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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
34180d00-7184-4e2c-8665-b71d2d7ff1cb | multi-loss-convolutional-network-with-time | 2306.08956 | null | https://arxiv.org/abs/2306.08956v1 | https://arxiv.org/pdf/2306.08956v1.pdf | Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement | The Dual-Path Convolution Recurrent Network (DPCRN) was proposed to effectively exploit time-frequency domain information. By combining the DPRNN module with Convolution Recurrent Network (CRN), the DPCRN obtained a promising performance in speech separation with a limited model size. In this paper, we explore self-att... | ['Jie Ji', 'Yi Zhou', 'Hongqing Liu', 'Liang Wan'] | 2023-06-15 | null | null | null | null | ['speech-separation', 'speech-enhancement'] | ['speech', 'speech'] | [ 8.52911621e-02 -1.18350551e-01 1.09053731e-01 -1.72274068e-01
-6.55367911e-01 1.99540347e-01 3.02566439e-01 -2.93573618e-01
-5.10168016e-01 3.17091763e-01 5.30425906e-01 -4.57533836e-01
1.44310454e-02 -4.79729414e-01 -4.90918905e-01 -7.80812800e-01
1.34003356e-01 -3.83197665e-01 2.04069793e-01 -2.30362967... | [14.851171493530273, 5.89178991317749] |
95c0bf84-d1bd-4997-ba10-3e00a6a72f1c | evaluating-bert-based-pre-training-language | 2203.07731 | null | https://arxiv.org/abs/2203.07731v1 | https://arxiv.org/pdf/2203.07731v1.pdf | Evaluating BERT-based Pre-training Language Models for Detecting Misinformation | It is challenging to control the quality of online information due to the lack of supervision over all the information posted online. Manual checking is almost impossible given the vast number of posts made on online media and how quickly they spread. Therefore, there is a need for automated rumour detection techniques... | ['Amitava Datta', 'Ghulam Mubashar Hassan', 'Rini Anggrainingsih'] | 2022-03-15 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-1.17494855e-02 -1.91400364e-01 -1.49259806e-01 -1.27237692e-01
-3.57132494e-01 -2.89555043e-01 8.84097636e-01 7.28010416e-01
-6.72748148e-01 7.85263777e-01 2.49076679e-01 -3.62944067e-01
-8.22014064e-02 -8.14286709e-01 -3.80324036e-01 -3.18978339e-01
-1.96163669e-01 3.92900229e-01 5.56694448e-01 -3.82152349... | [8.29907512664795, 10.149658203125] |
d0490626-4b42-49fb-9cbf-253aa25c6ef4 | uncertainty-guided-mixup-for-semi-supervised | 2107.06707 | null | https://arxiv.org/abs/2107.06707v1 | https://arxiv.org/pdf/2107.06707v1.pdf | Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data | Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data. However, the source data are not always available due to the privacy preserving consideration or bandwidth limitation. Source-free domain adaptation aims to solve the above pr... | ['Sheng Zhou', 'Zhen Zhang', 'Jiajun Bu', 'Ning Ma'] | 2021-07-14 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 4.40012693e-01 -6.46511018e-02 -6.25355244e-01 -6.12906814e-01
-1.11963570e+00 -6.89976692e-01 5.25124013e-01 3.13519291e-03
-4.68599886e-01 1.08135962e+00 3.61440450e-01 -1.43946148e-02
4.65054102e-02 -5.00579238e-01 -5.92306197e-01 -8.01611543e-01
4.73100066e-01 5.55213630e-01 1.35727242e-01 -6.79070801... | [10.389301300048828, 3.1688895225524902] |
9c543843-82fd-4d18-97ec-e57e5c98639a | swinir-image-restoration-using-swin | 2108.10257 | null | https://arxiv.org/abs/2108.10257v1 | https://arxiv.org/pdf/2108.10257v1.pdf | SwinIR: Image Restoration Using Swin Transformer | Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which... | ['Radu Timofte', 'Luc van Gool', 'Kai Zhang', 'Guolei Sun', 'JieZhang Cao', 'Jingyun Liang'] | 2021-08-23 | null | null | null | null | ['color-image-denoising', 'video-super-resolution', 'jpeg-compression-artifact-reduction', 'grayscale-image-denoising'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.82437301e-01 -3.96221608e-01 2.53797531e-01 -3.40964168e-01
-1.10508764e+00 1.37173042e-01 3.03307503e-01 -4.15295899e-01
-4.15069222e-01 5.53279221e-01 2.42376551e-01 9.27839279e-02
-7.38235861e-02 -7.33791411e-01 -9.18242276e-01 -7.53114641e-01
4.14985158e-02 -3.84234816e-01 2.84907162e-01 -5.60930669... | [11.096206665039062, -2.0992584228515625] |
003c6ddc-a32e-40fa-b6a9-abf3c0ed01e0 | qmsum-a-new-benchmark-for-query-based-multi | 2104.05938 | null | https://arxiv.org/abs/2104.05938v1 | https://arxiv.org/pdf/2104.05938v1.pdf | QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization | Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single sh... | ['Dragomir Radev', 'Xipeng Qiu', 'Yang Liu', 'Asli Celikyilmaz', 'Ahmed Hassan Awadallah', 'Rahul Jha', 'Mutethia Mutuma', 'Ahmad Zaidi', 'Tao Yu', 'Da Yin', 'Ming Zhong'] | 2021-04-13 | null | https://aclanthology.org/2021.naacl-main.472 | https://aclanthology.org/2021.naacl-main.472.pdf | naacl-2021-4 | ['meeting-summarization'] | ['natural-language-processing'] | [ 4.16868240e-01 1.77361801e-01 -3.06987576e-02 -5.12146294e-01
-1.62154830e+00 -7.38279045e-01 6.80828094e-01 6.58445179e-01
-3.10444385e-01 9.72939372e-01 9.75881398e-01 2.04789609e-01
-5.55724576e-02 -1.96845874e-01 -1.76212296e-01 -2.20098123e-01
1.83854923e-01 6.66071475e-01 1.11897074e-01 -2.88341045... | [12.603154182434082, 9.403907775878906] |
d5ad56c9-85ca-4b61-9eb2-dd8cd1192f2a | structure-guided-image-outpainting | 2212.12326 | null | https://arxiv.org/abs/2212.12326v1 | https://arxiv.org/pdf/2212.12326v1.pdf | Structure-guided Image Outpainting | Deep learning techniques have made considerable progress in image inpainting, restoration, and reconstruction in the last few years. Image outpainting, also known as image extrapolation, lacks attention and practical approaches to be fulfilled, owing to difficulties caused by large-scale area loss and less legitimate n... | ['Wenliang Jia', 'Weixi Cheng', 'Xi Wang'] | 2022-12-21 | null | null | null | null | ['image-outpainting', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.81446832e-01 1.29036963e-01 8.32384750e-02 -1.68509968e-02
-5.19838750e-01 -1.32077917e-01 4.36214685e-01 -5.24445295e-01
-1.23619758e-01 1.04914045e+00 2.18210563e-01 7.89855197e-02
2.70721018e-01 -9.55289781e-01 -9.67008650e-01 -6.79865539e-01
4.85949516e-01 -2.81406920e-02 9.17041600e-02 -2.02461377... | [11.405214309692383, -1.1830518245697021] |
75ab6cfd-afc3-4466-bba8-3d126a49eeb8 | spatial-mixup-directional-loudness | 2110.06126 | null | https://arxiv.org/abs/2110.06126v1 | https://arxiv.org/pdf/2110.06126v1.pdf | Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection | Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain. For sound event localization and detection (SELD) tasks several augmentation methods have been proposed, with most borrowing ideas from other domains such as images, speech, or... | ['Yuki Mitsufuji', 'Shusuke Takahashi', 'Yuichiro Koyama', 'Kazuki Shimada', 'Ricardo Falcon-Perez'] | 2021-10-12 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 4.58452523e-01 -1.54213324e-01 1.38315782e-01 -8.67389143e-02
-1.13687074e+00 -5.31893849e-01 6.94083393e-01 2.13383585e-01
-5.13816416e-01 6.24839246e-01 8.93755615e-01 -1.99534167e-02
2.91072726e-02 -3.82404447e-01 -7.51259029e-01 -8.50451112e-01
-2.00397059e-01 -1.26534432e-01 3.63819927e-01 -1.15929849... | [15.167342185974121, 5.417155742645264] |
87f3d47f-e8eb-49cf-98b6-78a6089e5300 | a-chinese-multi-type-complex-questions | 2111.06086 | null | https://arxiv.org/abs/2111.06086v1 | https://arxiv.org/pdf/2111.06086v1.pdf | A Chinese Multi-type Complex Questions Answering Dataset over Wikidata | Complex Knowledge Base Question Answering is a popular area of research in the past decade. Recent public datasets have led to encouraging results in this field, but are mostly limited to English and only involve a small number of question types and relations, hindering research in more realistic settings and in langua... | ['Zhou Zhao', 'Songfang Huang', 'Yifan He', 'Shushu Wang', 'Ran Qin', 'Fengqing Jiang', 'Qifan Pan', 'Yechen Xu', 'Lichao Zhang', 'Min Yang', 'Jianyun Zou'] | 2021-11-11 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-5.26240528e-01 3.12116921e-01 -1.66871622e-01 -5.03154516e-01
-1.39404690e+00 -8.49435985e-01 7.31412172e-02 1.03359535e-01
-6.61468506e-01 1.09450936e+00 4.96177822e-01 -4.78729010e-01
3.98601145e-02 -1.27526772e+00 -9.11142230e-01 9.39658955e-02
6.60785317e-01 1.16897488e+00 1.00754023e+00 -8.04618299... | [10.670328140258789, 7.942941665649414] |
23a1a3a5-38df-448d-89c8-15423833778f | reaching-kesten-stigum-threshold-in-the | 2305.10227 | null | https://arxiv.org/abs/2305.10227v1 | https://arxiv.org/pdf/2305.10227v1.pdf | Reaching Kesten-Stigum Threshold in the Stochastic Block Model under Node Corruptions | We study robust community detection in the context of node-corrupted stochastic block model, where an adversary can arbitrarily modify all the edges incident to a fraction of the $n$ vertices. We present the first polynomial-time algorithm that achieves weak recovery at the Kesten-Stigum threshold even in the presence ... | ['David Steurer', 'Yiding Hua', "Tommaso d'Orsi", 'Jingqiu Ding'] | 2023-05-17 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 5.09937704e-01 2.78598905e-01 -2.06872877e-02 4.54303920e-01
-1.00152969e+00 -1.24607563e+00 4.27077338e-02 3.75394970e-01
-2.29282901e-01 4.63470817e-01 2.18135049e-03 -5.02693415e-01
-5.02478778e-01 -9.09969628e-01 -1.22573888e+00 -1.16223371e+00
-7.57353961e-01 3.59641314e-01 2.49214604e-01 -5.80765307... | [6.758244514465332, 5.087464809417725] |
6c354141-f434-4f8f-ad4c-9b9f336f8c13 | win-fail-action-recognition | 2102.07355 | null | https://arxiv.org/abs/2102.07355v1 | https://arxiv.org/pdf/2102.07355v1.pdf | Win-Fail Action Recognition | Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a truer, deeper u... | ['Brendan Morris', 'Paritosh Parmar'] | 2021-02-15 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 4.13461536e-01 -3.98692936e-01 -8.69673491e-01 -3.50610137e-01
-8.74608278e-01 -5.61302423e-01 6.68561757e-01 -5.77957869e-01
-2.37775698e-01 4.43669289e-01 5.95263898e-01 -1.26497075e-01
-2.74026901e-01 -3.37591261e-01 -6.03806496e-01 -6.32419646e-01
-2.97948301e-01 3.27398509e-01 9.11120698e-02 -1.06041133... | [8.405569076538086, 0.6400545239448547] |
94dbaa38-263f-44a9-aee8-84c6dc7ae753 | unified-multi-modal-landmark-tracking-for | 2011.06838 | null | https://arxiv.org/abs/2011.06838v3 | https://arxiv.org/pdf/2011.06838v3.pdf | Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry | We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and pla... | ['Maurice Fallon', 'Sandipan Das', 'Marco Camurri', 'David Wisth'] | 2020-11-13 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 2.18836784e-01 -1.98033154e-02 5.52491397e-02 -1.66134745e-01
-6.46267474e-01 -7.34139562e-01 5.65869629e-01 2.36752182e-01
-9.22696769e-01 7.80781507e-01 -6.83541358e-01 -2.57478595e-01
-7.40299523e-02 -7.99269795e-01 -8.64387274e-01 -4.60106432e-01
-2.76797593e-01 8.69657576e-01 7.41919637e-01 -1.55988932... | [7.397130489349365, -2.1227917671203613] |
e39d4342-ab3d-4b3d-801e-85317af5d137 | verimedi-pill-identification-using-proxy | 2104.11231 | null | https://arxiv.org/abs/2104.11231v1 | https://arxiv.org/pdf/2104.11231v1.pdf | VeriMedi: Pill Identification using Proxy-based Deep Metric Learning and Exact Solution | We present the system that we have developed for the identification and verification of pills using images that are taken by the VeriMedi device. The VeriMedi device is an Internet of Things device that takes pictures of a filled pill vial from the bottom of the vial and uses the solution that is presented in this rese... | ['Alina Nescerecka', 'Stanislavs Hilcuks', 'Viktors Roze', 'Tekin Evrim Ozmermer'] | 2021-04-22 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.05263749e-01 1.53987408e-01 -2.47170422e-02 -3.85217100e-01
-1.78165182e-01 -5.84977746e-01 1.06648125e-01 6.18130453e-02
-2.47252613e-01 3.23623925e-01 -3.14020604e-01 -3.37955020e-02
1.56593427e-01 -9.56452012e-01 -6.58725798e-01 -7.80620992e-01
2.21988499e-01 6.68577015e-01 2.29497328e-01 2.14954033... | [14.592133522033691, -2.5858805179595947] |
0a0fa9c2-0c12-4af7-9e0e-dfaca11cb5f4 | jointly-learning-propagating-features-on-the | null | null | https://link.springer.com/chapter/10.1007/978-3-031-12423-5_1 | https://link.springer.com/chapter/10.1007/978-3-031-12423-5_1 | Jointly Learning Propagating Features on the Knowledge Graph for Movie Recommendation | Knowledge graphs are widely used as auxiliary information to improve the performance in recommender systems. This enables items to be aligned with knowledge entities and provides additional item attributes to facilitate learning interactions between users and items. However, the lack of user connections in the knowledg... | ['Qiong Chang', 'Jun Miyazaki', 'Yun Liu'] | 2022-08-22 | null | null | null | database-and-expert-systems-applications-2022 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'movie-recommendation'] | ['graphs', 'methodology', 'miscellaneous'] | [-1.95387453e-01 7.49383271e-02 -8.63837957e-01 -5.19205332e-01
