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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bc1c7fe6-2fb0-4ec9-968e-fbd1fbfc2fea | near-optimal-glimpse-sequences-for-improved | 1906.05462 | null | https://arxiv.org/abs/1906.05462v2 | https://arxiv.org/pdf/1906.05462v2.pdf | Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training | Hard visual attention is a promising approach to reduce the computational burden of modern computer vision methodologies. Hard attention mechanisms are typically non-differentiable. They can be trained with reinforcement learning but the high-variance training this entails hinders more widespread application. We show h... | ['Michael Teng', 'Frank Wood', 'William Harvey'] | 2019-06-13 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 4.00655508e-01 5.84042072e-01 -4.25663330e-02 -2.60008365e-01
-7.09234059e-01 -3.92910719e-01 5.58969676e-01 -9.64129493e-02
-6.97087646e-01 7.52434313e-01 -1.03571385e-01 -3.36522043e-01
-1.29859030e-01 -2.29718715e-01 -1.03706789e+00 -9.29647207e-01
2.37796202e-01 5.29962838e-01 1.32194072e-01 1.50934204... | [10.054261207580566, 1.9757760763168335] |
3864d243-3aff-4cad-9d36-c31f988390aa | evi-multilingual-spoken-dialogue-tasks-and-1 | 2204.13496 | null | https://arxiv.org/abs/2204.13496v1 | https://arxiv.org/pdf/2204.13496v1.pdf | EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification | Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this wo... | ['Paweł Budzianowski', 'Iñigo Casanueva', 'Michał Lis', 'Ivan Vulić', 'Georgios P. Spithourakis'] | 2022-04-28 | null | https://aclanthology.org/2022.findings-naacl.124 | https://aclanthology.org/2022.findings-naacl.124.pdf | findings-naacl-2022-7 | ['spoken-dialogue-systems', 'speaker-identification'] | ['speech', 'speech'] | [-2.93234617e-01 2.01162130e-01 1.64945535e-02 -5.43909252e-01
-8.83708596e-01 -9.43280399e-01 1.06678653e+00 5.51480711e-01
-8.98282230e-01 1.01003897e+00 4.86278415e-01 -5.33246994e-01
1.15896292e-01 -3.85848135e-01 1.36774048e-01 -3.27743024e-01
-3.15690041e-01 8.55123639e-01 3.21167894e-02 -4.07136172... | [12.871391296386719, 7.836047172546387] |
bbd794d1-ba77-48a9-97b1-bc0767e95ac2 | synthesizing-light-field-video-from-monocular | 2207.10357 | null | https://arxiv.org/abs/2207.10357v1 | https://arxiv.org/pdf/2207.10357v1.pdf | Synthesizing Light Field Video from Monocular Video | The hardware challenges associated with light-field(LF) imaging has made it difficult for consumers to access its benefits like applications in post-capture focus and aperture control. Learning-based techniques which solve the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have significantly reduc... | ['Kaushik Mitra', 'Sarah', 'Prasan Shedligeri', 'Shrisudhan Govindarajan'] | 2022-07-21 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 4.67829704e-01 -1.66486517e-01 -2.61709571e-01 -3.89761001e-01
-5.93051612e-01 -4.38095510e-01 3.21447909e-01 -5.37932634e-01
-1.48046970e-01 9.19220448e-01 3.58812571e-01 3.23035568e-02
-2.49637455e-01 -3.55096519e-01 -9.99460697e-01 -7.77582407e-01
1.44623876e-01 7.25527853e-02 1.37560785e-01 1.11376218... | [9.643309593200684, -2.6141679286956787] |
2ed6a06a-81e2-4516-9062-79a570b6100c | rccnet-an-efficient-convolutional-neural | 1810.02797 | null | https://arxiv.org/abs/1810.02797v3 | https://arxiv.org/pdf/1810.02797v3.pdf | RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification | Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell ... | ['S. H. Shabbeer Basha', 'Soumen Ghosh', 'Snehasis Mukherjee', 'Kancharagunta Kishan Babu', 'Viswanath Pulabaigari', 'Shiv Ram Dubey'] | 2018-09-30 | null | null | null | null | ['nuclei-classification'] | ['medical'] | [-2.32060254e-01 -1.04457535e-01 -1.21337280e-01 2.05104370e-02
-4.25997764e-01 -1.38314530e-01 2.22902045e-01 6.07246101e-01
-1.03728950e+00 8.34107935e-01 -8.84030536e-02 -3.71777773e-01
-1.18620723e-01 -9.12806869e-01 -8.78052562e-02 -1.14802742e+00
7.18360171e-02 2.48054698e-01 8.01314265e-02 -4.68073227... | [15.16122817993164, -2.878436803817749] |
ac8b97b8-929e-4eb7-8e0b-d0820d48a9f8 | an-equivariant-generative-framework-for | 2304.12436 | null | https://arxiv.org/abs/2304.12436v1 | https://arxiv.org/pdf/2304.12436v1.pdf | An Equivariant Generative Framework for Molecular Graph-Structure Co-Design | Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for \emph{de novo} molecule design. However, further refinement of met... | ['Enhong Chen', 'Chang-Yu Hsieh', 'Chee-Kong Lee', 'Qi Liu', 'Zaixi Zhang'] | 2023-04-12 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.70772806e-01 -2.11066306e-01 -5.68827629e-01 -4.72996831e-02
-4.93968934e-01 -7.07271397e-01 3.59483987e-01 3.74077231e-01
8.06879997e-02 1.23950100e+00 -7.11606368e-02 -6.13585055e-01
-1.93168178e-01 -9.88268018e-01 -7.11430073e-01 -1.10790110e+00
-1.22869365e-01 5.86867929e-01 -1.71232775e-01 -3.12953144... | [5.023231029510498, 5.695535182952881] |
8ac1cb63-59f1-4a0a-afca-2f108ae7fb80 | generalized-munchausen-reinforcement-learning | 2301.11476 | null | https://arxiv.org/abs/2301.11476v1 | https://arxiv.org/pdf/2301.11476v1.pdf | Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence | Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickly. This idea was initially proposed in a seminal paper on Conservative Policy Iteration, with approximations given by algorithms like TRPO a... | ['Martha White', 'Takamitsu Matsubara', 'Zheng Chen', 'Lingwei Zhu'] | 2023-01-27 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-2.84597009e-01 7.19340285e-03 -6.23614788e-01 -2.42487177e-01
-8.89739156e-01 -8.37255597e-01 4.59816545e-01 1.95078347e-02
-1.17405629e+00 1.24532223e+00 -8.15148056e-02 -8.43340814e-01
-5.01340568e-01 -4.78506982e-01 -7.68364251e-01 -7.90634215e-01
-6.46941960e-01 4.00427610e-01 3.43456686e-01 -4.69320327... | [4.119374752044678, 2.3391332626342773] |
ee4f039b-e29c-4684-aba6-b9bfa81a5fdd | optimality-preserving-reduction-of-chemical | 2301.08553 | null | https://arxiv.org/abs/2301.08553v1 | https://arxiv.org/pdf/2301.08553v1.pdf | Optimality-preserving Reduction of Chemical Reaction Networks | Across many disciplines, chemical reaction networks (CRNs) are an established population model defined as a system of coupled nonlinear ordinary differential equations. In many applications, for example, in systems biology and epidemiology, CRN parameters such as the kinetic reaction rates can be used as control inputs... | ['Andrea Vandin', 'Max Tschaikowski', 'Mirco Tribastone', 'Daniele Toller', 'Kim G. Larsen'] | 2023-01-20 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.11107504e-01 1.99042723e-01 -8.80319774e-02 4.58450407e-01
-6.14420287e-02 -7.98685789e-01 1.83419615e-01 4.08168882e-01
-4.87660497e-01 1.08444452e+00 -4.56510633e-01 -5.45612395e-01
-6.66649878e-01 -7.64595568e-01 -7.88108349e-01 -1.13848948e+00
-8.95551965e-02 7.24845946e-01 7.86805004e-02 -4.51883376... | [6.143272399902344, 4.208352565765381] |
08530d38-6b6d-4ec5-8990-c9b9f017db55 | graph-decoupling-attention-markov-networks | 2104.13718 | null | https://arxiv.org/abs/2104.13718v2 | https://arxiv.org/pdf/2104.13718v2.pdf | Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification | Graph neural networks (GNN) have been ubiquitous in graph node classification tasks. Most of GNN methods update the node embedding iteratively by aggregating its neighbors' information. However, they often suffer from negative disturbance, due to edges connecting nodes with different labels. One approach to alleviate t... | ['Junbin Gao', 'Junping Zhang', 'Jian Pu', 'Mingyuan Bai', 'Shouzhen Chen', 'Jie Chen'] | 2021-04-28 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 1.51899293e-01 2.97160506e-01 -2.64965624e-01 -4.67233777e-01
7.03311116e-02 -9.42889601e-02 3.78623188e-01 4.25581038e-01
-4.09229428e-01 5.30928314e-01 -1.12388562e-02 -9.13372934e-02
-2.30169535e-01 -1.11485434e+00 -5.09585381e-01 -8.27701151e-01
-5.19077256e-02 2.81291217e-01 2.30897039e-01 -1.43930033... | [7.336816787719727, 6.177712440490723] |
c18ef8b8-391e-4e64-b6c3-7b012611ad54 | knowledge-distilled-graph-neural-networks-for | 2304.06038 | null | https://arxiv.org/abs/2304.06038v1 | https://arxiv.org/pdf/2304.06038v1.pdf | Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection | Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge dist... | ['Pascal Frossard', 'Simona Petravic', 'Arun Venkitaraman', 'Qinyue Zheng'] | 2023-04-03 | null | null | null | null | ['seizure-detection', 'eeg', 'eeg'] | ['medical', 'methodology', 'time-series'] | [ 2.47575119e-01 2.74384081e-01 3.01221013e-01 -1.33051127e-01
-6.95432663e-01 -5.65743089e-01 1.24148831e-01 2.32181743e-01
-5.51807344e-01 8.28894734e-01 7.83501752e-03 -5.81874251e-02
-5.30521274e-01 -6.15954638e-01 -7.18828857e-01 -7.24451184e-01
-6.62355840e-01 3.31201196e-01 7.73856640e-02 -4.39152569... | [13.260712623596191, 3.5510711669921875] |
7bb22605-63e8-44be-be3d-c7aca5a41de8 | benchmarking-and-analyzing-3d-aware-image | 2306.12423 | null | https://arxiv.org/abs/2306.12423v1 | https://arxiv.org/pdf/2306.12423v1.pdf | Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase | Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final performance in terms of generality. Following the most popular and effective paradigm in this field, which incorporates a neural radiance fi... | ['Yujun Shen', 'Sida Peng', 'Yinghao Xu', 'Kecheng Zheng', 'Zifan Shi', 'Qiuyu Wang'] | 2023-06-21 | null | null | null | null | ['3d-aware-image-synthesis', 'benchmarking', 'benchmarking'] | ['computer-vision', 'miscellaneous', 'robots'] | [ 5.38871288e-02 -1.03286557e-01 -5.81287546e-03 -2.07700267e-01
-6.01052701e-01 -9.66290534e-01 8.26267302e-01 -4.69750434e-01
1.32260621e-01 3.11242491e-01 8.16270784e-02 -3.42723429e-01
1.14144370e-01 -9.65281844e-01 -8.50229979e-01 -6.91967487e-01
1.29213408e-01 -1.70238629e-01 1.42004073e-01 -3.93215239... | [11.422721862792969, -0.5390486717224121] |
4cafd361-217c-4cd4-adcd-cd28eb31d606 | mnhn-tree-tools-a-toolbox-for-tree-inference | null | null | https://academic.oup.com/bioinformatics/article/37/21/3947/6294927 | https://hal.science/hal-03451406/document | MNHN-Tree-Tools: a toolbox for tree inference using multi-scale clustering of a set of sequences | Abstract
Summary
Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high thr... | ['Julien Mozziconacci', 'Christophe Escudé', 'Loic Ponger', 'Thomas Haschka'] | 2021-06-08 | null | null | null | bioinformatics-2021-6 | ['multiple-sequence-alignment'] | ['medical'] | [ 3.03297997e-01 -5.50897717e-01 1.04704373e-01 -1.52460977e-01
-5.26630819e-01 -9.36232150e-01 2.26491585e-01 3.56515288e-01
-1.48992360e-01 8.05685103e-01 -7.60796294e-02 -6.87422931e-01
-1.65028244e-01 -7.39962876e-01 -3.48251700e-01 -1.00622892e+00
-1.71331942e-01 9.37050462e-01 5.12281954e-01 -4.45930921... | [4.896475791931152, 5.169152736663818] |
03ab8e3b-8bf9-4322-9bc8-30d4eebc23e1 | davd-net-deep-audio-aided-video-decompression | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_DAVD-Net_Deep_Audio-Aided_Video_Decompression_of_Talking_Heads_CVPR_2020_paper.pdf | DAVD-Net: Deep Audio-Aided Video Decompression of Talking Heads | Close-up talking heads are among the most common and salient object in video contents, such as face-to-face conversations in social media, teleconferences, news broadcasting, talk shows, etc. Due to the high sensitivity of human visual system to faces, compression distortions in talking heads videos are highly visible ... | [' Chengjie Tu', ' Xianye Ben', ' Xinliang Zhai', ' Xiaolin Wu', 'Xi Zhang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['video-reconstruction'] | ['computer-vision'] | [ 9.87712741e-02 3.36214036e-01 -3.76764610e-02 -4.33908910e-01
-4.20661211e-01 2.87997127e-02 1.50003418e-01 -8.24860454e-01
1.67335734e-01 3.00791591e-01 9.25737083e-01 1.66038871e-01
-8.64907354e-03 -5.97777188e-01 -7.62927532e-01 -6.31572962e-01
-3.83258052e-02 -1.72867030e-01 -1.77295819e-01 -2.08031356... | [13.195124626159668, -0.37861186265945435] |
fd9f31b9-cbf3-4d9e-9bc5-93ff4aaba3a6 | facial-action-unit-detection-using-attention | 1808.03457 | null | https://arxiv.org/abs/1808.03457v3 | https://arxiv.org/pdf/1808.03457v3.pdf | Facial Action Unit Detection Using Attention and Relation Learning | Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefi... | ['Zhilei Liu', 'Yunsheng Wu', 'Jianfei Cai', 'Zhiwen Shao', 'Lizhuang Ma'] | 2018-08-10 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.14234644e-01 -2.87573840e-02 -2.57705748e-01 8.58431496e-03
