix-norm | null | null | http://papers.nips.cc/paper/4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach | http://papers.nips.cc/paper/4663-accelerated-training-for-matrix-norm-regularization-a-boosting-approach.pdf | Accelerated Training for Matrix-norm Regularization: A Boosting Approach | Sparse learning models typically combine a smooth loss with a nonsmooth penalty, such as trace norm. Although recent developments in sparse approximation have offered promising solution methods, current approaches either apply only to matrix-norm constrained problems or provide suboptimal convergence rates. In this pap... | ['Yao-Liang Yu', 'Xinhua Zhang', 'Dale Schuurmans'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['multiview-learning'] | ['computer-vision'] | [-5.84721714e-02 -1.00756526e-01 -4.39628094e-01 -6.22058988e-01
-1.95117950e+00 -2.17861593e-01 1.95352644e-01 1.43522158e-01
-2.97450215e-01 7.94155180e-01 1.94194674e-01 -1.20848797e-01
-1.65629864e-01 -2.79737830e-01 -1.03509498e+00 -7.30566204e-01
-3.20805609e-01 3.38982671e-01 -2.92805403e-01 4.81454364... | [7.135993957519531, 4.495871543884277] |
ba0068d6-22ce-49dd-b417-aca7236467d7 | semi-automating-knowledge-base-construction | 2005.08146 | null | https://arxiv.org/abs/2005.08146v2 | https://arxiv.org/pdf/2005.08146v2.pdf | Semi-Automating Knowledge Base Construction for Cancer Genetics | In this work, we consider the exponentially growing subarea of genetics in cancer. The need to synthesize and centralize this evidence for dissemination has motivated a team of physicians to manually construct and maintain a knowledge base that distills key results reported in the literature. This is a laborious proces... | ['Kevin S. Hughes', 'Kanhua Yin', 'Somin Wadhwa', 'Byron C. Wallace'] | 2020-05-17 | null | https://openreview.net/forum?id=EQrvONEwh | https://openreview.net/pdf?id=EQrvONEwh | akbc-2020-6 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 6.48127079e-01 5.27483463e-01 -7.63260126e-01 -2.41041139e-01
-1.54495478e+00 -6.34195685e-01 4.22298133e-01 9.50026214e-01
-6.63243175e-01 9.93501067e-01 5.43045878e-01 -9.95214164e-01
-2.79294461e-01 -6.65540397e-01 -1.01395321e+00 -5.59171081e-01
4.78416272e-02 3.29999089e-01 -1.02738500e-01 3.16064566... | [8.517484664916992, 8.764847755432129] |
ff96c786-6351-4831-952e-a95d47e0b304 | probabilistic-model-of-narratives-over | 2004.06793 | null | https://arxiv.org/abs/2004.06793v1 | https://arxiv.org/pdf/2004.06793v1.pdf | Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model | Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary ex... | ['Ivan Garibay', 'Toktam A. Oghaz', 'Niloofar Yousefi', 'Jasser Jasser', 'Ece C. Mutlu'] | 2020-04-14 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [-3.97669002e-02 -8.19270611e-02 -4.83020425e-01 -2.77553853e-02
-1.10380578e+00 -6.80869401e-01 1.25928760e+00 9.14064765e-01
-3.19899470e-01 8.81902814e-01 1.09973538e+00 3.84508306e-03
-1.30323693e-01 -8.82867754e-01 -2.47613445e-01 -5.30516267e-01
-2.72705615e-01 1.27415303e-02 3.65513325e-01 -1.09462608... | [10.385971069335938, 7.3653459548950195] |
d57ed381-270e-4785-86ed-39a2dae8b706 | gaze-estimation-approach-using-deep | 2208.04298 | null | https://arxiv.org/abs/2208.04298v1 | https://arxiv.org/pdf/2208.04298v1.pdf | Gaze Estimation Approach Using Deep Differential Residual Network | Gaze estimation, which is a method to determine where a person is looking at given the person's full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze ... | ['Ahmad Chaddad', 'Ahmed Bouridane', 'Haoyu Wang', 'Xu Wang', 'Yujie Li', 'Longzhao Huang'] | 2022-08-08 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.39749482e-01 -2.91711255e-03 -7.06829205e-02 -5.18290162e-01
