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363d1c91-958a-42c8-adfc-1f7b75010216 | attentive-neural-controlled-differential | 2109.01876 | null | https://arxiv.org/abs/2109.01876v3 | https://arxiv.org/pdf/2109.01876v3.pdf | Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting | Neural networks inspired by differential equations have proliferated for the past several years. Neural ordinary differential equations (NODEs) and neural controlled differential equations (NCDEs) are two representative examples of them. In theory, NCDEs provide better representation learning capability for time-series... | ['Noseong Park', 'Solhee Park', 'Seoyoung Hong', 'Heejoo Shin', 'Sheo Yon Jhin'] | 2021-09-04 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.35922268e-01 -3.73275229e-03 3.76962066e-01 -1.72055587e-01
-8.75292197e-02 -3.04361939e-01 6.77861333e-01 -3.68422531e-02
-1.32842243e-01 6.16484106e-01 1.12558894e-01 -4.56330657e-01
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-5.78434765e-01 -1.39350910e-02 6.57151937e-02 -5.86432755... | [6.988212585449219, 3.1679301261901855] |
6e444c13-3a5e-4977-b01c-5dd183e02f39 | dynca-real-time-dynamic-texture-synthesis | 2211.11417 | null | https://arxiv.org/abs/2211.11417v2 | https://arxiv.org/pdf/2211.11417v2.pdf | DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata | Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos. However, they require a slow iterative optimization process to synthesize a single fixed-size short video, and they do not offer any post-training control over the synthesis process. We propose Dynamic Neural Cellular Automata (DyNCA), a f... | ['Sabine Süsstrunk', 'Tong Zhang', 'Yitao Xu', 'Ehsan Pajouheshgar'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pajouheshgar_DyNCA_Real-Time_Dynamic_Texture_Synthesis_Using_Neural_Cellular_Automata_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pajouheshgar_DyNCA_Real-Time_Dynamic_Texture_Synthesis_Using_Neural_Cellular_Automata_CVPR_2023_paper.pdf | cvpr-2023-1 | ['texture-synthesis'] | ['computer-vision'] | [ 3.84316981e-01 1.25071347e-01 2.05430202e-02 2.68296570e-01
-3.07937026e-01 -5.80664873e-01 7.29038596e-01 -5.96435130e-01
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5961df6b-aff0-411d-8e9e-58edd43ada57 | aida-upm-at-semeval-2022-task-5-exploring | null | null | https://aclanthology.org/2022.semeval-1.107 | https://aclanthology.org/2022.semeval-1.107.pdf | AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification | This paper describes the multimodal late fusion model proposed in the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) task. The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convol... | ['David Camacho', 'Javier Huertas-Tato', 'Alejandro Martín', 'Guillermo Villar-Rodríguez', 'Helena Liz', 'Álvaro Huertas-García'] | null | null | null | null | semeval-naacl-2022-7 | ['meme-classification'] | ['natural-language-processing'] | [ 2.30012491e-01 6.77162632e-02 -1.54326051e-01 -3.37542802e-01
-8.61879766e-01 -8.66231918e-02 8.51881921e-01 2.76915610e-01
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3.24324280e-01 4.07158256e-01 -1.60455272e-01 -2.31320381... | [8.565940856933594, 10.606555938720703] |
ed3316a1-dd63-4f05-afcd-64ff687b4948 | neural-network-for-heterogeneous-annotations | null | null | https://aclanthology.org/D16-1070 | https://aclanthology.org/D16-1070.pdf | Neural Network for Heterogeneous Annotations | null | ['Yue Zhang', 'Qun Liu', 'Hongshen Chen'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['multiview-learning'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.33184814453125, 3.6887059211730957] |
c0d5cc7f-7411-45d2-97ab-fb3a62ab8a45 | adaptive-remote-sensing-image-attribute | 2101.06438 | null | https://arxiv.org/abs/2101.06438v1 | https://arxiv.org/pdf/2101.06438v1.pdf | Adaptive Remote Sensing Image Attribute Learning for Active Object Detection | In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging configuration and detection performance, and do not take into account the importance of detection perform... | ['Chunhong Pan', 'Jian Wang', 'Yiwei Liu', 'Jiacheng Guo', 'Chunlei Huo', 'Nuo Xu'] | 2021-01-16 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 4.70330745e-01 -2.28355035e-01 1.23821404e-02 -3.52589458e-01
-1.89519465e-01 -2.40900189e-01 1.42015561e-01 1.57803863e-01
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-4.09361959e-01 -1.27066839e+00 -1.41198114e-01 -1.10159242e+00
1.23292513e-01 -1.02572754e-01 2.49762952e-01 -7.96937868... | [8.99502182006836, -0.9670644402503967] |
50ba233c-ecce-4377-832d-9e845f86877b | meta-two-sample-testing-learning-kernels-for | 2106.07636 | null | https://arxiv.org/abs/2106.07636v2 | https://arxiv.org/pdf/2106.07636v2.pdf | Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data | Modern kernel-based two-sample tests have shown great success in distinguishing complex, high-dimensional distributions with appropriate learned kernels. Previous work has demonstrated that this kernel learning procedure succeeds, assuming a considerable number of observed samples from each distribution. In realistic s... | ['Danica J. Sutherland', 'Jie Lu', 'Wenkai Xu', 'Feng Liu'] | 2021-06-14 | null | http://proceedings.neurips.cc/paper/2021/hash/2e6d9c6052e99fcdfa61d9b9da273ca2-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2e6d9c6052e99fcdfa61d9b9da273ca2-Paper.pdf | neurips-2021-12 | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 4.12427709e-02 -4.00260210e-01 -4.29425597e-01 -2.81225473e-01
-1.32355464e+00 -7.62745738e-01 7.77781427e-01 1.31202698e-01
-5.30307233e-01 9.60159481e-01 -4.80190724e-01 -6.81651175e-01
-4.26485687e-01 -4.36076581e-01 -4.96797264e-01 -8.59255672e-01
-3.48378718e-01 7.93013394e-01 5.72615504e-01 3.76328200... | [7.8446946144104, 4.004261016845703] |
7d07a5ab-a0c6-4c9b-91df-0d2539cb2398 | reinforcement-learning-for-low-thrust | 2008.08501 | null | https://arxiv.org/abs/2008.08501v1 | https://arxiv.org/pdf/2008.08501v1.pdf | Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions | This paper investigates the use of Reinforcement Learning for the robust design of low-thrust interplanetary trajectories in presence of severe disturbances, modeled alternatively as Gaussian additive process noise, observation noise, control actuation errors on thrust magnitude and direction, and possibly multiple mis... | ['Alessandro Zavoli', 'Lorenzo Federici'] | 2020-08-19 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-1.64470933e-02 2.58792490e-01 2.63068266e-03 -3.99903096e-02
-4.12944615e-01 -4.52355921e-01 9.57821369e-01 3.84165704e-01
-8.59383464e-01 1.21169710e+00 -2.97210008e-01 -6.90535665e-01
-9.02183294e-01 -6.27727747e-01 -8.11287701e-01 -1.03254676e+00
-5.46217144e-01 8.90168250e-01 -3.69991124e-01 -3.13873589... | [5.127792835235596, 2.2192676067352295] |
7b49a783-591f-4375-9c3f-a64cd94daa0e | cloud-transformers | 2007.11679 | null | https://arxiv.org/abs/2007.11679v4 | https://arxiv.org/pdf/2007.11679v4.pdf | Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks | We present a new versatile building block for deep point cloud processing architectures that is equally suited for diverse tasks. This building block combines the ideas of spatial transformers and multi-view convolutional networks with the efficiency of standard convolutional layers in two and three-dimensional dense g... | ['Kirill Mazur', 'Victor Lempitsky'] | 2020-07-22 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Mazur_Cloud_Transformers_A_Universal_Approach_to_Point_Cloud_Processing_Tasks_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Mazur_Cloud_Transformers_A_Universal_Approach_to_Point_Cloud_Processing_Tasks_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-reconstruction'] | ['computer-vision'] | [-7.58156478e-02 -2.51642615e-01 3.32212806e-01 -2.70474255e-01
-7.47293234e-01 -5.42189896e-01 7.78927863e-01 1.74335297e-02
-2.35947505e-01 4.01632190e-01 -1.89773008e-01 3.55250686e-02
1.87290028e-01 -1.33743286e+00 -1.12422442e+00 -6.03052378e-01
-9.55402479e-02 7.62186706e-01 3.64674121e-01 -2.56456882... | [8.048955917358398, -3.688333034515381] |
24fcdd6f-7c96-41b2-8ea9-e5202dc842ba | physics-informed-invertible-neural-network | 2306.17396 | null | https://arxiv.org/abs/2306.17396v1 | https://arxiv.org/pdf/2306.17396v1.pdf | Physics-informed invertible neural network for the Koopman operator learning | In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions. However, manually selecting observable functions that span the invariant subspace of the Koopman operator based on prior knowledge is inefficient and challenging, part... | ['Yue Qiu', 'Jianguo Huang', 'Yuhuang Meng'] | 2023-06-30 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 2.28707373e-01 3.98852192e-02 -3.04355383e-01 3.80313933e-01
-2.07657784e-01 -7.76693285e-01 4.28187668e-01 -6.60866559e-01
1.14719108e-01 7.30439186e-01 1.57989934e-01 -7.75779188e-01
-4.64000642e-01 -2.73111641e-01 -5.10271847e-01 -7.87197292e-01
-2.81305313e-01 -3.53157409e-02 -5.60464501e-01 -1.89582229... | [6.493125915527344, 3.452052116394043] |
635cb650-952e-46d3-a07e-437a3651297b | composition-and-deformance-measuring | 2306.03168 | null | https://arxiv.org/abs/2306.03168v1 | https://arxiv.org/pdf/2306.03168v1.pdf | Composition and Deformance: Measuring Imageability with a Text-to-Image Model | Although psycholinguists and psychologists have long studied the tendency of linguistic strings to evoke mental images in hearers or readers, most computational studies have applied this concept of imageability only to isolated words. Using recent developments in text-to-image generation models, such as DALLE mini, we ... | ['David A. Smith', 'Si Wu'] | 2023-06-05 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 7.07154274e-01 4.70763981e-01 3.79762322e-01 -4.83994395e-01
-5.79854548e-01 -7.73909926e-01 1.22018027e+00 1.68596953e-01
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4.51572210e-01 4.16157514e-01 1.10024989e-01 -2.38176629... | [11.195024490356445, 1.1612523794174194] |
ea85cf75-f667-40e6-a74d-7119e79da2c6 | multi-camera-calibration-free-bev | 2210.17252 | null | https://arxiv.org/abs/2210.17252v1 | https://arxiv.org/pdf/2210.17252v1.pdf | Multi-Camera Calibration Free BEV Representation for 3D Object Detection | In advanced paradigms of autonomous driving, learning Bird's Eye View (BEV) representation from surrounding views is crucial for multi-task framework. However, existing methods based on depth estimation or camera-driven attention are not stable to obtain transformation under noisy camera parameters, mainly with two cha... | ['Jihao Yin', 'Qian Zhang', 'Hongmei Zhu', 'Wenming Meng', 'Hongxiang Jiang'] | 2022-10-31 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 9.51612815e-02 -9.15054753e-02 9.11221188e-03 -4.02491540e-01
-7.72189736e-01 -6.79849029e-01 5.53514063e-01 -3.88838232e-01
-6.49831951e-01 4.45522428e-01 4.43859957e-02 -1.71014041e-01
1.84622258e-02 -7.48968363e-01 -1.12893331e+00 -6.00665510e-01
7.53614187e-01 8.69607553e-02 5.54051459e-01 -4.06771034... | [8.36109447479248, -2.3078646659851074] |
850f3ea2-fb61-4a00-bc03-596fdb1ebfb1 | probabilistic-surface-friction-estimation | 2010.08277 | null | https://arxiv.org/abs/2010.08277v3 | https://arxiv.org/pdf/2010.08277v3.pdf | Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements | Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition. Surface properties like friction are however difficult to estimate, as visual observation of the object does not convey enough information over these properties. In contrast, haptic ex... | ['Ville Kyrki', 'Fares J. Abu-Dakka', 'Francesco Verdoja', 'Tran Nguyen Le'] | 2020-10-16 | null | null | null | null | ['material-recognition'] | ['computer-vision'] | [ 8.54072198e-02 3.88312489e-02 -2.14587003e-01 1.30736992e-01
-2.29407489e-01 -6.23009980e-01 2.47803286e-01 4.91690576e-01
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-5.16714156e-01 -7.72898674e-01 -7.76545107e-01 -5.64503491e-01
-4.25779432e-01 4.75329459e-01 4.33314800e-01 -2.51473427... | [5.880251407623291, -0.8866768479347229] |
