paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f7cc39de-8100-458a-92da-4f7cb9f9b177 | adaptive-mask-sampling-and-manifold-to | null | null | https://ieeexplore.ieee.org/abstract/document/10097620 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10097620&tag=1 | Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification | For the abundant spectral and spatial information recorded in hyperspectral images (HSIs), fully exploring spectral-spatial relationships has attracted widespread attention in hyperspectral image classification (HSIC) community. However, there are still some intractable obstructs. For one thing, in the patch-based proc... | ['and Gongping Yang.', 'Yuwen Huang', 'Yikun Liu', 'Wei Li', 'Mingsong Li'] | 2023-04-07 | null | null | null | ieee-transactions-on-geoscience-and-remote-14 | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 3.42205554e-01 -5.30824065e-01 2.08216589e-02 -2.03529254e-01
-5.05169451e-01 -4.76094306e-01 2.28208661e-01 -2.80595392e-01
-6.19618706e-02 3.20070952e-01 -3.02231900e-04 -2.27923408e-01
-7.40486264e-01 -6.63365126e-01 -3.20707619e-01 -1.08279335e+00
-2.44224042e-01 -2.63735592e-01 -1.28441051e-01 -1.71729714... | [10.062873840332031, -1.8647524118423462] |
5f02b1c3-2056-421e-bb91-3bfb038e6236 | eventplus-a-temporal-event-understanding | 2101.04922 | null | https://arxiv.org/abs/2101.04922v2 | https://arxiv.org/pdf/2101.04922v2.pdf | EventPlus: A Temporal Event Understanding Pipeline | We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction. Event information, especially event temporal knowledge, is a type o... | ['Nanyun Peng', 'Rujun Han', 'Shikhar Singh', 'Nuan Wen', 'Kung-Hsiang Huang', 'Mu Yang', 'Jiao Sun', 'Mingyu Derek Ma'] | 2021-01-13 | null | https://aclanthology.org/2021.naacl-demos.7 | https://aclanthology.org/2021.naacl-demos.7.pdf | naacl-2021-4 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 5.66234738e-02 2.28356346e-01 -4.50567693e-01 -3.29337418e-01
-5.76177001e-01 -8.07440042e-01 5.66177130e-01 1.36779225e+00
-3.40919614e-01 8.31309617e-01 7.39162266e-01 -3.28823656e-01
-2.70147860e-01 -1.01606941e+00 -4.86642867e-01 5.73870577e-02
-5.01170874e-01 3.57771158e-01 6.75076127e-01 -1.03066161... | [8.830374717712402, 9.132889747619629] |
f8a29f68-40d1-43d1-8a90-071923d33bed | a-comparative-study-for-single-image-blind | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Lai_A_Comparative_Study_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Lai_A_Comparative_Study_CVPR_2016_paper.pdf | A Comparative Study for Single Image Blind Deblurring | Numerous single image blind deblurring algorithms have been proposed to restore latent sharp images under camera motion. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real blurred images. It is thus unclear how these algorithms would perform on images acquired "in the wi... | ['Ming-Hsuan Yang', 'Jia-Bin Huang', 'Wei-Sheng Lai', 'Narendra Ahuja', 'Zhe Hu'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['single-image-blind-deblurring'] | ['computer-vision'] | [ 2.71464020e-01 -6.19758308e-01 2.15204224e-01 -2.91361302e-01
-7.09012330e-01 -8.51076305e-01 5.75786710e-01 -4.67710525e-01
-4.52053934e-01 7.16588616e-01 7.28334963e-01 -3.58193547e-01
-2.82816708e-01 1.46437809e-01 -4.56525445e-01 -4.64706481e-01
9.01031345e-02 -2.75091141e-01 -2.51772883e-03 3.16858217... | [11.663654327392578, -2.7905895709991455] |
3c9ec195-05b5-44e5-9fec-bd5b6691b5ac | dual-mode-adaptive-svd-ghost-imaging | 2302.07269 | null | https://arxiv.org/abs/2302.07269v1 | https://arxiv.org/pdf/2302.07269v1.pdf | Dual-mode adaptive-SVD ghost imaging | In this paper, we present a dual-mode adaptive singular value decomposition ghost imaging (A-SVD GI), which can be easily switched between the modes of imaging and edge detection. It can adaptively localize the foreground pixels via a threshold selection method. Then only the foreground region is illuminated by the sin... | ['Fan Wang', 'Xuchen Shan', 'Yao Wang', 'Jiaqi Song', 'Baolei Liu', 'Dajing Wang'] | 2023-02-14 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 7.40524888e-01 -3.92816603e-01 1.91308156e-01 1.85783252e-01
-2.41955116e-01 -5.22064209e-01 2.20202103e-01 -5.93840063e-01
-5.14667749e-01 5.43878973e-01 -1.87752575e-01 -2.59018183e-01
-5.38291931e-02 -7.30218828e-01 -2.25390106e-01 -1.47238660e+00
2.12551832e-01 -1.48269400e-01 7.12850511e-01 6.53387159... | [11.361671447753906, -2.648261308670044] |
ea0f7b90-e1ff-4624-a040-78cff8b4ff92 | farmer-s-assistant-a-machine-learning-based | 2204.11340 | null | https://arxiv.org/abs/2204.11340v1 | https://arxiv.org/pdf/2204.11340v1.pdf | Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions | Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the con... | ['Aparna Bhonde', 'Nishit Jain', 'Akshay Chopade', 'Shloka Gupta'] | 2022-04-24 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 3.30545083e-02 3.88524169e-03 -3.59227598e-01 -1.25488192e-01
1.21239282e-01 -7.64938414e-01 -1.77164003e-01 7.35976577e-01
3.34696263e-01 6.15075469e-01 4.99899406e-03 -9.73237514e-01
-1.94320008e-01 -1.28341508e+00 -6.11608565e-01 -4.74595428e-01
1.43703207e-01 2.22682565e-01 -5.52839898e-02 -5.82424700... | [9.323701858520508, -1.570221185684204] |
0eea20be-dabe-4439-9eef-ff6c6b35dd6c | a-graph-multi-separator-problem-for-image | 2307.04592 | null | https://arxiv.org/abs/2307.04592v1 | https://arxiv.org/pdf/2307.04592v1.pdf | A Graph Multi-separator Problem for Image Segmentation | We propose a novel abstraction of the image segmentation task in the form of a combinatorial optimization problem that we call the multi-separator problem. Feasible solutions indicate for every pixel whether it belongs to a segment or a segment separator, and indicate for pairs of pixels whether or not the pixels belon... | ['Bjoern Andres', 'Jannik Presberger', 'Shengxian Zhao', 'Jannik Irmai'] | 2023-07-10 | null | null | null | null | ['semantic-segmentation', 'combinatorial-optimization'] | ['computer-vision', 'methodology'] | [ 8.56865048e-01 4.53476161e-01 -3.26546669e-01 -1.46108335e-02
-7.08802581e-01 -1.02654231e+00 -4.74655367e-02 4.27349597e-01
-2.40926355e-01 8.95763993e-01 -5.19028544e-01 -4.94073719e-01
-3.14511240e-01 -8.35582912e-01 -8.87798190e-01 -9.93888438e-01
-8.79593566e-02 8.32911491e-01 6.24507964e-01 2.77401328... | [14.369312286376953, -3.149040937423706] |
0a0a1ddb-c0ba-4a46-8369-1a2cbb64f21e | data-driven-simulation-of-inelastic-materials | 2101.10730 | null | https://arxiv.org/abs/2101.10730v2 | https://arxiv.org/pdf/2101.10730v2.pdf | Model-free Data-Driven simulation of inelastic materials using structured data sets, tangent space information and transition rules | Model-free data-driven computational mechanics replaces phenomenological constitutive functions by numerical simulations based on data sets of representative samples in stress-strain space. The distance of strain and stress pairs from the data set is minimized, subject to equilibrium and compatibility constraints. Alth... | ['Klaus Hackl', 'Kerem Ciftci'] | 2021-01-26 | null | null | null | null | ['non-linear-elasticity'] | ['miscellaneous'] | [ 3.81421372e-02 -2.69265890e-01 -9.98543203e-02 -1.22488022e-01
-3.17228809e-02 -1.99817330e-01 2.58195996e-01 3.27618450e-01
-5.69475114e-01 7.20692039e-01 -3.35014910e-01 4.90388632e-01
-8.25525820e-01 -8.71081412e-01 -4.78203237e-01 -9.82906640e-01
2.01851167e-02 8.84186149e-01 5.54097056e-01 -2.16839671... | [6.2734375, 3.2708609104156494] |
e0a42e12-007d-4da8-94c1-0ac61649e90c | associatively-segmenting-instances-and | 1902.09852 | null | http://arxiv.org/abs/1902.09852v2 | http://arxiv.org/pdf/1902.09852v2.pdf | Associatively Segmenting Instances and Semantics in Point Clouds | A 3D point cloud describes the real scene precisely and intuitively.To date
how to segment diversified elements in such an informative 3D scene is rarely
discussed. In this paper, we first introduce a simple and flexible framework to
segment instances and semantics in point clouds simultaneously. Then, we
propose two a... | ['Jiaya Jia', 'Chunhua Shen', 'Xiaoyong Shen', 'Shu Liu', 'Xinlong Wang'] | 2019-02-26 | associatively-segmenting-instances-and-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Associatively_Segmenting_Instances_and_Semantics_in_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Associatively_Segmenting_Instances_and_Semantics_in_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-4.88531627e-02 2.65739143e-01 -2.81286359e-01 -6.30643129e-01
-9.29972172e-01 -5.95373094e-01 4.88506943e-01 1.72093809e-01
4.80200201e-02 2.95168906e-01 -4.07111384e-02 8.46511498e-03
-7.30742440e-02 -9.04432535e-01 -9.79947805e-01 -3.74659389e-01
2.40885288e-01 8.20018530e-01 5.50530016e-01 -1.27699850... | [8.061857223510742, -3.1707301139831543] |
b154e35e-8e9c-4cf6-a5e5-8ca506fd7361 | abstract-to-executable-trajectory-translation | 2210.07658 | null | https://arxiv.org/abs/2210.07658v2 | https://arxiv.org/pdf/2210.07658v2.pdf | Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization | Training long-horizon robotic policies in complex physical environments is essential for many applications, such as robotic manipulation. However, learning a policy that can generalize to unseen tasks is challenging. In this work, we propose to achieve one-shot task generalization by decoupling plan generation and plan... | ['Hao Su', 'Yuzhe Qin', 'Zhiao Huang', 'Tongzhou Mu', 'Xiaochen Li', 'Stone Tao'] | 2022-10-14 | null | null | null | null | ['few-shot-imitation-learning'] | ['methodology'] | [ 1.64077714e-01 4.07002240e-01 -1.59458995e-01 -1.59675270e-01
-7.21961200e-01 -9.02714074e-01 8.45720470e-01 -5.29609323e-02
-3.10758501e-01 8.50163937e-01 3.08856577e-01 -3.68867248e-01
-7.94160962e-02 -5.44859946e-01 -1.11507535e+00 -3.65792930e-01
-1.41902000e-01 5.49938023e-01 2.02955469e-01 -4.67468381... | [4.540831565856934, 0.81562739610672] |
adb4b8c2-d7a0-4940-a17d-8ec03721f063 | a-probabilistic-hard-attention-model-for | 2111.07534 | null | https://arxiv.org/abs/2111.07534v1 | https://arxiv.org/pdf/2111.07534v1.pdf | A Probabilistic Hard Attention Model For Sequentially Observed Scenes | A visual hard attention model actively selects and observes a sequence of subregions in an image to make a prediction. The majority of hard attention models determine the attention-worthy regions by first analyzing a complete image. However, it may be the case that the entire image is not available initially but instea... | ['James J. Clark', 'Samrudhdhi B. Rangrej'] | 2021-11-15 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.01466048e-01 4.18262720e-01 -2.76645929e-01 -3.08343470e-01
-9.01230514e-01 -3.32188547e-01 5.74432790e-01 -3.03490251e-01
-3.21677566e-01 6.26652360e-01 3.00266892e-01 -1.18335672e-02
-4.12937254e-02 -5.29050648e-01 -8.98495257e-01 -1.07556617e+00
1.27759114e-01 3.86458158e-01 7.68456161e-02 2.07973883... | [9.51644515991211, 0.26236751675605774] |
7f34dfc8-df7b-4af4-b34c-b45ac3882f48 | accelerating-system-level-debug-using-rule | 2207.00622 | null | https://arxiv.org/abs/2207.00622v1 | https://arxiv.org/pdf/2207.00622v1.pdf | Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques | We propose a root-causing procedure for accelerating system-level debug using rule-based techniques. We describe the procedure and how it provides high quality debug hints for reducing the debug effort. This includes the heuristics for engineering features from logs of many tests, and the data analytics techniques for ... | ['Zurab Khasidashvili'] | 2022-07-02 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [-4.23581690e-01 1.89933553e-01 -4.22438622e-01 -3.15483928e-01