-8.44924077e-02 -2.27214787e-02 3.31046075e-01 -4.43230182e-01
-6.43130839e-01 6.16313457e-01 -2.23369077e-01 -2.74577811e-02
-2.12033950e-02 -2.96315879e-01 -6.47437692e-01 -9.07775164e-01
4.18062240e-01 -2.55231351e-01 6.02833852e-02 -2.38275882... | [14.069169998168945, 0.10357923060655594] |
8d187836-7eff-47eb-9eb3-54c72abbd292 | increasing-the-usefulness-of-already-existing | 2303.06727 | null | https://arxiv.org/abs/2303.06727v1 | https://arxiv.org/pdf/2303.06727v1.pdf | Increasing the usefulness of already existing annotations through WSI registration | Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the analysis of whole-slide-images (WSIs) of immunohistochemically (IHC) stained tissue sections could benefit from computational pathology meth... | ['Mattias Rantalainen', 'Johan Hartman', 'Daniel Budelmann', 'Stephanie Robertson', 'Balazs Acs', 'Viktoria Sartor', 'Philippe Weitz'] | 2023-03-12 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.39417121e-01 4.18433994e-01 -2.87295312e-01 -1.18615977e-01
-1.41511142e+00 -7.79135704e-01 2.75077075e-01 8.23456824e-01
-7.85233736e-01 5.16953707e-01 -5.42516820e-02 -4.83113825e-01
3.49398442e-02 -8.60702217e-01 -4.44924921e-01 -1.23354101e+00
-5.33539765e-02 6.46799147e-01 3.65441054e-01 1.39468178... | [15.174057006835938, -3.1046576499938965] |
22be240b-2f79-41b4-920e-84fcdda89a74 | truncated-tensor-schatten-p-norm-based | 2205.09390 | null | https://arxiv.org/abs/2205.09390v1 | https://arxiv.org/pdf/2205.09390v1.pdf | Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns | Rapid advances in sensor, wireless communication, cloud computing and data science have brought unprecedented amount of data to assist transportation engineers and researchers in making better decisions. However, traffic data in reality often has corrupted or incomplete values due to detector and communication malfunct... | ['Jian Sun', 'Guoyang Qin', 'Tong Nie'] | 2022-05-19 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [ 1.73315912e-01 -6.00273073e-01 -2.96509713e-01 -3.11534435e-01
-7.09642887e-01 -2.34282941e-01 1.19433537e-01 -5.00607312e-01
-2.23342925e-02 8.40772927e-01 4.69756812e-01 -3.64161015e-01
-6.12269461e-01 -5.43811917e-01 -7.19695091e-01 -1.03972924e+00
-9.48349014e-02 1.87893823e-01 -1.99672744e-01 -1.71215281... | [6.572211742401123, 2.149385690689087] |
afde3a5e-9610-4766-8c22-4c0522cc96da | progressive-residual-learning-for-single | 2103.07973 | null | https://arxiv.org/abs/2103.07973v1 | https://arxiv.org/pdf/2103.07973v1.pdf | Progressive residual learning for single image dehazing | The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when applied to real scenarios due to the unavailable or insufficient data problems. On the other hand, the physical model-based methods have be... | ['Wenqi Ren', 'Yuhua Qian', 'Deyu Li', 'Jiaying Liu', 'Bin Wang', 'Yudong Liang'] | 2021-03-14 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.27312645e-01 -2.14202449e-01 6.84598923e-01 -3.60355258e-01
-8.22276950e-01 3.78815755e-02 5.08131921e-01 1.34914517e-01
-1.97835937e-01 7.86072195e-01 7.30466247e-02 -2.75300324e-01
-6.88705087e-01 -7.79966354e-01 -3.71856153e-01 -1.34636414e+00
9.07895714e-02 1.93158820e-01 2.40210697e-01 -6.62046015... | [10.850862503051758, -3.2127110958099365] |
