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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
817c73b0-ec47-47a9-a5df-1d477a1eb79e | starss23-an-audio-visual-dataset-of-spatial | 2306.09126 | null | https://arxiv.org/abs/2306.09126v1 | https://arxiv.org/pdf/2306.09126v1.pdf | STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events | While direction of arrival (DOA) of sound events is generally estimated from multichannel audio data recorded in a microphone array, sound events usually derive from visually perceptible source objects, e.g., sounds of footsteps come from the feet of a walker. This paper proposes an audio-visual sound event localizatio... | ['Yuki Mitsufuji', 'Tuomas Virtanen', 'Shusuke Takahashi', 'Naoya Takahashi', 'Yuichiro Koyama', 'Aapo Hakala', 'Sharath Adavanne', 'Kengo Uchida', 'Daniel Krause', 'Parthasaarathy Sudarsanam', 'Archontis Politis', 'Kazuki Shimada'] | 2023-06-15 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-2.33354941e-01 -8.04310858e-01 3.22037637e-01 -7.40321307e-03
-1.27824593e+00 -6.34834886e-01 1.13524236e-01 2.65986085e-01
-3.20472956e-01 2.23271027e-01 5.11067033e-01 6.62364289e-02
2.82430649e-01 -2.52029270e-01 -5.36409259e-01 -4.02667135e-01
-4.13568497e-01 -3.29003304e-01 6.83879972e-01 3.62465769... | [15.069684028625488, 5.221979141235352] |
2c52b100-95b3-4d8a-8a1e-db90615f1653 | responsive-listening-head-generation-a | 2112.13548 | null | https://arxiv.org/abs/2112.13548v3 | https://arxiv.org/pdf/2112.13548v3.pdf | Responsive Listening Head Generation: A Benchmark Dataset and Baseline | We present a new listening head generation benchmark, for synthesizing responsive feedbacks of a listener (e.g., nod, smile) during a face-to-face conversation. As the indispensable complement to talking heads generation, listening head generation has seldomly been studied in literature. Automatically synthesizing list... | ['Tao Mei', 'Tiejun Zhao', 'Ting Yao', 'Wei zhang', 'Yalong Bai', 'Mohan Zhou'] | 2021-12-27 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 2.64597684e-01 4.54010010e-01 1.54333711e-01 -6.05047166e-01
-6.43569767e-01 -6.54840767e-01 8.65605593e-01 -5.30133486e-01
2.41855383e-01 3.56382102e-01 8.05233479e-01 1.89211965e-01
4.72071558e-01 -4.39990610e-01 -4.15938526e-01 -7.11946011e-01
1.27980232e-01 3.39061528e-01 -2.77542800e-01 -5.29372096... | [13.227076530456543, -0.40146520733833313] |
e46a3c43-28d7-4755-bd94-dbb2033f0034 | san-francisco-crime-classification | 1607.03626 | null | http://arxiv.org/abs/1607.03626v1 | http://arxiv.org/pdf/1607.03626v1.pdf | San Francisco Crime Classification | San Francisco Crime Classification is an online competition administered by
Kaggle Inc. The competition aims at predicting the future crimes based on a
given set of geographical and time-based features. In this paper, I achieved a
an accuracy that ranks at top %18, as of May 19th, 2016. I will explore the
data, and exp... | ['Yehya Abouelnaga'] | 2016-07-13 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-5.44228137e-01 -3.98592263e-01 -1.99303895e-01 -6.41683936e-01
-6.90784037e-01 -3.94886672e-01 7.66093671e-01 6.72843635e-01
-8.07566881e-01 7.72213221e-01 3.76717567e-01 -4.16177362e-01
-4.43248689e-01 -8.77434134e-01 -1.76865578e-01 2.31303647e-02
-5.50341547e-01 1.51153818e-01 2.77884249e-02 -5.14272928... | [6.721374034881592, 1.9105924367904663] |
d2d8fd3b-12af-462d-a684-210409639262 | expectile-quadrangle-and-applications | 2306.16351 | null | https://arxiv.org/abs/2306.16351v1 | https://arxiv.org/pdf/2306.16351v1.pdf | Expectile Quadrangle and Applications | The paper explores the concept of the \emph{expectile risk measure} within the framework of the Fundamental Risk Quadrangle (FRQ) theory. According to the FRQ theory, a quadrangle comprises four stochastic functions associated with a random variable: ``error'', ``regret'', ``risk'', and ``deviation''. These functions a... | ['Stan Uryasev', 'Anton Malandii', 'Viktor Kuzmenko'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.75318643e-01 3.73004407e-01 1.27547964e-01 -1.40416443e-01
-1.00662768e+00 -6.41349971e-01 5.04625738e-01 6.88186467e-01
-3.76244396e-01 8.43602598e-01 -9.44885090e-02 -4.33408171e-01
-7.21268475e-01 -1.01611280e+00 -3.60310286e-01 -6.84617400e-01
-4.65634733e-01 7.63752982e-02 -9.74248052e-02 -1.62043408... | [5.062130451202393, 3.955767869949341] |
5fc5db50-d324-41e0-aba3-ba1049254d1b | search-for-the-ugle-truth-an-investigation | 2305.06026 | null | https://arxiv.org/abs/2305.06026v2 | https://arxiv.org/pdf/2305.06026v2.pdf | Search for the UGLE Truth: An Investigation into Unsupervised GNN Learning Environments | Graph Neural Networks (GNNs) are a pertinent tool for any machine learning task due to their ability to learn functions over graph structures, a powerful and expressive data representation. The detection of communities, an unsupervised task has increasingly been performed with GNNs. Clustering nodes in a graph using th... | ['Ryan McConville', 'Will Leeney'] | 2023-05-10 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 3.81738633e-01 -1.06370293e-01 -6.00213222e-02 -2.11787686e-01
-4.37484980e-02 -6.60551488e-01 6.73248768e-01 5.34420609e-01
-5.25331199e-01 6.01533711e-01 1.09695747e-01 -4.25846130e-01
-6.61246121e-01 -7.56741703e-01 -1.85465187e-01 -7.96965301e-01
-6.58883750e-01 5.59840262e-01 -4.55334829e-03 -2.37247601... | [6.895699977874756, 5.7994794845581055] |
79380c80-589a-4f43-b289-ecdcfcdd9dc8 | additive-tree-structured-conditional | 2010.03171 | null | https://arxiv.org/abs/2010.03171v1 | https://arxiv.org/pdf/2010.03171v1.pdf | Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation | Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured funct... | ['Matthew B. Blaschko', 'Xingchen Ma'] | 2020-10-06 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 1.72131002e-01 2.60994257e-03 -1.28699848e-02 -5.19557595e-01
-7.24356711e-01 -3.20640355e-01 3.32932860e-01 -1.88384071e-01
-8.33245635e-01 8.42176080e-01 -1.89753160e-01 -4.24933404e-01
-5.33001304e-01 -5.07968366e-01 -8.30489397e-01 -8.36288273e-01
-7.47698396e-02 8.15645039e-01 3.09457272e-01 1.90572023... | [7.840514659881592, 3.5923988819122314] |
31ab486d-cf6d-4cfd-bea6-8cdb8b655c57 | accurate-tree-roots-positioning-and-sizing | 2205.13731 | null | https://arxiv.org/abs/2205.13731v1 | https://arxiv.org/pdf/2205.13731v1.pdf | Accurate Tree Roots Positioning and Sizing over Undulated Ground Surfaces by Common Offset GPR Measurements | Tree roots detection is a popular application of the Ground-penetrating radar (GPR). Normally, the ground surface above the tree roots is assumed to be flat, and standard processing methods based on hyperbolic fitting are applied to the hyperbolae reflection patterns of tree roots for detection purposes. When the surfa... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Lai Fern Ow', 'Yee Hui Lee', 'Wenhao Luo'] | 2022-05-27 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 4.50634599e-01 -5.24190180e-02 6.94536746e-01 -5.13216592e-02
-4.22002316e-01 -2.23714486e-02 -1.01934455e-01 6.73131049e-02
1.49731413e-01 4.56025243e-01 -4.49247420e-01 -4.75822240e-01
-3.52508485e-01 -1.31141961e+00 -3.20381910e-01 -8.11321080e-01
-4.19937313e-01 2.53535032e-01 5.46441913e-01 -4.47630018... | [6.810297966003418, 1.404147744178772] |
ee242d53-db4b-43b2-8b4f-af54eff13e27 | final-adaptation-reinforcement-learning-for-n | 2111.14375 | null | https://arxiv.org/abs/2111.14375v1 | https://arxiv.org/pdf/2111.14375v1.pdf | Final Adaptation Reinforcement Learning for N-Player Games | This paper covers n-tuple-based reinforcement learning (RL) algorithms for games. We present new algorithms for TD-, SARSA- and Q-learning which work seamlessly on various games with arbitrary number of players. This is achieved by taking a player-centered view where each player propagates his/her rewards back to previ... | ['Samineh Bagheri', 'Wolfgang Konen'] | 2021-11-29 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.56839323e-01 1.00351609e-01 -3.00405264e-01 2.95149237e-01
-9.63078082e-01 -8.26271355e-01 2.17433736e-01 -4.61367704e-02
-6.19730115e-01 1.27897191e+00 9.52662975e-02 -2.64192641e-01
-5.84464490e-01 -9.69714403e-01 -5.13951540e-01 -6.76122069e-01
-5.29202878e-01 7.57476091e-01 4.37563866e-01 -9.31382000... | [3.5775461196899414, 1.5411176681518555] |
c15dc6b6-6493-48c1-b66d-940dd8d67548 | learning-audio-driven-viseme-dynamics-for-3d | 2301.06059 | null | https://arxiv.org/abs/2301.06059v1 | https://arxiv.org/pdf/2301.06059v1.pdf | Learning Audio-Driven Viseme Dynamics for 3D Face Animation | We present a novel audio-driven facial animation approach that can generate realistic lip-synchronized 3D facial animations from the input audio. Our approach learns viseme dynamics from speech videos, produces animator-friendly viseme curves, and supports multilingual speech inputs. The core of our approach is a novel... | ['Di Kang', 'Xuefei Zhe', 'Changhai Chen', 'Tangli Xue', 'Yue Qian', 'Haoxian Zhang', 'Linchao Bao'] | 2023-01-15 | null | null | null | null | ['3d-face-animation'] | ['computer-vision'] | [ 1.21271203e-03 1.30872250e-01 5.55735491e-02 -9.08410251e-02
-9.77072239e-01 -5.26741505e-01 4.51375544e-01 -5.04343271e-01
1.75787106e-01 1.72311410e-01 3.00801277e-01 -1.94933526e-02
2.94940948e-01 -3.87467653e-01 -7.68075824e-01 -5.14912069e-01
-1.48728773e-01 5.04414737e-01 1.16745241e-01 -5.18749595... | [13.18025016784668, -0.43315181136131287] |
07dd8e90-34b3-4801-8d0f-46f170250eb0 | backpropagation-free-4d-continuous-ant-based | 2305.06715 | null | https://arxiv.org/abs/2305.06715v1 | https://arxiv.org/pdf/2305.06715v1.pdf | Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search | Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO). CANTS utilizes a continuous search space to indirectly-encode a neural architecture search space. Synthetic ant agents explore CANTS' c... | ['Travis Desell', 'Alexander Ororbia', 'Zeming Lyu', 'Karl Ricanek', 'AbdElRahman ElSaid'] | 2023-05-11 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.21790487e-01 -4.90856282e-02 -1.50556825e-02 -1.64979659e-02
6.16025269e-01 -3.41318876e-01 3.84961247e-01 1.56589568e-01
-7.89300740e-01 8.92599344e-01 -4.30019468e-01 -5.36092579e-01
-2.94280350e-01 -9.83607233e-01 -4.50821400e-01 -7.89004147e-01
4.53551896e-02 6.04247630e-01 6.01802289e-01 -4.21132416... | [8.174603462219238, 3.250377893447876] |
0c534966-c4b2-4d82-9728-5df10bdcef5f | one-ruler-for-all-languages-multi-lingual | 1805.02914 | null | http://arxiv.org/abs/1805.02914v1 | http://arxiv.org/pdf/1805.02914v1.pdf | One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning | Automatic evaluating the performance of Open-domain dialogue system is a
challenging problem. Recent work in neural network-based metrics has shown
promising opportunities for automatic dialogue evaluation. However, existing
methods mainly focus on monolingual evaluation, in which the trained metric is
not flexible eno... | ['Rui Yan', 'Mingyue Shang', 'Xiaowei Tong', 'Zhenxin Fu', 'Dongyan Zhao'] | 2018-05-08 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.87831837e-01 -1.23196192e-01 1.25875771e-01 -5.27214468e-01
-1.04119313e+00 -7.28185892e-01 8.78610134e-01 -3.42958830e-02
-6.51737869e-01 1.20311582e+00 4.12531495e-01 -1.05799794e-01
3.13391954e-01 -6.34799659e-01 -9.43539590e-02 -4.42933410e-01
2.51877904e-01 6.33903027e-01 1.41305238e-01 -8.89863610... | [12.636343955993652, 8.235668182373047] |
fd0c095c-114c-4558-a628-092d8c772126 | graph-self-attention-for-learning-graph | 2201.12787 | null | https://arxiv.org/abs/2201.12787v3 | https://arxiv.org/pdf/2201.12787v3.pdf | GRPE: Relative Positional Encoding for Graph Transformer | We propose a novel positional encoding for learning graph on Transformer architecture. Existing approaches either linearize a graph to encode absolute position in the sequence of nodes, or encode relative position with another node using bias terms. The former loses preciseness of relative position from linearization, ... | ['Seung-won Hwang', 'Juntae Kim', 'Donggeon Lee', 'WoongGi Chang', 'Wonpyo Park'] | 2022-01-30 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-2.12444305e-01 4.48762804e-01 -5.51334679e-01 -2.69017607e-01
-4.48315769e-01 -7.76947141e-01 4.81248558e-01 3.82086813e-01
2.47928813e-01 5.95022082e-01 2.88814545e-01 -5.03436148e-01
-2.19017327e-01 -1.08785093e+00 -8.59639108e-01 -5.26445210e-01
-2.55780458e-01 3.21868658e-01 2.54176229e-01 -2.30655000... | [7.009444713592529, 6.286719799041748] |
fe358efc-7f14-4230-88e2-7f9100b83545 | a-context-based-approach-for-dialogue-act | 1805.06280 | null | http://arxiv.org/abs/1805.06280v1 | http://arxiv.org/pdf/1805.06280v1.pdf | A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks | Dialogue act recognition is an important part of natural language
understanding. We investigate the way dialogue act corpora are annotated and
the learning approaches used so far. We find that the dialogue act is
context-sensitive within the conversation for most of the classes.