-6.91806853e-01 -6.58691674e-02 2.62941003e-01 -1.42869234e-01
-3.03468972e-01 2.25637525e-01 1.09570764e-01 3.44945699e-01
1.63505256e-01 -8.10035229e-01 -5.62289357e-01 -8.89555216e-01
4.48792912e-02 8.51604491e-02 3.62748414e-01 -3.09095681... | [13.633684158325195, 1.5248483419418335] |
28c9a16b-d5a6-4479-bfbc-7cf115a1172d | image-classification-on-small-datasets-via | 2202.11616 | null | https://arxiv.org/abs/2202.11616v2 | https://arxiv.org/pdf/2202.11616v2.pdf | ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing | Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called Chim... | ['Bodo Rosenhahn', 'Frederik Schubert', 'Christoph Reinders'] | 2022-02-23 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.48170072e-01 2.62304582e-02 -2.27827579e-01 -5.68953097e-01
-3.13784093e-01 -3.97443563e-01 6.89871967e-01 -9.34688449e-02
-6.27240837e-01 1.00843894e+00 -1.87098190e-01 -2.45129317e-01
1.24605335e-01 -9.83482540e-01 -1.01439226e+00 -6.11430287e-01
1.92739889e-01 5.76012492e-01 -8.98000225e-02 -2.61663925... | [9.391705513000488, 2.3600094318389893] |
36bed7c7-1a66-4717-99ae-35016ec03838 | quantifying-privacy-risks-of-masked-language | 2203.03929 | null | https://arxiv.org/abs/2203.03929v2 | https://arxiv.org/pdf/2203.03929v2.pdf | Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks | The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities -- to what extent do MLMs leak information about their training data? Prior attempts at measuring leakage of MLMs via members... | ['Reza Shokri', 'Taylor Berg-Kirkpatrick', 'Archit Uniyal', 'Kartik Goyal', 'FatemehSadat Mireshghallah'] | 2022-03-08 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 4.65488255e-01 4.71047014e-01 -2.07106516e-01 -4.71252084e-01
-1.19989789e+00 -1.00732517e+00 5.33227742e-01 5.54262817e-01
-5.12984753e-01 7.32018054e-01 1.07231021e-01 -1.24941587e+00
1.51584268e-01 -5.73674917e-01 -7.30094492e-01 -3.13434780e-01
-3.06525767e-01 -1.82259560e-01 8.66636634e-02 3.00820500... | [5.971431255340576, 7.10391092300415] |
08ac142e-0df4-4704-8dd2-45cbe735c179 | unsupervised-end-to-end-learning-for | 1711.08608 | null | http://arxiv.org/abs/1711.08608v2 | http://arxiv.org/pdf/1711.08608v2.pdf | Unsupervised End-to-end Learning for Deformable Medical Image Registration | We propose a registration algorithm for 2D CT/MRI medical images with a new
unsupervised end-to-end strategy using convolutional neural networks. The
contributions of our algorithm are threefold: (1) We transplant traditional
image registration algorithms to an end-to-end convolutional neural network
framework, while m... | ['Eric I-Chao Chang', 'Xiaoqing Guo', 'Yan Xu', 'Wen Yan', 'Yubo Fan', 'Siyuan Shan'] | 2017-11-23 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [-2.19951998e-02 2.76451558e-01 -2.05487505e-01 -7.03404725e-01
-9.56681371e-01 -2.69827157e-01 3.55925053e-01 3.35886121e-01
-6.21365368e-01 9.55487862e-02 4.23577815e-01 -1.83099538e-01
-3.96073386e-02 -5.63566208e-01 -3.28606069e-01 -7.40972996e-01
-5.67648888e-01 7.10008740e-01 -2.42109993e-03 -6.26749396... | [13.965644836425781, -2.5945050716400146] |
a3dbe36c-6814-4ed2-8377-8f0531fb5880 | distilled-pruning-using-synthetic-data-to-win | 2307.03364 | null | https://arxiv.org/abs/2307.03364v1 | https://arxiv.org/pdf/2307.03364v1.pdf | Distilled Pruning: Using Synthetic Data to Win the Lottery | This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenarios. Distilled datasets capture essential patterns from larger datasets... | ['Daniel Cummings', 'Luke McDermott'] | 2023-07-07 | null | null | null | null | ['network-pruning', 'model-compression', 'architecture-search'] | ['methodology', 'methodology', 'methodology'] | [ 2.24493355e-01 2.99250156e-01 -4.47208494e-01 -5.95216095e-01
-4.32812184e-01 -1.50632128e-01 1.77410752e-01 -6.86412454e-02
-7.61969864e-01 9.03890371e-01 1.06663350e-02 -6.36707604e-01
-6.65377378e-01 -1.00935316e+00 -7.97205806e-01 -3.58946830e-01
-9.42665637e-02 5.60638905e-01 7.47778043e-02 1.10179871... | [8.593067169189453, 3.2094011306762695] |
10b482ff-a653-491c-b673-ccaf0db15590 | 190508617 | 1905.08617 | null | https://arxiv.org/abs/1905.08617v2 | https://arxiv.org/pdf/1905.08617v2.pdf | Automatic Long-Term Deception Detection in Group Interaction Videos | Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video. In this paper, we propose a new ADD framework which captures long term deception in a group setting. We study deception ... | ['V. S. Subrahmanian', 'Bharat Singh', 'Chao Chen', 'Zhe Wu', 'Judee Burgoon', 'Norah Dunbar', 'Maksim Bolonkin', 'Chongyang Bai'] | 2019-05-15 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.10223757e-02 -4.63161767e-01 -3.81748714e-02 -2.07162216e-01
-6.87866747e-01 -7.19471335e-01 5.12808681e-01 -2.28603885e-01
-3.78702521e-01 4.60372657e-01 1.82282388e-01 -3.01777627e-02
-1.63545683e-01 -3.19828540e-01 -4.96838510e-01 -2.93947607e-01
-3.30719858e-01 -1.23788223e-01 2.06506506e-01 -3.48402709... | [13.200230598449707, 1.9875966310501099] |
b97303e6-05dd-4d6c-b8b2-464667640f7a | a-modular-framework-for-centrality-and | 2111.11623 | null | https://arxiv.org/abs/2111.11623v1 | https://arxiv.org/pdf/2111.11623v1.pdf | A Modular Framework for Centrality and Clustering in Complex Networks | The structure of many complex networks includes edge directionality and weights on top of their topology. Network analysis that can seamlessly consider combination of these properties are desirable. In this paper, we study two important such network analysis techniques, namely, centrality and clustering. An information... | ['Anwitaman Datta', 'Silivanxay Phetsouvanh', 'Frederique Oggier'] | 2021-11-23 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.61619455e-02 2.08786637e-01 -2.42256567e-01 7.92944431e-02
-7.14209303e-02 -8.99687350e-01 6.67025566e-01 5.42214751e-01
-1.54620022e-01 3.44562203e-01 1.23191968e-01 -6.77901864e-01
-8.40493083e-01 -1.08636534e+00 3.71961072e-02 -7.38580763e-01
-1.02855194e+00 7.00832844e-01 4.54496086e-01 -1.06388777... | [6.955436706542969, 5.256598472595215] |
03acd27d-1756-48e7-8b9a-7a4939b89bed | monte-carlo-graph-search-for-alphazero | 2012.11045 | null | https://arxiv.org/abs/2012.11045v1 | https://arxiv.org/pdf/2012.11045v1.pdf | Monte-Carlo Graph Search for AlphaZero | The AlphaZero algorithm has been successfully applied in a range of discrete domains, most notably board games. It utilizes a neural network, that learns a value and policy function to guide the exploration in a Monte-Carlo Tree Search. Although many search improvements have been proposed for Monte-Carlo Tree Search in... | ['Kristian Kersting', 'Patrick Korus', 'Johannes Czech'] | 2020-12-20 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 6.15563942e-03 4.02990282e-01 -4.77219522e-01 -1.92830727e-01
-5.61875284e-01 -8.02540541e-01 4.82021540e-01 1.12676788e-02
-3.22619796e-01 1.37208140e+00 6.24203905e-02 -8.59336793e-01
-6.27018154e-01 -1.04741406e+00 -5.35157621e-01 -4.37570840e-01
-5.68191767e-01 1.01306045e+00 7.27465153e-01 -4.27035779... | [3.8355906009674072, 1.5894814729690552] |
ba13c093-e3e0-4b38-b673-a1b7893a7279 | consisttl-modeling-consistency-in-transfer | 2212.04262 | null | https://arxiv.org/abs/2212.04262v1 | https://arxiv.org/pdf/2212.04262v1.pdf | ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation | Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods for NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization. In this paper, we... | ['Min Zhang', 'Lidia S. Chao', 'Derek F. Wong', 'Xuebo Liu', 'Zhaocong Li'] | 2022-12-08 | null | null | null | null | ['nmt', 'low-resource-neural-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 2.69713610e-01 3.31305623e-01 -7.06802189e-01 -5.23778200e-01
-1.29482543e+00 -6.42566264e-01 6.02455914e-01 -2.73033172e-01
-3.06530058e-01 9.07817304e-01 2.14203093e-02 -7.58284986e-01
5.37050068e-01 -6.27115130e-01 -1.44018281e+00 -4.14639622e-01
3.81533533e-01 1.15671921e+00 -1.28567219e-01 -2.36743122... | [11.642394065856934, 10.17068862915039] |
bd122165-a8c7-4a1d-99ee-5b08be0c2c23 | towards-visual-affordance-learning-a | 2203.14092 | null | https://arxiv.org/abs/2203.14092v2 | https://arxiv.org/pdf/2203.14092v2.pdf | Towards Visual Affordance Learning: A Benchmark for Affordance Segmentation and Recognition | The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision.~A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To in... | ['Zeyad Khalifa', 'Syed Afaq Ali Shah'] | 2022-03-26 | null | null | null | null | ['affordance-recognition'] | ['computer-vision'] | [-5.12098661e-04 -3.03561032e-01 -3.85843664e-01 -5.03815472e-01
-8.53636563e-02 -6.59832001e-01 4.82260287e-01 5.49005158e-02
-5.17478049e-01 2.64850140e-01 -1.73771456e-02 -3.06642771e-01
-2.20193177e-01 -6.38869941e-01 -8.79104555e-01 -5.29735208e-01
-8.96253139e-02 3.05742949e-01 2.12842152e-01 -3.68064493... | [5.1744561195373535, -0.1297093778848648] |
225a6a0d-d2c9-4474-bdbb-6df5a57104fd | efficient-framework-for-learning-code | 2009.02731 | null | https://arxiv.org/abs/2009.02731v8 | https://arxiv.org/pdf/2009.02731v8.pdf | Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations | We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two ways: (1) it can produce vector representation of code which can be appl... | ['Yijun Yu', 'Lingxiao Jiang', 'Nghi D. Q. Bui'] | 2020-09-06 | null | null | null | null | ['code-summarization', 'method-name-prediction'] | ['computer-code', 'natural-language-processing'] | [ 3.21567386e-01 1.81034371e-01 -3.85962486e-01 -4.31816489e-01
-1.34806597e+00 -8.24622512e-01 3.87933373e-01 5.65879524e-01
1.02963783e-01 1.09479778e-01 7.79643714e-01 -4.44625318e-01
2.12299794e-01 -3.29716325e-01 -6.12097502e-01 -1.82517320e-01
4.56100218e-02 3.30783188e-01 1.22567050e-01 -3.79426956... | [7.607229709625244, 7.966490745544434] |
3a805482-5f8f-4dbb-974b-4a09bd27244c | character-preserving-coherent-story | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2639_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620018.pdf | Character-Preserving Coherent Story Visualization | Story visualization aims at generating a sequence of images to narrate each sentence in a multi-sentence story. Different from video generation that focuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across differ... | ['Hong-Han Shuai', 'Huiao-Han Lu', 'Hung-Jen Chen', 'Zhi Rui Tam', 'Yun-Zhu Song'] | null | null | null | null | eccv-2020-8 | ['story-visualization'] | ['computer-vision'] | [ 3.56135041e-01 -2.62034703e-02 2.31947690e-01 -3.50123554e-01
-5.60113490e-01 -5.04844189e-01 7.85455644e-01 3.76493372e-02
2.71581441e-01 6.65367663e-01 7.43735909e-01 9.48203504e-02
1.65629104e-01 -8.10379803e-01 -7.94208527e-01 -6.51561081e-01
2.46708766e-01 -2.05910698e-01 3.35710973e-01 -1.77000776... | [11.157658576965332, 0.5632117986679077] |
754095b9-d975-45e0-8b8d-3668973e0a50 | unified-perception-efficient-video-panoptic | 2303.01991 | null | https://arxiv.org/abs/2303.01991v2 | https://arxiv.org/pdf/2303.01991v2.pdf | Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs | Depth-aware video panoptic segmentation is a promising approach to camera based scene understanding. However, the current state-of-the-art methods require costly video annotations and use a complex training pipeline compared to their image-based equivalents. In this paper, we present a new approach titled Unified Perce... | ['Gijs Dubbelman', 'Kurt Stolle'] | 2023-03-03 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 5.71449008e-03 -2.31353462e-01 -3.07184011e-01 -2.84762710e-01
-8.98319125e-01 -7.42002487e-01 4.78248060e-01 -3.24492812e-01
-6.31195188e-01 2.89715677e-01 -1.64810568e-01 -3.20656806e-01
2.19580188e-01 -5.93194962e-01 -9.36422765e-01 -4.63437319e-01
-6.70321733e-02 4.05558228e-01 1.04876542e+00 -9.67709497... | [9.060635566711426, -0.17126581072807312] |