4bea048c-0565-418e-9439-c9b0cbbf5758 | momentum-contrast-for-unsupervised-visual | 1911.05722 | null | https://arxiv.org/abs/1911.05722v3 | https://arxiv.org/pdf/1911.05722v3.pdf | Momentum Contrast for Unsupervised Visual Representation Learning | We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive... | ['Yuxin Wu', 'Haoqi Fan', 'Saining Xie', 'Ross Girshick', 'Kaiming He'] | 2019-11-13 | momentum-contrast-for-unsupervised-visual-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 7.49597102e-02 2.17218939e-02 -6.03529871e-01 -1.17587194e-01
-5.20536959e-01 -4.90256011e-01 8.46209764e-01 1.73381656e-01
-8.13499510e-01 5.04466653e-01 9.18125734e-02 -1.61439851e-01
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-5.80982938e-02 5.38742185e-01 3.55749540e-02 -2.25061804... | [9.447884559631348, 2.401327610015869] |
66e5cfd9-76a9-4b50-a254-0b74e7ddfb04 | unsupervised-cross-modal-alignment-of-speech | 1805.07467 | null | http://arxiv.org/abs/1805.07467v2 | http://arxiv.org/pdf/1805.07467v2.pdf | Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces | Recent research has shown that word embedding spaces learned from text
corpora of different languages can be aligned without any parallel data
supervision. Inspired by the success in unsupervised cross-lingual word
embeddings, in this paper we target learning a cross-modal alignment between
the embedding spaces of spee... | ['James Glass', 'Wei-Hung Weng', 'Yu-An Chung', 'Schrasing Tong'] | 2018-05-18 | unsupervised-cross-modal-alignment-of-speech-1 | http://papers.nips.cc/paper/7965-unsupervised-cross-modal-alignment-of-speech-and-text-embedding-spaces | http://papers.nips.cc/paper/7965-unsupervised-cross-modal-alignment-of-speech-and-text-embedding-spaces.pdf | neurips-2018-12 | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.84243935e-01 3.65615785e-01 -9.98143330e-02 -5.31053066e-01
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2.12986618e-01 6.88784540e-01 -1.41066983e-01 -4.01576728... | [14.40589427947998, 7.060170650482178] |
e5c583c4-f6bf-4ce4-a568-ac3cad092938 | further-improving-weakly-supervised-object | 2301.01060 | null | https://arxiv.org/abs/2301.01060v1 | https://arxiv.org/pdf/2301.01060v1.pdf | Further Improving Weakly-supervised Object Localization via Causal Knowledge Distillation | Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole obje... | ['Jun Xiao', 'Yi Yang', 'Fei Gao', 'Qiyi Li', 'Shengjian Wu', 'Yawei Luo', 'Feifei Shao'] | 2023-01-03 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 1.93236217e-01 5.04892282e-02 -2.86897361e-01 -3.01071793e-01
-3.24460834e-01 -4.85479861e-01 6.20467067e-01 2.34709322e-01
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2.08271608e-01 1.22776158e-01 3.30540001e-01 1.00635402... | [9.775738716125488, 1.5081939697265625] |
ba25ebf1-5883-4ff4-88ef-1ddb2277f2e2 | lexical-simplification-with-the-deep | null | null | https://aclanthology.org/I17-2073 | https://aclanthology.org/I17-2073.pdf | Lexical Simplification with the Deep Structured Similarity Model | We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used f... | ['John Lee', 'Xiaodong Liu', 'Lis Pereira'] | 2017-11-01 | lexical-simplification-with-the-deep-1 | https://aclanthology.org/I17-2073 | https://aclanthology.org/I17-2073.pdf | ijcnlp-2017-11 | ['learning-word-embeddings'] | ['methodology'] | [ 2.52550751e-01 -3.12892497e-02 -6.57482028e-01 -5.91366112e-01
-8.19623709e-01 -1.64391577e-01 7.85394549e-01 6.56519651e-01
-9.21193600e-01 4.35821414e-01 7.66034067e-01 -6.04571290e-02
-3.00220251e-01 -5.56022644e-01 -5.74794948e-01 6.19508743e-01
2.53784090e-01 1.29020977e+00 4.93471295e-01 -1.10009348... | [10.925140380859375, 10.33533763885498] |
b5e1676e-f1e9-4257-862f-5d91acc00489 | post-training-quantization-on-diffusion | 2211.15736 | null | https://arxiv.org/abs/2211.15736v3 | https://arxiv.org/pdf/2211.15736v3.pdf | Post-training Quantization on Diffusion Models | Denoising diffusion (score-based) generative models have recently achieved significant accomplishments in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise. Unfortunately, the generati... | ['Yan Yan', 'Bingzhe Wu', 'Bin Xie', 'Zhihang Yuan', 'Yuzhang Shang'] | 2022-11-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shang_Post-Training_Quantization_on_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shang_Post-Training_Quantization_on_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['noise-estimation'] | ['medical'] | [ 2.55684555e-01 -5.35517693e-01 -1.61497459e-01 -1.09198533e-01
-1.04994392e+00 -4.38350320e-01 4.35452193e-01 -2.38094747e-01
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-1.45466208e-01 -9.59455431e-01 -5.17689645e-01 -9.35892999e-01
2.54336923e-01 2.19213411e-01 2.68452764e-02 -1.98539227... | [11.248915672302246, -0.49389275908470154] |
ff29cb22-346b-417c-bd9e-70559cbbbd1e | geometric-signatures-of-switching-behavior-in | 2209.03324 | null | https://arxiv.org/abs/2209.03324v3 | https://arxiv.org/pdf/2209.03324v3.pdf | Geometric Signatures of Switching Behavior in Mechanobiology | The proteins involved in cells' mechanobiological processes have evolved specialized and surprising responses to applied forces. Biochemical transformations that show catch-to-slip switching and force-induced pathway switching serve important functions in cell adhesion, mechano-sensing and signaling, and protein foldin... | ['Robijn F. Bruinsma', 'Casey O. Barkan'] | 2022-09-07 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 5.43486476e-01 -2.22878337e-01 -7.33140349e-01 -1.46640524e-01
-3.41824561e-01 -7.54585803e-01 4.23796475e-01 3.51428688e-01
-1.67313918e-01 1.04628980e+00 -3.04288417e-02 -5.70771873e-01
-2.54227221e-01 -8.41234744e-01 -8.43971193e-01 -1.03007543e+00
-6.38163924e-01 4.05663192e-01 4.87713695e-01 -6.60188019... | [4.861719131469727, 5.186827659606934] |
5da06e85-ba96-4b34-9069-b4f1fc11ef40 | learning-multi-agent-intention-aware | 2307.03119 | null | https://arxiv.org/abs/2307.03119v1 | https://arxiv.org/pdf/2307.03119v1.pdf | Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance | Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets. Recent advance in model-free reinforcement learning (RL) provides a data-driven solution to the order execution problem. However, the existing works always... | ['Tie-Yan Liu', 'Yong Yu', 'Weinan Zhang', 'Dongsheng Li', 'Jiang Bian', 'Li Zhao', 'Weiqing Liu', 'Kan Ren', 'Zhenggang Tang', 'Yuchen Fang'] | 2023-07-06 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-2.61217296e-01 2.29762256e-01 -4.24486965e-01 -1.75470158e-01
-5.76488197e-01 -6.84190750e-01 4.88614768e-01 8.45579356e-02
-5.06205201e-01 1.04410434e+00 -9.58924443e-02 -1.67648554e-01
-5.25160491e-01 -8.97280335e-01 -6.09374583e-01 -9.09785807e-01
-4.55975652e-01 1.08034039e+00 -3.02470196e-02 -2.77527630... | [4.354043483734131, 3.6243700981140137] |
ecc8c2d1-c243-42c2-8b01-8e88727e6f01 | avsegformer-audio-visual-segmentation-with | 2307.01146 | null | https://arxiv.org/abs/2307.01146v2 | https://arxiv.org/pdf/2307.01146v2.pdf | AVSegFormer: Audio-Visual Segmentation with Transformer | The combination of audio and vision has long been a topic of interest in the multi-modal community. Recently, a new audio-visual segmentation (AVS) task has been introduced, aiming to locate and segment the sounding objects in a given video. This task demands audio-driven pixel-level scene understanding for the first t... | ['Tong Lu', 'Wenhai Wang', 'Guo Chen', 'Zhe Chen', 'Shengyi Gao'] | 2023-07-03 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 2.69522399e-01 -1.78004488e-01 -5.49213924e-02 -3.64061683e-01
-1.08984077e+00 -4.73885506e-01 2.88834870e-01 2.52577700e-02
-2.26048335e-01 1.51929989e-01 2.64612347e-01 9.67434868e-02
2.56432414e-01 -4.45964605e-01 -8.04050922e-01 -6.10533893e-01
1.69475913e-01 3.13360281e-02 6.63212478e-01 7.66492710... | [14.802176475524902, 4.744424819946289] |
88450bc7-a741-4d30-95bb-06ba5c725c57 | cleme-debiasing-multi-reference-evaluation | 2305.10819 | null | https://arxiv.org/abs/2305.10819v1 | https://arxiv.org/pdf/2305.10819v1.pdf | CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction | It is intractable to evaluate the performance of Grammatical Error Correction (GEC) systems since GEC is a highly subjective task. Designing an evaluation metric that is as objective as possible is crucial to the development of GEC task. Previous mainstream evaluation metrics, i.e., reference-based metrics, introduce b... | ['Ying Shen', 'Hai-Tao Zheng', 'Shirong Ma', 'Yangning Li', 'Qingyu Zhou', 'Yinghui Li', 'Jingheng Ye'] | 2023-05-18 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 6.82787597e-02 -9.73638073e-02 3.63683164e-01 -5.70534945e-01
-1.15689063e+00 -5.31821489e-01 2.14389384e-01 6.76363349e-01
-4.89272147e-01 7.79939175e-01 1.88834593e-01 -2.19858959e-01
-1.79651678e-01 -5.84885180e-01 -6.72956944e-01 -2.66098559e-01
2.81088263e-01 3.11728120e-01 3.13653618e-01 -3.24665636... | [11.071444511413574, 10.72245979309082] |
ab99e938-6c2c-46da-b973-7d8b1a2de4e4 | measuring-and-mitigating-constraint | 2305.15338 | null | https://arxiv.org/abs/2305.15338v1 | https://arxiv.org/pdf/2305.15338v1.pdf | Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing | In executable task-oriented semantic parsing, the system aims to translate users' utterances in natural language to machine-interpretable programs (API calls) that can be executed according to pre-defined API specifications. With the popularity of Large Language Models (LLMs), in-context learning offers a strong baseli... | ['Yi Zhang', 'Nikolaos Pappas', 'James Gung', 'Sailik Sengupta', 'Sebastien Jean', 'Shufan Wang'] | 2023-05-24 | null | null | null | null | ['semantic-retrieval', 'semantic-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.39601409e-01 2.31824726e-01 -2.47377679e-01 -6.01724803e-01
-1.20256782e+00 -8.86027038e-01 7.41576850e-01 -7.64172059e-03
-1.24494918e-01 2.82582879e-01 3.69304717e-01 -6.53436244e-01
6.74603134e-02 -4.23470438e-01 -9.45997000e-01 2.79595125e-02
9.03241038e-02 4.57794815e-01 2.01118633e-01 -6.53878897... | [10.563557624816895, 8.14789867401123] |
ae01aef6-d2d8-45d9-acd5-a8c83cf1719c | representation-online-matters-practical-end | 2305.15534 | null | https://arxiv.org/abs/2305.15534v2 | https://arxiv.org/pdf/2305.15534v2.pdf | Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems | As the use of online platforms continues to grow across all demographics, users often express a desire to feel represented in the content. To improve representation in search results and recommendations, we introduce end-to-end diversification, ensuring that diverse content flows throughout the various stages of these ... | ['Nadia Fawaz', 'Ashudeep Singh', 'Shloka Desai', 'Bhawna Juneja', 'Pedro Silva'] | 2023-05-24 | null | null | null | null | ['point-processes'] | ['methodology'] | [-3.71264279e-01 -3.51573139e-01 -4.61061388e-01 -2.54843026e-01
-7.68172920e-01 -1.09572875e+00 2.58064151e-01 2.17201009e-01
1.34297967e-01 3.18194449e-01 6.53603911e-01 -2.57053673e-01
-5.99699914e-01 -7.12521374e-01 -2.17526332e-01 -1.55521750e-01
1.00792609e-01 5.98149955e-01 -4.31475453e-02 -6.62382245... | [9.948929786682129, 5.756075382232666] |
0b8c2e67-732b-4bb8-9bef-503740eb12b0 | mlengineer-at-semeval-2020-task-7-bert-flair | null | null | https://aclanthology.org/2020.semeval-1.136 | https://aclanthology.org/2020.semeval-1.136.pdf | MLEngineer at SemEval-2020 Task 7: BERT-Flair Based Humor Detection Model (BFHumor) | Task 7, Assessing the Funniness of Edited News Headlines, in the International Workshop SemEval2020 introduces two sub-tasks to predict the funniness values of edited news headlines from the Reddit website. This paper proposes the BFHumor model of the MLEngineer team that participates in both sub-tasks in this competit... | ['Mahmoud Hammad', 'Malak Abdullah', 'Fara Shatnawi'] | 2020-12-01 | null | null | null | semeval-2020 | ['humor-detection'] | ['natural-language-processing'] | [-4.04960603e-01 5.93744934e-01 4.03587632e-02 1.30079299e-01
-6.32885158e-01 -3.30285132e-01 1.15467978e+00 3.60833794e-01
-5.13747215e-01 6.69802487e-01 9.12207723e-01 -2.58713663e-01
1.17128007e-01 -6.12154245e-01 -6.57171369e-01 -1.49772286e-01
1.97582111e-01 2.01553851e-01 1.23881297e-02 -7.43475974... | [8.848548889160156, 11.035077095031738] |