-5.90103030e-01 -5.24621487e-01 -1.72946274e-01 2.78237402e-01
4.65564549e-01 7.45594680e-01 -2.88940430e-01 -8.57211649e-01
-3.36082995e-01 -5.82320631e-01 -5.10868430e-01 1.36704758e-01
-4.80194479e-01 3.24305862e-01 5.22636712e-01 -2.31288180... | [7.557188987731934, 7.521271705627441] |
395d7779-d870-4b36-92bc-f8662cbcdd16 | belt-blockwise-missing-embedding-learning | 2105.10360 | null | https://arxiv.org/abs/2105.10360v3 | https://arxiv.org/pdf/2105.10360v3.pdf | Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices | Matrix completion has attracted attention in many fields, including statistics, applied mathematics, and electrical engineering. Most of the works focus on the independent sampling models under which the observed entries are sampled independently. Motivated by applications in the integration of knowledge graphs derived... | ['Junwei Lu', 'Tianxi Cai', 'Doudou Zhou'] | 2021-05-21 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 6.11091852e-01 1.09752499e-01 -1.28962263e-01 -5.63070215e-02
-5.34350872e-01 -3.40784848e-01 2.24104747e-01 3.77403259e-01
-4.97242749e-01 8.15522134e-01 3.22971106e-01 -4.35783058e-01
-6.28916323e-01 -5.47489643e-01 -6.42078638e-01 -9.41871881e-01
-3.70565653e-01 4.69644248e-01 -4.71019983e-01 -7.14548007... | [7.172196865081787, 4.747644901275635] |
5fc2a444-2240-42de-890c-9c106e43537b | lavender-unifying-video-language | 2206.07160 | null | https://arxiv.org/abs/2206.07160v1 | https://arxiv.org/pdf/2206.07160v1.pdf | LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling | Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence generation. However, existing video-language (VidL) models still require task-specific designs in model architecture and training objectives for... | ['Lijuan Wang', 'Ce Liu', 'Zicheng Liu', 'Chung-Ching Lin', 'Kevin Lin', 'Zhe Gan', 'Linjie Li'] | 2022-06-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_LAVENDER_Unifying_Video-Language_Understanding_As_Masked_Language_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_LAVENDER_Unifying_Video-Language_Understanding_As_Masked_Language_Modeling_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.82745263e-01 -1.99764743e-01 -2.72286773e-01 -3.82857949e-01
-1.23757875e+00 -5.26524961e-01 8.14049184e-01 -2.98250049e-01
-5.00415146e-01 3.97146046e-01 2.31574520e-01 -5.23321986e-01
4.14726943e-01 -2.12106571e-01 -9.03554440e-01 -4.59585428e-01
1.88118309e-01 2.37567574e-01 3.77053231e-01 -1.51437268... | [10.567750930786133, 1.162280559539795] |
6140aa15-3e62-4617-b734-6d81e6f2e59d | efficient-video-representation-learning-via | 2211.10636 | null | https://arxiv.org/abs/2211.10636v3 | https://arxiv.org/pdf/2211.10636v3.pdf | Efficient Video Representation Learning via Motion-Aware Token Selection | Recently emerged Masked Video Modeling techniques demonstrated their potential by significantly outperforming previous methods in self-supervised learning for video. However, they require an excessive amount of computations and memory while predicting uninformative tokens/frames due to random masking strategies, requir... | ['Sung Ju Hwang', 'Youngwan Lee', 'Jaehong Yoon', 'Sunil Hwang'] | 2022-11-19 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 2.97917336e-01 -1.27246723e-01 -4.31322932e-01 -1.81259796e-01
-5.88756859e-01 -2.74456590e-01 2.96557724e-01 1.56327877e-02
-5.71218133e-01 5.25727689e-01 1.11659408e-01 -1.85767233e-01
3.70659560e-01 -8.14784050e-01 -8.05662215e-01 -9.33277130e-01
-5.00457227e-01 7.43632540e-02 6.78533733e-01 3.30300122... | [9.063090324401855, 0.12572456896305084] |
3ce4dd50-486b-4a6e-bb2a-96779fb07eda | explainable-outfit-recommendation-with-joint | 1806.08977 | null | http://arxiv.org/abs/1806.08977v3 | http://arxiv.org/pdf/1806.08977v3.pdf | Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation | Most previous work on outfit recommendation focuses on designing visual
features to enhance recommendations. Existing work neglects user comments of
fashion items, which have been proved to be effective in generating
explanations along with better recommendation results. We propose a novel
neural network framework, neu... | ['Zhaochun Ren', 'Jun Ma', 'Zhumin Chen', 'Yujie Lin', 'Pengjie Ren', 'Maarten de Rijke'] | 2018-06-23 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 2.76215672e-01 3.46463844e-02 -3.85416418e-01 -5.96365631e-01
-7.67112970e-01 -3.23382378e-01 6.18316531e-01 -2.38084570e-01
3.78733426e-02 4.16783661e-01 9.47072566e-01 -5.49993634e-01
3.04085165e-01 -5.81537366e-01 -7.89918125e-01 -4.23571289e-01
5.49825907e-01 1.23510204e-01 -2.72294164e-01 -2.11189449... | [10.210273742675781, 5.624606132507324] |
43d946ce-2179-4151-b0b2-01993c8cf4df | srn-side-output-residual-network-for-object | 1807.06621 | null | http://arxiv.org/abs/1807.06621v2 | http://arxiv.org/pdf/1807.06621v2.pdf | SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond | In this paper, we establish a baseline for object reflection symmetry
detection in complex backgrounds by presenting a new benchmark and an
end-to-end deep learning approach, opening up a promising direction for
symmetry detection in the wild. The new benchmark, Sym-PASCAL, spans challenges
including object diversity, ... | ['Guoying Zhao', 'Jie Chen', 'Jianbin Jiao', 'Qixiang Ye', 'Wei Ke'] | 2018-07-17 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 5.22553980e-01 4.63276468e-02 1.90878823e-01 -4.67734843e-01
-7.73604751e-01 -4.10419732e-01 3.48583937e-01 -5.21368980e-01
-4.46910597e-02 4.18070331e-02 1.65188834e-01 -2.93419063e-02
7.18906820e-02 -4.98601705e-01 -1.09379709e+00 -4.67418253e-01
1.76333144e-01 1.03866227e-01 8.54486942e-01 -4.31715310... | [8.619140625, -1.7410895824432373] |
a1a77b3f-6208-4925-b317-29b0f4ec366c | only-pay-for-what-is-uncertain-variance | 2303.09033 | null | https://arxiv.org/abs/2303.09033v1 | https://arxiv.org/pdf/2303.09033v1.pdf | Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling | Most bandit algorithms assume that the reward variance or its upper bound is known. While variance overestimation is usually safe and sound, it increases regret. On the other hand, an underestimated variance may lead to linear regret due to committing early to a suboptimal arm. This motivated prior works on variance-aw... | ['Branislav Kveton', 'Aadirupa Saha'] | 2023-03-16 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 3.80321927e-02 2.68202901e-01 -9.43130672e-01 -2.91579574e-01
-1.09150755e+00 -7.05687523e-01 2.44222820e-01 -4.01890911e-02
-3.96916330e-01 1.26808345e+00 1.47121295e-01 -5.94569743e-01
-8.56103301e-01 -6.87285900e-01 -9.73368704e-01 -8.67259800e-01
-9.33898613e-03 7.58030117e-01 -1.14265837e-01 2.93448865... | [4.495402812957764, 3.258636474609375] |
9ccfec5d-7457-4c23-9b24-1709ec33eab1 | instance-level-sketch-based-retrieval-by-deep | 1811.11375 | null | https://arxiv.org/abs/1811.11375v2 | https://arxiv.org/pdf/1811.11375v2.pdf | Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network | Sketch has been employed as an effective communicative tool to express the abstract and intuitive meanings of object. Recognizing the free-hand sketch drawing is extremely useful in many real-world applications. While content-based sketch recognition has been studied for several decades, the instance-level Sketch-Based... | ['xiangyang xue', 'Yu-Gang Jiang', 'Shaogang Gong', 'Yanwei Fu', 'Peng Lu', 'Hangyu Lin'] | 2018-11-28 | null | null | null | null | ['sketch-based-image-retrieval', 'sketch-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.71471027e-02 -7.49729037e-01 -3.46972197e-01 -2.05544814e-01
-6.96937263e-01 -2.91355759e-01 6.43267274e-01 -3.92231792e-01
-1.16608195e-01 4.86164689e-01 -1.78877488e-01 7.19139427e-02
-5.17250717e-01 -7.95846522e-01 -5.30375481e-01 -6.37629926e-01
3.57946783e-01 2.93540418e-01 1.15375243e-01 -3.68324906... | [11.656868934631348, 0.6279942989349365] |
83c78ecb-c91b-43e9-9809-6c626970d251 | vsr-a-unified-framework-for-document-layout | 2105.06220 | null | https://arxiv.org/abs/2105.06220v1 | https://arxiv.org/pdf/2105.06220v1.pdf | VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations | Document layout analysis is crucial for understanding document structures. On this task, vision and semantics of documents, and relations between layout components contribute to the understanding process. Though many works have been proposed to exploit the above information, they show unsatisfactory results. NLP-based ... | ['Fei Wu', 'Yi Niu', 'ShiLiang Pu', 'Zhanzhan Cheng', 'Liang Qiao', 'Can Li', 'Peng Zhang'] | 2021-05-13 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 3.26203465e-01 -2.71262556e-01 -3.81927818e-01 -1.96891680e-01
-2.75373161e-01 -7.76891589e-01 6.16923034e-01 4.46996123e-01
-2.23252028e-02 1.92994207e-01 4.08736795e-01 -3.70024025e-01
-7.03286603e-02 -7.80064225e-01 -4.59373415e-01 -5.14417410e-01
3.82823408e-01 2.13054433e-01 1.62935898e-01 -5.85506558... | [11.608991622924805, 2.3663485050201416] |
87931b0b-8ab1-4f13-bf1f-dcc395efca08 | training-neural-networks-for | 1911.00405 | null | https://arxiv.org/abs/1911.00405v2 | https://arxiv.org/pdf/1911.00405v2.pdf | Training Neural Networks for Likelihood/Density Ratio Estimation | Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities. In classical approaches the two densities are assumed known or to belong to some known parametric family. In a data-driven version we replace this requirement with the availability of da... | ['Kalliopi Basioti', 'George V. Moustakides'] | 2019-11-01 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.90046102e-01 3.55748609e-02 -2.08654448e-01 -5.99227428e-01
-6.09282672e-01 -1.69130608e-01 2.88742900e-01 9.96827856e-02
-3.86090100e-01 1.13481486e+00 -4.36883628e-01 -3.72051626e-01
-4.22361702e-01 -8.93916488e-01 -7.35321522e-01 -6.84235752e-01
-2.81399667e-01 6.81002438e-01 3.55074406e-02 1.72591418... | [6.825510501861572, 3.8953490257263184] |
d0c5f5ef-0e62-488c-aa49-43227df058c5 | classifying-multi-channel-uwb-sar-imagery-via | 1810.02812 | null | http://arxiv.org/abs/1810.02812v1 | http://arxiv.org/pdf/1810.02812v1.pdf | Classifying Multi-channel UWB SAR Imagery via Tensor Sparsity Learning Techniques | Using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture
radar (SAR) technology for detecting buried and obscured targets, e.g. bomb or
mine, has been successfully demonstrated recently. Despite promising recent
progress, a significant open challenge is to distinguish obscured targets from
other (nat... | ['Vishal Monga', 'Tiep Vu', 'Lam Nguyen'] | 2018-10-04 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 5.42644322e-01 -5.68741381e-01 2.08484456e-01 -3.24632436e-01
-1.00122857e+00 -4.45366591e-01 3.92255634e-01 -3.70596588e-01
6.30484298e-02 7.19045401e-01 3.53087157e-01 -1.31095797e-01
-7.91375399e-01 -6.36777818e-01 -1.09454520e-01 -1.07954741e+00
-5.18541098e-01 2.32456569e-02 -7.78328031e-02 -4.11747336... | [6.824368476867676, 1.0212428569793701] |
c5bed6fa-31e1-4c34-a6d3-f8e9cf0d4db9 | supervised-nonnegative-matrix-factorization | 1809.10680 | null | http://arxiv.org/abs/1809.10680v2 | http://arxiv.org/pdf/1809.10680v2.pdf | Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk | ICU mortality risk prediction is a tough yet important task. On one hand, due
to the complex temporal data collected, it is difficult to identify the
effective features and interpret them easily; on the other hand, good
prediction can help clinicians take timely actions to prevent the mortality.
