paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bd7c161d-bb54-49ec-bd4d-ba33003463e5 | from-newspaper-to-microblogging-what-does-it | null | null | https://aclanthology.org/W13-1611 | https://aclanthology.org/W13-1611.pdf | From newspaper to microblogging: What does it take to find opinions? | null | ['Nina Kr{\\"u}ger', 'Manfred Stede', 'Jonathan Sonntag', 'Stefan Stieglitz', 'Wladimir Sidorenko'] | 2013-06-01 | null | null | null | ws-2013-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.496123313903809, 3.5282883644104004] |
5a0abd5f-c133-4fac-b58b-25e235d94c72 | cedas-a-compressed-decentralized-stochastic | 2301.05872 | null | https://arxiv.org/abs/2301.05872v1 | https://arxiv.org/pdf/2301.05872v1.pdf | CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence | In this paper, we consider solving the distributed optimization problem over a multi-agent network under the communication restricted setting. We study a compressed decentralized stochastic gradient method, termed ``compressed exact diffusion with adaptive stepsizes (CEDAS)", and show the method asymptotically achieves... | ['Shi Pu', 'Kun Huang'] | 2023-01-14 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.04685739e-01 2.91258126e-01 -2.01784750e-03 1.28817782e-01
-9.37733769e-01 -4.37694669e-01 -4.11866456e-02 4.39349562e-01
-5.66873908e-01 8.58782411e-01 9.44587663e-02 -3.06845218e-01
-5.97036839e-01 -7.42670953e-01 -6.18467689e-01 -1.09782970e+00
-8.50188136e-01 3.44805419e-01 -2.90979415e-01 -2.31642663... | [6.307094573974609, 4.79974365234375] |
57fd7e0f-53d7-447d-a7f1-0e381efa2a56 | euler-characteristic-tools-for-topological | 2303.14040 | null | https://arxiv.org/abs/2303.14040v1 | https://arxiv.org/pdf/2303.14040v1.pdf | Euler Characteristic Tools For Topological Data Analysis | In this article, we study Euler characteristic techniques in topological data analysis. Pointwise computing the Euler characteristic of a family of simplicial complexes built from data gives rise to the so-called Euler characteristic profile. We show that this simple descriptor achieve state-of-the-art performance in s... | ['Vadim Lebovici', 'Olympio Hacquard'] | 2023-03-24 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 9.49052498e-02 1.86394881e-02 -3.38119082e-02 -4.63150553e-02
-7.64809728e-01 -8.65483522e-01 6.61620915e-01 5.43564677e-01
-3.21431696e-01 4.13229346e-01 5.57048731e-02 1.01656102e-01
-5.83809197e-01 -7.50016391e-01 -7.57147551e-01 -9.34715271e-01
-8.45285356e-01 5.85992098e-01 4.76530679e-02 -2.83852339... | [7.3792405128479, 4.258005142211914] |
1825dd70-410a-4ea0-8d7d-f922e371dde2 | multi-stage-spatio-temporal-aggregation | 2301.00531 | null | https://arxiv.org/abs/2301.00531v1 | https://arxiv.org/pdf/2301.00531v1.pdf | Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification | In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local a... | ['Liang Lin', 'Jinrui Chen', 'Zhanglin Peng', 'Ruimao Zhang', 'Ziyi Tang'] | 2023-01-02 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 4.17028479e-02 -5.87619245e-01 3.55793834e-02 -4.02712196e-01
-4.85897213e-01 -2.61003971e-01 5.16259074e-01 -6.71903715e-02
-5.19632697e-01 3.38891238e-01 3.78266752e-01 4.32149142e-01
-2.84094810e-01 -5.80977380e-01 -2.36724108e-01 -9.36205804e-01
2.31880452e-02 -4.21772450e-02 -8.29691254e-03 -5.97352199... | [14.691429138183594, 0.9636807441711426] |
72b2ecd4-e948-4441-b1bf-d35761d2d58a | fast-l1-minimization-algorithms-for-robust | 1007.03753 | null | http://arxiv.org/abs/1007.3753v4 | http://arxiv.org/pdf/1007.3753v4.pdf | Fast L1-Minimization Algorithms For Robust Face Recognition | L1-minimization refers to finding the minimum L1-norm solution to an
underdetermined linear system b=Ax. Under certain conditions as described in
compressive sensing theory, the minimum L1-norm solution is also the sparsest
solution. In this paper, our study addresses the speed and scalability of its
algorithms. In par... | ['S. Shankar Sastry', 'Arvind Ganesh', 'Allen Y. Yang', 'Zihan Zhou', 'Yi Ma'] | 2010-07-21 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.63462687e-01 -1.21395171e-01 -9.57698934e-03 -1.49442479e-01
-6.91440642e-01 -2.33510956e-01 2.37883344e-01 -5.08312464e-01
7.91455582e-02 8.62378240e-01 1.69873700e-01 -3.95384245e-02
-4.50470865e-01 -1.97279572e-01 -7.93483496e-01 -8.41366947e-01
-1.40017018e-01 3.28979284e-01 -8.04593801e-01 -1.81359261... | [12.528475761413574, 0.40229612588882446] |
81591949-a7e9-4fc4-a2cb-9b4f5720dc2e | stereo-image-rain-removal-via-dual-view | 2211.10104 | null | https://arxiv.org/abs/2211.10104v2 | https://arxiv.org/pdf/2211.10104v2.pdf | Stereo Image Rain Removal via Dual-View Mutual Attention | Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e.g., rain removal and super-resolution. Stereo image restoration methods usually obtain better performance than monocular methods by learning the disparity between dual views either implicitly or exp... | ['Yi Yang', 'Richang Hong', 'Yang Zhao', 'ZhongQiu Zhao', 'Zhao Zhang', 'Yanyan Wei'] | 2022-11-18 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 5.10930270e-02 -4.98655349e-01 2.42057428e-01 -4.50767666e-01
-3.43567997e-01 -2.68617213e-01 3.37400168e-01 -5.90855002e-01
-2.30544820e-01 9.53912675e-01 4.02325898e-01 -3.69826667e-02
-1.07648797e-01 -8.05841088e-01 -6.39556706e-01 -1.07368255e+00
4.57236171e-01 -2.30557963e-01 4.04496282e-01 -4.57973301... | [10.82850456237793, -3.0538790225982666] |
3a6f2ef0-7f8b-4e12-9dd6-eca993942014 | deep-factorised-inverse-sketching | 1808.02313 | null | http://arxiv.org/abs/1808.02313v1 | http://arxiv.org/pdf/1808.02313v1.pdf | Deep Factorised Inverse-Sketching | Modelling human free-hand sketches has become topical recently, driven by
practical applications such as fine-grained sketch based image retrieval
(FG-SBIR). Sketches are clearly related to photo edge-maps, but a human
free-hand sketch of a photo is not simply a clean rendering of that photo's
edge map. Instead there i... | ['Yi-Zhe Song', 'Jifei Song', 'Kaiyue Pang', 'Da Li', 'Timothy M. Hospedales', 'Tao Xiang'] | 2018-08-07 | deep-factorised-inverse-sketching-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Kaiyue_Pang_Deep_Factorised_Inverse-Sketching_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Kaiyue_Pang_Deep_Factorised_Inverse-Sketching_ECCV_2018_paper.pdf | eccv-2018-9 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 5.23513734e-01 7.03044161e-02 -3.43505340e-03 -3.19187909e-01
-4.82151508e-01 -7.81362951e-01 1.07781541e+00 -2.82525271e-01
-1.46582872e-01 5.14993668e-01 4.03318763e-01 2.20432177e-01
-2.23963857e-01 -7.45312572e-01 -6.03795350e-01 -3.66207629e-01
4.18787986e-01 5.12096643e-01 1.71696663e-01 -2.36130670... | [11.852629661560059, 0.0787278264760971] |
71137bd3-4ddf-4311-9617-ac05d117d0f8 | reconstruction-error-based-anomaly-detection | 2305.10464 | null | https://arxiv.org/abs/2305.10464v1 | https://arxiv.org/pdf/2305.10464v1.pdf | Reconstruction Error-based Anomaly Detection with Few Outlying Examples | Reconstruction error-based neural architectures constitute a classical deep learning approach to anomaly detection which has shown great performances. It consists in training an Autoencoder to reconstruct a set of examples deemed to represent the normality and then to point out as anomalies those data that show a suffi... | ['Luca Ferragina', 'Fabio Fassetti', 'Fabrizio Angiulli'] | 2023-05-17 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 2.75226571e-02 3.17928255e-01 5.90561450e-01 -4.30596858e-01
-1.91242605e-01 -1.59326896e-01 6.22253954e-01 5.56212306e-01
-2.43364066e-01 3.59983534e-01 -1.44797921e-01 -2.46680453e-01
-2.77606696e-01 -1.03338730e+00 -7.05273688e-01 -7.14521885e-01
-1.30443931e-01 7.85164595e-01 1.54130638e-01 -4.47156161... | [7.584839820861816, 2.3150157928466797] |
fd010dc9-5356-4fa2-a284-57d7d8d25827 | leverage-lexical-knowledge-for-chinese-named | null | null | https://aclanthology.org/D19-1396 | https://aclanthology.org/D19-1396.pdf | Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network | The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowled... | ['Dianbo Sui', 'Yubo Chen', 'Jun Zhao', 'Shengping Liu', 'Kang Liu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.23590863e-01 -3.22957039e-01 -3.47408265e-01 -2.16974273e-01
-7.09591210e-01 -5.82971931e-01 1.65637165e-01 1.72267482e-01
-1.10041320e+00 8.16863298e-01 4.77118403e-01 -3.68128628e-01
1.89489007e-01 -8.06695402e-01 2.26648748e-02 -2.24422395e-01
1.71713889e-01 4.64958727e-01 2.44474113e-01 -6.02019906... | [9.796140670776367, 9.735673904418945] |
34774ded-98b3-4bb0-9785-330f0328ed49 | using-domain-knowledge-for-low-resource-named | 2203.14738 | null | https://arxiv.org/abs/2203.14738v1 | https://arxiv.org/pdf/2203.14738v1.pdf | Using Domain Knowledge for Low Resource Named Entity Recognition | In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the exist... | ['Yuan Shi'] | 2022-03-28 | null | null | null | null | ['low-resource-named-entity-recognition', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-2.25089602e-02 -3.99055451e-01 -2.11928830e-01 -5.19606054e-01
-2.20687121e-01 -5.14306068e-01 3.56697202e-01 9.59808901e-02
-1.09020030e+00 7.24646330e-01 3.29670399e-01 -1.86484531e-01
1.87502027e-01 -1.03827178e+00 -3.31924647e-01 -5.19884527e-01
5.01804113e-01 3.92140716e-01 2.50597417e-01 -2.26279497... | [9.810770034790039, 9.648488998413086] |
00e513b3-571b-4fbf-ab49-66c285d33225 | a-survey-on-non-autoregressive-generation-for | 2204.09269 | null | https://arxiv.org/abs/2204.09269v2 | https://arxiv.org/pdf/2204.09269v2.pdf | A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond | Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedu... | ['Tie-Yan Liu', 'Tao Qin', 'Min Zhang', 'Juntao Li', 'Junliang Guo', 'Lijun Wu', 'Yisheng Xiao'] | 2022-04-20 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.20750582e-01 3.41844797e-01 -4.84833241e-01 -4.21897978e-01
-1.27653778e+00 -4.40824211e-01 6.12314165e-01 -1.82459161e-01
-2.91659962e-02 8.21521759e-01 5.31152904e-01 -7.39318311e-01
2.43152022e-01 -5.82667112e-01 -7.40297973e-01 -5.53326190e-01
5.70010185e-01 7.84068763e-01 -4.51425374e-01 -4.38519567... | [11.754042625427246, 9.269105911254883] |
570462f6-b9c7-4517-81be-6e0c0e7aa84c | provable-convergence-of-tensor-decomposition | 2303.06815 | null | https://arxiv.org/abs/2303.06815v1 | https://arxiv.org/pdf/2303.06815v1.pdf | Provable Convergence of Tensor Decomposition-Based Neural Network Training | Advanced tensor decomposition, such as tensor train (TT), has been widely studied for tensor decomposition-based neural network (NN) training, which is one of the most common model compression methods. However, training NN with tensor decomposition always suffers significant accuracy loss and convergence issues. In thi... | ['Bo Shen', 'Chenyang Li'] | 2023-03-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [-8.65146071e-02 -2.82574356e-01 -3.05216938e-01 -6.57200664e-02
-2.85537928e-01 -6.59207255e-02 -1.51976615e-01 -3.54377478e-01
-4.93940353e-01 5.80770433e-01 -1.40283618e-03 -6.91909850e-01
-5.41287482e-01 -4.41173881e-01 -7.01099396e-01 -1.12932217e+00
-1.17404899e-02 4.07842100e-02 -7.64525384e-02 -1.88251287... | [8.204304695129395, 3.4071826934814453] |
7b58b649-6bfd-47ac-bbd6-2b5eca74d023 | zero-pixel-directional-boundary-by-vector-1 | 2203.08795 | null | https://arxiv.org/abs/2203.08795v2 | https://arxiv.org/pdf/2203.08795v2.pdf | Zero Pixel Directional Boundary by Vector Transform | Boundaries are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the label representation, which typically leads to class imbalance and, as a consequence, to thick boundaries that require non-differential post-processing steps to be thinned. In thi... | ['Luc van Gool', 'Ender Konukoglu', 'Yun Liu', 'Ajad Chhatkuli', 'Edoardo Mello Rella'] | 2022-03-16 | zero-pixel-directional-boundary-by-vector | https://openreview.net/forum?id=nxcABL7jbQh | https://openreview.net/pdf?id=nxcABL7jbQh | iclr-2022-4 | ['boundary-detection'] | ['computer-vision'] | [ 5.48468709e-01 3.09279829e-01 -8.57579336e-02 -4.99893785e-01
-7.65144825e-01 -3.38893920e-01 2.27935448e-01 4.77212191e-01
-3.61262172e-01 6.55566096e-01 -8.86811018e-02 -5.20920055e-03
4.52902950e-02 -7.17867076e-01 -8.38728428e-01 -7.93952703e-01
2.22639561e-01 4.29253399e-01 1.66170329e-01 9.40470770... | [9.57798957824707, 0.4595753252506256] |