b1c181b6-2888-43f7-adc3-ca7d7360f063 | lungattn-advanced-lung-sound-classification | null | null | https://iopscience.iop.org/article/10.1088/1361-6579/ac27b9 | https://iopscience.iop.org/article/10.1088/1361-6579/ac27b9 | LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram | Objective. Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians.Approach. We propose a deep learning architecture, LungAttn, which incorporates augmented attention ... | ['Guoxing Wang', 'Liebin Zhao', 'Yongfu Li', 'Yi Ma', 'Qianyu Guo', 'Shijian Liu', 'Hansong Wang', 'Jiajun Yuan', 'Jizuo Li'] | 2021-10-29 | null | null | null | physiological-measurement-2021-10 | ['sound-classification'] | ['audio'] | [-1.25615209e-01 -3.11544746e-01 2.63884128e-03 3.23746622e-01
-7.98724234e-01 -1.16049506e-01 -5.97998984e-02 -1.66904256e-01
-4.34008360e-01 4.09668446e-01 2.45324075e-01 -3.96382540e-01
-3.22554171e-01 -6.62643611e-01 -2.86329240e-01 -6.91788852e-01
2.51498908e-01 5.13219416e-01 5.51993489e-01 1.98640645... | [14.567703247070312, 3.9187674522399902] |
6247b2c0-9b63-4696-bd77-a8e867497f81 | differentiable-mathematical-programming-for | 2210.02159 | null | https://arxiv.org/abs/2210.02159v1 | https://arxiv.org/pdf/2210.02159v1.pdf | Differentiable Mathematical Programming for Object-Centric Representation Learning | We propose topology-aware feature partitioning into $k$ disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a partitioning method which is represented as a linear program. The method is topologically aware sinc... | ['Efstratios Gavves', 'Phillip Lippe', 'Adeel Pervez'] | 2022-10-05 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [-1.53383613e-01 1.24565102e-01 -4.27096158e-01 -7.12146759e-01
-8.01320493e-01 -6.16962016e-01 1.57433972e-02 2.63244599e-01
1.78505667e-02 6.91379011e-02 -2.68601745e-01 -8.62547606e-02
-6.58665895e-01 -1.04934084e+00 -8.11804295e-01 -2.32118100e-01
-4.34107214e-01 7.57197142e-01 3.06947589e-01 1.01435736... | [8.968878746032715, -0.29677027463912964] |
3f809d56-6674-4d24-b57f-1b3b15c1aca4 | detecting-gan-generated-imagery-using-color | 1812.08247 | null | http://arxiv.org/abs/1812.08247v1 | http://arxiv.org/pdf/1812.08247v1.pdf | Detecting GAN-generated Imagery using Color Cues | Image forensics is an increasingly relevant problem, as it can potentially
address online disinformation campaigns and mitigate problematic aspects of
social media. Of particular interest, given its recent successes, is the
detection of imagery produced by Generative Adversarial Networks (GANs), e.g.
`deepfakes'. Lever... | ['Michael Albright', 'Scott McCloskey'] | 2018-12-19 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 9.35834229e-01 2.01206937e-01 -2.85655018e-02 1.01801626e-01
-1.14681673e+00 -1.21880186e+00 8.49453568e-01 -4.81918603e-01
-1.08291626e-01 6.46180987e-01 1.91895023e-01 -5.87654293e-01
4.76461977e-01 -8.77601981e-01 -9.30879176e-01 -8.43775392e-01
1.63927376e-01 1.36587143e-01 -1.35098800e-01 -1.57562196... | [12.39962100982666, 1.0916692018508911] |
b2b19169-1824-4982-bf98-38b9f1cc990e | does-black-box-attribute-inference-attacks-on | 2306.00578 | null | https://arxiv.org/abs/2306.00578v1 | https://arxiv.org/pdf/2306.00578v1.pdf | Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk? | Graph neural networks (GNNs) have shown promising results on real-life datasets and applications, including healthcare, finance, and education. However, recent studies have shown that GNNs are highly vulnerable to attacks such as membership inference attack and link reconstruction attack. Surprisingly, attribute infere... | ['Megha Khosla', 'Oliver Sihlovec', 'Anmar Hizber', 'Iyiola E. Olatunji'] | 2023-06-01 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 4.60127920e-01 7.75867164e-01 -3.20921987e-01 -5.19500554e-01