These correspond to the... | ['Yuan Zhao', 'Yuan Luo', 'Fei Wang', 'Chengsheng Mao', 'Guoqing Chao'] | 2018-09-27 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 1.64335504e-01 -2.37026840e-01 -1.18395709e-01 -2.64130563e-01
-6.81844875e-02 -4.26044650e-02 5.48435077e-02 2.05873892e-01
-4.55337256e-01 8.56291533e-01 1.56846464e-01 -4.70631331e-01
-6.71695054e-01 -5.50973594e-01 -1.83061715e-02 -7.75783896e-01
-2.28729352e-01 3.43662590e-01 -7.47718140e-02 -1.52308539... | [7.73414421081543, 3.830409288406372] |
1360fca2-8031-477c-bd7f-f1684115170d | unituebingencl-at-semeval-2020-task-7-humor | null | null | https://aclanthology.org/2020.semeval-1.139 | https://aclanthology.org/2020.semeval-1.139.pdf | UniTuebingenCL at SemEval-2020 Task 7: Humor Detection in News Headlines | This paper describes the work done by the team UniTuebingenCL for the SemEval 2020 Task 7: {``}Assessing the Funniness of Edited News Headlines{''}. We participated in both sub-tasks: sub-task A, given the original and the edited headline, predicting the mean funniness of the edited headline; and sub-task B, given the ... | ['Lea Gr{\\"u}ner', 'Charlotte Ammer'] | 2020-12-01 | null | null | null | semeval-2020 | ['humor-detection'] | ['natural-language-processing'] | [-2.39152595e-01 4.67625380e-01 1.67844057e-01 -3.87117594e-01
-9.39354777e-01 -3.94522935e-01 8.58122647e-01 4.43110913e-01
-8.17782819e-01 6.28897250e-01 4.97823626e-01 -2.34824359e-01
-1.31147146e-01 -5.89472830e-01 -5.48441410e-01 -3.95625114e-01
-1.76692054e-01 2.26090387e-01 -1.08216681e-01 -2.79310405... | [8.744149208068848, 10.844122886657715] |
65e2c2ae-678e-43da-bde4-b56d54da2f46 | deep-operator-learning-based-surrogate-models | 2306.00810 | null | https://arxiv.org/abs/2306.00810v1 | https://arxiv.org/pdf/2306.00810v1.pdf | Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles | This paper designs surrogate models with uncertainty quantification capabilities to improve the thermal performance of rib-turbulated internal cooling channels effectively. To construct the surrogate, we use the deep operator network (DeepONet) framework, a novel class of neural networks designed to approximate mapping... | ['Guillermo Paniagua', 'Guang Lina', 'Amirhossein Mollaali', 'Christian Moya', 'Izzet Sahin'] | 2023-06-01 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [-1.69503123e-01 1.69795260e-01 2.21912786e-01 -4.05124091e-02
-3.58751178e-01 -3.97755742e-01 6.12433851e-01 -6.41428307e-02
-5.49206376e-01 1.19710910e+00 -1.13696016e-01 -3.71548444e-01
-5.26463985e-01 -9.83591557e-01 -8.30968380e-01 -1.12090862e+00
-2.09925711e-01 9.57927763e-01 2.06278116e-02 8.14409107... | [6.439406394958496, 3.3788089752197266] |
3a418f73-03a3-44ae-9011-731e1bdf601d | deep-fusion-of-gray-level-co-occurrence | 2205.05123 | null | https://arxiv.org/abs/2205.05123v2 | https://arxiv.org/pdf/2205.05123v2.pdf | Deep fusion of gray level co-occurrence matrices for lung nodule classification | Lung cancer is a severe menace to human health, due to which millions of people die because of late diagnoses of cancer; thus, it is vital to detect the disease as early as possible. The Computerized chest analysis Tomography of scan is assumed to be one of the efficient solutions for detecting and classifying lung nod... | ['AhmadReza Naghsh Nilchi', 'Hossein Karshenas', 'Ahmed Saihood'] | 2022-05-10 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.40269557e-01 -1.59764051e-01 -7.06351548e-02 1.03991076e-01
-9.07225311e-01 9.40287486e-02 4.37538534e-01 1.31746367e-01
-6.03287458e-01 4.54988956e-01 -7.30924904e-02 -4.25975442e-01
-8.84665698e-02 -7.82068908e-01 -9.98382568e-02 -1.09342158e+00
-5.07901125e-02 5.75636327e-01 4.94848758e-01 3.83641183... | [15.255053520202637, -2.217008352279663] |
4d398456-9c38-4714-94e6-2b918775130a | pix2nerf-unsupervised-conditional-p-gan-for | 2202.13162 | null | https://arxiv.org/abs/2202.13162v1 | https://arxiv.org/pdf/2202.13162v1.pdf | Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation | We propose a pipeline to generate Neural Radiance Fields~(NeRF) of an object or a scene of a specific class, conditioned on a single input image. This is a challenging task, as training NeRF requires multiple views of the same scene, coupled with corresponding poses, which are hard to obtain. Our method is based on $\p... | ['Luc van Gool', 'Dengxin Dai', 'Anton Obukhov', 'Shengqu Cai'] | 2022-02-26 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 6.21274769e-01 2.30460003e-01 2.72910446e-01 -4.24880445e-01
-1.08705425e+00 -5.72172463e-01 7.78308213e-01 -7.90684819e-01
1.73926115e-01 5.83922923e-01 3.48724693e-01 1.69439182e-01
3.92859131e-01 -1.15324938e+00 -1.22600937e+00 -7.33293295e-01
6.03556871e-01 5.60982168e-01 -8.39140862e-02 -1.09013349... | [9.265690803527832, -3.150806427001953] |
d7b0cd10-1828-4b90-95b2-feea925f7037 | reliable-prediction-intervals-with-directly | 2302.00872 | null | https://arxiv.org/abs/2302.00872v1 | https://arxiv.org/pdf/2302.00872v1.pdf | Reliable Prediction Intervals with Directly Optimized Inductive Conformal Regression for Deep Learning | By generating prediction intervals (PIs) to quantify the uncertainty of each prediction in deep learning regression, the risk of wrong predictions can be effectively controlled. High-quality PIs need to be as narrow as possible, whilst covering a preset proportion of real labels. At present, many approaches to improve ... | ['Anthony Bellotti', 'Haocheng Lei'] | 2023-02-02 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 3.32274884e-01 5.68892658e-01 -3.11523795e-01 -5.55931687e-01
-1.33091557e+00 -3.54253173e-01 4.35534537e-01 1.88918412e-01
-1.49801150e-01 9.92747366e-01 -1.50080711e-01 -1.27085492e-01
-4.16970700e-01 -1.13941562e+00 -1.13725102e+00 -7.35268950e-01
1.28959700e-01 7.94155300e-01 1.07551105e-01 6.04660809... | [7.957846164703369, 4.148825645446777] |
3ca7e99b-37ab-461e-a174-4e30f221c973 | e2v-sde-from-asynchronous-events-to-fast-and-1 | 2206.07578 | null | https://arxiv.org/abs/2206.07578v2 | https://arxiv.org/pdf/2206.07578v2.pdf | E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations | Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic range (HDR), high temporal resolution, and low power consumption. However, the results of event cameras should be processed into an alterna... | ['Sungroh Yoon', 'Jeonghee Jo', 'Seongsik Park', 'Byunggook Na', 'Dongjin Lee', 'Jongwan Kim'] | 2022-06-15 | e2v-sde-from-asynchronous-events-to-fast-and | http://openaccess.thecvf.com//content/CVPR2022/html/Kim_E2V-SDE_From_Asynchronous_Events_to_Fast_and_Continuous_Video_Reconstruction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kim_E2V-SDE_From_Asynchronous_Events_to_Fast_and_Continuous_Video_Reconstruction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-reconstruction'] | ['computer-vision'] | [ 1.51678756e-01 -6.40553415e-01 6.52447268e-02 -3.31709653e-01
-3.53152752e-01 -2.31565386e-01 3.67194444e-01 -4.51301903e-01
-3.59349608e-01 6.59448445e-01 1.53641716e-01 1.77896336e-01
-2.63355896e-02 -6.22598946e-01 -7.81615853e-01 -9.16977465e-01
1.26753241e-01 -1.19290635e-01 6.82656586e-01 2.22245052... | [10.769248962402344, -1.985932469367981] |
a8c30171-6568-44c2-ae21-64c3efd38be3 | basicvsr-improving-video-super-resolution | 2104.13371 | null | https://arxiv.org/abs/2104.13371v1 | https://arxiv.org/pdf/2104.13371v1.pdf | BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment | A recurrent structure is a popular framework choice for the task of video super-resolution. The state-of-the-art method BasicVSR adopts bidirectional propagation with feature alignment to effectively exploit information from the entire input video. In this study, we redesign BasicVSR by proposing second-order grid prop... | ['Chen Change Loy', 'Xiangyu Xu', 'Shangchen Zhou', 'Kelvin C. K. Chan'] | 2021-04-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chan_BasicVSR_Improving_Video_Super-Resolution_With_Enhanced_Propagation_and_Alignment_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chan_BasicVSR_Improving_Video_Super-Resolution_With_Enhanced_Propagation_and_Alignment_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 4.10804778e-01 -1.99151799e-01 -3.17833185e-01 2.39771474e-02
-8.37048471e-01 -3.15006763e-01 2.68152118e-01 -4.27011609e-01
-2.16191813e-01 7.14291513e-01 7.41344869e-01 3.56329083e-02
-1.74750879e-01 -4.89592373e-01 -6.65796340e-01 -4.97044027e-01
-2.74184555e-01 -3.85917902e-01 2.90836662e-01 -6.13594949... | [11.107913970947266, -1.9415841102600098] |
c78f4a51-82c0-4e2c-b2d1-9d2a143a6a1c | e-lpips-robust-perceptual-image-similarity | 1906.03973 | null | https://arxiv.org/abs/1906.03973v2 | https://arxiv.org/pdf/1906.03973v2.pdf | E-LPIPS: Robust Perceptual Image Similarity via Random Transformation Ensembles | It has been recently shown that the hidden variables of convolutional neural networks make for an efficient perceptual similarity metric that accurately predicts human judgment on relative image similarity assessment. First, we show that such learned perceptual similarity metrics (LPIPS) are susceptible to adversarial ... | ['Erik Härkönen', 'Markus Kettunen', 'Jaakko Lehtinen'] | 2019-06-10 | null | null | null | null | ['image-similarity-search'] | ['computer-vision'] | [ 5.39525807e-01 3.15459639e-01 3.75970066e-01 -4.93028998e-01
-4.14278895e-01 -9.44678724e-01 8.23025227e-01 -1.06578238e-01
-4.38713670e-01 3.63761306e-01 1.95434049e-01 -2.58630961e-01
-1.90647572e-01 -6.75065279e-01 -8.11865330e-01 -6.01537108e-01
-2.35117957e-01 -1.87386394e-01 1.76934570e-01 -6.13203526... | [10.098861694335938, 2.319463014602661] |
f66bc32f-c722-45b0-957b-34b23303a377 | terpret-a-probabilistic-programming-language | 1608.04428 | null | http://arxiv.org/abs/1608.04428v1 | http://arxiv.org/pdf/1608.04428v1.pdf | TerpreT: A Probabilistic Programming Language for Program Induction | We study machine learning formulations of inductive program synthesis; given
input-output examples, we try to synthesize source code that maps inputs to
corresponding outputs. Our aims are to develop new machine learning approaches
based on neural networks and graphical models, and to understand the
capabilities of mac... | ['Pushmeet Kohli', 'Nate Kushman', 'Daniel Tarlow', 'Rishabh Singh', 'Marc Brockschmidt', 'Jonathan Taylor', 'Alexander L. Gaunt'] | 2016-08-15 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 3.71195287e-01 4.39167947e-01 -7.47951984e-01 -6.09568417e-01
-9.35564339e-01 -7.29809642e-01 7.22069502e-01 1.12675682e-01
2.15313315e-01 4.88366663e-01 -3.96775790e-02 -1.09105766e+00
-9.68020875e-03 -1.16977513e+00 -9.90648985e-01 -1.12702578e-01
-9.08845440e-02 8.05073678e-01 -1.08425722e-01 2.73282588... | [8.411370277404785, 7.203490734100342] |
32098368-1a08-4c5c-8db8-418e50181595 | fast-vehicle-detection-and-tracking-on | 2207.01183 | null | https://arxiv.org/abs/2207.01183v2 | https://arxiv.org/pdf/2207.01183v2.pdf | Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation | We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the object detection base model. The nighttime video of this dataset is very challenging, and the detection accuracy (AP50) of the base model is about 54... | ['Wen-Huang Cheng', 'Hsueh-Ming Hang', 'Sandy Ardianto'] | 2022-07-04 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [-4.72265482e-01 -5.93243778e-01 -2.25603253e-01 9.82796866e-03
-4.94176745e-01 -4.75726426e-01 2.40621924e-01 -3.63193065e-01
-5.76959312e-01 5.37744582e-01 -5.38946509e-01 -5.55520833e-01
5.34390628e-01 -9.30053830e-01 -6.31918252e-01 -8.01225305e-01
-1.02731578e-01 -3.71216565e-01 1.51642573e+00 -1.20421261... | [8.050516128540039, -1.0316027402877808] |
f640b47a-6790-4562-aee0-0175b938fcdd | combining-generative-and-discriminative-1 | 1708.00790 | null | http://arxiv.org/abs/1708.00790v2 | http://arxiv.org/pdf/1708.00790v2.pdf | Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition | Unsupervised dependency parsing aims to learn a dependency parser from
unannotated sentences. Existing work focuses on either learning generative
models using the expectation-maximization algorithm and its variants, or
learning discriminative models using the discriminative clustering algorithm.
In this paper, we propo... | ['Wenjuan Han', 'Kewei Tu', 'Yong Jiang'] | 2017-08-02 | combining-generative-and-discriminative-2 | https://aclanthology.org/D17-1177 | https://aclanthology.org/D17-1177.pdf | emnlp-2017-9 | ['dependency-grammar-induction', 'unsupervised-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-2.71730989e-01 3.00801694e-01 -1.45033956e-01 -8.08746874e-01
-1.26569986e+00 -6.21878266e-01 3.83513719e-01 -2.82991499e-01
-2.74496049e-01 7.11231112e-01 2.83625335e-01 -3.59248132e-01
2.03730151e-01 -6.25776470e-01 -3.52311403e-01 -9.13413525e-01
-6.49363324e-02 8.43412995e-01 1.56043515e-01 5.83936945... | [10.347737312316895, 9.722945213317871] |
0a53623c-ce3d-4da6-b2d6-c9b47bab4c1f | sparse-gaussian-process-audio-source | 1810.12679 | null | http://arxiv.org/abs/1810.12679v3 | http://arxiv.org/pdf/1810.12679v3.pdf | Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain | Gaussian process (GP) audio source separation is a time-domain approach that
circumvents the inherent phase approximation issue of spectrogram based
methods. Furthermore, through its kernel, GPs elegantly incorporate prior
knowledge about the sources into the separation model. Despite these compelling
advantages, the c... | ['Dan Stowell', 'Mauricio A. Álvarez', 'Pablo A. Alvarado'] | 2018-10-30 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 9.02678445e-02 -3.18701297e-01 2.04615161e-01 1.60946682e-01
-1.51219833e+00 -7.13033915e-01 4.33566719e-01 -2.07959652e-01
2.49162968e-03 5.70251882e-01 2.69719988e-01 -1.28412306e-01
-5.39786816e-01 -2.79866785e-01 -3.40110421e-01 -1.13808632e+00
-2.45798379e-01 2.88137943e-01 1.14016928e-01 2.12235615... | [15.400691986083984, 5.602859020233154] |