13dc19d5-692f-4d62-83be-c850060871de | leveraging-just-a-few-keywords-for-fine | 1909.00415 | null | https://arxiv.org/abs/1909.00415v1 | https://arxiv.org/pdf/1909.00415v1.pdf | Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training | User-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be useful for both users and downstream opinion mining applications. Current supervised... | ['Luis Gravano', 'Giannis Karamanolakis', 'Daniel Hsu'] | 2019-09-01 | leveraging-just-a-few-keywords-for-fine-1 | https://aclanthology.org/D19-1468 | https://aclanthology.org/D19-1468.pdf | ijcnlp-2019-11 | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.54931620e-01 2.66806841e-01 -7.49859035e-01 -6.14142716e-01
-1.15474606e+00 -9.60055232e-01 6.83164775e-01 7.41826296e-01
-3.11743587e-01 5.35063386e-01 1.36663184e-01 -4.81402695e-01
3.08021575e-01 -9.69744265e-01 -6.25868738e-01 -5.78928590e-01
2.15154946e-01 4.09645081e-01 1.10708542e-01 -2.16560751... | [11.36135196685791, 6.697432994842529] |
1fe2cb3a-b1b7-4a70-9d91-763de8f2add8 | speaker-and-language-change-detection-using | 2302.09381 | null | https://arxiv.org/abs/2302.09381v1 | https://arxiv.org/pdf/2302.09381v1.pdf | Speaker and Language Change Detection using Wav2vec2 and Whisper | We investigate recent transformer networks pre-trained for automatic speech recognition for their ability to detect speaker and language changes in speech. We do this by simply adding speaker (change) or language targets to the labels. For Wav2vec2 pre-trained networks, we also investigate if the representation for the... | ['David A. van Leeuwen', 'Nik Vaessen', 'Tijn Berns'] | 2023-02-18 | null | null | null | null | ['change-detection', 'speaker-recognition'] | ['computer-vision', 'speech'] | [ 3.81544620e-01 4.22824085e-01 1.71439111e-01 -8.02530050e-01
-7.55001664e-01 -7.41739094e-01 1.02204382e+00 -1.15635373e-01
-4.40964907e-01 5.15148520e-01 3.83096188e-01 -7.38349378e-01
4.62087184e-01 -2.49420226e-01 -4.82201427e-01 -5.09016037e-01
-3.01010877e-01 5.26175261e-01 3.37413520e-01 -3.28168273... | [14.381579399108887, 6.394554138183594] |
371a431c-7ee2-4ebc-94a1-ed0e4dae491f | predicting-privacy-preferences-for-smart | 2302.10650 | null | https://arxiv.org/abs/2302.10650v1 | https://arxiv.org/pdf/2302.10650v1.pdf | Predicting Privacy Preferences for Smart Devices as Norms | Smart devices, such as smart speakers, are becoming ubiquitous, and users expect these devices to act in accordance with their preferences. In particular, since these devices gather and manage personal data, users expect them to adhere to their privacy preferences. However, the current approach of gathering these prefe... | ['Michael Luck', 'Natalia Criado', 'William Seymour', 'Marc Serramia'] | 2023-02-21 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 3.20088089e-01 4.69074965e-01 -3.01740915e-01 -1.32009745e+00
-6.02030993e-01 -8.19794357e-01 2.28092417e-01 1.26857176e-01
-3.76687020e-01 6.94515526e-01 8.64942491e-01 -3.45367461e-01
-1.44387797e-01 -6.92725360e-01 7.69927502e-02 -2.36181989e-01
2.72657365e-01 3.54190230e-01 1.07480409e-02 -1.13768548... | [12.304174423217773, 7.625743865966797] |
0de2815f-eab8-43f2-a98e-e9df489febb4 | legal-and-political-stance-detection-of | 2211.11724 | null | https://arxiv.org/abs/2211.11724v1 | https://arxiv.org/pdf/2211.11724v1.pdf | Legal and Political Stance Detection of SCOTUS Language | We analyze publicly available US Supreme Court documents using automated stance detection. In the first phase of our work, we investigate the extent to which the Court's public-facing language is political. We propose and calculate two distinct ideology metrics of SCOTUS justices using oral argument transcripts. We the... | ['Kathleen McKeown', 'Emily Allaway', 'Noah Bergam'] | 2022-11-21 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 9.67664346e-02 4.40460503e-01 -6.07575297e-01 -6.54975712e-01
-1.09501791e+00 -1.28603220e+00 1.27727878e+00 6.36644959e-01
-7.35060096e-01 8.93989682e-01 1.32613671e+00 -1.12510133e+00
1.61960442e-02 -8.17209780e-01 -4.29816157e-01 -2.68754840e-01
8.87923777e-01 7.07245767e-01 1.25672981e-01 -6.84095979... | [9.032991409301758, 9.93613052368164] |
53ede84e-9a9d-4110-86bf-13bfd4c4d71e | realgait-gait-recognition-for-person-re | 2201.04806 | null | https://arxiv.org/abs/2201.04806v2 | https://arxiv.org/pdf/2201.04806v2.pdf | RealGait: Gait Recognition for Person Re-Identification | Human gait is considered a unique biometric identifier which can be acquired in a covert manner at a distance. However, models trained on existing public domain gait datasets which are captured in controlled scenarios lead to drastic performance decline when applied to real-world unconstrained gait data. On the other h... | ['Anil K. Jain', 'Annan Li', 'Tianrui Chai', 'Yunhong Wang', 'Shaoxiong Zhang'] | 2022-01-13 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.37717763e-01 -7.17873573e-01 -1.61229432e-01 -2.71378756e-01
-2.33667940e-01 -4.59365308e-01 4.70432699e-01 -2.59362042e-01
-5.96781671e-01 7.91716456e-01 2.09574044e-01 2.24291176e-01
1.60146996e-01 -7.13887811e-01 -4.06514287e-01 -7.14238524e-01
1.05608232e-01 5.43734908e-01 8.18980560e-02 -1.21621132... | [14.27123737335205, 1.3794318437576294] |
3fd8b58c-f9fa-4d06-b2a7-574282442ef7 | consistent-multi-granular-rationale | 2305.09400 | null | https://arxiv.org/abs/2305.09400v1 | https://arxiv.org/pdf/2305.09400v1.pdf | Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification | The success of deep learning models on multi-hop fact verification has prompted researchers to understand the behavior behind their veracity. One possible way is erasure search: obtaining the rationale by entirely removing a subset of input without compromising the veracity prediction. Although extensively explored, ex... | ['Deyu Zhou', 'Yingjie Zhu', 'Jiasheng Si'] | 2023-05-16 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 2.62314022e-01 4.82910812e-01 -6.59861147e-01 -4.39062923e-01
-9.48577762e-01 -2.49475956e-01 5.16878843e-01 3.18749934e-01
3.23314905e-01 9.31928754e-01 6.41892254e-01 -3.55456144e-01
-1.70594931e-01 -5.17544806e-01 -6.84311330e-01 -4.93765473e-01
2.00794697e-01 -1.10884428e-01 -5.28534770e-01 1.77208949... | [9.162102699279785, 7.83399772644043] |
e667fc62-9171-4085-b363-e189e617e505 | adaptive-hybrid-activation-function-for-deep | null | null | https://doi.org/10.20535/SRIT.2308-8893.2022.1.07 | http://journal.iasa.kpi.ua/article/download/259203/255848/596453 | Adaptive hybrid activation function for deep neural networks | The adaptive hybrid activation function (AHAF) is proposed that combines the properties of the rectifier units and the squashing functions. The proposed function can be used as a drop-in replacement for ReLU, SiL and Swish activations for deep neural networks and can evolve to one of such functions during the training.... | ['Serhii Kostiuk', 'Yevgeniy Bodyanskiy'] | 2022-04-25 | null | null | null | system-research-and-information-technologies | ['activation-function-synthesis', 'architecture-search'] | ['methodology', 'methodology'] | [-1.03910547e-02 1.54131085e-01 -6.64058402e-02 -5.12076735e-01
2.18546063e-01 -2.69959778e-01 6.43287241e-01 -2.13217899e-01
-9.56390381e-01 8.56731236e-01 -1.97064295e-01 -4.23918605e-01
-5.39095819e-01 -5.95036685e-01 -4.34279621e-01 -9.62438047e-01
8.37448090e-02 3.00020874e-01 3.06705952e-01 -3.97525162... | [8.453715324401855, 3.0694262981414795] |
f15c2cf6-5455-4270-8f4f-b544f34c15b5 | a-stochastic-lqr-model-for-child-order | 2004.13797 | null | https://arxiv.org/abs/2004.13797v1 | https://arxiv.org/pdf/2004.13797v1.pdf | A Stochastic LQR Model for Child Order Placement in Algorithmic Trading | Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given time horizon. Few models ... | ['Jackie Jianhong Shen'] | 2020-04-28 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-4.93507534e-01 1.09375611e-01 -3.52602482e-01 7.34572336e-02
-3.08012187e-01 -1.20350289e+00 3.36731374e-01 1.47691742e-01
-1.40190095e-01 6.96912289e-01 -2.65886843e-01 -4.19441581e-01
-5.69570184e-01 -6.22620463e-01 -7.17595100e-01 -5.88629603e-01
-9.62484628e-02 1.11654413e+00 3.65073271e-02 -2.25798309... | [4.8195271492004395, 3.9224586486816406] |
0121220e-2e5a-49be-b62d-fc29ecd90001 | stock-price-prediction-using-convolutional | 2001.09769 | null | https://arxiv.org/abs/2001.09769v1 | https://arxiv.org/pdf/2001.09769v1.pdf | Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries | Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four ... | ['Sidra Mehtab', 'Jaydip Sen'] | 2020-01-10 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.22005105e-01 -3.54024470e-01 -2.91737944e-01 -3.81106198e-01
-2.01582983e-01 -4.67482001e-01 6.99919045e-01 -1.26810193e-01
-3.13797683e-01 9.52381849e-01 1.18547700e-01 -8.73278916e-01
-2.57410258e-01 -1.37705028e+00 -4.87728000e-01 -6.03562713e-01
-5.26740015e-01 4.16627973e-01 1.04565285e-01 -7.14692414... | [4.458344459533691, 4.227615833282471] |
ef1c940f-7ddd-4df4-b882-aa5af70deac4 | idms-instance-depth-for-multi-scale-monocular | 2212.01528 | null | https://arxiv.org/abs/2212.01528v2 | https://arxiv.org/pdf/2212.01528v2.pdf | IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection | Due to the lack of depth information of images and poor detection accuracy in monocular 3D object detection, we proposed the instance depth for multi-scale monocular 3D object detection method. Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilat... | ['Weijie Wu', 'Weibing Qiu', 'Liqiang Zhu', 'Chao Hu'] | 2022-12-03 | null | null | null | null | ['monocular-3d-object-detection', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [-1.71109438e-01 -5.13823748e-01 1.35382310e-01 -6.28169402e-02
-2.16015607e-01 -3.54371279e-01 4.14634466e-01 -3.44659656e-01
-7.54097223e-01 1.91921815e-01 -2.01881185e-01 -1.86637878e-01
2.56076723e-01 -7.23234117e-01 -4.72533494e-01 -7.79329062e-01
-6.07218109e-02 5.54595478e-02 1.04243267e+00 -6.30846061... | [7.993961811065674, -2.1741912364959717] |
1b650d7c-061f-41c3-9ee1-885f49ecd3bf | fedmix-mixed-supervised-federated-learning | 2205.01840 | null | https://arxiv.org/abs/2205.01840v1 | https://arxiv.org/pdf/2205.01840v1.pdf | FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation | The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that the training set for each local client is annotated in a similar fashio... | ['Kwang-Ting Cheng', 'Xin Yang', 'Huimin Wu', 'Xijie Huang', 'Dong Zhang', 'Zengqiang Yan', 'Jeffry Wicaksana'] | 2022-05-04 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 2.68647462e-01 3.29712331e-01 -7.67781198e-01 -6.96584821e-01
-8.92434299e-01 -4.10745323e-01 1.75884128e-01 8.87313336e-02
-3.67059231e-01 4.77514952e-01 -1.48801908e-01 -1.82941109e-01
-5.02304127e-03 -6.75696552e-01 -5.80884218e-01 -1.05939281e+00
2.72050679e-01 5.60418725e-01 2.63525933e-01 4.02648598... | [6.0248589515686035, 6.442208290100098] |
71428bbe-b36c-4d38-b33b-82061ad1282b | multi-perspective-semantic-information | 2008.01526 | null | https://arxiv.org/abs/2008.01526v1 | https://arxiv.org/pdf/2008.01526v1.pdf | Multi-Perspective Semantic Information Retrieval in the Biomedical Domain | Information Retrieval (IR) is the task of obtaining pieces of data (such as documents) that are relevant to a particular query or need from a large repository of information. IR is a valuable component of several downstream Natural Language Processing (NLP) tasks. Practically, IR is at the heart of many widely-used tec... | ['Samarth Rawal'] | 2020-07-17 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 6.65799379e-01 2.01444164e-01 -4.61366773e-01 -4.18000847e-01
-1.56684077e+00 -3.44478935e-01 3.93926173e-01 9.41133082e-01
-7.02100158e-01 7.52693117e-01 7.87743092e-01 -2.59147078e-01
-5.76552212e-01 -4.25545126e-01 -3.09549540e-01 -4.57812607e-01
8.94831419e-02 7.45641470e-01 -7.82016292e-02 -6.22135162... | [8.63451862335205, 8.688395500183105] |
483f9123-b8db-499b-b6c0-765eed7763f0 | understanding-model-complexity-for-temporal | 2303.07925 | null | https://arxiv.org/abs/2303.07925v6 | https://arxiv.org/pdf/2303.07925v6.pdf | Robust incremental learning pipelines for temporal tabular datasets with distribution shifts | In this paper, we present a robust deep incremental learning model for regression tasks on financial temporal tabular datasets. Using commonly available tabular and time-series prediction models as building blocks, a machine-learning model is built incrementally to adapt to distributional shifts in data. Using the conc... | ['Mauricio Barahona', 'Thomas Wong'] | 2023-03-14 | null | null | null | null | ['feature-engineering', 'time-series-prediction'] | ['methodology', 'time-series'] | [-3.45224738e-01 -2.52846152e-01 -4.57227081e-01 -6.47067189e-01