Nevertheless, previous models of dialogu... | ['Cornelius Weber', 'Sven Magg', 'Stefan Wermter', 'Chandrakant Bothe'] | 2018-05-16 | a-context-based-approach-for-dialogue-act-1 | https://aclanthology.org/L18-1307 | https://aclanthology.org/L18-1307.pdf | lrec-2018-5 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.38460255e-01 5.11814237e-01 -2.03697905e-01 -8.83582354e-01
-3.31715107e-01 -7.55470991e-01 1.22028196e+00 3.95225078e-01
-3.52009177e-01 1.05984318e+00 1.00366974e+00 -4.82569665e-01
1.58017397e-01 -5.82440376e-01 4.11911339e-01 -2.87944794e-01
1.14417247e-01 7.10058033e-01 4.58420366e-01 -9.40838814... | [12.918439865112305, 7.931952476501465] |
bafa5ac6-dc7b-4672-bd09-3c14723aa2da | rfr-wwanet-weighted-window-attention-based | 2305.04236 | null | https://arxiv.org/abs/2305.04236v2 | https://arxiv.org/pdf/2305.04236v2.pdf | RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration | The Swin transformer has recently attracted attention in medical image analysis due to its computational efficiency and long-range modeling capability. Owing to these properties, the Swin Transformer is suitable for establishing more distant relationships between corresponding voxels in different positions in complex a... | ['Guixia Liu', 'Weijie Wang', 'Lei Song', 'Tao Wang', 'Mingrui Ma'] | 2023-05-07 | null | null | null | null | ['image-registration', 'long-range-modeling'] | ['computer-vision', 'natural-language-processing'] | [ 2.87709355e-01 1.28330097e-01 -2.92216986e-01 -3.63967359e-01
-7.62930393e-01 -1.51274651e-01 2.94077545e-01 2.51488209e-01
-3.88292283e-01 3.26618522e-01 5.38305879e-01 4.09713686e-02
-7.93493807e-01 -8.18148196e-01 -4.16169524e-01 -7.85523713e-01
-3.13114285e-01 2.51169294e-01 5.79750359e-01 -3.46279472... | [14.097367286682129, -2.6507930755615234] |
ff5ab668-a30a-4f59-8be8-0e49737ab3c5 | generating-programmatic-referring-expressions | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1158-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1158-Paper.pdf | Generating Programmatic Referring Expressions via Program Synthesis | Incorporating symbolic reasoning into machine learning algorithms is a promising approach to improve performance on learning tasks that require logical reasoning. We study the problem of generating a programmatic variant of referring expressions that we call referring relational programs. In particular, given a symboli... | ['Aws Albarghouthi', 'Calvin Smith', 'Mayur Naik', 'Jiani Huang', 'Rishabh Singh', 'Osbert Bastani'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1158-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1158-Paper.pdf | icml-2020-1 | ['enumerative-search'] | ['computer-code'] | [ 5.34332633e-01 7.26406336e-01 -4.54609990e-01 -5.50637841e-01
-6.57504618e-01 -4.92309868e-01 8.00293922e-01 2.83934355e-01
-1.86617672e-01 4.02086169e-01 -1.00626983e-02 -5.24652183e-01
4.77995314e-02 -1.20381975e+00 -1.29471862e+00 -3.34696770e-01
1.40500680e-01 7.19755888e-01 1.17495872e-01 -6.58416152... | [8.666534423828125, 7.148620128631592] |
f319a97e-5896-4614-9987-16c9a5c505a4 | nmbr9-as-a-constraint-programming-challenge | 2001.04238 | null | https://arxiv.org/abs/2001.04238v1 | https://arxiv.org/pdf/2001.04238v1.pdf | Nmbr9 as a Constraint Programming Challenge | Modern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, con... | ['Mikael Zayenz Lagerkvist'] | 2020-01-13 | null | null | null | null | ['board-games', 'solitaire'] | ['playing-games', 'playing-games'] | [-2.80595690e-01 4.59687918e-01 3.16117145e-02 1.11722179e-01
-8.61595646e-02 -9.38587248e-01 2.11366966e-01 -1.33496478e-01
-3.14285576e-01 1.19472671e+00 -4.66315180e-01 -3.77988786e-01
-6.88226819e-01 -1.05338323e+00 -3.55145961e-01 -5.69820881e-01
-2.90086448e-01 1.33055353e+00 9.65617597e-01 -1.22589016... | [3.4511685371398926, 1.4808011054992676] |
7c481799-e564-465c-ac3d-e9c0a856b134 | hmm-based-writer-identification-in-music | 1707.06828 | null | http://arxiv.org/abs/1707.06828v2 | http://arxiv.org/pdf/1707.06828v2.pdf | HMM-based Writer Identification in Music Score Documents without Staff-Line Removal | Writer identification from musical score documents is a challenging task due
to its inherent problem of overlapping of musical symbols with staff lines.
Most of the existing works in the literature of writer identification in
musical score documents were performed after a preprocessing stage of staff
lines removal. In ... | ['Ayan Kumar Bhunia', 'Umapada Pal', 'Partha Pratim Roy'] | 2017-07-21 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.11022753e-01 -6.23526573e-01 2.46412098e-01 -3.28211263e-02
-6.48966789e-01 -7.76588380e-01 3.93758744e-01 3.97976860e-02
-4.08129722e-01 2.88708448e-01 1.05414316e-01 2.72340834e-01
-5.11917174e-01 -4.09870744e-01 -9.21837091e-02 -5.01087844e-01
3.23918939e-01 5.05530894e-01 5.92854977e-01 -1.28552377... | [15.860721588134766, 5.294817924499512] |
13c573d6-fcb5-4103-9fad-417e5c6949db | a-deep-cnn-architecture-with-novel-pooling | 2201.12664 | null | https://arxiv.org/abs/2201.12664v1 | https://arxiv.org/pdf/2201.12664v1.pdf | A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets | Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely-used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of datasets h... | ['Ephrem A. Retta', 'Eiad Almekhlafi', 'Jun Feng', 'Xia Sun', 'Richard Sutcliffe', 'Mustafa Mhamed'] | 2022-01-29 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-4.82239276e-01 -2.35019594e-01 2.33105734e-01 -6.14515841e-01
-3.99597794e-01 -4.20764089e-01 6.47340119e-01 1.68265954e-01
-5.49631715e-01 8.28006446e-01 -2.28882387e-01 -2.98289079e-02
2.21463785e-01 -9.22025263e-01 -1.44353211e-01 -8.06966126e-01
-1.11763753e-01 1.93845302e-01 -7.25106746e-02 -1.07056177... | [11.144991874694824, 7.060960292816162] |
8801fe14-f8de-462b-8195-1b3832a1c61a | demonstration-of-machine-learning-enhanced | 2212.08032 | null | https://arxiv.org/abs/2212.08032v1 | https://arxiv.org/pdf/2212.08032v1.pdf | Demonstration of machine-learning-enhanced Bayesian quantum state estimation | Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for use... | ['Brian T. Kirby', 'Thomas A. Searles', 'Ryan T. Glasser', 'Daniel E. Jones', 'Sangita Regmi', 'Amirali Khannejad', 'Atiyya A. Davis', 'Joseph M. Lukens', 'Sanjaya Lohani'] | 2022-12-15 | null | null | null | null | ['quantum-state-tomography', 'experimental-design'] | ['medical', 'methodology'] | [ 3.48587066e-01 -2.66926110e-01 -3.02171111e-01 -6.43536091e-01
-1.13214338e+00 -3.84103864e-01 7.49422133e-01 2.55836435e-02
-6.86204493e-01 1.10164201e+00 -4.33493108e-02 -6.97606683e-01
-1.99463755e-01 -7.10872114e-01 -3.33245784e-01 -8.77471566e-01
1.85427070e-01 4.84893799e-01 1.17963320e-02 2.16195490... | [5.601589202880859, 4.905053615570068] |
81cd8975-1b31-4c7a-a719-acdac5853836 | counter-hypothetical-particle-filters-for | 2305.17828 | null | https://arxiv.org/abs/2305.17828v1 | https://arxiv.org/pdf/2305.17828v1.pdf | Counter-Hypothetical Particle Filters for Single Object Pose Tracking | Particle filtering is a common technique for six degree of freedom (6D) pose estimation due to its ability to tractably represent belief over object pose. However, the particle filter is prone to particle deprivation due to the high-dimensional nature of 6D pose. When particle deprivation occurs, it can cause mode coll... | ['Odest Chadwicke Jenkins', 'Jasmine A. Berry', 'Jana Pavlasek', 'Elizabeth A. Olson'] | 2023-05-28 | null | null | null | null | ['pose-tracking', 'pose-estimation', '6d-pose-estimation-1'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.39470264e-01 2.75351733e-01 -1.81923844e-02 3.47768664e-02
-4.62328792e-01 -6.43445909e-01 7.87256360e-01 3.92911702e-01
-4.38449144e-01 8.60722721e-01 2.63202846e-01 -3.58049184e-01
-4.10388112e-01 -8.30174387e-01 -8.89813721e-01 -7.14506686e-01
4.21202034e-02 7.94289410e-01 4.55630422e-01 1.54227093... | [7.35066556930542, -1.0441350936889648] |
b4483be9-419e-4167-b1fc-74decbcbcbd3 | survaival-survival-analysis-with-the-eyes-of | 2305.18222 | null | https://arxiv.org/abs/2305.18222v1 | https://arxiv.org/pdf/2305.18222v1.pdf | survAIval: Survival Analysis with the Eyes of AI | In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-critical corner cases in a short period of time, as already presented in~\cite{kowol22simulator}. Our results show that incorporating these cor... | ['Hanno Gottschalk', 'Stefan Bracke', 'Kamil Kowol'] | 2023-05-23 | null | null | null | null | ['autonomous-vehicles', 'survival-analysis'] | ['computer-vision', 'miscellaneous'] | [-1.05056964e-01 1.69418350e-01 2.22394578e-02 -5.92015028e-01
-2.54309773e-01 -5.71805656e-01 4.29186434e-01 9.51782539e-02
-4.43344921e-01 5.63778877e-01 -2.98371404e-01 -8.41753721e-01
-2.56843239e-01 -7.86400914e-01 -7.68693686e-01 -9.66996774e-02
2.16047361e-01 3.41862708e-01 5.53760469e-01 -7.20744669... | [5.688711643218994, 1.1444401741027832] |
2799a3c4-dcd3-4e61-840c-cfb135641964 | variational-distillation-for-multi-view | 2206.09548 | null | https://arxiv.org/abs/2206.09548v1 | https://arxiv.org/pdf/2206.09548v1.pdf | Variational Distillation for Multi-View Learning | Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions. However, its great success is generally attributed to estimate the multivariate mutual information which is intractable when the network becomes ... | ['DaCheng Tao', 'Yuan Xie', 'Zongze Wu', 'Lizhuang Ma', 'Yanyun Qu', 'Wensheng Zhang', 'Cong Wang', 'Zhizhong Zhang', 'Xudong Tian'] | 2022-06-20 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.03278197e-01 8.70318711e-02 -5.49992800e-01 -4.24419731e-01
-1.01079500e+00 -5.67569435e-01 2.80668318e-01 -1.53943926e-01
1.26881659e-01 4.07796651e-01 2.75914729e-01 -2.77494062e-02
-5.84059536e-01 -4.49923784e-01 -4.05121088e-01 -8.32746267e-01
7.53673539e-02 4.81356651e-01 -4.63794321e-02 -6.16522878... | [8.511631965637207, 4.477954387664795] |
4e4a7ace-8a84-46db-8a6c-d5f681109a54 | pgtask-introducing-the-task-of-profile | 2304.06634 | null | https://arxiv.org/abs/2304.06634v1 | https://arxiv.org/pdf/2304.06634v1.pdf | PGTask: Introducing the Task of Profile Generation from Dialogues | Recent approaches have attempted to personalize dialogue systems by leveraging profile information into models. However, this knowledge is scarce and difficult to obtain, which makes the extraction/generation of profile information from dialogues a fundamental asset. To surpass this limitation, we introduce the Profile... | ['Luísa Coheur', 'Joao P. Carvalho', 'Rui Ribeiro'] | 2023-04-13 | null | null | null | null | ['pgtask'] | ['natural-language-processing'] | [ 4.19320285e-01 6.61722183e-01 -3.60451490e-01 -6.39672220e-01
-8.42622459e-01 -6.77522719e-01 9.98697817e-01 -6.69721812e-02
-1.86911970e-01 1.22617245e+00 6.93652391e-01 6.41474500e-02
1.38102189e-01 -5.93177617e-01 -1.46640375e-01 -3.47899884e-01
8.09418410e-02 7.35671103e-01 3.44225951e-02 -7.35810578... | [12.738418579101562, 8.159120559692383] |
69d8aa6d-649a-470f-b9d3-44288f9b4118 | usim-dal-uncertainty-aware-statistical-image | 2305.17520 | null | https://arxiv.org/abs/2305.17520v1 | https://arxiv.org/pdf/2305.17520v1.pdf | USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution | Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. However, the high cost of annotation and labeling makes it challenging to achieve accurate results. We propose incorporating active learning into dense regression models to address... | ['Biplab Banerjee', 'Zeynep Akata', 'Uddeshya Upadhyay', 'Vikrant Rangnekar'] | 2023-05-27 | null | null | null | null | ['image-super-resolution', 'active-learning', 'active-learning'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 5.46101034e-01 3.49178940e-01 -4.61644888e-01 -4.55050498e-01
-1.54178584e+00 8.23077094e-03 4.66547966e-01 1.92583472e-01
-8.03266704e-01 9.30718720e-01 2.72605687e-01 2.88379967e-01
-2.23044172e-01 -6.35932505e-01 -5.54275036e-01 -1.27796686e+00
2.48740092e-01 6.32525146e-01 2.58832365e-01 4.23153102... | [14.712980270385742, -2.1740567684173584] |
b0a9048d-e11a-40f9-a6bb-5019da4922e5 | is-more-data-better-re-thinking-the | 2209.10193 | null | https://arxiv.org/abs/2209.10193v1 | https://arxiv.org/pdf/2209.10193v1.pdf | Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning | Annotating abusive language is expensive, logistically complex and creates a risk of psychological harm. However, most machine learning research has prioritized maximizing effectiveness (i.e., F1 or accuracy score) rather than data efficiency (i.e., minimizing the amount of data that is annotated). In this paper, we us... | ['Scott A. Hale', 'Bertie Vidgen', 'Hannah Rose Kirk'] | 2022-09-21 | null | https://aclanthology.org/2022.trac-1.7 | https://aclanthology.org/2022.trac-1.7.pdf | trac-coling-2022-10 | ['abusive-language'] | ['natural-language-processing'] | [ 4.50164862e-02 4.07588065e-01 -9.07087386e-01 -5.20634115e-01
-9.25602496e-01 -6.52100861e-01 2.59382188e-01 7.48995304e-01
-1.13476932e+00 7.62186825e-01 9.39049795e-02 -3.83656740e-01
-5.81739433e-02 -5.32450318e-01 -3.02742958e-01 -2.69523531e-01