372100ef-2f49-4394-8b7e-4ac63433c920 | what-happened-3-seconds-ago-inferring-the | 2304.13651 | null | https://arxiv.org/abs/2304.13651v1 | https://arxiv.org/pdf/2304.13651v1.pdf | What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging | Inferring past human motion from RGB images is challenging due to the inherent uncertainty of the prediction problem. Thermal images, on the other hand, encode traces of past human-object interactions left in the environment via thermal radiation measurement. Based on this observation, we collect the first RGB-Thermal ... | ['Hang Zhao', 'Wei-Chiu Ma', 'Wenjie Ye', 'Zitian Tang'] | 2023-04-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 1.14018451e-02 -3.02676648e-01 -3.95595208e-02 -4.38436151e-01
-3.78502876e-01 -1.44102052e-01 3.46049875e-01 -8.08373928e-01
-6.57501400e-01 4.70845640e-01 3.14877808e-01 4.44662124e-02
3.12353104e-01 -3.96792978e-01 -6.70832932e-01 -7.32962489e-01
1.92492753e-01 1.07787661e-01 -7.53746331e-02 2.51585562... | [7.278650760650635, -0.5248172283172607] |
b48a44bd-ef2b-4eb6-af09-d22ab4c201e4 | probabilistic-3d-multi-object-tracking-for | 2001.05673 | null | https://arxiv.org/abs/2001.05673v1 | https://arxiv.org/pdf/2001.05673v1.pdf | Probabilistic 3D Multi-Object Tracking for Autonomous Driving | 3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop ... | ['Hsu-kuang Chiu', 'Jie Li', 'Jeannette Bohg', 'Antonio Prioletti'] | 2020-01-16 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-4.16641951e-01 -3.98349196e-01 -7.52109587e-02 -1.27189040e-01
-3.97662640e-01 -7.22176909e-01 8.74913335e-01 -1.24713235e-01
-7.55698383e-01 6.75782144e-01 -3.27393085e-01 -1.52835101e-01
-2.35587135e-01 -4.69128430e-01 -7.17682898e-01 -6.42605364e-01
-2.16058731e-01 9.33803260e-01 1.00891125e+00 -2.41643161... | [6.577953338623047, -2.0883679389953613] |
522fa1fc-7b46-4d6e-91f5-b7fc1bfbea08 | slicing-aided-hyper-inference-and-fine-tuning | 2202.06934 | null | https://arxiv.org/abs/2202.06934v5 | https://arxiv.org/pdf/2202.06934v5.pdf | Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection | Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Sli... | ['Alptekin Temizel', 'Sinan Onur Altinuc', 'Fatih Cagatay Akyon'] | 2022-02-14 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.59238994e-01 9.60613936e-02 1.65253669e-01 5.61900064e-03
-3.06776077e-01 -7.26279795e-01 6.67472184e-01 1.70667961e-01
-3.08709621e-01 3.63741577e-01 -3.87128651e-01 -2.25982100e-01
6.16290830e-02 -9.85609710e-01 -7.40853429e-01 -6.75821185e-01
-2.98904534e-03 1.36068553e-01 1.12039864e+00 4.49909344... | [8.600292205810547, -0.7961934804916382] |
f12d8eb9-94c5-43ca-b48e-6c01505d46e9 | deepbreath-deep-learning-of-breathing | 1708.06026 | null | http://arxiv.org/abs/1708.06026v1 | http://arxiv.org/pdf/1708.06026v1.pdf | DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings | We propose DeepBreath, a deep learning model which automatically recognises
people's psychological stress level (mental overload) from their breathing
patterns. Using a low cost thermal camera, we track a person's breathing
patterns as temperature changes around his/her nostril. The paper's technical
contribution is th... | ['Nadia Bianchi-Berthouze', 'Youngjun Cho', 'Simon J. Julier'] | 2017-08-20 | null | null | null | null | ['physiological-computing', 'mental-stress-detection'] | ['computer-vision', 'robots'] | [ 2.21783206e-01 -2.03014687e-01 2.93726057e-01 -3.64122897e-01
-2.79015213e-01 -4.09661740e-01 1.95884973e-01 8.05317909e-02
-5.63978374e-01 3.45359594e-01 1.37060106e-01 1.74223363e-01
-4.40889923e-03 -6.45602286e-01 -3.11038494e-02 -7.09041774e-01
-1.22963980e-01 -5.55802919e-02 -2.17863634e-01 -1.37919784... | [13.750341415405273, 3.101442575454712] |
c82da0bf-8681-42dc-bc0f-b20de73a4888 | lesion-based-contrastive-learning-for | 2107.08274 | null | https://arxiv.org/abs/2107.08274v1 | https://arxiv.org/pdf/2107.08274v1.pdf | Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images | Manually annotating medical images is extremely expensive, especially for large-scale datasets. Self-supervised contrastive learning has been explored to learn feature representations from unlabeled images. However, unlike natural images, the application of contrastive learning to medical images is relatively limited. ... | ['Xiaoying Tang', 'Junyan Lyu', 'Pujin Cheng', 'Li Lin', 'Yijin Huang'] | 2021-07-17 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 4.54503953e-01 1.63000241e-01 -4.37545091e-01 -7.76935518e-01
-9.03882265e-01 -2.90239215e-01 4.35079247e-01 -1.44801503e-02
-3.89020532e-01 7.55061269e-01 1.77315220e-01 -2.87074625e-01
-2.55405512e-02 -6.12418115e-01 -4.63954687e-01 -7.27284670e-01
6.54050037e-02 1.40460998e-01 -7.08257183e-02 1.33755088... | [15.791619300842285, -3.9559130668640137] |
e1d2b2b8-2fa3-40d1-aa88-965db2d2e199 | em-paste-em-guided-cut-paste-with-dall-e | 2212.07629 | null | https://arxiv.org/abs/2212.07629v1 | https://arxiv.org/pdf/2212.07629v1.pdf | EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation | We propose EM-PASTE: an Expectation Maximization(EM) guided Cut-Paste compositional dataset augmentation approach for weakly-supervised instance segmentation using only image-level supervision. The proposed method consists of three main components. The first component generates high-quality foreground object masks. To ... | ['Vibhav Vineet', 'Laurent Itti', 'Brian Nlong Zhao', 'Jiashu Xu', 'Yunhao Ge'] | 2022-12-15 | null | null | null | null | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 8.61890733e-01 3.52990717e-01 -3.92184883e-01 -4.12314415e-01
-1.18146253e+00 -5.84902048e-01 7.11605251e-01 -7.72582293e-02
-6.79500818e-01 6.59139514e-01 -4.45313007e-01 -9.24752727e-02
5.66758871e-01 -5.75120449e-01 -1.05838251e+00 -9.39001739e-01
4.92311746e-01 9.18216050e-01 6.92622662e-01 1.87699452... | [9.54545783996582, 0.5424870848655701] |
bac36c79-ba09-4fc5-bd42-0ee4dc4a887c | from-words-to-code-harnessing-data-for | 2305.01598 | null | https://arxiv.org/abs/2305.01598v2 | https://arxiv.org/pdf/2305.01598v2.pdf | From Words to Code: Harnessing Data for Program Synthesis from Natural Language | Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled programmers. Large language models (LLMs) demonstrate remarkable potential for generating code from natural language, but in the data manipu... | ['Ashish Tiwari', 'Mukul Singh', 'Sherry Shi', 'Mohammad Raza', 'Vu Le', 'Sumit Gulwani', 'Avrilia Floratou', 'Venkatesh Emani', 'Shaleen Deep', 'Jordan Henkel', 'Joyce Cahoon', 'Anirudh Khatry'] | 2023-05-02 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.05879861e-01 -4.32623103e-02 -3.60161364e-01 -6.47265434e-01
-8.42817664e-01 -9.85947490e-01 6.34310484e-01 6.65474057e-01
-2.31871739e-01 2.30701834e-01 1.22905552e-01 -4.69281137e-01
5.99048194e-03 -1.03086877e+00 -1.10139656e+00 -1.70936212e-01
9.63395238e-02 4.11097229e-01 3.85175318e-01 -1.42776310... | [8.143013000488281, 7.598193168640137] |
53d7c6da-5bef-45aa-a32d-01acd5327cf1 | reverse-perspective-network-for-perspective | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.pdf | Reverse Perspective Network for Perspective-Aware Object Counting | One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network expli... | [' Nicu Sebe', ' Qingming Huang', ' Li Su', ' Zhe Wu', ' Guorong Li', 'Yifan Yang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['object-counting'] | ['computer-vision'] | [ 2.01422349e-01 -2.17546389e-01 2.39086792e-01 -4.36048001e-01
-1.34605139e-01 -7.36049175e-01 4.05367583e-01 -6.66249812e-01
-5.12608290e-01 4.61593479e-01 -2.76916653e-01 4.71391296e-03
2.79773921e-01 -9.90557373e-01 -9.75904763e-01 -5.34247756e-01
2.53699094e-01 1.09223835e-01 4.73363727e-01 -2.57538468... | [8.697012901306152, -2.084717273712158] |
01ceca53-d518-45e4-80bd-b4d97706eec0 | tiny-object-detection-in-aerial-images | null | null | https://github.com/jwwangchn/AI-TOD | https://drive.google.com/file/d/1IiTp7gilwDCGr8QR_H9Covz8aVK7LXiI/view | Tiny Object Detection in Aerial Images | Object detection in Earth Vision has achieved great progress in recent years. However, tiny object detection in aerial images remains a very challenging problem since the tiny objects contain a small number of pixels and are easily confused with the background. To advance tiny object detection research in aerial images... | ['Gui-Song Xia', 'Ruixiang Zhang', 'Haowen Guo', 'Wen Yang', 'Jinwang Wang'] | 2021-01-10 | null | null | null | international-conference-on-pattern-2 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.34604105e-01 -4.95967895e-01 2.64890254e-01 -1.49472490e-01
-5.75985275e-02 -3.78527194e-01 4.36775200e-02 -1.36262283e-01
-4.21176821e-01 2.21208856e-01 -3.15079004e-01 -3.66851166e-02
3.08319420e-01 -1.03222799e+00 -5.95096469e-01 -5.54294288e-01
-2.85983115e-01 -7.27581605e-02 1.00091493e+00 -2.33640566... | [8.726428985595703, -0.7538167834281921] |
bf1b5c72-9854-48be-87b8-7c10885682b5 | why-the-firefly-algorithm-works | 1806.01632 | null | http://arxiv.org/abs/1806.01632v1 | http://arxiv.org/pdf/1806.01632v1.pdf | Why the Firefly Algorithm Works? | Firefly algorithm is a nature-inspired optimization algorithm and there have
been significant developments since its appearance about ten years ago. This
chapter summarizes the latest developments about the firefly algorithm and its
variants as well as their diverse applications. Future research directions are
also hig... | ['Xin-She Yang', 'Xingshi He'] | 2018-04-22 | null | null | null | null | ['nature-inspired-optimization-algorithm'] | ['computer-code'] | [-1.23575151e-01 -7.57882357e-01 -1.14424415e-01 -1.14543848e-01
3.62256289e-01 -5.11314094e-01 4.28960234e-01 2.36466676e-02
-6.67273462e-01 1.04003966e+00 -2.05861837e-01 1.96920529e-01
-4.54324871e-01 -8.50515127e-01 1.53944403e-01 -1.07909596e+00
-5.25533080e-01 2.60401338e-01 -8.75349622e-03 -8.37846875... | [5.687021732330322, 3.6041648387908936] |
95147f82-b49e-4d6f-8275-1a20cb6443f7 | divided-attention-unsupervised-multi-object | 2304.01430 | null | https://arxiv.org/abs/2304.01430v2 | https://arxiv.org/pdf/2304.01430v2.pdf | Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots | We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision. It consists of an adversarial conditional encoder-decoder architecture based on Slot Attention, modified to use the image as context to decode optical flow without attempting to reconstruct ... | ['Stefano Soatto', 'Yanchao Yang', 'Francesco Locatello', 'Zhengyang Hu', 'Dong Lao'] | 2023-04-04 | null | null | null | null | ['object-discovery', 'multi-object-discovery', 'motion-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.11838186e-01 2.28866145e-01 -2.56115258e-01 -1.80068359e-01
-6.13602757e-01 -8.27825904e-01 5.73710859e-01 -2.28360191e-01
-4.78179395e-01 7.18294561e-01 -3.12725641e-03 -4.14079815e-01
2.89881110e-01 -6.29951417e-01 -9.15842772e-01 -9.56302822e-01
2.47707441e-01 3.31580698e-01 4.34079230e-01 3.30725729... | [9.108579635620117, -0.3200002908706665] |
a9b5f469-0a13-4a03-bbb8-07bb25518d8d | muld-the-multitask-long-document-benchmark | 2202.07362 | null | https://arxiv.org/abs/2202.07362v1 | https://arxiv.org/pdf/2202.07362v1.pdf | MuLD: The Multitask Long Document Benchmark | The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficient techniques for processing much longer inputs. In this paper, we pres... | ['Noura Al Moubayed', 'G Thomas Hudson'] | 2022-02-15 | null | https://aclanthology.org/2022.lrec-1.392 | https://aclanthology.org/2022.lrec-1.392.pdf | lrec-2022-6 | ['summarization'] | ['natural-language-processing'] | [ 4.96311672e-02 1.20100178e-01 -3.87102842e-01 -5.26731431e-01
-1.40441740e+00 -1.06728804e+00 7.90668666e-01 3.75288367e-01
-5.13873219e-01 9.02166367e-01 6.90053523e-01 -7.21967518e-01
-3.46989892e-02 -6.03549898e-01 -7.13679075e-01 -3.76486629e-01
-3.27033162e-01 8.12401831e-01 3.54803592e-01 -2.21988440... | [10.957523345947266, 8.704994201660156] |
9d97ce82-69d3-4129-8553-5a2c424d2978 | talking-face-generation-by-conditional | 1804.04786 | null | https://arxiv.org/abs/1804.04786v3 | https://arxiv.org/pdf/1804.04786v3.pdf | Talking Face Generation by Conditional Recurrent Adversarial Network | Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face ... | ['Xiaolong Wang', 'Yang Song', 'Hairong Qi', 'Dawei Li', 'Jingwen Zhu'] | 2018-04-13 | null | null | null | null | ['talking-face-generation', 'lip-sync-1'] | ['computer-vision', 'computer-vision'] | [ 3.03311437e-01 -1.19364701e-01 -5.87411113e-02 -9.82216597e-02
-8.63466501e-01 -4.75871921e-01 4.48262393e-01 -4.45633054e-01
4.05650884e-02 4.87057030e-01 1.12277143e-01 2.97868140e-02
2.41329610e-01 -4.37581927e-01 -8.38655293e-01 -8.64884019e-01
3.21687222e-01 -2.30059266e-01 7.16639161e-02 1.01770252... | [13.213528633117676, -0.3822091221809387] |