-5.87229073e-01 -7.45130956e-01 7.93399870e-01 2.92935997e-01
-1.85273990e-01 7.17334867e-01 -8.81172493e-02 -7.41390705e-01
-5.14488757e-01 -8.04412246e-01 -8.42695057e-01 -5.39273202e-01
-5.68789184e-01 7.59571671e-01 4.89055589e-02 -3.86164993... | [7.0923309326171875, 3.299100160598755] |
58b68c92-8271-4225-a8ad-8f7a04f5eca9 | defending-against-adversarial-attacks-by-3 | 1909.06137 | null | https://arxiv.org/abs/1909.06137v1 | https://arxiv.org/pdf/1909.06137v1.pdf | Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix | We propose a scheme for defending against adversarial attacks by suppressing the largest eigenvalue of the Fisher information matrix (FIM). Our starting point is one explanation on the rationale of adversarial examples. Based on the idea of the difference between a benign sample and its adversarial example is measured ... | ['Chaomin Shen', 'Yaxin Peng', 'Jinsong Fan', 'Guixu Zhang'] | 2019-09-13 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.31276205e-01 4.03613657e-01 2.70023972e-01 -1.09690920e-01
-4.81430180e-02 -9.56183314e-01 4.64001864e-01 -3.73610914e-01
-6.68942153e-01 6.60654426e-01 -3.52312028e-01 -5.69996655e-01
-1.63774326e-01 -9.19989705e-01 -9.82901573e-01 -1.19193029e+00
-2.55539030e-01 -1.17354833e-01 3.94550145e-01 -5.12642682... | [5.638783931732178, 7.814046382904053] |
e5eb62bb-5e44-4e2c-b94e-be1b23a9c912 | utopia-unconstrained-tracking-objects-without | 2306.09613 | null | https://arxiv.org/abs/2306.09613v1 | https://arxiv.org/pdf/2306.09613v1.pdf | UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation | Multiple Object Tracking (MOT) aims to find bounding boxes and identities of targeted objects in consecutive video frames. While fully-supervised MOT methods have achieved high accuracy on existing datasets, they cannot generalize well on a newly obtained dataset or a new unseen domain. In this work, we first address t... | ['Khoa Luu', 'Bhiksha Raj', 'Samee U. Khan', 'John Gauch', 'Kha Gia Quach', 'Pha Nguyen'] | 2023-06-16 | null | null | null | null | ['object-tracking', 'multiple-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 2.71358311e-01 -3.57773989e-01 -2.56962031e-01 -2.37322375e-01
-4.54285532e-01 -4.59982574e-01 5.34953713e-01 -5.52251041e-02
-5.53639352e-01 1.09254754e+00 -2.62234449e-01 4.48093653e-01
-2.25005701e-01 -3.36481422e-01 -9.05802190e-01 -7.59569764e-01
-2.06546903e-01 9.09940004e-01 9.24396455e-01 3.49705573... | [6.385303974151611, -2.1037094593048096] |
996131cf-9699-49eb-ad91-335e14d65612 | improving-language-identification-for | 2001.11019 | null | https://arxiv.org/abs/2001.11019v1 | https://arxiv.org/pdf/2001.11019v1.pdf | Improving Language Identification for Multilingual Speakers | Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, however, is discrimination of languages for multilingual speakers, d... | ['Andrew Titus', 'Nanxin Chen', 'Jan Silovsky', 'Arnab Ghoshal', 'Roger Hsiao', 'Mary Young'] | 2020-01-29 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-2.50857323e-01 -3.64039630e-01 -3.44837494e-02 -5.86608887e-01
-1.38847899e+00 -1.06219256e+00 7.64247596e-01 -1.90749899e-01
-6.30472779e-01 4.74326670e-01 5.22277653e-01 -5.40740669e-01
5.16875565e-01 -2.81410874e-03 -7.90482908e-02 -4.11754221e-01
3.29525262e-01 6.75453544e-01 1.37890667e-01 -1.18861675... | [14.19935417175293, 6.628518104553223] |
98381987-01b4-4a55-82b0-a04affe763a7 | towards-good-practices-for-deep-3d-hand-pose | 1707.07248 | null | http://arxiv.org/abs/1707.07248v1 | http://arxiv.org/pdf/1707.07248v1.pdf | Towards Good Practices for Deep 3D Hand Pose Estimation | 3D hand pose estimation from single depth image is an important and
challenging problem for human-computer interaction. Recently deep convolutional
networks (ConvNet) with sophisticated design have been employed to address it,
but the improvement over traditional random forest based methods is not so
apparent. To explo... | ['Guijin Wang', 'Hengkai Guo', 'Cairong Zhang', 'Xinghao Chen'] | 2017-07-23 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [-2.50894636e-01 -2.14185923e-01 -3.18594903e-01 -2.95864314e-01
-5.40921092e-01 -2.73965508e-01 6.98133484e-02 -9.26958621e-01
-5.10696352e-01 7.06128538e-01 1.67659014e-01 1.38025478e-01
5.60282841e-02 -3.80427986e-01 -7.46240914e-01 -6.54522598e-01
-2.69479543e-01 6.75958812e-01 3.89583260e-01 -1.82395756... | [6.615572929382324, -0.6621056199073792] |
de4fe771-b516-4ecc-8f8f-17d76b50eac8 | estimating-treatment-effects-from-irregular-1 | 2303.02320 | null | https://arxiv.org/abs/2303.02320v1 | https://arxiv.org/pdf/2303.02320v1.pdf | Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders | Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc. Real-world time series can include large-scale, irregular, and intermittent time series observations, raising significant challen... | ['Yan Liu', 'Hao Niu', 'Chuizheng Meng', 'Xiangchen Song', 'Yujing Wang', 'James Enouen', 'Defu Cao'] | 2023-03-04 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 2.03678861e-01 -2.69633830e-01 -5.54087102e-01 -1.57285497e-01
-7.05875993e-01 -1.42506048e-01 1.86301142e-01 1.69438317e-01
-4.88491133e-02 1.17894006e+00 6.28535509e-01 -4.78563130e-01
-5.38111091e-01 -7.31744051e-01 -7.66871095e-01 -7.63716102e-01
-3.75431508e-01 1.16467141e-01 -4.67200875e-01 1.11713208... | [7.938438892364502, 5.299957275390625] |
7346ab2d-2157-4acf-b562-7a2e6e4f8eb0 | shuttleset-a-human-annotated-stroke-level | 2306.04948 | null | https://arxiv.org/abs/2306.04948v1 | https://arxiv.org/pdf/2306.04948v1.pdf | ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis | With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement. This is attributed to the availability of public ground-truth datasets. While there are a few available datasets for t... | ['Wen-Chih Peng', 'Tsi-Ui Ik', 'Yung-Chang Huang', 'Wei-Yao Wang'] | 2023-06-08 | null | null | null | null | ['sports-analytics', 'action-detection'] | ['computer-vision', 'computer-vision'] | [-1.75691187e-01 -1.43790483e-01 -5.77922761e-01 3.71428989e-02
-8.20171893e-01 -6.98843896e-01 3.40109527e-01 3.58624637e-01
-4.51780409e-01 3.56990904e-01 7.90865362e-01 -8.88204277e-02
-3.51353705e-01 -9.65053916e-01 -4.50399280e-01 -1.22776903e-01
-2.54858613e-01 5.74701190e-01 3.98771346e-01 -6.09208643... | [6.663517951965332, 0.33256813883781433] |
f53b6c74-0ca7-4871-bd4a-9633aff9ce7e | unifying-vision-and-language-tasks-via-text | 2102.02779 | null | https://arxiv.org/abs/2102.02779v2 | https://arxiv.org/pdf/2102.02779v2.pdf | Unifying Vision-and-Language Tasks via Text Generation | Existing methods for vision-and-language learning typically require designing task-specific architectures and objectives for each task. For example, a multi-label answer classifier for visual question answering, a region scorer for referring expression comprehension, and a language decoder for image captioning, etc. To... | ['Mohit Bansal', 'Hao Tan', 'Jie Lei', 'Jaemin Cho'] | 2021-02-04 | null | null | null | null | ['conditional-text-generation', 'visual-commonsense-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 3.06746960e-01 2.26171315e-01 -1.02149971e-01 -6.22149169e-01
-1.26335287e+00 -7.04546154e-01 7.68875778e-01 -6.93221688e-02
-3.45196396e-01 4.92171526e-01 2.80709416e-01 -5.26675522e-01
4.93664235e-01 -5.05867958e-01 -1.08292711e+00 -5.04051208e-01
7.27854788e-01 7.15575337e-01 1.17258122e-02 1.59794334... | [10.875555992126465, 1.6745952367782593] |
d1d73d63-1723-4124-aa0a-34b4dca97ff8 | spanbert-improving-pre-training-by | 1907.10529 | null | https://arxiv.org/abs/1907.10529v3 | https://arxiv.org/pdf/1907.10529v3.pdf | SpanBERT: Improving Pre-training by Representing and Predicting Spans | We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on ... | ['Omer Levy', 'Luke Zettlemoyer', 'Mandar Joshi', 'Danqi Chen', 'Yinhan Liu', 'Daniel S. Weld'] | 2019-07-24 | spanbert-improving-pre-training-by-1 | https://aclanthology.org/2020.tacl-1.5 | https://aclanthology.org/2020.tacl-1.5.pdf | tacl-2020-1 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 3.36957350e-02 7.32801795e-01 -7.02311039e-01 -7.01690018e-02
-1.67623508e+00 -8.44134390e-01 2.84470677e-01 2.50645429e-01
-2.92436659e-01 1.02515101e+00 6.95760489e-01 -1.48536652e-01
-8.68526846e-02 -5.75567901e-01 -6.39384806e-01 -5.88419177e-02
-7.56944120e-02 9.12249029e-01 4.10031259e-01 -3.15457791... | [9.364073753356934, 9.3938570022583] |
75930f4b-ae81-4133-988e-7babc2414ec5 | re-imagine-the-negative-prompt-algorithm | 2304.04968 | null | https://arxiv.org/abs/2304.04968v3 | https://arxiv.org/pdf/2304.04968v3.pdf | Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond | Although text-to-image diffusion models have made significant strides in generating images from text, they are sometimes more inclined to generate images like the data on which the model was trained rather than the provided text. This limitation has hindered their usage in both 2D and 3D applications. To address this p... | ['Mingyuan Zhou', 'Amir Sadeghian', 'Ali Sadeghian', 'Huangjie Zheng', 'Mohammadreza Armandpour'] | 2023-04-11 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.73712954e-01 3.31319481e-01 2.04672173e-01 -2.50012964e-01
-5.99686682e-01 -7.80956686e-01 9.68580425e-01 -4.97744739e-01
-1.42685607e-01 4.18510705e-01 3.95726115e-01 -5.47658563e-01
1.66323408e-01 -6.91834927e-01 -3.91471386e-01 -4.95901138e-01
3.87709051e-01 5.08042455e-01 1.43492356e-01 -1.93381280... | [11.287439346313477, -0.1668214201927185] |
c70d3ca1-196f-454b-bf58-e36414dc755e | a-novel-active-solution-for-two-dimensional | 2212.06958 | null | https://arxiv.org/abs/2212.06958v1 | https://arxiv.org/pdf/2212.06958v1.pdf | A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection | Identity authentication is the process of verifying one's identity. There are several identity authentication methods, among which biometric authentication is of utmost importance. Facial recognition is a sort of biometric authentication with various applications, such as unlocking mobile phones and accessing bank acco... | ['Matineh Pooshideh'] | 2022-12-14 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 4.27776128e-01 -5.06377161e-01 -7.56990984e-02 7.55925179e-02
-4.66977417e-01 -7.61493385e-01 5.79085052e-01 5.90843745e-02
-2.23408669e-01 2.58474797e-01 -3.43090415e-01 -4.95848626e-01
1.43513223e-02 -5.70427299e-01 -1.17488228e-01 -8.26374948e-01
-4.07065190e-02 -1.47740217e-02 1.99308507e-02 -2.30070487... | [13.168493270874023, 1.1412678956985474] |
cb53a284-e850-4a4b-9ecf-e901a69a2d57 | transient-hemodynamics-prediction-using-an | 2302.06557 | null | https://arxiv.org/abs/2302.06557v1 | https://arxiv.org/pdf/2302.06557v1.pdf | Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model | Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurately acquire high-resolution hemodynamic information that would be required to assess complex neurovascular pathologies. Therefore, computati... | ['Andreas Maier', 'Annette Birkhold', 'Markus Kowarschik', 'Laura Pfaff', 'Maximilian Rohleder', 'Mareike Thies', 'Fabian Wagner', 'Katharina Zinn', 'Noah Maul'] | 2023-02-13 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [-3.07215452e-01 -3.91726047e-01 5.75213194e-01 -7.13102743e-02
-3.97996515e-01 -2.60488629e-01 3.54141623e-01 5.08487821e-01
-6.85624361e-01 1.10865283e+00 -3.03590328e-01 -8.17190051e-01
-2.34042071e-02 -8.72746050e-01 -2.08577648e-01 -7.78334379e-01
-4.61185604e-01 8.18855107e-01 2.68664688e-01 2.79749278... | [6.403521537780762, 3.2640106678009033] |
db2307a8-39c6-4b1a-8c82-ad0cd4a9bf5e | predicting-human-activities-using-stochastic | 1708.00945 | null | http://arxiv.org/abs/1708.00945v1 | http://arxiv.org/pdf/1708.00945v1.pdf | Predicting Human Activities Using Stochastic Grammar | This paper presents a novel method to predict future human activities from
partially observed RGB-D videos. Human activity prediction is generally
difficult due to its non-Markovian property and the rich context between human
and environments.