884d73a8-3fb1-4806-9a7f-f9e7f5d446d6 | ldedit-towards-generalized-text-guided-image | 2210.02249 | null | https://arxiv.org/abs/2210.02249v1 | https://arxiv.org/pdf/2210.02249v1.pdf | LDEdit: Towards Generalized Text Guided Image Manipulation via Latent Diffusion Models | Research in vision-language models has seen rapid developments off-late, enabling natural language-based interfaces for image generation and manipulation. Many existing text guided manipulation techniques are restricted to specific classes of images, and often require fine-tuning to transfer to a different style or dom... | ['Kanchana Vaishnavi Gandikota', 'Paramanand Chandramouli'] | 2022-10-05 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 7.79407561e-01 -1.28279060e-01 -1.41455680e-01 -4.41870570e-01
-5.22235274e-01 -7.00238168e-01 1.26130843e+00 -4.86495554e-01
-3.35500330e-01 3.23648900e-01 1.17082648e-01 -1.13086283e-01
1.49926841e-01 -8.93564403e-01 -9.81738448e-01 -4.90741581e-01
7.88336813e-01 6.53669953e-01 6.33082092e-02 -3.07651460... | [11.366618156433105, -0.1959502249956131] |
df041d34-5760-43a3-841f-7e384839247d | schema-driven-information-extraction-from | 2305.14336 | null | https://arxiv.org/abs/2305.14336v1 | https://arxiv.org/pdf/2305.14336v1.pdf | Schema-Driven Information Extraction from Heterogeneous Tables | In this paper, we explore the question of whether language models (LLMs) can support cost-efficient information extraction from complex tables. We introduce schema-driven information extraction, a new task that uses LLMs to transform tabular data into structured records following a human-authored schema. To assess vari... | ['Alan Ritter', 'Dayne Freitag', 'Gabriel Stanovsky', 'Junmo Kang', 'Fan Bai'] | 2023-05-23 | null | null | null | null | ['table-extraction', 'instruction-following'] | ['miscellaneous', 'natural-language-processing'] | [ 3.51650454e-02 3.64656210e-01 -6.81779623e-01 -3.25154185e-01
-1.39390552e+00 -8.96643043e-01 5.48917294e-01 8.80518377e-01
-2.81702816e-01 7.85931408e-01 8.62933919e-02 -8.78835499e-01
1.25172148e-02 -8.70005131e-01 -1.06192148e+00 2.52528131e-01
1.72035888e-01 4.13016826e-01 2.63602704e-01 -1.07150212... | [9.618781089782715, 7.854340076446533] |
24f16ff6-6df3-4b42-8fb3-1cf8fd1a27ba | end-to-end-person-search-sequentially-trained | 2201.09604 | null | https://arxiv.org/abs/2201.09604v1 | https://arxiv.org/pdf/2201.09604v1.pdf | End-to-end Person Search Sequentially Trained on Aggregated Dataset | In video surveillance applications, person search is a challenging task consisting in detecting people and extracting features from their silhouette for re-identification (re-ID) purpose. We propose a new end-to-end model that jointly computes detection and feature extraction steps through a single deep Convolutional N... | ['Romaric Audigier', 'Jaonary Rabarisoa', 'Angelique Loesch'] | 2022-01-24 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.72225565e-01 -4.37538534e-01 -4.16303426e-02 -4.93994176e-01
-6.52223051e-01 -6.58846796e-01 9.36184168e-01 1.50913119e-01
-1.00225663e+00 4.37692791e-01 3.68443251e-01 1.84519202e-01
1.31545454e-01 -6.19181514e-01 -5.18654883e-01 -2.95613676e-01
7.20394030e-02 3.89763087e-01 2.85397053e-01 1.67510629... | [14.698363304138184, 0.8578429222106934] |
22675a2f-c526-471c-9cda-0e86167fe52b | optical-font-recognition-in-smartphone | 1810.08016 | null | http://arxiv.org/abs/1810.08016v1 | http://arxiv.org/pdf/1810.08016v1.pdf | Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection | In this paper, we consider the problem of detecting counterfeit identity
documents in images captured with smartphones. As the number of documents
contain special fonts, we study the applicability of convolutional neural
networks (CNNs) for detection of the conformance of the fonts used with the
ones, corresponding to ... | ['Alexander V. Sheshkus', 'Ekaterina S. Gushchanskaia', 'Yulia S. Chernyshova', 'Mikhail A. Aliev'] | 2018-10-18 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 1.02645680e-01 -5.10188222e-01 2.46639028e-01 -1.50581971e-01
-4.34790760e-01 -9.00524080e-01 8.34485173e-01 1.27020106e-01
-6.21179461e-01 5.11845291e-01 -3.29900920e-01 -6.33598089e-01
-9.59902536e-03 -8.32621217e-01 -7.65253901e-01 -6.59221411e-01
4.35112715e-01 4.53334332e-01 2.82538384e-02 -3.22316229... | [12.396055221557617, 1.0529228448867798] |
831b50f1-5a92-403a-adfe-220e404995ae | exploring-generative-models-for-joint | 2208.07130 | null | https://arxiv.org/abs/2208.07130v1 | https://arxiv.org/pdf/2208.07130v1.pdf | Exploring Generative Models for Joint Attribute Value Extraction from Product Titles | Attribute values of the products are an essential component in any e-commerce platform. Attribute Value Extraction (AVE) deals with extracting the attributes of a product and their values from its title or description. In this paper, we propose to tackle the AVE task using generative frameworks. We present two types of... | ['Pawan Goyal', 'Tapas Nayak', 'Kalyani Roy'] | 2022-08-15 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 4.54807699e-01 1.87581062e-01 -3.10310662e-01 -6.31135225e-01
-6.48206592e-01 -7.29292572e-01 9.03445184e-01 6.48546889e-02
-2.98406720e-01 8.73675048e-01 2.81642437e-01 -3.53509903e-01
-2.79858232e-01 -1.11426139e+00 -4.38788503e-01 -7.47380853e-01
9.66564491e-02 6.06015027e-01 -2.53936686e-02 -3.31872106... | [9.979840278625488, 6.252597332000732] |
d8dbf4d8-e2bd-496f-bc62-cbdba9d8b906 | deception-detection-with-feature-augmentation | 2305.01011 | null | https://arxiv.org/abs/2305.01011v1 | https://arxiv.org/pdf/2305.01011v1.pdf | Deception Detection with Feature-Augmentation by soft Domain Transfer | In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets. Although numerous research has been done to detect deception in all these domains, information shortage in a new event necessitates these domains to associate with each... | ['Omprakash Gnawali', 'Arjun Mukherjee', 'Sadat Shahriar'] | 2023-05-01 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.78197646e-01 -3.02604102e-02 -6.88166559e-01 -3.41989160e-01
-2.75703758e-01 -7.23870873e-01 1.00160527e+00 9.84859914e-02
-1.51506975e-01 9.72162366e-01 4.80399936e-01 -4.11065876e-01
3.82469356e-01 -8.43655527e-01 -5.77263951e-01 -1.59422100e-01
1.56969294e-01 3.31400275e-01 1.98263124e-01 -7.39682794... | [8.086013793945312, 10.202168464660645] |
90fabe5c-6406-46fd-9ed6-07e8bd40bdce | counterfactual-explanation-and-instance | 2301.08939 | null | https://arxiv.org/abs/2301.08939v1 | https://arxiv.org/pdf/2301.08939v1.pdf | Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial Networks | The image-based diagnosis is now a vital aspect of modern automation assisted diagnosis. To enable models to produce pixel-level diagnosis, pixel-level ground-truth labels are essentially required. However, since it is often not straight forward to obtain the labels in many application domains such as in medical image,... | ['Shakeeb Murtaza', 'Zeeshan Nisar', 'Tehseen Zia'] | 2023-01-21 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 9.18595433e-01 6.17708027e-01 -1.78604558e-01 -2.46044621e-01
-6.26955748e-01 -6.31663501e-01 7.61995316e-01 -9.63262767e-02
8.11060965e-02 1.14685786e+00 -1.88605115e-01 -5.34109712e-01
-8.06832407e-03 -1.03797102e+00 -9.70843971e-01 -9.25497830e-01
2.83204734e-01 3.91979247e-01 -7.55471289e-02 2.95322184... | [11.72917652130127, -0.4271060526371002] |
d4fad827-e2ff-4d53-b088-fa66f300c7ff | argumentative-link-prediction-using-residual | null | null | https://aclanthology.org/W18-5201 | https://aclanthology.org/W18-5201.pdf | Argumentative Link Prediction using Residual Networks and Multi-Objective Learning | We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our mode... | ['Andrea Galassi', 'Marco Lippi', 'Paolo Torroni'] | 2018-11-01 | null | null | null | ws-2018-11 | ['component-classification'] | ['natural-language-processing'] | [ 9.35344920e-02 1.00765073e+00 -7.45122433e-01 -2.45826855e-01
-1.79861352e-01 -5.79049408e-01 9.70833302e-01 7.67963529e-01
-5.15790701e-01 7.67337024e-01 4.95019406e-01 -1.05156565e+00
-4.10671473e-01 -9.73852277e-01 -6.57510519e-01 1.89097315e-01
-1.17223725e-01 6.85794532e-01 3.51327866e-01 -8.04355383... | [9.590736389160156, 9.611942291259766] |
a180f349-3725-4aa7-a98c-e3c3828f4726 | knowledge-based-reasoning-and-learning-under | 2306.00790 | null | https://arxiv.org/abs/2306.00790v1 | https://arxiv.org/pdf/2306.00790v1.pdf | Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork | Ad hoc teamwork refers to the problem of enabling an agent to collaborate with teammates without prior coordination. Data-driven methods represent the state of the art in ad hoc teamwork. They use a large labeled dataset of prior observations to model the behavior of other agent types and to determine the ad hoc agent'... | ['Mohan Sridharan', 'Hasra Dodampegama'] | 2023-06-01 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-6.21458627e-02 2.93119073e-01 5.98808303e-02 -3.00020814e-01
-1.17020264e-01 -7.91373074e-01 1.10004210e+00 5.72802782e-01
-2.95657933e-01 1.08355331e+00 1.07960843e-01 -1.33549020e-01
-4.44038004e-01 -6.18276060e-01 -6.19815767e-01 -3.26540679e-01
-3.13238680e-01 1.38727069e+00 3.92536134e-01 -9.57666814... | [3.8241028785705566, 1.7936419248580933] |
539cd59d-8f1b-484b-b710-d02335904eed | labelprompt-effective-prompt-based-learning | 2302.08068 | null | https://arxiv.org/abs/2302.08068v1 | https://arxiv.org/pdf/2302.08068v1.pdf | LabelPrompt: Effective Prompt-based Learning for Relation Classification | Recently, prompt-based learning has become a very popular solution in many Natural Language Processing (NLP) tasks by inserting a template into model input, which converts the task into a cloze-style one to smoothing out differences between the Pre-trained Language Model (PLM) and the current task. But in the case of r... | ['XiaoJun Wu', 'Tianyang Xu', 'ZhenHua Feng', 'Xiaoning Song', 'Wenjie Zhang'] | 2023-02-16 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [ 4.45647538e-01 4.66296822e-01 -2.86593139e-01 -5.23176253e-01
-5.03112435e-01 -3.18600684e-01 7.74878919e-01 4.11392063e-01
-6.63856804e-01 6.12185419e-01 1.23737693e-01 -2.02016369e-01
-3.88548933e-02 -7.41057754e-01 -5.32350838e-01 -5.26407421e-01
2.01343760e-01 5.59431195e-01 2.94344425e-01 -2.57751673... | [9.639092445373535, 8.716363906860352] |
f1468dab-b057-4771-9232-fce02e8b5352 | rethinking-deconvolution-for-2d-human-pose | 2111.04226 | null | https://arxiv.org/abs/2111.04226v1 | https://arxiv.org/pdf/2111.04226v1.pdf | Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing | In this study, we present a pragmatic lightweight pose estimation model. Our model can achieve real-time predictions using low-power embedded devices. This system was found to be very accurate and achieved a 94.5% accuracy of SOTA HRNet 256x192 using a computational cost of only 3.8% on COCO test dataset. Our model ado... | ['Eigo Mori', 'Masayuki Yamazaki'] | 2021-11-08 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.59008563e-01 9.61474553e-02 -2.32984945e-02 -4.52360898e-01
-6.17158771e-01 -2.26537362e-01 2.03449473e-01 -5.42167187e-01
-8.09675932e-01 4.13931191e-01 -5.55180665e-03 -5.10894835e-01
5.28089881e-01 -4.39626873e-01 -8.52508843e-01 -7.68017843e-02
-7.92476237e-02 1.16810732e-01 6.06779009e-02 -9.60570872... | [8.556902885437012, 2.8162100315093994] |
d98a8a65-e2d0-4c66-aafd-93194656faac | fmtfusing-multi-task-convolutional-neural | 2003.00406 | null | https://arxiv.org/abs/2003.00406v1 | https://arxiv.org/pdf/2003.00406v1.pdf | FMT:Fusing Multi-task Convolutional Neural Network for Person Search | Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity... | ['Xiao Wang', 'Sulan Zhai', 'Jin Tang', 'Shunqiang Liu'] | 2020-03-01 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-2.17364877e-01 -4.99150157e-01 1.73341215e-01 -3.88138235e-01
-5.07773399e-01 -5.62435985e-01 7.53383577e-01 -8.07231516e-02
-1.02252185e+00 5.98824859e-01 1.93552613e-01 4.05699879e-01
-5.29481359e-02 -7.14277506e-01 -5.46699107e-01 -4.69454944e-01
3.42556268e-01 8.53591263e-01 2.46267483e-01 1.17022045... | [14.819445610046387, 0.8215851187705994] |
e8995b43-46f5-4366-b8c8-443d2797e1fc | applications-of-machine-learning-in-fintech | null | null | https://ieeexplore.ieee.org/document/9491903 | https://ieeexplore.ieee.org/document/9491903 | Applications of Machine Learning in Fintech Credit Card Fraud Detection | Fintech utilizes innovative technology to offer
improved monetary administrations and financial solutions.
According to data from the prediction of Autonomous Research
artificial intelligence (AI) technologies will allow financial
institutions to reduce their operational costs by 22% by 2030.