-4.07425202e-02 2.89794922e-01 -1.21067263e-01 1.66172445... | [8.689231872558594, 10.449369430541992] |
3d0567ad-0bbf-42ef-9caf-42367452ef40 | computational-choreography-using-human-motion | 2210.04366 | null | https://arxiv.org/abs/2210.04366v2 | https://arxiv.org/pdf/2210.04366v2.pdf | Computational Choreography using Human Motion Synthesis | Should deep learning models be trained to analyze human performance art? To help answer this question, we explore an application of deep neural networks to synthesize artistic human motion. Problem tasks in human motion synthesis can include predicting the motions of humans in-the-wild, as well as generating new sequen... | ['Trevor Kirkby', 'Patrick Perrine'] | 2022-10-09 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 2.29880452e-01 7.63253644e-02 -4.54487056e-02 1.00526989e-01
-4.17105913e-01 -4.09908503e-01 6.52693510e-01 -1.06940615e+00
-1.75279051e-01 7.02011645e-01 7.08995402e-01 -1.49296165e-01
4.24650669e-01 -8.03002715e-01 -7.34352887e-01 -4.91594791e-01
4.28497046e-01 5.42792141e-01 1.12138897e-01 -6.93026364... | [7.271836757659912, -0.12449961155653] |
fad679ee-6e00-4284-95fb-0a6c92b84e33 | context-sensitive-neocortical-neurons | 2207.07338 | null | https://arxiv.org/abs/2207.07338v6 | https://arxiv.org/pdf/2207.07338v6.pdf | Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing | Deep learning (DL) has big-data processing capabilities that are as good, or even better, than those of humans in many real-world domains, but at the cost of high energy requirements that may be unsustainable in some applications and of errors, that, though infrequent, can be large. We hypothesise that a fundamental we... | ['Khubaib Ahmed', 'Mohsin Raza', 'Mario Franco', 'Ahsan Adeel'] | 2022-07-15 | null | null | null | null | ['audio-signal-processing'] | ['audio'] | [ 3.28258306e-01 1.70103893e-01 2.99214751e-01 -6.54757470e-02
-1.30106419e-01 -2.90487409e-01 6.25498593e-01 5.91035485e-01
-9.13517356e-01 9.34154451e-01 2.71648075e-02 -3.88097800e-02
-1.54506043e-01 -1.13223326e+00 -6.91172838e-01 -1.08077180e+00
-2.85806149e-01 3.97935629e-01 4.13608760e-01 -3.33824992... | [8.131750106811523, 2.8970937728881836] |
70152e28-3d5a-4cf2-bc99-850f7b74994d | domain-knowledge-empowered-structured-neural | 2009.07373 | null | https://arxiv.org/abs/2009.07373v2 | https://arxiv.org/pdf/2009.07373v2.pdf | Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction | Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding. Prior systems leverage deep learning and pre-trained language models to improve the performance of the task. However, these systems often suffer from two short-comings: 1) whe... | ['Nanyun Peng', 'Yichao Zhou', 'Rujun Han'] | 2020-09-15 | null | https://aclanthology.org/2020.emnlp-main.461 | https://aclanthology.org/2020.emnlp-main.461.pdf | emnlp-2020-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 4.10527796e-01 3.24197620e-01 -8.47198725e-01 -6.66482091e-01
-7.68686175e-01 -2.13065237e-01 6.22132778e-01 4.53686118e-01
-8.50010633e-01 1.00992179e+00 4.79569376e-01 -2.99217939e-01
-3.42121035e-01 -6.63461745e-01 -7.65588403e-01 -3.30429345e-01
-3.93253624e-01 7.01906621e-01 1.66863412e-01 6.03894144... | [8.936067581176758, 8.968766212463379] |
547f2f5b-4f50-45e8-b69e-d451b18f8187 | walk-and-learn-facial-attribute | 1604.06433 | null | http://arxiv.org/abs/1604.06433v3 | http://arxiv.org/pdf/1604.06433v3.pdf | Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data | The way people look in terms of facial attributes (ethnicity, hair color,
facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat,
hoodies, etc.) is highly dependent on geo-location and weather condition,
respectively. This work explores, for the first time, the use of this
contextual information, ... | ['Rogerio Schmidt Feris', 'Jing Wang', 'Yu Cheng'] | 2016-04-21 | walk-and-learn-facial-attribute-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Wang_Walk_and_Learn_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wang_Walk_and_Learn_CVPR_2016_paper.pdf | cvpr-2016-6 | ['facial-attribute-classification'] | ['computer-vision'] | [ 2.53154431e-03 -4.25277621e-01 -6.68054447e-02 -8.60037446e-01
-3.75316918e-01 -7.64409184e-01 7.97217607e-01 4.03723009e-02
-5.11686921e-01 6.47810161e-01 3.47259879e-01 5.16429603e-01
4.41268571e-02 -8.56539905e-01 -7.28340149e-01 -5.88298559e-01
-4.12095189e-02 4.44518954e-01 -2.89252877e-01 -1.51121303... | [14.448375701904297, 0.9567000865936279] |
e34f8e34-f386-4a27-8310-b5a7b2da6319 | efficient-lightweight-3d-cnn-using-frame | 2105.06340 | null | https://arxiv.org/abs/2105.06340v4 | https://arxiv.org/pdf/2105.06340v4.pdf | 3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame | Facial expression spotting is the preliminary step for micro- and macro-expression analysis. The task of reliably spotting such expressions in video sequences is currently unsolved. The current best systems depend upon optical flow methods to extract regional motion features, before categorisation of that motion into a... | ['SuJing Wang', 'Jingting Li', 'Connah Kendrick', 'Ryan Cunningham', 'Adrian K. Davison', 'Moi Hoon Yap', 'Chuin Hong Yap'] | 2021-05-13 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 1.04397915e-01 -3.39549631e-01 -1.05617523e-01 -7.05687702e-01
-8.96528244e-01 -3.13631058e-01 5.32814622e-01 -4.91416126e-01
-8.30764532e-01 6.51557148e-01 4.88800518e-02 3.03681821e-01
3.73537064e-01 -1.54720217e-01 -5.64414203e-01 -8.62392724e-01
-4.31277663e-01 -2.68333793e-01 1.79847747e-01 -2.80620098... | [13.622672080993652, 1.758375644683838] |
dff3060f-9e9b-41f0-8e6d-60e4b220381e | imbalance-agnostic-source-free-domain | 2305.12649 | null | https://arxiv.org/abs/2305.12649v1 | https://arxiv.org/pdf/2305.12649v1.pdf | Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment | Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a well-trained source model to an unlabeled target domain without access to the source data. One key challenge is the lack of source data during domain adaptation. To handle this, we propose to mine the hidden knowledge of the source model and exploit it... | ['Yanxia Liu', 'Qing Du', 'Dong Liu', 'Shuaicheng Niu', 'Zhen Qiu', 'Yifan Zhang', 'Mingkui Tan', 'Hongbin Lin'] | 2023-05-22 | null | null | null | null | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation', 'pseudo-label'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 3.80374104e-01 -1.83960512e-01 -3.98488730e-01 -4.86955732e-01
-8.46079767e-01 -6.58289433e-01 5.66067934e-01 6.09910078e-02
-7.70306289e-02 9.25620258e-01 -5.71546815e-02 -1.98200226e-01
7.63485059e-02 -7.66710758e-01 -7.58466780e-01 -7.32236385e-01
4.95681345e-01 9.26661909e-01 1.12173051e-01 -2.20007256... | [10.338887214660645, 3.111502170562744] |
05dff2c1-0836-41eb-a304-6ca7a81e5a1c | machine-learning-cryptanalysis-of-a-quantum | 1905.02342 | null | https://arxiv.org/abs/1905.02342v2 | https://arxiv.org/pdf/1905.02342v2.pdf | Machine Learning Cryptanalysis of a Quantum Random Number Generator | Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are ba... | ['Syed Muhamad Assad', 'Nhan Duy Truong', 'Ping Koy Lam', 'Omid Kavehei', 'Jing Yan Haw'] | 2019-05-07 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 7.34948039e-01 -1.99963059e-02 1.52709529e-01 4.83737975e-01
-8.43947589e-01 -7.45023847e-01 8.21130335e-01 -2.13266805e-01
-2.79043674e-01 9.41860557e-01 -3.16782683e-01 -6.62257373e-01
-1.79114118e-01 -1.16552162e+00 -6.15115523e-01 -1.38751042e+00
-8.16582143e-02 1.56626523e-01 -1.30398348e-01 -4.85428542... | [5.524283409118652, 5.077869415283203] |
640a0402-3c5b-4108-8b0b-69d76f8b0ec8 | geometric-perception-based-efficient-text | 2302.03873 | null | https://arxiv.org/abs/2302.03873v1 | https://arxiv.org/pdf/2302.03873v1.pdf | Geometric Perception based Efficient Text Recognition | Every Scene Text Recognition (STR) task consists of text localization \& text recognition as the prominent sub-tasks. However, in real-world applications with fixed camera positions such as equipment monitor reading, image-based data entry, and printed document data extraction, the underlying data tends to be regular s... | ['D. Y. Silva', 'D. R. Jayakodi', 'P. N. Deelaka'] | 2023-02-08 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.28796321e-01 -3.07944685e-01 9.40808728e-02 -4.33296353e-01
-3.03989887e-01 -4.27578598e-01 5.76016963e-01 6.18370622e-02
-3.67309242e-01 2.58483499e-01 -4.23231423e-01 -2.41823107e-01
8.14734846e-02 -7.43251443e-01 -6.89205348e-01 -6.59245789e-01
5.69657087e-01 3.66304457e-01 -6.29322231e-02 2.59999901... | [11.97212028503418, 2.1942291259765625] |
f017630d-c7ad-4214-82a6-fb51daf86bff | ms-unique-multi-model-and-sharpness-weighted | 1811.08947 | null | http://arxiv.org/abs/1811.08947v1 | http://arxiv.org/pdf/1811.08947v1.pdf | MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation | In this paper, we train independent linear decoder models to estimate the
perceived quality of images. More specifically, we calculate the responses of
individual non-overlapping image patches to each of the decoders and scale
these responses based on the sharpness characteristics of filter set. We use
multiple linear ... | ['Ghassan AlRegib', 'Dogancan Temel', 'Mohit Prabhushankar'] | 2018-11-21 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 9.60911959e-02 -4.19729918e-01 6.45699948e-02 -4.99951094e-01
-9.12666500e-01 -3.54177654e-01 2.36698508e-01 1.76465809e-01
-3.65002960e-01 3.43291789e-01 2.19479203e-01 3.62949789e-01
-2.53483236e-01 -6.31511748e-01 -6.39914632e-01 -7.28295803e-01
-6.56319112e-02 -1.33813903e-01 3.51154149e-01 3.16672847... | [11.766498565673828, -1.9175348281860352] |
889ba110-3a41-4aff-92d1-fce850c134d5 | respiratory-diseases-recognition-through | null | null | https://ieeexplore.ieee.org/document/9080747 | https://ieeexplore.ieee.org/document/9080747 | Respiratory diseases recognition through respiratory sound with the help of deep neural network | Prediction of respiratory diseases such as COPD(Chronic obstructive pulmonary disease), URTI(upper respiratory tract infection), Bronchiectasis, Pneumonia, Bronchiolitis with the help of deep neural networks or deep learning. We have constructed a deep neural network model that takes in respiratory sound as input and c... | ['Srinibas Rana', 'Victor Basu'] | 2020-04-30 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [-1.58143207e-01 1.15462027e-01 -2.82943785e-01 1.81278065e-01
3.18103135e-01 -3.12449157e-01 2.37655081e-02 -1.80937216e-01
-4.07267697e-02 6.51587725e-01 3.07786375e-01 -7.65715718e-01
-5.76644778e-01 -1.19538975e+00 -6.96793795e-02 -5.30802369e-01
1.71034276e-01 1.15829909e+00 1.72458246e-01 1.80416510... | [14.535948753356934, 3.8154642581939697] |
193c9a3d-40a6-41b1-9aea-ef589f167e0c | a-deep-learning-framework-for-nuclear | 2203.03420 | null | https://arxiv.org/abs/2203.03420v2 | https://arxiv.org/pdf/2203.03420v2.pdf | A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images | Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing. However, it is very challenging due to its high-level heterogeneity and wide variations. This work proposes a deep neural network to simultaneously achieve nuclear classification and segmentation, which is des... | ['Jinxi Xiang', 'Xiyue Wang', 'Sen yang'] | 2022-03-04 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 1.71725988e-01 9.28340182e-02 -2.51982287e-02 -2.87818342e-01
-5.34579754e-01 -3.69451553e-01 3.10412228e-01 3.65583450e-01
-3.77696633e-01 6.04526281e-01 -2.34853327e-02 -2.34438851e-01
-3.64627838e-02 -1.07872927e+00 -1.37982070e-01 -1.21234453e+00
3.64295751e-01 5.57506263e-01 3.00234765e-01 9.22127515... | [14.905811309814453, -3.072237968444824] |
3781e629-1692-42e1-b684-6475cd795fbc | torchxrayvision-a-library-of-chest-x-ray | 2111.00595 | null | https://arxiv.org/abs/2111.00595v1 | https://arxiv.org/pdf/2111.00595v1.pdf | TorchXRayVision: A library of chest X-ray datasets and models | TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest X-ray datasets. In addition, a number of classification and representation learning models with dif... | ['Hadrien Bertrand', 'Mohammad Hashir', 'Rupert Brooks', 'Akshay Chaudhari', 'Matthew P Lungren', 'Matteo Guarrera', 'Parsa Torabian', 'Paul Morrison', 'Paul Bertin', 'Joseph D. Viviano', 'Joseph Paul Cohen'] | 2021-10-31 | null | null | null | null | ['medical-image-retrieval', 'small-data', 'medical-x-ray-image-segmentation', 'medical-image-retrieval'] | ['computer-vision', 'computer-vision', 'medical', 'medical'] | [-1.60372928e-02 -3.09246659e-01 -5.86045563e-01 -9.58343685e-01
-1.37165570e+00 -7.31393322e-02 3.04925531e-01 5.86677939e-02
-2.34045565e-01 2.17060104e-01 2.91105092e-01 -3.51981938e-01
-2.13762879e-01 -6.16057992e-01 -2.14286089e-01 -5.42479813e-01
1.42360941e-01 6.36421800e-01 7.74315596e-02 -1.36799619... | [15.225720405578613, -1.949194073677063] |