We use a stochastic grammar model to capture the compositional structure o... | ['Song-Chun Zhu', 'Siyuan Qi', 'Siyuan Huang', 'Ping Wei'] | 2017-08-02 | predicting-human-activities-using-stochastic-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Qi_Predicting_Human_Activities_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Qi_Predicting_Human_Activities_ICCV_2017_paper.pdf | iccv-2017-10 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.75641793e-01 1.05913363e-01 -7.79274479e-02 -6.40829861e-01
-2.80764382e-02 -4.69709635e-01 6.98004484e-01 5.58691733e-02
4.11177753e-03 5.49420893e-01 7.52639711e-01 -1.66087225e-02
-4.12456580e-02 -7.51178086e-01 -6.23707294e-01 -2.50104845e-01
-5.41992605e-01 2.85540104e-01 8.11798096e-01 2.20899269... | [8.051088333129883, 0.5777020454406738] |
c7accbf7-0035-4d4c-95aa-fb5d301ad619 | adaptivepaste-code-adaptation-through | 2205.11023 | null | https://arxiv.org/abs/2205.11023v2 | https://arxiv.org/pdf/2205.11023v2.pdf | AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations | In software development, it is common for programmers to copy-paste or port code snippets and then adapt them to their use case. This scenario motivates the code adaptation task -- a variant of program repair which aims to adapt variable identifiers in a pasted snippet of code to the surrounding, preexisting source cod... | ['Alexey Svyatkovskiy', 'Miltiadis Allamanis', 'Neel Sundaresan', 'Jinu Jang', 'Xiaoyu Liu'] | 2022-05-23 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.06137209e-01 1.71612516e-01 -3.01056743e-01 -5.11856735e-01
-6.47324324e-01 -6.36278987e-01 -1.14979044e-01 4.91770446e-01
-6.71680197e-02 2.57011026e-01 6.87655658e-02 -6.89224601e-01
2.29656965e-01 -4.41510439e-01 -9.02316630e-01 1.92144543e-01
-7.56949335e-02 -2.40399599e-01 3.49070489e-01 -1.00156277... | [7.663318157196045, 7.763908863067627] |
a6ce2860-937c-4dca-90c7-210f4d624bab | meta-learning-to-detect-rare-objects | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf | Meta-Learning to Detect Rare Objects | Few-shot learning, i.e., learning novel concepts from few examples, is fundamental to practical visual recognition systems. While most of existing work has focused on few-shot classification, we make a step towards few-shot object detection, a more challenging yet under-explored task. We develop a conceptually simple b... | [' Martial Hebert', ' Deva Ramanan', 'Yu-Xiong Wang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['novel-concepts'] | ['reasoning'] | [ 3.65584940e-01 -3.51117343e-01 -2.98355639e-01 -3.60317141e-01
-1.01345432e+00 -3.37674618e-01 8.99617851e-01 2.87561957e-02
-2.66241223e-01 2.90378928e-01 4.81171981e-02 7.18753338e-02
-1.61156468e-02 -5.09971440e-01 -5.69284439e-01 -7.82337725e-01
1.68875679e-01 9.46918130e-02 4.72746074e-01 -1.07226759... | [9.961604118347168, 2.7070326805114746] |
8f6fd620-84f9-4930-8529-75d335217176 | twitmo-a-twitter-data-topic-modeling-and | 2207.11236 | null | https://arxiv.org/abs/2207.11236v1 | https://arxiv.org/pdf/2207.11236v1.pdf | Twitmo: A Twitter Data Topic Modeling and Visualization Package for R | We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-tagged Tweets from Twitter and and provides a comprehensive and user-friendly toolbox to generate topic distributions from Latent Dirichlet A... | ['Thomas Kneib', 'Krisztina Kis-Katos', 'Benjamin Säfken', 'Christoph Weisser', 'Gillian Kant', 'Andreas Buchmüller'] | 2022-07-08 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-5.21651089e-01 1.19024366e-01 -3.36325355e-02 -5.89392960e-01
-8.61649275e-01 -7.57018447e-01 1.00158429e+00 5.96959114e-01
-2.00157445e-02 3.74520451e-01 6.89808547e-01 -4.71242517e-01
-1.08087473e-01 -1.08637214e+00 5.92548735e-02 -7.84690380e-01
-2.15479687e-01 8.14108074e-01 2.69997001e-01 -2.40211934... | [10.417296409606934, 7.0645365715026855] |
487541ca-7107-4c45-9863-fc3fe0cfbc02 | spqr-a-sparse-quantized-representation-for | 2306.03078 | null | https://arxiv.org/abs/2306.03078v1 | https://arxiv.org/pdf/2306.03078v1.pdf | SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression | Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. However, quantization down to 3-4... | ['Dan Alistarh', 'Torsten Hoefler', 'Alexander Borzunov', 'Saleh Ashkboos', 'Elias Frantar', 'Denis Kuznedelev', 'Vage Egiazarian', 'Ruslan Svirschevski', 'Tim Dettmers'] | 2023-06-05 | null | null | null | null | ['quantization'] | ['methodology'] | [ 9.37333480e-02 -7.94870108e-02 -6.65528774e-01 -3.16297829e-01
-1.51610279e+00 -1.88099876e-01 2.59858489e-01 4.55082864e-01
-7.39946842e-01 5.03671467e-01 2.22193241e-01 -7.37741590e-01
1.24931604e-01 -7.61937737e-01 -8.76752257e-01 -4.12182212e-01
-1.58238456e-01 7.87753105e-01 2.02428177e-02 1.28234267... | [8.661556243896484, 3.4595792293548584] |
b83aa113-235f-4598-8e1d-11db7b19c6ab | csi-net-unified-human-body-characterization | 1810.03064 | null | http://arxiv.org/abs/1810.03064v2 | http://arxiv.org/pdf/1810.03064v2.pdf | CSI-Net: Unified Human Body Characterization and Pose Recognition | We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the
representation of WiFi signals. Using CSI-Net, we jointly solved two body
characterization problems: biometrics estimation (including body fat, muscle,
water, and bone rates) and person recognition. We also demonstrated the
application of CSI-Net on tw... | ['Fei Wang', 'Shiyuan Zhang', 'Dong Huang', 'Xu He', 'Jinsong Han'] | 2018-10-07 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.53031969e-01 -4.97140251e-02 -3.18149507e-01 -4.55245614e-01
-3.94526929e-01 -1.03535742e-01 -2.17636287e-01 -7.81754255e-01
-2.98201740e-01 5.41662693e-01 4.63978618e-01 2.27869704e-01
6.21000305e-02 -7.02547550e-01 -5.79871297e-01 -6.57507896e-01
-4.86184031e-01 4.64582980e-01 -2.68909723e-01 2.35121071... | [6.878854274749756, 0.20472149550914764] |
445983af-1ff0-4d49-ad53-e641c9ae483a | neural-dynamic-focused-topic-model-1 | 2301.10988 | null | https://arxiv.org/abs/2301.10988v1 | https://arxiv.org/pdf/2301.10988v1.pdf | Neural Dynamic Focused Topic Model | Topic models and all their variants analyse text by learning meaningful representations through word co-occurrences. As pointed out by Williamson et al. (2010), such models implicitly assume that the probability of a topic to be active and its proportion within each document are positively correlated. This correlation ... | ['César Ojeda', 'Ramsés J. Sánchez', 'Kostadin Cvejoski'] | 2023-01-26 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 1.11348957e-01 3.29389066e-01 -4.49953377e-01 -1.14901885e-01
-6.69779003e-01 -5.80498695e-01 1.42958581e+00 4.13955718e-01
-4.90808368e-01 9.11102295e-01 3.92287850e-01 -3.21418464e-01
-2.42110521e-01 -7.51426578e-01 -8.56779575e-01 -7.49005020e-01
-2.03326806e-01 1.04279828e+00 5.08122444e-01 -4.97963950... | [10.391434669494629, 6.972903728485107] |
d086ae3e-f134-436c-896c-e74bb07e37ff | deep-learning-decoding-of-mental-state-in-non | 1911.05661 | null | http://arxiv.org/abs/1911.05661v1 | http://arxiv.org/pdf/1911.05661v1.pdf | Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface | Brain computer interface (BCI) has been popular as a key approach to monitor
our brains recent year. Mental states monitoring is one of the most important
BCI applications and becomes increasingly accessible. However, the mental state
prediction accuracy and generality through encephalogram (EEG) are not good
enough fo... | [] | 2019-11-11 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 1.48199618e-01 -1.10561639e-01 2.84164846e-01 -4.03945863e-01
7.91245624e-02 1.00211866e-01 4.04227674e-01 -1.74716115e-01
-5.04835784e-01 1.03110290e+00 5.24367206e-02 -2.98670650e-01
-1.74727201e-01 -8.18920553e-01 -3.94229650e-01 -5.37460744e-01
-2.19263047e-01 4.13964726e-02 1.14159018e-01 -3.76939178... | [13.104564666748047, 3.4445858001708984] |
bd5963dc-ad31-4904-bd5d-c6559f0a5622 | transparency-strategy-based-data-augmentation | 2203.10609 | null | https://arxiv.org/abs/2203.10609v2 | https://arxiv.org/pdf/2203.10609v2.pdf | A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms | Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks. Recent methods have proved the efficiency of image augmentation on data deficiency or data imbalance issues. In this paper, we propose a novel transparency strateg... | ['Ha Q. Nguyen', 'Chi Phan', 'Hieu H. Pham', 'Huyen T. X. Nguyen', 'Sam B. Tran'] | 2022-03-20 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 5.05196869e-01 6.04854167e-01 -4.65574473e-01 -7.02304780e-01
-7.84817278e-01 2.29278192e-01 5.43594599e-01 3.08403313e-01
-3.27059925e-01 6.77692533e-01 1.83326676e-01 -7.36077487e-01
5.37095144e-02 -8.50578666e-01 -7.47409940e-01 -8.42395842e-01
1.87634110e-01 2.74447203e-01 4.33959290e-02 2.95038149... | [15.1694917678833, -2.3900599479675293] |
984a8a57-eeae-4942-97e4-49ef15b306d8 | hifi-a-unified-framework-for-neural-vocoding | 2203.13086 | null | https://arxiv.org/abs/2203.13086v2 | https://arxiv.org/pdf/2203.13086v2.pdf | HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement | Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we show that this success can be extended to other tasks of conditional audio generation. In particular, building upon HiFi vocoders, we propose a... | ['Dmitry Vetrov', 'Oleg Ivanov', 'Aibek Alanov', 'Pavel Andreev'] | 2022-03-24 | null | null | null | null | ['bandwidth-extension', 'audio-generation', 'bandwidth-extension'] | ['audio', 'audio', 'speech'] | [ 2.91929960e-01 1.63464189e-01 1.06588118e-01 4.60756905e-02
-8.94375563e-01 -3.14255178e-01 7.22386599e-01 -6.27064943e-01
-1.18999556e-01 1.11652362e+00 5.79844296e-01 -2.21322402e-01
4.17160057e-02 -7.04591095e-01 -7.78544188e-01 -6.28959239e-01
-2.50719607e-01 8.77739191e-02 1.64931953e-01 -3.53896737... | [15.423089981079102, 5.968552589416504] |
7f00dc08-c215-404b-9636-23219ebf77e8 | attention-based-feature-compression-for-cnn | 2211.13745 | null | https://arxiv.org/abs/2211.13745v2 | https://arxiv.org/pdf/2211.13745v2.pdf | Attention-based Feature Compression for CNN Inference Offloading in Edge Computing | This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel autoencoder-based CNN architecture (AECNN), for effective feature extraction at end-device. We design a feature compression module based on t... | ['Qi Zhang', 'Alexandros Iosifidis', 'Nan Li'] | 2022-11-24 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 1.44691363e-01 1.26232892e-01 -1.31214336e-01 -3.50366980e-01
-2.28290290e-01 2.11659268e-01 -2.52093300e-02 -3.14119428e-01
-6.58017695e-01 6.61509097e-01 3.59403998e-01 -1.77614987e-01
-2.25608230e-01 -1.01394880e+00 -9.79866862e-01 -5.06273925e-01
1.42316133e-01 -6.18432881e-03 -5.61712682e-02 9.99411345... | [8.458355903625488, 2.839570999145508] |
f163097b-9652-4485-9e4d-1dd89f650177 | hierarchical-sketch-induction-for-paraphrase | 2203.03463 | null | https://arxiv.org/abs/2203.03463v2 | https://arxiv.org/pdf/2203.03463v2.pdf | Hierarchical Sketch Induction for Paraphrase Generation | We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch. We introduce Hierarchical Refinement Quantized Variational Autoencoders (HRQ-VAE), a method for learning decompositions of dense encodings as a sequence of discrete latent variabl... | ['Mirella Lapata', 'Hao Tang', 'Tom Hosking'] | 2022-03-07 | null | https://aclanthology.org/2022.acl-long.178 | https://aclanthology.org/2022.acl-long.178.pdf | acl-2022-5 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [-1.20540798e-01 4.99187559e-01 -1.92535087e-01 -3.80248487e-01
-9.25863385e-01 -8.27140808e-01 6.05271935e-01 -2.71462113e-01
1.39632672e-01 7.31052935e-01 9.71071899e-01 -3.41737598e-01
2.50893176e-01 -1.23569357e+00 -1.04345942e+00 -3.61509502e-01
2.95949101e-01 8.50493908e-01 -2.32936203e-01 -3.42561305... | [11.722087860107422, 9.218084335327148] |
02eb1013-114d-4f09-adc9-06797c1f9b9c | graph-attention-network-based-single-pixel | 2109.05466 | null | https://arxiv.org/abs/2109.05466v2 | https://arxiv.org/pdf/2109.05466v2.pdf | Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation | In this paper, we present a single-pixel compressive direction of arrival (DoA) estimation technique leveraging a graph attention network (GAT)-based deep-learning framework. The physical layer compression is achieved using a coded-aperture technique, probing the spectrum of far-field sources that are incident on the a... | ['Güneş Karabulut Kurt', 'Okan Yurduseven', 'Kürşat Tekbıyık'] | 2021-09-12 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 6.69947803e-01 2.31360510e-01 4.96081620e-01 -2.97460072e-02
-9.03143823e-01 -2.00048134e-01 5.86183012e-01 -3.61330301e-01
-2.86829621e-01 5.40811419e-01 3.96869868e-01 -3.91237617e-01
-6.66733205e-01 -1.05736172e+00 -6.35335267e-01 -1.30455327e+00
-4.27443743e-01 1.43959880e-01 -4.92446840e-01 1.73372686... | [10.632256507873535, -2.141759157180786] |