-3.26872408e-01 -8.77265692e-01 2.31549636e-01 4.62401032e-01
-1.27315819e-01 1.02415359e+00 -2.83991426e-01 -8.17562521e-01
-3.06196719e-01 -1.42377853e+00 -1.03234982e+00 -5.42993903e-01
-4.03334707e-01 4.50065583e-01 -1.17660820e-01 -1.17892902... | [5.964350700378418, 7.165371894836426] |
1d2676b3-ba5f-4ff4-8880-2d61f82d70eb | echovpr-echo-state-networks-for-visual-place | 2110.05572 | null | https://arxiv.org/abs/2110.05572v3 | https://arxiv.org/pdf/2110.05572v3.pdf | EchoVPR: Echo State Networks for Visual Place Recognition | Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models to recover the position of a query image (or images) from sequential datasets that include both spatial and temporal components. Recently, E... | ['Luca Manneschi', 'Eleni Vasilaki', 'Michael Mangan', 'Andrew Philippides', 'Andrew B. Barron', 'Mark Scerri', 'Anil Ozdemir'] | 2021-10-11 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 9.97191742e-02 -4.04639363e-01 -5.64553514e-02 -1.73221469e-01
-3.36597145e-01 -7.28035688e-01 1.03459835e+00 2.31328327e-02
-8.56930315e-01 5.76508641e-01 8.63210112e-02 -4.80614543e-01
-6.12579286e-01 -4.91484731e-01 -8.21441114e-01 -4.96733755e-01
-8.90641749e-01 2.83699960e-01 5.35055161e-01 -6.39394403... | [7.634484767913818, -1.8901467323303223] |
e0c43bed-a9a4-45e0-8111-c5e59a84b327 | semeval-2014-task-8-broad-coverage-semantic | null | null | https://aclanthology.org/S14-2008 | https://aclanthology.org/S14-2008.pdf | SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing | null | ['Jan Haji{\\v{c}}', 'Daniel Zeman', 'Yusuke Miyao', 'Stephan Oepen', 'Angelina Ivanova', 'Yi Zhang', 'Marco Kuhlmann', 'Dan Flickinger'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['semantic-dependency-parsing'] | ['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... | [-7.2930908203125, 3.8570878505706787] |
875fac6f-ab9d-4f66-b1a7-f9878960857f | fseval-a-benchmarking-framework-for-feature | null | null | https://joss.theoj.org/papers/10.21105/joss.04611 | https://www.theoj.org/joss-papers/joss.04611/10.21105.joss.04611.pdf | fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms | The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables users to run experiments in parallel and distributed over multiple machines, and export the results to... | ['George Azzopardi', 'Ahmad Alsahaf', 'Jeroen G. S. Overschie'] | 2022-11-23 | null | null | null | journal-of-open-source-software-2022-11 | ['automated-feature-engineering', 'feature-engineering', 'classification-with-costly-features'] | ['methodology', 'methodology', 'miscellaneous'] | [-4.15864468e-01 -8.01459014e-01 -2.22067639e-01 -4.63483155e-01
-8.67451787e-01 -8.91639352e-01 2.03939453e-01 3.09553325e-01
-1.39610872e-01 6.14044964e-01 -2.64086783e-01 -1.13318279e-01
-1.76120549e-01 -1.01370025e+00 -4.61172521e-01 -6.37107909e-01
-8.81377608e-03 6.99250042e-01 4.40259904e-01 8.90960097... | [7.409721851348877, 4.391233921051025] |
e2117266-6f76-4689-94f6-b166fe72c5aa | scandmm-a-deep-markov-model-of-scanpath | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.pdf | ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images | Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism. Most existing scanpath prediction methods for 360deg images do not give a complete treatment of the time-dependency when predicting human scanpath, resulting in inferior performance and poor ... | ['Zhou Wang', 'Shiqi Wang', 'Hanwei Zhu', 'Yuming Fang', 'Xiangjie Sui'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['saliency-detection', 'image-quality-assessment', 'scanpath-prediction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.44815663e-01 2.03704625e-01 -4.79856730e-01 -5.09685636e-01
