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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4d80b483-9d32-4251-9ce6-cbf293006401 | a-question-answering-approach-to-emotion | 1708.05482 | null | http://arxiv.org/abs/1708.05482v2 | http://arxiv.org/pdf/1708.05482v2.pdf | A Question Answering Approach to Emotion Cause Extraction | Emotion cause extraction aims to identify the reasons behind a certain
emotion expressed in text. It is a much more difficult task compared to emotion
classification. Inspired by recent advances in using deep memory networks for
question answering (QA), we propose a new approach which considers emotion
cause identifica... | ['Lin Gui', 'Jiachen Du', 'Jiannan Hu', 'Ruifeng Xu', 'Yulan He', 'Qin Lu'] | 2017-08-18 | null | null | null | null | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 4.37361985e-01 9.72051546e-02 1.05143063e-01 -5.06662548e-01
-9.59348500e-01 -4.29800063e-01 5.54438472e-01 6.85269952e-01
-6.76452339e-01 7.23757207e-01 6.08492792e-01 -1.01153217e-01
9.00691599e-02 -7.04492271e-01 -5.99911690e-01 -2.69770205e-01
1.25496626e-01 1.26199409e-01 -1.12083763e-01 -4.09920454... | [12.691495895385742, 6.235157012939453] |
f7d19781-c6e6-4141-a0c0-be40f0bc9603 | quality-aware-pre-trained-models-for-blind | 2303.00521 | null | https://arxiv.org/abs/2303.00521v2 | https://arxiv.org/pdf/2303.00521v2.pdf | Quality-aware Pre-trained Models for Blind Image Quality Assessment | Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years. However, the paucity of labeled data somewhat restrains deep learning-based BIQA methods from unleashing their full potential.... | ['Xing Wen', 'Mading Li', 'Ming Sun', 'Kun Yuan', 'Kai Zhao'] | 2023-03-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_Quality-Aware_Pre-Trained_Models_for_Blind_Image_Quality_Assessment_CVPR_2023_paper.pdf | cvpr-2023-1 | ['blind-image-quality-assessment', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 2.60940999e-01 -3.70800614e-01 1.56712934e-01 -5.18011928e-01
-1.02038813e+00 -3.03244859e-01 2.37947673e-01 -1.58331141e-01
-3.13992292e-01 6.43940032e-01 4.28348571e-01 -1.30652055e-01
-4.48314566e-03 -6.66489422e-01 -7.32092559e-01 -8.45565736e-01
-1.18691828e-02 -1.61637723e-01 4.05233055e-02 -2.84080416... | [11.873327255249023, -1.8172905445098877] |
7b2c8cf5-5497-4aa3-9ba9-7a7618734247 | pose-guided-knowledge-transfer-for-object | null | null | https://www.researchgate.net/publication/340684849_Pose-Guided_Knowledge_Transfer_for_Object_Part_Segmentation | http://vision.soic.indiana.edu/papers/poseguided2020cvprw.pdf | Pose-Guided Knowledge Transfer for Object Part Segmentation | Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive. Recently, Fang et al. augmented object part segmentation datasets by using keypoint locations as weak supervision to transfer a source object inst... | ['David J. Crandall', 'Md Alimoor Reza', 'Prianka Banik', 'Qingyang Xiao', 'Shujon Naha'] | 2020-04-01 | null | null | null | null | ['semantic-part-detection'] | ['computer-vision'] | [ 4.70016748e-01 2.39899710e-01 -3.77680987e-01 -4.89330351e-01
-9.20180023e-01 -9.84486461e-01 3.10379326e-01 1.01634234e-01
-1.80381790e-01 6.45401657e-01 -3.85201514e-01 2.07470417e-01
1.28448591e-01 -5.81340492e-01 -1.23158836e+00 -4.32606071e-01
3.17768723e-01 1.00864363e+00 9.63668585e-01 3.06073166... | [9.235370635986328, 0.5131736993789673] |
ef380980-0ad6-4b78-a9a0-0cdcc37b4379 | transfer-learning-for-tensor-gaussian | 2211.09391 | null | https://arxiv.org/abs/2211.09391v1 | https://arxiv.org/pdf/2211.09391v1.pdf | Transfer learning for tensor Gaussian graphical models | Tensor Gaussian graphical models (GGMs), interpreting conditional independence structures within tensor data, have important applications in numerous areas. Yet, the available tensor data in one single study is often limited due to high acquisition costs. Although relevant studies can provide additional data, it remain... | ['Junhui Wang', 'Yaoming Zhen', 'Mingyang Ren'] | 2022-11-17 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.93786000e-02 -1.39971495e-01 -4.45241839e-01 -3.64654601e-01