-8.41405317e-02 -6.26205921e-01 3.28556120e-01 3.05529773e-01
-3.93102616e-01 6.63508832e-01 6.21104419e-01 -8.62665921e-02
-4.76223528e-01 -9.30362701e-01 -6.05470955e-01 -3.43110293e-01
1.93505604e-02 1.77800879e-01 4.00536448e-01 -2.75509089... | [10.19851016998291, 5.620154857635498] |
3829547a-3c23-4311-a57c-7d96e5eb79ea | red-sfa-relation-discovery-based-slow-feature | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.pdf | ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering | For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an i... | ['Jun Li', 'Zhang Zhang', 'Kaiqi Huang', 'Tieniu Tan', 'Peipei Yang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['time-series-clustering'] | ['time-series'] | [-3.28834802e-01 -1.56824693e-01 -9.41244140e-02 -1.72898665e-01
-5.89526474e-01 -3.23828727e-01 4.07058626e-01 3.06417525e-01
-1.95026621e-01 6.59356296e-01 1.61105677e-01 1.30885899e-01
-6.34360015e-01 -7.15387523e-01 -3.62620652e-01 -1.10041022e+00
-4.19415295e-01 5.55235624e-01 4.30722475e-01 2.53514182... | [7.802774429321289, 4.74257230758667] |
6a16db71-a07d-4ff9-8ff6-9771ea85ce32 | sok-pragmatic-assessment-of-machine-learning | 2305.00550 | null | https://arxiv.org/abs/2305.00550v1 | https://arxiv.org/pdf/2305.00550v1.pdf | SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection | Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For Network Intrusion Detection (NID), however, scientific advances in ML are still seen with skepticism by practitioners. This disconnection is due to the intrinsically limited scope of research papers, many of which primarily aim to dem... | ['Johannes Schneider', 'Pavel Laskov', 'Giovanni Apruzzese'] | 2023-04-30 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 6.80494756e-02 -2.53479719e-01 -3.83007497e-01 -1.07652657e-01
-2.95880139e-01 -7.35896945e-01 5.22763133e-01 -1.50004271e-02
-4.03570414e-01 5.02661467e-01 -4.79114413e-01 -1.00230980e+00
-3.66032481e-01 -5.55736840e-01 -4.51435447e-01 -5.65371394e-01
-2.10580111e-01 3.16435933e-01 2.15446994e-01 -2.10404351... | [5.440761089324951, 7.351468086242676] |
1a8255d9-b127-4324-be20-c801b9c85803 | minimax-rates-in-network-analysis-graphon | 1811.06055 | null | http://arxiv.org/abs/1811.06055v2 | http://arxiv.org/pdf/1811.06055v2.pdf | Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing | This paper surveys some recent developments in fundamental limits and optimal
algorithms for network analysis. We focus on minimax optimal rates in three
fundamental problems of network analysis: graphon estimation, community
detection, and hypothesis testing. For each problem, we review state-of-the-art
results in the... | ['Zongming Ma', 'Chao Gao'] | 2018-11-14 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 5.28526247e-01 2.32993960e-01 -7.31581748e-01 -1.19402952e-01
-3.35573405e-01 -5.01871586e-01 1.28780678e-01 4.14478958e-01
-2.93800861e-01 1.02221739e+00 -4.16850299e-01 -5.61503708e-01
-7.59667158e-01 -7.63077676e-01 -5.87873340e-01 -6.02376938e-01
-1.22610974e+00 5.23523152e-01 9.86702144e-02 7.14795664... | [6.92963171005249, 5.26082181930542] |
d7e3b217-b7e2-40b1-9db6-11269b21b596 | translating-translationese-a-two-step | 1906.05683 | null | https://arxiv.org/abs/1906.05683v1 | https://arxiv.org/pdf/1906.05683v1.pdf | Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation | Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then `translating' the... | ['Jonathan May', 'Nima Pourdamghani', 'Kevin Knight', 'Nada Aldarrab', 'Marjan Ghazvininejad'] | 2019-06-11 | translating-translationese-a-two-step-1 | https://aclanthology.org/P19-1293 | https://aclanthology.org/P19-1293.pdf | acl-2019-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.98410970e-01 2.66056299e-01 -4.50611830e-01 -5.11778951e-01
-1.10315108e+00 -9.49862421e-01 7.25494921e-01 -5.64878620e-02
-5.64534187e-01 8.84343266e-01 6.47928774e-01 -6.86322927e-01
4.72156525e-01 -6.42808557e-01 -7.38256991e-01 -2.53139377e-01
4.00809050e-01 1.17026126e+00 -2.10284725e-01 -3.90099049... | [11.593015670776367, 10.347818374633789] |
5746436f-4700-4b5f-a64b-0332bbc15853 | vqfr-blind-face-restoration-with-vector | 2205.06803 | null | https://arxiv.org/abs/2205.06803v3 | https://arxiv.org/pdf/2205.06803v3.pdf | VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder | Although generative facial prior and geometric prior have recently demonstrated high-quality results for blind face restoration, producing fine-grained facial details faithful to inputs remains a challenging problem. Motivated by the classical dictionary-based methods and the recent vector quantization (VQ) technique, ... | ['Ming-Ming Cheng', 'Ying Shan', 'Gen Li', 'Chao Dong', 'Liangbin Xie', 'Xintao Wang', 'YuChao Gu'] | 2022-05-13 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 1.96714830e-02 -2.36605778e-02 1.46657275e-02 -1.16218910e-01
-5.21008253e-01 -7.14640692e-02 4.67480034e-01 -5.32347620e-01
3.27811427e-02 5.03508627e-01 5.50852180e-01 1.71812400e-01
-1.91113967e-02 -1.02946007e+00 -6.16623223e-01 -1.00467050e+00
4.56628978e-01 -1.47698075e-01 3.41106970e-05 -4.02088255... | [12.853065490722656, -0.08181171864271164] |
d67118cd-1063-441c-b2c0-b20f3c34ee4c | finite-volume-least-squares-neural-network-fv | 2110.10895 | null | https://arxiv.org/abs/2110.10895v3 | https://arxiv.org/pdf/2110.10895v3.pdf | Least-Squares Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Laws: Discrete Divergence Operator | A least-squares neural network (LSNN) method was introduced for solving scalar linear and nonlinear hyperbolic conservation laws (HCLs) in [7, 6]. This method is based on an equivalent least-squares (LS) formulation and uses ReLU neural network as approximating functions, making it ideal for approximating discontinuous... | ['Min Liu', 'Jingshuang Chen', 'Zhiqiang Cai'] | 2021-10-21 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-1.89554691e-01 -1.19046345e-01 3.32605809e-01 1.29168943e-01
-2.04278037e-01 -1.38679177e-01 1.97687820e-01 3.12646985e-01
-5.23650050e-01 1.38668287e+00 -5.34668624e-01 -1.79819271e-01
1.38737662e-02 -7.47625470e-01 -6.86693609e-01 -8.61709893e-01
-3.20152491e-02 7.81340301e-02 1.39768511e-01 -5.62982738... | [6.46898078918457, 3.4138011932373047] |
a8b72856-c16d-427b-9ad2-a929c295645a | diverse-facial-inpainting-guided-by-exemplars | 2202.06358 | null | https://arxiv.org/abs/2202.06358v3 | https://arxiv.org/pdf/2202.06358v3.pdf | Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars | We present EXE-GAN, a novel exemplar-guided facial inpainting framework using generative adversarial networks. Our approach can not only preserve the quality of the input facial image but also complete the image with exemplar-like facial attributes. We achieve this by simultaneously leveraging the global style of the i... | ['Jiankai Lyu', 'Min Wang', 'YongLiang Yang', 'Kaijie Shi', 'Xiaogang Jin', 'Xianta Jiang', 'Hanli Zhao', 'Wanglong Lu'] | 2022-02-13 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 1.62627622e-01 1.50342911e-01 -5.95410876e-02 -6.80968761e-01
-6.36992514e-01 -2.97474682e-01 3.43037158e-01 -8.04626644e-01
-9.58684161e-02 8.54799509e-01 2.13003814e-01 3.82180870e-01
-6.32951558e-02 -9.85762239e-01 -1.08947420e+00 -8.23609412e-01
3.55649918e-01 1.91889003e-01 -6.65589929e-01 -2.20767543... | [12.559693336486816, -0.19695869088172913] |
1991903b-f7b0-4a2e-a004-f8f2d077d652 | selfdocseg-a-self-supervised-vision-based | 2305.00795 | null | https://arxiv.org/abs/2305.00795v2 | https://arxiv.org/pdf/2305.00795v2.pdf | SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation | Document layout analysis is a known problem to the documents research community and has been vastly explored yielding a multitude of solutions ranging from text mining, and recognition to graph-based representation, visual feature extraction, etc. However, most of the existing works have ignored the crucial fact regard... | ['Umapada Pal', 'Saumik Bhattacharya', 'Josep Lladós', 'Ayan Banerjee', 'Siladittya Manna', 'Sanket Biswas', 'Subhajit Maity'] | 2023-05-01 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 4.29795742e-01 -1.22288410e-02 -3.67853254e-01 -4.44048226e-01
-6.15961194e-01 -9.60259378e-01 7.46140063e-01 2.57420212e-01
-2.01159105e-01 4.84821826e-01 -9.23642591e-02 -3.56523037e-01
1.04105184e-02 -6.68035805e-01 -6.76133037e-01 -5.93785465e-01
2.44819939e-01 8.10540140e-01 5.25929213e-01 1.02607161... | [11.586316108703613, 2.3896355628967285] |
5e2532fa-a949-443e-a87c-43624860ca1e | a-neural-span-based-continual-named-entity | 2302.12200 | null | https://arxiv.org/abs/2302.12200v1 | https://arxiv.org/pdf/2302.12200v1.pdf | A Neural Span-Based Continual Named Entity Recognition Model | Named Entity Recognition (NER) models capable of Continual Learning (CL) are realistically valuable in areas where entity types continuously increase (e.g., personal assistants). Meanwhile the learning paradigm of NER advances to new patterns such as the span-based methods. However, its potential to CL has not been ful... | ['Qingcai Chen', 'Yunan Zhang'] | 2023-02-23 | null | null | null | null | ['continual-named-entity-recognition'] | ['natural-language-processing'] | [-1.40200198e-01 1.41988993e-01 -3.77372533e-01 -2.73670346e-01
-5.89146197e-01 -5.81668735e-01 3.62552792e-01 3.13416630e-01
-7.59948790e-01 1.16519058e+00 1.37001500e-01 -1.90469265e-01
-2.51540512e-01 -5.55784225e-01 -6.69619739e-01 -3.52213472e-01
-3.00313886e-02 4.22677755e-01 4.06817675e-01 6.63047805... | [9.5950345993042, 9.31495475769043] |
2ad6c393-5070-4713-9653-d05e93646600 | self-supervised-novel-2d-view-synthesis-of | 2306.14709 | null | https://arxiv.org/abs/2306.14709v1 | https://arxiv.org/pdf/2306.14709v1.pdf | Self-supervised novel 2D view synthesis of large-scale scenes with efficient multi-scale voxel carving | The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it is important for novel view synthesis methods to stay grounded in the physical world as much as possible, while also being able to imagine it... | ['Marius Leordeanu', 'Alina Marcu', 'Dragos Costea', 'Alexandra Budisteanu'] | 2023-06-26 | null | null | null | null | ['novel-view-synthesis'] | ['computer-vision'] | [ 2.43788749e-01 7.88044482e-02 5.46499014e-01 -7.53378794e-02
-5.07889390e-01 -9.09189284e-01 5.87112725e-01 -4.02053416e-01
-5.30493297e-02 7.44546354e-01 4.45065051e-02 6.27624989e-02
2.37439364e-01 -9.92338717e-01 -8.18370759e-01 -2.04646662e-01
-2.59871539e-02 7.53391683e-01 6.77422464e-01 -4.31353688... | [8.891100883483887, -2.874819755554199] |
10b54210-33dd-41fe-a71b-fdadea7796a9 | m2h2-a-multimodal-multiparty-hindi-dataset | 2108.01260 | null | https://arxiv.org/abs/2108.01260v1 | https://arxiv.org/pdf/2108.01260v1.pdf | M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations | Humor recognition in conversations is a challenging task that has recently gained popularity due to its importance in dialogue understanding, including in multimodal settings (i.e., text, acoustics, and visual). The few existing datasets for humor are mostly in English. However, due to the tremendous growth in multilin... | ['Soujanya Poria', 'Louis-Philippe Morency', 'Pushpak Bhattacharyya', 'Asif Ekbal', 'Amir Zadeh', 'Navonil Majumder', 'Gopendra Vikram Singh', 'Dushyant Singh Chauhan'] | 2021-08-03 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [-4.75731045e-01 -3.05409044e-01 9.88890007e-02 -2.28720218e-01
-9.25711095e-01 -6.30232811e-01 7.96770513e-01 -1.33821517e-01
-9.00425166e-02 6.17567837e-01 9.99364376e-01 -3.17187719e-02
5.64746857e-01 -1.72707662e-01 -3.29161167e-01 -6.68824136e-01
3.74727309e-01 4.65693951e-01 -3.55077207e-01 -7.24574506... | [13.03865909576416, 5.381781578063965] |
84c97538-c1fa-41c1-9739-e9d74c9f27b4 | iexam-a-novel-online-exam-monitoring-and | 2206.13356 | null | https://arxiv.org/abs/2206.13356v1 | https://arxiv.org/pdf/2206.13356v1.pdf | iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition | Online exams via video conference software like Zoom have been adopted in many schools due to COVID-19. While it is convenient, it is challenging for teachers to supervise online exams from simultaneously displayed student Zoom windows. In this paper, we propose iExam, an intelligent online exam monitoring and analysis... | ['Tan Lee', 'Jimmy H. M. Lee', 'Xiao Yi', 'Daoyuan Wu', 'Xu Yang'] | 2022-06-27 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-1.14732636e-02 -5.19779623e-01 -3.88727710e-02 -3.68064255e-01
-5.18294811e-01 -7.63090611e-01 3.18358429e-02 1.36945248e-01