07ff3aa8-28a4-4080-97e7-21bd52ac9ce1 | self-attentive-multi-context-one-class | null | null | https://aclanthology.org/P19-1398 | https://aclanthology.org/P19-1398.pdf | Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text | There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words. In this paper we introduce a new anomaly detection method{---}Context Vector Data Description (CVDD){---}which builds upon word embeddin... | ['V', 'Yury Zemlyanskiy', 'Thomas Schnake', 'Robert ermeulen', 'Marius Kloft', 'Lukas Ruff'] | 2019-07-01 | null | null | null | acl-2019-7 | ['contextual-anomaly-detection'] | ['miscellaneous'] | [-8.50599818e-03 -9.47733670e-02 -8.27370286e-02 -5.13404191e-01
-3.89653057e-01 -3.34675223e-01 7.35098302e-01 1.06279206e+00
-4.92866367e-01 2.14332983e-01 5.85942864e-01 -4.01054144e-01
2.16722876e-01 -4.93801326e-01 -2.57842422e-01 -3.88815880e-01
-1.87948421e-01 2.15188354e-01 9.13619027e-02 -4.13018823... | [10.518207550048828, 8.612540245056152] |
14301513-8c41-4566-98b2-93d7809011af | ontology-matching-techniques-a-gold-standard | 1811.10191 | null | http://arxiv.org/abs/1811.10191v1 | http://arxiv.org/pdf/1811.10191v1.pdf | Ontology Matching Techniques: A Gold Standard Model | Typically an ontology matching technique is a combination of much different
type of matchers operating at various abstraction levels such as structure,
semantic, syntax, instance etc. An ontology matching technique which employs
matchers at all possible abstraction levels is expected to give, in general,
best results i... | ['Alok Chauhan', 'Vijayakumar V', 'Layth Sliman'] | 2018-11-26 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 3.39670658e-01 1.11946210e-01 -1.44552559e-01 -4.57552105e-01
-2.72172898e-01 -3.85898560e-01 6.80489182e-01 8.12658131e-01
-4.08473432e-01 3.20276648e-01 1.97353378e-01 -9.10520628e-02
-9.64664519e-01 -1.23745906e+00 -4.92572924e-03 3.61568481e-02
-8.65909010e-02 7.47391164e-01 4.62179154e-01 -6.08853638... | [9.205137252807617, 8.0642671585083] |
0c8f74fd-4998-4a37-8a39-85fc891c292e | label-only-membership-inference-attack | 2207.13766 | null | https://arxiv.org/abs/2207.13766v1 | https://arxiv.org/pdf/2207.13766v1.pdf | Label-Only Membership Inference Attack against Node-Level Graph Neural Networks | Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, graph classification, and link prediction. Previous studies have indicated that GNNs are vulnerable ... | ['Jing Xu', 'Stjepan Picek', 'Jiaxin Li', 'Mauro Conti'] | 2022-07-27 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 1.72583729e-01 4.74540353e-01 -6.10126972e-01 -1.32519737e-01
-3.37616801e-01 -7.39313245e-01 2.42353037e-01 3.59014571e-01
-3.58159155e-01 6.50351644e-01 -3.85099918e-01 -9.36118484e-01
-1.67870313e-01 -1.30497694e+00 -9.81045306e-01 -5.39037287e-01
-4.57231164e-01 2.85180181e-01 3.57297093e-01 2.31036246... | [6.025822162628174, 7.241660118103027] |
a868e647-19d5-437d-865a-64ed1c6e483b | human-action-recognition-in-egocentric | 2306.05147 | null | https://arxiv.org/abs/2306.05147v1 | https://arxiv.org/pdf/2306.05147v1.pdf | Human Action Recognition in Egocentric Perspective Using 2D Object and Hands Pose | Egocentric action recognition is essential for healthcare and assistive technology that relies on egocentric cameras because it allows for the automatic and continuous monitoring of activities of daily living (ADLs) without requiring any conscious effort from the user. This study explores the feasibility of using 2D ha... | ['Martin Kampel', 'Wiktor Mucha'] | 2023-06-08 | null | null | null | null | ['action-classification', 'action-recognition-in-videos', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.27680409e-01 3.93088907e-02 -5.60399532e-01 -2.55333096e-01
-4.40304071e-01 -2.37009287e-01 4.23391879e-01 -3.34528118e-01
-7.09573388e-01 7.31905222e-01 5.10110438e-01 1.65698171e-01
-5.72668985e-02 -4.13941622e-01 -1.33555740e-01 -9.40173984e-01
1.52588142e-02 3.78127158e-01 2.17813358e-01 2.33285889... | [7.501162528991699, 0.41396650671958923] |
be7921f3-dd0d-4ec1-938c-08563771831d | federated-td-learning-over-finite-rate | 2305.08104 | null | https://arxiv.org/abs/2305.08104v1 | https://arxiv.org/pdf/2305.08104v1.pdf | Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling | Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints. However, whether similar speedups can be established for reinforcement learning remains much less understood theoretically. Towards this direction, we... | ['George J. Pappas', 'Aritra Mitra', 'Nicolò Dal Fabbro'] | 2023-05-14 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.49024466e-01 6.11382984e-02 -2.64824480e-01 -8.38499144e-02
-8.76211464e-01 -6.86456621e-01 5.21035671e-01 4.77210701e-01
-7.85300970e-01 1.14564514e+00 -8.64003524e-02 -4.90592331e-01
-3.29899102e-01 -7.24713862e-01 -9.68310237e-01 -9.53937650e-01
-9.46041584e-01 4.68842834e-01 -2.44713366e-01 4.91834395... | [4.222477912902832, 2.573914051055908] |
3832a388-9221-4d4f-8439-1188742655da | low-rank-laplacian-uniform-mixed-model-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Dong_Low-Rank_Laplacian-Uniform_Mixed_Model_for_Robust_Face_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Low-Rank_Laplacian-Uniform_Mixed_Model_for_Robust_Face_Recognition_CVPR_2019_paper.pdf | Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recognition | Sparse representation based methods have successfully put forward a general framework for robust face recognition through linear reconstruction and sparsity constraints. However, residual modeling in existing works is not yet robust enough when dealing with dense noise. In this paper, we aim at recognizing identities f... | [' Lina Lian', ' Huicheng Zheng', 'Jiayu Dong'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['robust-face-recognition'] | ['computer-vision'] | [ 3.41324657e-01 -6.92978561e-01 1.01860173e-01 -3.83623332e-01
-5.79794466e-01 -1.01530209e-01 2.89259821e-01 -7.61100054e-01
-8.20183381e-02 6.28690720e-01 2.08081514e-01 2.25662500e-01
-2.63870418e-01 -4.38300222e-01 -5.17219126e-01 -9.78129923e-01
4.22208160e-01 -7.86786247e-03 -2.11962596e-01 1.57089494... | [12.5476713180542, 0.3956843316555023] |
421f0ca7-beec-41fe-990e-59530468ea1d | debiased-batch-normalization-via-gaussian | 2203.01723 | null | https://arxiv.org/abs/2203.01723v2 | https://arxiv.org/pdf/2203.01723v2.pdf | Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification | Generalizable person re-identification aims to learn a model with only several labeled source domains that can perform well on unseen domains. Without access to the unseen domain, the feature statistics of the batch normalization (BN) layer learned from a limited number of source domains is doubtlessly biased for unsee... | ['Zheng-Jun Zha', 'Kecheng Zheng', 'Liang Li', 'Zhipeng Huang', 'Jiawei Liu'] | 2022-03-03 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [-7.12197274e-02 -6.55635670e-02 2.00012416e-01 -5.50857425e-01
-3.83530587e-01 -8.06945622e-01 4.06169295e-01 -2.57816583e-01
-5.16562819e-01 7.29962230e-01 1.29981022e-02 3.45156521e-01
-2.37257779e-01 -8.76175582e-01 -4.55458552e-01 -8.20479929e-01
4.14665014e-01 9.43685949e-01 7.70483017e-02 -1.49376288... | [14.717079162597656, 1.1177418231964111] |
a1b773fc-2a20-4862-8fe5-d1819a6f71aa | an-asymmetric-modeling-for-action-assessment | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7352_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750222.pdf | An Asymmetric Modeling for Action Assessment | Action assessment is a task of assessing the performance of an action. It is widely applicable to many real-world scenarios such as medical treatment and sporting events. However, existing methods for action assessment are mostly limited to individual actions, especially lacking modeling of the asymmetric relations amo... | ['Yao-Wei Wang', 'Jian-Huang Lai', 'Jia-Hui Pan', 'Wei-Shi Zheng', 'Chengying Gao', 'Wei Zeng', 'Jibin Gao'] | null | null | null | null | eccv-2020-8 | ['action-assessment'] | ['computer-vision'] | [ 1.08362727e-01 -1.02495447e-01 -1.20552972e-01 -2.09917650e-01
-2.40817189e-01 -5.05735397e-01 8.30485821e-01 -1.84377208e-01
-5.84871829e-01 5.62495112e-01 6.20065808e-01 -1.04345664e-01
-3.20594847e-01 -8.57274532e-01 -2.76495159e-01 -8.62488091e-01
-2.03240424e-01 7.69071162e-01 8.59911323e-01 -5.30229151... | [8.220709800720215, 0.5701557397842407] |
24001142-4d57-4025-b36c-1486e8d54fbd | edu-ap-elementary-discourse-unit-based | null | null | https://aclanthology.org/2022.sigdial-1.19 | https://aclanthology.org/2022.sigdial-1.19.pdf | EDU-AP: Elementary Discourse Unit based Argument Parser | Neural approaches to end-to-end argument mining (AM) are often formulated as dependency parsing (DP), which relies on token-level sequence labeling and intricate post-processing for extracting argumentative structures from text. Although such methods yield reasonable results, operating solely with tokens increases the ... | ['Rohini Srihari', 'Souvik Das', 'Sougata Saha'] | null | null | null | null | sigdial-acl-2022-9 | ['dependency-parsing', 'argument-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.63004231e-01 8.40775907e-01 -5.10762572e-01 -3.95364732e-01
-1.22993600e+00 -8.80087197e-01 6.01058900e-01 6.76209390e-01
-5.11840105e-01 1.04630089e+00 6.94210649e-01 -8.92452717e-01
1.46402881e-01 -7.54618645e-01 -7.80286670e-01 -1.34714186e-01
-2.05235481e-02 4.86044586e-01 1.51460052e-01 -1.64750755... | [10.57597541809082, 9.464433670043945] |
c9d1fad2-46d7-4b41-b464-34c6c45aa5ac | modeling-global-and-local-node-contexts-for | 2001.11003 | null | https://arxiv.org/abs/2001.11003v2 | https://arxiv.org/pdf/2001.11003v2.pdf | Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs | Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding co... | ['Leonardo F. R. Ribeiro', 'Yue Zhang', 'Iryna Gurevych', 'Claire Gardent'] | 2020-01-29 | null | null | null | null | ['graph-to-sequence', 'kg-to-text'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.34205604e-01 7.28065431e-01 -3.61969531e-01 -3.20461720e-01
-4.84932393e-01 -6.83606982e-01 1.11220860e+00 9.43905354e-01
-1.99275509e-01 5.54086626e-01 5.14178872e-01 -4.69350249e-01
-1.96941182e-01 -1.36615431e+00 -6.17077529e-01 -3.87601227e-01
-3.54808897e-01 7.15920091e-01 2.07389891e-01 -3.42833340... | [10.108146667480469, 8.15916633605957] |
f1665956-cf32-463c-8f48-f2d4b382488d | mmwave-mapping-and-slam-for-5g-and-beyond | 2211.16024 | null | https://arxiv.org/abs/2211.16024v1 | https://arxiv.org/pdf/2211.16024v1.pdf | MmWave Mapping and SLAM for 5G and Beyond | Device localization and radar-like mapping are at the heart of integrated sensing and communication, enabling not only new services and applications, but can also improve communication quality with reduced overheads. These forms of sensing are however susceptible to data association problems, due to the unknown relatio... | ['Henk Wymeersch', 'Mikko Valkama', 'Lennart Svensson', 'Sunwoo Kim', 'Jukka Talvitie', 'Hyowon Kim', 'Ossi Kaltiokallio', 'Yu Ge'] | 2022-11-29 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 6.05726898e-01 -1.99969485e-01 -5.26033789e-02 -5.29642045e-01
-7.20996618e-01 -5.71137011e-01 6.62998736e-01 1.65928841e-01
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-5.84902108e-01 7.77121246e-01 1.48989081e-01 2.95566060... | [6.188792705535889, 0.8990471363067627] |
4de1f278-b77b-4416-b89e-f1adff2db370 | efficiently-leveraging-multi-level-user | 2206.12781 | null | https://arxiv.org/abs/2206.12781v2 | https://arxiv.org/pdf/2206.12781v2.pdf | Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network | Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various elaborately designed graph neural networks (GNNs) to capture the pair-wise relationships among items, seemingly suggesting the design of mor... | ['Sunghun Kim', 'Haohan Wang', 'Xing Xie', 'Yan Zhang', 'Jaeboum Kim', 'Yueqi Xie', 'Chaozhuo Li', 'Jiayan Guo', 'Peiyan Zhang'] | 2022-06-26 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.00892549e-02 -1.27101481e-01 -2.27602527e-01 -4.09723580e-01
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-3.43517989e-01 2.89405376e-01 3.50378752e-01 -6.72414780... | [10.139397621154785, 5.578834533691406] |
338a93ca-13b6-4421-ac7d-19a240d04fc8 | german-dialect-identification-and-mapping-for | null | null | https://aclanthology.org/2022.eurali-1.10 | https://aclanthology.org/2022.eurali-1.10.pdf | German Dialect Identification and Mapping for Preservation and Recovery | Many linguistic projects which focus on dialects do collection of audio data, analysis, and linguistic interpretation on the data. The outcomes of such projects are good language resources because dialects are among less-resources languages as most of them are oral traditions. Our project Dialektatlas Mittleres Westdeu... | ['Sabine Roller', 'Aynalem Tesfaye Misganaw'] | null | null | null | null | eurali-lrec-2022-6 | ['dialect-identification'] | ['natural-language-processing'] | [-2.35362872e-01 -1.96818173e-01 -1.70290813e-01 -6.97959363e-01
-1.12761188e+00 -8.15213561e-01 4.70257491e-01 4.02843386e-01
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-1.60596147e-01 5.60116231e-01 -7.00621307e-03 -6.15608454... | [10.341818809509277, 10.422983169555664] |