0998518b-71ae-4b95-a1d0-efe877b9fb1e | towards-legally-enforceable-hate-speech | 2305.13677 | null | https://arxiv.org/abs/2305.13677v1 | https://arxiv.org/pdf/2305.13677v1.pdf | Towards Legally Enforceable Hate Speech Detection for Public Forums | Hate speech is a serious issue on public forums, and proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex task that is highly open to subjective interpretations. Existing works do not ali... | ['Samuel Dahan', 'Xiaodan Zhu', 'Rohan Bhambhoria', 'Chu Fei Luo'] | 2023-05-23 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 3.46906334e-01 -3.88734695e-03 -3.26951951e-01 -3.35421443e-01
-6.88816190e-01 -1.11245000e+00 8.93340230e-01 2.19074965e-01
-2.36724854e-01 6.17505074e-01 6.63121998e-01 -5.46607673e-01
3.41694872e-03 -3.53204906e-01 -8.56491998e-02 -4.27963465e-01
3.09113413e-01 4.76651080e-02 1.47422567e-01 -2.71653384... | [8.73484992980957, 10.49686336517334] |
bd081b39-bd4f-4f38-950e-80989f7330ca | unicode-analogies-an-anti-objectivist-visual | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.pdf | Unicode Analogies: An Anti-Objectivist Visual Reasoning Challenge | Analogical reasoning enables agents to extract relevant information from scenes, and efficiently navigate them in familiar ways. While progressive-matrix problems (PMPs) are becoming popular for the development and evaluation of analogical reasoning in computer vision, we argue that the dominant methodology in this... | ['Tim Miller', 'Krista A. Ehinger', 'Steven Spratley'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.51439798e-01 6.97967485e-02 4.77281630e-01 3.13722827e-02
-8.62787887e-02 -8.08424056e-01 1.15862215e+00 1.90376580e-01
-4.48179930e-01 5.67127943e-01 1.94334880e-01 -6.17236674e-01
-6.60982311e-01 -5.84930420e-01 -5.41990519e-01 -1.16759464e-01
2.04905331e-01 8.19653511e-01 9.63177830e-02 -8.64284635... | [10.632253646850586, 2.2695798873901367] |
cfc8c030-8e16-4ec2-bac6-f0477a1f1032 | a-multilevel-framework-for-ai-governance | 2307.03198 | null | https://arxiv.org/abs/2307.03198v1 | https://arxiv.org/pdf/2307.03198v1.pdf | A multilevel framework for AI governance | To realize the potential benefits and mitigate potential risks of AI, it is necessary to develop a framework of governance that conforms to ethics and fundamental human values. Although several organizations have issued guidelines and ethical frameworks for trustworthy AI, without a mediating governance structure, thes... | ['John S. Seberger', 'Prabu David', 'Hyesun Choung'] | 2023-07-04 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-2.09848717e-01 3.75379145e-01 -3.05547893e-01 -3.53573382e-01
-5.06194048e-02 -5.15939951e-01 4.91221905e-01 3.06393355e-01
-2.18585134e-01 6.89212680e-01 6.49495065e-01 -2.58882552e-01
-1.80295482e-01 -7.07243502e-01 -1.87092036e-01 -4.43939209e-01
3.98602039e-01 -4.14889395e-01 -3.52542907e-01 -4.19110745... | [9.04535961151123, 6.325340270996094] |
f3345328-baaa-4544-bbf8-d64299ada656 | learning-to-grow-artificial-hippocampi-in | 2303.08250 | null | https://arxiv.org/abs/2303.08250v2 | https://arxiv.org/pdf/2303.08250v2.pdf | Learning to Grow Artificial Hippocampi in Vision Transformers for Resilient Lifelong Learning | Lifelong learning without catastrophic forgetting (i.e., resiliency) possessed by human intelligence is entangled with sophisticated memory mechanisms in the brain, especially the long-term memory (LM) maintained by Hippocampi. With the dominance of Transformers in deep learning, it is a pressing need to explore what w... | ['Tianfu Wu', 'Michelle Dai', 'Chinmay Savadikar'] | 2023-03-14 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.02839112e-01 6.07138798e-02 4.96694714e-01 1.12193719e-01
1.01207308e-01 -2.32205749e-01 6.45756543e-01 -2.52525717e-01
-3.39021951e-01 8.03973198e-01 1.84988976e-02 9.77076776e-03
-5.14439285e-01 -6.48946285e-01 -9.88761246e-01 -9.31880176e-01
-3.60514432e-01 2.84499645e-01 6.20248437e-01 -4.63050097... | [9.862603187561035, 3.435033082962036] |
6b52460d-f7c9-4185-b05e-ad4399b10316 | discriminative-multiple-canonical-correlation | 2103.00361 | null | https://arxiv.org/abs/2103.00361v1 | https://arxiv.org/pdf/2103.00361v1.pdf | Discriminative Multiple Canonical Correlation Analysis for Information Fusion | In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion. DMCCA is capable of extracting more discriminative characteristics from multimodal information representations. Specifically, it finds the projected directions which simultaneousl... | ['Ling Guan', 'Enqing Chen', 'Lin Qi', 'Lei Gao'] | 2021-02-28 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-7.36074373e-02 -7.65610993e-01 1.03691496e-01 -2.59050131e-01
-9.72327888e-01 -5.99404812e-01 4.74488407e-01 -6.88201413e-02
-6.73559904e-02 3.16852868e-01 2.61364311e-01 2.48503238e-02
-2.99442559e-01 -2.43371084e-01 4.93512908e-03 -9.98949051e-01
-2.50666022e-01 -6.85960278e-02 -3.85347277e-01 -1.22937284... | [13.163491249084473, 5.084508895874023] |
0ceef3fe-9e9c-4fc1-b379-4fd9a5864de5 | transdocs-optical-character-recognition-with | 2304.07637 | null | https://arxiv.org/abs/2304.07637v1 | https://arxiv.org/pdf/2304.07637v1.pdf | TransDocs: Optical Character Recognition with word to word translation | While OCR has been used in various applications, its output is not always accurate, leading to misfit words. This research work focuses on improving the optical character recognition (OCR) with ML techniques with integration of OCR with long short-term memory (LSTM) based sequence to sequence deep learning models to pe... | ['Phani Krishna Uppala', 'Abhishek Bamotra'] | 2023-04-15 | null | null | null | null | ['optical-character-recognition', 'word-translation'] | ['computer-vision', 'natural-language-processing'] | [ 6.56242430e-01 -3.94537359e-01 -2.66624123e-01 -3.02961826e-01
-1.12981689e+00 -6.08845770e-01 6.37526512e-01 -2.09585816e-01
-5.53274453e-01 1.09045696e+00 4.15644944e-01 -5.96301973e-01
5.95133603e-01 -2.94660181e-01 -8.51613224e-01 -3.94891053e-01
5.37005007e-01 5.24342418e-01 -3.49571198e-01 -1.85338572... | [14.20512580871582, 7.257715225219727] |
0e4a67ed-a57c-419c-a373-5e02cba1ae0e | semeval-2017-task-3-community-question-1 | 1912.00730 | null | https://arxiv.org/abs/1912.00730v1 | https://arxiv.org/pdf/1912.00730v1.pdf | SemEval-2017 Task 3: Community Question Answering | We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from... | ['Hamdy Mubarak', 'Lluís Màrquez', 'Doris Hoogeveen', 'Preslav Nakov', 'Timothy Baldwin', 'Karin Verspoor', 'Alessandro Moschitti'] | 2019-12-02 | semeval-2017-task-3-community-question | https://aclanthology.org/S17-2003 | https://aclanthology.org/S17-2003.pdf | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-4.00097109e-02 -1.32267401e-01 5.94286025e-01 -3.15925360e-01
-1.45785558e+00 -9.13121700e-01 6.34477615e-01 5.77968121e-01
-5.86500883e-01 8.25616777e-01 3.03084642e-01 -3.35985452e-01
9.61261168e-02 -2.73171186e-01 -5.17301202e-01 -2.26333514e-01
1.69183224e-01 4.76492673e-01 8.23596418e-01 -5.30152261... | [11.372658729553223, 8.028656959533691] |
dc6a827d-d61d-4217-b515-4d625def0cbc | clustering-tweets-usingwikipedia-concepts | null | null | https://aclanthology.org/L14-1640 | https://aclanthology.org/L14-1640.pdf | Clustering tweets usingWikipedia concepts | Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent res... | ['Yunqing Xia', 'Weizhi Wang', 'Raymond Lau', 'Guoyu Tang', 'Fang Zheng'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['text-clustering'] | ['natural-language-processing'] | [-3.78752261e-01 -2.76999712e-01 -4.11897838e-01 -2.60111570e-01
8.28445926e-02 -4.64821309e-01 9.17926073e-01 6.58559561e-01
-5.95841110e-01 7.61234343e-01 6.51219249e-01 -9.13441554e-02
-3.17435443e-01 -9.04019713e-01 -1.35775641e-01 -2.27677673e-01
5.49562946e-02 4.35589254e-01 2.11644366e-01 -6.52515888... | [10.468135833740234, 8.197687149047852] |
3d63de81-99e3-4d34-8e7d-fc75281a583a | modeling-input-uncertainty-in-neural-network | null | null | https://aclanthology.org/D18-1542 | https://aclanthology.org/D18-1542.pdf | Modeling Input Uncertainty in Neural Network Dependency Parsing | Recently introduced neural network parsers allow for new approaches to circumvent data sparsity issues by modeling character level information and by exploiting raw data in a semi-supervised setting. Data sparsity is especially prevailing when transferring to non-standard domains. In this setting, lexical normalization... | ['Rob van der Goot', 'Gertjan van Noord'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['lexical-normalization'] | ['natural-language-processing'] | [ 4.08085316e-01 3.31933618e-01 -4.46794897e-01 -5.31145930e-01
-3.58706176e-01 -4.83581454e-01 4.10405606e-01 5.70480525e-01
-1.12810218e+00 7.73809075e-01 4.93768603e-01 -3.01229119e-01
7.67628551e-02 -1.00518584e+00 -5.58585286e-01 -5.85859060e-01
1.78892568e-01 3.46446037e-01 1.63146377e-01 -2.23838329... | [10.296049118041992, 9.6600923538208] |
7ee067dd-ae28-4cf8-9b93-7820bf9d0fbd | hardware-accelerated-mars-sample-localization | 2206.02622 | null | https://arxiv.org/abs/2206.02622v2 | https://arxiv.org/pdf/2206.02622v2.pdf | Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations | The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetc... | ['Levin Gerdes', 'Gonzalo Jesús Paz-Delgado', 'Carlos Jesús Pérez-del-Pulgar', 'Raúl Castilla-Arquillo'] | 2022-06-06 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.40639812e-01 3.22055668e-01 1.43905610e-01 -3.92230600e-01
-1.24632023e-01 -2.73028910e-01 5.25896490e-01 2.58024663e-01
-5.61218143e-01 4.60388273e-01 -9.35018659e-01 -3.02614599e-01
1.27320485e-02 -1.07790160e+00 -5.71637750e-01 -3.38528693e-01
-5.05145252e-01 1.22973144e+00 1.06125496e-01 -5.16344011... | [7.719748497009277, -1.7949564456939697] |
ef7bcfe5-d967-4daa-ae53-38ab4553c55d | machine-learning-on-big-data-from-twitter-to | 2005.08817 | null | https://arxiv.org/abs/2005.08817v3 | https://arxiv.org/pdf/2005.08817v3.pdf | Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter | The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized i... | ['Sijia Li', 'Chengda Zheng', 'Chen Chen', 'Jia Xue', 'Tingshao Zhu', 'Junxiang Chen'] | 2020-05-18 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.88269848e-01 1.32838368e-01 -6.41768038e-01 -3.51417661e-02
-4.59965527e-01 -6.04314685e-01 7.38264859e-01 1.20150054e+00
-5.51605403e-01 9.24783349e-01 7.13526070e-01 -4.06683117e-01
8.28958377e-02 -6.09243512e-01 -4.02370214e-01 -6.17656052e-01
-3.07433665e-01 1.77982062e-01 -6.30746007e-01 -6.26558006... | [8.452508926391602, 9.733599662780762] |
ec9f4aa8-250d-489b-80ea-b5e39e8669ee | recurrent-batch-normalization | 1603.09025 | null | http://arxiv.org/abs/1603.09025v5 | http://arxiv.org/pdf/1603.09025v5.pdf | Recurrent Batch Normalization | We propose a reparameterization of LSTM that brings the benefits of batch
normalization to recurrent neural networks. Whereas previous works only apply
batch normalization to the input-to-hidden transformation of RNNs, we
demonstrate that it is both possible and beneficial to batch-normalize the
hidden-to-hidden transi... | ['Çağlar Gülçehre', 'César Laurent', 'Tim Cooijmans', 'Nicolas Ballas', 'Aaron Courville'] | 2016-03-30 | null | null | null | null | ['sequential-image-classification'] | ['computer-vision'] | [ 2.36529320e-01 1.03413537e-01 -2.41079614e-01 -7.04692483e-01
-5.71048081e-01 -3.15913379e-01 5.73809922e-01 -2.06591845e-01
-8.09267581e-01 6.69339061e-01 3.60640764e-01 -9.09524977e-01
2.77304709e-01 -5.05649745e-01 -6.25069082e-01 -5.86968422e-01
6.36485070e-02 1.32778749e-01 -5.23737743e-02 -1.95223734... | [10.858165740966797, 6.468289375305176] |
a2f85bb3-3299-4418-8d18-3f98f9cc421a | a-phoneme-informed-neural-network-model-for | 2304.05917 | null | https://arxiv.org/abs/2304.05917v1 | https://arxiv.org/pdf/2304.05917v1.pdf | A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription | Note-level automatic music transcription is one of the most representative music information retrieval (MIR) tasks and has been studied for various instruments to understand music. However, due to the lack of high-quality labeled data, transcription of many instruments is still a challenging task. In particular, in the... | ['Juhan Nam', 'Li Su', 'Sangeon Yong'] | 2023-04-12 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 2.30420113e-01 -7.29239583e-01 -8.97861421e-02 1.00104168e-01
-1.11931396e+00 -9.08593833e-01 1.23242401e-01 7.15730712e-02
-1.18500464e-01 3.59500289e-01 4.67658728e-01 1.71692058e-01
-2.44727820e-01 -2.15109035e-01 -2.17201516e-01 -7.36301661e-01
-5.16661443e-03 -6.23206869e-02 -1.48717269e-01 -1.12488389... | [15.725540161132812, 5.546482563018799] |