-7.10299313e-02 1.41253352e-01 -4.21171069e-01 -4.51720685e-01
-2.65081406e-01 -7.21279860e-01 -6.18140042e-01 -4.63239133e-01
2.27736026e-01 1.45575136e-01 5.07818758e-01 2.19619274... | [13.815382957458496, 0.6723130941390991] |
838a121d-e6b1-4094-b9e7-631ac7ddc15e | autoregressive-neural-tensornet-bridging | 2304.01996 | null | https://arxiv.org/abs/2304.01996v2 | https://arxiv.org/pdf/2304.01996v2.pdf | ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation | Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of the Hilbert space with respect to the particle number, a direct simulation is intractable. While repr... | ['Marin Soljačić', 'Di Luo', 'Eddie Chen', 'Laker Newhouse', 'Zhuo Chen'] | 2023-04-04 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 2.66676825e-02 -2.81575531e-01 -8.84919465e-02 -5.47354296e-02
-2.56144196e-01 -2.08639950e-01 6.24852717e-01 -3.64536226e-01
-3.06503922e-01 7.93089747e-01 1.16622306e-01 -5.01428723e-01
-3.12940508e-01 -1.20570219e+00 -7.67343163e-01 -9.86367643e-01
-3.38670999e-01 8.75537217e-01 -2.34311789e-01 -7.20273614... | [5.605201721191406, 4.987043380737305] |
8f6f990d-3765-40ec-b52e-bcbac41fe761 | learning-to-transduce-with-unbounded-memory | 1506.02516 | null | http://arxiv.org/abs/1506.02516v3 | http://arxiv.org/pdf/1506.02516v3.pdf | Learning to Transduce with Unbounded Memory | Recently, strong results have been demonstrated by Deep Recurrent Neural
Networks on natural language transduction problems. In this paper we explore
the representational power of these models using synthetic grammars designed to
exhibit phenomena similar to those found in real transduction problems such as
machine tra... | ['Mustafa Suleyman', 'Edward Grefenstette', 'Karl Moritz Hermann', 'Phil Blunsom'] | 2015-06-08 | learning-to-transduce-with-unbounded-memory-1 | http://papers.nips.cc/paper/5648-learning-to-transduce-with-unbounded-memory | http://papers.nips.cc/paper/5648-learning-to-transduce-with-unbounded-memory.pdf | neurips-2015-12 | ['natural-language-transduction'] | ['natural-language-processing'] | [ 6.04630768e-01 3.72248083e-01 2.03911543e-01 -5.11536039e-02
-6.94378197e-01 -6.03237152e-01 1.15122509e+00 -2.67333090e-01
-3.48530561e-01 9.76138771e-01 4.37432528e-01 -1.14637244e+00
3.07431161e-01 -1.36962187e+00 -1.03060949e+00 -5.48642218e-01
-1.51479393e-01 7.75838792e-01 9.30510089e-02 -9.59954560... | [10.714518547058105, 7.274586200714111] |
e4f33d7d-c74a-425a-aeb8-9c1c924a720b | gmml-is-all-you-need | 2205.14986 | null | https://arxiv.org/abs/2205.14986v1 | https://arxiv.org/pdf/2205.14986v1.pdf | GMML is All you Need | Vision transformers have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply confined local, or long range global. However, they are known to be data hungry. This has motivated the research in self-supervised transforme... | ['Josef Kittler', 'Muhammad Awais', 'Sara Atito'] | 2022-05-30 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.46752036e-01 4.03183103e-01 -2.37093121e-01 -5.04009664e-01
-4.50189620e-01 -6.68094099e-01 8.96313310e-01 1.74484789e-01
-2.07346722e-01 3.47576886e-01 3.20911586e-01 -1.85115337e-01
-2.06450745e-01 -8.21871042e-01 -8.36032093e-01 -8.52602303e-01
2.31567055e-01 3.72893721e-01 4.08103675e-01 -1.71603441... | [9.648063659667969, 1.769453525543213] |
ff44ba7c-1bba-451b-920e-4734ed762779 | supervised-and-unsupervised-machine | null | null | https://aclanthology.org/D19-5206 | https://aclanthology.org/D19-5206.pdf | Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English | This paper presents the NICT{'}s supervised and unsupervised machine translation systems for the WAT2019 Myanmar-English and Khmer-English translation tasks. For all the translation directions, we built state-of-the-art supervised neural (NMT) and statistical (SMT) machine translation systems, using monolingual data cl... | ['Masao Utiyama', 'Aye Myat Mon', 'Atsushi Fujita', 'Hour Kaing', 'Benjamin Marie', 'Eiichiro Sumita', 'Chenchen Ding'] | 2019-11-01 | null | null | null | ws-2019-11 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.91976154e-01 5.62939756e-02 -5.84318638e-01 -5.20390630e-01
-1.26945972e+00 -7.90609658e-01 1.01888430e+00 -3.32444161e-01
-7.08030820e-01 1.35404825e+00 3.77504468e-01 -1.12312508e+00
8.45399126e-02 -2.56975770e-01 -6.67099953e-01 -4.02372390e-01
5.27579248e-01 1.34533381e+00 -5.49192667e-01 -6.77171886... | [11.5717134475708, 10.392926216125488] |
dcb1457c-6a58-4244-8994-dff56a0df3f8 | challenges-and-opportunities-in-multi-device | 2206.15432 | null | https://arxiv.org/abs/2206.15432v1 | https://arxiv.org/pdf/2206.15432v1.pdf | Challenges and Opportunities in Multi-device Speech Processing | We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and m... | ['Tao Zhang', 'Israel Cohen', 'Arun Nair', 'Jarred Barber', 'Gregory Ciccarelli'] | 2022-06-27 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.79476690e-01 -3.13938797e-01 -4.01658654e-01 -2.79687822e-01
-1.21906269e+00 -4.30633843e-01 8.84614438e-02 -1.53989479e-01
4.95872609e-02 5.08454084e-01 6.50767148e-01 -2.63403893e-01
-3.70210141e-01 1.41807154e-01 -4.44971099e-02 -5.96638918e-01
9.92654637e-03 -7.61640742e-02 -2.03810379e-01 2.17007715... | [14.735675811767578, 6.060156345367432] |
755d87c2-e41f-4957-a019-74057e31c967 | the-classification-of-optical-galaxy | 2206.06165 | null | https://arxiv.org/abs/2206.06165v2 | https://arxiv.org/pdf/2206.06165v2.pdf | The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques | In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount of data generated by modern surveys. This has brought into question the viability... | ['Mattia Vaccari', 'Clement N. Nyirenda', 'Ezra Fielding'] | 2022-06-13 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [-4.08874154e-01 -4.24183626e-03 5.62907517e-01 -6.27164900e-01
-1.33686453e-01 -7.16087699e-01 8.65971863e-01 6.20921910e-01
-6.96140945e-01 3.04450423e-01 2.39184916e-01 -2.41711050e-01
-3.04889947e-01 -1.02634072e+00 -1.49443775e-01 -9.29687619e-01
-4.27339524e-02 1.02343059e+00 1.98083997e-01 6.98263198... | [7.934167861938477, 2.966944932937622] |
7cf284d1-4186-4d99-9d49-083793aeb974 | exploring-the-value-of-multi-view-learning-1 | null | null | https://aclanthology.org/2022.findings-naacl.23 | https://aclanthology.org/2022.findings-naacl.23.pdf | Exploring the Value of Multi-View Learning for Session-Aware Query Representation | Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user’... | ['Lynda Tamine', 'Karen Pinel-Sauvagnat', 'Gilles Hubert', 'Jose Moreno', 'Diego Ortiz'] | null | null | null | null | findings-naacl-2022-7 | ['multi-view-learning', 'document-ranking'] | ['computer-vision', 'natural-language-processing'] | [-2.93080751e-02 -5.20337880e-01 -7.10079670e-01 -4.76598710e-01
-9.44779038e-01 -8.89817357e-01 1.23057067e+00 5.63487947e-01
-5.14784157e-01 -6.75216243e-02 7.34977186e-01 -6.69702590e-02
-7.69691110e-01 -6.31116331e-01 -2.80870140e-01 -4.11660880e-01
-1.37059778e-01 7.09067106e-01 2.13215783e-01 -5.62697768... | [11.652288436889648, 7.530436038970947] |
3baafc22-5a28-44d2-8e1b-01a1440100fa | docee-a-large-scale-and-fine-grained | null | null | https://openreview.net/forum?id=bC5NiJmbK2 | https://openreview.net/pdf?id=bC5NiJmbK2 | DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction | Event extraction aims to identify an event and then extract the arguments participating in the event. Despite the great success in sentence-level event extraction, events are more naturally presented in the form of documents, with event arguments scattering in multiple sentences. However, a major barrier to promote doc... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 3.97588998e-01 5.52834451e-01 -1.31061703e-01 -2.71972656e-01
-1.11229467e+00 -8.26988459e-01 8.33451688e-01 7.79888690e-01
-7.16835082e-01 1.00886965e+00 6.41585529e-01 -1.41513556e-01
-1.98377278e-02 -7.19732940e-01 -6.58148050e-01 -1.35866255e-01
-2.66455203e-01 4.72087830e-01 5.41308165e-01 -3.79988253... | [9.070842742919922, 9.29626178741455] |
0e0be9fd-70ae-4943-95eb-ff2ade0120be | spatially-multi-conditional-image-generation | 2203.13812 | null | https://arxiv.org/abs/2203.13812v2 | https://arxiv.org/pdf/2203.13812v2.pdf | Spatially Multi-conditional Image Generation | In most scenarios, conditional image generation can be thought of as an inversion of the image understanding process. Since generic image understanding involves solving multiple tasks, it is natural to aim at generating images via multi-conditioning. However, multi-conditional image generation is a very challenging pro... | ['Luc van Gool', 'Thomas Probst', 'Danda Pani Paudel', 'Nikola Popovic', 'Ritika Chakraborty'] | 2022-03-25 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 7.17674792e-01 2.32816607e-01 -1.21776812e-01 -1.64515346e-01
-8.47289443e-01 -5.00336826e-01 7.24036455e-01 -3.16166699e-01
-2.55393088e-01 7.16989279e-01 5.36034331e-02 -2.82663763e-01
1.52705938e-01 -9.38235700e-01 -1.05963135e+00 -1.06496871e+00
4.04017329e-01 3.15514117e-01 7.22998977e-02 -6.14009388... | [11.340142250061035, -0.3468974530696869] |
b01e3cd7-9ad1-4c77-8ab9-2f12cf0faa38 | data-augmentation-for-improving-tail-traffic | 2306.04823 | null | https://arxiv.org/abs/2306.04823v1 | https://arxiv.org/pdf/2306.04823v1.pdf | Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems | Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request. In such system, the agent is responsible for serving thousands of skills and interpretations which create a long-tail distribution due to the natural fr... | ['Sungjin Lee', 'Jaeyoung Do', 'Mohammad Kachuee', 'Fatemeh Sheikholeslami', 'Ting-Wei Wu'] | 2023-06-07 | null | null | null | null | ['long-tail-learning'] | ['methodology'] | [ 3.24478477e-01 -1.51605815e-01 -1.10387959e-01 -6.01891696e-01
-9.15843487e-01 -5.94957590e-01 8.29682887e-01 -3.14931363e-01
-2.19869599e-01 8.24470818e-01 5.40096104e-01 -3.80735993e-01
-9.55219865e-02 -6.75881982e-01 -5.41416764e-01 -7.08702326e-01
1.88488886e-02 1.03258288e+00 2.09676072e-01 -4.84276772... | [12.66756534576416, 8.149604797363281] |
f51dcd8f-1422-4521-bc6e-82f23db47696 | boosting-radiology-report-generation-by | 2305.04561 | null | https://arxiv.org/abs/2305.04561v2 | https://arxiv.org/pdf/2305.04561v2.pdf | Boosting Radiology Report Generation by Infusing Comparison Prior | Recent transformer-based models have made significant strides in generating radiology reports from chest X-ray images. However, a prominent challenge remains: these models often lack prior knowledge, resulting in the generation of synthetic reports that mistakenly reference non-existent prior exams. This discrepancy ca... | ['Michael Krauthammer', 'Fabio Rinaldi', 'Ryo Sakamoto', 'Mizuho Nishio', 'Koji Fujimoto', 'Morteza Rohanian', 'Farhad Nooralahzadeh', 'Sanghwan Kim'] | 2023-05-08 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.78956997e-01 6.76500499e-01 -2.98964679e-01 -3.56493652e-01
-1.56455827e+00 -6.71207488e-01 9.02298391e-01 3.66188943e-01
6.23995326e-02 9.67842340e-01 5.64173281e-01 -6.60333037e-01
-1.90927032e-02 -9.27853882e-01 -8.09916735e-01 -1.69131935e-01
3.41378897e-01 5.09752810e-01 1.37197673e-01 3.00360247... | [15.038304328918457, -1.3919190168380737] |
6a1af8f1-f501-495f-b08f-65f8100f95ad | on-predictive-information-sub-optimality-of-1 | 1910.09578 | null | https://arxiv.org/abs/1910.09578v2 | https://arxiv.org/pdf/1910.09578v2.pdf | On Predictive Information in RNNs | Certain biological neurons demonstrate a remarkable capability to optimally compress the history of sensory inputs while being maximally informative about the future. In this work, we investigate if the same can be said of artificial neurons in recurrent neural networks (RNNs) trained with maximum likelihood. Empirical... | ['Zhe Dong', 'Alexander A. Alemi', 'Deniz Oktay', 'Ben Poole'] | 2019-10-21 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 5.42623460e-01 5.05455732e-01 -3.44663769e-01 -4.85697359e-01
-1.71256423e-01 -4.25004244e-01 6.59331620e-01 -1.32850930e-01
-7.23806918e-01 9.58154500e-01 6.34068906e-01 -4.56322551e-01
-2.10243508e-01 -6.18846238e-01 -7.39340842e-01 -6.74760699e-01
-3.04143816e-01 1.19549021e-01 9.21268463e-02 8.36123154... | [7.760252475738525, 3.469567060470581] |
c0e1e644-4b48-47f7-a494-5a16c21f50d1 | deepprivacy-a-generative-adversarial-network | 1909.04538 | null | https://arxiv.org/abs/1909.04538v1 | https://arxiv.org/pdf/1909.04538v1.pdf | DeepPrivacy: A Generative Adversarial Network for Face Anonymization | We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information. Our model is based on a conditional generative adversarial network... | ['Rudolf Mester', 'Håkon Hukkelås', 'Frank Lindseth'] | 2019-09-10 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 2.27533743e-01 5.23813665e-01 3.96142304e-01 -5.52553177e-01