c6fa8d47-44a9-4e14-b5b3-a7e542cd8c68 | segmented-convolutional-gated-recurrent | null | null | https://doi.org/10.1016/j.neucom.2018.11.109 | https://sci-hub.se/10.1016/j.neucom.2018.11.109 | Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar | The automatic detection and recognition of human activities are valuable for physical security, gaming, and intelligent interface. Compared to an optical recognition system, radar is more robust to variations in lighting conditions and occlusions. The centimeter-wave ultra-wideband radar can even track human motion whe... | ['Yongpeng Dai', 'Yongping Song', 'Hao Du', 'Tian Jin', 'Yuan He'] | 2019-04-27 | null | null | null | neurocomputing-2019-4 | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 4.55166996e-01 -6.35814965e-01 9.08799544e-02 -1.28787398e-01
-2.03800395e-01 -3.87943774e-01 6.97491229e-01 -6.96254373e-01
-4.92731422e-01 6.98223114e-01 1.10155165e-01 -2.59963423e-01
-2.23304421e-01 -8.92355740e-01 -1.48168296e-01 -8.99915338e-01
-5.15177369e-01 -2.28662133e-01 5.62267080e-02 -5.57226464... | [6.800650596618652, 0.41226014494895935] |
0b2f3a68-a80c-4d1d-a54f-aef86f6716e2 | skygpt-probabilistic-short-term-solar | 2306.11682 | null | https://arxiv.org/abs/2306.11682v1 | https://arxiv.org/pdf/2306.11682v1.pdf | SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT | In recent years, deep learning-based solar forecasting using all-sky images has emerged as a promising approach for alleviating uncertainty in PV power generation. However, the stochastic nature of cloud movement remains a major challenge for accurate and reliable solar forecasting. With the recent advances in generati... | ['Adam Brandt', 'Quentin Paletta', 'Andea Scott', 'Eric Zelikman', 'Yuhao Nie'] | 2023-06-20 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-6.81800693e-02 -2.60355622e-01 2.13556826e-01 -2.95098186e-01
-8.06207240e-01 -8.66878569e-01 9.25319135e-01 -6.26399636e-01
6.79916501e-01 1.00705409e+00 2.80207038e-01 -2.54928976e-01
5.49352616e-02 -9.29799557e-01 -1.09985399e+00 -1.32718801e+00
-7.72267506e-02 3.03149611e-01 -5.94279496e-03 7.51774684... | [6.457332134246826, 2.702085256576538] |
5d9f283d-2e6a-453b-8813-1bd82efdf5f4 | ctds-centralized-teacher-with-decentralized | 2203.08412 | null | https://arxiv.org/abs/2203.08412v1 | https://arxiv.org/pdf/2203.08412v1.pdf | CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning | Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has become one of the most widely used MARL paradigms. In CTDE, centralized information is dedicated to learning the allocation of the team rew... | ['Houqiang Li', 'Jiangcheng Zhu', 'Wengang Zhou', 'Mingyu Yang', 'Xunhan Hu', 'Jian Zhao'] | 2022-03-16 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-4.10758078e-01 1.51124761e-01 -2.78904855e-01 -4.86506559e-02
-5.78829408e-01 -2.32656822e-01 5.34221113e-01 2.17600256e-01
-5.42536438e-01 1.13289857e+00 -2.88124174e-01 -1.78323731e-01
-2.96972096e-01 -8.03038657e-01 -6.75195992e-01 -1.09469473e+00
-1.32611901e-01 7.38901615e-01 3.27564031e-01 -2.23215967... | [3.7441394329071045, 2.1084611415863037] |
d1813e22-0de8-436e-be43-e2d89f6d99f2 | focus-attention-promoting-faithfulness-and | 2105.11921 | null | https://arxiv.org/abs/2105.11921v1 | https://arxiv.org/pdf/2105.11921v1.pdf | Focus Attention: Promoting Faithfulness and Diversity in Summarization | Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously learn to focus on salient content, while deciding what to generate, at each decoding step. With the motivation to narrow this gap, we introd... | ['Ryan Mcdonald', 'Sascha Rothe', 'Joshua Maynez', 'Shashi Narayan', 'Rahul Aralikatte'] | 2021-05-25 | null | https://aclanthology.org/2021.acl-long.474 | https://aclanthology.org/2021.acl-long.474.pdf | acl-2021-5 | ['extreme-summarization'] | ['natural-language-processing'] | [ 4.98221278e-01 5.67933679e-01 -2.97716141e-01 -3.15890282e-01
-1.58245325e+00 -6.24356627e-01 8.03038538e-01 3.50006402e-01
-2.57668942e-01 1.29898512e+00 1.46157670e+00 -1.35004520e-01
1.55067533e-01 -5.15770733e-01 -6.34844661e-01 -3.73621374e-01
2.63420314e-01 6.11913681e-01 -2.32460842e-01 -3.81972760... | [12.313438415527344, 9.334712028503418] |
f1344e87-41ab-4a08-af54-114b891110cb | aspect-specific-context-modeling-for-aspect | 2207.08099 | null | https://arxiv.org/abs/2207.08099v1 | https://arxiv.org/pdf/2207.08099v1.pdf | Aspect-specific Context Modeling for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) aims at predicting sentiment polarity (SC) or extracting opinion span (OE) expressed towards a given aspect. Previous work in ABSA mostly relies on rather complicated aspect-specific feature induction. Recently, pretrained language models (PLMs), e.g., BERT, have been used as cont... | ['Dawei Song', 'Bo Zhang', 'Chen Zhang', 'Fang Ma'] | 2022-07-17 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.92558557e-01 4.45438437e-02 -1.71135306e-01 -6.56626761e-01
-7.73909986e-01 -6.24185026e-01 8.43489587e-01 6.71697855e-02
-1.29049048e-01 3.08284163e-01 1.02493927e-01 -4.47757959e-01
2.25469738e-01 -1.00703180e+00 -5.68742156e-01 -5.62104046e-01
2.18858674e-01 8.79216790e-02 5.32749966e-02 -7.57516623... | [11.445094108581543, 6.720444202423096] |
600bfa5a-7c7c-46c4-acec-6f4adacef537 | 3dcrowdnet-2d-human-pose-guided3d-crowd-human | 2104.07300 | null | https://arxiv.org/abs/2104.07300v3 | https://arxiv.org/pdf/2104.07300v3.pdf | Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes | We consider the problem of recovering a single person's 3D human mesh from in-the-wild crowded scenes. While much progress has been in 3D human mesh estimation, existing methods struggle when test input has crowded scenes. The first reason for the failure is a domain gap between training and testing data. A motion capt... | ['Kyoung Mu Lee', 'JoonKyu Park', 'Gyeongsik Moon', 'Hongsuk Choi'] | 2021-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Choi_Learning_To_Estimate_Robust_3D_Human_Mesh_From_In-the-Wild_Crowded_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Choi_Learning_To_Estimate_Robust_3D_Human_Mesh_From_In-the-Wild_Crowded_CVPR_2022_paper.pdf | cvpr-2022-1 | ['2d-human-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.18781734e-01 -2.22325742e-01 7.03812763e-02 -2.58207202e-01
-7.85297215e-01 -2.89512575e-01 3.49817544e-01 -1.92408919e-01
-6.09959602e-01 6.92540169e-01 3.18318456e-01 4.03513074e-01
2.32223243e-01 -6.66200757e-01 -8.49759638e-01 -6.26075208e-01
-1.61028076e-02 8.88555706e-01 5.56647480e-01 -1.51679337... | [7.049046516418457, -0.8750078678131104] |
24bcebf7-7af8-4130-8e1e-adf591f3c696 | joint-hierarchical-priors-and-adaptive | 2307.02273 | null | https://arxiv.org/abs/2307.02273v1 | https://arxiv.org/pdf/2307.02273v1.pdf | Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression | Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs. Despite significant progress, current NIC methods still rely on ConvNet-based entropy coding, limited in modeling long-range dependencies du... | ['Luce Morin', 'Wassim Hamidouche', 'Ahmed Ghorbel'] | 2023-07-05 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 4.94054407e-01 -1.05352208e-01 -8.31487551e-02 -2.22961962e-01
-5.09093583e-01 1.64389163e-02 5.39388299e-01 -2.45489538e-01
-6.09643221e-01 4.14224625e-01 3.32156003e-01 -3.11658561e-01
-2.44952127e-01 -5.82671106e-01 -9.25083041e-01 -7.91563332e-01
-1.80535674e-01 3.88250686e-02 -5.05615259e-03 9.79164317... | [11.314224243164062, -1.5987895727157593] |
816a0142-6c8f-405e-849e-ab9d95a0e1bb | cosda-continual-source-free-domain-adaptation | 2304.06627 | null | https://arxiv.org/abs/2304.06627v1 | https://arxiv.org/pdf/2304.06627v1.pdf | CoSDA: Continual Source-Free Domain Adaptation | Without access to the source data, source-free domain adaptation (SFDA) transfers knowledge from a source-domain trained model to target domains. Recently, SFDA has gained popularity due to the need to protect the data privacy of the source domain, but it suffers from catastrophic forgetting on the source domain due to... | ['Shuicheng Yan', 'Wei Chen', 'Minfeng Zhu', 'Chao Du', 'Tianyu Pang', 'Hesun Chen', 'Zhaorui Yang', 'Haozhe Feng'] | 2023-04-13 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-9.48347375e-02 -2.66722664e-02 -2.36111611e-01 -3.73418897e-01
-7.05794394e-01 -5.43573081e-01 5.38888514e-01 -5.07912189e-02
-3.85299027e-01 1.02109194e+00 1.02767929e-01 -2.22143799e-01
1.14748947e-01 -6.45471573e-01 -9.24192131e-01 -7.40137517e-01
5.31805694e-01 3.72586459e-01 3.68954629e-01 -1.99014425... | [10.389276504516602, 3.2277822494506836] |
c7b67039-8e59-4c67-93e8-21a7319a7c00 | 190600434 | 1906.00434 | null | https://arxiv.org/abs/1906.00434v1 | https://arxiv.org/pdf/1906.00434v1.pdf | Deep Unknown Intent Detection with Margin Loss | Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. ... | ['Ting-En Lin', 'Hua Xu'] | 2019-06-02 | deep-unknown-intent-detection-with-margin | https://aclanthology.org/P19-1548 | https://aclanthology.org/P19-1548.pdf | acl-2019-7 | ['open-intent-detection'] | ['natural-language-processing'] | [-1.81744486e-01 1.91713236e-02 -3.49062532e-01 -7.45490074e-01
-7.30650902e-01 -3.05659503e-01 5.38827717e-01 -3.76285389e-02
-7.29750574e-01 8.28552365e-01 2.13153154e-01 -2.75472760e-01
2.22550020e-01 -2.82205820e-01 -5.84522247e-01 -3.00002247e-01
-1.76495999e-01 2.25094289e-01 5.15629388e-02 -1.09573379... | [12.120235443115234, 7.560116767883301] |
02537be3-bd08-4824-be9e-269f1f73892f | on-the-effects-of-video-grounding-on-language | null | null | https://aclanthology.org/2022.mmmpie-1.1 | https://aclanthology.org/2022.mmmpie-1.1.pdf | On the Effects of Video Grounding on Language Models | Transformer-based models trained on text and vision modalities try to improve the performance on multimodal downstream tasks or tackle the problem Transformer-based models trained on text and vision modalities try to improve the performance on multimodal downstream tasks or tackle the problem of lack of grounding, e.g.... | ['Marco Kuhlmann', 'Ehsan Doostmohammadi'] | null | null | null | null | mmmpie-coling-2022-10 | ['video-grounding'] | ['computer-vision'] | [ 1.32464111e-01 3.98002118e-01 1.86495706e-01 -1.78180918e-01
-6.42467260e-01 -5.88159919e-01 9.42303717e-01 4.06959280e-02
-6.37878001e-01 5.13624012e-01 4.08411860e-01 -6.54194236e-01
7.32812285e-02 -8.05734873e-01 -9.33718026e-01 -6.35448575e-01
2.39231080e-01 3.32429022e-01 1.38332754e-01 -3.12183082... | [10.82286548614502, 1.6919746398925781] |
6f9e18db-6c9e-429f-a652-8f8f4043764d | laughter-during-cooperative-and-competitive | null | null | https://aclanthology.org/2022.smila-1.10 | https://aclanthology.org/2022.smila-1.10.pdf | Laughter During Cooperative and Competitive Games | This exploratory study investigates the extent to which social context influences the frequency of laughter. In a within-subjects design, dyads of strangers played two simple laughter-inducing games in a cooperative and competitive setting, ostensibly to earn money individually and as a team. We examined the frequency ... | ['William Curran', 'Ian Sneddon', 'Gary McKeown', 'Magdalena Rychlowska'] | null | null | null | null | smila-lrec-2022-6 | ['general-knowledge'] | ['miscellaneous'] | [-9.32272598e-02 1.18484668e-01 4.11159366e-01 2.29808941e-01
-2.33691797e-01 -6.85332954e-01 4.32557344e-01 2.23949607e-02
-6.50463879e-01 6.38412058e-01 3.02045286e-01 -1.39027676e-02
-1.39261127e-01 -7.70730376e-01 -1.96146265e-01 -7.20589221e-01
1.08068317e-01 -8.01977888e-02 -6.82200193e-02 -3.76329869... | [12.515290260314941, 7.762846946716309] |
3e89cec5-bae0-4083-9079-053b38b0300a | recurrent-neural-network-training-with | 1606.04449 | null | http://arxiv.org/abs/1606.04449v2 | http://arxiv.org/pdf/1606.04449v2.pdf | Recurrent neural network training with preconditioned stochastic gradient descent | This paper studies the performance of a recently proposed preconditioned
stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN)
training. PSGD adaptively estimates a preconditioner to accelerate gradient
descent, and is designed to be simple, general and easy to use, as stochastic
gradient desce... | ['Xi-Lin Li'] | 2016-06-14 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 7.15624616e-02 -1.59571901e-01 1.13587894e-01 -4.99682933e-01
-1.96392432e-01 1.25107430e-02 5.24126053e-01 -5.51731944e-01
-9.12951350e-01 1.05947971e+00 3.18948925e-02 -8.42037976e-01
2.82462448e-01 -1.98199227e-01 -6.41364992e-01 -8.18223476e-01
1.46392286e-02 3.90236586e-01 1.06637537e-01 -4.33786422... | [10.854975700378418, 6.36912202835083] |
77b9a8b7-5074-4ff1-aa94-ae80e8e2f02e | towards-unseen-triples-effective-text-image | 2306.13420 | null | https://arxiv.org/abs/2306.13420v1 | https://arxiv.org/pdf/2306.13420v1.pdf | Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation | Scene Graph Generation (SGG) aims to structurally and comprehensively represent objects and their connections in images, it can significantly benefit scene understanding and other related downstream tasks. Existing SGG models often struggle to solve the long-tailed problem caused by biased datasets. However, even if th... | ['Hanzi Wang', 'Ying Shan', 'Tianxiang Hou', 'Zhongang Qi', 'Wenxi Ma', 'Qianji Di'] | 2023-06-23 | null | null | null | null | ['scene-graph-generation', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 4.15865272e-01 2.84678638e-01 -2.72085607e-01 -4.86955702e-01