a8d7c610-8272-4554-bf04-531453c4641d | rethinking-textual-adversarial-defense-for | 2208.10251 | null | https://arxiv.org/abs/2208.10251v1 | https://arxiv.org/pdf/2208.10251v1.pdf | Rethinking Textual Adversarial Defense for Pre-trained Language Models | Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversarial examples with slight perturbations on different levels (sentence / word / character), adversarial attacks can fool PrLMs to generate inco... | ['Hai Zhao', 'Zhuosheng Zhang', 'Rongzhou Bao', 'Jiayi Wang'] | 2022-07-21 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.57804179e-01 4.89300229e-02 1.17859274e-01 2.36283746e-02
-6.45194769e-01 -1.47752798e+00 7.72064388e-01 -9.27531943e-02
8.57764557e-02 5.35924077e-01 1.13286167e-01 -6.46968365e-01
3.38111341e-01 -1.16061211e+00 -7.47401476e-01 -5.38477004e-01
-1.18526414e-01 3.89933959e-02 3.81054312e-01 -7.68877149... | [5.949075698852539, 8.031827926635742] |
5f035982-950e-4ce7-9981-6cf6b7e3bb0a | removing-word-level-spurious-alignment | 2104.13872 | null | https://arxiv.org/abs/2104.13872v2 | https://arxiv.org/pdf/2104.13872v2.pdf | Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning | Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images. In previous work, pseudo-captions, i.e., sentences that contain the detected o... | ['Yuji Matsumoto', 'Taro Watanabe', 'Atsushi Hashimoto', 'Yoshitaka Ushiku', 'Ukyo Honda'] | 2021-04-28 | null | https://aclanthology.org/2021.eacl-main.323 | https://aclanthology.org/2021.eacl-main.323.pdf | eacl-2021-2 | ['image-sentence-alignment'] | ['natural-language-processing'] | [ 8.56781006e-01 2.98530787e-01 1.16712525e-01 -6.12531364e-01
-1.00562000e+00 -3.91159803e-01 5.13021886e-01 1.28794909e-01
-7.03569949e-01 7.86004424e-01 7.66088590e-02 -7.39901420e-03
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3.93229276e-01 2.77474016e-01 5.05656123e-01 -1.01037778... | [10.929567337036133, 0.9922721982002258] |
6a67fddc-f933-4f9c-99d4-e564399808f4 | global-relational-models-of-source-code | null | null | https://openreview.net/forum?id=B1lnbRNtwr | https://openreview.net/pdf?id=B1lnbRNtwr | Global Relational Models of Source Code | Models of code can learn distributed representations of a program's syntax and semantics to predict many non-trivial properties of a program. Recent state-of-the-art models leverage highly structured representations of programs, such as trees, graphs and paths therein (e.g. data-flow relations), which are precise and a... | ['David Bieber', 'Petros Maniatis', 'Rishabh Singh', 'Charles Sutton', 'Vincent J. Hellendoorn'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['program-repair', 'variable-misuse', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning'] | [ 1.22107066e-01 3.73117954e-01 -8.09057057e-01 -1.61529839e-01
-5.94908535e-01 -4.79564965e-01 5.14777780e-01 9.49428320e-01
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-5.61668694e-01 -1.58708412e-02 3.69059980e-01 -4.06588286... | [7.533560276031494, 7.846220970153809] |
f334a39a-ed8d-48d5-ac94-fc0862fb218a | visually-explaining-3d-cnn-predictions-for | 2207.12859 | null | https://arxiv.org/abs/2207.12859v1 | https://arxiv.org/pdf/2207.12859v1.pdf | Visually explaining 3D-CNN predictions for video classification with an adaptive occlusion sensitivity analysis | This paper proposes a method for visually explaining the decision-making process of 3D convolutional neural networks (CNN) with a temporal extension of occlusion sensitivity analysis. The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D temporal-spatial data space and then measure the c... | ['Kazuhiro Fukui', 'Koichiro Niinuma', 'Naoya Sogi', 'Tomoki Uchiyama'] | 2022-07-26 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [-3.94545794e-02 -3.60270739e-01 -8.46597105e-02 -3.12670052e-01
1.95634604e-01 -6.02381468e-01 4.08377826e-01 -2.60961413e-01
-5.73228359e-01 4.48570400e-01 -5.58060408e-02 -4.96057332e-01
-1.60927415e-01 -5.67727447e-01 -6.35775626e-01 -7.91265726e-01
-1.71133026e-01 -2.33821481e-01 3.23487520e-01 6.34050593... | [9.161250114440918, -0.6081537008285522] |
2b8d304a-5d93-43e2-8e1a-5f32c24235d0 | selfevolve-a-code-evolution-framework-via | 2306.02907 | null | https://arxiv.org/abs/2306.02907v1 | https://arxiv.org/pdf/2306.02907v1.pdf | SelfEvolve: A Code Evolution Framework via Large Language Models | Large language models (LLMs) have already revolutionized code generation, after being pretrained on publicly available code data. However, while various methods have been proposed to augment LLMs with retrieved knowledge and enhance the quality of code generation, the performance of these retrieval-based methods is lim... | ['Yu Wang', 'Yuhao Wang', 'Shuyang Jiang'] | 2023-06-05 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-2.27701012e-02 6.82067079e-03 -4.36331809e-01 -1.85106352e-01
-1.24739826e+00 -7.13660121e-01 4.68836904e-01 9.18157548e-02
-5.93584068e-02 3.11648518e-01 1.90810531e-01 -6.80187881e-01
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1.10664412e-01 1.57176986e-01 2.62313783e-01 -3.48168135... | [7.768386363983154, 7.846240997314453] |
91377a65-f02b-4d54-a17c-a415e03c19c7 | time-series-prediction-for-food | 2209.06889 | null | https://arxiv.org/abs/2209.06889v1 | https://arxiv.org/pdf/2209.06889v1.pdf | Time Series Prediction for Food sustainability | With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone. Doing so can improve people's livelihoods, health, and ecosystems for the present and future generations. Sustainable development, a paradigm of the United Nations, ... | ['Fiona Victoria Stanley Jothiraj'] | 2022-09-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.93280607e-01 -1.25247210e-01 -4.90658194e-01 1.39139429e-01
3.21482807e-01 -2.31685624e-01 3.44323635e-01 4.17600006e-01
-4.61575836e-02 8.83013546e-01 7.36912787e-02 -5.38857877e-01
-9.52608362e-02 -1.31722236e+00 -1.41413257e-01 -7.17827439e-01
2.19325554e-02 3.17130387e-02 -1.52373955e-01 -3.62807244... | [9.369535446166992, -1.5701687335968018] |
66fc6f5f-d535-4f90-b29b-07b4007d6401 | cross-modal-face-and-voice-style-transfer | 2302.13838 | null | https://arxiv.org/abs/2302.13838v2 | https://arxiv.org/pdf/2302.13838v2.pdf | Cross-modal Face- and Voice-style Transfer | Image-to-image translation and voice conversion enable the generation of a new facial image and voice while maintaining some of the semantics such as a pose in an image and linguistic content in audio, respectively. They can aid in the content-creation process in many applications. However, as they are limited to the c... | ['Yuki Mitsufuji', 'Mayank K. Singh', 'Naoya Takahashi'] | 2023-02-27 | null | null | null | null | ['voice-conversion', 'open-question', 'voice-conversion'] | ['audio', 'natural-language-processing', 'speech'] | [ 3.97837460e-01 1.03354387e-01 -7.09765479e-02 -4.86516684e-01
-1.03717935e+00 -8.17988575e-01 7.89613366e-01 -5.39169312e-01
1.72142629e-02 5.47986925e-01 4.25254047e-01 1.44105554e-01
4.38655943e-01 -5.84018171e-01 -1.06118107e+00 -5.46549678e-01
5.66671848e-01 3.83764058e-01 -2.48407930e-01 6.02102019... | [12.710094451904297, -0.23396185040473938] |
bcb92066-6e58-4a2d-944f-88021f9508e9 | learning-based-repetitive-precision-motion | 2111.10246 | null | https://arxiv.org/abs/2111.10246v1 | https://arxiv.org/pdf/2111.10246v1.pdf | Learning-Based Repetitive Precision Motion Control with Mismatch Compensation | Learning-based control methods utilize run-time data from the underlying process to improve the controller performance under model mismatch and unmodeled disturbances. This is beneficial for optimizing industrial processes, where the dynamics are difficult to model, and the repetitive nature of the process can be explo... | ['John Lygeros', 'Alisa Rupenyan', 'Dawn M. Tilbury', 'Kira Barton', 'Efe C. Balta'] | 2021-11-19 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.04931700e-01 1.65101260e-01 -1.69940531e-01 2.87759691e-01
-3.63388658e-01 -5.75151205e-01 3.98124367e-01 1.50909677e-01
-2.62758005e-02 7.70623922e-01 -4.48669434e-01 -2.90807635e-01
-5.11573434e-01 -3.77298564e-01 -7.09918082e-01 -9.27608073e-01
6.97686300e-02 2.94437498e-01 -4.23452929e-02 -6.37432262... | [5.081645965576172, 2.319078207015991] |
532f6987-8236-4d89-852a-e9332a40dd79 | solving-the-rubiks-cube-with-approximate | null | null | https://openreview.net/forum?id=Hyfn2jCcKm | https://openreview.net/pdf?id=Hyfn2jCcKm | Solving the Rubik's Cube with Approximate Policy Iteration | Recently, Approximate Policy Iteration (API) algorithms have achieved super-human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a r... | ['Pierre Baldi', 'Alexander Shmakov', 'Stephen McAleer', 'Forest Agostinelli'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['rubik-s-cube'] | ['graphs'] | [-2.45659456e-01 2.75564790e-01 -1.04723752e-01 2.95619428e-01
-7.28899479e-01 -1.02824831e+00 2.39209324e-01 -3.37474234e-02
-5.29242694e-01 1.26648176e+00 -1.51000500e-01 -6.39159799e-01
-3.06081444e-01 -9.73040581e-01 -7.16071665e-01 -5.99515796e-01
-2.19185010e-01 1.18441415e+00 3.17981303e-01 -4.32654798... | [3.6498239040374756, 1.5622774362564087] |
1ca1abaa-b000-4cd5-bfcf-eeef82126f45 | sc-depthv3-robust-self-supervised-monocular | 2211.03660 | null | https://arxiv.org/abs/2211.03660v1 | https://arxiv.org/pdf/2211.03660v1.pdf | SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenes | Self-supervised monocular depth estimation has shown impressive results in static scenes. It relies on the multi-view consistency assumption for training networks, however, that is violated in dynamic object regions and occlusions. Consequently, existing methods show poor accuracy in dynamic scenes, and the estimated d... | ['Chunhua Shen', 'Ian Reid', 'Wei Yin', 'Huangying Zhan', 'Jia-Wang Bian', 'Libo Sun'] | 2022-11-07 | null | null | null | null | ['indoor-monocular-depth-estimation'] | ['computer-vision'] | [ 1.70304418e-01 -1.35420188e-01 -2.72404671e-01 -5.54384589e-01
-5.13320148e-01 -5.10647535e-01 3.09331357e-01 -6.73614800e-01
-1.36039793e-01 8.38107526e-01 2.72002339e-01 1.78965613e-01
3.11418593e-01 -5.59497356e-01 -7.26804078e-01 -7.10328102e-01
4.46533918e-01 1.25774786e-01 3.17739546e-01 3.48789483... | [8.821861267089844, -2.4718008041381836] |
f73849ca-c2eb-470c-b9e5-b7236e23e62a | low-bit-quantization-and-quantization-aware | null | null | https://openreview.net/forum?id=rJxVxiiDoX | https://openreview.net/pdf?id=rJxVxiiDoX | Low-bit quantization and quantization-aware training for small-footprint keyword spotting | We investigate low-bit quantization to reduce computational cost of deep neural network (DNN) based keyword spotting (KWS). We propose approaches to further reduce quantization bits via integrating quantization into keyword spotting model training, which we refer to as quantization-aware training. Our experimental resu... | ['Shiv Naga Prasad Vitaladevuni', 'Oleg Rybakov', 'Spyros Matsoukas', 'Chris Beauchene', 'Ming Sun', 'Yusuf Goren', 'Yuriy Mishchenko'] | 2018-10-19 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 1.77432373e-01 -3.09440941e-01 -5.64468801e-01 -3.59110206e-01
-1.14925110e+00 -2.31625080e-01 3.22229445e-01 3.87661578e-03
-9.29500937e-01 5.23970187e-01 4.35227722e-01 -7.23618805e-01
-4.60560992e-02 -7.82528698e-01 -8.37440550e-01 -2.44814485e-01
3.94204229e-01 5.22127748e-02 -1.14508942e-01 1.97667126... | [8.713595390319824, 3.374701738357544] |
a2e90714-add9-416e-be37-993a827d7ee9 | on-the-complexity-of-multi-agent-decision | 2305.00684 | null | https://arxiv.org/abs/2305.00684v1 | https://arxiv.org/pdf/2305.00684v1.pdf | On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring | A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees, and how these considerations change as we move from few to many agents. We study this question in a general framework for inte... | ['Alexander Rakhlin', 'Noah Golowich', 'Dean P. Foster', 'Dylan J. Foster'] | 2023-05-01 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [ 5.44704869e-02 4.75675493e-01 -4.61054921e-01 6.87438026e-02
-9.37530160e-01 -7.49715388e-01 4.68753666e-01 2.44971618e-01
-8.71384501e-01 1.03907847e+00 -1.77821234e-01 -5.21047533e-01
-7.81590581e-01 -7.21506476e-01 -5.84954739e-01 -1.00430322e+00
-4.68067527e-01 7.38334417e-01 7.48045817e-02 -1.42799884... | [4.220224857330322, 2.721081495285034] |
3879ecae-7be2-4928-8c04-9f0aa552ea90 | multi-rate-adaptive-transform-coding-for | 2210.14308 | null | https://arxiv.org/abs/2210.14308v2 | https://arxiv.org/pdf/2210.14308v2.pdf | Multi-rate adaptive transform coding for video compression | Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression. Despite impressive performance of end-to-end optimized compression with deep neural networks, the high computational and sp... | ['Jingning Han', 'Cheng Chen', 'Bohan Li', 'Lyndon R. Duong'] | 2022-10-25 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 7.34648943e-01 8.91145095e-02 -5.25703132e-01 -3.77388358e-01
-7.22583652e-01 -1.80040985e-01 4.86024022e-01 -2.21707955e-01
-2.76406080e-01 5.27105868e-01 5.98804235e-01 -3.65570277e-01
1.00979827e-01 -7.23785102e-01 -7.36989737e-01 -4.41459507e-01
-3.03708702e-01 7.63576918e-06 1.73973322e-01 -1.24878071... | [11.37735652923584, -1.5717798471450806] |
eacce34e-2c51-48f4-8f50-53ee8e5d5c57 | a-deep-learning-based-native-language | null | null | https://aclanthology.org/W17-5047 | https://aclanthology.org/W17-5047.pdf | A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017 | This paper proposes a deep-learning based native-language identification (NLI) using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI Shared Task 2017 where the NLI Shared Task 2017 aims to detect the native language of an essay or speech response of a standardized assessment of English proficien... | ['Yun-Keun Lee', 'Jeon-Gue Park', 'Yun-Kyung Lee', 'Hyung-Bae Jeon', 'Yoo Rhee Oh', 'Hwa Jeon Song'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-5.01560457e-02 -4.16869223e-01 -4.35013860e-01 -3.94959748e-01
-1.04654038e+00 -5.90983450e-01 6.97673857e-01 2.57194698e-01
-5.67443967e-01 4.06542659e-01 5.46241641e-01 -6.25450611e-01
-2.94584155e-01 -7.28491366e-01 -1.02721080e-01 -6.33400440e-01
5.43781281e-01 3.13715607e-01 -2.70057857e-01 1.98034253... | [10.349732398986816, 10.521793365478516] |