-4.58407253e-01 -8.17460060e-01 5.65261126e-01 -5.82276940e-01
-2.44552881e-01 8.81605208e-01 -5.57342730e-02 5.56210987e-02
3.56012762e-01 -9.28053677e-01 -1.10047579e+00 -7.48817682e-01
-2.18624395e-04 4.41402763e-01 -2.98754960e-01 -1.23522490... | [12.806829452514648, 0.6326466202735901] |
c877763b-1bac-47b7-97a0-610f43903fca | ppdl-predicate-probability-distribution-based | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_PPDL_Predicate_Probability_Distribution_Based_Loss_for_Unbiased_Scene_Graph_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_PPDL_Predicate_Probability_Distribution_Based_Loss_for_Unbiased_Scene_Graph_CVPR_2022_paper.pdf | PPDL: Predicate Probability Distribution Based Loss for Unbiased Scene Graph Generation | Scene Graph Generation (SGG) has attracted more and more attention from visual researchers in recent years, since Scene Graph (SG) is valuable in many downstream tasks due to its rich structural-semantic details. However, the application value of SG on downstream tasks is severely limited by the predicate classific... | ['Xiaojie Yuan', 'Ning Jiang', 'Guoqing Zhao', 'Qijie Bai', 'Haiwei Zhang', 'Wei Li'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.64423740e-01 3.71927917e-01 -2.51948923e-01 -4.43895638e-01
-4.77328062e-01 -4.23955470e-01 5.12008667e-01 4.28346753e-01
-6.07923120e-02 6.28413677e-01 4.29566145e-01 -1.91835552e-01
-2.03865752e-01 -1.08692825e+00 -9.27241981e-01 -9.22040403e-01
4.40374613e-01 4.23217386e-01 5.80140829e-01 9.21261683... | [10.28222370147705, 1.7744718790054321] |
f7816713-5d5a-43d0-93cb-f89ba72b5027 | pnat-non-autoregressive-transformer-by | null | null | https://openreview.net/forum?id=BJe932EYwS | https://openreview.net/pdf?id=BJe932EYwS | PNAT: Non-autoregressive Transformer by Position Learning | Non-autoregressive generation is a new paradigm for text generation. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling of output words is an essential problem in non-autoregressive text generation. In this paper, we propose PNAT, which explicitly models posi... | ['Lei LI', 'Jiajun Chen', 'ShuJian Huang', 'Mingxuan Wang', 'Jiangtao Feng', 'Hao Zhou', 'Yu Bao'] | 2019-09-25 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.50893790e-01 3.52319181e-01 -2.92066485e-01 2.86234226e-02
-1.14794278e+00 -6.36919081e-01 1.30797172e+00 -3.18348587e-01
3.86232249e-02 1.18620872e+00 7.85269320e-01 -4.78859395e-01
4.77295309e-01 -7.90755630e-01 -8.49301755e-01 -6.80552840e-01
7.82336891e-01 9.78185892e-01 -3.10638964e-01 -6.38674796... | [11.898306846618652, 9.115544319152832] |
1352c9e0-68bb-484a-b6cb-e96f32f4852b | rgb-d-mapping-and-tracking-in-a-plenoxel | 2307.03404 | null | https://arxiv.org/abs/2307.03404v1 | https://arxiv.org/pdf/2307.03404v1.pdf | RGB-D Mapping and Tracking in a Plenoxel Radiance Field | Building on the success of Neural Radiance Fields (NeRFs), recent years have seen significant advances in the domain of novel view synthesis. These models capture the scene's volumetric radiance field, creating highly convincing dense photorealistic models through the use of simple, differentiable rendering equations. ... | ['Rudolf Mester', 'Annette Stahl', 'Yeonsoo Park', 'Andreas L. Teigen'] | 2023-07-07 | null | null | null | null | ['3d-reconstruction', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.80320019e-01 1.06711447e-01 3.18157464e-01 -1.91972896e-01
-8.32305104e-02 -5.02496600e-01 7.58416712e-01 -5.89296997e-01
-1.01146460e-01 7.03183949e-01 -9.58934277e-02 -2.51130342e-01
-3.44387069e-02 -9.03501391e-01 -6.86057150e-01 -5.28287411e-01
2.62972593e-01 3.37598562e-01 7.92765543e-02 -6.20082378... | [9.124744415283203, -3.0262973308563232] |
bffe246a-ff37-4376-8785-cb3e49710513 | improving-low-resource-question-answering | 2211.14880 | null | https://arxiv.org/abs/2211.14880v1 | https://arxiv.org/pdf/2211.14880v1.pdf | Improving Low-Resource Question Answering using Active Learning in Multiple Stages | Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we propose a novel approach that combines data augmentation via question-an... | ['Thang Vu', 'A. Cristiano I. Malossi', 'Jasmina Bogojeska', 'Andrea Bartezzaghi', 'Maximilian Schmidt'] | 2022-11-27 | null | null | null | null | ['question-answer-generation', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.54742324e-01 3.77383888e-01 -1.10490695e-01 -5.15911162e-01
-1.14424336e+00 -8.74342680e-01 5.58857322e-01 4.86356527e-01
-8.78992260e-01 8.70704174e-01 2.48108208e-02 -2.75597095e-01
1.51127819e-02 -6.67778134e-01 -6.51136220e-01 -2.51177907e-01
4.56407577e-01 9.88088727e-01 5.02403796e-01 -4.43441153... | [11.203863143920898, 8.06934642791748] |
78a9eb60-e2a5-49da-9313-645a5e06cb75 | multimodal-search-on-iconclass-using-vision | 2306.16529 | null | https://arxiv.org/abs/2306.16529v1 | https://arxiv.org/pdf/2306.16529v1.pdf | Multimodal Search on Iconclass using Vision-Language Pre-Trained Models | Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search,... | ['Harald Sack', 'Tabea Tietz', 'Oleksandra Bruns', 'Mary Ann Tan', 'Etienne Posthumus', 'Cristian Santini'] | 2023-06-23 | null | null | null | null | ['retrieval', 'information-retrieval'] | ['methodology', 'natural-language-processing'] | [-3.40820141e-02 -1.32295325e-01 -4.39573467e-01 1.20335318e-01
-6.05030835e-01 -1.02585304e+00 1.26516759e+00 5.94034135e-01
-6.00676835e-01 6.36960506e-01 4.67274755e-01 -1.53427467e-01
-5.90586305e-01 -7.91828156e-01 -1.36680380e-01 -1.88393965e-01
4.11731988e-01 3.96691531e-01 4.42588389e-01 -7.17798352... | [10.91682243347168, 0.7185744643211365] |
c375c28d-d189-41c7-8748-32bd6226a821 | conslt-a-token-level-contrastive-framework | 2204.04916 | null | https://arxiv.org/abs/2204.04916v3 | https://arxiv.org/pdf/2204.04916v3.pdf | A Token-level Contrastive Framework for Sign Language Translation | Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine Translation (NMT) methods, which usually require large-scale corpus for training, to achieve SLT. However, the publicly available SLT corpu... | ['Xiaodong Shi', 'Yidong Chen', 'Cong Hu', 'Pei Yu', 'Liang Zhang', 'PeiGen Ye', 'Biao Fu'] | 2022-04-11 | null | null | null | null | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [ 4.78575468e-01 -1.84352726e-01 -3.21445286e-01 -4.91638809e-01
-1.44688797e+00 -1.76822379e-01 1.64147466e-01 -4.92014796e-01
-7.74939358e-01 8.53704035e-01 5.48868477e-01 -4.81389016e-01
5.28561294e-01 -4.83316153e-01 -1.03273535e+00 -7.20176637e-01
4.08062935e-01 3.78676504e-01 -1.44100890e-01 -2.36432195... | [9.216670036315918, -6.531961917877197] |
3f2f4a8c-368f-4994-a8e8-f91793b348e5 | conversational-product-search-based-on | 1909.02071 | null | https://arxiv.org/abs/1909.02071v1 | https://arxiv.org/pdf/1909.02071v1.pdf | Conversational Product Search Based on Negative Feedback | Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. During the interactions, the system could ask questions on certain aspects of the ideal products to clarify the users' needs. For example, prev... | ['Qingyao Ai', 'W. Bruce Croft', 'Keping Bi', 'Yongfeng Zhang'] | 2019-09-04 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.86781883e-01 6.79240003e-02 -5.07142246e-01 -7.75159895e-01
-4.42073375e-01 -7.97611594e-01 4.45591450e-01 4.37377304e-01
-5.43803096e-01 5.34139395e-01 6.08298481e-01 -4.20198232e-01
-2.25969002e-01 -5.86823046e-01 -1.83045223e-01 -2.23674521e-01
2.70293951e-01 8.49023879e-01 1.12342857e-01 -6.44924104... | [12.213225364685059, 7.708831787109375] |
d179e3ef-132f-43e0-a4e2-8eb61fe249b8 | graphmdn-leveraging-graph-structure-and-deep | 2010.13668 | null | https://arxiv.org/abs/2010.13668v1 | https://arxiv.org/pdf/2010.13668v1.pdf | GraphMDN: Leveraging graph structure and deep learning to solve inverse problems | The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean, graph-structured data. GNNs have achieved state-of-the-art results across an impressive array of graph-based machine learning problems. Nev... | ['Sohrob Kazerounian', 'Daniel C. Hannah', 'Tuomas P. Oikarinen'] | 2020-10-26 | null | null | null | null | ['multi-hypotheses-3d-human-pose-estimation'] | ['computer-vision'] | [-7.00588599e-02 3.83188695e-01 -1.36928931e-01 -2.93144643e-01
-4.21014279e-01 -3.19402605e-01 9.36182320e-01 -9.92132351e-02
-2.44257957e-01 3.17815661e-01 2.59756356e-01 -2.87769169e-01
-1.38123691e-01 -8.42874587e-01 -6.91018760e-01 -6.24367356e-01
-3.83880943e-01 9.51096296e-01 -5.36616854e-02 -2.79608537... | [6.940205097198486, 6.109101295471191] |
12c043eb-96cb-4220-8508-cf1f7840eb28 | drcove-an-augmented-word-representation | null | null | https://aclanthology.org/2019.icon-1.26 | https://aclanthology.org/2019.icon-1.26.pdf | DRCoVe: An Augmented Word Representation Approach using Distributional and Relational Context | Word representation using the distributional information of words from a sizeable corpus is considered efficacious in many natural language processing and text mining applications. However, distributional representation of a word is unable to capture distant relational knowledge, representing the relational semantics. ... | ['Mohd Fazil', 'Muhammad Abulaish', 'Md. Aslam Parwez'] | null | null | null | null | icon-2019-12 | ['word-similarity'] | ['natural-language-processing'] | [ 1.25331342e-01 2.39640146e-01 -4.22567904e-01 -4.50380653e-01
-2.02509865e-01 -2.11640507e-01 7.24818528e-01 9.31299329e-01
-8.19759667e-01 5.14645278e-01 8.11855733e-01 -1.95327312e-01
-4.47137773e-01 -1.27304637e+00 -2.25476045e-02 -5.22791326e-01
2.25122064e-01 3.31743002e-01 6.52697757e-02 -4.39607173... | [10.217287063598633, 8.725737571716309] |
0dc91104-df5f-44c9-bc34-f7548252efa6 | contrastively-reinforced-attention | null | null | https://www.bmvc2020-conference.com/conference/papers/paper_0656.html | https://www.bmvc2020-conference.com/assets/papers/0656.pdf | Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image Recognition | Fine-grained visual classification is inherently challenging because of its inter-class similarity and intra-class variance. However, by contrasting the images with same/different labels, a human can instinctively notice that the key clues lie in certain objects while other objects are ignorable. Inspired by this, we p... | ['Kenji Mase', 'Jien Kato', 'Yu Wang', 'Dichao Liu'] | 2020-09-08 | null | null | null | bmvc-2020-9 | ['fine-grained-image-recognition', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [-6.48552254e-02 -2.37972736e-01 -3.20962779e-02 -3.33010405e-01
-5.80031462e-02 -3.77116174e-01 4.72837806e-01 1.24672599e-01
-4.87621754e-01 5.01356363e-01 1.84805453e-01 -3.05675762e-03
1.04188137e-01 -6.57236457e-01 -6.96884811e-01 -6.97508872e-01
2.58962631e-01 8.96971151e-02 3.49354118e-01 -9.17198136... | [9.776799201965332, 2.006225109100342] |
43f20597-6e83-44ef-84b8-04d0499cf3af | strategy-for-rapid-diabetic-retinopathy | 2305.04724 | null | https://arxiv.org/abs/2305.04724v1 | https://arxiv.org/pdf/2305.04724v1.pdf | Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing | In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the reti... | ['S. Anusuya', 'V. Banupriya'] | 2023-05-08 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-9.64299217e-02 -2.40700468e-01 5.36321700e-02 -2.52388537e-01
-9.14680362e-02 -5.45763150e-02 1.72326285e-02 -4.84129339e-02
-5.11011362e-01 6.15573347e-01 1.42134801e-01 -2.55914658e-01
-2.01745242e-01 -7.57772982e-01 -7.21818954e-02 -7.86067247e-01
7.77561516e-02 -4.30790521e-02 -5.57967052e-02 1.26782209... | [15.838804244995117, -3.980299472808838] |
85951139-d8d9-4f4a-8c4a-86d4dcb33b5c | clireval-evaluating-machine-translation-as-a | null | null | https://aclanthology.org/2020.acl-demos.18 | https://aclanthology.org/2020.acl-demos.18.pdf | CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task | We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR). Contrary to what the project name might suggest, CLIReval does not actually require any annotated CLIR dataset. Instead, it automatically transforms translations and ref... | ['Suzanna Sia', 'Kevin Duh', 'Shuo Sun'] | 2020-07-01 | null | null | null | acl-2020-6 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-5.08912392e-02 -2.24412709e-01 -5.56395590e-01 -1.78160429e-01
-1.76882017e+00 -1.15385997e+00 1.23385298e+00 1.34260446e-01
-7.94504702e-01 7.89778233e-01 5.19839585e-01 -5.97232044e-01
-5.37256934e-02 -3.36979091e-01 -5.57731569e-01 -1.86652780e-01
7.86521494e-01 9.58585799e-01 3.15860584e-02 -4.46936905... | [11.522103309631348, 10.181771278381348] |
8b6bc2f3-c219-49d1-900a-cac176f2c594 | coverless-information-hiding-based-on-1 | 1802.03528 | null | http://arxiv.org/abs/1802.03528v1 | http://arxiv.org/pdf/1802.03528v1.pdf | Coverless information hiding based on Generative Model | A new coverless image information hiding method based on generative model is