12af87c1-87d7-47df-a3dd-a92f4e386b84 | hidden-poison-machine-unlearning-enables | 2212.10717 | null | https://arxiv.org/abs/2212.10717v1 | https://arxiv.org/pdf/2212.10717v1.pdf | Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks | We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The ad... | ['Ayush Sekhari', 'Gautam Kamath', 'Jayadev Acharya', 'Jack Douglas', 'Jimmy Z. Di'] | 2022-12-21 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.48816943e-01 2.61991888e-01 -2.23448440e-01 -9.82844532e-02
-6.59516990e-01 -1.15105164e+00 5.46626210e-01 2.94130534e-01
-5.00748992e-01 8.17358196e-01 -1.77495360e-01 -4.46865231e-01
5.75768501e-02 -7.37698078e-01 -1.01358593e+00 -9.65915740e-01
3.04679815e-02 3.98043454e-01 4.97830473e-02 8.71393159... | [5.8069963455200195, 7.656182289123535] |
0c8f6e26-935f-498e-a3a2-104adc43b61e | i-vector-based-features-embedding-for-heart | 1904.11914 | null | https://arxiv.org/abs/1904.11914v3 | https://arxiv.org/pdf/1904.11914v3.pdf | Statistical feature embedding for heart sound classification | Cardiovascular Disease (CVD) is considered as one of the principal causes of death in the world. Over recent years, this field of study has attracted researchers' attention to investigate heart sounds' patterns for disease diagnostics. In this study, an approach is proposed for normal/abnormal heart sound classificatio... | ['Bagher BabaAli', 'Saeedreza Shehnepoor', 'Mohammad Adiban'] | 2019-04-26 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 1.65846989e-01 -1.07122645e-01 8.07674304e-02 3.70484293e-02
-6.54453695e-01 -7.12667406e-02 3.38162303e-01 4.30014700e-01
-2.25522116e-01 3.56667340e-01 3.23340654e-01 -3.25094461e-01
4.15550694e-02 -5.35988390e-01 1.24986567e-01 -7.81946957e-01
-2.03513861e-01 3.98075068e-03 1.80360526e-02 3.42120886... | [14.355836868286133, 3.3724446296691895] |
0ab87949-5be8-4342-88e1-0269f1284852 | financial-news-annotation-by-weakly | null | null | https://aclanthology.org/2020.finnlp-1.1 | https://aclanthology.org/2020.finnlp-1.1.pdf | Financial News Annotation by Weakly-Supervised Hierarchical Multi-label Learning | null | ['Guangwei Shi', 'Jidong Lu', 'Jian Gao', 'Mengjun Ni', 'Chenyu Wang', 'Yuefeng Lin', 'Zhongchen Miao', 'Hang Jiang'] | null | null | null | null | finnlp-coling-2020-1 | ['news-annotation'] | ['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... | [-7.465348720550537, 3.877047061920166] |
79cfd587-208c-445a-89d1-ffae4420b874 | evaluating-community-detection-algorithms-for | 2007.08635 | null | https://arxiv.org/abs/2007.08635v1 | https://arxiv.org/pdf/2007.08635v1.pdf | Evaluating Community Detection Algorithms for Progressively Evolving Graphs | Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. U... | ['Souaad Boudebza', 'Remy Cazabet', 'Giulio Rossetti'] | 2020-07-16 | null | null | null | null | ['dynamic-community-detection'] | ['graphs'] | [ 1.40892386e-01 1.31803066e-01 2.18052149e-01 2.66790777e-01
-2.00518236e-01 -1.09738696e+00 8.78653705e-01 4.04200137e-01
-1.07481107e-02 7.68738389e-01 1.62027441e-02 -2.05772191e-01
-5.90186417e-01 -7.79444575e-01 -1.80792034e-01 -7.39058018e-01
-8.53971303e-01 8.22858274e-01 6.47289574e-01 -2.84930855... | [6.973922252655029, 5.282011985778809] |
9ea46082-2b05-4a06-a12c-8d5b768f588f | patch-based-object-centric-transformers-for | 2206.04003 | null | https://arxiv.org/abs/2206.04003v2 | https://arxiv.org/pdf/2206.04003v2.pdf | Patch-based Object-centric Transformers for Efficient Video Generation | In this work, we present Patch-based Object-centric Video Transformer (POVT), a novel region-based video generation architecture that leverages object-centric information to efficiently model temporal dynamics in videos. We build upon prior work in video prediction via an autoregressive transformer over the discrete la... | ['Pieter Abbeel', 'Stephen James', 'Ryo Okumura', 'Wilson Yan'] | 2022-06-08 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 5.06565906e-02 1.53580233e-01 -3.73384506e-01 5.91104515e-02
-6.78649306e-01 -5.81748188e-01 7.52827883e-01 -2.52454668e-01
2.16674238e-01 5.32407582e-01 7.43934929e-01 -1.30509257e-01
4.68468703e-02 -7.13131309e-01 -1.17338634e+00 -3.95286947e-01
-1.70791268e-01 4.78538483e-01 1.61162108e-01 -1.45546615... | [10.64059066772461, -0.46211811900138855] |
fc47d5af-31d9-42fa-bc82-ff475f32cfee | bevscope-enhancing-self-supervised-depth | 2306.11598 | null | https://arxiv.org/abs/2306.11598v1 | https://arxiv.org/pdf/2306.11598v1.pdf | BEVScope: Enhancing Self-Supervised Depth Estimation Leveraging Bird's-Eye-View in Dynamic Scenarios | Depth estimation is a cornerstone of perception in autonomous driving and robotic systems. The considerable cost and relatively sparse data acquisition of LiDAR systems have led to the exploration of cost-effective alternatives, notably, self-supervised depth estimation. Nevertheless, current self-supervised depth esti... | ['Hang Zhao', 'Tianbao Zhang', 'Ruowen Zhao', 'Yucheng Mao'] | 2023-06-20 | null | null | null | null | ['depth-estimation'] | ['computer-vision'] | [ 1.61873579e-01 -5.34612052e-02 -2.77180701e-01 -6.20886326e-01
-7.94294715e-01 -4.87772018e-01 6.36240661e-01 -7.14385659e-02
-5.37556112e-01 5.98984420e-01 -1.75638739e-02 -1.14921279e-01
-6.95378408e-02 -6.59833729e-01 -5.47320127e-01 -5.28883100e-01
1.04278000e-02 2.13768423e-01 5.34923136e-01 -1.61951128... | [8.12161636352539, -2.3675172328948975] |
0b8b5195-1ac5-4b8a-90e2-9e0d04ce71f0 | fenerf-face-editing-in-neural-radiance-fields | 2111.15490 | null | https://arxiv.org/abs/2111.15490v2 | https://arxiv.org/pdf/2111.15490v2.pdf | FENeRF: Face Editing in Neural Radiance Fields | Previous portrait image generation methods roughly fall into two categories: 2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency. 3D-aware GAN methods can maintain view consistency but their generated images are not locally editable. To overcome these limitations, we pr... | ['Jue Wang', 'Yebin Liu', 'Qi Zhang', 'Xiaoyu Li', 'Yong Zhang', 'Xuan Wang', 'Jingxiang Sun'] | 2021-11-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sun_FENeRF_Face_Editing_in_Neural_Radiance_Fields_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 5.14329493e-01 4.91457880e-01 9.74607095e-02 -4.93729115e-01
-6.69907987e-01 -7.88584650e-01 7.48366654e-01 -9.05121565e-01
5.79715133e-01 8.61459672e-01 4.39072669e-01 3.13667893e-01
2.28959396e-01 -1.17616832e+00 -8.88309896e-01 -6.10267282e-01
6.88616276e-01 5.92505395e-01 -3.80959630e-01 -2.47702524... | [12.42754077911377, -0.4876845180988312] |
1f61f5c0-0593-4d2f-894c-52041c87b598 | extreme-face-inpainting-with-sketch-guided | 2105.06033 | null | https://arxiv.org/abs/2105.06033v1 | https://arxiv.org/pdf/2105.06033v1.pdf | Extreme Face Inpainting with Sketch-Guided Conditional GAN | Recovering badly damaged face images is a useful yet challenging task, especially in extreme cases where the masked or damaged region is very large. One of the major challenges is the ability of the system to generalize on faces outside the training dataset. We propose to tackle this extreme inpainting task with a cond... | ['Andreas Savakis', 'Nilesh Pandey'] | 2021-05-13 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 6.35141015e-01 3.27120781e-01 2.48366773e-01 -3.36459219e-01
-6.48302197e-01 -4.70098466e-01 2.09915370e-01 -5.99226475e-01
-2.92337481e-02 9.01982605e-01 2.90558249e-01 2.12360486e-01
3.10195565e-01 -8.53210151e-01 -1.06512475e+00 -6.99540019e-01
2.89382160e-01 1.53187349e-01 -1.52613193e-01 3.83325517... | [12.562405586242676, -0.21204376220703125] |
cfc5ee02-aba4-4217-8c9b-6310d8cb0455 | neural-implicit-surface-reconstruction-from | 2210.01548 | null | https://arxiv.org/abs/2210.01548v1 | https://arxiv.org/pdf/2210.01548v1.pdf | Neural Implicit Surface Reconstruction from Noisy Camera Observations | Representing 3D objects and scenes with neural radiance fields has become very popular over the last years. Recently, surface-based representations have been proposed, that allow to reconstruct 3D objects from simple photographs. However, most current techniques require an accurate camera calibration, i.e. camera param... | ['Patrik Huber', 'Sarthak Gupta'] | 2022-10-02 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 2.71578729e-01 -2.75028050e-01 2.03055695e-01 -5.37292361e-01
-7.37596273e-01 -4.97449130e-01 3.03757995e-01 -4.20474559e-01
-8.34872574e-02 3.66562426e-01 -1.29941881e-01 1.30772546e-01
7.31873140e-03 -8.15047741e-01 -1.01820624e+00 -6.12411380e-01
3.76916796e-01 4.17575836e-01 7.86075220e-02 3.31529714... | [9.050745010375977, -2.887497663497925] |
720db600-684a-4268-b379-2a134f9811c6 | deep-bayes-factor-scoring-for-authorship | 2008.10105 | null | https://arxiv.org/abs/2008.10105v1 | https://arxiv.org/pdf/2008.10105v1.pdf | Deep Bayes Factor Scoring for Authorship Verification | The PAN 2020 authorship verification (AV) challenge focuses on a cross-topic/closed-set AV task over a collection of fanfiction texts. Fanfiction is a fan-written extension of a storyline in which a so-called fandom topic describes the principal subject of the document. The data provided in the PAN 2020 AV task is quit... | ['Dorothea Kolossa', 'Robert M. Nickel', 'Julian Rupp', 'Benedikt Boenninghoff'] | 2020-08-23 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [-5.55977151e-02 -2.80201316e-01 -1.35518357e-01 -4.30032790e-01
-1.25840020e+00 -7.66474247e-01 1.11016166e+00 2.76699424e-01
-1.92958206e-01 3.45207214e-01 5.13391078e-01 1.04081407e-02
-1.87782541e-01 -3.68557006e-01 -4.31606770e-01 -3.19276899e-01
3.03276449e-01 7.34930873e-01 -1.47061497e-01 1.80547684... | [9.611775398254395, 10.559517860412598] |
90b61869-f2f4-4065-9682-988c4dfc629c | patchnets-patch-based-generalizable-deep | 2008.01639 | null | https://arxiv.org/abs/2008.01639v2 | https://arxiv.org/pdf/2008.01639v2.pdf | PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations | Implicit surface representations, such as signed-distance functions, combined with deep learning have led to impressive models which can represent detailed shapes of objects with arbitrary topology. Since a continuous function is learned, the reconstructions can also be extracted at any arbitrary resolution. However, l... | ['Michael Zollhöfer', 'Christian Theobalt', 'Carsten Stoll', 'Edgar Tretschk', 'Ayush Tewari', 'Vladislav Golyanik'] | 2020-08-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2547_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610290.pdf | eccv-2020-8 | ['point-cloud-completion'] | ['computer-vision'] | [ 1.89730134e-02 4.00303483e-01 2.36970410e-01 -3.15926433e-01
-7.15436518e-01 -7.69379497e-01 6.16991162e-01 5.34025952e-02
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-3.03220819e-03 -1.16652274e+00 -1.19915223e+00 -6.14896297e-01
1.60066634e-01 7.61015832e-01 4.46087599e-01 -3.35469425... | [8.668328285217285, -3.637852191925049] |
14a3377e-bb87-497a-b43c-3194887c6fe1 | scalable-privacy-preserving-cancer-type | 2204.05496 | null | https://arxiv.org/abs/2204.05496v1 | https://arxiv.org/pdf/2204.05496v1.pdf | Scalable privacy-preserving cancer type prediction with homomorphic encryption | Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer type from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer type... | ['Michail Maniatakos', 'Mark Gerstein', 'Leo Chen', 'Gamze Gursoy', 'Eduardo Chielle', 'Esha Sarkar'] | 2022-04-12 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 2.93313295e-01 -8.25806856e-02 -4.36830163e-01 -4.41203088e-01
-1.17797947e+00 -8.32839429e-01 1.00960284e-02 5.66702783e-01
-7.64521658e-01 8.95454049e-01 6.25354722e-02 -8.31060469e-01
5.10144718e-02 -9.07893538e-01 -7.19499767e-01 -9.20419574e-01
-1.57124341e-01 3.08110088e-01 -3.35461199e-01 2.69889146... | [6.121723651885986, 6.532205104827881] |
a1670a6c-768b-41a6-8d4c-2549122d2b5a | entitycs-improving-zero-shot-cross-lingual | 2210.12540 | null | https://arxiv.org/abs/2210.12540v2 | https://arxiv.org/pdf/2210.12540v2.pdf | EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching | Accurate alignment between languages is fundamental for improving cross-lingual pre-trained language models (XLMs). Motivated by the natural phenomenon of code-switching (CS) in multilingual speakers, CS has been used as an effective data augmentation method that offers language alignment at the word- or phrase-level, ... | ['Ignacio Iacobacci', 'Fenia Christopoulou', 'Chenxi Whitehouse'] | 2022-10-22 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'word-alignment'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.70649216e-02 6.68593273e-02 -5.26225805e-01 -4.13639724e-01
-9.03135061e-01 -6.26441002e-01 4.45900112e-01 5.27526319e-01
-5.30138135e-01 5.10585487e-01 4.42787707e-01 -7.36871898e-01
3.88511568e-01 -5.02330661e-01 -8.30627859e-01 -7.11894706e-02
2.79971343e-02 2.77337492e-01 -4.59179888e-03 -6.19104207... | [11.021403312683105, 9.95093059539795] |
936be60a-54f1-4c0c-ac8b-acaa49c0c0b0 | uni6dv2-noise-elimination-for-6d-pose | 2208.06416 | null | https://arxiv.org/abs/2208.06416v2 | https://arxiv.org/pdf/2208.06416v2.pdf | Uni6Dv2: Noise Elimination for 6D Pose Estimation | Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images. We discover that the principal reasons of Uni6D performance limitations are Instance-Outside and Instance-Inside noise. Uni6D's simple pipeline design inherently introduces Instance-Ou... | ['Xiaoke Jiang', 'Rui Zhao', 'Liwei Wu', 'Guoqiang Jin', 'Jianqiu Chen', 'Tianpeng Bao', 'Ye Zheng', 'Mingshan Sun'] | 2022-08-15 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 3.34522903e-01 2.79679179e-01 1.81165159e-01 -6.59418821e-01