Throughout this ... | ['J.', 'Saniie', 'F.', 'Lacruz'] | 2021-07-26 | null | null | null | ieee-international-conference-on-electro | ['fraud-detection'] | ['miscellaneous'] | [-7.26853371e-01 1.85919300e-01 -6.43524081e-02 -4.74191397e-01
2.02553913e-01 -2.04621136e-01 4.93262708e-01 5.47978766e-02
-5.74250221e-01 1.04421163e+00 -6.19428493e-02 -5.76809227e-01
9.00048241e-02 -1.47343063e+00 -4.69052315e-01 -2.47569144e-01
-1.24924093e-01 6.24335349e-01 -4.07914519e-01 -4.79288995... | [7.812346935272217, 5.207097053527832] |
051ae130-0564-4b23-9f44-0dac9fc5d19f | adaptiveweighted-attention-network-with | 2005.09305 | null | https://arxiv.org/abs/2005.09305v1 | https://arxiv.org/pdf/2005.09305v1.pdf | AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images | Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs). Nevertheless, most CNN-based SR algorithms neglect to explore the camera spectral sensitivity (CSS) prior and interdependencies among intermediate fea... | ['Fei Liu', 'Yunsong Li', 'Jiaojiao Li', 'Chaoxiong Wu', 'Rui Song'] | 2020-05-19 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 5.94715416e-01 -3.72868508e-01 3.08703668e-02 -3.56277436e-01
-1.06675148e+00 -3.59056503e-01 3.14155549e-01 -4.83042181e-01
-1.09833449e-01 5.27704179e-01 4.03920352e-01 -1.78142488e-01
-4.72335428e-01 -7.90130973e-01 -7.86483407e-01 -1.02941096e+00
5.40349372e-02 -3.97149056e-01 -2.02840135e-01 -2.78718799... | [10.282776832580566, -2.0004968643188477] |
932a1e27-ba57-448f-a9c8-5935bfdb00f1 | vietnamese-end-to-end-speech-recognition | null | null | https://github.com/vietai/ASR | https://github.com/vietai/ASR | Vietnamese end-to-end speech recognition using wav2vec 2.0 | Our models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. We use wav2vec2 architecture for the pre-trained model. For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CT... | ['Thai Binh Nguyen'] | 2021-09-02 | null | null | null | https-github-com-vietai-asr-2021-9 | ['handwriting-recognition'] | ['computer-vision'] | [ 1.73363686e-01 -7.09991455e-02 -1.30163521e-01 -5.30971646e-01
-1.02254295e+00 -6.10532165e-01 3.22858632e-01 -4.56902623e-01
-7.20782518e-01 5.95831811e-01 5.78897357e-01 -6.21625721e-01
2.68122196e-01 -3.44112575e-01 -5.57674468e-01 -4.04499203e-01
-4.54527810e-02 3.88851136e-01 1.72247350e-01 -2.60516822... | [14.397676467895508, 6.65601110458374] |
e5ef0347-0a42-40bd-b5ed-6a177eb9727c | effective-few-shot-named-entity-linking-by | 2207.05280 | null | https://arxiv.org/abs/2207.05280v2 | https://arxiv.org/pdf/2207.05280v2.pdf | Effective Few-Shot Named Entity Linking by Meta-Learning | Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these... | ['Jianyong Wang', 'Zhiyuan Liu', 'Wei zhang', 'Haitao Yuan', 'Ning Liu', 'Zhengyan Zhang', 'Zhenyu Li', 'Xiuxing Li'] | 2022-07-12 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 5.21799065e-02 2.40931362e-01 -4.85963792e-01 -3.08204979e-01
-9.33692098e-01 -4.14589822e-01 4.66239214e-01 2.32826263e-01
-4.36479747e-01 1.05036795e+00 3.11052017e-02 -9.36351717e-02
1.46865949e-01 -1.02746844e+00 -7.86257803e-01 -3.69240820e-01
4.24207032e-01 4.88688916e-01 4.30587411e-01 -6.04949772... | [9.473503112792969, 9.012835502624512] |
f319b986-2729-4533-800c-cb17c339cf5f | care-mi-chinese-benchmark-for-misinformation | 2307.01458 | null | https://arxiv.org/abs/2307.01458v1 | https://arxiv.org/pdf/2307.01458v1.pdf | CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care | The recent advances in NLP, have led to a new trend of applying LLMs to real-world scenarios. While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful consequences, especial... | ['Noa Garcia', 'Bowen Wang', 'Lu Wei', 'Mingbai Bai', 'Wangyue Li', 'Liangzhi Li', 'Tong Xiang'] | 2023-07-04 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 2.98589706e-01 6.51741087e-01 -1.77704450e-02 -4.36842114e-01
-1.18911529e+00 -6.89342499e-01 8.69522512e-01 4.03652310e-01
-2.86792070e-01 1.11871386e+00 5.51011801e-01 -4.03267831e-01
-7.98392296e-02 -8.82586598e-01 -7.44062841e-01 -2.24030882e-01
3.25998366e-01 5.92619240e-01 7.70857185e-02 -3.45523119... | [11.732660293579102, 8.816365242004395] |
c868aecd-b820-473e-8803-9d903ba81d1e | personalized-embedding-based-e-commerce | 2102.06156 | null | https://arxiv.org/abs/2102.06156v1 | https://arxiv.org/pdf/2102.06156v1.pdf | Personalized Embedding-based e-Commerce Recommendations at eBay | Recommender systems are an essential component of e-commerce marketplaces, helping consumers navigate massive amounts of inventory and find what they need or love. In this paper, we present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in ... | ['Sriganesh Madhvanath', 'Yuri M. Brovman', 'Tian Wang'] | 2021-02-11 | null | null | null | null | ['data-ablation'] | ['computer-vision'] | [-2.07600638e-01 1.19386479e-01 -1.96015149e-01 -5.28210878e-01
-5.31160057e-01 -5.81700444e-01 3.85366887e-01 7.98219889e-02
-2.74207711e-01 4.37281132e-01 3.19513410e-01 -2.57055879e-01
-1.97758958e-01 -1.07704735e+00 -6.88394666e-01 -1.60844967e-01
-3.91594544e-02 7.73707926e-01 -4.32671010e-02 -6.93107069... | [10.050504684448242, 5.80099630355835] |
26212bc1-6718-474d-b474-242c785a6563 | fixed-point-quantization-aware-training-for | 2303.02284 | null | https://arxiv.org/abs/2303.02284v1 | https://arxiv.org/pdf/2303.02284v1.pdf | Fixed-point quantization aware training for on-device keyword-spotting | Fixed-point (FXP) inference has proven suitable for embedded devices with limited computational resources, and yet model training is continually performed in floating-point (FLP). FXP training has not been fully explored and the non-trivial conversion from FLP to FXP presents unavoidable performance drop. We propose a ... | ['Yuzong Liu', 'Sree Hari Krishnan Parthasarathi', 'Santosh Kumar Cheekatmalla', 'Robbie Armitano', 'Francesco Caliva', 'Alex Escott', 'Om Oza', 'Sashank Macha'] | 2023-03-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.18138003e-01 -2.74410695e-01 -5.14091074e-01 -5.79701483e-01
-1.30876839e+00 -4.70868975e-01 2.18733877e-01 1.78928435e-01
-5.68346322e-01 7.76736796e-01 -4.16555524e-01 -1.11160278e+00
-1.16549414e-02 -5.73237896e-01 -1.04807436e+00 -4.24828142e-01
-5.62594458e-02 8.13285038e-02 3.74106139e-01 9.48280767... | [8.60448932647705, 3.194683074951172] |
c8020946-8754-4af4-a9bd-a75373e6d690 | on-evaluation-of-document-classification | 2306.12550 | null | https://arxiv.org/abs/2306.12550v1 | https://arxiv.org/pdf/2306.12550v1.pdf | On Evaluation of Document Classification using RVL-CDIP | The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% p... | ['Kevin Leach', 'Gordon Lim', 'Stefan Larson'] | 2023-06-21 | null | null | null | null | ['classification-1', 'benchmarking', 'document-classification', 'benchmarking'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'robots'] | [ 2.55030662e-01 -2.08812267e-01 -3.73379111e-01 -4.89867270e-01
-1.22756422e+00 -1.07289255e+00 8.53465080e-01 7.34392822e-01
-4.57376570e-01 7.92989612e-01 1.45671219e-01 -5.65696180e-01
-1.87060669e-01 -4.79969174e-01 -2.92723864e-01 -5.17409205e-01
2.37222224e-01 4.36441749e-01 2.87842713e-02 2.48030096... | [9.097908020019531, 4.5942301750183105] |
b8898cff-a0cf-4865-8fbc-4218f9a22ecc | adaptive-modeling-against-adversarial-attacks | 2112.12431 | null | https://arxiv.org/abs/2112.12431v1 | https://arxiv.org/pdf/2112.12431v1.pdf | Adaptive Modeling Against Adversarial Attacks | Adversarial training, the process of training a deep learning model with adversarial data, is one of the most successful adversarial defense methods for deep learning models. We have found that the robustness to white-box attack of an adversarially trained model can be further improved if we fine tune this model in inf... | ['Teck Khim Ng', 'Zhiwen Yan'] | 2021-12-23 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 2.68952429e-01 3.36804211e-01 2.51249015e-01 -4.07737672e-01
-6.52447820e-01 -1.19952703e+00 6.49259388e-01 -4.72521722e-01
-4.88342494e-01 7.93327630e-01 -1.48134023e-01 -7.03609586e-01
3.66377175e-01 -1.00552452e+00 -1.05754447e+00 -6.76941395e-01
-1.19067118e-01 3.50031227e-01 3.29349637e-01 -2.72324741... | [5.663578033447266, 7.839147567749023] |
0c674e74-a1a0-45a1-8dfb-c859aaf2349d | fast-and-private-submodular-and-k-submodular | 2006.15744 | null | https://arxiv.org/abs/2006.15744v1 | https://arxiv.org/pdf/2006.15744v1.pdf | Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints | The problem of maximizing nonnegative monotone submodular functions under a certain constraint has been intensively studied in the last decade, and a wide range of efficient approximation algorithms have been developed for this problem. Many machine learning problems, including data summarization and influence maximiza... | ['Yuichi Yoshida', 'Akbar Rafiey'] | 2020-06-28 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3576-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3576-Paper.pdf | icml-2020-1 | ['data-summarization'] | ['miscellaneous'] | [ 2.78650016e-01 5.45779765e-01 -3.20328534e-01 -6.51114881e-01
-7.82736421e-01 -6.75615251e-01 -2.44918451e-01 3.01677316e-01
-4.71038043e-01 9.62102532e-01 7.04290345e-02 8.86036977e-02
-4.91319865e-01 -1.09764874e+00 -8.60282004e-01 -9.07252252e-01
-7.91312233e-02 5.18286526e-01 -1.05462655e-01 -1.80418417... | [6.562194347381592, 4.946418285369873] |
9aef55d6-f64f-410f-b305-588d8535edfa | effective-early-stopping-of-point-cloud | 2209.15308 | null | https://arxiv.org/abs/2209.15308v1 | https://arxiv.org/pdf/2209.15308v1.pdf | Effective Early Stopping of Point Cloud Neural Networks | Early stopping techniques can be utilized to decrease the time cost, however currently the ultimate goal of early stopping techniques is closely related to the accuracy upgrade or the ability of the neural network to generalize better on unseen data without being large or complex in structure and not directly with its ... | ['Anna Puig', 'Maria Salamó', 'Thanasis Zoumpekas'] | 2022-09-30 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 8.31216499e-02 1.02469698e-01 9.95447338e-02 -3.16129476e-01
-1.35871023e-01 -3.59794974e-01 2.30929360e-01 4.05874878e-01
-1.02151942e+00 4.55847472e-01 -8.66226077e-01 -4.56264645e-01
-4.85897750e-01 -7.27011621e-01 -7.21372306e-01 -6.77516341e-01
-3.00846905e-01 8.50059271e-01 3.08562517e-01 6.40844628... | [7.975240707397461, -3.3508074283599854] |
9908b552-f504-40ff-8e8d-a8a82a538506 | agent-based-simulators-for-covid-19 | 2209.02887 | null | https://arxiv.org/abs/2209.02887v2 | https://arxiv.org/pdf/2209.02887v2.pdf | Agent based simulators for epidemic modelling: Simulating larger models using smaller ones | Agent-based simulators (ABS) are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an epidemic in a city (or a region). They provide the flexibility to accurately model a heterogeneous population with time and location varying, person-specific interacti... | ['Shubhada Agrawal', 'Sandeep Juneja', 'Daksh Mittal'] | 2022-09-07 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.82601679e-02 1.40868038e-01 7.16087520e-02 3.96266669e-01
-6.14969656e-02 -5.36505580e-01 8.58256042e-01 5.52060068e-01
-4.44569528e-01 1.12509477e+00 -1.76525593e-01 -6.01355135e-01
-4.60596710e-01 -1.14990473e+00 -4.99420106e-01 -1.08121300e+00
-7.61146605e-01 1.21443164e+00 5.16468883e-01 -3.40985328... | [5.975902080535889, 4.392340183258057] |
ed669143-5c35-4379-bb0a-fb019abf27d5 | self-supervised-relation-alignment-for-scene | 2302.01403 | null | https://arxiv.org/abs/2302.01403v1 | https://arxiv.org/pdf/2302.01403v1.pdf | Self-Supervised Relation Alignment for Scene Graph Generation | The goal of scene graph generation is to predict a graph from an input image, where nodes correspond to identified and localized objects and edges to their corresponding interaction predicates. Existing methods are trained in a fully supervised manner and focus on message passing mechanisms, loss functions, and/or bias... | ['Leonid Sigal', 'Renjie Liao', 'Bicheng Xu'] | 2023-02-02 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 9.57400262e-01 8.43632221e-01 -1.70675009e-01 -5.64864039e-01
-5.19524992e-01 -2.78237939e-01 9.42732990e-01 4.55074668e-01
-2.42203921e-02 3.84038329e-01 2.30230272e-01 -1.44669384e-01
-1.00786670e-03 -1.07585418e+00 -1.05511057e+00 -5.88905215e-01
-3.98851968e-02 6.19276643e-01 5.21125734e-01 -1.76590323... | [10.37927532196045, 1.5708391666412354] |
7baf59b3-7597-471e-aea6-12ce49be6fd1 | veram-view-enhanced-recurrent-attention-model | 1808.06698 | null | http://arxiv.org/abs/1808.06698v1 | http://arxiv.org/pdf/1808.06698v1.pdf | VERAM: View-Enhanced Recurrent Attention Model for 3D Shape Classification | Multi-view deep neural network is perhaps the most successful approach in 3D
shape classification. However, the fusion of multi-view features based on max
or average pooling lacks a view selection mechanism, limiting its application
in, e.g., multi-view active object recognition by a robot. This paper presents
VERAM, a... | ['Zhixin Sun', 'Lintao Zheng', 'Yan Zhang', 'Songle Chen', 'Kai Xu'] | 2018-08-20 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-5.98839819e-02 1.95603251e-01 -6.95435703e-02 -3.90538037e-01
-5.91637373e-01 -3.75073552e-01 4.84632879e-01 -2.66705573e-01
-2.21750885e-01 4.44888026e-01 -5.94919696e-02 1.49805427e-01
-9.36969072e-02 -7.05209613e-01 -7.83483505e-01 -8.43867779e-01
1.69889033e-01 5.26303351e-01 1.25052467e-01 8.84529129... | [8.206077575683594, -3.5799734592437744] |
3a655929-b0ef-4642-91c1-91e2ea762bb4 | incorporating-features-learned-by-an-enhanced | 1806.03256 | null | http://arxiv.org/abs/1806.03256v1 | http://arxiv.org/pdf/1806.03256v1.pdf | Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction | The 2017 ASSISTments Data Mining competition aims to use data from a
longitudinal study for predicting a brand-new outcome of students which had
never been studied before by the educational data mining research community.