8293cabc-f3a0-40e7-9e07-98544a76d61f | deep-learning-based-framework-for-iranian | 2201.06825 | null | https://arxiv.org/abs/2201.06825v1 | https://arxiv.org/pdf/2201.06825v1.pdf | Deep Learning Based Framework for Iranian License Plate Detection and Recognition | License plate recognition systems have a very important role in many applications such as toll management, parking control, and traffic management. In this paper, a framework of deep convolutional neural networks is proposed for Iranian license plate recognition. The first CNN is the YOLOv3 network that detects the Ira... | ['Roozbeh Rajabi', 'Mojtaba Shahidi Zandi'] | 2022-01-18 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-2.83473492e-01 -6.50718510e-01 2.25062743e-01 -1.12562038e-01
-3.69765222e-01 -5.81664145e-01 3.29984754e-01 -8.34395468e-01
-6.17076159e-01 6.02066636e-01 -4.44831669e-01 -2.90199101e-01
3.33527833e-01 -9.95442152e-01 -5.95045626e-01 -6.27849817e-01
3.88453960e-01 6.77453399e-01 7.35363960e-01 -2.83545464... | [9.828861236572266, -4.953187942504883] |
51b0316d-f158-4d17-bcc9-6d6fb1626749 | medical-diffusion-on-a-budget-textual | 2303.13430 | null | https://arxiv.org/abs/2303.13430v1 | https://arxiv.org/pdf/2303.13430v1.pdf | Medical diffusion on a budget: textual inversion for medical image generation | Diffusion-based models for text-to-image generation have gained immense popularity due to recent advancements in efficiency, accessibility, and quality. Although it is becoming increasingly feasible to perform inference with these systems using consumer-grade GPUs, training them from scratch still requires access to la... | ['Henkjan Huisman', 'Richard P. G. ten Broek', 'Anindo Saha', 'Bram de Wilde'] | 2023-03-23 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.02911866e-01 3.44156325e-01 -1.02887284e-02 -1.44420043e-01
-9.14044619e-01 -4.78551686e-01 5.16895235e-01 3.21785033e-01
-6.83015943e-01 8.21849048e-01 2.67236471e-01 -4.30607736e-01
-8.94248411e-02 -7.78646708e-01 -4.35798675e-01 -7.84688592e-01
-2.74640232e-01 4.58740234e-01 1.29197631e-02 2.47885212... | [14.31087875366211, -1.9556688070297241] |
056e2253-462f-421a-a96f-b16e5d6d57f5 | hierarchical-neural-implicit-pose-network-for | 2112.00958 | null | https://arxiv.org/abs/2112.00958v1 | https://arxiv.org/pdf/2112.00958v1.pdf | Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting | We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses. HIPNet can disentangle subject-specific details from pose-specific details, effectively enabling us to retarget motion from one subject to another or to animate between keyframes through latent space interpolation. To this... | ['Sameh Khamis', 'Sanja Fidler', 'Maria Shugrina', 'Kangxue Yin', 'Sourav Biswas'] | 2021-12-02 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-1.22215366e-02 2.18389779e-01 -2.93193191e-01 -2.93594331e-01
-7.76823103e-01 -3.66093010e-01 5.73098302e-01 -3.25627148e-01
-3.15219849e-01 7.66876757e-01 4.16793078e-01 3.64136100e-01
2.30825320e-01 -4.04070467e-01 -7.56676018e-01 -6.01731122e-01
-3.19453925e-01 5.14589190e-01 3.01192820e-01 -2.14126915... | [7.02618932723999, -1.157141923904419] |
9334b422-0263-484a-91e9-c52df7826666 | predicting-stock-price-movement-after | 2206.12528 | null | https://arxiv.org/abs/2206.12528v2 | https://arxiv.org/pdf/2206.12528v2.pdf | Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks | In the current stock market, computer science and technology are more and more widely used to analyse stocks. Not same as most related machine learning stock price prediction work, this work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual report... | ['Yue Wang', 'Fengyu Han'] | 2022-06-25 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-9.12327349e-01 -2.00387031e-01 -6.91502094e-01 -2.31836382e-02
2.04288825e-01 -7.92214513e-01 6.71493769e-01 -1.34536490e-01
-4.93008554e-01 1.23942494e+00 2.47941747e-01 -7.02285945e-01
9.47151780e-02 -1.23536956e+00 -3.61688763e-01 -5.40006697e-01
-1.51370376e-01 1.15276895e-01 5.41568577e-01 -4.01402771... | [4.5296244621276855, 4.216752529144287] |
df535c8b-f345-4ff7-97af-abb4649609e8 | predictive-maneuver-planning-with-deep | 2306.09055 | null | https://arxiv.org/abs/2306.09055v1 | https://arxiv.org/pdf/2306.09055v1.pdf | Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving | This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning approaches often have to improve their abilities to handle the variabilities of real-world driving scenarios. By learning from its experience, a Reinforceme... | ['Narasimhan Sundararajan', 'Suresh Sundaram', 'Vishruth Veerendranath', 'Jayabrata Chowdhury'] | 2023-06-15 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-4.19931233e-01 3.07043105e-01 -1.54784948e-01 -3.92866105e-01
-4.44333196e-01 -4.07946557e-01 7.87120879e-01 -1.30500853e-01
-4.62959766e-01 1.12492251e+00 1.75000921e-01 -5.19016504e-01
-1.69198990e-01 -9.18039978e-01 -7.17509151e-01 -7.39611745e-01
-4.88251656e-01 7.22928941e-01 6.67113006e-01 -8.15250397... | [5.115784168243408, 1.33548903465271] |
450cc8e8-c322-4785-8b98-0ea9bd76f22c | efficient-gaussian-process-model-on-class | 2210.06120 | null | https://arxiv.org/abs/2210.06120v1 | https://arxiv.org/pdf/2210.06120v1.pdf | Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning | Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite its presence in several ZSL datasets. In this paper, we propose a Neural Network model that learns a latent feature embedding and a Gaussian ... | ['Russell Tsuchida', 'Lars Petersson', 'Nick Barnes', 'Changkun Ye'] | 2022-10-11 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.09923715e-02 5.93507141e-02 -6.53150737e-01 -6.92921460e-01
-8.48739028e-01 9.43762884e-02 5.16084194e-01 3.37352604e-01
-1.72901317e-01 7.56431162e-01 -3.04877073e-01 -2.89468747e-02
-2.98494458e-01 -1.13911891e+00 -6.64765000e-01 -7.33591974e-01
1.57605857e-01 8.88872802e-01 1.52192980e-01 9.83023345... | [9.762779235839844, 3.239572525024414] |
93665ccc-9b5d-4697-a2b8-c584175718ba | using-the-fast-fourier-transform-in-binding | 1708.07045 | null | http://arxiv.org/abs/1708.07045v1 | http://arxiv.org/pdf/1708.07045v1.pdf | Using the Fast Fourier Transform in Binding Free Energy Calculations | According to implicit ligand theory, the standard binding free energy is an
exponential average of the binding potential of mean force (BPMF), an
exponential average of the interaction energy between the ligand apo ensemble
and a rigid receptor. Here, we use the Fast Fourier Transform (FFT) to
efficiently estimate BPMF... | ['Huan-Xiang Zhou', 'Trung Hai Nguyen', 'David D. L. Minh'] | 2017-07-27 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 5.22428036e-01 -4.27733734e-02 1.29698604e-01 -3.21930110e-01
-7.02030122e-01 -6.44465923e-01 5.09148300e-01 1.61898121e-01
-8.58143210e-01 1.44822001e+00 -1.28936067e-01 -6.04250968e-01
-1.71368986e-01 -4.34131891e-01 -8.07200909e-01 -1.34878433e+00
-4.67211664e-01 7.67120719e-01 2.26721331e-01 -3.48146319... | [4.8104472160339355, 5.323380470275879] |
39f59b9d-0942-4f7f-bf58-b95055d493ce | edgeserve-an-execution-layer-for | 2303.08028 | null | https://arxiv.org/abs/2303.08028v1 | https://arxiv.org/pdf/2303.08028v1.pdf | EdgeServe: An Execution Layer for Decentralized Prediction | The relevant features for a machine learning task may be aggregated from data sources collected on different nodes in a network. This problem, which we call decentralized prediction, creates a number of interesting systems challenges in managing data routing, placing computation, and time-synchronization. This paper pr... | ['Sanjay Krishnan', 'Ted Shaowang'] | 2023-03-02 | null | null | null | null | ['human-activity-recognition', 'network-intrusion-detection', 'human-activity-recognition'] | ['computer-vision', 'miscellaneous', 'time-series'] | [-2.76801318e-01 -1.36117965e-01 -6.61842525e-01 -7.68946826e-01
-3.25303167e-01 -1.40781835e-01 6.00519061e-01 6.30152166e-01
-1.33113891e-01 6.17982447e-01 3.09659932e-02 -6.66284887e-03
-2.42963225e-01 -9.39865351e-01 -3.01102012e-01 -4.18550551e-01
-5.02716720e-01 6.89279318e-01 5.47939420e-01 2.17471734... | [6.079092502593994, 6.212115287780762] |
6fd35c29-d5df-4cdb-bc9d-120d1a54b2e5 | deep-reinforcement-learning-based-resource-1 | 2305.06249 | null | https://arxiv.org/abs/2305.06249v1 | https://arxiv.org/pdf/2305.06249v1.pdf | Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network | Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing enviro... | ['Jingjing Zhang', 'Yue Gao', 'Jiasheng Wu', 'Lin Wang'] | 2023-05-10 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.82149452e-01 -1.72791153e-01 -5.58998525e-01 -1.44945279e-01
1.66150033e-01 -7.95495331e-01 7.95489922e-02 -7.61843085e-01
2.82240510e-02 9.77407217e-01 -2.10460857e-01 -1.21333051e+00
-3.67140383e-01 -8.51168752e-01 -1.01263866e-01 -4.99344468e-01
-8.91825378e-01 6.09048486e-01 -1.40597641e-01 7.05507398... | [5.914285182952881, 1.6793313026428223] |
f550d301-e9c7-436b-9fa9-5730f81cc7e2 | cuslink-single-linkage-agglomerative | 2306.16354 | null | https://arxiv.org/abs/2306.16354v1 | https://arxiv.org/pdf/2306.16354v1.pdf | cuSLINK: Single-linkage Agglomerative Clustering on the GPU | In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only $O(Nk)$ space and uses a parameter $k$ to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly o... | ['Tim Oates', 'Brad Rees', 'John Zedlewski', 'Edward Raff', 'Joe Eaton', 'Mahesh Doijade', 'Alex Fender', 'Divye Gala', 'Corey J. Nolet'] | 2023-06-28 | null | null | null | null | ['graph-construction', 'clustering'] | ['graphs', 'methodology'] | [-3.75541389e-01 -2.63558090e-01 6.22974057e-03 -3.36980015e-01
-6.76537275e-01 -7.88103878e-01 2.50218242e-01 7.66533136e-01
-4.70584661e-01 1.93944097e-01 -2.27519661e-01 -7.10217416e-01
-1.71970710e-01 -1.10064173e+00 -4.65613127e-01 -4.36216265e-01
-6.68867350e-01 7.68988490e-01 4.21041608e-01 2.48654671... | [7.086864948272705, 5.284994125366211] |
1eb11e1d-bf3e-4965-abbb-4fc9f0fd6c42 | generating-annotated-high-fidelity-images | 2006.12150 | null | https://arxiv.org/abs/2006.12150v3 | https://arxiv.org/pdf/2006.12150v3.pdf | Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects | Recent developments related to generative models have made it possible to generate diverse high-fidelity images. In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic complex images containing distinct objects. These models are generally conditi... | ['Bryan G. Cardenas', 'Devanshu Arya', 'Deepak K. Gupta'] | 2020-06-22 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 4.53433365e-01 2.72249013e-01 2.99741596e-01 -2.25642309e-01
-8.21072876e-01 -3.89444172e-01 8.54131162e-01 -1.70731485e-01
-2.43464392e-02 8.36175978e-01 2.05584154e-01 2.68311799e-01
-1.75525412e-01 -1.01770532e+00 -1.15440321e+00 -1.02226591e+00
4.11780059e-01 6.85066164e-01 1.97797664e-03 -5.22306524... | [11.320099830627441, -0.4158773124217987] |
813b8586-e735-4d05-9cc4-15d8582a5086 | multi-objective-matrix-normalization-for-fine | 2003.13272 | null | https://arxiv.org/abs/2003.13272v2 | https://arxiv.org/pdf/2003.13272v2.pdf | Multi-Objective Matrix Normalization for Fine-grained Visual Recognition | Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent methods have shown that the matrix power normalization can stabilize the second-order information in bilinear features, but some problems, e.g., redundant information and over-fitting, remain to be resolved. In this paper, we prop... | ['Yongdong Zhang', 'Zheng-Jun Zha', 'Hantao Yao', 'Hongtao Xie', 'Shaobo Min'] | 2020-03-30 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-2.30269283e-01 -7.46502995e-01 -2.85565913e-01 -4.82812136e-01
-8.06343079e-01 -2.80931324e-01 2.07900524e-01 -1.12555049e-01
-3.82539719e-01 5.65721095e-01 3.03553045e-01 1.71945505e-02
-9.98893529e-02 -5.92221737e-01 -5.51976323e-01 -9.94828641e-01
2.95069456e-01 -2.10993826e-01 1.62929704e-04 -1.19391479... | [13.39547061920166, 0.5360827445983887] |
568334c6-53b8-4ed6-9a6a-9293bb7a44ce | privacy-guarantees-for-de-identifying-text | 2008.03101 | null | https://arxiv.org/abs/2008.03101v2 | https://arxiv.org/pdf/2008.03101v2.pdf | Privacy Guarantees for De-identifying Text Transformations | Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data. At the same time, there is a clear need for protecting the privacy of the users whose data is collected and processed. For text collections, such as, e.g., transcripts of voice interactions o... | ['Dietrich Klakow', 'David Ifeoluwa Adelani', 'Ali Davody', 'Thomas Kleinbauer'] | 2020-08-07 | null | null | null | null | ['dialog-act-classification'] | ['natural-language-processing'] | [ 4.35507476e-01 3.33297461e-01 1.87209230e-02 -7.32232451e-01