0cadca5c-3980-42c3-b221-12eba82932ac | confidence-ranking-for-ctr-prediction | 2307.01206 | null | https://arxiv.org/abs/2307.01206v1 | https://arxiv.org/pdf/2307.01206v1.pdf | Confidence Ranking for CTR Prediction | Model evolution and constant availability of data are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and online learn with recently available data to update the models periodical... | ['Jingping Shao', 'Zhangang Lin', 'Xiwei Zhao', 'Pei Wang', 'Congcong Liu', 'Jian Zhu'] | 2023-06-28 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.69999406e-01 -1.90908358e-01 -4.58488882e-01 -7.69037783e-01
-9.77568746e-01 -6.78247392e-01 4.31686759e-01 3.38030308e-01
-4.41527188e-01 5.93750894e-01 -2.74202049e-01 -3.56266022e-01
-3.28327000e-01 -6.02478266e-01 -8.65059376e-01 -3.59712809e-01
-3.35863084e-01 1.00146842e+00 4.36183363e-01 -3.12260032... | [9.918739318847656, 5.422080993652344] |
7f0cc19e-abb2-4740-89a2-fed1bf77c4f9 | streaming-speaker-attributed-asr-with-token | 2203.16685 | null | https://arxiv.org/abs/2203.16685v2 | https://arxiv.org/pdf/2203.16685v2.pdf | Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings | This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize ``who spoke what'' with low latency even when multiple people are speaking simultaneously. Our model is based on token-level serialized output training (t-SOT) which was recently proposed to transcribe mult... | ['Takuya Yoshioka', 'Jinyu Li', 'Zhuo Chen', 'Yashesh Gaur', 'Xiaofei Wang', 'Zhong Meng', 'Xiong Xiao', 'Yu Wu', 'Jian Wu', 'Naoyuki Kanda'] | 2022-03-30 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.52115065e-01 -8.10501203e-02 2.65609384e-01 -7.16903687e-01
-1.64522862e+00 -5.54447949e-01 5.77037334e-01 1.04635186e-01
-4.19205070e-01 1.25601575e-01 3.53482008e-01 -2.79889673e-01
5.17706275e-01 1.32812440e-01 -5.69688201e-01 -7.31789231e-01
-4.87557277e-02 6.95829690e-01 4.10019793e-02 -2.70591434... | [14.597254753112793, 6.345139026641846] |
3262e47d-a1fe-46f2-a9cf-9abdca1d2335 | benchmarking-and-analyzing-3d-human-pose-and | 2209.10529 | null | https://arxiv.org/abs/2209.10529v1 | https://arxiv.org/pdf/2209.10529v1.pdf | Benchmarking and Analyzing 3D Human Pose and Shape Estimation Beyond Algorithms | 3D human pose and shape estimation (a.k.a. "human mesh recovery") has achieved substantial progress. Researchers mainly focus on the development of novel algorithms, while less attention has been paid to other critical factors involved. This could lead to less optimal baselines, hindering the fair and faithful evaluati... | ['Ziwei Liu', 'Tianwei Zhang', 'Lei Yang', 'Zhongang Cai', 'Hui En Pang'] | 2022-09-21 | null | null | null | null | ['3d-human-pose-and-shape-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 1.06981814e-01 2.97928788e-02 -2.99069434e-01 -2.37329021e-01
-1.15586030e+00 -4.02247578e-01 4.47471291e-01 -1.10628523e-01
-4.11421478e-01 5.43380082e-01 3.67472589e-01 1.22036815e-01
-1.19172834e-01 -5.21038294e-01 -9.00723755e-01 -6.16119802e-01
3.84340361e-02 5.79888582e-01 1.44654289e-01 -3.29702497... | [7.092249870300293, -1.0915299654006958] |
bf01cd7c-5612-43f4-96da-3b70712b95bf | automatic-readability-assessment-of-german-1 | 2209.04299 | null | https://arxiv.org/abs/2209.04299v1 | https://arxiv.org/pdf/2209.04299v1.pdf | Automatic Readability Assessment of German Sentences with Transformer Ensembles | Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based app... | ['Stephan Bialonski', 'Niklas Grieger', 'Tobias Bornheim', 'Patrick Gustav Blaneck'] | 2022-09-09 | automatic-readability-assessment-of-german | https://aclanthology.org/2022.germeval-1.10 | https://aclanthology.org/2022.germeval-1.10.pdf | germeval-2022-9 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-4.83055748e-02 2.26552531e-01 3.19313526e-01 -3.65022600e-01
-1.07709014e+00 -5.75517058e-01 7.63861597e-01 6.77766919e-01
-4.96145964e-01 8.69274378e-01 6.05242610e-01 -3.88462096e-01
-2.57023007e-01 -9.15843189e-01 -3.83546203e-01 -2.60771632e-01
1.58467859e-01 6.26853406e-01 2.75172926e-02 -4.53133881... | [11.060815811157227, 10.20193099975586] |
27662242-f885-4efb-bda8-15230d139cf8 | blind-audio-source-separation-with-minimum | 1907.02404 | null | https://arxiv.org/abs/1907.02404v2 | https://arxiv.org/pdf/1907.02404v2.pdf | Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF | Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider on this paper the blind audio source separation problem which consists in isolating and extracting each of the sources. To perform this task, nonnegative matrix factorization (NMF) based on the Kullback-Leibl... | ['Nicolas Gillis', 'Man Shun Ang', 'Valentin Leplat'] | 2019-07-04 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 3.81072700e-01 -9.77265239e-02 3.26750129e-01 9.23456699e-02
-8.41571391e-01 -6.13449574e-01 2.70116597e-01 7.83948302e-02
-4.51480359e-01 7.33476877e-01 1.08496606e-01 -2.06594676e-01
-5.55146158e-01 -3.82776111e-01 -5.27139723e-01 -9.69893336e-01
-2.35737965e-01 3.76386940e-01 -1.64326072e-01 -2.99777120... | [15.310540199279785, 5.633208274841309] |
fd53123d-0f1b-4136-8e83-c6751b9c01be | differentiable-top-k-classification-learning-1 | 2206.07290 | null | https://arxiv.org/abs/2206.07290v1 | https://arxiv.org/pdf/2206.07290v1.pdf | Differentiable Top-k Classification Learning | The top-k classification accuracy is one of the core metrics in machine learning. Here, k is conventionally a positive integer, such as 1 or 5, leading to top-1 or top-5 training objectives. In this work, we relax this assumption and optimize the model for multiple k simultaneously instead of using a single k. Leveragi... | ['Oliver Deussen', 'Christian Borgelt', 'Hilde Kuehne', 'Felix Petersen'] | 2022-06-15 | differentiable-top-k-classification-learning | https://openreview.net/forum?id=6PTUd_zPdHL | https://openreview.net/pdf?id=6PTUd_zPdHL | null | ['classification'] | ['methodology'] | [ 1.45474464e-01 -6.25855178e-02 -5.61499059e-01 -7.11542904e-01
-9.98615265e-01 -4.18690950e-01 2.70102888e-01 2.61643738e-01
-6.98106587e-01 4.58760381e-01 5.25522195e-02 -1.79077342e-01
-2.22948343e-01 -7.66536772e-01 -8.02972257e-01 -5.16919732e-01
-1.30497143e-01 2.66169399e-01 2.46953845e-01 5.92009304... | [9.321444511413574, 3.316835641860962] |
642f8cef-1df1-4738-8bc4-f7dfbe46f4de | achieving-long-term-fairness-in-submodular | 2304.04700 | null | https://arxiv.org/abs/2304.04700v1 | https://arxiv.org/pdf/2304.04700v1.pdf | Achieving Long-term Fairness in Submodular Maximization through Randomization | Submodular function optimization has numerous applications in machine learning and data analysis, including data summarization which aims to identify a concise and diverse set of data points from a large dataset. It is important to implement fairness-aware algorithms when dealing with data items that may contain sensit... | ['Twumasi Mensah-Boateng', 'Jing Yuan', 'Shaojie Tang'] | 2023-04-10 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.13504866e-01 3.92345011e-01 -8.30161870e-01 -6.55419707e-01
-3.96036237e-01 -3.93151850e-01 -1.50730342e-01 8.42068076e-01
-5.05747616e-01 1.09551775e+00 3.57306927e-01 3.76634635e-02
-4.99743640e-01 -8.26066256e-01 -4.39034730e-01 -6.83421493e-01
-1.51972800e-01 6.07779860e-01 -4.82751191e-01 -1.27884876... | [6.611871242523193, 4.9490132331848145] |
45d4b083-812e-40bd-86ad-e2ac51d15a31 | example-based-explanations-with-adversarial | 2203.16141 | null | https://arxiv.org/abs/2203.16141v1 | https://arxiv.org/pdf/2203.16141v1.pdf | Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis | Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretability of classification results, especially ones based on deep learning, many explanation methods have been proposed using prototypes. However,... | ['Björn W. Schuller', 'Wolfgang Nejdl', 'Thanh Tam Nguyen', 'Zhao Ren', 'Yi Chang'] | 2022-03-30 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 1.47288442e-01 4.93195683e-01 7.74857309e-03 -5.96354008e-01
-6.63591623e-01 -3.39829952e-01 4.44211423e-01 2.29916692e-01
1.60775304e-01 6.74515188e-01 1.49203032e-01 -5.62103808e-01
-4.35080171e-01 -5.93403280e-01 -8.36270332e-01 -5.36510944e-01
1.46586552e-01 6.24338388e-01 -1.67577788e-01 3.60674001... | [8.755579948425293, 5.6591081619262695] |
d4eebd3b-a0b0-4d0d-a799-92116db95eeb | boxe-a-box-embedding-model-for-knowledge-base | 2007.06267 | null | https://arxiv.org/abs/2007.06267v2 | https://arxiv.org/pdf/2007.06267v2.pdf | BoxE: A Box Embedding Model for Knowledge Base Completion | Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one... | ['Tommaso Salvatori', 'İsmail İlkan Ceylan', 'Ralph Abboud', 'Thomas Lukasiewicz'] | 2020-07-13 | null | http://proceedings.neurips.cc/paper/2020/hash/6dbbe6abe5f14af882ff977fc3f35501-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/6dbbe6abe5f14af882ff977fc3f35501-Paper.pdf | neurips-2020-12 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-2.25515038e-01 3.70268822e-01 -6.59679234e-01 -1.45083994e-01
-2.48404473e-01 -6.64761722e-01 7.23154247e-01 3.70005190e-01
-8.20902586e-02 8.67011964e-01 5.52964568e-01 -4.98532236e-01
-5.60511768e-01 -1.38387215e+00 -9.93011117e-01 -3.36655915e-01
-4.92810816e-01 4.51617777e-01 3.71758372e-01 -2.69523770... | [8.837167739868164, 7.764684677124023] |
a7b849d8-71a0-4638-ad24-9a1dc08f4dd5 | dl-corrector-remapper-a-grid-free-bias | 2210.12293 | null | https://arxiv.org/abs/2210.12293v1 | https://arxiv.org/pdf/2210.12293v1.pdf | DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting | Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw climate observational data, the gold standard ground truth. In this work we develo... | ['Karthik Kashinath', 'Akshay Subramaniam', 'Jaideep Pathak', 'Tao Ge'] | 2022-10-21 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-2.81842262e-01 -3.69032443e-01 5.33242762e-01 -5.89349329e-01
-6.04843259e-01 -6.80910826e-01 8.97220254e-01 -1.65377006e-01
-2.11166710e-01 1.13984478e+00 3.69472474e-01 -6.76020205e-01
-2.97401488e-01 -1.16842580e+00 -5.33261657e-01 -1.06877673e+00
-3.42914134e-01 5.24595439e-01 -1.38708308e-01 -7.48727024... | [6.581233978271484, 2.9632344245910645] |
9e25523e-6550-4e5e-9713-41bbb072d870 | long-range-3d-with-quadocular-thermal-lwir | 1911.06975 | null | https://arxiv.org/abs/1911.06975v2 | https://arxiv.org/pdf/1911.06975v2.pdf | Long Range 3D with Quadocular Thermal (LWIR) Camera | Long Wave Infrared (LWIR) cameras provide images regardles of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights. These features make LWIR cameras attractive for autonomous navigation, security and military applications. Thermal images can be used similarly to the visible ran... | ['Andrey Filippov', 'Oleg Dzhimiev'] | 2019-11-16 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.87061894e-01 -2.21376151e-01 3.98004740e-01 -3.30901235e-01
-3.84951025e-01 -8.30889404e-01 3.36176246e-01 -3.86388928e-01
-9.63750541e-01 6.07949495e-01 9.07840021e-03 -3.46251845e-01
1.22797370e-01 -6.10316753e-01 -4.45363253e-01 -9.06332493e-01
4.38530743e-01 -1.34777069e-01 4.08975631e-01 -1.09227166... | [9.91514778137207, -2.7109646797180176] |
949902ec-653a-409a-8862-519c1bd5c1a8 | towards-3d-human-pose-estimation-in-the-wild | 1704.02447 | null | http://arxiv.org/abs/1704.02447v2 | http://arxiv.org/pdf/1704.02447v2.pdf | Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach | In this paper, we study the task of 3D human pose estimation in the wild.
This task is challenging due to lack of training data, as existing datasets are
either in the wild images with 2D pose or in the lab images with 3D pose.
We propose a weakly-supervised transfer learning method that uses mixed 2D
and 3D labels i... | ['Qi-Xing Huang', 'xiangyang xue', 'Xiao Sun', 'Yichen Wei', 'Xingyi Zhou'] | 2017-04-08 | towards-3d-human-pose-estimation-in-the-wild-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Zhou_Towards_3D_Human_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhou_Towards_3D_Human_ICCV_2017_paper.pdf | iccv-2017-10 | ['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.00993611e-01 3.11804384e-01 -1.05091758e-01 -5.52584410e-01
-8.06466460e-01 -4.01732802e-01 2.82627910e-01 -2.70359337e-01
-7.36756146e-01 4.45861489e-01 2.28215888e-01 1.35095552e-01
4.77522850e-01 -5.50546706e-01 -1.01094639e+00 -3.00723344e-01
5.53356074e-02 8.25290024e-01 1.08439893e-01 -1.89096034... | [6.9361572265625, -0.9661102294921875] |
416b7faf-8157-4de8-a9a0-dc02337becab | a-practical-framework-for-unsupervised | 2304.01864 | null | https://arxiv.org/abs/2304.01864v1 | https://arxiv.org/pdf/2304.01864v1.pdf | A Practical Framework for Unsupervised Structure Preservation Medical Image Enhancement | Medical images are extremely valuable for supporting medical diagnoses. However, in practice, low-quality (LQ) medical images, such as images that are hazy/blurry, have uneven illumination, or are out of focus, among others, are often obtained during data acquisition. This leads to difficulties in the screening and dia... | ['Hitoshi Iyatomi', 'Atsushi Fukuda', 'Quan Huu Cap'] | 2023-04-04 | null | null | null | null | ['medical-image-enhancement', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.99382401e-01 -1.52646631e-01 1.52732506e-01 -1.50197119e-01
-7.92878449e-01 -2.92805493e-01 1.99603721e-01 7.72956684e-02
-1.28689244e-01 7.22719431e-01 3.14063102e-01 -1.38477966e-01
-3.01955760e-01 -6.77277446e-01 -3.70186627e-01 -1.14565301e+00
1.47696018e-01 -1.64284691e-01 3.27583961e-02 -1.56838223... | [13.460837364196777, -2.31612229347229] |
904dbf92-ff58-477e-ad4d-251dbef09629 | t5ql-taming-language-models-for-sql | 2209.10254 | null | https://arxiv.org/abs/2209.10254v1 | https://arxiv.org/pdf/2209.10254v1.pdf | T5QL: Taming language models for SQL generation | Automatic SQL generation has been an active research area, aiming at streamlining the access to databases by writing natural language with the given intent instead of writing SQL. Current SOTA methods for semantic parsing depend on LLMs to achieve high predictive accuracy on benchmark datasets. This reduces their appli... | ['António Alegria', 'Hugo Veiga', 'David Aparício', 'Samuel Arcadinho'] | 2022-09-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 2.53373563e-01 2.64319450e-01 -2.71231443e-01 -6.83716476e-01