proposed, we feed the secret image to the generative model database, and
generate a meaning-normal and independent image different from the secret
image, then, the generated image is transmitted to the receiver and is fed to
the generative mod... | ['Xintao Duan', 'Haoxian Song'] | 2018-02-10 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 9.49439645e-01 1.04408320e-02 2.41601691e-01 1.61269203e-01
2.64433026e-01 -3.02205384e-01 1.53684139e-01 -7.80786335e-01
-2.93371081e-01 4.32702810e-01 -3.75983752e-02 -3.44548881e-01
3.38482589e-01 -1.20487034e+00 -4.02345926e-01 -1.34173119e+00
-1.72111746e-02 -6.62578419e-02 4.93372262e-01 -3.43718559... | [4.301815032958984, 8.050455093383789] |
8fdd5c6c-c024-4df1-995f-c6626e2a9dca | optimization-derived-learning-with-essential | 2206.07875 | null | https://arxiv.org/abs/2206.07875v1 | https://arxiv.org/pdf/2206.07875v1.pdf | Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training | Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization. However, previous ODL approaches regard the training and hyper-training procedures as two separated stages, meaning that the hyper-training variables ... | ['Yixuan Zhang', 'Jin Zhang', 'Shangzhi Zeng', 'Xuan Liu', 'Risheng Liu'] | 2022-06-16 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [-8.77196267e-02 -1.44450054e-01 -1.16509654e-01 -7.24088252e-02
-6.36340439e-01 7.88619816e-02 2.53552020e-01 -1.42230302e-01
-2.29378432e-01 7.34876692e-01 -1.31036773e-01 -1.99480876e-01
-6.68789804e-01 -6.52669013e-01 -7.46898174e-01 -1.30063558e+00
7.50960484e-02 1.28274977e-01 -2.68443048e-01 -1.24770351... | [10.848241806030273, -2.0687458515167236] |
caf88a98-4632-4647-b216-41d8a3ec32d6 | histopathology-image-classification-using | 2306.14459 | null | https://arxiv.org/abs/2306.14459v1 | https://arxiv.org/pdf/2306.14459v1.pdf | Histopathology Image Classification using Deep Manifold Contrastive Learning | Contrastive learning has gained popularity due to its robustness with good feature representation performance. However, cosine distance, the commonly used similarity metric in contrastive learning, is not well suited to represent the distance between two data points, especially on a nonlinear feature manifold. Inspired... | ['Won-Ki Jeong', 'Jing Wei Tan'] | 2023-06-26 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'clustering', 'classification-1'] | ['computer-vision', 'methodology', 'methodology', 'methodology'] | [-6.48044273e-02 -4.26600218e-01 -1.23447679e-01 -4.24666494e-01
-1.33269739e+00 -3.79700005e-01 4.49820340e-01 8.98768604e-01
-8.60670269e-01 3.13370138e-01 1.52103081e-02 -2.56286323e-01
-7.67627597e-01 -5.85560143e-01 -2.19409376e-01 -1.04717374e+00
-3.65674317e-01 6.29407391e-02 9.40927416e-02 4.42180643... | [15.038496971130371, -2.6955556869506836] |
a6f1fa12-e8c6-4b06-9295-41a4b4e88e29 | deep-inside-outside-recursive-autoencoder | null | null | https://aclanthology.org/2020.coling-main.322 | https://aclanthology.org/2020.coling-main.322.pdf | Deep Inside-outside Recursive Autoencoder with All-span Objective | Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing. During its forward computation, it provides phrase and contextual representations for all spans in the input sentence. By utilizing the contextual representation of each leaf-level span, the span of... | ['Kewei Tu', 'Jiong Cai', 'Ruyue Hong'] | 2020-12-01 | null | null | null | coling-2020-8 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.62006542e-01 5.68173528e-01 -2.56370783e-01 -4.67315555e-01
-7.14622736e-01 -6.91200435e-01 -1.38308868e-01 7.68971667e-02
-2.99218178e-01 6.86790764e-01 6.22212172e-01 -7.25456059e-01
6.27632558e-01 -1.04291296e+00 -7.34058321e-01 -3.04029942e-01
8.98469537e-02 1.53128669e-01 6.37243018e-02 -2.93218344... | [10.378005981445312, 9.555119514465332] |
976f2f05-e6a7-4bf1-823a-5a36bee6383d | chatgpt-vs-state-of-the-art-models-a | 2304.14177 | null | https://arxiv.org/abs/2304.14177v2 | https://arxiv.org/pdf/2304.14177v2.pdf | ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task | Transformer-based language models, including ChatGPT, have demonstrated exceptional performance in various natural language generation tasks. However, there has been limited research evaluating ChatGPT's keyphrase generation ability, which involves identifying informative phrases that accurately reflect a document's co... | ['José Portela', 'Alvaro J. López-López', 'Roberto Martínez-Cruz'] | 2023-04-27 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 9.28229466e-02 2.60540992e-02 -3.24974924e-01 1.81280330e-01
-1.37772226e+00 -9.88194406e-01 1.42306530e+00 4.23161268e-01
-2.03291491e-01 1.30892110e+00 7.67523944e-01 -5.03760517e-01
-9.92164314e-02 -8.83568466e-01 -4.46763337e-01 -2.57660538e-01
1.78637877e-01 1.08544397e+00 2.33048707e-01 -6.36593521... | [12.281021118164062, 8.938755989074707] |
fbf21530-23ff-42c3-9635-7b041cb47bd5 | deep-feature-statistics-mapping-for | 2209.05321 | null | https://arxiv.org/abs/2209.05321v2 | https://arxiv.org/pdf/2209.05321v2.pdf | Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment | The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the firs... | ['Sam Kwong', 'Shiqi Wang', 'Lingyu Zhu', 'Hanwei Zhu', 'Baoliang Chen'] | 2022-09-12 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.74019203e-01 -5.13654590e-01 -5.47713637e-02 -3.44694465e-01
-9.58582044e-01 -4.57856387e-01 4.84195769e-01 -7.29511902e-02
-9.74859148e-02 5.51190972e-01 7.77689070e-02 -1.76945284e-01
-4.45206374e-01 -6.32356048e-01 -8.38574827e-01 -7.38892138e-01
3.61842364e-02 -1.71255976e-01 2.69573361e-01 -1.13176055... | [11.810952186584473, -1.850890040397644] |
f454c166-6e05-46cd-9010-9c5e7fba28c2 | on-the-robustness-of-alphafold-a-covid-19 | 2301.04093 | null | https://arxiv.org/abs/2301.04093v2 | https://arxiv.org/pdf/2301.04093v2.pdf | On the Robustness of AlphaFold: A COVID-19 Case Study | Protein folding neural networks (PFNNs) such as AlphaFold predict remarkably accurate structures of proteins compared to other approaches. However, the robustness of such networks has heretofore not been explored. This is particularly relevant given the broad social implications of such technologies and the fact that b... | ['Susmit Jha', 'Arvind Ramanathan', 'Rickard Ewetz', 'Alvaro Velasquez', 'George Atia', 'Andre Beckus', 'Sumit Jha', 'Ismail Alkhouri'] | 2023-01-10 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 6.45034850e-01 3.66582721e-01 3.35792005e-01 -1.66608259e-01
-5.30780315e-01 -9.75093186e-01 2.65864909e-01 2.35514566e-01
-5.30075073e-01 1.38196588e+00 -7.93899968e-02 -6.00049138e-01
1.75193235e-01 -6.25889182e-01 -1.45689118e+00 -1.15143478e+00
-2.61466622e-01 3.57125372e-01 2.01756313e-01 -4.06068116... | [4.689091682434082, 5.617979526519775] |
13dd7228-df0d-48c1-9075-3ec1b5e06ca4 | gait-recognition-using-fmcw-radar-and | null | null | https://ieeexplore.ieee.org/abstract/document/9160199 | https://ieeexplore.ieee.org/abstract/document/9160199 | Gait Recognition using FMCW Radar and Temporal Convolutional Deep Neural Networks | The capability of human identification in specific scenarios and in a quickly and accurately manner, is a critical aspect in various surveillance applications. In particular, in this context, classical survaillance systems are based on videocameras, requiring high computational/storing resources, which are very sensiti... | ['Danilo Orlando', 'Carmine Clemente', 'Marta Cimitile', 'Filippo Biondi', 'Mario Luca Bernardi', 'Pia Addabbo'] | 2020-08-06 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.05133837e-02 -7.28222430e-01 2.56197006e-01 -2.32650533e-01
-6.67455494e-02 -3.57317865e-01 6.77381516e-01 7.43162930e-02
-9.20852780e-01 7.66259789e-01 -4.46333826e-01 -2.26547401e-02
-5.74368894e-01 -7.87482858e-01 -7.46390298e-02 -9.45266843e-01
-6.74871206e-01 3.03009957e-01 -7.64580220e-02 -2.15272292... | [13.991700172424316, 1.416465401649475] |
593ff917-1663-4295-8790-f8f1a0f183f4 | task-agnostic-distillation-of-encoder-decoder | 2305.12330 | null | https://arxiv.org/abs/2305.12330v1 | https://arxiv.org/pdf/2305.12330v1.pdf | Task-agnostic Distillation of Encoder-Decoder Language Models | Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks. The intriguing task-agnostic property has driven a shifted focus from task-specific to task-agnostic distillation of LMs. While task-agnostic, compute-efficient, performance-preserved LMs can be yielded by task-a... | ['Dawei Song', 'Jingang Wang', 'Yang Yang', 'Chen Zhang'] | 2023-05-21 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.76878631e-01 1.94956213e-01 -2.39625841e-01 -1.54259562e-01
-9.83763278e-01 -7.09935844e-01 9.09822404e-01 -5.14272377e-02
-5.51627934e-01 6.46536291e-01 4.85661864e-01 -6.49682403e-01
-1.23302445e-01 -2.75735140e-01 -5.63490689e-01 -2.87910432e-01
1.64872438e-01 8.40018928e-01 5.45238219e-02 -6.33924067... | [11.537111282348633, 8.803693771362305] |
4f10cf6b-d806-4da6-bcc6-c63beb55a717 | fpga-implementation-of-convolutional-neural-1 | 2306.13557 | null | https://arxiv.org/abs/2306.13557v2 | https://arxiv.org/pdf/2306.13557v2.pdf | FPGA Implementation of Convolutional Neural Network for Real-Time Handwriting Recognition | Machine Learning (ML) has recently been a skyrocketing field in Computer Science. As computer hardware engineers, we are enthusiastic about hardware implementations of popular software ML architectures to optimize their performance, reliability, and resource usage. In this project, we designed a highly-configurable, re... | ['Qikun Liu', 'Lingkai Zhao', 'Shichen Qiao', 'Eric J. Hoffman', 'Haining Qiu'] | 2023-06-23 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 9.86032560e-02 -4.52001452e-01 -1.81166753e-01 -7.10735679e-01
2.15771347e-01 -5.51905036e-01 4.88672853e-02 -3.12816277e-02
-6.71817005e-01 2.67645389e-01 -5.38664281e-01 -1.14777184e+00
1.37838706e-01 -8.27650309e-01 -6.22177839e-01 -5.02985835e-01
-1.30324215e-01 -2.98415393e-01 3.35860074e-01 -3.97973286... | [8.341452598571777, 2.7584147453308105] |
dfb3c801-ebbb-40dc-9214-9f5de1fb1173 | doubleadapt-a-meta-learning-approach-to | 2306.09862 | null | https://arxiv.org/abs/2306.09862v1 | https://arxiv.org/pdf/2306.09862v1.pdf | DoubleAdapt: A Meta-learning Approach to Incremental Learning for Stock Trend Forecasting | Stock trend forecasting is a fundamental task of quantitative investment where precise predictions of price trends are indispensable. As an online service, stock data continuously arrive over time. It is practical and efficient to incrementally update the forecast model with the latest data which may reveal some new pa... | ['Yanyan Shen', 'Shuming Kong', 'Lifan Zhao'] | 2023-06-16 | null | null | null | null | ['meta-learning', 'incremental-learning'] | ['methodology', 'methodology'] | [-3.90490323e-01 -4.24314827e-01 -4.63191390e-01 -4.76293981e-01
-4.26995605e-01 -8.40497375e-01 4.05168742e-01 2.92096555e-01
-2.74975061e-01 6.36099637e-01 1.53667971e-01 -4.56124961e-01
-2.69077756e-02 -8.00773084e-01 -8.04525256e-01 -5.56732595e-01
-2.38732174e-01 7.04030931e-01 4.19913769e-01 -3.28833580... | [4.483198165893555, 4.195487022399902] |
9b4039e0-947d-4f44-a199-1267db156bcb | robust-android-malware-detection-system | 2101.12031 | null | https://arxiv.org/abs/2101.12031v1 | https://arxiv.org/pdf/2101.12031v1.pdf | Robust Android Malware Detection System against Adversarial Attacks using Q-Learning | The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attacks. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep... | ['Mohit Sewak', 'Piyush Nikam', 'Sanjay K. Sahay', 'Hemant Rathore'] | 2021-01-27 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 1.82654798e-01 9.61559117e-02 -3.37171912e-01 2.21688136e-01
-1.91242859e-01 -8.82576287e-01 7.31727123e-01 -1.50124624e-01
-1.40806466e-01 5.79334021e-01 -4.24471945e-01 -9.96230960e-01
7.07643153e-03 -9.39396799e-01 -8.47190976e-01 -4.95968133e-01
-2.71653593e-01 -1.82277665e-01 5.70882738e-01 -3.91459376... | [14.405033111572266, 9.668720245361328] |
f25aa696-cb32-4d42-993a-c4ea75f7e3d3 | 6d-pose-estimation-with-combined-deep | 2111.06276 | null | https://arxiv.org/abs/2111.06276v1 | https://arxiv.org/pdf/2111.06276v1.pdf | 6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping | Real-time robotic grasping, supporting a subsequent precise object-in-hand operation task, is a priority target towards highly advanced autonomous systems. However, such an algorithm which can perform sufficiently-accurate grasping with time efficiency is yet to be found. This paper proposes a novel method with a 2-sta... | ['Chyi-Yeu Lin', 'Joel Vidal', 'Yu-Ru Chen', 'Trung-Son Le', 'Tuan-Tang Le'] | 2021-11-11 | null | null | null | null | ['3d-object-recognition', 'robotic-grasping'] | ['computer-vision', 'robots'] | [-8.33556056e-02 -2.89428324e-01 2.61557758e-01 -3.88920009e-01