-9.25101042e-01 -9.26898539e-01 5.56746125e-01 4.78183329e-01
-8.05387318e-01 6.37434065e-01 6.60278141e-01 -4.54240888e-01
2.82650024e-01 -4.92475510e-01 -3.30616862e-01 -6.20268643e-01
3.53332609e-01 3.06027323e-01 -2.36104026e-01 -1.19170301... | [6.031612873077393, 7.009664058685303] |
5b679bc6-e5af-493f-b1cb-4377b1b4da06 | lisa-localized-image-stylization-with-audio | 2211.11381 | null | https://arxiv.org/abs/2211.11381v1 | https://arxiv.org/pdf/2211.11381v1.pdf | LISA: Localized Image Stylization with Audio via Implicit Neural Representation | We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization. Sound often provides information about the specific context of the scene and is closely related to a certain part of the scene or object. However, existing image stylization works have fo... | ['Sangpil Kim', 'Jinkyu Kim', 'Sang Ho Yoon', 'Wonmin Byeon', 'Chanyoung Kim', 'Seung Hyun Lee'] | 2022-11-21 | null | null | null | null | ['image-stylization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 6.28820896e-01 -8.21770579e-02 1.10735856e-01 -5.65694161e-02
-8.24209630e-01 -5.48702180e-01 3.51208687e-01 -5.80482818e-02
-1.30982295e-01 3.66681904e-01 4.14437801e-01 1.24290220e-01
2.19781399e-01 -6.87615097e-01 -1.11459959e+00 -6.60212696e-01
3.48356575e-01 1.00185335e-01 2.73202151e-01 1.86263010... | [14.810810089111328, 4.696695327758789] |
80571a77-99cd-4fd1-bf33-390546b383da | multi-scale-aggregation-using-feature-pyramid | 2004.03194 | null | https://arxiv.org/abs/2004.03194v4 | https://arxiv.org/pdf/2004.03194v4.pdf | Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances | Currently, the most widely used approach for speaker verification is the deep speaker embedding learning. In this approach, we obtain a speaker embedding vector by pooling single-scale features that are extracted from the last layer of a speaker feature extractor. Multi-scale aggregation (MSA), which utilizes multi-sca... | ['Youngmoon Jung', 'Seong Min Kye', 'Yeunju Choi', 'Hoirin Kim', 'Myunghun Jung'] | 2020-04-07 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-8.30875188e-02 -6.27017915e-02 4.54489850e-02 -7.61728942e-01
-1.09808481e+00 -3.49478245e-01 5.08477807e-01 2.59467959e-01
-6.12569392e-01 9.42623168e-02 4.75339949e-01 1.23286471e-01
1.52179137e-01 -3.41823041e-01 -3.07058901e-01 -8.17416966e-01
-2.16154486e-01 -1.85783401e-01 2.02598616e-01 -2.37426028... | [14.395940780639648, 6.046393871307373] |
09d679a2-6f3e-49b7-9c01-2c7a300bd1b0 | finding-counterfactual-explanations-through | 2204.03429 | null | https://arxiv.org/abs/2204.03429v1 | https://arxiv.org/pdf/2204.03429v1.pdf | Finding Counterfactual Explanations through Constraint Relaxations | Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user constraints. A common approach to recover infeasibility is to eliminate the constraints that cause the conflicts in the system. This approach allows the system to provide an explanation as: "if the user is willing to dr... | ["Barry O'Sullivan", 'Begum Genc', 'Sharmi Dev Gupta'] | 2022-04-07 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.09265089e-01 7.89055467e-01 -4.37951773e-01 -4.66781110e-01
-2.73661494e-01 -8.60663354e-01 3.10300916e-01 2.54654974e-01
-2.29500383e-01 1.14242542e+00 1.28149241e-01 -8.13664198e-01
-4.36922610e-01 -6.97545469e-01 -4.56871957e-01 -3.79728168e-01
-8.81324187e-02 6.92052126e-01 1.12180397e-01 -2.10673735... | [8.622931480407715, 5.985284805297852] |
356c47fe-9583-427f-82a9-9610317644c2 | a-review-on-the-use-of-deep-learning-in | 1812.10360 | null | http://arxiv.org/abs/1812.10360v1 | http://arxiv.org/pdf/1812.10360v1.pdf | A Review on The Use of Deep Learning in Android Malware Detection | Android is the predominant mobile operating system for the past few years.
The prevalence of devices that can be powered by Android magnetized not merely
application developers but also malware developers with criminal intention to
design and spread malicious applications that can affect the normal work of
Android phon... | ['Abdelmonim Naway', 'Yuancheng LI'] | 2018-12-26 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.61280835e-01 -1.73906863e-01 -9.76247013e-01 3.38818759e-01
-1.19642708e-02 -7.85053909e-01 6.82431877e-01 -6.07658744e-01
-1.20799309e-02 5.48302472e-01 -3.62639725e-01 -1.27409565e+00
1.09348632e-01 -3.96631896e-01 -5.09854138e-01 -5.23207843e-01
2.32506376e-02 -1.99059799e-01 1.61117792e-01 -1.81061998... | [14.427237510681152, 9.683520317077637] |
44cad76e-f45d-4e67-80ec-dba21a804c40 | explaining-clinical-decision-support-systems | 2010.05759 | null | https://arxiv.org/abs/2010.05759v3 | https://arxiv.org/pdf/2010.05759v3.pdf | Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization | Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its unde... | ['Horst-Michael Groß', 'Michael Sühling', 'Alexander Mühlberg', 'Stephen Ahmad', 'Oliver Taubmann', 'Alexander Katzmann'] | 2020-10-09 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.40066504e-01 5.84614277e-01 -1.94089159e-01 -5.16012609e-01
-5.36940455e-01 -2.74123460e-01 4.83846277e-01 3.64332736e-01
-2.93411136e-01 3.57052565e-01 2.55269349e-01 -8.79881144e-01
-2.81331271e-01 -5.15409887e-01 -2.66171694e-01 -7.40314722e-01
-3.00408676e-02 3.85501564e-01 -1.80929840e-01 -1.23749197... | [15.153916358947754, -2.4432404041290283] |
f8726e0c-de4a-4c23-9212-65f87e4d8876 | open-vocabulary-panoptic-segmentation-with | 2208.08984 | null | https://arxiv.org/abs/2208.08984v2 | https://arxiv.org/pdf/2208.08984v2.pdf | Open-Vocabulary Universal Image Segmentation with MaskCLIP | In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background semantic labeling + foreground instance segmentation) for arbitrary categories of text-based descriptions in inference time. We first build a ... | ['Zhuowen Tu', 'Jieke Wang', 'Zheng Ding'] | 2022-08-18 | null | null | null | null | ['panoptic-segmentation', 'open-vocabulary-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 0.6849126 0.38103032 -0.37401512 -0.57474494 -1.1974416 -0.9425775
0.68249744 -0.16593638 -0.36226752 0.31229874 -0.08621425 -0.4184441
0.55603737 -0.43082455 -1.074418 -0.59250873 0.58757156 0.8169365
0.4551439 0.35570163 0.03935736 0.03572635 -1.6358391 0.65794384
0.7317679 1.2381592 0.514... | [9.687036514282227, 0.7538371086120605] |
634ff0e7-351c-474f-868b-6e9f8624664a | non-intrusive-load-monitoring-with-an | 1912.00759 | null | https://arxiv.org/abs/1912.00759v3 | https://arxiv.org/pdf/1912.00759v3.pdf | Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network | Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combin... | ['Veronica Piccialli', 'Antonio M. Sudoso'] | 2019-11-15 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.98978716e-01 3.58622223e-01 7.33174384e-02 -4.04188752e-01
-8.10683548e-01 -4.35730547e-01 7.83308208e-01 1.18567288e-01
-8.03708211e-02 5.77825308e-01 3.62368852e-01 -1.42873675e-01
-5.43627404e-02 -5.81906021e-01 -8.87201726e-01 -9.45121884e-01
1.07488111e-01 4.75978911e-01 -4.03349519e-01 6.37329891... | [16.06941032409668, 7.582823276519775] |
b390b974-dc1f-4a0f-8cdc-145efd0a89b4 | storir-stochastic-room-impulse-response | 2008.07231 | null | https://arxiv.org/abs/2008.07231v1 | https://arxiv.org/pdf/2008.07231v1.pdf | StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation | In this paper we introduce StoRIR - a stochastic room impulse response generation method dedicated to audio data augmentation in machine learning applications. This technique, in contrary to geometrical methods like image-source or ray tracing, does not require prior definition of room geometry, absorption coefficients... | ['Michał Romaniuk', 'Mateusz Matuszewski', 'Piotr Masztalski', 'Karol Piaskowski'] | 2020-08-17 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.33326927e-01 1.44405738e-01 9.81885433e-01 -4.56388183e-02
-1.06004405e+00 -4.52710927e-01 4.79761064e-01 2.33957380e-01
-4.08780128e-01 5.48150778e-01 4.37790811e-01 -4.94373202e-01
-6.64582849e-02 -8.06992531e-01 -6.62435412e-01 -8.55621934e-01
6.85478980e-03 1.71951637e-01 3.71502675e-02 -3.76277357... | [15.219470024108887, 5.756626129150391] |
c7687aa8-8b8a-4523-8aba-e103487e7d48 | transfer-learning-in-deep-learning-models-for | 2301.10663 | null | https://arxiv.org/abs/2301.10663v2 | https://arxiv.org/pdf/2301.10663v2.pdf | Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data | Precise load forecasting in buildings could increase the bill savings potential and facilitate optimized strategies for power generation planning. With the rapid evolution of computer science, data-driven techniques, in particular the Deep Learning models, have become a promising solution for the load forecasting probl... | ['Huangjie Gong', 'Samy Faddel', 'Moustafa Shomer', 'Menna Nawar'] | 2023-01-25 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-3.68507564e-01 -3.22596520e-01 5.98505624e-02 -1.84035093e-01
-3.80369574e-01 -1.72434784e-02 5.02424002e-01 -1.84548467e-01
4.98891734e-02 9.96347904e-01 1.91623300e-01 -4.12696451e-01
-2.48533338e-01 -1.32173526e+00 -4.14956421e-01 -1.04379463e+00
-1.67409897e-01 2.87121952e-01 -1.44172996e-01 -4.80855227... | [6.240879058837891, 2.8063790798187256] |
a468b4d8-63ca-445c-ab27-c2ed87f12ec9 | inference-of-binary-regime-models-with-jump | 1910.10606 | null | https://arxiv.org/abs/1910.10606v4 | https://arxiv.org/pdf/1910.10606v4.pdf | Inference of Binary Regime Models with Jump Discontinuities | Identifying the instances of jumps in a discrete-time-series sample of a jump diffusion model is a challenging task. We have developed a novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. The consistency of the volatility estimator has been ob... | ['Sharan Rajani', 'Anindya Goswami', 'Milan Kumar Das'] | 2019-10-23 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-2.80926209e-02 -3.42462569e-01 6.69139549e-02 -1.76272273e-01
-6.05416894e-01 -9.61818337e-01 1.08616459e+00 2.00337902e-01
-2.43111938e-01 8.36837411e-01 -2.21001625e-01 -6.76384330e-01
-4.37165588e-01 -7.90302455e-01 -4.54426169e-01 -7.83951461e-01
-4.45699483e-01 7.03139901e-01 5.40970087e-01 -1.45100608... | [4.8256330490112305, 4.126916885375977] |
df86ba90-63e0-4ccf-a80d-6aedb9b086c9 | simfle-simple-facial-landmark-encoding-for | 2303.07648 | null | https://arxiv.org/abs/2303.07648v1 | https://arxiv.org/pdf/2303.07648v1.pdf | SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild | One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in this paper, we propose a self-supervised simple facial landmark encoding (SimFLE) method that can lear... | ['Seongsik Park', 'Jiyong Moon'] | 2023-03-14 | null | null | null | null | ['face-alignment', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.64165604e-01 1.55107841e-01 -1.44792199e-01 -9.35125351e-01
-8.78863573e-01 -2.70844489e-01 3.37219417e-01 -5.16430855e-01
-3.52465361e-01 4.66116160e-01 2.72392780e-01 2.23015636e-01
1.11082293e-01 -4.07045275e-01 -8.29532444e-01 -7.99343765e-01
-2.39852637e-01 -3.14789079e-02 -1.74191758e-01 -3.16578269... | [13.573103904724121, 1.1483415365219116] |
6ef8c5b6-10ac-42d6-a843-2b6c415cba78 | word-embedding-algorithms-as-generalized-low | 1911.02639 | null | https://arxiv.org/abs/1911.02639v1 | https://arxiv.org/pdf/1911.02639v1.pdf | Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form | Word embedding algorithms produce very reliable feature representations of words that are used by neural network models across a constantly growing multitude of NLP tasks. As such, it is imperative for NLP practitioners to understand how their word representations are produced, and why they are so impactful. The presen... | ['Kian Kenyon-Dean'] | 2019-11-06 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 9.85523388e-02 2.87889302e-01 -4.48640496e-01 -2.05817431e-01
-5.48762918e-01 -6.44619226e-01 1.09460878e+00 3.83735090e-01
-8.33831608e-01 3.60912591e-01 5.36521673e-01 -5.86869836e-01
-1.75086334e-01 -8.16109002e-01 -5.14896989e-01 -6.84796631e-01
-6.70625493e-02 3.50544333e-01 -1.46820098e-01 -3.11452687... | [10.544248580932617, 8.664650917053223] |
7a605e3f-3539-42a9-9d3e-e2f2c4275dcd | learning-sequential-and-structural | null | null | https://aclanthology.org/2021.findings-acl.251 | https://aclanthology.org/2021.findings-acl.251.pdf | Learning Sequential and Structural Information for Source Code Summarization | null | ['Jee-Hyong Lee', 'CheolWon Na', 'JinYeong Bak', 'YunSeok Choi'] | null | null | null | null | findings-acl-2021-8 | ['code-summarization'] | ['computer-code'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.334739685058594, 3.7157199382781982] |