Specifically, it facilitates research in developing predictive models that
predict whether the fir... | ['Dit-yan Yeung', 'Chun-kit Yeung', 'Zizheng Lin', 'Kai Yang'] | 2018-06-06 | null | null | null | null | ['job-prediction'] | ['natural-language-processing'] | [-2.32349426e-01 5.72050549e-03 -7.31709659e-01 -3.14480543e-01
-3.05429816e-01 -3.60121936e-01 4.76414084e-01 9.21740115e-01
-2.94866413e-01 4.34496552e-01 2.04824701e-01 -7.39004254e-01
-7.97592521e-01 -1.24865162e+00 -6.75752521e-01 -2.02835321e-01
3.22491407e-01 1.77440181e-01 1.38526544e-01 -4.57602084... | [10.129183769226074, 7.175673484802246] |
5c22ca9a-8607-40ac-a89b-4373077739c7 | a-tutorial-on-thompson-sampling | 1707.02038 | null | https://arxiv.org/abs/1707.02038v3 | https://arxiv.org/pdf/1707.02038v3.pdf | A Tutorial on Thompson Sampling | Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of p... | ['Daniel Russo', 'Benjamin Van Roy', 'Zheng Wen', 'Ian Osband', 'Abbas Kazerouni'] | 2017-07-07 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 5.05077541e-01 3.80603462e-01 -1.16539788e+00 -4.34290886e-01
-6.74770892e-01 -5.59783041e-01 4.88857776e-01 2.99519956e-01
-6.92647278e-01 1.30212855e+00 1.46896824e-01 -6.34397388e-01
-7.27169335e-01 -9.15989995e-01 -6.09956801e-01 -7.82305479e-01
-3.72787625e-01 9.57644343e-01 -3.62804867e-02 1.36230245... | [4.4627461433410645, 3.155402421951294] |
dde180e4-c268-44ec-9a45-fe5240f3fe9f | symmetric-nonnegative-matrix-factorization | null | null | https://epubs.siam.org/doi/abs/10.1137/1.9781611972825.10?mobileUi=0 | https://www.cc.gatech.edu/~hpark/papers/DaDingParkSDM12.pdf | Symmetric Nonnegative Matrix Factorization for Graph Clustering | Nonnegative matrix factorization (NMF) provides a lower rank approximation of a nonnegative matrix, and has been successfully used as a clustering method. In this paper, we offer some conceptual understanding for the capabilities and shortcomings of NMF as a clustering method. Then, we propose Symmetric NMF (SymNMF) as... | ['Da Kuang', 'Chris Ding', 'Haesun Park'] | 2012-02-27 | null | null | null | sdm-2012-2 | ['spectral-graph-clustering'] | ['graphs'] | [-9.13493186e-02 -1.86601117e-01 -2.02230990e-01 -1.94979347e-02
-8.92406181e-02 -7.03549683e-01 3.87487650e-01 -1.28901824e-01
-5.15942723e-02 8.31485316e-02 2.20012411e-01 -3.77859741e-01
-5.71151674e-01 -5.51413298e-01 -3.07066202e-01 -8.84779513e-01
-3.47675472e-01 4.85496789e-01 -2.90149599e-01 -2.45555669... | [7.369706153869629, 4.865300178527832] |
d425ded3-9f7d-47d2-b3f0-c10a06996091 | tree-decomposed-graph-neural-network | 2108.11022 | null | https://arxiv.org/abs/2108.11022v1 | https://arxiv.org/pdf/2108.11022v1.pdf | Tree Decomposed Graph Neural Network | Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neighborhood information. Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through a... | ['Tyler Derr', 'Yu Wang'] | 2021-08-25 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-5.81429414e-02 3.97462770e-02 -2.46055856e-01 -2.21945196e-01
2.11157516e-01 -4.12248224e-01 5.54856002e-01 3.79327387e-01
-1.31510273e-01 3.85859698e-01 3.82806093e-01 -3.33589673e-01
-5.32854736e-01 -1.20228827e+00 -5.88673949e-01 -8.34020197e-01
-4.55733269e-01 -6.98772818e-02 2.88156092e-01 -3.44428211... | [7.04511022567749, 6.18927526473999] |
17560032-dcfc-4f63-b2bd-10ea7d9cdc5e | benchmark-performance-of-machine-and-deep | 2003.01345 | null | https://arxiv.org/abs/2003.01345v1 | https://arxiv.org/pdf/2003.01345v1.pdf | Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification | In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it pro-vides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning based Urdu text docume... | ['Muhammad Usman Ghani', 'Muhammad Ali Ibrahim', 'Waqar Mahmood', 'Sheraz Ahmad', 'Muhammad Nabeel Asim', 'Andreas Dengel'] | 2020-03-03 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-2.20737115e-01 -2.91516840e-01 -2.19404012e-01 -2.60882884e-01
-9.56938386e-01 -5.97964823e-01 8.33902597e-01 5.62918901e-01
-4.31716174e-01 1.10016680e+00 2.63375580e-01 -5.49387932e-01
-8.11522976e-02 -1.06396592e+00 -3.90315443e-01 -3.82746637e-01
-8.17023143e-02 2.70717144e-01 -1.36645377e-01 -4.26221788... | [10.21345329284668, 8.669140815734863] |
043d75aa-48c8-4943-b3a4-49a34fab70b6 | pishgu-universal-path-prediction-architecture | 2210.08057 | null | https://arxiv.org/abs/2210.08057v3 | https://arxiv.org/pdf/2210.08057v3.pdf | Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems | Path prediction is an essential task for many real-world Cyber-Physical Systems (CPS) applications, from autonomous driving and traffic monitoring/management to pedestrian/worker safety. These real-world CPS applications need a robust, lightweight path prediction that can provide a universal network architecture for mu... | ['Hamed Tabkhi', 'Christopher Neff', 'Armin Danesh Pazho', 'Vinit Katariya', 'Ghazal Alinezhad Noghre'] | 2022-10-14 | null | null | null | null | ['trajectory-forecasting'] | ['computer-vision'] | [-3.35249841e-01 -7.73958340e-02 -2.65720248e-01 -2.95197487e-01
-3.85829881e-02 -5.82995474e-01 3.65566581e-01 3.45429480e-02
-7.83639178e-02 3.81883621e-01 -9.56322905e-03 -7.36088693e-01
-1.62777156e-01 -1.02612376e+00 -7.57458508e-01 -3.59962195e-01
-1.95108116e-01 3.00209314e-01 1.04889357e+00 -5.84452093... | [6.097592830657959, 0.8030121922492981] |
d4582b76-4b98-4cce-ae42-a23a1fb09d79 | clusterfug-clustering-fully-connected-graphs | 2301.12159 | null | https://arxiv.org/abs/2301.12159v2 | https://arxiv.org/pdf/2301.12159v2.pdf | ClusterFuG: Clustering Fully connected Graphs by Multicut | We propose a graph clustering formulation based on multicut (a.k.a. weighted correlation clustering) on the complete graph. Our formulation does not need specification of the graph topology as in the original sparse formulation of multicut, making our approach simpler and potentially better performing. In contrast to u... | ['Paul Swoboda', 'Ahmed Abbas'] | 2023-01-28 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.29375653e-02 3.62685680e-01 -1.18265308e-01 -1.80014610e-01
-5.64099073e-01 -8.04475427e-01 5.36429882e-01 3.53445470e-01
-3.38408947e-01 3.96363020e-01 5.20852618e-02 -1.57562107e-01
-7.55577087e-01 -9.55228388e-01 -4.28453326e-01 -8.74514043e-01
-5.56012034e-01 8.64188612e-01 4.14671928e-01 2.33098432... | [7.035454750061035, 5.199778079986572] |
609a93fb-df99-4e29-a30e-3d1ba7310ea2 | accurate-protein-structure-prediction-by | 1911.05531 | null | https://arxiv.org/abs/1911.05531v1 | https://arxiv.org/pdf/1911.05531v1.pdf | Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations | Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino... | ["Itsik Pe'er", 'Weilong Fu', 'Linyong Nan', 'Yuan Gao', 'Mohammed AlQuraishi', 'Jinhao Lei', 'Fan Wu', 'Yueqi Wang', 'Weiyi Lu', 'Sashank Karri', 'Iddo Drori', 'Dimitri Leggas', 'Darshan Thaker', 'Chen Keasar', 'Anand Kannan', 'Daniel Jeong', 'Arjun Srivatsa', 'Antonio Moretti'] | 2019-11-09 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 1.69647902e-01 -3.19464691e-02 -3.00369322e-01 -3.41628075e-01
-4.39004630e-01 -7.06075013e-01 3.07327151e-01 6.89651489e-01
-6.02062583e-01 1.25255203e+00 3.67826521e-01 -5.20006061e-01
1.69568714e-02 -3.70868981e-01 -1.01243293e+00 -1.09806812e+00
-3.56563896e-01 7.60718882e-01 1.94228515e-01 -3.95330250... | [4.780913829803467, 5.572076797485352] |
be1f2b95-7539-4c5d-951d-cc04b602246b | temporalmaxer-maximize-temporal-context-with | 2303.09055 | null | https://arxiv.org/abs/2303.09055v1 | https://arxiv.org/pdf/2303.09055v1.pdf | TemporalMaxer: Maximize Temporal Context with only Max Pooling for Temporal Action Localization | Temporal Action Localization (TAL) is a challenging task in video understanding that aims to identify and localize actions within a video sequence. Recent studies have emphasized the importance of applying long-term temporal context modeling (TCM) blocks to the extracted video clip features such as employing complex se... | ['Kwanghoon Sohn', 'Kwonyoung Kim', 'Tuan N. Tang'] | 2023-03-16 | null | null | null | null | ['video-understanding', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.68950126e-01 -2.55932063e-01 -3.82218540e-01 -2.02277333e-01
-7.37786055e-01 -3.90107363e-01 5.35621285e-01 -9.84366164e-02
-6.50879085e-01 3.81402671e-01 4.69098896e-01 5.28285727e-02
3.66992541e-02 -1.66711822e-01 -7.56531060e-01 -6.45463169e-01
-3.56757969e-01 -4.15310353e-01 4.68824506e-01 -2.90926360... | [8.750630378723145, 0.5473912358283997] |
d2848e18-76f9-4a19-b4df-8d329a1f63a2 | probabilistically-robust-optimization-of-irs | 2201.13151 | null | https://arxiv.org/abs/2201.13151v1 | https://arxiv.org/pdf/2201.13151v1.pdf | Probabilistically Robust Optimization of IRS-aided SWIPT Under Coordinated Spectrum Underlay | This study considers the Joint Transmit/Reflect Beamforming and Power Splitting (JTRBPS) optimization problem in a spectrum underlay setting, such that the transmit sum-energy of the intelligent reflecting surface (IRS)-aided secondary transmitter (ST) is minimized subject to the quality-of-service requirements of the ... | ['Ioannis Krikidis', 'Konstantinos Ntougias'] | 2022-01-31 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 7.01817691e-01 6.04465365e-01 -1.65523574e-01 2.12014616e-02
-8.72320890e-01 -3.98387730e-01 4.40398194e-02 -7.26855755e-01
-2.62420122e-02 8.44809771e-01 3.01091671e-01 -4.84124362e-01
-7.89020836e-01 -4.60193694e-01 -3.64347488e-01 -1.50531614e+00
-4.93949860e-01 -1.78849533e-01 -8.78862441e-01 -2.50617355... | [6.134361267089844, 1.4507794380187988] |
eb937ba6-f7dc-44c4-aa52-ff1f64776ce7 | statistical-component-separation-for-targeted | 2306.15012 | null | https://arxiv.org/abs/2306.15012v1 | https://arxiv.org/pdf/2306.15012v1.pdf | Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures | Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal. In this work, we tackle simpler "statistical component separation" problems that focus on recovering a predefined set of statistical descriptors of a target signal from ... | ['Michael Eickenberg', 'Bruno Régaldo-Saint Blancard'] | 2023-06-26 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 4.26694363e-01 -2.77409494e-01 5.46069264e-01 -9.34648588e-02
-9.40697849e-01 -3.79187554e-01 9.61702824e-01 1.56612277e-01
-6.28950894e-01 5.50432384e-01 1.14136701e-02 1.82694465e-01
-5.62440455e-01 -5.76519191e-01 -4.05439198e-01 -1.48830163e+00
-5.86502180e-02 3.91004086e-01 1.24683708e-01 -3.14519018... | [11.602570533752441, -2.4107418060302734] |
4b4b85e8-3154-473d-8f3d-5859faa753f8 | explainable-predictive-process-monitoring | 2008.01807 | null | https://arxiv.org/abs/2008.01807v2 | https://arxiv.org/pdf/2008.01807v2.pdf | Explainable Predictive Process Monitoring | Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is repo... | ['Nicolò Navarin', 'Bernat Coma-Puig', 'Josep Carmona', 'Riccardo Galanti', 'Massimiliano de Leoni'] | 2020-08-04 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.89279193e-01 7.36868382e-01 -5.88543154e-02 -1.81858882e-01
1.70083016e-01 -3.68312448e-01 6.03641748e-01 7.96527207e-01
-1.09251449e-02 5.40385842e-01 6.43073246e-02 -7.14595914e-01
-8.52526724e-01 -8.85946691e-01 -7.92488530e-02 -4.35616881e-01
-3.19647342e-01 8.25356781e-01 2.49731198e-01 -2.74388224... | [8.619668006896973, 5.970236301422119] |