-6.87678754e-01 -3.53972912e-01 6.23242855e-01 9.94124264e-02
-1.00225858e-01 6.71378970e-01 1.67259619e-01 -2.14552861e-02
-1.33093745e-02 -1.01381242e+00 -1.16580355e+00 -6.66903675e-01
3.30208570e-01 7.26901233e-01 6.42774820e-01 -2.74799556... | [10.318424224853516, 1.7435222864151] |
b70ca5d8-b508-4c50-82ee-1fa9bada9d0e | learning-a-high-fidelity-pose-invariant-model | 1806.08472 | null | http://arxiv.org/abs/1806.08472v2 | http://arxiv.org/pdf/1806.08472v2.pdf | Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization | Face frontalization refers to the process of synthesizing the frontal view of
a face from a given profile. Due to self-occlusion and appearance distortion in
the wild, it is extremely challenging to recover faithful results and preserve
texture details in a high-resolution. This paper proposes a High Fidelity Pose
Inva... | ['Yibo Hu', 'Zhenan Sun', 'Jie Cao', 'Ran He', 'Hongwen Zhang'] | 2018-06-22 | learning-a-high-fidelity-pose-invariant-model-1 | http://papers.nips.cc/paper/7551-learning-a-high-fidelity-pose-invariant-model-for-high-resolution-face-frontalization | http://papers.nips.cc/paper/7551-learning-a-high-fidelity-pose-invariant-model-for-high-resolution-face-frontalization.pdf | neurips-2018-12 | ['robust-face-recognition'] | ['computer-vision'] | [ 5.48148572e-01 2.98092160e-02 1.79214239e-01 -5.12247503e-01
-7.15301752e-01 -4.69586194e-01 6.32078469e-01 -9.95057583e-01
1.82173103e-01 3.71192783e-01 3.44646305e-01 3.54948670e-01
7.65185058e-02 -8.86632740e-01 -1.01849699e+00 -8.32299829e-01
5.41251779e-01 3.58136207e-01 -3.86943758e-01 -2.01147243... | [12.90057373046875, -0.05809829756617546] |
f11b4491-b212-4537-b3d8-f03fff41864f | godel-large-scale-pre-training-for-goal | 2206.11309 | null | https://arxiv.org/abs/2206.11309v1 | https://arxiv.org/pdf/2206.11309v1.pdf | GODEL: Large-Scale Pre-Training for Goal-Directed Dialog | We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information ... | ['Jianfeng Gao', 'Bill Dolan', 'Zhou Yu', 'Elnaz Nouri', 'Lars Liden', 'Chris Brockett', 'Pengcheng He', 'Michel Galley', 'Baolin Peng'] | 2022-06-22 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-1.59161985e-01 6.41465902e-01 -4.05216664e-02 -7.35159039e-01
-1.16216588e+00 -9.83787775e-01 1.09419835e+00 2.18994319e-01
-4.97977734e-01 9.99301910e-01 9.84716713e-01 -8.17003548e-02
2.25625739e-01 -5.22836864e-01 1.89997286e-01 -7.65961334e-02
1.98389158e-01 1.17018628e+00 3.73482317e-01 -1.04751706... | [12.790836334228516, 8.024829864501953] |
1aecadcb-05cf-45d6-9732-b7c45df48fde | robustnet-improving-domain-generalization-in | 2103.15597 | null | https://arxiv.org/abs/2103.15597v2 | https://arxiv.org/pdf/2103.15597v2.pdf | RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening | Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To address this issue, this paper proposes a novel instance selective whitening loss to improve the robustness of the segmentation networks for unse... | ['Jaegul Choo', 'Seungryong Kim', 'Joanne Kim', 'Huiwon Yun', 'Sanghun Jung', 'Sungha Choi'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Choi_RobustNet_Improving_Domain_Generalization_in_Urban-Scene_Segmentation_via_Instance_Selective_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-segmentation'] | ['computer-vision'] | [ 3.56386036e-01 -8.26810598e-02 1.33773714e-01 -5.43866754e-01
-3.93152982e-01 -7.73317754e-01 5.63180208e-01 -1.96968675e-01
-5.02225876e-01 8.48821938e-01 -1.12321004e-01 -3.34215015e-01
9.07274242e-03 -5.71627736e-01 -7.63356090e-01 -1.00979638e+00
1.14914417e-01 8.20844546e-02 7.25817204e-01 -3.23954314... | [9.022075653076172, -0.6366678476333618] |
def3f7e9-8a2b-4b4c-a3a7-e0d590bc7f21 | filter-sharing-efficient-learning-of | 1612.02575 | null | http://arxiv.org/abs/1612.02575v1 | http://arxiv.org/pdf/1612.02575v1.pdf | Filter sharing: Efficient learning of parameters for volumetric convolutions | Typical convolutional neural networks (CNNs) have several millions of
parameters and require a large amount of annotated data to train them. In
medical applications where training data is hard to come by, these
sophisticated machine learning models are difficult to train. In this paper, we
propose a method to reduce th... | ['Sheshadri Thiruvenkadam', 'Rahul Venkataramani', 'Hariharan Ravishankar', 'Vivek Vaidya', 'Prasad Sudhakar'] | 2016-12-08 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [ 3.13513875e-01 2.82603264e-01 1.17555276e-01 -4.15951371e-01
-3.06653857e-01 -4.44681138e-01 1.32834375e-01 1.37727499e-01
-7.30487168e-01 3.91308188e-01 -2.71598727e-01 -6.70214713e-01
8.80967006e-02 -6.94957614e-01 -8.02007973e-01 -5.75914741e-01
-5.27124777e-02 2.09964111e-01 4.43559170e-01 -4.42012027... | [14.767833709716797, -2.6654231548309326] |
5607c8e9-53e5-4097-a14e-033dd293c2ce | dynamic-bicycle-dispatching-of-dockless | 2101.07437 | null | https://arxiv.org/abs/2101.07437v1 | https://arxiv.org/pdf/2101.07437v1.pdf | Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems using Multi-objective Reinforcement Learning | As a new generation of Public Bicycle-sharing Systems (PBS), the dockless PBS (DL-PBS) is an important application of cyber-physical systems and intelligent transportation. How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for DL-PBS. In this p... | ['Zeng Zeng', 'Philip S. Yu', 'Keqin Li', 'Kenli Li', 'Jianguo Chen'] | 2021-01-19 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-7.66253352e-01 -2.16924384e-01 -5.64606011e-01 3.31995115e-02
-4.84237492e-01 -2.92806774e-01 -1.89893723e-01 -4.62650001e-01
-3.80583704e-01 9.63686168e-01 -2.78961621e-02 -4.87239599e-01
-6.44795239e-01 -1.22851777e+00 -6.24397576e-01 -9.16736364e-01
-6.07808605e-02 1.17517769e+00 2.22481117e-01 -6.74878657... | [5.53166389465332, 1.7908034324645996] |
e12cd517-7477-4949-a6e0-fa8fafe4a5b0 | deep-manta-a-coarse-to-fine-many-task-network | 1703.07570 | null | http://arxiv.org/abs/1703.07570v1 | http://arxiv.org/pdf/1703.07570v1.pdf | Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image | In this paper, we present a novel approach, called Deep MANTA (Deep
Many-Tasks), for many-task vehicle analysis from a given image. A robust
convolutional network is introduced for simultaneous vehicle detection, part
localization, visibility characterization and 3D dimension estimation. Its
architecture is based on a ... | ['Céline Teulière', 'Jaonary Rabarisoa', 'Florian Chabot', 'Mohamed Chaouch', 'Thierry Chateau'] | 2017-03-22 | deep-manta-a-coarse-to-fine-many-task-network-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Chabot_Deep_MANTA_A_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Chabot_Deep_MANTA_A_CVPR_2017_paper.pdf | cvpr-2017-7 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-3.72700632e-01 -2.30558097e-01 -5.80015630e-02 -2.49826312e-01
-7.56341994e-01 -8.12976122e-01 9.05015886e-01 -7.19934329e-02
-5.05874932e-01 1.77386999e-01 -4.70754862e-01 -7.58667737e-02
3.94519538e-01 -6.27445400e-01 -1.33995438e+00 -7.11213231e-01
-6.59212396e-02 9.84789610e-01 5.57722986e-01 -1.08738199... | [7.906476020812988, -2.12926983833313] |
5c22d43e-36de-40a3-9609-d540f7b9fae6 | progressive-self-training-with-discriminator | null | null | https://aclanthology.org/2021.emnlp-main.23 | https://aclanthology.org/2021.emnlp-main.23.pdf | Progressive Self-Training with Discriminator for Aspect Term Extraction | Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data. While self-training is potentially an effective method to address this issue, the pseudo-labels i... | ['Ruifeng Xu', 'Min Yang', 'Qin Zhao', 'Zhiyuan Wen', 'Qianlong Wang'] | null | null | null | null | emnlp-2021-11 | ['term-extraction', 'extract-aspect'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.49194011e-01 3.51248801e-01 -6.47541165e-01 -5.56338310e-01
-1.10042787e+00 -6.91392183e-01 5.86100817e-01 2.84436166e-01
-3.89876902e-01 7.40501463e-01 2.89585352e-01 -2.23219305e-01
3.69367689e-01 -6.10260248e-01 -5.02681673e-01 -5.42595327e-01
4.50578094e-01 4.10575539e-01 1.01104863e-01 -2.10106373... | [11.376204490661621, 6.729957103729248] |
be03a056-706e-45a1-94e4-753e9a849a2e | contextual-argument-component-classification | 2102.10290 | null | https://arxiv.org/abs/2102.10290v1 | https://arxiv.org/pdf/2102.10290v1.pdf | Contextual Argument Component Classification for Class Discussions | Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different context... | ['Diane Litman', 'Luca Lugini'] | 2021-02-20 | null | https://aclanthology.org/2020.coling-main.128 | https://aclanthology.org/2020.coling-main.128.pdf | coling-2020-8 | ['component-classification'] | ['natural-language-processing'] | [ 5.46334088e-01 4.10045624e-01 -6.22485399e-01 -5.40725768e-01
-8.20084691e-01 -9.00425494e-01 7.64859557e-01 1.10973573e+00
-2.71446496e-01 7.81204462e-01 9.08964396e-01 -9.35708463e-01
-1.68987736e-01 -6.41030014e-01 -5.33135355e-01 -1.83839798e-01
4.91916060e-01 5.60555235e-02 5.03800809e-01 -2.12865531... | [10.506486892700195, 9.424967765808105] |
ef047aae-892b-4f59-840b-0ce7f027b032 | 3han-a-deep-neural-network-for-fake-news | 2306.12014 | null | https://arxiv.org/abs/2306.12014v1 | https://arxiv.org/pdf/2306.12014v1.pdf | 3HAN: A Deep Neural Network for Fake News Detection | The rapid spread of fake news is a serious problem calling for AI solutions. We employ a deep learning based automated detector through a three level hierarchical attention network (3HAN) for fast, accurate detection of fake news. 3HAN has three levels, one each for words, sentences, and the headline, and constructs a ... | ['Shrisha Rao', 'Nigel Fernandez', 'Sneha Singhania'] | 2023-06-21 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-3.00359219e-01 1.94472983e-01 -3.40622127e-01 -1.38961524e-01
-4.81622726e-01 -3.87979954e-01 7.56162822e-01 2.77842611e-01
-1.58673942e-01 4.21832830e-01 8.04846048e-01 -3.40138167e-01
4.33957398e-01 -8.86283219e-01 -9.74328041e-01 -4.46808964e-01
9.89902914e-02 2.44208932e-01 2.46382847e-01 -5.75501263... | [8.11937427520752, 10.239374160766602] |
462c0f41-37a9-4e58-a90b-6f7e17970725 | joint-modelling-of-emotion-and-abusive | 2005.14028 | null | https://arxiv.org/abs/2005.14028v1 | https://arxiv.org/pdf/2005.14028v1.pdf | Joint Modelling of Emotion and Abusive Language Detection | The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online. Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection. While achievi... | ['Pushkar Mishra', 'Ekaterina Shutova', 'Santhosh Rajamanickam', 'Helen Yannakoudakis'] | 2020-05-28 | joint-modelling-of-emotion-and-abusive-1 | https://aclanthology.org/2020.acl-main.394 | https://aclanthology.org/2020.acl-main.394.pdf | acl-2020-6 | ['abuse-detection'] | ['natural-language-processing'] | [-8.69236663e-02 -4.03229520e-02 -6.22418933e-02 -3.53248179e-01
-4.39836770e-01 -4.67533708e-01 7.58992434e-01 4.19902951e-01
-6.00371420e-01 5.51313698e-01 3.34649771e-01 -7.62753487e-02
1.37029871e-01 -4.35360312e-01 -1.12889986e-02 -5.17849505e-01
-2.85428226e-01 2.53169656e-01 -2.71599561e-01 -1.38233930... | [8.72294807434082, 10.409008026123047] |
9a13e58a-a98d-4590-9574-38218d59d9d0 | on-label-granularity-and-object-localization | 2207.10225 | null | https://arxiv.org/abs/2207.10225v1 | https://arxiv.org/pdf/2207.10225v1.pdf | On Label Granularity and Object Localization | Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled at different levels of granularity. Is it an animal, a bird, or a great horned owl? Which image-level labels should we use? In this paper we... | ['Oisin Mac Aodha', 'Andrew Howard', 'Serge Belongie', 'Pietro Perona', 'Marco Fornoni', 'Xuan Yang', 'Grant van Horn', 'Kimberly Wilber', 'Elijah Cole'] | 2022-07-20 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [-9.85739529e-02 -6.37602881e-02 -5.09361506e-01 -4.78221416e-01
-8.68962049e-01 -8.76958013e-01 5.57073891e-01 5.46919048e-01
-7.18772590e-01 7.43523419e-01 1.91620901e-01 -1.58604439e-02
-7.06669912e-02 -8.02285731e-01 -9.05341804e-01 -7.01909900e-01
-2.14529499e-01 4.65805590e-01 5.82413137e-01 6.58950135... | [9.642200469970703, 2.0068936347961426] |
20a5d8d9-dc25-49ff-8f0c-7f3d11abd350 | selinet-sentiment-enriched-lightweight | 2307.02773 | null | https://arxiv.org/abs/2307.02773v1 | https://arxiv.org/pdf/2307.02773v1.pdf | SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images | In this paper, we propose a sentiment-enriched lightweight network SeLiNet and an end-to-end on-device pipeline for contextual emotion recognition in images. SeLiNet model consists of body feature extractor, image aesthetics feature extractor, and learning-based fusion network which jointly estimates discrete emotion a... | ['Barath Raj KR', 'Sumit Kumar', 'Shwetank Choudhary', 'Tuneer Khargonkar'] | 2023-07-06 | null | null | null | null | ['emotion-recognition'] | ['computer-vision'] | [ 1.97823599e-01 3.11542243e-01 2.69664437e-01 -5.50286174e-01