-9.26558614e-01 -5.12191415e-01 3.34829569e-01 4.33700979e-01
-1.89331144e-01 6.27810419e-01 -4.09450904e-02 -7.05946147e-01
1.29226804e-01 -1.64249051e+00 -1.01843047e+00 1.48203731e-01
4.02476460e-01 7.99390912e-01 5.93927681e-01 -2.35218033... | [9.876947402954102, 7.819868087768555] |
59a2e7bc-106b-4177-82eb-670c473024ec | cryptgpu-fast-privacy-preserving-machine | 2104.10949 | null | https://arxiv.org/abs/2104.10949v1 | https://arxiv.org/pdf/2104.10949v1.pdf | CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU | We introduce CryptGPU, a system for privacy-preserving machine learning that implements all operations on the GPU (graphics processing unit). Just as GPUs played a pivotal role in the success of modern deep learning, they are also essential for realizing scalable privacy-preserving deep learning. In this work, we start... | ['David J. Wu', 'Yuan Tian', 'Brian Knott', 'Sijun Tan'] | 2021-04-22 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 1.24960616e-01 -4.52619512e-03 -1.61439423e-02 -6.84527755e-01
-8.83129478e-01 -1.10491490e+00 5.45681477e-01 3.39271367e-01
-1.03821027e+00 4.93694603e-01 -6.19087368e-04 -1.05890799e+00
5.52396178e-01 -1.16844678e+00 -1.12021828e+00 -9.95898426e-01
-2.01266482e-01 5.99478073e-02 2.49949619e-02 -9.91696715... | [5.873862266540527, 6.848789691925049] |
fb952fce-034f-4e12-a4da-5ed4a0dfde7e | ponder-point-cloud-pre-training-via-neural | 2301.00157 | null | https://arxiv.org/abs/2301.00157v1 | https://arxiv.org/pdf/2301.00157v1.pdf | Ponder: Point Cloud Pre-training via Neural Rendering | We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud features should be able to encode rich geometry and appearance cues and render realistic images, we train a point-cloud encoder within a devised p... | ['Wanli Ouyang', 'Xiaowei Zhou', 'Tong He', 'Sida Peng', 'Di Huang'] | 2022-12-31 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [ 2.24847853e-01 2.30525434e-01 4.11087126e-02 -6.06955230e-01
-9.41573679e-01 -4.32275355e-01 7.23831415e-01 3.26765701e-02
3.82228848e-03 1.79742604e-01 -2.55520791e-01 -4.70236272e-01
2.26960033e-01 -1.02367496e+00 -1.27885318e+00 -3.35955560e-01
-1.08869337e-01 6.73503220e-01 2.16970548e-01 -2.25906476... | [8.487683296203613, -3.4736037254333496] |
5fecc78a-aa2b-43c1-b7e6-e2df585ea582 | static-background-removal-in-vehicular-radar | 2307.01444 | null | https://arxiv.org/abs/2307.01444v1 | https://arxiv.org/pdf/2307.01444v1.pdf | Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain | A significant challenge in autonomous driving systems lies in image understanding within complex environments, particularly dense traffic scenarios. An effective solution to this challenge involves removing the background or static objects from the scene, so as to enhance the detection of moving targets as key componen... | ['Lyutianyang Zhang', 'Sumit Roy', 'Xiangyu Gao'] | 2023-07-04 | null | null | null | null | ['autonomous-driving', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.55273652e-01 -7.17225373e-01 4.24855381e-01 -1.58895060e-01
-8.25930417e-01 -6.18901193e-01 5.47976553e-01 -3.77413481e-01
-4.55959558e-01 4.65228707e-01 -1.53707847e-01 -4.91920292e-01
-3.19556385e-01 -7.86065578e-01 -2.46315047e-01 -9.75068271e-01
-1.52196541e-01 -9.05035436e-02 3.97331446e-01 -2.17125908... | [6.7047014236450195, 0.9209728240966797] |
8ff239c6-485a-423c-90af-e6028fe4b20a | crystal-graph-neural-networks-for-data-mining | null | null | https://www.researchgate.net/publication/333667001_Crystal_Graph_Neural_Networks_for_Data_Mining_in_Materials_Science | https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf | Crystal Graph Neural Networks for Data Mining in Materials Science | Machine learning methods have been employed for materials prediction in various ways. It has recently been proposed that a crystalline material is represented by a multigraph called a crystal graph. Convolutional neural networks adapted to those graphs have successfully predicted bulk properties of materials with the u... | ['Takenori Yamamoto'] | 2019-05-27 | null | null | null | technical-report-rimcs-llc-2019-5 | ['formation-energy'] | ['miscellaneous'] | [ 8.14539194e-02 2.33342111e-01 -4.17409152e-01 -4.40222651e-01
2.55144946e-02 2.03018993e-01 4.54111308e-01 4.83380944e-01
-1.10340536e-01 9.62151647e-01 -1.68142021e-01 -3.82648438e-01
-3.86965990e-01 -1.38151336e+00 -9.13089156e-01 -1.05272400e+00
-3.33546370e-01 9.79678392e-01 2.81667799e-01 -3.75429541... | [5.335961818695068, 5.524656295776367] |
814867a8-26ab-426a-bb3b-9b85f54935f0 | replacing-language-model-for-style-transfer | 2211.07343 | null | https://arxiv.org/abs/2211.07343v1 | https://arxiv.org/pdf/2211.07343v1.pdf | Replacing Language Model for Style Transfer | We introduce replacing language model (RLM), a sequence-to-sequence language modeling framework for text style transfer. Our method autoregressively replaces each token in the original sentence with a text span in the target style. In contrast, the new span is generated via a non-autoregressive masked language model. T... | ['Ruineng Li', 'Pengyu Cheng'] | 2022-11-14 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 4.68681723e-01 1.42596871e-01 -1.14307381e-01 -6.10690176e-01
-8.00900042e-01 -5.40429354e-01 9.23081636e-01 -5.07342935e-01
-3.30810308e-01 7.84922183e-01 7.71732628e-01 -4.56092834e-01
7.82239079e-01 -7.43792713e-01 -5.52839100e-01 -3.23311657e-01
5.52227139e-01 5.12504458e-01 -2.11621732e-01 -4.93873835... | [11.665252685546875, 9.494989395141602] |
2a005b3c-5167-4281-9429-91bbac9bb0c4 | self-supervised-non-uniform-kernel-estimation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fang_Self-Supervised_Non-Uniform_Kernel_Estimation_With_Flow-Based_Motion_Prior_for_Blind_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fang_Self-Supervised_Non-Uniform_Kernel_Estimation_With_Flow-Based_Motion_Prior_for_Blind_CVPR_2023_paper.pdf | Self-Supervised Non-Uniform Kernel Estimation With Flow-Based Motion Prior for Blind Image Deblurring | Many deep learning-based solutions to blind image deblurring estimate the blur representation and reconstruct the target image from its blurry observation. However, these methods suffer from severe performance degradation in real-world scenarios because they ignore important prior information about motion blur (e.g... | ['Guangming Shi', 'Jinjian Wu', 'Xin Li', 'Weisheng Dong', 'Fangfang Wu', 'Zhenxuan Fang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [-2.29041159e-01 -8.75804186e-01 -7.32556581e-02 -2.61736661e-01
-4.94765669e-01 -5.72420716e-01 1.76041439e-01 -7.67633438e-01
-6.37528114e-03 8.17297399e-01 7.53166974e-01 -1.91825807e-01
-1.96120605e-01 -2.52534926e-01 -6.55759394e-01 -8.51621270e-01
1.38738811e-01 -3.08655232e-01 -5.02572767e-02 3.36454988... | [11.542720794677734, -2.6800520420074463] |
8c2081c4-2145-472a-b116-8ca0f43ab6f0 | towards-end-to-end-text-spotting-in-natural | 1906.06013 | null | https://arxiv.org/abs/1906.06013v6 | https://arxiv.org/pdf/1906.06013v6.pdf | Towards End-to-End Text Spotting in Natural Scenes | Text spotting in natural scene images is of great importance for many image understanding tasks. It includes two sub-tasks: text detection and recognition. In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as ima... | ['Peng Wang', 'Chunhua Shen', 'Hui Li'] | 2019-06-14 | null | null | null | null | ['text-spotting', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 5.82604408e-01 -5.82616508e-01 -1.46965742e-01 -3.19262207e-01
-6.02424383e-01 -4.85895634e-01 7.87863791e-01 3.08169037e-01
-7.83650517e-01 1.44414976e-01 -8.43052343e-02 -3.53232503e-01
1.95792437e-01 -6.32044554e-01 -4.70596313e-01 -7.91001618e-01
7.42133856e-01 4.72255528e-01 3.37915301e-01 5.82547374... | [11.966407775878906, 2.255176067352295] |
98be0e4b-af97-451f-b329-72febeb1ab97 | times-are-changing-investigating-the-pace-of | null | null | https://aclanthology.org/W19-4718 | https://aclanthology.org/W19-4718.pdf | Times Are Changing: Investigating the Pace of Language Change in Diachronic Word Embeddings | We propose Word Embedding Networks, a novel method that is able to learn word embeddings of individual data slices while simultaneously aligning and ordering them without feeding temporal information a priori to the model. This gives us the opportunity to analyse the dynamics in word embeddings on a large scale in a pu... | ['Br', 'David Lassner', 'Stephanie l'] | 2019-08-01 | null | null | null | ws-2019-8 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-3.32505524e-01 -8.89529139e-02 -2.94866145e-01 -2.46321797e-01
1.33481935e-01 -7.58239865e-01 1.16955066e+00 6.37977660e-01
-1.04917359e+00 5.35122275e-01 5.49625218e-01 -5.17001510e-01
-2.80578643e-01 -9.60778475e-01 -2.37178370e-01 -6.21261716e-01
-5.63260555e-01 5.40255547e-01 4.15604621e-01 -5.95831096... | [10.212876319885254, 8.923996925354004] |
d702a890-ddf2-48a2-8a34-91740d1d75f4 | saroco-detecting-satire-in-a-novel-romanian | 2105.06456 | null | https://arxiv.org/abs/2105.06456v3 | https://arxiv.org/pdf/2105.06456v3.pdf | SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles | In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of t... | ['Radu Tudor Ionescu', 'Mihaela Gaman', 'Ana-Cristina Rogoz'] | 2021-05-13 | null | https://aclanthology.org/2021.acl-short.136 | https://aclanthology.org/2021.acl-short.136.pdf | acl-2021-5 | ['satire-detection'] | ['natural-language-processing'] | [ 6.56942418e-03 7.31981173e-02 -5.76883018e-01 -1.32916257e-01
-1.06579125e+00 -8.38306069e-01 1.11016369e+00 2.73175895e-01
-6.73829854e-01 4.06614184e-01 1.11090815e+00 -2.81616688e-01
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3.45517278e-01 7.55268037e-01 3.06098815e-02 -8.88266563... | [8.593968391418457, 10.462284088134766] |
c2c25b29-8f3d-4c6d-8ea6-9b994f0137fe | learning-to-segment-rigid-motions-from-two | 2101.03694 | null | https://arxiv.org/abs/2101.03694v1 | https://arxiv.org/pdf/2101.03694v1.pdf | Learning to Segment Rigid Motions from Two Frames | Appearance-based detectors achieve remarkable performance on common scenes, but tend to fail for scenarios lack of training data. Geometric motion segmentation algorithms, however, generalize to novel scenes, but have yet to achieve comparable performance to appearance-based ones, due to noisy motion estimations and de... | ['Deva Ramanan', 'Gengshan Yang'] | 2021-01-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Learning_To_Segment_Rigid_Motions_From_Two_Frames_CVPR_2021_paper.pdf | cvpr-2021-1 | ['motion-segmentation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [ 9.59801376e-02 -6.32925406e-02 -1.58611700e-01 -1.60707906e-01
-6.30865693e-01 -7.66441703e-01 6.41918004e-01 -4.31841582e-01
-4.37435627e-01 3.12607706e-01 5.37911952e-02 -3.26958783e-02
3.20464194e-01 -4.03341830e-01 -6.17748082e-01 -6.29080892e-01
6.65021464e-02 7.71415412e-01 7.71863461e-01 -1.36115164... | [8.549690246582031, -1.7266886234283447] |
8a4be2f3-f7ce-4d51-b950-9cfcee755eb1 | stochastic-dimension-reduced-second-order | 2301.12174 | null | https://arxiv.org/abs/2301.12174v1 | https://arxiv.org/pdf/2301.12174v1.pdf | Stochastic Dimension-reduced Second-order Methods for Policy Optimization | In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods. Specifically, we propose a dimension-reduced second-order method (DR-... | ['Yinyu Ye', 'Dongdong Ge', 'Qi Deng', 'Chenghan Xie', 'Jinsong Liu'] | 2023-01-28 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.81295842e-01 -6.34650060e-04 -3.17451954e-01 7.81973004e-02
-7.07250357e-01 -6.29512548e-01 2.89758027e-01 -4.90140766e-02
-8.89264703e-01 1.09802973e+00 1.55870140e-01 -9.38691735e-01
-3.71918738e-01 -3.01024675e-01 -6.25715077e-01 -6.60716116e-01
-2.87473857e-01 2.27996022e-01 2.47810572e-01 -2.49825820... | [4.2526679039001465, 2.6476738452911377] |
fa1fbd52-b7f6-478f-ac4c-d0a72df52fb1 | gpt-4-a-review-on-advancements-and | 2305.03195 | null | https://arxiv.org/abs/2305.03195v1 | https://arxiv.org/pdf/2305.03195v1.pdf | Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing | Generative Pre-trained Transformer 4 (GPT-4) is the fourth-generation language model in the GPT series, developed by OpenAI, which promises significant advancements in the field of natural language processing (NLP). In this research article, we have discussed the features of GPT-4, its potential applications, and the c... | ['Mursal Dawodi', 'Jawid Ahmad Baktash'] | 2023-05-04 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 1.28433496e-01 7.70101786e-01 -3.56385484e-02 -1.83078676e-01
-1.22616637e+00 -7.79991329e-01 7.36924648e-01 1.33957192e-01
-1.34957582e-01 1.00833726e+00 6.03918672e-01 -6.58427119e-01
1.00590862e-01 -5.24569392e-01 -4.59167361e-01 -3.30105424e-03
1.26316756e-01 9.20498252e-01 -5.75733483e-02 -5.65039992... | [11.441564559936523, 9.202988624572754] |
71762e1b-ffe1-4815-9490-f02dd49c37e1 | finrl-podracer-high-performance-and-scalable | 2111.05188 | null | https://arxiv.org/abs/2111.05188v1 | https://arxiv.org/pdf/2111.05188v1.pdf | FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance | Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The development of deep reinforcement learning techniques is partially addressing these issues. Unfortu... | ['Jian Guo', 'Anwar Walid', 'Zhaoran Wang', 'Jiahao Zheng', 'Xiao-Yang Liu', 'Zechu Li'] | 2021-11-07 | null | null | null | null | ['stock-trend-prediction'] | ['time-series'] | [-1.23891497e+00 -6.15561664e-01 -1.81147441e-01 -2.46049643e-01
-6.67645037e-01 -7.64214456e-01 7.07607150e-01 8.19872692e-02
-4.09494996e-01 6.72306120e-01 -2.93294638e-01 -6.57257617e-01
-1.41303698e-02 -1.18282962e+00 -7.05265760e-01 -4.77066904e-01
-2.84177780e-01 8.94603610e-01 1.86278760e-01 -3.87587011... | [4.441044330596924, 4.012338638305664] |