-5.34972787e-01 -2.53544480e-01 2.26476744e-01 -3.34126860e-01
-1.17217265e-01 1.00148296e+00 7.03637004e-02 -1.96391061e-01
-6.07074916e-01 -3.59240144e-01 -4.05628532e-01 -1.18881536e+00
-6.36304379e-01 5.41297615e-01 1.63376167e-01 1.56377479... | [7.360343933105469, 4.727543354034424] |
ce79c145-d6d8-4053-957f-6fbdb2485bc5 | non-invasive-experimental-identification-of-a | 2306.14195 | null | https://arxiv.org/abs/2306.14195v1 | https://arxiv.org/pdf/2306.14195v1.pdf | Non-Invasive Experimental Identification of a Single Particle Model for LiFePO4 Cells | The rapid spread of Lithium-ions batteries (LiBs) for electric vehicles calls for the development of accurate physical models for Battery Management Systems (BMSs). In this work, the electrochemical Single Particle Model (SPM) for a high-power LiFePO4 cell is experimentally identified through a set of non-invasive test... | ['Sergio M. Savaresi', 'Stefano Radrizzani', 'Matteo Corno', 'Andrea Trivella'] | 2023-06-25 | null | null | null | null | ['management'] | ['miscellaneous'] | [-6.66809604e-02 -4.48621064e-01 -1.63564518e-01 1.04211360e-01
-3.48508418e-01 -7.07526088e-01 7.33679354e-01 6.16519451e-01
-2.24343285e-01 1.29441619e+00 -6.24001324e-01 -5.64182580e-01
-4.55330640e-01 -3.37436646e-01 -6.74262106e-01 -1.06948137e+00
1.44043760e-02 7.03039885e-01 3.97999346e-01 3.47812101... | [6.293051719665527, 2.74676251411438] |
f7dbfa07-8cac-4254-9c69-36c0ebc427ed | ucnn-exploiting-computational-reuse-in-deep | 1804.06508 | null | http://arxiv.org/abs/1804.06508v1 | http://arxiv.org/pdf/1804.06508v1.pdf | UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition | Convolutional Neural Networks (CNNs) have begun to permeate all corners of
electronic society (from voice recognition to scene generation) due to their
high accuracy and machine efficiency per operation. At their core, CNN
computations are made up of multi-dimensional dot products between weight and
input vectors. This... | ['Christopher W. Fletcher', 'Jiyong Yu', 'Rohit Agrawal', 'Mengjia Yan', 'Michael Pellauer', 'Kartik Hegde'] | 2018-04-18 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 2.37553164e-01 -5.25553189e-02 -4.65235978e-01 -3.43276113e-01
1.22816861e-01 -2.99779773e-01 2.97603250e-01 2.23633274e-01
-8.75513792e-01 7.97601789e-02 3.65904242e-01 -8.46100867e-01
4.21195358e-01 -1.03278124e+00 -7.45528877e-01 -3.73245537e-01
1.36214450e-01 -4.82059836e-01 3.58627647e-01 -3.71581577... | [8.402100563049316, 2.8605592250823975] |
5d70cd95-da5d-43c7-8c20-9b772562dfea | fisher-matrix-based-fault-detection-for-pmus | 2208.04637 | null | https://arxiv.org/abs/2208.04637v1 | https://arxiv.org/pdf/2208.04637v1.pdf | Fisher Matrix Based Fault Detection for PMUs Data in Power Grids | Abnormal event detection is critical in the safe operation of power system. In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection pro... | ['Hongxia Wang', 'Bo wang', 'Dandan Jiang', 'Ke Chen'] | 2022-08-09 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-1.13969937e-01 -4.21794951e-01 2.80606329e-01 3.90788727e-02
-2.30129272e-01 -4.72343683e-01 -4.47815731e-02 2.13436142e-01
7.41116107e-02 8.72701406e-01 -3.69749069e-01 -4.78973925e-01
-6.25394106e-01 -9.88132119e-01 -1.58226103e-01 -1.10807335e+00
-3.69497299e-01 1.86909929e-01 4.08881217e-01 1.42769322... | [6.223239421844482, 2.472055435180664] |
f2dd0ad6-0241-48ae-83d0-babafc058847 | universal-recurrent-neural-network-grammar | null | null | https://aclanthology.org/2021.rocling-1.1 | https://aclanthology.org/2021.rocling-1.1.pdf | Universal Recurrent Neural Network Grammar | Modern approaches to Constituency Parsing are mono-lingual supervised approaches which require large amount of labelled data to be trained on, thus limiting their utility to only a handful of high-resource languages. To address this issue of data-sparsity for low-resource languages we propose Universal Recurrent Neural... | ['Colm O’riordan', 'Chinmay Choudhary'] | null | null | null | null | rocling-2021-10 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.87334701e-01 3.94179553e-01 -5.06288528e-01 -4.77622181e-01
-1.13937974e+00 -9.41697896e-01 4.38937843e-01 -7.21418634e-02