-6.32413089e-01 -2.82505870e-01 2.85795480e-01 -2.25071922e-01
-4.83974189e-01 3.89622539e-01 -5.19145370e-01 -1.20230429e-01
-4.60505873e-01 -7.74590313e-01 -1.01583707e+00 -9.26670134e-01
-5.08461654e-01 6.87424541e-01 1.01861000e-01 -1.30940482... | [5.862091064453125, -0.9391540288925171] |
a254d767-341c-423b-902e-974d974e005e | real-time-video-highlights-for-yahoo-esports | 1611.08780 | null | http://arxiv.org/abs/1611.08780v1 | http://arxiv.org/pdf/1611.08780v1.pdf | Real-Time Video Highlights for Yahoo Esports | Esports has gained global popularity in recent years and several companies
have started offering live streaming videos of esports games and events. This
creates opportunities to develop large scale video understanding systems for
new product features and services. We present a technique for detecting
highlights from li... | ['Yale Song'] | 2016-11-27 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-1.41430780e-01 -3.11241329e-01 -3.25253874e-01 -1.60151795e-01
-5.53923368e-01 -6.44682467e-01 2.49736235e-01 1.84993312e-01
-3.65215212e-01 1.16393358e-01 -2.24137474e-02 7.72623122e-02
4.59386170e-01 -8.92285287e-01 -7.95241117e-01 -9.20068100e-02
-3.88836116e-01 2.28055147e-03 8.49975049e-01 -4.98005390... | [8.23173713684082, 0.19720645248889923] |
cb801c82-4146-4622-941b-33169fc1c6c1 | retinal-vessel-segmentation-via-a-multi | 2304.12856 | null | https://arxiv.org/abs/2304.12856v1 | https://arxiv.org/pdf/2304.12856v1.pdf | Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning | Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addre... | ['Imran Razzak', 'Antonio Robles-Kelly', 'Syed S. Naqvi', 'Tariq M. Khan'] | 2023-04-25 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 4.17092532e-01 -1.00805119e-01 1.60119757e-01 -2.42649913e-01
-8.26249242e-01 -5.48543274e-01 2.80712217e-01 -1.36269927e-01
-6.31966949e-01 9.05510783e-01 1.64101884e-01 -5.11576951e-01
-2.27437064e-01 -5.85204303e-01 -4.26673889e-01 -8.55926991e-01
1.52630895e-01 8.27394426e-02 4.39481407e-01 1.30011234... | [15.765632629394531, -3.942861795425415] |
76209560-e97a-4a0c-a915-54d85b3f8e28 | gusum-graph-based-unsupervised-summarization-1 | null | null | https://aclanthology.org/2022.textgraphs-1.5 | https://aclanthology.org/2022.textgraphs-1.5.pdf | GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT | Unsupervised extractive document summarization aims to extract salient sentences from a document without requiring a labelled corpus. In existing graph-based methods, vertex and edge weights are usually created by calculating sentence similarities. In this paper, we develop a Graph-Based Unsupervised Summarization(GUSU... | ['Mark Lee', 'Phillip Smith', 'Tuba Gokhan'] | null | null | null | null | coling-textgraphs-2022-10 | ['graph-ranking', 'sentence-embeddings', 'sentence-embeddings', 'extractive-document-summarization', 'document-summarization'] | ['graphs', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.29644966e-01 4.30887550e-01 -1.09270543e-01 -3.70985806e-01
-3.26228768e-01 -4.11577940e-01 4.99104053e-01 1.33457458e+00
-4.74239111e-01 6.63850844e-01 1.17512727e+00 1.35958984e-01
-2.92350888e-01 -9.71047342e-01 -3.50783587e-01 -4.19086516e-01
2.79508159e-02 1.82422936e-01 8.25724676e-02 -3.56195897... | [12.529722213745117, 9.540606498718262] |
bb27dd9c-dc87-44d0-98e7-430fd90ae04a | one-sided-matrix-completion-from-two | 2306.04049 | null | https://arxiv.org/abs/2306.04049v1 | https://arxiv.org/pdf/2306.04049v1.pdf | One-sided Matrix Completion from Two Observations Per Row | Given only a few observed entries from a low-rank matrix $X$, matrix completion is the problem of imputing the missing entries, and it formalizes a wide range of real-world settings that involve estimating missing data. However, when there are too few observed entries to complete the matrix, what other aspects of the u... | ['Gregory Valiant', 'Percy Liang', 'Steven Cao'] | 2023-06-06 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 4.74843532e-01 7.92330876e-02 -5.47591820e-02 4.52949591e-02
-1.03175020e+00 -8.85926664e-01 -4.46498320e-02 4.85107768e-03
-3.65087837e-01 8.94126356e-01 3.67392093e-01 -3.27823669e-01
-5.15933812e-01 -6.93880200e-01 -1.35359538e+00 -7.97363639e-01
-5.91054857e-01 7.09060907e-01 -6.93880200e-01 -4.39675897... | [6.913506507873535, 4.729947566986084] |
9f2e7e9a-11f9-45f7-8959-ba7ceca6546a | citeprompt-using-prompts-to-identify-citation | 2304.12730 | null | https://arxiv.org/abs/2304.12730v2 | https://arxiv.org/pdf/2304.12730v2.pdf | CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers | Citations in scientific papers not only help us trace the intellectual lineage but also are a useful indicator of the scientific significance of the work. Citation intents prove beneficial as they specify the role of the citation in a given context. In this paper, we present CitePrompt, a framework which uses the hithe... | ['Imon Mukherjee', 'Debarshi Kumar Sanyal', 'Avishek Lahiri'] | 2023-04-25 | null | null | null | null | ['citation-intent-classification', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.04848564e-02 -3.90665606e-02 -6.41759396e-01 -6.81594312e-02
-1.16615450e+00 -6.98825300e-01 1.28191829e+00 2.97618359e-01
-6.30811155e-01 7.12515831e-01 4.98018563e-01 -4.62975353e-01
-4.02760714e-01 -4.68165398e-01 -5.88870347e-01 -6.04748905e-01
4.05899197e-01 5.89685738e-01 1.19787887e-01 2.42777735... | [9.787849426269531, 8.260281562805176] |
117fd6de-9485-489a-af4d-139b87206eae | taking-actions-separately-a-bidirectionally | null | null | https://aclanthology.org/2022.coling-1.395 | https://aclanthology.org/2022.coling-1.395.pdf | Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation | Training Neural Machine Translation (NMT) models suffers from sparse parallel data, in the infrequent translation scenarios towards low-resource source languages. The existing solutions primarily concentrate on the utilization of Parent-Child (PC) transfer learning. It transfers well-trained NMT models on high-resource... | ['Guodong Zhou', 'Jianmin Yao', 'Minhan Xu', 'Yu Hong', 'Xiaolin Xing'] | null | null | null | null | coling-2022-10 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.91593164e-01 2.68508270e-02 -3.46557707e-01 -1.49308741e-01
-1.21718931e+00 -7.01474249e-01 8.02740216e-01 -5.44612825e-01
-2.57525921e-01 9.72999096e-01 2.56452292e-01 -6.67630494e-01
4.90663409e-01 -7.37787426e-01 -8.82838547e-01 -6.45197213e-01
2.41058305e-01 7.84934044e-01 -3.49272221e-01 -4.81126666... | [11.663949966430664, 10.204864501953125] |
29554054-ae46-45bd-a9e3-11b7dcafe9fa | evaluation-of-a-tree-based-pipeline | 1603.06212 | null | http://arxiv.org/abs/1603.06212v1 | http://arxiv.org/pdf/1603.06212v1.pdf | Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science | As the field of data science continues to grow, there will be an
ever-increasing demand for tools that make machine learning accessible to
non-experts. In this paper, we introduce the concept of tree-based pipeline
optimization for automating one of the most tedious parts of machine
learning---pipeline design. We imple... | ['Randal S. Olson', 'Nathan Bartley', 'Jason H. Moore', 'Ryan J. Urbanowicz'] | 2016-03-20 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 3.81507911e-02 -1.64808705e-02 1.56486392e-01 -4.42628354e-01
-6.11606538e-01 -8.16865742e-01 4.25703414e-02 4.03956056e-01
-2.83193856e-01 2.33546183e-01 -1.05263464e-01 -6.62903488e-01
9.00652483e-02 -7.06083536e-01 -5.72756112e-01 -3.62699270e-01
-9.45298001e-02 4.97811705e-01 1.91893607e-01 9.99029609... | [8.446456909179688, 4.407183647155762] |
c452af14-ba39-4c06-98bc-6e4b195719ef | intermediate-and-future-frame-prediction-of | 2303.04405 | null | https://arxiv.org/abs/2303.04405v1 | https://arxiv.org/pdf/2303.04405v1.pdf | Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network | Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satell... | ['Wanseok Seo', 'Hyesook Lee', 'Hyungkun Bae', 'Heesun Park', 'Hyungon Ry', 'Yeji Choi', 'Minseok Seo'] | 2023-03-08 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-7.92919174e-02 -4.83718157e-01 -3.26036178e-02 -1.79752901e-01
-3.03078324e-01 -3.88134420e-01 6.21041954e-01 -4.89694744e-01
-5.31400740e-01 8.37116599e-01 1.65955469e-01 -4.40633565e-01
1.10282414e-01 -1.18502510e+00 -3.83519500e-01 -9.45651650e-01
-5.14220119e-01 4.21990082e-02 1.12556435e-01 -4.88806933... | [9.710369110107422, -1.6653954982757568] |
401e7d48-5816-4873-b059-b3ce7f11bbcd | deep-learning-for-environmentally-robust | 1705.10874 | null | http://arxiv.org/abs/1705.10874v3 | http://arxiv.org/pdf/1705.10874v3.pdf | Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments | Eliminating the negative effect of non-stationary environmental noise is a
long-standing research topic for automatic speech recognition that stills
remains an important challenge. Data-driven supervised approaches, including
ones based on deep neural networks, have recently emerged as potential
alternatives to traditi... | ['Björn Schuller', 'Amr El-Desoky Mousa', 'Jouni Pohjalainen', 'Jürgen Geiger', 'Wenyu Jin', 'Zixing Zhang'] | 2017-05-30 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 2.89917916e-01 -1.56457335e-01 4.34875727e-01 -3.40700448e-01
-8.71360898e-01 -2.39942580e-01 4.04068172e-01 -2.95687169e-01
-4.96170193e-01 4.62932289e-01 4.06717360e-01 -3.09340239e-01
-1.64823711e-01 -4.40591007e-01 -5.86520433e-01 -1.15202391e+00
2.34174863e-01 -1.39167592e-01 -2.09568918e-01 -1.57731235... | [15.062457084655762, 5.886836528778076] |
9842fe24-69fd-44e0-bd13-08934fddcfc8 | hierarchical-3d-feature-learning-for-pancreas | 2109.01667 | null | https://arxiv.org/abs/2109.01667v1 | https://arxiv.org/pdf/2109.01667v1.pdf | Hierarchical 3D Feature Learning for Pancreas Segmentation | We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to m... | ['Concetto Spampinato', 'Ulas Bagci', 'Simone Palazzo', 'Ismail Irmakci', 'Giovanni Bellitto', 'Federica Proietto Salanitri'] | 2021-09-03 | null | null | null | null | ['pancreas-segmentation', 'automated-pancreas-segmentation'] | ['medical', 'medical'] | [ 2.27880880e-01 3.59043926e-01 -1.49844229e-01 -5.83876610e-01
-9.43540275e-01 -3.79117608e-01 2.03194410e-01 4.42570001e-01
-3.89918417e-01 4.15624022e-01 4.70673472e-01 -1.90002218e-01
3.51870470e-02 -6.00087404e-01 -8.74334872e-01 -7.23576725e-01
-8.42512667e-01 7.62939572e-01 6.87884465e-02 4.32304114... | [14.53028678894043, -2.5816714763641357] |
48d8e3ea-9438-4c83-94b3-03aa50cc009b | gsdf-geometry-driven-signed-distance | 2304.11970 | null | https://arxiv.org/abs/2304.11970v1 | https://arxiv.org/pdf/2304.11970v1.pdf | gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction | Signed distance functions (SDFs) is an attractive framework that has recently shown promising results for 3D shape reconstruction from images. SDFs seamlessly generalize to different shape resolutions and topologies but lack explicit modelling of the underlying 3D geometry. In this work, we exploit the hand structure a... | ['Ivan Laptev', 'Cordelia Schmid', 'ShiZhe Chen', 'Zerui Chen'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_gSDF_Geometry-Driven_Signed_Distance_Functions_for_3D_Hand-Object_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-reconstruction', '3d-shape-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.22812487e-01 -4.40506279e-01 7.53976181e-02 -3.40325177e-01
-2.14143202e-01 -9.86479998e-01 6.34478331e-01 -5.10858893e-01
-9.71339494e-02 6.40202940e-01 3.90591770e-01 4.88690659e-02
-1.02507934e-01 -3.30229044e-01 -7.87739217e-01 -4.64638382e-01
2.61155158e-01 8.77718449e-01 8.65364894e-02 -1.02420244... | [6.610786437988281, -1.0673197507858276] |
65c0aad4-3f9c-4015-a1a7-43c1c85a5835 | diagnosing-and-remedying-shot-sensitivity | 2207.03398 | null | https://arxiv.org/abs/2207.03398v1 | https://arxiv.org/pdf/2207.03398v1.pdf | Diagnosing and Remedying Shot Sensitivity with Cosine Few-Shot Learners | Few-shot recognition involves training an image classifier to distinguish novel concepts at test time using few examples (shot). Existing approaches generally assume that the shot number at test time is known in advance. This is not realistic, and the performance of a popular and foundational method has been shown to s... | ['Bharath Hariharan', 'Luming Tang', 'Davis Wertheimer'] | 2022-07-07 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.28206474e-01 -5.94386995e-01 -4.69621718e-01 -4.24962252e-01
-1.02824450e+00 -3.45500052e-01 8.87234211e-01 4.16952074e-02
-4.83489037e-01 6.46406293e-01 4.18259427e-02 2.14990571e-01
-3.56944084e-01 -7.51767337e-01 -5.30696690e-01 -4.85264033e-01