faee39eb-e6c3-48d0-a5ed-cc406933e840 | unified-multi-intent-order-and-slot | null | null | https://aclanthology.org/2020.icon-workshop.2 | https://aclanthology.org/2020.icon-workshop.2.pdf | Unified Multi Intent Order and Slot Prediction using Selective Learning Propagation | Natural Language Understanding (NLU) involves two important task namely Intent Determination(ID) and Slot Filling (SF). With recent advancements in Intent Determination and Slot Filling tasks, explorations on handling of multiple intent information in a single utterance is increasing to make the NLU more conversation-b... | ['Divya Verma Gogoi', 'Kritika Yadav', 'Priyank Chhipa', 'Bharatram Natarajan'] | null | null | null | null | icon-2020-12 | ['slot-filling'] | ['natural-language-processing'] | [ 3.94197941e-01 4.23671752e-01 -5.03351510e-01 -4.48576987e-01
-8.76008213e-01 -2.66224980e-01 5.28460503e-01 2.99115956e-01
-6.33090734e-01 9.64882672e-01 7.28759944e-01 -5.62522888e-01
4.22948115e-02 -4.78128046e-01 -2.98141748e-01 -4.98070419e-02
2.02967197e-01 1.06703377e+00 2.52065420e-01 -6.29578471... | [12.566165924072266, 7.375655651092529] |
3280ca67-0220-4d54-a05d-94a04aa4ad17 | loopy-a-research-friendly-mix-framework-for | 2305.01051 | null | https://arxiv.org/abs/2305.01051v1 | https://arxiv.org/pdf/2305.01051v1.pdf | LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music | Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years. However, music genres like electronic dance music (EDM) has always been relatively less investigated compared to others. Considering its wide range of applications, we present a Python pack... | ['Xinyu Li'] | 2023-05-01 | null | null | null | null | ['audio-generation', 'music-generation', 'music-generation', 'music-information-retrieval'] | ['audio', 'audio', 'music', 'music'] | [ 2.18589325e-02 -1.75588623e-01 8.08369592e-02 7.18598142e-02
-1.17973042e+00 -9.13793683e-01 5.87139487e-01 -1.95363730e-01
-3.82378772e-02 4.46115166e-01 3.21987569e-01 1.33828819e-01
-3.39048833e-01 -6.08753026e-01 -5.11094928e-01 -5.60647011e-01
6.90798312e-02 5.78376174e-01 1.11295946e-01 -3.63549262... | [15.933167457580566, 5.449570178985596] |
ada25be3-7780-4aab-960e-673008c1c7a4 | multi-compartment-neuron-and-population | 2301.07275 | null | https://arxiv.org/abs/2301.07275v1 | https://arxiv.org/pdf/2301.07275v1.pdf | Multi-compartment Neuron and Population Encoding improved Spiking Neural Network for Deep Distributional Reinforcement Learning | Inspired by the information processing with binary spikes in the brain, the spiking neural networks (SNNs) exhibit significant low energy consumption and are more suitable for incorporating multi-scale biological characteristics. Spiking Neurons, as the basic information processing unit of SNNs, are often simplified in... | ['Zhuoya Zhao', 'Feifei Zhao', 'Yi Zeng', 'Yinqian Sun'] | 2023-01-18 | null | null | null | null | ['distributional-reinforcement-learning', 'atari-games'] | ['methodology', 'playing-games'] | [ 1.71765629e-02 -3.83690506e-01 3.01859289e-01 2.22102165e-01
1.05191506e-01 -3.43666255e-01 4.96143669e-01 -1.72452524e-03
-7.22070873e-01 1.10293555e+00 -3.38769443e-02 1.71850920e-02
-9.40457880e-02 -1.16797340e+00 -6.37179315e-01 -1.39914989e+00
1.88237727e-01 2.63664544e-01 4.89897996e-01 -6.02146804... | [8.178202629089355, 2.507765769958496] |
413df58e-3d3c-4f67-b059-f17c0e01a51e | non-local-color-image-denoising-with | 1611.06757 | null | http://arxiv.org/abs/1611.06757v2 | http://arxiv.org/pdf/1611.06757v2.pdf | Non-Local Color Image Denoising with Convolutional Neural Networks | We propose a novel deep network architecture for grayscale and color image
denoising that is based on a non-local image model. Our motivation for the
overall design of the proposed network stems from variational methods that
exploit the inherent non-local self-similarity property of natural images. We
build on this con... | ['Stamatios Lefkimmiatis'] | 2016-11-21 | non-local-color-image-denoising-with-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Lefkimmiatis_Non-Local_Color_Image_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lefkimmiatis_Non-Local_Color_Image_CVPR_2017_paper.pdf | cvpr-2017-7 | ['color-image-denoising'] | ['computer-vision'] | [-5.93549525e-03 -2.38123655e-01 3.37635487e-01 -4.61188734e-01
-7.14650333e-01 -3.32963675e-01 5.23125112e-01 1.67853191e-01
-7.17346907e-01 4.96960521e-01 -1.65578157e-01 -1.37339300e-02
-9.24111307e-02 -9.95557547e-01 -7.55293906e-01 -9.65007722e-01
-2.61982143e-01 -3.92977335e-02 4.04977202e-01 -3.00673723... | [11.52446460723877, -2.2931137084960938] |
0a14f754-0065-4f48-91de-8a3e9a7a0693 | rethinking-generalization-in-few-shot-1 | 2206.07267 | null | https://arxiv.org/abs/2206.07267v3 | https://arxiv.org/pdf/2206.07267v3.pdf | Rethinking Generalization in Few-Shot Classification | Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted. While this might be acceptable in many classification scenarios, it poses a significant challenge for applications where the set of classes differs significantly ... | ['Tom Drummond', 'Mehrtash Harandi', 'Rongkai Ma', 'Markus Hiller'] | 2022-06-15 | rethinking-generalization-in-few-shot | https://arxiv.org/abs/2206.07267 | https://arxiv.org/pdf/2206.07267.pdf | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 6.17375672e-01 5.57605550e-02 -3.10178488e-01 -5.92363119e-01
-6.78090394e-01 -3.39356601e-01 4.90450561e-01 2.09255025e-01
-5.50373316e-01 4.55647260e-01 -7.31127560e-02 1.64430141e-02
-2.10401505e-01 -7.59260416e-01 -9.66409385e-01 -8.51084530e-01
6.71895668e-02 3.55594635e-01 2.83940166e-01 -1.94692731... | [9.940317153930664, 2.420868396759033] |
954ee451-7e92-4adf-b23b-ecbb36c683b2 | changing-the-representation-examining-1 | 2210.06312 | null | https://arxiv.org/abs/2210.06312v1 | https://arxiv.org/pdf/2210.06312v1.pdf | Changing the Representation: Examining Language Representation for Neural Sign Language Production | Neural Sign Language Production (SLP) aims to automatically translate from spoken language sentences to sign language videos. Historically the SLP task has been broken into two steps; Firstly, translating from a spoken language sentence to a gloss sequence and secondly, producing a sign language video given a sequence ... | ['Richard Bowden', 'Ben Saunders', 'Harry Walsh'] | 2022-09-16 | changing-the-representation-examining | https://aclanthology.org/2022.sltat-1.18 | https://aclanthology.org/2022.sltat-1.18.pdf | sltat-lrec-2022-6 | ['sign-language-translation', 'sign-language-production'] | ['computer-vision', 'natural-language-processing'] | [ 5.35529792e-01 1.00689232e-02 -5.67349531e-02 -4.88178760e-01
-1.10716820e+00 -6.42027378e-01 7.91039586e-01 -7.08156526e-01
-7.88454354e-01 4.87389565e-01 7.98169076e-01 -2.14186236e-01
3.84315759e-01 -3.57858896e-01 -6.73388660e-01 -6.72551394e-01
2.46542141e-01 5.06507754e-01 2.74964601e-01 -1.95083737... | [9.204023361206055, -6.531381607055664] |
bc372d7c-2060-488a-9f4f-68be74de6fa3 | an-interpretable-determinantal-choice-model | 2302.11477 | null | https://arxiv.org/abs/2302.11477v1 | https://arxiv.org/pdf/2302.11477v1.pdf | An Interpretable Determinantal Choice Model for Subset Selection | Understanding how subsets of items are chosen from offered sets is critical to assortment planning, wireless network planning, and many other applications. There are two seemingly unrelated subset choice models that capture dependencies between items: intuitive and interpretable random utility models; and tractable det... | ['Alex Coy', 'David B. Shmoys', 'Sander Aarts'] | 2023-02-22 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 1.31984904e-01 9.28756222e-02 -6.08980000e-01 -5.01427233e-01
-4.13345516e-01 -6.25039220e-01 3.70116055e-01 -5.13686687e-02
-2.97548890e-01 1.29426527e+00 2.79836267e-01 -8.25893164e-01
-1.06151950e+00 -7.64622390e-01 -4.33425188e-01 -6.15229189e-01
-9.08916771e-01 9.23323035e-01 -8.59736428e-02 -3.21324617... | [4.758420944213867, 3.138922929763794] |
5b0e7c4a-568f-4445-9ef7-0a6316dbd0e5 | perturbation-inactivation-based-adversarial | 2207.06035 | null | https://arxiv.org/abs/2207.06035v1 | https://arxiv.org/pdf/2207.06035v1.pdf | Perturbation Inactivation Based Adversarial Defense for Face Recognition | Deep learning-based face recognition models are vulnerable to adversarial attacks. To curb these attacks, most defense methods aim to improve the robustness of recognition models against adversarial perturbations. However, the generalization capacities of these methods are quite limited. In practice, they are still vul... | ['Zhenan Sun', 'Yunlong Wang', 'Yuhao Zhu', 'Min Ren'] | 2022-07-13 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.57175928e-01 -1.57365814e-01 -8.99541937e-03 -1.23514697e-01
-3.14464241e-01 -1.16603851e+00 5.58427215e-01 -4.72392648e-01
2.31191497e-02 5.84179580e-01 -1.43693760e-01 -4.48237121e-01
1.32312924e-01 -9.93907094e-01 -9.06017661e-01 -1.09497166e+00
2.35578343e-01 1.58549592e-01 1.18550181e-01 -4.84390408... | [5.562038421630859, 7.899300575256348] |
d83da0af-8ad6-4b9c-a20b-2a89e17ac4f4 | rethink-darts-search-space-and-renovate-a-new | 2306.06852 | null | https://arxiv.org/abs/2306.06852v1 | https://arxiv.org/pdf/2306.06852v1.pdf | Rethink DARTS Search Space and Renovate a New Benchmark | DARTS search space (DSS) has become a canonical benchmark for NAS whereas some emerging works pointed out the issue of narrow accuracy range and claimed it would hurt the method ranking. We observe some recent studies already suffer from this issue that overshadows the meaning of scores. In this work, we first propose ... | ['Zhiming Ding', 'Jiuling Zhang'] | 2023-06-12 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.69016093e-01 -2.86035240e-01 -3.60797524e-01 -2.41164133e-01
-1.05684447e+00 -8.50166202e-01 7.59375751e-01 -5.22211008e-02
-5.61434090e-01 7.06724226e-01 4.22221631e-01 -7.09746361e-01
-4.07530993e-01 -4.82415915e-01 -5.55678606e-01 -5.50612807e-01
3.06549460e-01 3.92943323e-01 1.90980300e-01 -3.36606652... | [10.427644729614258, 8.228387832641602] |
165cac9b-6e9a-4d81-bbc8-95291560830e | influence-based-mini-batching-for-graph | 2212.09083 | null | https://arxiv.org/abs/2212.09083v1 | https://arxiv.org/pdf/2212.09083v1.pdf | Influence-Based Mini-Batching for Graph Neural Networks | Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches. To solve this, previous methods have relied on sampling or graph clustering. While these approaches often lead to good training convergence, they introduce significant overhead due to expensive random d... | ['Stephan Günnemann', 'Chendi Qian', 'Johannes Gasteiger'] | 2022-12-18 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 6.80489168e-02 4.01959091e-01 -2.71827251e-01 -4.18945223e-01
-7.38172829e-01 -4.33366776e-01 5.06391287e-01 3.98766488e-01
-4.95370060e-01 8.86568606e-01 -2.33546242e-01 -6.80371642e-01
-1.43650487e-01 -1.01041114e+00 -1.21262312e+00 -4.39688534e-01
-1.37914076e-01 9.34618294e-01 9.23167095e-02 1.88209832... | [7.011512756347656, 5.980539798736572] |
2ed0946d-ef2b-4888-bf93-d4140eac579a | dilated-scale-aware-attention-convnet-for | 2012.08149 | null | https://arxiv.org/abs/2012.08149v1 | https://arxiv.org/pdf/2012.08149v1.pdf | Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting | Object counting aims to estimate the number of objects in images. The leading counting approaches focus on the single category counting task and achieve impressive performance. Note that there are multiple categories of objects in real scenes. Multi-class object counting expands the scope of application of object count... | ['Zhanyu Ma', 'Yixiao Zheng', 'Dingkang Liang', 'Wei Xu'] | 2020-12-15 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.56781733e-01 -5.24064302e-01 -3.13006602e-02 -4.90033507e-01