-4.15148139e-01 8.10640037e-01 6.48920655e-01 -9.37298179e-01
4.17758644e-01 -9.16987300e-01 -7.25795150e-01 -3.47802669e-01
3.57033946e-02 3.45012456e-01 -6.66934177e-02 -7.10775256... | [10.4840087890625, 9.890600204467773] |
520d7a33-9db7-4ae0-b379-02eb633f4104 | dynamic-texture-recognition-via-nuclear | 2102.00841 | null | https://arxiv.org/abs/2102.00841v1 | https://arxiv.org/pdf/2102.00841v1.pdf | Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces | Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data. Based on the conjecture that the most distinctive characteristic of a dynamic texture is the appearance of its individual frames, this work proposes ... | ['Hao Shen', 'Julian Wörmann', 'Alexander Sagel'] | 2021-02-01 | null | null | null | null | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 2.29037598e-01 -7.76599288e-01 -1.97014362e-01 -6.29001558e-01
-5.72877228e-01 -3.53572726e-01 8.24230134e-01 8.54637846e-02
-3.34552616e-01 1.33666411e-01 -4.36516516e-02 -8.13106727e-03
-4.83041883e-01 -8.84936452e-01 -2.84852177e-01 -1.17047489e+00
-4.59622294e-01 3.07556957e-01 8.97781134e-01 -2.55395383... | [10.179844856262207, -0.3613486886024475] |
884c99a1-d7a1-4f43-a54c-19c165ad1a1a | graphvf-controllable-protein-specific-3d | 2304.12825 | null | https://arxiv.org/abs/2304.12825v1 | https://arxiv.org/pdf/2304.12825v1.pdf | GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow | Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent models leverage geometric constraints to generate ligand molecules that bind cohesively with specific protein pockets. However, these models cannot effectively generate 3D molecules with 2D skeletal curtailments an... | ['Jian Tang', 'Ming Zhang', 'Hongyu Guo', 'Zhihao Zhan', 'Fang Sun'] | 2023-02-23 | null | null | null | null | ['drug-discovery', '3d-molecule-generation'] | ['medical', 'medical'] | [ 1.12471983e-01 1.67339876e-01 -4.71056074e-01 -9.21481848e-02
-7.51717508e-01 -9.18151796e-01 2.71713853e-01 2.27904603e-01
2.14700684e-01 1.55327594e+00 2.33548552e-01 -6.12337530e-01
-6.73619956e-02 -9.49151874e-01 -9.18834329e-01 -8.06066215e-01
1.70619227e-02 7.00621009e-01 5.54437470e-03 -2.55029321... | [4.989903926849365, 5.765566349029541] |
d7f11d1d-3bcc-4225-987d-efc6b904f483 | deforming-autoencoders-unsupervised | 1806.06503 | null | http://arxiv.org/abs/1806.06503v1 | http://arxiv.org/pdf/1806.06503v1.pdf | Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance | In this work we introduce Deforming Autoencoders, a generative model for
images that disentangles shape from appearance in an unsupervised manner. As in
the deformable template paradigm, shape is represented as a deformation between
a canonical coordinate system (`template') and an observed image, while
appearance is m... | ['Nikos Paragios', 'Zhixin Shu', 'Mihir Sahasrabudhe', 'Alp Guler', 'Iasonas Kokkinos', 'Dimitris Samaras'] | 2018-06-18 | deforming-autoencoders-unsupervised-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhixin_Shu_Deforming_Autoencoders_Unsupervised_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 2.63614029e-01 2.51354396e-01 5.19437909e-01 -5.91524243e-01
-3.79243642e-02 -9.20632958e-01 8.71071875e-01 -4.21969056e-01
-3.56176853e-01 3.80603969e-01 5.72619811e-02 2.01564461e-01
-7.36893015e-03 -9.26342428e-01 -7.71158874e-01 -1.06467962e+00
1.53041229e-01 6.22537673e-01 -3.79260629e-01 -2.79701740... | [12.52906608581543, -0.20328475534915924] |
fd0d3228-2447-4cba-b030-a1be7af14e6a | lesionpaste-one-shot-anomaly-detection-for | 2203.06354 | null | https://arxiv.org/abs/2203.06354v1 | https://arxiv.org/pdf/2203.06354v1.pdf | LesionPaste: One-Shot Anomaly Detection for Medical Images | Due to the high cost of manually annotating medical images, especially for large-scale datasets, anomaly detection has been explored through training models with only normal data. Lacking prior knowledge of true anomalies is the main reason for the limited application of previous anomaly detection methods, especially i... | ['Xiaoying Tang', 'Yijin Huang', 'Weikai Huang'] | 2022-03-12 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 6.98200285e-01 2.47711703e-01 1.13810122e-01 -4.38115418e-01