-9.01354384e-03 3.56345922e-01 5.72072446e-01 -3.20932299... | [9.91685962677002, 2.8479621410369873] |
93d3ff16-9b62-4e0f-8ece-fc129c97180a | location-based-training-for-multi-channel | 2110.04289 | null | https://arxiv.org/abs/2110.04289v1 | https://arxiv.org/pdf/2110.04289v1.pdf | Location-based training for multi-channel talker-independent speaker separation | Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial information afforded by microphone arrays, we propose a new training approach to resolving permutation ambiguities for multi-channel speaker separatio... | ['DeLiang Wang', 'Ke Tan', 'Hassan Taherian'] | 2021-10-08 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 7.05752969e-02 -6.88032985e-01 3.35337874e-03 -4.02412355e-01
-1.30034709e+00 -8.47922325e-01 2.67034739e-01 -1.29577890e-01
-3.53239179e-01 4.85626966e-01 2.98565328e-01 -6.02795660e-01
-7.42128253e-01 -1.73825249e-01 -4.27070409e-01 -1.09775484e+00
-4.26183671e-01 4.42623854e-01 2.98610888e-02 5.78434132... | [14.873675346374512, 5.903517246246338] |
a26e617f-26cb-4f4b-92f5-7a936be0e01a | towards-a-unified-approach-to-single-image | 2103.14204 | null | https://arxiv.org/abs/2103.14204v1 | https://arxiv.org/pdf/2103.14204v1.pdf | Towards a Unified Approach to Single Image Deraining and Dehazing | We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit. Via depth-aware fusion of multi-layer rain streaks according to the camera imaging mechanism, the new model can better capture the... | ['Jun Chen', 'Linhui Dai', 'Zhihao Shi', 'Yongrui Ma', 'Xiaohong Liu'] | 2021-03-26 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.15267055e-02 -1.73190594e-01 5.90756238e-01 -3.51945579e-01
-1.89488038e-01 -1.98265880e-01 4.08581793e-01 -1.93008482e-01
-1.51068434e-01 9.76553619e-01 -1.72236040e-01 -1.55229747e-01
-4.34009820e-01 -9.55952764e-01 -4.79250014e-01 -1.31469083e+00
-4.10392791e-01 2.17595443e-01 6.73642159e-01 -5.72634101... | [10.895261764526367, -3.227623462677002] |
cf8b6c9a-9d15-42c9-b684-47859333f97e | more-than-classification-a-unified-framework | 2305.17607 | null | https://arxiv.org/abs/2305.17607v1 | https://arxiv.org/pdf/2305.17607v1.pdf | More than Classification: A Unified Framework for Event Temporal Relation Extraction | Event temporal relation extraction~(ETRE) is usually formulated as a multi-label classification task, where each type of relation is simply treated as a one-hot label. This formulation ignores the meaning of relations and wipes out their intrinsic dependency. After examining the relation definitions in various ETRE tas... | ['Dongyan Zhao', 'Chang Liu', 'Yansong Feng', 'Shengqi Zhu', 'Yutong Hu', 'Quzhe Huang'] | 2023-05-28 | null | null | null | null | ['temporal-relation-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.86059850e-02 9.33309942e-02 -6.48309946e-01 -6.55410528e-01
-5.86592078e-01 -8.05491269e-01 6.66592181e-01 5.67571998e-01
-2.92423755e-01 8.35165858e-01 1.26124322e-01 -4.51725066e-01
-2.50884891e-01 -9.86162484e-01 -6.39397264e-01 -4.90810275e-01
-3.09979707e-01 6.72091424e-01 4.51344937e-01 -3.09455872... | [9.056347846984863, 9.139460563659668] |
8756ee41-4123-464e-aea6-c61be932e183 | discrete-wavelet-transform-for-generative | null | null | https://ieeexplore.ieee.org/document/9716101 | https://ieeexplore.ieee.org/document/9716101 | Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors | Driver identification is a central research area in intelligent transportation systems, with applications in commercial freight transport and usage-based insurance. One way to perform the identification is to use smartphones as the main sensor devices. After extracting features from smartphone-embedded sensors, various... | ['Johan Wahlström', 'Mehdi Ghatee', 'Rouhollah Ahmadian'] | 2022-04-01 | null | null | null | ieee-sensors-journal-2022-4 | ['image-augmentation'] | ['computer-vision'] | [ 4.95512098e-01 -8.63391906e-02 -2.41459742e-01 -2.33162716e-01
-7.55854845e-01 -3.53291065e-01 5.27521729e-01 -3.62463295e-01
-3.69333804e-01 7.91852474e-01 -1.88516781e-01 -3.69295329e-01
1.88779324e-01 -9.69994843e-01 -6.41658068e-01 -9.57217216e-01
4.03959066e-01 -7.48054534e-02 -4.07507196e-02 -3.69499356... | [7.943689823150635, -0.578368067741394] |
7263628e-5599-4d91-981e-79913b18addf | leveraging-rationales-to-improve-human-task | 2002.04202 | null | https://arxiv.org/abs/2002.04202v1 | https://arxiv.org/pdf/2002.04202v1.pdf | Leveraging Rationales to Improve Human Task Performance | Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans. In response to the proliferation of such models, the field of Explainable AI (XAI) has sought to develop techniques that enhance the transparency and interpretability of machine learning... | ['Sonia Chernova', 'Devleena Das'] | 2020-02-11 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [ 4.64545071e-01 7.17416108e-01 -2.50066407e-02 -3.00525010e-01
-6.01194739e-01 -7.45183408e-01 8.76152277e-01 2.15422913e-01
-3.66371393e-01 5.43127537e-01 4.80981469e-01 -6.27522886e-01
1.41738176e-01 -4.97403502e-01 -5.42588651e-01 -8.29116702e-02
3.39157045e-01 5.40577948e-01 4.52168211e-02 -3.44420105... | [9.416234016418457, 6.89768648147583] |
ba7cd129-5415-411f-8252-3bcb73ec50a8 | gcfagg-global-and-cross-view-feature | 2305.06799 | null | https://arxiv.org/abs/2305.06799v1 | https://arxiv.org/pdf/2305.06799v1.pdf | GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering | Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views v... | ['Weisi Lin', 'Liang Liao', 'Guanghui Yue', 'Chang Tang', 'Chenlei Lv', 'Yuanyang Zhang', 'Weiqing Yan'] | 2023-05-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-4.33084339e-01 -3.87788057e-01 2.64887530e-02 -5.82994938e-01
-7.98181593e-01 -7.82908440e-01 6.22078359e-01 -1.80631399e-01
2.88747489e-01 -4.82634604e-02 6.16933584e-01 4.91125256e-01
-1.83622837e-01 -5.01378298e-01 -3.83684427e-01 -1.05027580e+00
3.49431753e-01 5.85240960e-01 9.03041735e-02 1.37013197... | [8.373421669006348, 4.591564178466797] |
a30fbddb-262e-430f-8889-ebf3abc29418 | mtldesc-looking-wider-to-describe-better | 2203.07003 | null | https://arxiv.org/abs/2203.07003v1 | https://arxiv.org/pdf/2203.07003v1.pdf | MTLDesc: Looking Wider to Describe Better | Limited by the locality of convolutional neural networks, most existing local features description methods only learn local descriptors with local information and lack awareness of global and surrounding spatial context. In this work, we focus on making local descriptors "look wider to describe better" by learning loca... | ['Xiaopeng Zhang', 'Bin Fan', 'Weiliang Meng', 'Shibiao Xu', 'Yuyang Zhang', 'Rongtao Xu', 'Changwei Wang'] | 2022-03-14 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-4.04017806e-01 -7.36162364e-01 -3.69147956e-01 -8.12689543e-01
-1.22004211e+00 -4.53521192e-01 7.92623580e-01 3.76934677e-01
-5.12651265e-01 7.04237819e-01 4.83752966e-01 1.80763260e-01
-4.14261222e-01 -6.73231125e-01 -7.11876690e-01 -7.40989983e-01
-1.57522336e-01 -1.07756644e-01 8.76754075e-02 -3.14483643... | [7.857123851776123, -1.9443432092666626] |
956f3a59-0213-4bd5-86a4-c2942982b251 | vesselness-features-and-the-inverse | 1306.1609 | null | http://arxiv.org/abs/1306.1609v1 | http://arxiv.org/pdf/1306.1609v1.pdf | Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR | Over the course of the last decade, infrared (IR) and particularly thermal IR
imaging based face recognition has emerged as a promising complement to
conventional, visible spectrum based approaches which continue to struggle when
applied in the real world. While inherently insensitive to visible spectrum
illumination c... | ['Reza Shoja Ghiass', 'Xavier Maldague', 'Hakim Bendada', 'Ognjen Arandjelovic'] | 2013-06-07 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 6.60937130e-01 -9.55176130e-02 -1.36326347e-02 -3.64884168e-01
-5.04502416e-01 -4.50581402e-01 4.59461004e-01 -4.02971655e-01
-4.80113536e-01 3.61273408e-01 -1.24593265e-01 2.07085609e-01
3.71127166e-02 -4.33725059e-01 -4.23834801e-01 -1.08447230e+00
1.89074665e-01 -3.31608839e-02 -2.00470239e-01 -2.82624692... | [13.214086532592773, 0.8852028250694275] |
ad306c9c-5ead-4cee-be2c-82e43d845fb9 | deep-learning-for-handling-kernel-model | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Nan_Deep_Learning_for_Handling_Kernelmodel_Uncertainty_in_Image_Deconvolution_CVPR_2020_paper.pdf | Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution | Most existing non-blind image deconvolution methods assume that the given blurring kernel is error-free. In practice, blurring kernel often is estimated via some blind deblurring algorithm which is not exactly the truth. Also, the convolution model is only an approximation to practical blurring effect. It is known that... | [' Hui Ji', 'Yuesong Nan'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-deconvolution'] | ['computer-vision'] | [ 1.21723458e-01 -5.73152184e-01 1.95227712e-01 -1.47088319e-01
-3.02813917e-01 -3.58806074e-01 3.69639426e-01 -9.84979391e-01
-3.53565574e-01 1.04538715e+00 6.87511384e-01 -2.56894141e-01
-2.05226302e-01 -2.12171935e-02 -8.20751607e-01 -9.07387972e-01
2.17476245e-02 -1.39520764e-01 -7.12503642e-02 7.09045231... | [11.627039909362793, -2.7455899715423584] |
bcdf4c17-d07a-46a2-b790-18fcba0ac0e1 | accurate-fine-grained-segmentation-of-human | 2306.03934 | null | https://arxiv.org/abs/2306.03934v1 | https://arxiv.org/pdf/2306.03934v1.pdf | Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling | Purpose: Interpreting chest radiographs (CXR) remains challenging due to the ambiguity of overlapping structures such as the lungs, heart, and bones. To address this issue, we propose a novel method for extracting fine-grained anatomical structures in CXR using pseudo-labeling of three-dimensional computed tomography (... | ['Rainer Stiefelhagen', 'Jens Kleesiek', 'Ken Herrmann', 'Simon Reiß', 'Moon Kim', 'Matthias A. Fink', 'Alexander Jaus', 'Constantin Seibold'] | 2023-06-06 | null | null | null | null | ['computed-tomography-ct', 'anatomy'] | ['methodology', 'miscellaneous'] | [ 1.45617858e-01 4.99208450e-01 -8.45915750e-02 -5.29011309e-01
-1.17375195e+00 -8.48849773e-01 2.75446344e-02 3.42708915e-01
-2.65694439e-01 5.43579936e-01 2.74039567e-01 -7.38142252e-01
-2.39443064e-01 -4.63048220e-01 -3.48356575e-01 -3.89373660e-01
1.82115138e-01 1.09368002e+00 4.97025728e-01 2.80634016... | [14.9302339553833, -2.138698101043701] |
78f1200e-abf6-40c2-a8f4-07689bb3c5ee | rudsi-graph-based-word-sense-induction | 2209.13750 | null | https://arxiv.org/abs/2209.13750v1 | https://arxiv.org/pdf/2209.13750v1.pdf | RuDSI: graph-based word sense induction dataset for Russian | We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russian, RuDSI is completely data-driven (based on texts from Russian National Corpus), with no external w... | ['Andrey Kutuzov', 'Elisey Rykov', 'Ekaterina Gavrishina', 'Anna Aksenova'] | 2022-09-28 | null | https://aclanthology.org/2022.textgraphs-1.9 | https://aclanthology.org/2022.textgraphs-1.9.pdf | coling-textgraphs-2022-10 | ['graph-clustering'] | ['graphs'] | [ 3.45319897e-01 6.04446471e-01 -3.86052936e-01 -1.93212405e-01
-4.70688760e-01 -8.31208944e-01 6.15954340e-01 5.30186892e-01
-6.23980224e-01 1.13591039e+00 5.17004251e-01 -6.96749032e-01
-2.64385968e-01 -8.01681459e-01 1.12004049e-01 -4.01160955e-01
3.26700181e-01 8.45093071e-01 4.78163183e-01 -7.25228608... | [10.223651885986328, 9.158869743347168] |
753d0e42-6c59-4f06-9bff-c4c2a21959ba | kite-keypoint-conditioned-policies-for | 2306.16605 | null | https://arxiv.org/abs/2306.16605v2 | https://arxiv.org/pdf/2306.16605v2.pdf | KITE: Keypoint-Conditioned Policies for Semantic Manipulation | While natural language offers a convenient shared interface for humans and robots, enabling robots to interpret and follow language commands remains a longstanding challenge in manipulation. A crucial step to realizing a performant instruction-following robot is achieving semantic manipulation, where a robot interprets... | ['Jeannette Bohg', 'Dorsa Sadigh', 'Suneel Belkhale', 'Priya Sundaresan'] | 2023-06-29 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.63822353e-01 4.46792878e-02 -2.45819211e-01 -3.37753564e-01
-5.44448316e-01 -8.70031714e-01 7.01116443e-01 8.29402953e-02
-3.17318410e-01 2.41218820e-01 1.26628309e-01 -2.91985273e-01
-2.63391435e-01 -4.62481111e-01 -1.05541611e+00 -4.77673054e-01
4.75849062e-02 6.36224627e-01 1.10259064e-01 -4.29170758... | [4.731771469116211, 0.4973049759864807] |
00f1ce51-ccee-4757-916e-1342fd31760f | early-mfcc-and-hpcp-fusion-for-robust-cover | 1707.04680 | null | http://arxiv.org/abs/1707.04680v1 | http://arxiv.org/pdf/1707.04680v1.pdf | Early MFCC And HPCP Fusion for Robust Cover Song Identification | While most schemes for automatic cover song identification have focused on