-4.12439972e-01 -5.65867662e-01 5.41759193e-01 2.56771415e-01
-7.95493186e-01 4.07065660e-01 -3.33368927e-01 -4.78545092e-02
2.55812973e-01 -1.00301528e+00 -5.12262523e-01 -4.85899240e-01
1.42915443e-01 2.59114653e-01 8.07584941e-01 3.22652876... | [8.85564136505127, 0.22492338716983795] |
b78ada50-69da-4c8b-a2ef-a07137db1d78 | similarity-weighted-construction-of | 2305.08495 | null | https://arxiv.org/abs/2305.08495v1 | https://arxiv.org/pdf/2305.08495v1.pdf | Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks | Arguments often do not make explicit how a conclusion follows from its premises. To compensate for this lack, we enrich arguments with structured background knowledge to support knowledge-intense argumentation tasks. We present a new unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKG... | ['Anette Frank', 'Philipp Cimiano', 'Philipp Heinisch', 'Juri Opitz', 'Moritz Plenz'] | 2023-05-15 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.29696596e-01 1.02102673e+00 -5.72450340e-01 -2.08793208e-01
-9.18451369e-01 -9.70062137e-01 6.72106981e-01 7.97560573e-01
-2.76763827e-01 8.79881382e-01 6.02056742e-01 -6.76423073e-01
-6.39333129e-01 -8.28093171e-01 -7.16499865e-01 -1.06389463e-01
3.72956872e-01 7.78097272e-01 4.83444989e-01 -2.16318294... | [9.625129699707031, 8.252413749694824] |
6c070691-9295-4037-bbcd-1a4affa7f465 | cmid-a-unified-self-supervised-learning | 2304.09670 | null | https://arxiv.org/abs/2304.09670v1 | https://arxiv.org/pdf/2304.09670v1.pdf | CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding | Self-supervised learning (SSL) has gained widespread attention in the remote sensing (RS) and earth observation (EO) communities owing to its ability to learn task-agnostic representations without human-annotated labels. Nevertheless, most existing RS SSL methods are limited to learning either global semantic separable... | ['Feng Gu', 'Zhenshi Li', 'Pengfeng Xiao', 'Xueliang Zhang', 'Dilxat Muhtar'] | 2023-04-19 | null | null | null | null | ['change-detection', 'scene-classification'] | ['computer-vision', 'computer-vision'] | [ 4.32004333e-01 -3.97460312e-02 -1.09551266e-01 -5.33811629e-01
-4.67283517e-01 -6.36104763e-01 8.31881940e-01 2.03870505e-01
-2.50283092e-01 4.16047782e-01 -7.60865733e-02 -5.86532176e-01
-1.99526578e-01 -9.27174389e-01 -6.61279976e-01 -7.84485042e-01
-1.55443817e-01 7.11726397e-02 3.63724440e-01 -3.52936864... | [9.631325721740723, -1.3061445951461792] |
7896b94f-719a-44cd-9bbb-d57632f00457 | a-dataset-of-dynamic-reverberant-sound-scenes | 2106.06999 | null | https://arxiv.org/abs/2106.06999v2 | https://arxiv.org/pdf/2106.06999v2.pdf | A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection | This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD). The dataset is based on emulation of real recordings of static or moving sound events under real conditions of reverberation and ambient noise, using spatial room impulse responses captur... | ['Tuomas Virtanen', 'Prerak Srivastava', 'Antoine Deleforge', 'Daniel Krause', 'Sharath Adavanne', 'Archontis Politis'] | 2021-06-13 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 2.78284967e-01 -2.80320019e-01 8.49936187e-01 8.58122855e-02
-9.48912084e-01 -6.54356778e-01 6.77122116e-01 2.75752217e-01
-5.90402246e-01 6.31394684e-01 5.87840259e-01 -6.56580776e-02
-2.11361423e-01 -4.02906626e-01 -5.89872241e-01 -7.43821204e-01
-3.09977174e-01 -6.74618781e-02 5.33104897e-01 -2.26231605... | [15.144186973571777, 5.344782829284668] |
a05e2dba-beaf-4901-9274-2de85fccf218 | dual-space-compressed-sensing | 2207.07627 | null | https://arxiv.org/abs/2207.07627v1 | https://arxiv.org/pdf/2207.07627v1.pdf | Dual-space Compressed Sensing | Compressed sensing (CS) is a powerful method routinely employed to accelerate image acquisition. It is particularly suited to situations when the image under consideration is sparse but can be sampled in a basis where it is non-sparse. Here we propose an alternate CS regime in situations where the image can be sampled ... | ['Ashok Ajoy', 'Xudong Lv'] | 2022-07-15 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 1.09501290e+00 -2.22312152e-01 2.11726815e-01 -3.83442752e-02
-8.22179198e-01 -5.23965776e-01 5.07224977e-01 -8.77794027e-02
-7.44151175e-01 8.42503786e-01 -9.32048038e-02 -1.90788478e-01
-1.29774868e-01 -6.39336228e-01 -5.40607452e-01 -1.24454451e+00
-2.54697055e-01 6.93102717e-01 1.30531996e-01 -1.28305450... | [12.803607940673828, -2.750864267349243] |
de9e5749-b391-4025-9ec8-5ebb32518ccc | monoplflownet-permutohedral-lattice-flownet | 2111.12325 | null | https://arxiv.org/abs/2111.12325v1 | https://arxiv.org/pdf/2111.12325v1.pdf | MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene FlowEstimation with Monocular Images | Real-scale scene flow estimation has become increasingly important for 3D computer vision. Some works successfully estimate real-scale 3D scene flow with LiDAR. However, these ubiquitous and expensive sensors are still unlikely to be equipped widely for real application. Other works use monocular images to estimate sce... | ['Truong Nguyen', 'Runfa Li'] | 2021-11-24 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-8.12339261e-02 -4.03507859e-01 -2.26989448e-01 -1.95886970e-01
-3.00060123e-01 -6.94098175e-01 6.37961686e-01 -2.90179312e-01
-5.29199600e-01 8.59395802e-01 -7.26245418e-02 -2.57943541e-01
4.22764987e-01 -1.02626514e+00 -6.26706064e-01 -3.22338253e-01
-2.98052002e-02 6.28633499e-01 7.27081299e-01 2.20304597... | [8.599967956542969, -2.12333083152771] |
cf5355b3-cd0e-4b18-abce-74378ecfb5f1 | a-hierarchy-of-graph-neural-networks-based-on-1 | 1911.05256 | null | https://arxiv.org/abs/1911.05256v1 | https://arxiv.org/pdf/1911.05256v1.pdf | A Hierarchy of Graph Neural Networks Based on Learnable Local Features | Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been proposed, but there is limited understanding on both how to compare different architectures and how to construct GNNs systematically. Here, we p... | ['Alexander M. Rush', 'Michael Lingzhi Li', 'Meng Dong', 'Jiawei Zhou'] | 2019-11-13 | null | https://openreview.net/forum?id=ryeEr0EFvS | https://openreview.net/pdf?id=ryeEr0EFvS | null | ['graph-regression'] | ['graphs'] | [ 2.03602314e-01 5.09365201e-01 -2.74759173e-01 -3.67034405e-01
-3.78812030e-02 -6.25590324e-01 6.85004830e-01 2.38691524e-01
-8.16190615e-02 5.88113248e-01 1.28668919e-01 -5.66547334e-01
-5.29273331e-01 -1.13101923e+00 -5.30788839e-01 -4.54664886e-01
-7.79442847e-01 4.59163278e-01 2.01677084e-01 -4.16909337... | [6.915899276733398, 6.189515590667725] |
cb5e0290-bb0a-4305-b1d8-6af9c6600ad1 | extraction-of-pharmacokinetic-evidence-of | 1412.0744 | null | http://arxiv.org/abs/1412.0744v2 | http://arxiv.org/pdf/1412.0744v2.pdf | Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature | Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a
subject of intense scientific interest. Biomedical literature mining can aid
DDI research by extracting evidence for large numbers of potential interactions
from published literature and clinical databases. Though DDI is investigated in
domai... | ['Heng-Yi Wu', 'Anália Lourenço', 'Artemy Kolchinsky', 'Lang Li', 'Luis M. Rocha'] | 2014-12-02 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 2.76335537e-01 -1.02102362e-01 -5.45386791e-01 -2.47251451e-01
-8.23382437e-01 -5.24039924e-01 6.45912588e-01 1.09188688e+00
-5.99905074e-01 1.31340730e+00 3.15302581e-01 -7.74370611e-01
-5.10237813e-01 -3.15527469e-01 -6.83106065e-01 -4.45421338e-01
-5.33111930e-01 4.44911927e-01 -1.89651325e-01 2.56277174... | [8.396174430847168, 8.653517723083496] |
76aa8e0b-2669-4f1b-8067-28c75734742f | traffic-signs-in-the-wild-highlights-from-the | 1810.06169 | null | http://arxiv.org/abs/1810.06169v2 | http://arxiv.org/pdf/1810.06169v2.pdf | Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions] | Robust and reliable traffic sign detection is necessary to bring autonomous
vehicles onto our roads. State-of-the-art algorithms successfully perform
traffic sign detection over existing databases that mostly lack severe
challenging conditions. VIP Cup 2017 competition focused on detecting such
traffic signs under chal... | ['Ghassan AlRegib', 'Dogancan Temel'] | 2018-10-15 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [-3.37460518e-01 -7.55033374e-01 -4.75190103e-01 -4.52093005e-01
-9.04567480e-01 -4.17299271e-01 6.57243729e-01 -1.09407139e+00
-5.40689409e-01 6.57372236e-01 -2.14454725e-01 -3.87960374e-01
2.34582841e-01 -9.69440490e-02 -5.16506851e-01 -4.93227601e-01
-4.36516814e-02 4.64893669e-01 8.59244585e-01 -3.67957443... | [7.97849178314209, -0.8265339732170105] |
5e447185-1460-48f6-bcbe-fd06dc552e4e | gated-recurrent-unit-for-video-denoising | 2210.09135 | null | https://arxiv.org/abs/2210.09135v1 | https://arxiv.org/pdf/2210.09135v1.pdf | Gated Recurrent Unit for Video Denoising | Current video denoising methods perform temporal fusion by designing convolutional neural networks (CNN) or combine spatial denoising with temporal fusion into basic recurrent neural networks (RNNs). However, there have not yet been works which adapt gated recurrent unit (GRU) mechanisms for video denoising. In this le... | ['Jongseong Choi', 'Seungwon Choi', 'Kai Guo'] | 2022-10-17 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 6.93589002e-02 -5.22542715e-01 3.23574990e-01 -2.57669061e-01
-4.08741832e-01 1.75492644e-01 6.31353110e-02 -3.43318880e-01
-6.01594269e-01 6.70099020e-01 3.41271371e-01 1.15842365e-01
1.43605143e-01 -8.18470240e-01 -5.06604612e-01 -1.20239782e+00
2.04714507e-01 -7.84625530e-01 4.26524311e-01 -4.58067894... | [11.311957359313965, -2.1174118518829346] |
f732bd16-4d93-4d2b-830b-15c821db2321 | an-exploration-of-hierarchical-attention | 2210.05529 | null | https://arxiv.org/abs/2210.05529v1 | https://arxiv.org/pdf/2210.05529v1.pdf | An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification | Non-hierarchical sparse attention Transformer-based models, such as Longformer and Big Bird, are popular approaches to working with long documents. There are clear benefits to these approaches compared to the original Transformer in terms of efficiency, but Hierarchical Attention Transformer (HAT) models are a vastly u... | ['Desmond Elliott', 'Prodromos Malakasiotis', 'Manos Fergadiotis', 'Xiang Dai', 'Ilias Chalkidis'] | 2022-10-11 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-1.48557305e-01 2.55962461e-02 4.06666324e-02 -2.87664682e-01
-1.29311264e+00 -7.22790897e-01 7.95217335e-01 2.07811370e-01
-4.28849936e-01 4.49142158e-01 8.09816420e-01 -4.65303421e-01
3.53914164e-02 -3.86751384e-01 -7.29367077e-01 -4.62229341e-01
2.30948523e-01 6.33702874e-01 2.20977023e-01 -1.47384524... | [11.053434371948242, 8.369871139526367] |
31f6118d-bf71-4976-8117-ae811a97f4bd | deep-network-for-simultaneous-decomposition | 1801.05458 | null | http://arxiv.org/abs/1801.05458v2 | http://arxiv.org/pdf/1801.05458v2.pdf | Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery | Classifying buried and obscured targets of interest from other natural and
manmade clutter objects in the scene is an important problem for the U.S. Army.