8.70552063e-02 -9.82735679e-02 4.50160623e-01 -2.53305882e-01
-1.10170811e-01 2.21661367e-02 7.75377005e-02 -5.34975469e-01
-1.23706050e-01 -2.52404928e-01 -8.89753580e-01 -7.08346605e-01
-6.07222579e-02 -5.68652852e-03 5.87634623e-01 -2.64872432... | [10.105895042419434, 1.0962235927581787] |
58bae9e7-134b-42e9-8840-769b93002981 | fusion-gcn-multimodal-action-recognition | 2109.12946 | null | https://arxiv.org/abs/2109.12946v1 | https://arxiv.org/pdf/2109.12946v1.pdf | Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks | In this paper, we present Fusion-GCN, an approach for multimodal action recognition using Graph Convolutional Networks (GCNs). Action recognition methods based around GCNs recently yielded state-of-the-art performance for skeleton-based action recognition. With Fusion-GCN, we propose to integrate various sensor data mo... | ['Dietrich Paulus', 'Raphael Memmesheimer', 'Michael Duhme'] | 2021-09-27 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 5.67188025e-01 1.52337447e-01 -3.05111080e-01 -3.51223260e-01
-8.90488148e-01 -7.98124075e-02 7.60103583e-01 -4.60582711e-02
-5.04917622e-01 3.24418396e-01 9.12148297e-01 1.12698160e-01
-8.30461159e-02 -7.70174146e-01 -6.70545459e-01 -6.25975251e-01
-2.11298212e-01 2.27938145e-01 1.90222234e-01 -1.79814786... | [7.830733776092529, 0.456231027841568] |
c878610d-1df3-4d04-a20b-6daed2e36711 | cross-lingual-transfer-learning-for-phrase | 2306.02579 | null | https://arxiv.org/abs/2306.02579v1 | https://arxiv.org/pdf/2306.02579v1.pdf | Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model | Phrase break prediction is a crucial task for improving the prosody naturalness of a text-to-speech (TTS) system. However, most proposed phrase break prediction models are monolingual, trained exclusively on a large amount of labeled data. In this paper, we address this issue for low-resource languages with limited lab... | ['Jae-Min Kim', 'Jong-Hwan Kim', 'Hyun-Wook Yoon', 'Hoyeon Lee'] | 2023-06-05 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-2.11200818e-01 -2.88465410e-01 -7.50216663e-01 -2.75918007e-01
-1.77585709e+00 -6.36748612e-01 4.48722653e-02 -6.45996109e-02
-6.40796900e-01 6.12272263e-01 4.27335471e-01 -4.96804088e-01
6.46776557e-01 -3.13387394e-01 -6.35363936e-01 -2.21733093e-01
3.38986695e-01 4.76003796e-01 1.63786978e-01 -4.86783653... | [14.396265029907227, 6.9437689781188965] |
668a89b7-9d2b-4883-b617-3ba0b20caf26 | autoselect-automatic-and-dynamic-detection | 2012.05894 | null | https://arxiv.org/abs/2012.05894v1 | https://arxiv.org/pdf/2012.05894v1.pdf | AutoSelect: Automatic and Dynamic Detection Selection for 3D Multi-Object Tracking | 3D multi-object tracking is an important component in robotic perception systems such as self-driving vehicles. Recent work follows a tracking-by-detection pipeline, which aims to match past tracklets with detections in the current frame. To avoid matching with false positive detections, prior work filters out detectio... | ['Kris Kitani', 'Xinshuo Weng'] | 2020-12-10 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 5.36783524e-02 -5.36218762e-01 -1.77480727e-01 -2.96589464e-01
-7.03042924e-01 -9.40909684e-01 3.75796258e-01 9.64379609e-02
-7.01562524e-01 3.65123749e-01 -2.55036950e-01 -1.53164983e-01
1.26862854e-01 -6.87514663e-01 -6.76418960e-01 -4.51238811e-01
-1.11641679e-02 2.43382290e-01 1.29924262e+00 3.40440720... | [6.604880332946777, -2.0943405628204346] |