note-based features such as HPCP and chord profiles, a few recent papers
surprisingly showed that local self-similarities of MFCC-based features also
have classification power for this task. Since MFCC and HPCP capture
complementary information,... | ['Tralie Christopher J.'] | 2017-07-15 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 1.90452978e-01 -5.39417744e-01 -1.11304987e-02 -1.99180469e-01
-1.31163251e+00 -1.18115258e+00 3.65288645e-01 4.64386851e-01
-1.46033019e-01 5.47146678e-01 3.55095953e-01 1.47053406e-01
-5.58126390e-01 -4.95967746e-01 -2.04560608e-01 -7.84229338e-01
-8.25200438e-01 3.13991636e-01 3.87818605e-01 -1.56182483... | [15.882391929626465, 5.316154479980469] |
0acebeb5-eb41-4b29-aa50-100bf7786ad5 | sparse-local-embeddings-for-extreme-multi | null | null | http://papers.nips.cc/paper/5969-sparse-local-embeddings-for-extreme-multi-label-classification | http://papers.nips.cc/paper/5969-sparse-local-embeddings-for-extreme-multi-label-classification.pdf | Sparse Local Embeddings for Extreme Multi-label Classification | The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hen... | ['Manik Varma', 'Kush Bhatia', 'Purushottam Kar', 'Prateek Jain', 'Himanshu Jain'] | 2015-12-01 | null | null | null | neurips-2015-12 | ['extreme-multi-label-classification'] | ['methodology'] | [ 3.70858878e-01 7.00062737e-02 -3.75029922e-01 -4.73119438e-01
-1.13644004e+00 -8.75896394e-01 4.69409853e-01 5.16363025e-01
-4.02586937e-01 3.92258316e-01 6.99946731e-02 -1.44105554e-01
-4.40911502e-01 -6.13946319e-01 -2.85834283e-01 -8.52226079e-01
-1.40136555e-01 8.86914492e-01 1.17329776e-01 1.31368354... | [9.540684700012207, 4.339142322540283] |
7883f75d-d99b-4fe7-819d-aa736840eeea | receptive-field-analysis-of-temporal | 2204.06439 | null | https://arxiv.org/abs/2204.06439v3 | https://arxiv.org/pdf/2204.06439v3.pdf | Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation | Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. Temporal convolutional networks (TCNs) are commonly used for sequence modelling in speech enhancement tasks. A fea... | ['Thomas Hain', 'Stefan Goetze', 'William Ravenscroft'] | 2022-04-13 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 3.72821331e-01 -2.38964930e-01 3.75415802e-01 -2.13978052e-01
-5.26390135e-01 -1.85205847e-01 6.02801561e-01 -2.68547237e-01
-5.85094333e-01 5.51697075e-01 5.30627310e-01 -6.05714619e-01
-1.33822300e-02 -2.58909553e-01 -5.17106533e-01 -1.06206715e+00
-1.92236856e-01 -2.88185298e-01 1.13710828e-01 -3.34224761... | [14.881120681762695, 5.947079658508301] |
428191da-aa45-4126-a506-52209e91e8d6 | robust-matrix-completion-with-heavy-tailed | 2206.04276 | null | https://arxiv.org/abs/2206.04276v1 | https://arxiv.org/pdf/2206.04276v1.pdf | Robust Matrix Completion with Heavy-tailed Noise | This paper studies low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of... | ['Jianqing Fan', 'Bingyan Wang'] | 2022-06-09 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 8.17120597e-02 -1.82240218e-01 -2.01069135e-02 -7.09882081e-02
-1.21684980e+00 -5.15477538e-01 4.00830023e-02 -9.91819873e-02
-4.13809955e-01 7.81283677e-01 2.74263054e-01 -1.51510939e-01
-6.40713871e-01 -2.67580837e-01 -8.50188732e-01 -1.14183021e+00
-3.60399969e-02 3.30306381e-01 -3.54912013e-01 -1.36841401... | [6.987147808074951, 4.585371494293213] |
f093e7db-c0f2-4449-9308-150bd07e2792 | eeg-and-emg-dataset-for-the-detection-of | 2305.11996 | null | https://arxiv.org/abs/2305.11996v2 | https://arxiv.org/pdf/2305.11996v2.pdf | EEG and EMG dataset for the detection of errors introduced by an active orthosis device | This paper presents a dataset containing recordings of the electroencephalogram (EEG) and the electromyogram (EMG) from eight subjects who were assisted in moving their right arm by an active orthosis device. The supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the ort... | ['Elsa Andrea Kirchner', 'Frank Kirchner', 'Marc Tabie', 'Tobias Rossol', 'Su Kyoung Kim', 'Julia Habenicht', 'Judith Bütefür', 'Kartik Chari', 'Niklas Kueper'] | 2023-05-19 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.34844434e-01 1.90462887e-01 2.18686625e-01 -8.69934540e-03
-2.02906057e-01 -1.29441112e-01 -1.97781563e-01 -2.39078980e-02
-6.49677515e-01 9.89459634e-01 2.79797949e-02 3.97646762e-02
-1.59667343e-01 -7.06238821e-02 -7.11401522e-01 -5.42866170e-01
-4.93428737e-01 -3.97847258e-02 9.92242321e-02 8.14587623... | [6.899194717407227, 0.21701283752918243] |
0e610e68-1079-4656-8e16-7de7e40e8d0e | minimal-adversarial-examples-for-deep | 2008.12066 | null | https://arxiv.org/abs/2008.12066v4 | https://arxiv.org/pdf/2008.12066v4.pdf | Minimal Adversarial Examples for Deep Learning on 3D Point Clouds | With recent developments of convolutional neural networks, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e.g., object recognition, semantic segmentation. In a safety-critical environment, it is however not well understood how such deep learning models are vuln... | ['Sai-Kit Yeung', 'Duc Thanh Nguyen', 'Binh-Son Hua', 'Jaeyeon Kim'] | 2020-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kim_Minimal_Adversarial_Examples_for_Deep_Learning_on_3D_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kim_Minimal_Adversarial_Examples_for_Deep_Learning_on_3D_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-recognition', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 1.61188304e-01 2.91724980e-01 4.56537783e-01 -9.85298157e-02
-5.82352817e-01 -1.04602802e+00 6.63769722e-01 1.00040346e-01
-2.66206115e-01 3.50719392e-01 -7.67917871e-01 -6.87668562e-01
1.66617125e-01 -1.12423861e+00 -1.37848425e+00 -6.00240767e-01
-2.37877518e-01 5.28906524e-01 4.14516151e-01 -3.31451863... | [7.711121559143066, -4.461941242218018] |
4c76a597-4ca9-4425-8b9a-1eec1a02d84f | inferring-the-ground-truth-through | 1807.11836 | null | http://arxiv.org/abs/1807.11836v1 | http://arxiv.org/pdf/1807.11836v1.pdf | Inferring the ground truth through crowdsourcing | Universally valid ground truth is almost impossible to obtain or would come
at a very high cost. For supervised learning without universally valid ground
truth, a recommended approach is applying crowdsourcing: Gathering a large data
set annotated by multiple individuals of varying possibly expertise levels and
inferri... | ['Jean Pierre Char'] | 2018-07-31 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.77689689e-01 5.64331234e-01 -4.50472087e-02 -2.57900953e-01
-7.70928502e-01 -7.21542060e-01 4.32850093e-01 9.62097704e-01
-4.84051555e-01 1.57905424e+00 -5.36061585e-01 -3.41168612e-01
1.23834789e-01 -8.09809566e-01 -6.28859103e-01 -1.06048906e+00
4.38372046e-01 8.25159907e-01 3.20980519e-01 -4.92973663... | [9.542546272277832, 4.040847301483154] |
7f071ab5-b56b-4c37-a0e1-f62759880db5 | disc-a-dataset-for-integrated-sensing-and | 2306.09469 | null | https://arxiv.org/abs/2306.09469v1 | https://arxiv.org/pdf/2306.09469v1.pdf | DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems | In this paper we present DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response in... | ['Joerg Widmer', 'Michele Rossi', 'Jesus Omar Lacruz', 'Jacopo Pegoraro'] | 2023-06-15 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'benchmarking', 'benchmarking', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'robots', 'time-series'] | [ 6.19376957e-01 2.55672429e-02 -1.55435234e-01 -1.30095422e-01
-6.87108457e-01 -3.42836864e-02 -1.70593406e-03 -3.02972585e-01
-2.70328850e-01 1.04212570e+00 2.52393693e-01 -1.04502015e-01
-4.93624598e-01 -9.52489614e-01 -1.53080150e-01 -8.01866770e-01
-9.87202525e-01 5.10652959e-01 -4.76032011e-02 1.20129049... | [6.6740617752075195, 0.745560348033905] |
4836cfe1-573e-4215-b026-fffe5fc42ebd | toward-semi-automatic-misconception-discovery | 2103.04448 | null | https://arxiv.org/abs/2103.04448v1 | https://arxiv.org/pdf/2103.04448v1.pdf | Toward Semi-Automatic Misconception Discovery Using Code Embeddings | Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highlighting patterns in student data, which domain experts can then inspect... | ['Thomas W. Price', 'Poorvaja Penmetsa', 'Samiha Marwan', 'Wengran Wang', 'Krupal Shah', 'Yang Shi'] | 2021-03-07 | null | null | null | null | ['code-classification', 'misconceptions'] | ['computer-code', 'miscellaneous'] | [-8.23012590e-02 1.73079044e-01 -1.06536865e-01 -3.67588401e-01
-2.40204766e-01 -7.59245694e-01 1.46327227e-01 1.15622842e+00
1.27692685e-01 1.84913993e-01 1.38881400e-01 -1.21368361e+00
-1.45554543e-01 -8.83792102e-01 -9.51480865e-01 -5.99895418e-02
1.82260498e-01 6.39253063e-03 3.52185011e-01 -2.59072095... | [9.859573364257812, 7.382505893707275] |
a920e84a-2d7a-4b65-b252-6261f11d7ba0 | interpretable-deep-clustering | 2306.04785 | null | https://arxiv.org/abs/2306.04785v1 | https://arxiv.org/pdf/2306.04785v1.pdf | Interpretable Deep Clustering | Clustering is a fundamental learning task widely used as a first step in data analysis. For example, biologists often use cluster assignments to analyze genome sequences, medical records, or images. Since downstream analysis is typically performed at the cluster level, practitioners seek reliable and interpretable clus... | ['Ofir Lindenbaum', 'Jonathan Svirsky'] | 2023-06-07 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 1.10224679e-01 -1.53514192e-01 -1.34698629e-01 -7.73109317e-01
-8.01718295e-01 -7.67162383e-01 2.71249324e-01 8.19940686e-01
-1.03211947e-01 3.16198587e-01 -8.47453438e-03 -1.14284731e-01
-3.88565272e-01 -6.22785151e-01 -8.04128706e-01 -9.03460324e-01
-3.12330097e-01 7.91687191e-01 -2.85429597e-01 5.22156656... | [9.159907341003418, 3.2818098068237305] |
4c0aae86-91d2-4af5-8ee3-c8e4dc60800b | minute-ventilation-measurement-using | 2208.13319 | null | https://arxiv.org/abs/2208.13319v1 | https://arxiv.org/pdf/2208.13319v1.pdf | Minute ventilation measurement using Plethysmographic Imaging and lighting parameters | Breathing disorders such as sleep apnea is a critical disorder that affects a large number of individuals due to the insufficient capacity of the lungs to contain/exchange oxygen and carbon dioxide to ensure that the body is in the stable state of homeostasis. Respiratory Measurements such as minute ventilation can be ... | ['Krishna Vardhan', 'Bo Ji', 'Karen Li', 'Ludwik Sams', 'Daniel Minati'] | 2022-08-29 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.68069485e-02 -2.60674767e-02 -1.35317519e-01 -5.38371563e-01
7.09526017e-02 -2.63178468e-01 -4.26771551e-01 2.24640742e-01
-3.62296164e-01 7.54385769e-01 2.49162927e-01 -4.42614377e-01
2.00115070e-01 -1.04202235e+00 -4.01871465e-02 -5.37270427e-01
2.34531134e-01 2.32324585e-01 7.94479344e-03 1.95193306... | [13.866375923156738, 3.0626401901245117] |
796994bb-50c8-4209-b91a-9c7a72645c90 | what-if-generating-code-to-answer-simulation | 2204.07835 | null | https://arxiv.org/abs/2204.07835v1 | https://arxiv.org/pdf/2204.07835v1.pdf | What If: Generating Code to Answer Simulation Questions | Many texts, especially in chemistry and biology, describe complex processes. We focus on texts that describe a chemical reaction process and questions that ask about the process's outcome under different environmental conditions. To answer questions about such processes, one needs to understand the interactions between... | ['Kira Radinsky', 'Gal Peretz'] | 2022-04-16 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.43014586e-01 2.26645157e-01 6.81141391e-02 -2.28674680e-01
-2.71394968e-01 -8.85248780e-01 9.30981338e-01 6.68092549e-01
-2.12236151e-01 2.62766361e-01 -3.91775891e-02 -6.29109621e-01
1.71141058e-01 -1.21596289e+00 -1.12866795e+00 -4.03015584e-01
1.73389211e-01 6.63745761e-01 2.55823910e-01 -2.32480869... | [8.195311546325684, 7.4516401290893555] |
2acb498d-bab5-4c84-81a7-90c1baf9c6bd | learning-to-score-olympic-events | 1611.05125 | null | http://arxiv.org/abs/1611.05125v3 | http://arxiv.org/pdf/1611.05125v3.pdf | Learning To Score Olympic Events | Estimating action quality, the process of assigning a "score" to the
execution of an action, is crucial in areas such as sports and health care.
Unlike action recognition, which has millions of examples to learn from, the
action quality datasets that are currently available are small -- typically
comprised of only a fe... | ['Brendan Tran Morris', 'Paritosh Parmar'] | 2016-11-16 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 2.11493168e-02 -1.32483974e-01 -5.98397374e-01 -6.29630566e-01
-1.16260505e+00 -2.27863975e-02 3.54148775e-01 3.61362875e-01
-4.65190113e-01 7.48221874e-01 9.35962379e-01 -2.78371759e-02
-1.62540883e-01 -9.50994790e-01 -6.67697430e-01 -3.16862583e-01
-1.97231337e-01 8.17279592e-02 3.04869711e-02 -4.30930555... | [8.032525062561035, 0.6317633390426636] |
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