-9.24605250e-01 -3.56953979e-01 1.20112076e-01 9.03039873e-02
-4.90727574e-01 1.25366226e-01 2.73836285e-01 4.74268824e-01
4.10439402e-01 -2.41234675e-01 -2.89452732e-01 -2.30953321e-01
1.69462264e-01 -2.89396346e-01 -4.13401753e-01 9.41807181... | [13.297731399536133, 5.0336689949035645] |
b0eb5181-f514-4701-88f1-10c24b168b01 | low-rank-convex-sparse-thermal-matrix | 2010.06784 | null | https://arxiv.org/abs/2010.06784v1 | https://arxiv.org/pdf/2010.06784v1.pdf | Low-rank Convex/Sparse Thermal Matrix Approximation for Infrared-based Diagnostic System | Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and s... | ['Xavier P. V. Maldague', 'Clemente Ibarra Castanedo', 'Bardia Yousefi'] | 2020-10-14 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.65703756e-01 9.66771785e-03 1.62077006e-02 -4.04568501e-02
-8.04998696e-01 -1.69127017e-01 -4.14024293e-02 -1.75610438e-01
-6.40649274e-02 1.32085815e-01 5.33975661e-01 -1.00050397e-01
-6.19837821e-01 -3.94241035e-01 -2.20300302e-01 -1.42365503e+00
-2.71308661e-01 4.58359480e-01 -7.00919107e-02 5.64795993... | [12.22036361694336, 0.23648008704185486] |
8de9d460-29fb-479a-abd9-1038cc92643e | hybrid-instance-aware-temporal-fusion-for | 2112.01695 | null | https://arxiv.org/abs/2112.01695v2 | https://arxiv.org/pdf/2112.01695v2.pdf | Hybrid Instance-aware Temporal Fusion for Online Video Instance Segmentation | Recently, transformer-based image segmentation methods have achieved notable success against previous solutions. While for video domains, how to effectively model temporal context with the attention of object instances across frames remains an open problem. In this paper, we propose an online video instance segmentatio... | ['Yan Lu', 'Xiao Li', 'Jinglu Wang', 'Xiang Li'] | 2021-12-03 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.84938991e-01 -1.88843146e-01 -4.81936216e-01 -4.50319260e-01
-8.20323825e-01 -5.38286984e-01 5.65078378e-01 7.40815401e-02
-3.23721558e-01 4.87612784e-01 -5.26028611e-02 6.01450130e-02
-9.35793445e-02 -5.34209609e-01 -9.97064114e-01 -6.06204987e-01
2.67580990e-02 -6.32683933e-02 6.45773053e-01 8.89150202... | [9.256998062133789, 0.04123135283589363] |
d8e0a868-7151-49c0-b2db-769f0a3aceb2 | extractive-summarization-via-chatgpt-for | 2304.04193 | null | https://arxiv.org/abs/2304.04193v1 | https://arxiv.org/pdf/2304.04193v1.pdf | Extractive Summarization via ChatGPT for Faithful Summary Generation | Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent introduction of ChatGPT has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstrea... | ['Jiawei Zhang', 'Xiao Liu', 'Haopeng Zhang'] | 2023-04-09 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 4.18586254e-01 5.89334786e-01 -2.83342153e-01 -2.22001508e-01
-1.52698123e+00 -7.11409688e-01 8.55375171e-01 6.93726480e-01
-3.65730733e-01 9.43965971e-01 1.15915453e+00 -4.12711680e-01
2.17153743e-01 -4.39554632e-01 -4.48166370e-01 -1.94088444e-01
2.48506218e-01 5.30138254e-01 2.09437653e-01 -5.70789874... | [12.46371841430664, 9.466949462890625] |
13cc305a-fa84-463e-8d4d-2ec87c28aee4 | machine-learning-can-guide-experimental | 2211.00625 | null | https://arxiv.org/abs/2211.00625v1 | https://arxiv.org/pdf/2211.00625v1.pdf | Machine learning can guide experimental approaches for protein digestibility estimations | Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins. In this study, we propose a machine learning approach to predict the true ileal digestibility coefficient of food items. The model mak... | ['Ranveer Chandra', 'Swati Sharma', 'Maria Angels de Luis Balaguer', 'Anvita Bhagavathula', 'Sara Malvar'] | 2022-11-01 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 4.36640196e-02 -4.17175563e-03 -6.51427746e-01 -3.12017888e-01
1.74896091e-01 -8.12357008e-01 -7.78503641e-02 1.19092035e+00
-1.34000599e-01 2.94981360e-01 5.11937976e-01 -4.35001612e-01
-1.00841038e-01 -9.70418096e-01 -9.58671093e-01 -5.36334276e-01
-3.67490321e-01 1.68815553e-01 -1.43801540e-01 -3.76848191... | [11.529703140258789, 4.4971489906311035] |
ca2928b3-e5b4-49a9-9237-d39465513179 | learning-dynamic-graphs-from-all-contextual | 2306.15927 | null | https://arxiv.org/abs/2306.15927v1 | https://arxiv.org/pdf/2306.15927v1.pdf | Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting | Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies. Although this forecasting problem can be formulated as a multivariate time-se... | ['Cyrus Shahabi', 'Yao-Yi Chiang', 'Maria Despoina Siampou', 'Haoji Hu', 'Sina Shaham', 'Haowen Lin', 'Arash Hajisafi'] | 2023-06-28 | null | null | null | null | ['management', 'decision-making', 'multivariate-time-series-forecasting'] | ['miscellaneous', 'reasoning', 'time-series'] | [ 4.98477332e-02 -1.11861803e-01 -5.90345263e-01 -5.57687819e-01
-2.02676922e-01 -1.42066255e-01 6.34991169e-01 5.49940825e-01
8.93738717e-02 6.34134889e-01 6.80605710e-01 -8.38197172e-01
-4.80849534e-01 -1.23808765e+00 -5.87398827e-01 -4.27490950e-01
-8.33275914e-01 4.06849146e-01 6.59895539e-02 -4.55505222... | [6.619620323181152, 2.432827949523926] |
1fa352c5-79a8-4c3d-bf5f-a6f162438e5c | the-development-of-standard-perceptual | null | null | https://doi.org/10.6180/jase.202202_25(1).0022 | http://jase.tku.edu.tw/articles/jase-202202-25-1-0022.pdf | The Development of Standard Perceptual Attributes in Indonesian for Soundscape Evaluation: Result from Initial Study | ISO 12913-1, 12913-2, and 12913-3 have standardized soundscape evaluation from different aspects such as definition and framework, data collection methods, and data analysis. Central to ISO 12913-2 is that an acoustic environment can be evaluated based on perceptual attributes standardized only in English. These percep... | ['Ni Putu Amanda Nitidara', 'Sugeng Joko Sarwono', 'Winda Setiasari', 'Anugrah Sabdono Sudarsono'] | 2021-08-10 | null | null | null | journal-of-applied-science-and-engineering | ['soundscape-evaluation'] | ['audio'] | [-2.59489566e-01 -7.59997070e-01 7.31163561e-01 -1.47329137e-01
-4.45817888e-01 -8.85196447e-01 2.02862039e-01 5.55911899e-01
-5.52696466e-01 5.08952737e-01 5.01432359e-01 -2.64731556e-01
-5.35937369e-01 -5.04604578e-01 -8.76397416e-02 -5.32355070e-01
6.45191520e-02 -2.22133100e-01 1.41148075e-01 -3.59322160... | [15.177106857299805, 5.605398654937744] |
82d76d62-1d6b-4a35-b7a4-9e7e3d76fd63 | refinement-of-predicted-missing-parts-enhance | 2010.04278 | null | https://arxiv.org/abs/2010.04278v1 | https://arxiv.org/pdf/2010.04278v1.pdf | Refinement of Predicted Missing Parts Enhance Point Cloud Completion | Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural networks to directly estimate the whole point cloud through encoder-decoder models fed by the incomplete point set. By predicting the complete ... | ['Cristian Lopez', 'Ivan Sipiran', 'Alexander Apaza', 'Alexis Mendoza'] | 2020-10-08 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.51807472e-01 3.87281984e-01 -6.19162526e-03 -6.14143670e-01
-1.03254211e+00 -2.80967444e-01 2.84243792e-01 9.25491303e-02
-8.49500969e-02 2.57417202e-01 -1.06818698e-01 -3.05152102e-03
2.36745059e-01 -9.34425235e-01 -1.38604403e+00 -2.15010643e-01
1.01655819e-01 1.12478817e+00 7.07089305e-02 -5.20079434... | [8.315632820129395, -3.5398099422454834] |
f6d00f27-4677-4079-b52a-60b8575faa2f | an-experimental-study-of-the-transferability | 2012.10258 | null | https://arxiv.org/abs/2012.10258v1 | https://arxiv.org/pdf/2012.10258v1.pdf | An Experimental Study of the Transferability of Spectral Graph Networks | Spectral graph convolutional networks are generalizations of standard convolutional networks for graph-structured data using the Laplacian operator. A common misconception is the instability of spectral filters, i.e. the impossibility to transfer spectral filters between graphs of variable size and topology. This misbe... | ['Xavier Bresson', 'Axel Nilsson'] | 2020-12-18 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-1.46122072e-02 2.81117827e-01 2.15646829e-02 1.52073801e-01
-8.80779177e-02 -8.08159292e-01 2.73667246e-01 3.90960693e-01
-1.07728764e-01 7.62082696e-01 3.01021058e-02 -6.77362740e-01
-3.20379913e-01 -9.94219065e-01 -8.17357183e-01 -4.20209378e-01
-6.40396059e-01 7.93166980e-02 3.76767308e-01 -3.27835023... | [6.872424602508545, 6.1190948486328125] |
22faaf79-b397-4648-8124-26f0ff1810dd | robust-brain-age-estimation-via-regression | 2306.05514 | null | https://arxiv.org/abs/2306.05514v1 | https://arxiv.org/pdf/2306.05514v1.pdf | Robust Brain Age Estimation via Regression Models and MRI-derived Features | The determination of biological brain age is a crucial biomarker in the assessment of neurological disorders and understanding of the morphological changes that occur during aging. Various machine learning models have been proposed for estimating brain age through Magnetic Resonance Imaging (MRI) of healthy controls. H... | ['Imdad Ullah Khan', 'Murray Patterson', 'Shafiq Alam', 'Sarwan Ali', 'Usama Sardar', 'Mansoor Ahmed'] | 2023-06-08 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.85866114e-02 -2.66175270e-01 1.49716094e-01 -5.55422843e-01
-6.84069037e-01 -4.08062674e-02 5.93094230e-01 5.71231663e-01
-1.02414489e+00 7.28730679e-01 6.43899888e-02 2.38615694e-03
-1.43484920e-01 -5.60214579e-01 -3.30451965e-01 -7.74953544e-01
-6.96865261e-01 4.07771021e-01 2.14906946e-01 1.48861259... | [14.08324146270752, -1.5273454189300537] |
e53537d4-c9ef-4648-8407-627716d30dce | deep-snapshot-hdr-reconstruction-based-on-the | 2105.05824 | null | https://arxiv.org/abs/2105.05824v1 | https://arxiv.org/pdf/2105.05824v1.pdf | Deep Snapshot HDR Reconstruction Based on the Polarization Camera | The recent development of the on-chip micro-polarizer technology has made it possible to acquire four spatially aligned and temporally synchronized polarization images with the same ease of operation as a conventional camera. In this paper, we investigate the use of this sensor technology in high-dynamic-range (HDR) im... | ['Hong Zhang', 'Kangkang Hu', 'Xuesong Wu', 'Juiwen Ting'] | 2021-05-12 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 4.18093294e-01 -4.06521171e-01 3.31033349e-01 -2.14344189e-01
-3.97033125e-01 -7.08058417e-01 3.55869770e-01 -7.54623830e-01
-3.67842793e-01 6.56298876e-01 4.27404158e-02 -7.21249580e-02
1.03875250e-01 -6.61815405e-01 -7.34622538e-01 -1.19478166e+00
3.01200479e-01 3.18516076e-01 8.95240605e-02 -4.99419458... | [10.236690521240234, -2.570350408554077] |
91759d28-8523-415d-9a9e-bf6c0fbefc4e | vq-ar-vector-quantized-autoregressive | 2205.15894 | null | https://arxiv.org/abs/2205.15894v1 | https://arxiv.org/pdf/2205.15894v1.pdf | VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting | Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making. In practice, deep learning based time series models come in many forms, but at a high level learn some continuous representation of the past and use it... | ['Kyung-Min Kim', 'Max Nihlén Ramström', 'Young-Jin Park', 'Kashif Rasul'] | 2022-05-31 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 5.44158705e-02 2.26405282e-02 -3.05287540e-01 -5.97227275e-01
-5.84881485e-01 -5.57320893e-01 1.08719230e+00 7.73308575e-02
-7.97111094e-02 6.15918577e-01 3.21244776e-01 -7.92130589e-01
-2.21462891e-01 -9.43335652e-01 -6.40478551e-01 -6.81480229e-01
-4.49425638e-01 3.85829926e-01 -1.95160285e-01 -4.68635798... | [6.986021995544434, 3.1470634937286377] |
1f61fb26-7124-423c-ba16-19204a7489ae | view-guided-point-cloud-completion | 2104.05666 | null | https://arxiv.org/abs/2104.05666v2 | https://arxiv.org/pdf/2104.05666v2.pdf | View-Guided Point Cloud Completion | This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extr... | ['Yue Gao', 'Yandong Guo', 'Xibin Zhao', 'Hai Wan', 'Changqing Zou', 'Siqi Li', 'Yutong Feng', 'Xuancheng Zhang'] | 2021-04-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_View-Guided_Point_Cloud_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 1.46014929e-01 -3.83070186e-02 7.44405091e-02 -3.43090594e-01
-1.32486033e+00 -8.45632076e-01 8.10924888e-01 -6.14982396e-02
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3.35770130e-01 8.39729249e-01 4.35041815e-01 -6.11480065... | [8.383389472961426, -3.175233840942383] |
7730b19d-2b49-4a3e-bf93-f1b6762d3c5d | building-manufacturing-deep-learning-models | 2306.00202 | null | https://arxiv.org/abs/2306.00202v1 | https://arxiv.org/pdf/2306.00202v1.pdf | Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation | Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a ... | ['Ting-Yan Wu', 'Rih-Teng Wu', 'Elisa Bertino', 'Adrian Shuai Li'] | 2023-05-31 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.46875519e-01 5.89617006e-02 -3.71135861e-01 -5.77074587e-01
-6.28489494e-01 -3.71566564e-02 2.22784579e-02 4.64955091e-01
-1.55170903e-01 9.03955936e-01 -1.64144218e-01 -3.85409035e-02
1.22622214e-01 -1.11632776e+00 -7.23147154e-01 -6.86938763e-01
4.80293036e-01 6.76574051e-01 -2.12048087e-02 -2.67438423... | [7.599326133728027, 2.1553118228912354] |