88ee5fb4-f26c-4db1-8534-111a9ebbab18 | fidnet-lidar-point-cloud-semantic | 2109.03787 | null | https://arxiv.org/abs/2109.03787v1 | https://arxiv.org/pdf/2109.03787v1.pdf | FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding | Projecting the point cloud on the 2D spherical range image transforms the LiDAR semantic segmentation to a 2D segmentation task on the range image. However, the LiDAR range image is still naturally different from the regular 2D RGB image; for example, each position on the range image encodes the unique geometry informa... | ['Xinming Huang', 'Lin Bai', 'Yiming Zhao'] | 2021-09-08 | null | null | null | null | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.01561683e-01 1.62197396e-01 -8.88906419e-02 -8.06059718e-01
-6.74459100e-01 -3.97964507e-01 2.32831448e-01 -2.35275224e-01
-4.96689498e-01 3.27521712e-01 -3.32341433e-01 -6.41430795e-01
-2.91327015e-02 -1.19765282e+00 -1.02687252e+00 -4.27531272e-01
2.49499589e-01 7.17045426e-01 5.18658280e-01 8.38330835... | [8.056082725524902, -3.0396382808685303] |
83cea8ea-8c77-47f6-a56e-caa4c20a2932 | multi-objective-consensus-clustering | 2002.10241 | null | https://arxiv.org/abs/2002.10241v2 | https://arxiv.org/pdf/2002.10241v2.pdf | Multi-objective Consensus Clustering Framework for Flight Search Recommendation | In the travel industry, online customers book their travel itinerary according to several features, like cost and duration of the travel or the quality of amenities. To provide personalized recommendations for travel searches, an appropriate segmentation of customers is required. Clustering ensemble approaches were dev... | ['Nicolas Pasquier', 'Simon Nanty', 'Sujoy Chatterjee', 'Maria A. Zuluaga'] | 2020-02-20 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [-3.22345197e-01 -3.99819285e-01 -1.75857142e-01 -7.99730420e-01
-6.56657934e-01 -8.23484540e-01 3.38704467e-01 5.51712751e-01
-5.43239415e-01 2.28761479e-01 -1.51024893e-01 -1.49505064e-01
-1.13007355e+00 -9.51886714e-01 -3.62909995e-02 -9.81768727e-01
2.04970334e-02 1.48895466e+00 -2.52194032e-02 -3.15468639... | [7.6097493171691895, 4.493999481201172] |
f0035e3e-daaf-4b08-b6ab-6b6cd4511313 | an-efficient-multilingual-language-model | 2305.15020 | null | https://arxiv.org/abs/2305.15020v1 | https://arxiv.org/pdf/2305.15020v1.pdf | An Efficient Multilingual Language Model Compression through Vocabulary Trimming | Multilingual language model (LM) have become a powerful tool in NLP especially for non-English languages. Nevertheless, model parameters of multilingual LMs remain large due to the larger embedding matrix of the vocabulary covering tokens in different languages. On the contrary, monolingual LMs can be trained in a targ... | ['Jose Camacho-Collados', 'Yi Zhou', 'Asahi Ushio'] | 2023-05-24 | null | null | null | null | ['model-compression'] | ['methodology'] | [-3.74746978e-01 2.81454355e-01 -4.24572498e-01 -3.39496098e-02
-1.00657415e+00 -8.19375157e-01 6.47351503e-01 -3.57885808e-02
-7.81384110e-01 1.18272078e+00 1.42231703e-01 -5.92741966e-01
3.10280502e-01 -6.45880818e-01 -8.78342927e-01 -6.76675022e-01
2.83108145e-01 7.76871145e-01 3.80017310e-02 -3.82368296... | [11.072182655334473, 10.070366859436035] |
8ac9c5a4-163d-4a01-b344-4f13b0c6677c | the-brain-tumor-segmentation-brats-challenge-3 | 2305.19369 | null | https://arxiv.org/abs/2305.19369v1 | https://arxiv.org/pdf/2305.19369v1.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa) | Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive... | ['Udunna C Anazodo', 'Abiodun Fatade', 'Farouk Dako', 'Spyridon Bakas', 'Ujjwal Baid', 'Bjoern H Menze', 'Zeke Meier', 'Elaine Johansson', 'Gian-Marco Conte', 'Maire Piraud', 'Christina Bukas', 'Koen van Leemput', 'Ariana Familiar', 'Zhifan Jiang', 'Xinyang Liu', 'Chunhao Wang', 'Zachary Reitman', 'Walter Wiggins', 'Ru... | 2023-05-30 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 2.77692489e-02 -2.64011994e-02 -1.93749085e-01 -3.74119193e-03