Targets of interest are often represented by signals captured using
low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar
(SAR) technology. Thi... | ['Vishal Monga', 'Tiantong Guo', 'Tiep Vu', 'Lam Nguyen'] | 2018-01-16 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 7.50349998e-01 -2.80124843e-01 2.24131793e-01 -2.34573811e-01
-9.36028838e-01 -1.75748050e-01 2.10060522e-01 -5.21879911e-01
-1.92167014e-01 9.30375755e-01 1.30150914e-01 -1.50077865e-01
-6.91247523e-01 -8.56417000e-01 5.98720163e-02 -1.19606805e+00
-3.83496732e-01 2.61843726e-02 3.09282728e-02 -3.86609793... | [6.851171493530273, 1.0251059532165527] |
e318dac9-86b4-48ed-bdf5-81c90185f4ae | lean-light-and-efficient-audio-classification | 2305.12712 | null | https://arxiv.org/abs/2305.12712v1 | https://arxiv.org/pdf/2305.12712v1.pdf | LEAN: Light and Efficient Audio Classification Network | Over the past few years, audio classification task on large-scale dataset such as AudioSet has been an important research area. Several deeper Convolution-based Neural networks have shown compelling performance notably Vggish, YAMNet, and Pretrained Audio Neural Network (PANN). These models are available as pretrained ... | ['Sumit Kumar', 'Punuru Sri Lakshmi', 'CR Karthik', 'Shwetank Choudhary'] | 2023-05-22 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 2.25863665e-01 -1.59878597e-01 -1.36829689e-01 -3.00099492e-01
-1.27691650e+00 -3.12587261e-01 -1.97351500e-02 -7.93981552e-02
-3.43756139e-01 4.73089993e-01 3.20016921e-01 -4.01918590e-01
-1.36966646e-01 -5.64187586e-01 -7.92745709e-01 -3.76897752e-01
-3.28788757e-01 -5.02003506e-02 3.54477286e-01 -4.16943356... | [15.066252708435059, 5.328159809112549] |
e11a532f-1452-4745-bd62-b19720ac4b69 | anvita-machine-translation-system-for-wat | null | null | https://aclanthology.org/2021.wat-1.30 | https://aclanthology.org/2021.wat-1.30.pdf | ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task | This paper describes ANVITA-1.0 MT system, architected for submission to WAT2021 MultiIndicMT shared task by mcairt team, where the team participated in 20 translation directions: English→Indic and Indic→English; Indic set comprised of 10 Indian languages. ANVITA-1.0 MT system comprised of two multi-lingual NMT models ... | ['Prasanna Kumar K R', 'Chitra Viswanathan', 'Biswajit Paul', 'Sivabhavani J', 'Pavanpankaj Vegi'] | null | null | null | null | acl-wat-2021-8 | ['transliteration'] | ['natural-language-processing'] | [-8.33837613e-02 1.60243124e-01 -3.06943029e-01 -3.15930605e-01
-1.46743560e+00 -9.08383667e-01 1.12501442e+00 -3.81705135e-01
-5.58559775e-01 1.05579185e+00 4.52809900e-01 -9.90581095e-01
1.94849893e-02 -3.18310529e-01 -6.15745366e-01 -4.29351717e-01
4.65813279e-01 1.04737949e+00 -1.19962066e-01 -6.23475313... | [11.545969009399414, 10.4279146194458] |
1876d6ea-f833-4620-b1d0-802c1bfa5471 | a-method-of-accounting-bigrams-in-topic | null | null | https://aclanthology.org/W15-0901 | https://aclanthology.org/W15-0901.pdf | A Method of Accounting Bigrams in Topic Models | null | ['Natalia Loukachevitch', 'Michael Nokel'] | 2015-06-01 | null | null | null | ws-2015-6 | ['text-clustering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.4445905685424805, 3.6369826793670654] |
7ff15aa1-fc87-42c3-9363-ba0d70a8739d | spatial-temporal-graph-convolution-with-graph | 2211.06161 | null | https://arxiv.org/abs/2211.06161v1 | https://arxiv.org/pdf/2211.06161v1.pdf | Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection | Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanw... | ['Bo Liu', 'Bin Guo', 'Fugen Zhou', 'Yunpeng Zhao'] | 2022-11-11 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 1.11671975e-02 6.28096163e-02 6.35799542e-02 -5.04408121e-01
1.97254419e-01 -1.62611127e-01 3.67055893e-01 2.90347248e-01
-4.81015146e-01 5.58918297e-01 5.77014945e-02 -3.06693494e-01
-4.70412672e-01 -9.88372147e-01 -3.87573153e-01 -5.74486136e-01
-7.96232283e-01 3.82727921e-01 3.97725224e-01 -3.43199015... | [12.424206733703613, 3.369744300842285] |
57dc83bd-a662-4782-9c22-fa17a8f81740 | dynamic-label-graph-matching-for-unsupervised | 1709.09297 | null | http://arxiv.org/abs/1709.09297v1 | http://arxiv.org/pdf/1709.09297v1.pdf | Dynamic Label Graph Matching for Unsupervised Video Re-Identification | Label estimation is an important component in an unsupervised person
re-identification (re-ID) system. This paper focuses on cross-camera label
estimation, which can be subsequently used in feature learning to learn robust
re-ID models. Specifically, we propose to construct a graph for samples in each
camera, and then ... | ['Andy J. Ma', 'Jiawei Li', 'Mang Ye', 'P C Yuen', 'Liang Zheng'] | 2017-09-27 | dynamic-label-graph-matching-for-unsupervised-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Ye_Dynamic_Label_Graph_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Ye_Dynamic_Label_Graph_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.72770584e-01 -1.26028121e-01 -2.54578054e-01 -5.37602663e-01
-5.85247695e-01 -4.95611668e-01 7.47388244e-01 3.33580524e-01
-3.63730818e-01 4.72540855e-01 2.90065944e-01 4.67455208e-01
-5.55070490e-02 -4.91489232e-01 -4.21046764e-01 -4.85560864e-01
3.83538031e-03 6.82231545e-01 -1.03190988e-02 3.06679308... | [14.788086891174316, 1.0451322793960571] |
807f4d3d-1d8f-44e2-9616-652e57b8d137 | learning-to-decompose-hypothetical-question | 2210.16865 | null | https://arxiv.org/abs/2210.16865v1 | https://arxiv.org/pdf/2210.16865v1.pdf | Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts | Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems. However, despite the many datasets and resources built as part of this effort, the majority have... | ['Dan Roth', 'Xiaodong Yu', 'Kyle Richardson', 'Ben Zhou'] | 2022-10-30 | null | null | null | null | ['semantic-parsing', 'strategyqa'] | ['natural-language-processing', 'reasoning'] | [-6.92413480e-04 5.72144151e-01 -2.84548908e-01 -5.70751429e-01
-1.62264442e+00 -9.98928308e-01 5.02485752e-01 -1.88932649e-03
-1.79004669e-01 7.88569808e-01 5.82747221e-01 -7.01429546e-01
2.80404061e-01 -3.27734828e-01 -1.05894387e+00 -1.76299199e-01
2.90225893e-01 1.26332915e+00 1.91432443e-02 -3.46888751... | [10.662507057189941, 8.563526153564453] |
3f10ebc7-ea4c-415e-b671-1681e9ec4cdf | unsupervised-uncertainty-estimation-using | 1901.01550 | null | http://arxiv.org/abs/1901.01550v1 | http://arxiv.org/pdf/1901.01550v1.pdf | Unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection | In this paper, we address the problem of quantifying reliability of
computational saliency for videos, which can be used to improve saliency-based
video processing and enable more reliable performance and risk assessment of
such processing. Our approach is twofold. First, we explore spatial
correlations in both salienc... | ['Tariq Alshawi', 'Ghassan AlRegib', 'Zhiling Long'] | 2019-01-06 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 2.60717750e-01 -2.78737068e-01 -1.50139332e-01 -3.88163060e-01
-6.48760617e-01 -3.78853083e-01 3.90965968e-01 2.39698172e-01
-3.70975256e-01 6.92357421e-01 1.92100853e-01 1.08737700e-01
-2.53286600e-01 -3.64599556e-01 -9.82818723e-01 -5.80407679e-01
-2.27713302e-01 -2.68500835e-01 7.91968882e-01 1.96207449... | [9.727913856506348, -0.2800421714782715] |
16ff8a53-0119-4e1f-90e8-f6ef042abc0c | a-survey-on-graph-neural-networks-for | 2007.12374 | null | https://arxiv.org/abs/2007.12374v1 | https://arxiv.org/pdf/2007.12374v1.pdf | A Survey on Graph Neural Networks for Knowledge Graph Completion | Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval. However, the Knowledge Graphs are often incomplete, thus leading to poor performance. As a result, there has been a lot of interest in the task of Knowledge Base Completion. More recen... | ['Siddhant Arora'] | 2020-07-24 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [ 2.34254837e-01 3.12775195e-01 -6.83704913e-01 -3.02029997e-01
-1.57333598e-01 -4.33929324e-01 5.25937259e-01 5.57852983e-01
-8.94901454e-02 9.05011833e-01 4.32893746e-02 -3.55557501e-01
-5.20645499e-01 -1.01824880e+00 -6.00331366e-01 -4.77587044e-01
-1.60319418e-01 3.33292961e-01 2.32463881e-01 -1.83770791... | [8.900680541992188, 7.857755184173584] |
6df29830-282a-4e7e-a02c-eec49c124512 | squibs-constrained-arc-eager-dependency | null | null | https://aclanthology.org/J14-2001 | https://aclanthology.org/J14-2001.pdf | Squibs: Constrained Arc-Eager Dependency Parsing | null | ['Ryan Mcdonald', 'Joakim Nivre', 'Yoav Goldberg'] | 2014-06-01 | null | null | null | cl-2014-6 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.281475067138672, 3.6850943565368652] |
1dbb828f-c11a-470d-a6e2-11be0325be65 | learning-meta-representations-of-one-shot | 2205.10621 | null | https://arxiv.org/abs/2205.10621v2 | https://arxiv.org/pdf/2205.10621v2.pdf | Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction | Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly been studied. Compared to KGs, TKGs contain rich temporal information, thus requiring temporal reasoning techniques for modeling. This poses ... | ['Volker Tresp', 'Zhen Han', 'Yunpu Ma', 'Bailan He', 'Zifeng Ding'] | 2022-05-21 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-3.13632041e-01 3.53835642e-01 -1.00965142e+00 -1.69679791e-01
-4.74916637e-01 6.54508471e-02 4.23158020e-01 5.35750270e-01
1.98948622e-01 7.04483986e-01 -1.31151956e-02 -4.82239664e-01
-6.76629901e-01 -1.32786262e+00 -5.87081075e-01 -2.75318027e-01
-6.88088655e-01 5.91228247e-01 1.13254082e+00 -4.13918436... | [8.615819931030273, 7.934966564178467] |
cb83b00a-45ef-4a44-901d-0480a63ec089 | predictive-engagement-an-efficient-metric-for | 1911.01456 | null | https://arxiv.org/abs/1911.01456v2 | https://arxiv.org/pdf/1911.01456v2.pdf | Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems | User engagement is a critical metric for evaluating the quality of open-domain dialogue systems. Prior work has focused on conversation-level engagement by using heuristically constructed features such as the number of turns and the total time of the conversation. In this paper, we investigate the possibility and effic... | ['Nanyun Peng', 'Aram Galstyan', 'Ralph Weischedel', 'Sarik Ghazarian'] | 2019-11-04 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 3.49873677e-02 7.33224571e-01 -8.07082728e-02 -6.55875742e-01
-1.05479038e+00 -8.14670801e-01 8.92252147e-01 4.25785840e-01
-4.39079285e-01 7.82394648e-01 8.34744215e-01 -2.48599783e-01
-4.24854197e-02 -5.13945818e-01 1.50804192e-01 -1.41597956e-01
1.15152420e-02 5.69580376e-01 -8.20511431e-02 -5.98443925... | [12.8040771484375, 8.067361831665039] |
c10d6b4f-84b1-4c81-ba3b-ab473b524cc1 | document-level-relation-extraction-with-1 | 2012.11384 | null | https://arxiv.org/abs/2012.11384v1 | https://arxiv.org/pdf/2012.11384v1.pdf | Document-Level Relation Extraction with Reconstruction | In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally mod... | ['Tiejun Zhao', 'Kehai Chen', 'Wang Xu'] | 2020-12-21 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-6.49569929e-02 4.86145884e-01 -6.41620159e-01 -1.79426551e-01
-1.48060635e-01 -2.34136552e-01 4.74853188e-01 5.21465659e-01
-1.25527233e-01 6.82221711e-01 3.59122753e-01 -2.51535028e-01
-1.84016362e-01 -1.48175919e+00 -5.78000367e-01 -4.17555571e-01
-1.39107808e-01 5.71725965e-01 2.44441345e-01 -3.84279549... | [9.183684349060059, 8.524307250976562] |
82c38871-f55e-4349-b971-6f039303dd97 | 3dshape2vecset-a-3d-shape-representation-for | 2301.11445 | null | https://arxiv.org/abs/2301.11445v3 | https://arxiv.org/pdf/2301.11445v3.pdf | 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models | We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept of neural fields has previously been combined with a global laten... | ['Peter Wonka', 'Matthias Niessner', 'Jiapeng Tang', 'Biao Zhang'] | 2023-01-26 | null | null | null | null | ['point-cloud-completion', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 7.96745345e-02 2.89683133e-01 2.89955318e-01 -2.63252705e-01
-7.11250067e-01 -6.14548504e-01 1.12317514e+00 -3.45005602e-01
3.60578179e-01 4.21664476e-01 5.64189255e-01 -1.81406364e-01
1.26336679e-01 -1.33915985e+00 -1.01335073e+00 -8.00266325e-01
1.16640732e-01 8.15047979e-01 -2.48901367e-01 -7.71514848... | [8.90849494934082, -3.648859977722168] |
80e1c959-5970-4213-bac0-8d294e5b9cd0 | ibir-bug-report-driven-fault-injection | 2012.06506 | null | https://arxiv.org/abs/2012.06506v1 | https://arxiv.org/pdf/2012.06506v1.pdf | IBIR: Bug Report driven Fault Injection | Much research on software engineering and software testing relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-world software faults since it "blindly" injects large numbers of faults. It remains indeed challenging to inject few but realistic faults t... | ['Yves Le Traon', 'Jacques Klein', 'Tegawendé F. Bissyandé', 'Maxime Cordy', 'Mike Papadakis', 'Anil Koyuncu', 'Ahmed Khanfir'] | 2020-12-11 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.41068295e-01 1.48070082e-01 -4.36346605e-02 2.36345127e-01
-6.46484554e-01 -7.72645414e-01 1.90840140e-01 4.58871931e-01
2.38246128e-01 6.29495025e-01 -4.66686785e-01 -5.08912921e-01
-5.58872558e-02 -9.37857032e-01 -1.12694705e+00 -2.08501533e-01
-4.22864631e-02 3.36503208e-01 6.19965076e-01 -2.99533010... | [7.529779434204102, 7.637044429779053] |
b2b7413c-12fd-44f3-9c9c-ebbb1e705418 | real-to-sim-deep-learning-with-auto-tuning-to | 2209.03210 | null | https://arxiv.org/abs/2209.03210v3 | https://arxiv.org/pdf/2209.03210v3.pdf | Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter | Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcemen... | ['Dennis Hong', 'Marcel Menner', 'Feng Xu', 'Yusuke Tanaka', 'Alexander Schperberg'] | 2022-09-07 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 4.05710116e-02 3.57473731e-01 -4.37944084e-01 7.52459764e-02
-1.55226156e-01 -4.73182023e-01 4.08686787e-01 -4.19965684e-01
-4.00057584e-01 9.16595578e-01 -3.62392306e-01 -5.64398766e-01
-3.61392528e-01 -5.35209954e-01 -1.15753484e+00 -7.38915980e-01
-7.02019408e-02 4.87885118e-01 -5.77488728e-02 -4.74998266... | [4.904374599456787, 1.9377496242523193] |
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