4ae76b34-acf6-47f0-968c-79b26e5a43f9 | person-image-generation-with-semantic | 2008.07884 | null | https://arxiv.org/abs/2008.07884v1 | https://arxiv.org/pdf/2008.07884v1.pdf | Person image generation with semantic attention network for person re-identification | Pose variation is one of the key factors which prevents the network from learning a robust person re-identification (Re-ID) model. To address this issue, we propose a novel person pose-guided image generation method, which is called the semantic attention network. The network consists of several semantic attention bloc... | ['Meichen Liu', 'Shuzhi Sam Ge', 'Kejun Wang', 'Juihang Ji'] | 2020-08-18 | null | null | null | null | ['pose-guided-image-generation'] | ['computer-vision'] | [ 1.45540148e-01 -1.44170105e-01 1.93399444e-01 -5.78614354e-01
-3.02500963e-01 -3.08956057e-01 5.01406848e-01 -4.95503724e-01
-4.60414201e-01 5.50300539e-01 3.54465127e-01 5.58242679e-01
2.45309219e-01 -6.70364499e-01 -7.49053180e-01 -6.29371464e-01
5.29809296e-01 5.57661653e-01 2.11362559e-02 -1.69881091... | [14.631427764892578, 0.8882884383201599] |
c0de0590-2877-44a7-b0db-f2b0e8ecbe00 | mgpsn-motion-guided-pseudo-siamese-network | 2110.03302 | null | https://arxiv.org/abs/2110.03302v5 | https://arxiv.org/pdf/2110.03302v5.pdf | MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings | Head detection in the indoor video is an essential component of building occupancy detection. While deep models have achieved remarkable progress in general object detection, they are not satisfying enough in complex indoor scenes. The indoor surveillance video often includes cluttered background objects, among which h... | ['Peng Liu', 'Qianchuan Zhao', 'Xiaoteng Ma', 'Kailai Sun'] | 2021-10-07 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 1.45479605e-01 -4.01711553e-01 1.89733282e-01 -2.25628361e-01
-7.25174487e-01 -2.83256769e-01 2.23753810e-01 -2.82728404e-01
-4.93640810e-01 7.03117907e-01 4.94412601e-01 2.15239003e-02
-6.25443161e-02 -5.89108646e-01 -7.58584380e-01 -1.11081839e+00
-3.09959292e-01 -3.88215482e-02 3.90896022e-01 -4.35071774... | [7.660022258758545, -0.7855173945426941] |
a269f6d6-a8ee-4971-9a58-d74cba007085 | afnet-m-adaptive-fusion-network-with-masks | 2205.11785 | null | https://arxiv.org/abs/2205.11785v1 | https://arxiv.org/pdf/2205.11785v1.pdf | AFNet-M: Adaptive Fusion Network with Masks for 2D+3D Facial Expression Recognition | 2D+3D facial expression recognition (FER) can effectively cope with illumination changes and pose variations by simultaneously merging 2D texture and more robust 3D depth information. Most deep learning-based approaches employ the simple fusion strategy that concatenates the multimodal features directly after fully-con... | ['Feng Zhao', 'Zhaoqing Zhu', 'Hanting Li', 'Mingzhe Sui'] | 2022-05-24 | null | null | null | null | ['3d-facial-expression-recognition', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.09725699e-01 -2.13279337e-01 4.53024469e-02 -6.66565776e-01
-3.77075523e-01 -5.60100283e-03 4.47077543e-01 -4.00168657e-01
-4.51016039e-01 3.74720961e-01 1.78729147e-01 2.26520732e-01
-4.94173095e-02 -6.28431261e-01 -3.52850497e-01 -8.74017596e-01
3.47299548e-03 -2.18933895e-01 6.68689311e-02 -4.84245688... | [13.58326244354248, 1.5468226671218872] |
2639da95-c8ac-43c8-8cee-36df694eccd3 | reco-retrieve-and-co-segment-for-zero-shot-1 | 2206.07045 | null | https://arxiv.org/abs/2206.07045v1 | https://arxiv.org/pdf/2206.07045v1.pdf | ReCo: Retrieve and Co-segment for Zero-shot Transfer | Semantic segmentation has a broad range of applications, but its real-world impact has been significantly limited by the prohibitive annotation costs necessary to enable deployment. Segmentation methods that forgo supervision can side-step these costs, but exhibit the inconvenient requirement to provide labelled exampl... | ['Samuel Albanie', 'Weidi Xie', 'Gyungin Shin'] | 2022-06-14 | reco-retrieve-and-co-segment-for-zero-shot | https://arxiv.org/abs/2206.07045 | https://arxiv.org/pdf/2206.07045 | null | ['unsupervised-semantic-segmentation-with', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.16164052e-01 5.36864460e-01 -3.24460506e-01 -4.13919449e-01
-1.24093568e+00 -8.99016023e-01 7.79146016e-01 -8.01787004e-02
-4.68357831e-01 6.18064225e-01 -9.49451774e-02 -2.38857567e-01
-1.99318640e-02 -7.54209936e-01 -7.59326041e-01 -3.79863828e-01
2.51909137e-01 8.34717691e-01 4.55578953e-01 -1.28740132... | [9.701573371887207, 0.8656459450721741] |
f5e61680-02b4-425d-9347-4e4c741c74f7 | skin-lesion-classification-using-hybrid-deep | 1702.08434 | null | http://arxiv.org/abs/1702.08434v2 | http://arxiv.org/pdf/1702.08434v2.pdf | Skin Lesion Classification Using Hybrid Deep Neural Networks | Skin cancer is one of the major types of cancers with an increasing incidence
over the past decades. Accurately diagnosing skin lesions to discriminate
between benign and malignant skin lesions is crucial to ensure appropriate
patient treatment. While there are many computerised methods for skin lesion
classification, ... | ['Amirreza Mahbod', 'Isabella Ellinger', 'Rupert Ecker', 'Gerald Schaefer', 'Chunliang Wang'] | 2017-02-27 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.01330757e-01 -3.25315334e-02 -1.32308483e-01 -1.42543316e-01
-8.35499406e-01 -1.94116458e-01 8.22826385e-01 4.60350364e-01
-7.13255644e-01 6.90618455e-01 -3.67388502e-03 -3.65051329e-01
-1.95935085e-01 -7.37194598e-01 -4.23082486e-02 -8.94323945e-01
7.84959830e-03 -1.58883601e-01 1.48173943e-01 -2.37015009... | [15.677809715270996, -2.994845390319824] |
aced68e1-4350-47cc-b1db-91be5780f822 | learning-knowledge-graph-based-world-models | 2106.09608 | null | https://arxiv.org/abs/2106.09608v2 | https://arxiv.org/pdf/2106.09608v2.pdf | Learning Knowledge Graph-based World Models of Textual Environments | World models improve a learning agent's ability to efficiently operate in interactive and situated environments. This work focuses on the task of building world models of text-based game environments. Text-based games, or interactive narratives, are reinforcement learning environments in which agents perceive and inter... | ['Mark O. Riedl', 'Prithviraj Ammanabrolu'] | 2021-06-17 | null | http://proceedings.neurips.cc/paper/2021/hash/1e747ddbea997a1b933aaf58a7953c3c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/1e747ddbea997a1b933aaf58a7953c3c-Paper.pdf | neurips-2021-12 | ['text-based-games'] | ['playing-games'] | [ 4.76208270e-01 5.29961765e-01 2.11708307e-01 2.10605055e-01
-6.91376686e-01 -8.18391562e-01 1.23305738e+00 7.80195091e-03
-4.02126819e-01 1.01922750e+00 6.84995234e-01 -2.12836027e-01
-1.06856629e-01 -1.50108516e+00 -8.75423551e-01 -2.60860413e-01
-2.30865747e-01 1.19177008e+00 5.56219876e-01 -7.61962593... | [3.8079473972320557, 1.2841827869415283] |
2ca3c165-6cae-4bad-b68b-0b4c369705c1 | a-global-constraint-for-mining-sequential | 1511.08350 | null | http://arxiv.org/abs/1511.08350v1 | http://arxiv.org/pdf/1511.08350v1.pdf | A global Constraint for mining Sequential Patterns with GAP constraint | Sequential pattern mining (SPM) under gap constraint is a challenging task.
Many efficient specialized methods have been developed but they are all
suffering from a lack of genericity. The Constraint Programming (CP) approaches
are not so effective because of the size of their encodings. In[7], we have
proposed the glo... | ['Thierry Charnois', 'Samir Loudni', 'Amina Kemmar', 'Yahia Lebbah', 'Patrice Boizumault'] | 2015-11-26 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 5.24066031e-01 -4.91993222e-03 -3.58772844e-01 -2.21061885e-01
-7.05742463e-02 -2.67942220e-01 1.87369302e-01 5.45859262e-02
-1.82967186e-01 7.96024799e-01 -8.61601457e-02 -4.15572137e-01
-4.56728220e-01 -9.60708201e-01 -3.38886112e-01 -4.77534235e-01
-9.70708951e-02 4.23599750e-01 9.27765071e-01 -1.11034967... | [8.285184860229492, 6.293193340301514] |
85b4477e-b13c-4738-8e52-c19a3480cdc9 | ppmn-pixel-phrase-matching-network-for-one | 2208.05647 | null | https://arxiv.org/abs/2208.05647v1 | https://arxiv.org/pdf/2208.05647v1.pdf | PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding | Panoptic Narrative Grounding (PNG) is an emerging task whose goal is to segment visual objects of things and stuff categories described by dense narrative captions of a still image. The previous two-stage approach first extracts segmentation region proposals by an off-the-shelf panoptic segmentation model, then conduct... | ['Si Liu', 'Xiaolin Wei', 'Xiaoming Wei', 'Junshi Huang', 'Tianrui Hui', 'Zi-han Ding', 'Zihan Ding'] | 2022-08-11 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 4.16504681e-01 4.19018343e-02 -5.44578493e-01 -2.41562322e-01
-1.11274624e+00 -5.40706038e-01 6.37392282e-01 -1.58409663e-02
-4.12750363e-01 3.76803607e-01 2.85199195e-01 4.92719375e-02
3.37149650e-01 -9.65639412e-01 -6.53853118e-01 -6.00176990e-01
3.46725792e-01 3.00384313e-01 7.16817379e-01 -1.80201501... | [9.712986946105957, 0.5432993769645691] |
a99fc49c-b97e-42ca-92d1-6ade487cd41e | long-tail-visual-relationship-recognition | 2004.00436 | null | https://arxiv.org/abs/2004.00436v7 | https://arxiv.org/pdf/2004.00436v7.pdf | Exploring Long Tail Visual Relationship Recognition with Large Vocabulary | Several approaches have been proposed in recent literature to alleviate the long-tail problem, mainly in object classification tasks. In this paper, we make the first large-scale study concerning the task of Long-Tail Visual Relationship Recognition (LTVRR). LTVRR aims at improving the learning of structured visual rel... | ['Jun Chen', 'Aniket Agarwal', 'Kenneth Church', 'Jiaji Huang', 'Sherif Abdelkarim', 'Panos Achlioptas', 'Mohamed Elhoseiny', 'Boyang Li'] | 2020-03-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Abdelkarim_Exploring_Long_Tail_Visual_Relationship_Recognition_With_Large_Vocabulary_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Abdelkarim_Exploring_Long_Tail_Visual_Relationship_Recognition_With_Large_Vocabulary_ICCV_2021_paper.pdf | iccv-2021-1 | ['visual-relationship-detection'] | ['computer-vision'] | [-1.83579504e-01 -5.97186163e-02 -1.99304715e-01 -5.01448274e-01
-3.63072485e-01 -5.23785830e-01 7.71111012e-01 8.97650048e-02
-3.22919816e-01 4.91673410e-01 4.38417457e-02 -3.79188746e-01
-1.08682122e-02 -4.78747278e-01 -9.35398757e-01 -6.25138819e-01
-3.43727730e-02 6.38607085e-01 4.05586839e-01 -3.74692261... | [10.472249984741211, 1.6637970209121704] |
600e1806-1866-435a-a526-5b5b2086d50c | generative-models-for-graph-based-protein | null | null | http://papers.nips.cc/paper/9711-generative-models-for-graph-based-protein-design | http://papers.nips.cc/paper/9711-generative-models-for-graph-based-protein-design.pdf | Generative Models for Graph-Based Protein Design | Engineered proteins offer the potential to solve many problems in biomedicine, energy, and materials science, but creating designs that succeed is difficult in practice. A significant aspect of this challenge is the complex coupling between protein sequence and 3D structure, with the task of finding a viable design oft... | ['Vikas Garg', 'Regina Barzilay', 'Tommi Jaakkola', 'John Ingraham'] | 2019-12-01 | null | https://openreview.net/forum?id=SJgxrLLKOE | https://openreview.net/pdf?id=SJgxrLLKOE | iclr-workshop-deepgenstruct-2019 | ['protein-design'] | ['medical'] | [ 3.59500319e-01 -4.60088477e-02 -3.15042175e-02 -2.00949147e-01
-4.38029438e-01 -8.01336348e-01 3.46023500e-01 4.06578988e-01
-8.02653953e-02 9.09259737e-01 3.78464848e-01 -6.83259785e-01
-1.60580799e-01 -7.17511475e-01 -1.11985159e+00 -9.10958171e-01
-8.02397728e-02 5.13186157e-01 -2.56381303e-01 -3.81153166... | [4.752908706665039, 5.610057353973389] |
eb3acb4f-3ee2-4a1f-8bf0-0d4285e4461e | using-textual-interface-to-align-external | 2305.13710 | null | https://arxiv.org/abs/2305.13710v1 | https://arxiv.org/pdf/2305.13710v1.pdf | Using Textual Interface to Align External Knowledge for End-to-End Task-Oriented Dialogue Systems | Traditional end-to-end task-oriented dialogue systems have been built with a modularized design. However, such design often causes misalignment between the agent response and external knowledge, due to inadequate representation of information. Furthermore, its evaluation metrics emphasize assessing the agent's pre-lexi... | ['Zhou Yu', 'Derek Chen', 'Deema Alnuhait', 'Qingyang Wu'] | 2023-05-23 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [-6.06548041e-02 4.97528762e-01 2.37123802e-01 -5.58054447e-01
-8.84557426e-01 -6.55934453e-01 8.33952785e-01 2.39175960e-01
-8.06906402e-01 8.14073086e-01 5.48116148e-01 -8.26464593e-02
6.13301322e-02 -7.04579294e-01 4.48638089e-02 -7.12651312e-02
6.20107949e-01 1.14546871e+00 3.88062805e-01 -7.12252140... | [12.849360466003418, 8.076214790344238] |
b23e58b4-231e-4fd6-95f7-b1dfbb65d70b | learning-monocular-depth-in-dynamic | 2305.07397 | null | https://arxiv.org/abs/2305.07397v1 | https://arxiv.org/pdf/2305.07397v1.pdf | Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention | The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoning from 2D monocular images, multi-frame monocular methods are developed to leverage the perspective correlation information from sequential... | ['Xianzhi Li', 'Jian Pu', 'Yuanzhu Gan', 'Yunzhe Wu', 'Zhi-Gang Fan', 'Zhuozheng Li', 'Zizhang Wu'] | 2023-05-12 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [-2.43473817e-02 -2.25369349e-01 6.45647421e-02 -7.31728435e-01
-5.85274220e-01 -3.13820004e-01 6.21514320e-01 -2.65737027e-01
-4.84230071e-01 4.10420060e-01 6.20177574e-02 5.44392392e-02
-4.76158522e-02 -6.46499991e-01 -8.14096570e-01 -7.83712327e-01
3.24150532e-01 1.86385885e-01 3.35709423e-01 3.25358026... | [8.3619966506958, -2.2247402667999268] |
589c6852-4e84-4a58-814b-2e4b62672887 | comparison-of-semantic-segmentation | 1805.08105 | null | http://arxiv.org/abs/1805.08105v1 | http://arxiv.org/pdf/1805.08105v1.pdf | Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection | Horizon or skyline detection plays a vital role towards mountainous visual
geo-localization, however most of the recently proposed visual geo-localization
approaches rely on \textbf{user-in-the-loop} skyline detection methods.