6f7469e0-a8bc-4ec7-a44b-cbfcaca5a17e | 190412181 | 1904.12181 | null | http://arxiv.org/abs/1904.12181v1 | http://arxiv.org/pdf/1904.12181v1.pdf | Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks | Recent progress in biomedical image segmentation based on deep convolutional
neural networks (CNNs) has drawn much attention. However, its vulnerability
towards adversarial samples cannot be overlooked. This paper is the first one
that discovers that all the CNN-based state-of-the-art biomedical image
segmentation mode... | ['Guanbin Li?', 'Huiyou Chang', 'Haofeng Li', 'Yizhou Yu', 'Sibei Yang', 'Xiang He'] | 2019-04-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.66616440e-01 1.65838411e-03 3.26874182e-02 -1.91167474e-01
-6.25076473e-01 -6.65648162e-01 7.74687901e-02 3.72548588e-02
-6.10318542e-01 5.67818999e-01 -7.97280148e-02 -4.67258513e-01
6.83248639e-02 -7.59278238e-01 -8.82642269e-01 -1.15741265e+00
-1.80159539e-01 -3.57837886e-01 5.15233576e-01 -2.76704073... | [5.61004638671875, 7.806966304779053] |
8454c4c3-b6af-4f13-9ff5-c7987318bfb5 | emphasis-an-emotional-phoneme-based-acoustic | 1806.09276 | null | http://arxiv.org/abs/1806.09276v2 | http://arxiv.org/pdf/1806.09276v2.pdf | EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System | We present EMPHASIS, an emotional phoneme-based acoustic model for speech
synthesis system. EMPHASIS includes a phoneme duration prediction model and an
acoustic parameter prediction model. It uses a CBHG-based regression network to
model the dependencies between linguistic features and acoustic features. We
modify the... | [] | 2018-06-26 | null | null | null | null | ['parameter-prediction', 'emotional-speech-synthesis'] | ['miscellaneous', 'speech'] | [-3.02710235e-01 3.57159823e-01 -1.10654399e-01 -5.94823718e-01
-5.11070549e-01 -5.22966869e-02 5.67226186e-02 -4.09232706e-01
2.24262383e-02 6.19901955e-01 6.27663672e-01 -2.35355422e-01
3.70589405e-01 -5.47708929e-01 4.79623713e-02 -6.44779027e-01
-1.46780768e-02 1.60065498e-02 1.29862502e-02 -5.18539846... | [14.71563720703125, 6.474836349487305] |
0b909fbd-a533-4f8f-a683-0a865f1272cd | data-copying-in-generative-models-a-formal | 2302.13181 | null | https://arxiv.org/abs/2302.13181v2 | https://arxiv.org/pdf/2302.13181v2.pdf | Data-Copying in Generative Models: A Formal Framework | There has been some recent interest in detecting and addressing memorization of training data by deep neural networks. A formal framework for memorization in generative models, called "data-copying," was proposed by Meehan et. al. (2020). We build upon their work to show that their framework may fail to detect certain ... | ['Kamalika Chaudhuri', 'Sanjoy Dasgupta', 'Robi Bhattacharjee'] | 2023-02-25 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.82278121e-01 3.90933365e-01 -1.40069917e-01 -3.43477964e-01
-6.40883148e-01 -5.21424413e-01 8.58788192e-01 -7.76943797e-03
-5.65954447e-01 9.25378203e-01 2.67178286e-03 -4.52628076e-01
-1.69165686e-01 -1.14279306e+00 -1.12857234e+00 -6.14003778e-01
-1.78109497e-01 3.59718174e-01 -3.45702805e-02 5.64971082... | [8.456254005432129, 3.538506507873535] |
3e21ea09-c326-4bdc-bb09-40321ef5b151 | explicitly-antisymmetrized-neural-network | 2112.03491 | null | https://arxiv.org/abs/2112.03491v1 | https://arxiv.org/pdf/2112.03491v1.pdf | Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation | The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with an antisymmetric layer to satisfy the antisymmetry requirements of the electronic wavefunction. H... | ['Lin Lin', 'Gil Goldshlager', 'Jeffmin Lin'] | 2021-12-07 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 4.36870784e-01 -2.38841511e-02 9.19224322e-02 -1.37767419e-01
-2.28676602e-01 -4.92049605e-01 7.69082427e-01 -4.03173119e-01
-4.26263779e-01 1.14863324e+00 -8.02546963e-02 -5.22629678e-01
-1.61937118e-01 -8.80240858e-01 -7.61240840e-01 -1.25524759e+00
1.68138593e-01 4.31159049e-01 3.12472526e-02 -7.83330917... | [5.399946212768555, 5.117935657501221] |
a8db19f1-7983-4a36-9e1f-f4a366a2c78b | hear-to-segment-unmixing-the-audio-to-guide | 2305.07223 | null | https://arxiv.org/abs/2305.07223v1 | https://arxiv.org/pdf/2305.07223v1.pdf | Hear to Segment: Unmixing the Audio to Guide the Semantic Segmentation | In this paper, we focus on a recently proposed novel task called Audio-Visual Segmentation (AVS), where the fine-grained correspondence between audio stream and image pixels is required to be established. However, learning such correspondence faces two key challenges: (1) audio signals inherently exhibit a high degree ... | ['Yabiao Wang', 'Mingmin Chi', 'Jiangning Zhang', 'Zhenye Gan', 'Yuxi Li', 'Yuhang Ling'] | 2023-05-12 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 5.23731530e-01 -3.22209120e-01 5.64555451e-02 -2.22793147e-01
-1.00664890e+00 -4.79356170e-01 2.12879539e-01 1.00678839e-01
-2.03327358e-01 2.31505767e-01 7.87395164e-02 2.29142651e-01
2.93866061e-02 -3.46244037e-01 -7.84338892e-01 -7.90301442e-01
-2.51079258e-02 -5.55899106e-02 4.61889446e-01 1.29056603... | [14.794636726379395, 4.8265380859375] |
0f2c732e-19da-4b10-9a76-d546a47bbcea | exploring-textual-and-speech-information-in | 1810.07455 | null | http://arxiv.org/abs/1810.07455v1 | http://arxiv.org/pdf/1810.07455v1.pdf | Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation | In spite of the recent success of Dialogue Act (DA) classification, the
majority of prior works focus on text-based classification with oracle
transcriptions, i.e. human transcriptions, instead of Automatic Speech
Recognition (ASR)'s transcriptions. In spoken dialog systems, however, the
agent would only have access to... | ['Quan Hung Tran', 'Laurent Besacier', 'Gholamreza Haffari', 'Xuanli He', 'William Havard', 'Ingrid Zukerman'] | 2018-10-17 | exploring-textual-and-speech-information-in-2 | https://aclanthology.org/U18-1007 | https://aclanthology.org/U18-1007.pdf | alta-2018-12 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.83626652e-01 7.80757889e-02 2.41299242e-01 -6.62770629e-01
-1.22322130e+00 -8.83959055e-01 8.75750899e-01 -1.71039715e-01
-4.11202461e-01 9.86126244e-01 4.69792455e-01 -3.88800830e-01
3.03685337e-01 -2.04634666e-01 -9.87990797e-02 -8.33778262e-01
5.51203549e-01 8.49645317e-01 -5.38753392e-03 -5.75526357... | [14.192205429077148, 6.800258636474609] |
507ebd80-db02-4f64-99bc-68ca47146b53 | rotation-invariance-and-extensive-data | 2109.00823 | null | https://arxiv.org/abs/2109.00823v2 | https://arxiv.org/pdf/2109.00823v2.pdf | Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge | Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen ta... | ['Viktor H. Koelzer', 'Maxime W. Lafarge'] | 2021-09-02 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 5.20321727e-01 3.94434333e-01 1.50388494e-01 -4.59016085e-01
-1.33539939e+00 -4.13240463e-01 5.77381253e-01 3.93858641e-01
-8.14293683e-01 9.41150427e-01 -1.63035035e-01 -2.28393093e-01
-2.59454399e-01 -3.83891821e-01 -7.38875747e-01 -1.03856170e+00
-4.52663973e-02 8.96271646e-01 3.36821586e-01 1.33461639... | [15.097122192382812, -3.044065475463867] |
cf3883ac-d5e4-408d-8813-e3c26ee71f6b | mammoganesis-controlled-generation-of-high | 2010.05177 | null | https://arxiv.org/abs/2010.05177v1 | https://arxiv.org/pdf/2010.05177v1.pdf | MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education | During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions. Unfortunately, medico-legal and technical hurdles make it difficult to access and query medical... | ['Ghina Berjawi', 'Elie Najem', 'Ghida Saheb', 'Cyril Zakka'] | 2020-10-11 | null | null | null | null | ['radiologist-binary-classification', 'medical-image-generation'] | ['medical', 'medical'] | [ 6.44453168e-01 6.35202169e-01 1.17733650e-01 -5.63854039e-01
-1.30055821e+00 -5.63388228e-01 3.58942509e-01 2.15406105e-01
-2.73719847e-01 6.46573186e-01 2.42518321e-01 -6.55372500e-01
-2.20459439e-02 -6.17536783e-01 -9.01114821e-01 -6.71379983e-01
-2.88320392e-01 5.11964858e-01 -1.38252624e-03 7.72735551... | [14.395525932312012, -1.9228919744491577] |
a187299f-da02-474f-89c9-6c517a17562d | no-pain-big-gain-classify-dynamic-point-cloud | 2203.11113 | null | https://arxiv.org/abs/2203.11113v2 | https://arxiv.org/pdf/2203.11113v2.pdf | No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces | Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification since the unstructured points make it hard or even impossible to efficiently and effectively trace point-wise correspondences. To capture 3D... | ['Andrew Markham', 'Niki Trigoni', 'Bing Wang', 'Qingyong Hu', 'Kaichen Zhou', 'Jia-Xing Zhong'] | 2022-03-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-classification'] | ['computer-vision'] | [-3.99339795e-01 -5.73184013e-01 1.33804837e-02 -4.72085774e-02
-2.57844459e-02 -7.59571016e-01 3.73559237e-01 -2.18451276e-01
-4.16535974e-01 3.03694218e-01 -4.85065937e-01 -7.19769359e-01
-1.52434960e-01 -8.32773328e-01 -7.03237653e-01 -3.45089108e-01
-3.59403998e-01 6.43734634e-01 4.24170136e-01 -3.19836199... | [8.519189834594727, -2.082984685897827] |
12a870ec-a9f3-4089-b207-72ee9f0d3f33 | weakly-supervised-representation-learning-for-1 | 2302.04064 | null | https://arxiv.org/abs/2302.04064v1 | https://arxiv.org/pdf/2302.04064v1.pdf | Weakly-supervised Representation Learning for Video Alignment and Analysis | Many tasks in video analysis and understanding boil down to the need for frame-based feature learning, aiming to encapsulate the relevant visual content so as to enable simpler and easier subsequent processing. While supervised strategies for this learning task can be envisioned, self and weakly-supervised alternatives... | ['Ehud Rivlin', 'Michael Elad', 'George Leifman', 'Guy Bar-Shalom'] | 2023-02-08 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 3.47060144e-01 -1.02326192e-01 -2.75092214e-01 -5.63835263e-01
-7.50897527e-01 -3.22803319e-01 8.04355204e-01 5.01141012e-01
-6.13330483e-01 5.37268877e-01 3.63248199e-01 1.39225438e-01
-2.95248330e-01 -4.02556986e-01 -7.51209080e-01 -8.39994788e-01
-4.14374024e-01 1.39304683e-01 3.40284914e-01 1.83435138... | [8.496333122253418, 0.6457517743110657] |
ff966ba0-4b26-494a-a46b-55a087edad9e | unipoll-a-unified-social-media-poll | 2306.06851 | null | https://arxiv.org/abs/2306.06851v1 | https://arxiv.org/pdf/2306.06851v1.pdf | UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization | Social media platforms are essential outlets for expressing opinions, providing a valuable resource for capturing public viewpoints via text analytics. However, for many users, passive browsing is their preferred mode of interaction, leading to their perspectives being overlooked by text analytics methods. Meanwhile, s... | ['Jing Li', 'Yanlin Song', 'Rong Xiang', 'Yixia Li'] | 2023-06-12 | null | null | null | null | ['poll-generation', 'question-generation', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-8.26947466e-02 2.59955496e-01 -3.76194566e-01 -2.92054772e-01
-1.03957427e+00 -7.14962423e-01 8.36139917e-01 4.66406941e-01
-3.71534050e-01 8.31703722e-01 8.28261018e-01 -5.34794867e-01
1.69147193e-01 -9.77690995e-01 -1.27363965e-01 -4.28315103e-01
6.45558774e-01 4.65549767e-01 2.82175187e-02 -7.00551331... | [11.848621368408203, 8.263736724853516] |
9eae454e-a79b-4a91-8e8b-bab9bb4c25a3 | joint-extraction-of-entities-and-relations-2 | 1908.08672 | null | https://arxiv.org/abs/1908.08672v2 | https://arxiv.org/pdf/1908.08672v2.pdf | Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction | Recent works on relational triple extraction have shown the superiority of jointly extracting entities and relations over the pipelined extraction manner. However, most existing joint models fail to balance the modeling of entity features and the joint decoding strategy, and thus the interactions between the entity lev... | ['Yi Chang', 'Mark Steedman', 'Sujian Li', 'Yuan Tian', 'Yantao Jia', 'Zhepei Wei', 'Mohammad Javad Hosseini'] | 2019-08-23 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-3.45591158e-02 4.70280856e-01 -3.39545101e-01 -3.54899764e-01
-9.62987006e-01 -5.28019369e-01 5.69292188e-01 1.71968609e-01
-3.70767027e-01 7.69326866e-01 2.64138699e-01 -2.80800402e-01
-3.74028496e-02 -6.49322391e-01 -9.47183371e-01 -5.46207309e-01
-7.03707710e-02 3.05520773e-01 1.89137936e-01 -4.87543605... | [9.285296440124512, 8.724596977233887] |
04dfb5b4-870c-4a41-be96-413c4931f84e | playing-with-embeddings-evaluating-embeddings | null | null | https://aclanthology.org/W17-5305 | https://aclanthology.org/W17-5305.pdf | Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games | Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on co-occurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of usin... | ['Anmol Gulati', 'Kumar Krishna Agrawal'] | 2017-09-01 | null | null | null | ws-2017-9 | ['text-based-games'] | ['playing-games'] | [-1.67492971e-01 5.44879556e-01 -1.40477931e-02 -2.52231002e-01