75fa74e8-5ab2-44ad-912f-dfc44b43f1ee | belfusion-latent-diffusion-for-behavior | 2211.14304 | null | https://arxiv.org/abs/2211.14304v2 | https://arxiv.org/pdf/2211.14304v2.pdf | BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction | Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints' dispersion. This has led to methods predicting fast and motion-divergent movements, which ar... | ['Cristina Palmero', 'Sergio Escalera', 'German Barquero'] | 2022-11-25 | null | null | null | null | ['human-pose-forecasting', 'stochastic-human-motion-prediction'] | ['computer-vision', 'computer-vision'] | [-1.54417679e-01 1.90619320e-01 -3.38588178e-01 -1.02590509e-01
-4.36258525e-01 -3.90157014e-01 7.54390955e-01 -5.35905659e-01
-1.33205533e-01 7.71654129e-01 7.44527102e-01 3.25078696e-01
9.28963944e-02 -6.20653510e-01 -7.88381755e-01 -7.22561002e-01
-8.57884511e-02 3.45308244e-01 1.44873351e-01 -3.76314580... | [7.29479455947876, -0.12489165365695953] |
cd547836-a6a0-4723-8489-efa067f67a4c | ots-a-one-shot-learning-approach-for-text | 2304.00746 | null | https://arxiv.org/abs/2304.00746v2 | https://arxiv.org/pdf/2304.00746v2.pdf | OTS: A One-shot Learning Approach for Text Spotting in Historical Manuscripts | Historical manuscript processing poses challenges like limited annotated training data and novel class emergence. To address this, we propose a novel One-shot learning-based Text Spotting (OTS) approach that accurately and reliably spots novel characters with just one annotated support sample. Drawing inspiration from ... | ['Hongjian Zhan', 'WenBo Hu', 'Yue Lu', 'Bing Yin', 'Cong Liu'] | 2023-04-03 | null | null | null | null | ['text-spotting', 'one-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.96490014e-01 -4.54126894e-01 -1.16153039e-01 -3.84007514e-01
-9.33331549e-01 -4.08095688e-01 6.06558144e-01 2.49437302e-01
-5.95436931e-01 7.49841869e-01 1.75887626e-02 -1.73782520e-02
-3.70109409e-01 -7.56675422e-01 -6.87108815e-01 -7.08273888e-01
1.91076458e-01 5.22891462e-01 3.02730232e-01 -4.55207340... | [11.799678802490234, 2.285433530807495] |
299edeb9-5c5b-4c99-bab2-774ed0c3e382 | deepdeform-learning-non-rigid-rgb-d | 1912.04302 | null | https://arxiv.org/abs/1912.04302v2 | https://arxiv.org/pdf/1912.04302v2.pdf | DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data | Applying data-driven approaches to non-rigid 3D reconstruction has been difficult, which we believe can be attributed to the lack of a large-scale training corpus. Unfortunately, this method fails for important cases such as highly non-rigid deformations. We first address this problem of lack of data by introducing a n... | ['Matthias Nießner', 'Christian Theobalt', 'Michael Zollhöfer', 'Aljaž Božič'] | 2019-12-09 | null | null | null | null | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 2.93491453e-01 7.95990750e-02 4.18124013e-02 -6.15716219e-01
-1.22850025e+00 -5.61610579e-01 5.86820543e-01 -3.21664572e-01
-3.39649141e-01 4.45493281e-01 5.91507435e-01 1.92682639e-01
1.36440881e-02 -5.19216299e-01 -1.01208079e+00 -4.81096894e-01
3.38655710e-01 6.55574739e-01 5.32846868e-01 -1.99947417... | [8.328749656677246, -2.467207193374634] |
83c076aa-e81f-4dfe-a3e8-ee992ecdf8c3 | a-distribution-dependent-mumford-shah-model | 2203.15058 | null | https://arxiv.org/abs/2203.15058v2 | https://arxiv.org/pdf/2203.15058v2.pdf | A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation | Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial in hyperspectral image analysis. The spectra... | ['Benjamin Berkels', 'Chandrajit Bajaj', 'Jan-Christopher Cohrs'] | 2022-03-28 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 1.06804383e+00 -4.87180889e-01 3.31276134e-02 -3.37562978e-01
-9.70830142e-01 -7.10485637e-01 2.75911510e-01 1.08133502e-01
-4.26228285e-01 6.32128775e-01 -4.58607763e-01 -2.47174621e-01
-5.38981080e-01 -8.35475922e-01 -3.56415361e-01 -1.27349484e+00
1.22139379e-01 4.28279072e-01 -1.49662390e-01 5.80657162... | [10.024361610412598, -1.9564764499664307] |
cd070148-2ae8-4e4c-9744-3e8953e8c039 | vln-trans-translator-for-the-vision-and | 2302.09230 | null | https://arxiv.org/abs/2302.09230v1 | https://arxiv.org/pdf/2302.09230v1.pdf | VLN-Trans: Translator for the Vision and Language Navigation Agent | Language understanding is essential for the navigation agent to follow instructions. We observe two kinds of issues in the instructions that can make the navigation task challenging: 1. The mentioned landmarks are not recognizable by the navigation agent due to the different vision abilities of the instructor and the m... | ['Parisa Kordjamshidi', 'Yue Zhang'] | 2023-02-18 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [-1.95869192e-01 4.42806892e-02 2.39171997e-01 -5.49377084e-01
-4.34838593e-01 -7.17231214e-01 4.28621829e-01 3.66219617e-02
-5.35232484e-01 3.09887111e-01 -6.74319789e-02 -5.35461307e-01
1.38370186e-01 -5.62162161e-01 -8.00134480e-01 -3.57778013e-01
1.76920876e-01 6.66847169e-01 6.14191592e-01 -4.25170720... | [4.462834358215332, 0.46994268894195557] |
7a2c8116-4cac-42e0-8128-5075e7f74543 | efficient-mask-correction-for-click-based | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.pdf | Efficient Mask Correction for Click-Based Interactive Image Segmentation | The goal of click-based interactive image segmentation is to extract target masks with the input of positive/negative clicks. Every time a new click is placed, existing methods run the whole segmentation network to obtain a corrected mask, which is inefficient since several clicks may be needed to reach satisfactor... | ['Fan Wang', 'Zhibin Wang', 'Jianlong Yuan', 'Fei Du'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.43679762e-01 -4.75623235e-02 -2.83232089e-02 -4.61590588e-01
-8.13366115e-01 -4.70962614e-01 1.09064430e-01 -1.15066938e-01
-5.72560430e-01 3.53419691e-01 -3.17383319e-01 -2.16517717e-01
3.47233683e-01 -6.95870459e-01 -5.95628798e-01 -4.30845469e-01
4.62119550e-01 2.65424073e-01 1.02973402e+00 1.45612331... | [9.522497177124023, -0.07616961747407913] |
e671bce3-a9cd-4c83-9520-182ffe22bc4c | dimsum-laysumm-20-bart-based-approach-for | 2010.09252 | null | https://arxiv.org/abs/2010.09252v1 | https://arxiv.org/pdf/2010.09252v1.pdf | Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization | Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on the BART model. We leverage sentence labels as extra supervision signals to improve... | ['Pascale Fung', 'Wenliang Dai', 'Dan Su', 'Tiezheng Yu'] | 2020-10-19 | null | null | null | null | ['lay-summarization', 'scientific-article-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.91972065e-01 6.46580875e-01 -5.12063682e-01 -1.87288284e-01
-1.42242026e+00 -3.03127617e-01 7.86597073e-01 4.31355000e-01
-2.49563485e-01 1.60386133e+00 1.02960563e+00 -2.44081989e-01
-9.08533335e-02 -4.63727742e-01 -1.00088739e+00 -8.23888406e-02
4.93163049e-01 3.27124119e-01 -7.30647519e-02 8.63956809... | [12.489481925964355, 9.552290916442871] |
fb898fe4-5e50-4b4a-a2b5-f48bcf3a178e | volumetric-supervised-contrastive-learning | 2206.08158 | null | https://arxiv.org/abs/2206.08158v1 | https://arxiv.org/pdf/2206.08158v1.pdf | Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation | In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity of unlabeled data that is left unused simply because traditional deep learning methods rely on access to fully labeled volumes. To rectify t... | ['Ghassan AlRegib', 'Mohit Prabhushankar', 'Kiran Kokilepersaud'] | 2022-06-16 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 3.72867733e-01 2.44345739e-01 2.54105255e-02 -7.14875281e-01
-9.18073714e-01 -5.54273725e-01 5.88508904e-01 3.58461708e-01
-5.72392702e-01 8.63568604e-01 -2.13759273e-01 -1.92006588e-01
-5.40935695e-02 -1.08049500e+00 -7.37773120e-01 -8.03536713e-01
1.55405030e-01 7.66435742e-01 5.57172596e-01 -1.52549505... | [7.445321559906006, 2.0022830963134766] |
c394931c-17df-489c-9ab3-9e086b80c6d4 | investigating-eeg-based-functional | 2004.01973 | null | https://arxiv.org/abs/2004.01973v1 | https://arxiv.org/pdf/2004.01973v1.pdf | Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition | Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals... | ['Bao-liang Lu', 'Wei-Long Zheng', 'Xun Wu'] | 2020-04-04 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-8.73723328e-02 -3.93848151e-01 2.20655531e-01 -3.65358084e-01
8.12415555e-02 -2.20222205e-01 2.21287176e-01 -1.55433506e-01
-6.06685460e-01 9.51061428e-01 -4.30499054e-02 1.58196360e-01
-5.55236876e-01 -4.06447828e-01 -9.05976743e-02 -8.63864899e-01
-5.40604949e-01 -3.32519144e-01 -5.78036368e-01 -2.39962995... | [13.143817901611328, 3.450885772705078] |
dd2c7ea3-99d2-4f28-8d09-3244639cc516 | infwide-image-and-feature-space-wiener | 2207.08201 | null | https://arxiv.org/abs/2207.08201v2 | https://arxiv.org/pdf/2207.08201v2.pdf | INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions | Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dom... | ['Qionghai Dai', 'Liheng Bian', 'Jinli Suo', 'Yuxiao Cheng', 'Zhihong Zhang'] | 2022-07-17 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.41451234e-01 -5.80148101e-01 3.72074336e-01 -1.35752320e-01
-3.62397134e-01 -2.65354812e-01 4.36551243e-01 -1.22075152e+00
-1.10398516e-01 9.15073872e-01 4.88902748e-01 -1.06933847e-01
-3.25686544e-01 -2.75718987e-01 -4.99245793e-01 -1.33374882e+00
4.65843499e-01 -5.38818359e-01 -1.82426900e-01 -1.13311402... | [11.335759162902832, -2.7218456268310547] |
89b356cb-ca1c-4276-856c-f646a3778356 | transg-transformer-based-skeleton-graph | 2303.06819 | null | https://arxiv.org/abs/2303.06819v2 | https://arxiv.org/pdf/2303.06819v2.pdf | TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification | Person re-identification (re-ID) via 3D skeleton data is an emerging topic with prominent advantages. Existing methods usually design skeleton descriptors with raw body joints or perform skeleton sequence representation learning. However, they typically cannot concurrently model different body-component relations, and ... | ['Chunyan Miao', 'Haocong Rao'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rao_TranSG_Transformer-Based_Skeleton_Graph_Prototype_Contrastive_Learning_With_Structure-Trajectory_Prompted_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rao_TranSG_Transformer-Based_Skeleton_Graph_Prototype_Contrastive_Learning_With_Structure-Trajectory_Prompted_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'graph-reconstruction'] | ['computer-vision', 'graphs'] | [ 1.33991437e-02 -8.74101296e-02 -4.83702451e-01 -2.07815796e-01
-1.25535443e-01 -2.60537714e-01 7.41447151e-01 4.49701883e-02
6.64248019e-02 1.80993617e-01 6.28050089e-01 4.89592254e-01
-2.87105501e-01 -8.14100981e-01 -1.54879838e-01 -3.63002032e-01
-2.04045042e-01 6.37538552e-01 2.21081987e-01 -2.92889833... | [14.525135040283203, 1.1826584339141846] |
557bc8a9-fc77-4ee7-bb74-aa667dfc67f2 | audio-visual-speech-enhancement-with-score | 2306.01432 | null | https://arxiv.org/abs/2306.01432v1 | https://arxiv.org/pdf/2306.01432v1.pdf | Audio-Visual Speech Enhancement with Score-Based Generative Models | This paper introduces an audio-visual speech enhancement system that leverages score-based generative models, also known as diffusion models, conditioned on visual information. In particular, we exploit audio-visual embeddings obtained from a self-super\-vised learning model that has been fine-tuned on lipreading. The ... | ['Timo Gerkmann', 'Simone Frintrop', 'Julius Richter'] | 2023-06-02 | null | null | null | null | ['lipreading', 'speech-enhancement'] | ['computer-vision', 'speech'] | [ 1.83580235e-01 2.82499582e-01 -1.29969418e-01 -1.65979519e-01
-1.18451381e+00 -3.76501441e-01 6.41838133e-01 -2.45381668e-01
-2.06750423e-01 3.84475917e-01 8.24448705e-01 -1.85978681e-01
1.11552700e-02 -3.10114026e-01 -4.68632311e-01 -8.41117203e-01
1.79294020e-01 -3.58710915e-01 -5.02216555e-02 3.78481891... | [14.904451370239258, 5.9462456703186035] |
6c32e613-1177-45cf-9e11-e48da4c2bc03 | self-discriminative-learning-for-unsupervised | null | null | https://aclanthology.org/N19-1255 | https://aclanthology.org/N19-1255.pdf | Self-Discriminative Learning for Unsupervised Document Embedding | Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents ... | ['Shou-De Lin', 'Chin-Hua Hu', 'Hong-You Chen', 'Leila Wehbe'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['document-embedding'] | ['methodology'] | [ 2.03804478e-01 3.95091027e-01 -6.51561558e-01 -6.38476968e-01
-5.39731920e-01 -5.46364725e-01 1.08611441e+00 6.71797872e-01
-5.70470870e-01 4.86060351e-01 5.13721347e-01 -1.99954808e-02
-3.42512667e-01 -6.63786173e-01 -2.95147985e-01 -6.46087646e-01
-1.05434731e-01 8.66798818e-01 1.27584368e-01 -6.39817044... | [10.438349723815918, 8.277606964111328] |
8a3a9867-2c54-4f1d-8663-a57e1197f079 | learning-gradient-fields-for-shape-generation | 2008.06520 | null | https://arxiv.org/abs/2008.06520v2 | https://arxiv.org/pdf/2008.06520v2.pdf | Learning Gradient Fields for Shape Generation | In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. Point cloud generation thus amounts to moving randomly sampled points to high-density areas. We genera... | ['Hadar Averbuch-Elor', 'Ruojin Cai', 'Serge Belongie', 'Guandao Yang', 'Zekun Hao', 'Noah Snavely', 'Bharath Hariharan'] | 2020-08-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/462_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480375.pdf | eccv-2020-8 | ['point-cloud-generation'] | ['computer-vision'] | [ 8.98430198e-02 2.17893422e-01 1.71810798e-02 -1.17850840e-01