Detecting such a segmenting boundary fully autonomously would definitely be a
step forward fo... | ['George Bebis', 'Martin Čadík', 'Touqeer Ahmad', 'Pavel Campr'] | 2018-05-21 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 5.54698333e-02 2.37170860e-01 1.99057475e-01 -4.37351108e-01
-1.03739190e+00 -7.60170460e-01 6.78378463e-01 1.39696568e-01
-5.30911148e-01 8.04185152e-01 -3.53167623e-01 -3.37695301e-01
2.27778312e-02 -9.62141454e-01 -8.91790986e-01 -6.86459780e-01
-2.37994552e-01 4.95910197e-01 6.11634135e-01 -3.50661278... | [8.800299644470215, -1.4879510402679443] |
e62b7a45-d31a-428f-9615-2b6c5046b7b2 | on-mutual-information-maximization-for | 1907.13625 | null | https://arxiv.org/abs/1907.13625v2 | https://arxiv.org/pdf/1907.13625v2.pdf | On Mutual Information Maximization for Representation Learning | Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective... | ['Paul K. Rubenstein', 'Josip Djolonga', 'Michael Tschannen', 'Sylvain Gelly', 'Mario Lucic'] | 2019-07-31 | null | https://openreview.net/forum?id=rkxoh24FPH | https://openreview.net/pdf?id=rkxoh24FPH | iclr-2020-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.69838142e-01 2.90382087e-01 -1.33949697e-01 -4.34346706e-01
-5.33449113e-01 -5.04946768e-01 1.11118996e+00 1.58296347e-01
-4.23847169e-01 6.74273431e-01 3.19066554e-01 6.30123764e-02
-6.27371192e-01 -7.38882422e-01 -5.12178957e-01 -8.62558901e-01
-5.33619747e-02 6.12683117e-01 -2.24862233e-01 -2.12348804... | [9.013495445251465, 3.1408417224884033] |
0eeaeb37-7bd9-4309-b377-33d68b99416a | deep-learning-methods-for-drug-response | 2211.10442 | null | https://arxiv.org/abs/2211.10442v1 | https://arxiv.org/pdf/2211.10442v1.pdf | Deep learning methods for drug response prediction in cancer: predominant and emerging trends | Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimat... | ['Rick L. Stevens', 'Jamie Overbeek', 'Austin Clyde', 'Oleksandr Narykov', 'Yitan Zhu', 'Thomas S. Brettin', 'Alexander Partin'] | 2022-11-18 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.90768856e-01 -3.05459231e-01 -1.08271646e+00 -1.25205785e-01
-8.70224595e-01 -3.13241363e-01 5.92574477e-01 5.42834938e-01
-2.02584922e-01 9.19803798e-01 2.45141655e-01 -4.48189199e-01
-5.06283820e-01 -7.74823964e-01 -7.56765306e-02 -1.07093751e+00
9.63472575e-02 5.26815295e-01 -2.71714211e-01 -1.71618253... | [5.73466682434082, 5.717627048492432] |
a57542c0-e33e-432e-b5bd-a0884bf3de16 | from-association-to-generation-text-only | 2304.13273 | null | https://arxiv.org/abs/2304.13273v3 | https://arxiv.org/pdf/2304.13273v3.pdf | From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping | With the development of Vision-Language Pre-training Models (VLPMs) represented by CLIP and ALIGN, significant breakthroughs have been achieved for association-based visual tasks such as image classification and image-text retrieval by the zero-shot capability of CLIP without fine-tuning. However, CLIP is hard to apply... | ['Jitao Sang', 'Yi Zhang', 'Ming Yan', 'Junyang Wang'] | 2023-04-26 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 4.34957474e-01 1.88683763e-01 -3.76644135e-01 -2.64538109e-01
-1.13683319e+00 -2.97336996e-01 8.38984787e-01 -2.85665542e-01
-6.24433570e-02 6.59722090e-01 5.97711980e-01 -1.03968158e-01
3.12955052e-01 -4.74646777e-01 -1.04571414e+00 -3.20074767e-01
4.68851209e-01 3.50977093e-01 1.14969648e-01 -1.82831198... | [11.034482955932617, 1.0886456966400146] |
a9f3c241-c50c-4323-bb81-51b90e0af189 | all-optical-neural-network-quantum-state | 2103.06457 | null | https://arxiv.org/abs/2103.06457v2 | https://arxiv.org/pdf/2103.06457v2.pdf | All-optical neural network quantum state tomography | Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the "imaging" technique in the quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extens... | ['Shengwang Du', 'Bei Zeng', 'Xuanying Lai', 'Ningping Cao', 'Chenfeng Cao', 'Ying Zuo'] | 2021-03-11 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.17987049e-01 1.24310516e-01 1.44219652e-01 -3.59107077e-01
-2.47996956e-01 -2.87082821e-01 3.22617531e-01 -3.18151534e-01
-6.84836328e-01 7.72346020e-01 -2.49067754e-01 -5.47947407e-01
-1.38169080e-01 -1.01770568e+00 -7.71591246e-01 -1.16740239e+00
2.45459095e-01 1.89918295e-01 -2.63097845e-02 -4.58891749... | [5.577231407165527, 4.944746494293213] |
3e38686b-9d88-4e97-b4fb-df993e3fdc23 | analysis-of-the-operation-of-industrial | 2112.08258 | null | https://arxiv.org/abs/2112.08258v1 | https://arxiv.org/pdf/2112.08258v1.pdf | Analysis of the Operation of Industrial Trucks based on Position Data | Indoor positioning systems (IPSs) can make an important contribution to the analysis and optimization of internal transport processes. The overall aim of this work is to examine how position data can be used to analyze the operation of industrial trucks in warehouses. This is achieved by developing a concept for the an... | ['Jochen Kreutzfeldt', 'Johannes Hinckeldeyn', 'Hendrik Rose', 'Jakob Schyga'] | 2021-12-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-8.27371851e-02 -4.21811700e-01 5.82073152e-01 -2.58855611e-01
-3.58148158e-01 -3.69416893e-01 4.21811491e-01 3.86087090e-01
-3.67783040e-01 1.76560283e-01 -6.96087554e-02 -5.44677138e-01
-7.84424245e-01 -6.88362360e-01 -9.81480181e-02 -9.59903598e-01
-1.75500527e-01 4.90304291e-01 6.09731197e-01 -4.74875301... | [6.558043003082275, 1.7863552570343018] |
4685edec-b3c0-4a92-a5a2-993dfd1b107b | connecting-levels-of-analysis-in-the | 2305.06037 | null | https://arxiv.org/abs/2305.06037v2 | https://arxiv.org/pdf/2305.06037v2.pdf | Connecting levels of analysis in the computational era | Neuroscience and artificial intelligence are closely intertwined, but so are the physics of dynamical system, philosophy and psychology. Each of these fields try in their own way to relate observations at the level of molecules, synapses, neurons or behavior, to a function. An influential conceptual approach to this en... | ['André Longtin', 'Richard Naud'] | 2023-05-10 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-2.07229145e-03 -1.81201130e-01 -4.82801124e-02 1.14521459e-01
3.47572267e-01 -5.50663769e-01 1.08905733e+00 3.32893610e-01
-4.63958919e-01 8.57894897e-01 8.82069990e-02 -3.64646912e-01
-4.64713335e-01 -6.37021780e-01 -2.79410720e-01 -7.78619766e-01
-8.30350593e-02 2.88468599e-01 3.05771768e-01 -4.40044969... | [5.673561096191406, 4.149896621704102] |
53dc0609-6034-44b2-9591-4d44e1a97796 | pingan-vcgroup-s-solution-for-icdar-2021-1 | 2105.01846 | null | https://arxiv.org/abs/2105.01846v1 | https://arxiv.org/pdf/2105.01846v1.pdf | PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex | This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX. This competition has two sub-tasks: Table Structure Reconstruction (TSR) and Table Content Reconstruction (TCR). We treat both sub-tasks as two individual image-to-sequence recognition problems. We leverage o... | ['Rong Xiao', 'Xin Tang', 'Bingcong Li', 'Yihao Chen', 'Peng Gao', 'Jiaquan Ye', 'Xianbiao Qi', 'Yelin He'] | 2021-05-05 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.15151775e-01 -3.49159122e-01 -1.58280149e-01 -2.41313949e-01
-1.23145401e+00 -7.84781277e-01 6.44410908e-01 1.99875548e-01
-4.62147772e-01 5.52622318e-01 3.02936584e-01 -2.58760780e-01
2.94432223e-01 -5.53591371e-01 -1.22128081e+00 -2.04633653e-01
3.48591149e-01 5.08979499e-01 -2.08673641e-01 1.31115943... | [11.692536354064941, 2.9895665645599365] |
d9fc9c48-33ea-42ec-a5b9-198579cb82ae | a-heat-map-based-algorithm-for-recognizing | 1502.06076 | null | http://arxiv.org/abs/1502.06076v1 | http://arxiv.org/pdf/1502.06076v1.pdf | A Heat-Map-based Algorithm for Recognizing Group Activities in Videos | In this paper, a new heat-map-based (HMB) algorithm is proposed for group
activity recognition. The proposed algorithm first models human trajectories as
series of "heat sources" and then applies a thermal diffusion process to create
a heat map (HM) for representing the group activities. Based on this heat map,
a new k... | ['Zhenzhong Chen', 'Bin Sheng', 'Weiyao Lin', 'Jianxin Wu', 'Hang Chu'] | 2015-02-21 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 1.91298261e-01 -5.84940910e-01 -2.94591606e-01 -5.24787158e-02
-3.95114273e-01 -2.35323414e-01 7.61114776e-01 4.54548784e-02
-7.20270351e-02 2.28503615e-01 3.23452771e-01 -2.22353600e-02
-1.23658098e-01 -8.30862105e-01 -2.77430773e-01 -9.55122054e-01
-1.22869231e-01 -5.34828939e-02 5.56413710e-01 1.13259949... | [8.158425331115723, 0.37691929936408997] |
049ac831-0f1a-4690-8d02-70625453f600 | a-cognitive-account-of-the-puzzle-of | 2305.00296 | null | https://arxiv.org/abs/2305.00296v1 | https://arxiv.org/pdf/2305.00296v1.pdf | A Cognitive Account of the Puzzle of Ideography | In this commentary article to 'The Puzzle of Ideography' by Morin, we put forth a new cognitive account of the puzzle of ideography, that complements the standardization account of Morin. Efficient standardization of spoken language is phenomenologically attributed to a modality effect coupled with chunking of cognitiv... | ['Xerxes D. Arsiwalla'] | 2023-04-29 | null | null | null | null | ['chunking'] | ['natural-language-processing'] | [-8.81133154e-02 1.95659459e-01 -2.15823457e-01 4.96718325e-02
-3.84148918e-02 -7.52678216e-01 8.36692989e-01 -5.52031994e-02
-3.32344770e-01 2.65017897e-01 1.12258065e+00 -5.36788762e-01
-5.51898301e-01 -6.63147867e-02 -3.06989461e-01 -1.88304767e-01
3.33999991e-01 4.14134830e-01 -1.67218804e-01 -7.32278049... | [10.080007553100586, 8.451208114624023] |
f18a661c-3224-4bfc-aa6b-faec2676a35a | subjective-and-objective-quality-assessment-4 | 2303.08050 | null | https://arxiv.org/abs/2303.08050v2 | https://arxiv.org/pdf/2303.08050v2.pdf | Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images | Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practice, the quality of CGIs consistently suffers from poor rendering during production, inevitable compression artifacts during the transmi... | ['Guangtao Zhai', 'Xiongkuo Min', 'Qiyuan Wang', 'Jun He', 'Quan Zhou', 'Wei Lu', 'Tao Wang', 'Wei Sun', 'ZiCheng Zhang'] | 2023-03-14 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 1.69256881e-01 -6.37221396e-01 4.18570846e-01 -2.95857519e-01
-6.10170722e-01 -1.07148513e-01 2.70210475e-01 7.04121143e-02
-1.53492674e-01 2.35578597e-01 -1.38305333e-02 -1.46720171e-01
-1.93912312e-01 -9.93460059e-01 -6.47016644e-01 -5.16586065e-01
-1.98051214e-01 -6.24180771e-02 1.43372595e-01 -3.87384921... | [11.803017616271973, -1.8398901224136353] |
7beaf2d8-0857-44c1-86c7-ccf3dd6c82ab | reconstructing-continuously-heterogeneous | 1909.05215 | null | https://arxiv.org/abs/1909.05215v3 | https://arxiv.org/pdf/1909.05215v3.pdf | Reconstructing continuous distributions of 3D protein structure from cryo-EM images | Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the three-dimensional structure of a macromolecule from $10^{4-7}$ noisy and randomly orien... | ['Tristan Bepler', 'Joseph H. Davis', 'Ellen D. Zhong', 'Bonnie Berger'] | 2019-09-11 | null | https://openreview.net/forum?id=SJxUjlBtwB | https://openreview.net/pdf?id=SJxUjlBtwB | iclr-2020-1 | ['3d-volumetric-reconstruction', 'cryogenic-electron-microscopy-cryo-em'] | ['computer-vision', 'computer-vision'] | [ 9.94725749e-02 -1.71265319e-01 3.30210596e-01 -5.35452843e-01
-8.33578229e-01 -3.86192143e-01 3.94084454e-01 -1.36996478e-01
-6.21402442e-01 9.75324214e-01 -1.31387398e-01 -5.04665673e-01
-3.43887471e-02 -3.45649660e-01 -1.10035610e+00 -1.31694770e+00
4.67407219e-02 1.37421083e+00 -9.23646763e-02 1.26732618... | [13.295774459838867, -3.069888114929199] |
652408aa-ae3f-44b5-a3bf-9603408dd9d3 | model-aided-federated-reinforcement-learning | 2306.02029 | null | https://arxiv.org/abs/2306.02029v1 | https://arxiv.org/pdf/2306.02029v1.pdf | Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks | Deploying teams of cooperative unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms. Multi-agent reinforcement learning (MARL) has emerged as an effective solution, but often requires extensive and costly rea... | ['Marco Caccamo', 'David Gesbert', 'Harald Bayerlein', 'Omid Esrafilian', 'Jichao Chen'] | 2023-06-03 | null | null | null | null | ['multi-agent-reinforcement-learning', 'trajectory-planning'] | ['methodology', 'robots'] | [-1.25672385e-01 -1.18150547e-01 -2.45918017e-02 8.80517960e-02
-5.43034077e-01 -8.23223650e-01 2.44175762e-01 2.22787499e-01
-5.20290196e-01 1.20052838e+00 -5.36213577e-01 -3.94789368e-01
-7.34061658e-01 -1.09108722e+00 -7.57722020e-01 -1.12926769e+00
-6.51668966e-01 6.41170561e-01 -1.34447679e-01 -1.38428822... | [5.777977466583252, 1.5890781879425049] |
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