-5.89584112e-01 -4.07226503e-01 8.09605777e-01 2.90313035e-01
-1.13432026e+00 4.83935505e-01 3.48704875e-01 -5.73911428e-01
-1.87851667e-01 -7.58098781e-01 -6.17547989e-01 -2.26024687e-01
-5.43866277e-01 5.77841222e-01 -2.43832543e-01 -7.28780985... | [4.475819110870361, 0.8890737891197205] |
14849a66-2f8c-4f3e-b2c3-f2eaffc1c13e | partial-attack-supervision-and-regional | 2111.04336 | null | https://arxiv.org/abs/2111.04336v1 | https://arxiv.org/pdf/2111.04336v1.pdf | Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection | Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-CoV-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face ... | ['Naser Damer', 'Arjan Kuijper', 'Fadi Boutros', 'Meiling Fang'] | 2021-11-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.41051370e-01 1.19058698e-01 3.01582903e-01 -4.74962741e-01
-4.20241892e-01 -4.85860676e-01 6.50140882e-01 -4.04225141e-01
-3.12588900e-01 4.87909019e-01 -2.09349886e-01 -3.94577652e-01
-1.46515325e-01 -5.04255414e-01 -5.35793662e-01 -1.03400528e+00
-3.29700440e-01 3.82479578e-01 1.59091458e-01 -2.44174242... | [13.191229820251465, 0.8266248106956482] |
c4e0e589-84e7-404c-9a2f-b36a0ac4daba | cico-domain-aware-sign-language-retrieval-via | 2303.12793 | null | https://arxiv.org/abs/2303.12793v1 | https://arxiv.org/pdf/2303.12793v1.pdf | CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning | This work focuses on sign language retrieval-a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain vis... | ['Wenqiang Zhang', 'Dong Chen', 'Jianmin Bao', 'Fangyun Wei', 'Yiting Cheng'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 4.26509753e-02 -6.25218272e-01 -5.53234458e-01 -3.22040081e-01
-1.27024877e+00 -7.56528914e-01 9.00211990e-01 -7.89789617e-01
-6.04651034e-01 4.06692803e-01 7.77716219e-01 3.97638511e-03
-1.06596455e-01 -2.28871256e-01 -6.49203420e-01 -7.60768592e-01
1.40763342e-01 1.64343253e-01 3.27057280e-02 -7.59004802... | [9.211539268493652, -6.519320964813232] |
b86f6561-6d22-47a7-baa3-d8448e6d68c4 | entropic-one-class-classifiers | 1407.7556 | null | http://arxiv.org/abs/1407.7556v3 | http://arxiv.org/pdf/1407.7556v3.pdf | Entropic one-class classifiers | The one-class classification problem is a well-known research endeavor in
pattern recognition. The problem is also known under different names, such as
outlier and novelty/anomaly detection. The core of the problem consists in
modeling and recognizing patterns belonging only to a so-called target class.
All other patte... | ['Witold Pedrycz', 'Alireza Sadeghian', 'Lorenzo Livi'] | 2014-07-28 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 2.34463945e-01 -8.48281458e-02 5.37444726e-02 -3.70896012e-01
-1.60720050e-01 -4.10377920e-01 8.48593235e-01 7.19597042e-01
-2.72319317e-01 3.78702819e-01 -3.96791071e-01 -1.73855841e-01
-4.84502137e-01 -9.70586419e-01 -4.20376241e-01 -1.14034128e+00
-1.79942131e-01 6.72505558e-01 3.01749915e-01 -7.05833063... | [8.004076957702637, 3.9954519271850586] |
474e5537-5c4a-40e0-a83f-31f0719d5a2d | imle-net-an-interpretable-multi-level-multi | 2204.05116 | null | https://arxiv.org/abs/2204.05116v1 | https://arxiv.org/pdf/2204.05116v1.pdf | IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification | Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist ma... | ['U. Deva Priyakumar', 'Raju. S. Bapi', 'Shanmukh Alle', 'Vivek Talwar', 'Likith Reddy'] | 2022-04-06 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 1.10987544e-01 -1.01628400e-01 7.72473030e-03 -4.04377520e-01
-8.33615839e-01 -5.41342676e-01 -3.62122923e-01 6.05695188e-01
-2.99223475e-02 6.19328856e-01 -9.78746340e-02 -8.09747458e-01
-3.85527790e-01 -3.01605701e-01 -1.57100514e-01 -5.45936704e-01
-6.25081718e-01 8.28873813e-02 -2.72506118e-01 2.82565743... | [14.315068244934082, 3.290292263031006] |
6681b52f-c218-42e2-b44b-0fd58f7ffaeb | sogar-self-supervised-spatiotemporal | 2305.06310 | null | https://arxiv.org/abs/2305.06310v2 | https://arxiv.org/pdf/2305.06310v2.pdf | SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition | This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we created local and global views with varying frame rates. Our self-supervised objective ensures that... | ['Khoa Luu', 'Page Daniel Dobbs', 'Xin Li', 'Han-Seok Seo', 'Alexander H Nelson', 'Pha Nguyen', 'Naga VS Raviteja Chappa'] | 2023-04-27 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 9.64405984e-02 -3.22164744e-01 -7.36231804e-01 -3.45047593e-01
-7.10880339e-01 -5.96137762e-01 8.05812418e-01 -1.58934250e-01
-4.17560190e-01 6.04502857e-01 6.91567659e-01 3.86547074e-02
-4.42244798e-01 -6.41361356e-01 -6.93915844e-01 -5.68548262e-01
-5.73579013e-01 -7.77518675e-02 2.66660899e-01 1.61848422... | [8.38140869140625, 0.67973393201828] |
5d574ba5-19ae-45c5-b295-df7be8470097 | hw-tscs-participation-at-wmt-2020-automatic | null | null | https://aclanthology.org/2020.wmt-1.85 | https://aclanthology.org/2020.wmt-1.85.pdf | HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task | The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottlenec... | ['Yimeng Chen', 'Shiliang Sun', 'Shimin Tao', 'Ying Qin', 'Lizhi Lei', 'Zongyao Li', 'Jiaxin Guo', 'Hengchao Shang', 'Daimeng Wei', 'Minghan Wang', 'Hao Yang'] | null | null | null | null | wmt-emnlp-2020-11 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 1.44411206e-01 1.33858800e-01 -3.71344954e-01 -4.28171724e-01
-1.24467623e+00 -4.93307203e-01 6.37740552e-01 -4.33748335e-01
-9.97522175e-01 9.10137951e-01 4.26367700e-01 -6.58577859e-01
4.89678621e-01 -3.95353079e-01 -7.33947277e-01 -5.01890155e-03
4.23000544e-01 9.38637376e-01 2.52326399e-01 -6.43336713... | [11.667025566101074, 10.290751457214355] |
6507b4db-221d-4a36-8331-d2f2f057a1e2 | improving-factual-consistency-in | 2211.06196 | null | https://arxiv.org/abs/2211.06196v1 | https://arxiv.org/pdf/2211.06196v1.pdf | Improving Factual Consistency in Summarization with Compression-Based Post-Editing | State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may in... | ['Caiming Xiong', 'Chien-Sheng Wu', 'Jesse Vig', 'Prafulla Kumar Choubey', 'Alexander R. Fabbri'] | 2022-11-11 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 4.27873939e-01 4.73337829e-01 -2.74868459e-01 -1.98835298e-01
-1.34347761e+00 -7.28208780e-01 4.20182198e-01 1.06731665e+00
-4.44725275e-01 1.00182831e+00 5.36477387e-01 -2.58885473e-01
-4.62635607e-03 -6.36218548e-01 -9.27911222e-01 7.86869526e-02
1.19152263e-01 5.28611839e-01 2.95917809e-01 -1.35676086... | [12.360733032226562, 9.41561508178711] |
1bf9bc79-4742-4709-9aa2-b0ad1c2e15c7 | a-two-phase-paradigm-for-joint-entity | 2208.08659 | null | https://arxiv.org/abs/2208.08659v1 | https://arxiv.org/pdf/2208.08659v1.pdf | A Two-Phase Paradigm for Joint Entity-Relation Extraction | An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in tur... | ['Huijun Liu', 'Yuke Ji', 'Jun Ma', 'Shasha Li', 'Jie Yu', 'Hao Xu', 'Bin Ji'] | 2022-08-18 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.91250801e-01 2.39197269e-01 -4.96230781e-01 -1.76678851e-01
-5.18552780e-01 -3.36423337e-01 6.17693663e-01 6.26090527e-01
-4.52461511e-01 1.00224221e+00 -1.22290842e-01 -1.38788551e-01
-4.79934514e-01 -1.17822802e+00 -2.14493901e-01 -7.01601923e-01
-3.11639100e-01 7.94476211e-01 3.19158942e-01 -1.06437072... | [9.192660331726074, 8.591378211975098] |
18b1950f-7db6-490e-90d7-180a724e5aa0 | mitigating-spurious-correlations-for-self | 2212.04282 | null | https://arxiv.org/abs/2212.04282v1 | https://arxiv.org/pdf/2212.04282v1.pdf | Mitigating Spurious Correlations for Self-supervised Recommendation | Recent years have witnessed the great success of self-supervised learning (SSL) in recommendation systems. However, SSL recommender models are likely to suffer from spurious correlations, leading to poor generalization. To mitigate spurious correlations, existing work usually pursues ID-based SSL recommendation or util... | ['Fuli Feng', 'Yang Zhang', 'Wenjie Wang', 'Yiyan Xu', 'Xinyu Lin'] | 2022-12-08 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 3.50393057e-01 -2.35928208e-01 -2.77256578e-01 -6.07659459e-01
-2.79844642e-01 -3.82135570e-01 4.31080163e-01 1.79380029e-02
5.50825819e-02 4.99826491e-01 2.80796230e-01 -9.79681090e-02
-5.23476005e-01 -7.06967533e-01 -7.00590670e-01 -5.77463269e-01
-5.92285432e-02 -1.52442873e-01 1.67347565e-01 -3.18231463... | [10.103570938110352, 5.51362943649292] |
9c8446b0-efd5-495a-8478-b0bd1e5e4199 | deep-learning-based-multi-label-image | 2301.04212 | null | https://arxiv.org/abs/2301.04212v1 | https://arxiv.org/pdf/2301.04212v1.pdf | Deep Learning based Multi-Label Image Classification of Protest Activities | With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of user-generated data can be used to enhance our understanding of significant societal issues p... | ['Jialu Wang', 'Kosaku Sato', 'Yingzhou Lu'] | 2023-01-10 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [-2.12136477e-01 -3.44613381e-02 2.01505497e-02 -1.54027328e-01
-5.77419877e-01 -4.52815354e-01 8.41869831e-01 7.20245004e-01
-4.11632836e-01 7.62679219e-01 6.50950074e-01 -5.73275030e-01
4.72251624e-02 -1.27370155e+00 -4.49505091e-01 -4.77910578e-01
-2.37336218e-01 3.09197068e-01 5.70153212e-03 -7.01542556... | [9.466228485107422, -1.255569338798523] |
186e4c95-f824-446a-b921-cc3a25cbb166 | temporal-needle-a-view-and-appearance | 1612.04854 | null | http://arxiv.org/abs/1612.04854v1 | http://arxiv.org/pdf/1612.04854v1.pdf | Temporal-Needle: A view and appearance invariant video descriptor | The ability to detect similar actions across videos can be very useful for
real-world applications in many fields. However, this task is still challenging
for existing systems, since videos that present the same action, can be taken
from significantly different viewing directions, performed by different actors
and back... | ['Michal Irani', 'Michal Yarom'] | 2016-12-14 | null | null | null | null | ['unsupervised-video-clustering'] | ['computer-vision'] | [ 9.32555199e-02 -6.42010450e-01 -7.51156509e-02 -1.26219630e-01
-3.27999294e-01 -8.64189148e-01 5.60659707e-01 3.43376249e-01
-3.27611506e-01 2.30120569e-01 3.53456549e-02 4.77166504e-01
-3.83904248e-01 -4.52165663e-01 -5.28255403e-01 -7.80935228e-01
-4.61938083e-01 6.13606051e-02 1.00603390e+00 -2.17758715... | [8.263177871704102, 0.1360042542219162] |
b6d38c8a-9023-468b-934b-3ca53e3d3b45 | pushing-the-limits-of-deep-cnns-for | 1603.04525 | null | http://arxiv.org/abs/1603.04525v2 | http://arxiv.org/pdf/1603.04525v2.pdf | Pushing the Limits of Deep CNNs for Pedestrian Detection | Compared to other applications in computer vision, convolutional neural
networks have under-performed on pedestrian detection. A breakthrough was made
very recently by using sophisticated deep CNN models, with a number of
hand-crafted features, or explicit occlusion handling mechanism. In this work,
we show that by re-... | ['Anton Van Den Hengel', 'Chunhua Shen', 'Qichang Hu', 'Peng Wang', 'Fatih Porikli'] | 2016-03-15 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.32892966e-01 -1.40477195e-02 1.68923050e-01 -4.14954424e-01
-5.20389080e-01 -2.83285648e-01 6.09138072e-01 1.42459050e-01
-9.45899904e-01 6.91462040e-01 -1.66225567e-01 -3.15748930e-01
3.68308961e-01 -9.65211689e-01 -7.90092528e-01 -6.28487587e-01
-2.64709353e-01 8.26706812e-02 7.21971929e-01 -8.98760259... | [8.40793228149414, -0.5640954971313477] |
002074c6-10a7-41a9-a9ab-cab15744bf3e | multimodal-feature-extraction-for-memes | 2207.03317 | null | https://arxiv.org/abs/2207.03317v1 | https://arxiv.org/pdf/2207.03317v1.pdf | Multimodal Feature Extraction for Memes Sentiment Classification | In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feeling... | ['Tomas Horvath', 'Tsegaye Misikir Tashu', 'Sofiane Ouaari'] | 2022-07-07 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.20725922e-01 -2.26343080e-01 -2.03435585e-01 -4.73622203e-01
-2.27492586e-01 -3.97425860e-01 6.65563345e-01 4.79514867e-01
-4.27589267e-01 7.23300338e-01 4.63512003e-01 4.21864599e-01
2.73466200e-01 -9.29702878e-01 -5.29868126e-01 -6.03272915e-01
4.18357581e-01 -2.18283564e-01 -1.43900603e-01 -4.34228361... | [8.56832504272461, 10.659457206726074] |
cd2bbc05-1145-4833-a4d3-0387ca4fad36 | 3d-lmnet-latent-embedding-matching-for | 1807.07796 | null | http://arxiv.org/abs/1807.07796v2 | http://arxiv.org/pdf/1807.07796v2.pdf | 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image | 3D reconstruction from single view images is an ill-posed problem. Inferring
the hidden regions from self-occluded images is both challenging and ambiguous.