-1.13686907e+00 -3.66644442e-01 5.07205606e-01 6.97752774e-01
-8.04912925e-01 5.97563267e-01 4.79475200e-01 -8.48030984e-01
-1.89961180e-01 -6.48460209e-01 4.86714765e-02 -9.15406823e-01
-1.72624633e-01 8.37025702e-01 3.39457020e-02 1.26073975... | [14.735286712646484, -2.454430341720581] |
6b2feeaa-4be0-4674-a5e8-1009822ad931 | perceiving-unseen-3d-objects-by-poking-the | 2302.13375 | null | https://arxiv.org/abs/2302.13375v1 | https://arxiv.org/pdf/2302.13375v1.pdf | Perceiving Unseen 3D Objects by Poking the Objects | We present a novel approach to interactive 3D object perception for robots. Unlike previous perception algorithms that rely on known object models or a large amount of annotated training data, we propose a poking-based approach that automatically discovers and reconstructs 3D objects. The poking process not only enable... | ['Xiaowei Zhou', 'Hujun Bao', 'Yunzhou Song', 'Linghao Chen'] | 2023-02-26 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 2.19615418e-02 3.42563957e-01 -2.64170486e-02 -3.08273435e-01
-8.93259346e-02 -5.94910741e-01 3.51603299e-01 1.10066114e-02
1.46361113e-01 3.82814735e-01 -3.00723642e-01 1.93522591e-02
-1.69384688e-01 -8.24194968e-01 -1.21327186e+00 -4.96427923e-01
-2.58831680e-01 1.05686069e+00 6.98767900e-01 2.21242785... | [5.877902507781982, -0.889857828617096] |
2143b651-8de4-453f-9976-ad64aa98f650 | w2kpe-keyphrase-extraction-with-word-word | 2303.13463 | null | https://arxiv.org/abs/2303.13463v1 | https://arxiv.org/pdf/2303.13463v1.pdf | W2KPE: Keyphrase Extraction with Word-Word Relation | This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials. We model the challenge as a single-class Named Entity Recognition task and developed techniques for better performance on the ... | ['Wei Wang', 'Shichen Dong', 'Wen Cheng'] | 2023-03-22 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.50598556e-01 5.39846383e-02 -3.18652272e-01 -1.88791975e-01
-1.33547282e+00 -8.01710665e-01 7.35333025e-01 6.13530457e-01
-1.13815832e+00 6.77882791e-01 5.71177483e-01 -2.33201370e-01
9.73863900e-02 -4.98863578e-01 -8.91776919e-01 -4.86242235e-01
6.21571168e-02 2.67822117e-01 3.49020004e-01 1.00894086... | [12.28503131866455, 8.8950834274292] |
c93b94cf-c2a8-43df-8758-f185e07a1a06 | neural-network-training-with-asymmetric | 2201.13377 | null | https://arxiv.org/abs/2201.13377v1 | https://arxiv.org/pdf/2201.13377v1.pdf | Neural Network Training with Asymmetric Crosspoint Elements | Analog crossbar arrays comprising programmable nonvolatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically degrades the classification performance of networks trained with conv... | ['Seyoung Kim', 'Wilfried Haensch', 'John Rozen', 'Jesus A. del Alamo', 'Tomasz Nowicki', 'Teodor K. Todorov', 'Tayfun Gokmen', 'Murat Onen'] | 2022-01-31 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 4.24826384e-01 -9.23300385e-02 -1.67469695e-01 -2.43548527e-01
2.27564842e-01 -6.53434217e-01 4.39242482e-01 6.52112365e-02
-7.44952917e-01 9.15826142e-01 -4.95943248e-01 -7.98345029e-01
3.27665247e-02 -9.80751991e-01 -8.41765642e-01 -1.10911167e+00
1.08836271e-01 2.89204925e-01 1.55520841e-01 -3.08798254... | [8.247868537902832, 2.555638074874878] |
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