-7.62583971e-01 -5.80184639e-01 2.39815101e-01 -1.39408052e-01
-5.45182705e-01 3.93674195e-01 -1.18549310e-01 -2.08714560e-01
1.09202944e-01 -9.15358067e-01 -7.99556792e-01 -7.23792553e-01
3.97550911e-01 3.57311428e-01 4.64534014e-01 2.24418640... | [8.034953117370605, -2.860666275024414] |
070f7a93-9827-4ffb-ab06-2e849dfeed37 | a-novel-filter-based-on-three-variables | 2210.14609 | null | https://arxiv.org/abs/2210.14609v1 | https://arxiv.org/pdf/2210.14609v1.pdf | A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images | The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In fact, the removal of irrelevant, noisy and redundant bands helps incr... | ['Chafik Nacir', 'Ahmed Hammouch', 'Elkebir Sarhrouni', 'Asma Elmaizi'] | 2022-10-26 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 6.06416702e-01 -4.89020109e-01 2.96669513e-01 -2.36255199e-01
-2.17988163e-01 -4.34427679e-01 2.96666861e-01 1.01797774e-01
-3.79345179e-01 8.35086107e-01 3.35649066e-02 4.70086224e-02
-9.81312156e-01 -1.04756999e+00 1.33780137e-01 -1.08781040e+00
-5.12362830e-02 -1.74519401e-02 -1.30568817e-01 -6.71608225... | [9.774955749511719, -1.8509254455566406] |
7e77afd9-4de5-4dce-a44e-e802d0c5a469 | extending-word-level-quality-estimation-for | 2209.11378 | null | https://arxiv.org/abs/2209.11378v1 | https://arxiv.org/pdf/2209.11378v1.pdf | Extending Word-Level Quality Estimation for Post-Editing Assistance | We define a novel concept called extended word alignment in order to improve post-editing assistance efficiency. Based on extended word alignment, we further propose a novel task called refined word-level QE that outputs refined tags and word-level correspondences. Compared to original word-level QE, the new task is ab... | ['Masaaki Nagata', 'Takehito Utsuro', 'Yizhen Wei'] | 2022-09-23 | null | null | null | null | ['word-alignment', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.33548534e-01 -9.43932161e-02 -2.57225454e-01 -2.70308018e-01
-1.03062212e+00 -6.32850528e-01 1.10213973e-01 2.61640191e-01
-7.98253000e-01 7.06401825e-01 3.95021558e-01 -4.42662448e-01
-1.76107883e-02 -7.80805588e-01 -5.10991216e-01 -2.48328283e-01
3.31262767e-01 2.97846645e-01 1.93665728e-01 -5.75757504... | [11.187239646911621, 10.190994262695312] |
22fca94a-7733-414f-b6d3-610f4a501cc3 | experiments-on-paraphrase-identification | 2006.02648 | null | https://arxiv.org/abs/2006.02648v2 | https://arxiv.org/pdf/2006.02648v2.pdf | Experiments on Paraphrase Identification Using Quora Question Pairs Dataset | We modeled the Quora question pairs dataset to identify a similar question. The dataset that we use is provided by Quora. The task is a binary classification. We tried several methods and algorithms and different approach from previous works. For feature extraction, we used Bag of Words including Count Vectorizer, and ... | ['Ruben Stefanus', 'Andreas Chandra'] | 2020-06-04 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [-5.78313172e-01 -3.00499737e-01 -4.37570393e-01 -4.00462747e-01
-1.23133206e+00 -6.23818994e-01 4.99285102e-01 3.50473017e-01
-4.42917526e-01 7.88896859e-01 5.76396883e-01 -2.53839761e-01
-1.94148362e-01 -1.06310678e+00 -4.17938292e-01 -2.58081168e-01
2.54521072e-01 1.76936805e-01 2.62946218e-01 -4.14975315... | [11.37753677368164, 8.509770393371582] |
8de5c9d5-ace1-4215-98e0-acab0c3a7080 | trading-off-price-for-data-quality-to-achieve | 2306.13440 | null | https://arxiv.org/abs/2306.13440v1 | https://arxiv.org/pdf/2306.13440v1.pdf | Trading-off price for data quality to achieve fair online allocation | We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the protected attributes -- which is often unrealistic in practice. Instead they can purchase data that help estimate them from sources of differen... | ['Vianney Perchet', 'Patrick Loiseau', 'Nicolas Gast', 'Mathieu Molina'] | 2023-06-23 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 4.02589003e-03 1.70474514e-01 -6.57139957e-01 -4.19003397e-01
-1.06816316e+00 -8.53571534e-01 1.39703164e-02 4.56256002e-01
-7.97694504e-01 1.04860139e+00 9.43026096e-02 -3.85172129e-01
-4.98957306e-01 -8.66629064e-01 -7.03843951e-01 -6.68169081e-01
2.19341703e-02 7.18198061e-01 -2.33541518e-01 7.57000549... | [4.558324337005615, 3.3769919872283936] |
e945bdf2-8801-4cf5-a85e-70141b876ed7 | image-as-a-foreign-language-beit-pretraining-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Image_as_a_Foreign_Language_BEiT_Pretraining_for_Vision_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Image_as_a_Foreign_Language_BEiT_Pretraining_for_Vision_and_CVPR_2023_paper.pdf | Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks | A big convergence of language, vision, and multimodal pretraining is emerging. In this work, we introduce a general-purpose multimodal foundation model BEiT-3, which achieves excellent transfer performance on both vision and vision-language tasks. Specifically, we advance the big convergence from three aspects: bac... | ['Furu Wei', 'Subhojit Som', 'Saksham Singhal', 'Owais Khan Mohammed', 'Kriti Aggarwal', 'Qiang Liu', 'Zhiliang Peng', 'Johan Bjorck', 'Li Dong', 'Hangbo Bao', 'Wenhui Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-reasoning', 'cross-modal-retrieval', 'visual-reasoning'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [-1.00523099e-01 -1.01490512e-01 -2.94486014e-03 -4.92204100e-01
-1.07548952e+00 -6.53170824e-01 7.51402676e-01 -8.34468976e-02
-6.39135659e-01 1.92372441e-01 -1.37753342e-03 -4.63072181e-01
4.61253375e-01 -3.84171724e-01 -9.82024014e-01 -3.71095449e-01
4.12746072e-01 5.42064130e-01 9.59696323e-02 -3.44659030... | [10.884016036987305, 1.6042437553405762] |
17126d66-ab45-484b-b58a-b9f1af76b708 | truly-unordered-probabilistic-rule-sets-for | 2206.08804 | null | https://arxiv.org/abs/2206.08804v3 | https://arxiv.org/pdf/2206.08804v3.pdf | Truly Unordered Probabilistic Rule Sets for Multi-class Classification | Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2) existi... | ['Matthijs van Leeuwen', 'Lincen Yang'] | 2022-06-17 | null | null | null | null | ['classification'] | ['methodology'] | [ 4.38655466e-01 3.26055974e-01 -5.99877298e-01 -4.37680125e-01
-6.81980848e-01 -7.27837861e-01 5.26663959e-01 2.93938309e-01
-7.25329891e-02 1.10728705e+00 5.41312918e-02 -5.74899793e-01
-8.96270931e-01 -9.50302660e-01 -7.12239742e-01 -5.71729362e-01
9.53539275e-03 9.69471574e-01 2.78247029e-01 5.37957251... | [8.808249473571777, 6.39182186126709] |
06711db8-b679-4b07-bf8b-59951eb03f71 | attacking-point-cloud-segmentation-with-color | 2112.05871 | null | https://arxiv.org/abs/2112.05871v4 | https://arxiv.org/pdf/2112.05871v4.pdf | On Adversarial Robustness of Point Cloud Semantic Segmentation | Recent research efforts on 3D point cloud semantic segmentation (PCSS) have achieved outstanding performance by adopting neural networks. However, the robustness of these complex models have not been systematically analyzed. Given that PCSS has been applied in many safety-critical applications like autonomous driving, ... | ['Yufei Ding', 'Boyuan Feng', 'Zhou Li', 'Zhe Zhou', 'Jiacen Xu'] | 2021-12-11 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.92663306e-01 6.41667545e-02 8.89899433e-02 -1.22616343e-01
-3.24073493e-01 -9.34035122e-01 3.47843230e-01 -8.50174874e-02
-2.70899743e-01 2.15550423e-01 -4.98185515e-01 -7.61880159e-01
1.14744768e-01 -8.93916488e-01 -8.61258626e-01 -6.09993577e-01
-5.38114607e-02 -4.74467911e-02 7.58587897e-01 -1.85688585... | [7.699694633483887, -4.4511237144470215] |
6ff90d4d-7687-4488-946e-45a71e9a3d2a | cnn-based-cost-volume-analysis-as-confidence | 1905.07287 | null | https://arxiv.org/abs/1905.07287v2 | https://arxiv.org/pdf/1905.07287v2.pdf | CNN-based Cost Volume Analysis as Confidence Measure for Dense Matching | Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous driving, which needs a high degree of confidence as mandatory prerequisite. Especially, the introduction of deep learning based methods result... | ['Christian Heipke', 'Max Mehltretter'] | 2019-05-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-7.66759813e-02 5.63538559e-02 4.23788615e-02 -5.99017739e-01
-4.93323505e-01 1.06030144e-01 7.13593960e-01 5.04246235e-01
-7.21999109e-01 9.09950197e-01 -7.35615045e-02 -7.01239109e-02
-1.00454383e-01 -1.01071513e+00 -6.15426898e-01 -5.20566642e-01
-8.94238800e-02 4.73006099e-01 4.43042785e-01 -6.67895749... | [8.66506290435791, -2.121777296066284] |
75181cb9-9f73-426e-9d98-2764428d575c | training-generative-adversarial-networks-from | 1905.12660 | null | https://arxiv.org/abs/1905.12660v2 | https://arxiv.org/pdf/1905.12660v2.pdf | Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators | Generative adversarial networks (GANs) have shown great success in applications such as image generation and inpainting. However, they typically require large datasets, which are often not available, especially in the context of prediction tasks such as image segmentation that require labels. Therefore, methods such as... | ['Simon Dixon', 'Sebastian Ewert', 'Daniel Stoller'] | 2019-05-29 | null | https://openreview.net/forum?id=Hye1RJHKwB | https://openreview.net/pdf?id=Hye1RJHKwB | iclr-2020-1 | ['audio-source-separation'] | ['audio'] | [ 6.68783009e-01 4.11605716e-01 5.29370876e-03 -2.25138083e-01
-1.36829710e+00 -7.58059502e-01 6.87039316e-01 -1.75394878e-01
-3.48446310e-01 9.01529431e-01 1.25118226e-01 -1.89831167e-01
4.31746542e-01 -8.19854736e-01 -7.70440578e-01 -1.00007343e+00
4.04364735e-01 6.43350482e-01 -1.55368149e-01 1.19015230... | [11.636958122253418, -0.20047180354595184] |
6c38993d-70d4-4efe-b9f5-5e31bb6ce918 | reproducible-scaling-laws-for-contrastive | 2212.07143 | null | https://arxiv.org/abs/2212.07143v1 | https://arxiv.org/pdf/2212.07143v1.pdf | Reproducible scaling laws for contrastive language-image learning | Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previo... | ['Jenia Jitsev', 'Ludwig Schmidt', 'Christoph Schuhmann', 'Cade Gordon', 'Gabriel Ilharco', 'Mitchell Wortsman', 'Ross Wightman', 'Romain Beaumont', 'Mehdi Cherti'] | 2022-12-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-attribute-detection', 'zero-shot-cross-modal-retrieval'] | ['computer-vision', 'miscellaneous'] | [-2.14370772e-01 -7.46006072e-01 -4.07268912e-01 -4.55914855e-01
-1.10411358e+00 -8.96987379e-01 4.85561639e-01 1.39536276e-01
-8.23067844e-01 2.56391406e-01 2.05604613e-01 -4.32539493e-01
-5.23721203e-02 -3.82020622e-01 -7.63162196e-01 -4.64364797e-01
1.54558420e-01 4.00868863e-01 1.95548370e-01 -6.45308048... | [9.406370162963867, 2.221642255783081] |
dd56b7a0-5f43-4503-9f7f-8093c22541ae | convergence-and-price-of-anarchy-guarantees | 2206.07642 | null | https://arxiv.org/abs/2206.07642v1 | https://arxiv.org/pdf/2206.07642v1.pdf | Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games | We study the performance of policy gradient methods for the subclass of Markov games known as Markov potential games (MPGs), which extends the notion of normal-form potential games to the stateful setting and includes the important special case of the fully cooperative setting where the agents share an identical reward... | ['Thinh T. Doan', 'Qi Zhang', 'Dingyang Chen'] | 2022-06-15 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.49752522e-01 4.91936266e-01 -4.53725159e-01 9.92931649e-02
-7.57124722e-01 -7.22685277e-01 2.41436169e-01 -1.72312811e-01
-7.80491114e-01 1.15588164e+00 9.27353743e-03 -8.11042547e-01
-5.67956507e-01 -6.52802527e-01 -9.24093366e-01 -9.45209801e-01
-4.47334051e-01 5.08786738e-01 7.15989619e-02 -4.49425042... | [4.222933769226074, 2.6328647136688232] |
44dbfced-f434-4ed9-ad3f-c1cd4bc1d424 | weakly-and-self-supervised-learning-for | 1708.02731 | null | http://arxiv.org/abs/1708.02731v1 | http://arxiv.org/pdf/1708.02731v1.pdf | Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting | This paper proposes a weakly- and self-supervised deep convolutional neural
network (WSSDCNN) for content-aware image retargeting. Our network takes a
source image and a target aspect ratio, and then directly outputs a retargeted
image. Retargeting is performed through a shift map, which is a pixel-wise
mapping from th... | ['Yu-Wing Tai', 'Tae-Hyun Oh', 'Jinsun Park', 'In So Kweon', 'Donghyeon Cho'] | 2017-08-09 | weakly-and-self-supervised-learning-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Cho_Weakly-_and_Self-Supervised_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Cho_Weakly-_and_Self-Supervised_ICCV_2017_paper.pdf | iccv-2017-10 | ['image-retargeting'] | ['computer-vision'] | [ 6.76483631e-01 3.05510581e-01 -2.12562278e-01 -3.99457455e-01
-3.97113532e-01 -5.40908277e-01 3.51004928e-01 9.18337330e-02
-5.24395466e-01 3.77831787e-01 1.53457865e-01 5.54719418e-02
3.03295314e-01 -8.87830019e-01 -9.76223707e-01 -7.08703041e-01
5.69618940e-01 -2.03872502e-01 5.54449439e-01 -2.23488271... | [11.285279273986816, -1.001785397529602] |
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