We propose a two-pronged approach to address these issues. To better
incorporate the data prior and generate meaningful reconstructions, we propose
3D-LMNet, a lat... | ['Priyanka Mandikal', 'R. Venkatesh Babu', 'Mayank Agarwal', 'K L Navaneet'] | 2018-07-20 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction', 'single-view-3d-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.81734133e-01 4.13425624e-01 -8.47923011e-02 -4.14338857e-01
-1.03473556e+00 -5.60153604e-01 8.12696338e-01 -3.98492128e-01
1.28883764e-01 4.98343915e-01 5.55331349e-01 -4.73925360e-02
-3.36736590e-02 -6.89240813e-01 -9.73273516e-01 -5.42784572e-01
4.17287886e-01 6.22970343e-01 -5.00667728e-02 1.69644982... | [8.87582778930664, -3.1731197834014893] |
a50025b3-bf1a-4d12-9a83-da4a25f169f9 | vflow-more-expressive-generative-flows-with | 2002.09741 | null | https://arxiv.org/abs/2002.09741v2 | https://arxiv.org/pdf/2002.09741v2.pdf | VFlow: More Expressive Generative Flows with Variational Data Augmentation | Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architectural constraints on generative flows, making them less expressive than other types of generative models. In this work, we study a previousl... | ['Tian Tian', 'Jun Zhu', 'Jianfei Chen', 'Biqi Chenli', 'Cheng Lu'] | 2020-02-22 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1619-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1619-Paper.pdf | icml-2020-1 | ['normalising-flows'] | ['methodology'] | [-4.22362983e-01 2.32550487e-01 -3.45079750e-01 -4.13344890e-01
-4.80846196e-01 -7.03033507e-01 8.18422914e-01 -7.66538262e-01
-9.51211751e-02 1.12274230e+00 4.58563983e-01 -5.87958753e-01
-3.59868199e-01 -1.04280365e+00 -6.62124932e-01 -7.96783984e-01
-1.56736732e-01 9.41744268e-01 -7.05927461e-02 3.58937204... | [7.191006183624268, 3.774919271469116] |
922d7318-7efe-41ae-804d-4dcb776b5dcf | rade-resource-efficient-supervised-anomaly | 1909.11877 | null | https://arxiv.org/abs/1909.11877v2 | https://arxiv.org/pdf/1909.11877v2.pdf | RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods | Decision-tree-based ensemble classification methods (DTEMs) are a prevalent tool for supervised anomaly detection. However, due to the continued growth of datasets, DTEMs result in increasing drawbacks such as growing memory footprints, longer training times, and slower classification latencies at lower throughput. In ... | ['Yaniv Ben-Itzhak', 'Shay Vargaftik', 'Isaac Keslassy', 'Ariel Orda'] | 2019-09-26 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-2.68022008e-02 -5.19075274e-01 4.78784414e-03 -3.69792819e-01
-3.46207023e-01 -4.17785466e-01 3.26788127e-01 4.69008535e-01
-5.84679782e-01 8.32519829e-01 -4.67970580e-01 -7.68615544e-01
-4.48439956e-01 -1.03262711e+00 -2.08992839e-01 -6.22339785e-01
-7.37623125e-02 5.70356309e-01 5.82560360e-01 -1.64393231... | [7.580688953399658, 2.8902974128723145] |
126113dc-37f7-44d4-abaf-084816d8f257 | application-of-quantum-computers-in-foreign | 2203.15716 | null | https://arxiv.org/abs/2203.15716v1 | https://arxiv.org/pdf/2203.15716v1.pdf | Application of Quantum Computers in Foreign Exchange Reserves Management | The main purpose of this article is to evaluate possible applications of quantum computers in foreign exchange reserves management. The capabilities of quantum computers are demonstrated by means of risk measurement using the quantum Monte Carlo method and portfolio optimization using a linear equations system solver (... | ['Martin Veselý'] | 2022-03-29 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.41844612e-01 -9.99883041e-02 2.11563349e-01 -4.21215072e-02
-6.60803199e-01 -5.47183692e-01 6.25288486e-01 -1.63856477e-01
-4.43192780e-01 1.15680504e+00 -3.55687827e-01 -9.61778641e-01
-3.59489210e-02 -1.03882575e+00 -1.04070380e-01 -6.05338991e-01
-1.39584258e-01 8.31351936e-01 -2.78429985e-01 -6.89424157... | [5.5658183097839355, 4.895813941955566] |
2741bd3a-a778-44b2-b4c7-e6134432fdec | the-oracle-estimator-is-suboptimal-for-global | 2112.07521 | null | https://arxiv.org/abs/2112.07521v2 | https://arxiv.org/pdf/2112.07521v2.pdf | Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimisation | Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise. Non-linear shrinkage is a popular estimator based on how the Oracle eigenvalues can be computed using only data from the calibration window. Contrary to common belief, NLS is not optimal for portfolio optim... | ['Damien Challet', 'Christian Bongiorno'] | 2021-12-14 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.21742137e-01 -8.40499699e-02 -1.33746862e-01 -3.71521056e-01
-9.06432629e-01 -1.00742626e+00 3.08196723e-01 -1.82650343e-01
-3.89846772e-01 7.57929802e-01 8.04681852e-02 -5.90785742e-01
-5.60647726e-01 -5.84523380e-01 -5.08164585e-01 -7.00678051e-01
1.34006113e-01 3.76903206e-01 -2.69965440e-01 1.84128404... | [5.048413276672363, 3.9874789714813232] |
5efa67f8-714b-4072-ac5f-837c1180f79a | on-the-power-of-deep-but-naive-partial-label | 2010.11600 | null | https://arxiv.org/abs/2010.11600v2 | https://arxiv.org/pdf/2010.11600v2.pdf | On the Power of Deep but Naive Partial Label Learning | Partial label learning (PLL) is a class of weakly supervised learning where each training instance consists of a data and a set of candidate labels containing a unique ground truth label. To tackle this problem, a majority of current state-of-the-art methods employs either label disambiguation or averaging strategies. ... | ['Joon Suk Huh', 'Junghoon Seo'] | 2020-10-22 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 2.56024778e-01 5.03363848e-01 -5.28358042e-01 -3.36773366e-01
-6.79846227e-01 -5.55802405e-01 8.77123594e-01 8.48171338e-02
-5.01123846e-01 9.02952492e-01 4.51957658e-02 -2.36219808e-01
-1.17596939e-01 -6.48715079e-01 -7.43741691e-01 -9.59885418e-01
1.56885400e-01 7.56295979e-01 2.10747540e-01 -1.33926675... | [9.46529483795166, 3.8872976303100586] |
85be6334-1ed0-4dc2-bcdf-cd29410761d4 | blind-graph-matching-using-graph-signals | 2306.15747 | null | https://arxiv.org/abs/2306.15747v1 | https://arxiv.org/pdf/2306.15747v1.pdf | Blind Graph Matching Using Graph Signals | Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks to identifying similar biological network functions across species. However, when the underlying graphs are unknown, th... | ['Hoi-To Wai', 'Anna Scaglione', 'Hang Liu'] | 2023-06-27 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 6.09966040e-01 3.24298441e-01 1.46288812e-01 -6.77449629e-02
-3.66277188e-01 -6.32655025e-01 4.92574275e-01 2.77620137e-01
7.69082755e-02 5.02185643e-01 -1.39256805e-01 -7.78374597e-02
-4.74637777e-01 -8.26694429e-01 -6.80182874e-01 -5.64780414e-01
-3.68709326e-01 5.91555297e-01 1.25562660e-02 1.78524479... | [6.939715385437012, 5.204104900360107] |
eb64d735-ffed-42b2-98ab-950af73bccf8 | analyzing-and-characterizing-user-intent-in | 1804.08759 | null | http://arxiv.org/abs/1804.08759v1 | http://arxiv.org/pdf/1804.08759v1.pdf | Analyzing and Characterizing User Intent in Information-seeking Conversations | Understanding and characterizing how people interact in information-seeking
conversations is crucial in developing conversational search systems. In this
paper, we introduce a new dataset designed for this purpose and use it to
analyze information-seeking conversations by user intent distribution,
co-occurrence, and fl... | ['Yongfeng Zhang', 'W. Bruce Croft', 'Johanne R. Trippas', 'Liu Yang', 'Minghui Qiu', 'Chen Qu'] | 2018-04-23 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.41127333e-01 6.25967026e-01 -3.59822273e-01 -6.54379487e-01
-9.27164614e-01 -1.01415408e+00 9.29840147e-01 2.36657947e-01
-1.97443247e-01 4.95521277e-01 9.55560327e-01 -5.66863060e-01
4.57098931e-02 -9.06515494e-02 2.13174775e-01 6.63815439e-02
1.40821457e-01 8.21566582e-01 2.53989577e-01 -7.78366268... | [12.367846488952637, 7.847703456878662] |
36e7eba4-b381-4f7e-b09d-eacd479f54ed | structure-informed-shadow-removal-networks | 2301.03182 | null | https://arxiv.org/abs/2301.03182v1 | https://arxiv.org/pdf/2301.03182v1.pdf | Structure-Informed Shadow Removal Networks | Shadow removal is a fundamental task in computer vision. Despite the success, existing deep learning-based shadow removal methods still produce images with shadow remnants. These shadow remnants typically exist in homogeneous regions with low intensity values, making them untraceable in the existing image-to-image mapp... | ['Rynson W. H. Lau', 'Ivor W. Tsang', 'Wei Feng', 'Ke Xu', 'Zhanghan Ke', 'Lan Fu', 'Qing Guo', 'Yuhao Liu'] | 2023-01-09 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 8.20743382e-01 4.30429280e-02 2.61167765e-01 -2.77987659e-01
-5.01870411e-03 -4.67690438e-01 4.24999744e-01 -4.61614460e-01
4.50468287e-02 6.05518043e-01 3.56785916e-02 -4.42352295e-01
5.19850791e-01 -8.11157048e-01 -7.89265215e-01 -1.06653810e+00
3.75583798e-01 -1.43788725e-01 9.51234639e-01 -2.12128624... | [10.846511840820312, -4.101534366607666] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.