-1.23802495e+00 -5.31761587e-01 8.20915341e-01 -5.02053052e-02
6.18247129e-02 5.57525396e-01 1.56901162e-02 -2.20827349e-02
2.34471247e-01 -1.27045667e+00 -1.10461020e+00 -5.68804979e-01
1.41559793e-02 1.35487747e+00 1.68610930e-01 1.52895883... | [8.876984596252441, -3.664203643798828] |
bc224593-0d4b-4988-8e60-1331b9beff60 | learning-with-proper-partial-labels | 2112.12303 | null | https://arxiv.org/abs/2112.12303v2 | https://arxiv.org/pdf/2112.12303v2.pdf | Learning with Proper Partial Labels | Partial-label learning is a kind of weakly-supervised learning with inexact labels, where for each training example, we are given a set of candidate labels instead of only one true label. Recently, various approaches on partial-label learning have been proposed under different generation models of candidate label sets.... | ['Masashi Sugiyama', 'Jiaqi Lv', 'Zhenguo Wu'] | 2021-12-23 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 1.58637270e-01 4.50639546e-01 -7.56471813e-01 -7.40747869e-01
-1.11779618e+00 -6.82248712e-01 3.59616786e-01 1.42683163e-01
-3.12524945e-01 9.59873676e-01 -1.44984081e-01 -2.52140462e-01
-3.12593400e-01 -6.61516249e-01 -7.74340451e-01 -7.80651987e-01
3.02783251e-01 5.17731428e-01 -6.13770485e-02 3.96706045... | [9.190786361694336, 4.153933048248291] |
a6a1e7bd-6759-4017-8675-7c90cbc005a7 | auditory-neural-response-inspired-sound-event | 2306.11427 | null | https://arxiv.org/abs/2306.11427v1 | https://arxiv.org/pdf/2306.11427v1.pdf | Auditory Neural Response Inspired Sound Event Detection Based on Spectro-temporal Receptive Field | Sound event detection (SED) is one of tasks to automate function by human auditory system which listens and understands auditory scenes. Therefore, we were inspired to make SED recognize sound events in the way human auditory system does. Spectro-temporal receptive field (STRF), an approach to describe the relationship... | ['Yong-Hwa Park', 'Hyeonuk Nam', 'Deokki Min'] | 2023-06-20 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [-4.99312393e-02 -4.70529974e-01 8.22830021e-01 -1.47600859e-01
-6.62797570e-01 -6.45573437e-01 3.19257587e-01 6.52064458e-02
-7.22534478e-01 1.58730417e-01 4.47402626e-01 -1.92930773e-01
-1.40253862e-03 -6.52062595e-01 -7.16181278e-01 -3.20475638e-01
-1.14634432e-01 -6.00971043e-01 7.58191466e-01 -1.90419361... | [15.180243492126465, 5.223280429840088] |
00091978-665e-412d-937b-6a662c78d342 | learning-to-discriminate-information-for-1 | 2109.03393 | null | https://arxiv.org/abs/2109.03393v3 | https://arxiv.org/pdf/2109.03393v3.pdf | Learning to Discriminate Information for Online Action Detection: Analysis and Application | Online action detection, which aims to identify an ongoing action from a streaming video, is an important subject in real-world applications. For this task, previous methods use recurrent neural networks for modeling temporal relations in an input sequence. However, these methods overlook the fact that the input image ... | ['Changick Kim', 'Chanho Jung', 'Yoonhyung Kim', 'Seokeon Choi', 'Jinyoung Moon', 'Hyunjun Eun', 'Sumin Lee'] | 2021-09-08 | null | null | null | null | ['action-anticipation', 'online-action-detection'] | ['computer-vision', 'computer-vision'] | [ 8.47360253e-01 -1.72869280e-01 -4.83186334e-01 -1.84926957e-01
-5.46949744e-01 -2.74760872e-01 5.05517364e-01 -6.94071278e-02
-4.11705941e-01 4.59373474e-01 5.43689311e-01 1.36901259e-01
1.10138319e-01 -5.08205354e-01 -4.35854822e-01 -8.14510465e-01
-2.24886477e-01 -2.36815169e-01 5.17532408e-01 3.77727598... | [8.367554664611816, 0.4986291527748108] |
a652736a-0166-4721-ac00-58adaa409413 | shape-constraint-recurrent-flow-for-6d-object-1 | 2306.13266 | null | https://arxiv.org/abs/2306.13266v1 | https://arxiv.org/pdf/2306.13266v1.pdf | Shape-Constraint Recurrent Flow for 6D Object Pose Estimation | Most recent 6D object pose methods use 2D optical flow to refine their results. However, the general optical flow methods typically do not consider the target's 3D shape information during matching, making them less effective in 6D object pose estimation. In this work, we propose a shape-constraint recurrent matching f... | ['Yinlin Hu', 'Jiaojiao Li', 'Rui Song', 'Yang Hai'] | 2023-06-23 | shape-constraint-recurrent-flow-for-6d-object | http://openaccess.thecvf.com//content/CVPR2023/html/Hai_Shape-Constraint_Recurrent_Flow_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hai_Shape-Constraint_Recurrent_Flow_for_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pose-estimation', 'optical-flow-estimation', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.28806451e-01 -3.65841836e-01 -1.57477230e-01 -3.67264062e-01
-3.68358940e-01 -5.50749242e-01 3.10844153e-01 5.34734279e-02
-3.42460603e-01 5.53019606e-02 1.65148869e-01 2.27776036e-01
-2.92619094e-02 -5.36602497e-01 -7.23549545e-01 -3.94279569e-01
1.17799580e-01 7.78470397e-01 2.16503993e-01 1.48496419... | [7.49861478805542, -2.6279945373535156] |
8c3336ad-88b6-4a65-acd4-2077502e1516 | simple-yet-powerful-an-overlooked | null | null | https://openreview.net/forum?id=cL4tgY1ZxS | https://openreview.net/pdf?id=cL4tgY1ZxS | Simple yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. Traditional NER research ignores nested entities, which are entities contained in other entity mentions. Although several methods have been proposed to address this cas... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-2.94272929e-01 2.74314135e-01 -1.28039107e-01 -4.59932923e-01
-9.11745787e-01 -7.26724207e-01 8.02598000e-01 5.32360435e-01
-1.11125052e+00 8.84420156e-01 5.98083496e-01 -5.33462882e-01
9.27585289e-02 -6.06055498e-01 -4.69943613e-01 -1.86892271e-01
-1.83436245e-01 5.96321881e-01 2.45507047e-01 -3.08609217... | [9.768868446350098, 9.584970474243164] |
9e2bc2fd-f73a-4057-a4ff-64923c0fdb91 | fusion-aware-point-convolution-for-online | 2003.06233 | null | https://arxiv.org/abs/2003.06233v4 | https://arxiv.org/pdf/2003.06233v4.pdf | Fusion-Aware Point Convolution for Online Semantic 3D Scene Segmentation | Online semantic 3D segmentation in company with real-time RGB-D reconstruction poses special challenges such as how to perform 3D convolution directly over the progressively fused 3D geometric data, and how to smartly fuse information from frame to frame. We propose a novel fusion-aware 3D point convolution which opera... | ['Chenyang Zhu', 'Lintao Zheng', 'Kai Xu', 'Jiazhao Zhang'] | 2020-03-13 | fusion-aware-point-convolution-for-online-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Fusion-Aware_Point_Convolution_for_Online_Semantic_3D_Scene_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Fusion-Aware_Point_Convolution_for_Online_Semantic_3D_Scene_Segmentation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['rgb-d-reconstruction'] | ['computer-vision'] | [-7.12668225e-02 -2.61908192e-02 2.45034412e-01 -3.25283885e-01
-1.05858612e+00 -6.64211035e-01 3.23141158e-01 3.78371954e-01
-3.69113833e-01 1.67115346e-01 -3.49305004e-01 -1.92280725e-01
-1.19618796e-01 -1.16777229e+00 -8.04368854e-01 -5.39765716e-01
-1.82289049e-01 6.89668596e-01 7.40726352e-01 -9.84986722... | [8.354684829711914, -2.9504592418670654] |
37e3c5e9-6539-4b47-afb1-ec379a7d736e | semi-automated-segmentation-of-geoscientific | 2303.11404 | null | https://arxiv.org/abs/2303.11404v1 | https://arxiv.org/pdf/2303.11404v1.pdf | Semi-Automated Segmentation of Geoscientific Data Using Superpixels | Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy. However, direct observation of geology is often prevented by surface cover such as overburden or vegetation. In such cases, remote and in-situ surveys are frequently conducted to collect physical measur... | ['Eldad Haber', 'Conrad P. Koziol'] | 2023-03-20 | null | null | null | null | ['superpixels'] | ['computer-vision'] | [ 2.56205827e-01 7.92670473e-02 3.28033008e-02 -4.15702909e-01
-4.48339939e-01 -5.92327595e-01 4.66268182e-01 4.04667675e-01
-3.93592179e-01 1.00433123e+00 9.85171720e-02 -2.41989866e-01
6.15270995e-02 -1.31789207e+00 -8.10162008e-01 -7.43816614e-01
-8.02785456e-02 5.25997281e-01 3.82398009e-01 -1.16890721... | [9.609047889709473, -1.3933149576187134] |
ebb2b3b6-4903-4744-931f-cc4a53e6bb8f | distributed-representations-for-biological | 1608.05949 | null | http://arxiv.org/abs/1608.05949v2 | http://arxiv.org/pdf/1608.05949v2.pdf | Distributed Representations for Biological Sequence Analysis | Biological sequence comparison is a key step in inferring the relatedness of
various organisms and the functional similarity of their components. Thanks to
the Next Generation Sequencing efforts, an abundance of sequence data is now
available to be processed for a range of bioinformatics applications. Embedding
a biolo... | ['James M. Hogan', 'Dhananjay Kimothi', 'Akshay Soni', 'Pravesh Biyani'] | 2016-08-21 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 6.38046741e-01 -3.88590604e-01 -1.40939072e-01 -2.76366949e-01
-5.02926052e-01 -8.18869174e-01 5.25148213e-01 7.79456139e-01
-6.54976010e-01 6.39300883e-01 5.07283509e-01 -4.50960070e-01
3.25477533e-02 -3.78042996e-01 -3.45507026e-01 -9.41431880e-01
-9.89217237e-02 2.81539857e-01 -1.42141148e-01 -1.34735510... | [4.791141033172607, 5.5687947273254395] |
1f928458-5cc1-4c2b-934d-12f87bbbe80a | alleviating-matthew-effect-of-offline | 2307.04571 | null | https://arxiv.org/abs/2307.04571v1 | https://arxiv.org/pdf/2307.04571v1.pdf | Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation | Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice in decision-making processes like interactive recommendation. Offline RL faces the value overestimation problem. To address it, existing me... | ['Xiangnan He', 'Zhong Zhang', 'Shiqi Wang', 'Peng Jiang', 'Biao Li', 'Yuan Zhang', 'Jiawei Chen', 'Kexin Huang', 'Chongming Gao'] | 2023-07-10 | null | null | null | null | ['reinforcement-learning-1', 'recommendation-systems', 'offline-rl', 'decision-making'] | ['methodology', 'miscellaneous', 'playing-games', 'reasoning'] | [-3.47159624e-01 2.63760716e-01 -8.33954453e-01 -1.88734576e-01
-4.83269542e-01 -6.83139145e-01 1.13312423e-01 -1.42932683e-01
-4.16615129e-01 9.92584229e-01 4.27060783e-01 -6.26077354e-01
-1.83777973e-01 -6.77885354e-01 -5.96540928e-01 -8.40947628e-01
-1.26293406e-01 2.76851237e-01 -2.28043735e-01 -4.24480408... | [4.153491020202637, 2.41606068611145] |
7a861b7a-3ead-4cc3-bd06-6d51c87005d6 | referring-expression-comprehension-using | 2306.04451 | null | https://arxiv.org/abs/2306.04451v1 | https://arxiv.org/pdf/2306.04451v1.pdf | Referring Expression Comprehension Using Language Adaptive Inference | Different from universal object detection, referring expression comprehension (REC) aims to locate specific objects referred to by natural language expressions. The expression provides high-level concepts of relevant visual and contextual patterns, which vary significantly with different expressions and account for onl... | ['Xi Li', 'Yongjian Fu', 'Huanzhang Dou', 'Peihan Miao', 'Wei Su'] | 2023-06-06 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 1.70107156e-01 1.63913146e-01 -2.20554218e-01 -4.73218620e-01
-2.69284040e-01 -7.47389495e-01 5.25864840e-01 -1.65387150e-02
-3.25184494e-01 4.34690267e-01 1.46885529e-01 -3.46044838e-01
-1.46814436e-01 -8.13772738e-01 -4.77116138e-01 -5.59140146e-01
8.72150734e-02 3.32726449e-01 4.30519670e-01 -3.86518657... | [10.463554382324219, 1.5058119297027588] |
813178da-86d9-4be0-930e-5a46941eda9b | marginal-utility-for-planning-in-continuous | 2006.06054 | null | https://arxiv.org/abs/2006.06054v2 | https://arxiv.org/pdf/2006.06054v2.pdf | Marginal Utility for Planning in Continuous or Large Discrete Action Spaces | Sample-based planning is a powerful family of algorithms for generating intelligent behavior from a model of the environment. Generating good candidate actions is critical to the success of sample-based planners, particularly in continuous or large action spaces. Typically, candidate action generation exhausts the acti... | ['Michael Bowling', 'Levi H. S. Lelis', 'Zaheen Farraz Ahmad'] | 2020-06-10 | null | http://proceedings.neurips.cc/paper/2020/hash/14da15db887a4b50efe5c1bc66537089-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/14da15db887a4b50efe5c1bc66537089-Paper.pdf | neurips-2020-12 | ['action-generation'] | ['computer-vision'] | [ 6.47519052e-01 8.28486681e-01 -3.75762612e-01 -5.55282570e-02
-1.28829896e+00 -6.33876920e-01 1.09633183e+00 1.23679623e-01
-4.93082196e-01 1.46248519e+00 5.87094426e-01 -3.40458095e-01
-2.14844882e-01 -8.41438830e-01 -7.19099820e-01 -5.48847318e-01
-2.47902125e-01 9.20987070e-01 2.88409412e-01 -2.07692653... | [4.141141891479492, 1.7359895706176758] |
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