-7.85362303e-01 -3.59143950e-02 3.91628563e-01 5.65291047e-01
-3.66758853e-01 4.19154197e-01 -1.80067077e-01 -3.96442600e-02
7.49886408e-02 -5.64596236e-01 -5.98530948e-01 -8.86154473e-01
-1.42748216e-02 4.19785082e-01 3.69275361e-01 1.18448474... | [7.641907691955566, 2.1407017707824707] |
af3d3a28-b3c9-4c70-873d-4086e6fec012 | analysing-the-effect-of-clarifying-questions | 2008.03717 | null | https://arxiv.org/abs/2008.03717v2 | https://arxiv.org/pdf/2008.03717v2.pdf | Analysing the Effect of Clarifying Questions on Document Ranking in Conversational Search | Recent research on conversational search highlights the importance of mixed-initiative in conversations. To enable mixed-initiative, the system should be able to ask clarifying questions to the user. However, the ability of the underlying ranking models (which support conversational search) to account for these clarify... | ['Nikos Voskarides', 'Mohammad Aliannejadi', 'Evangelos Kanoulas', 'Antonios Minas Krasakis'] | 2020-08-09 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.37561485e-01 6.02953613e-01 -1.15149550e-01 -3.35567385e-01
-1.00723255e+00 -9.52467501e-01 1.18241060e+00 2.59867370e-01
-4.78488386e-01 7.46113002e-01 1.27482319e+00 -6.66806161e-01
-4.36779767e-01 -3.44177544e-01 -1.76372364e-01 6.34268522e-02
4.52620894e-01 8.12614381e-01 3.12756568e-01 -7.39394367... | [12.134659767150879, 7.817296028137207] |
1de85c6b-3d0f-4e59-89a2-a4535578a5e3 | supervised-anomaly-detection-method-combining | 2212.11507 | null | https://arxiv.org/abs/2212.11507v1 | https://arxiv.org/pdf/2212.11507v1.pdf | Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections | The external visual inspections of rolling stock's underfloor equipment are currently being performed via human visual inspection. In this study, we attempt to partly automate visual inspection by investigating anomaly inspection algorithms that use image processing technology. As the railroad maintenance studies tend ... | ['Gaurang Gavai', 'Ryosuke Mori', 'Takuro Hoshi', 'Yohei Baba'] | 2022-12-22 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 2.24806368e-01 5.39100403e-03 6.96852744e-01 -7.51937255e-02
1.93671316e-01 -4.16241199e-01 1.45367011e-01 -1.56290874e-01
3.80326919e-02 1.57063767e-01 -7.01859117e-01 -4.13781375e-01
1.15382008e-01 -8.75519872e-01 -5.97117543e-01 -6.99068189e-01
1.89033404e-01 1.85121328e-01 4.12093848e-01 -5.00958681... | [7.5145344734191895, 2.0634045600891113] |
caf497f6-6455-4573-baf4-06bf4d5c876f | nerf-lidar-generating-realistic-lidar-point | 2304.14811 | null | https://arxiv.org/abs/2304.14811v1 | https://arxiv.org/pdf/2304.14811v1.pdf | NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields | Labeling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently. Recently, Neural Radiance Fields (NeRF) have been proposed for novel view synthesis ... | ['Li Zhang', 'Shaochen Kuang', 'Feihu Zhang', 'Junge Zhang'] | 2023-04-28 | null | null | null | null | ['self-driving-cars', 'point-cloud-generation', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.09339602e-01 -2.62665981e-03 -4.67485376e-03 -7.38414049e-01
-5.71475506e-01 -5.56702554e-01 6.53425097e-01 -1.34043083e-01
-2.92376757e-01 7.10149646e-01 -6.96049929e-01 -6.60491586e-01
2.05231488e-01 -1.36365867e+00 -1.02129340e+00 -2.91060954e-01
1.62177742e-01 1.12181163e+00 3.35529745e-01 -2.99125403... | [8.189236640930176, -2.635352611541748] |
aebcdf6c-5a20-4945-bb2c-7a909447cb81 | photi-lakeice-dataset | null | null | https://arxiv.org/abs/2002.07875v1 | https://arxiv.org/pdf/2002.07875.pdf | Photi-LakeIce Dataset | Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way... | ['Laura Leal-Taixe', 'Manu Tom', 'Konrad Schindler', 'Emmanuel Baltsavias', 'Rajanie Prabha', 'Mathias Rothermel'] | 2020-02-18 | null | null | null | isprs-congress-2020-2 | ['webcam-rgb-image-classification', 'lake-ice-detection', 'lake-detection', 'change-detection-for-remote-sensing-images', 'lake-ice-detection', 'segmentation-of-remote-sensing-imagery', 'remote-sensing-image-classification', 'the-semantic-segmentation-of-remote-sensing'] | ['computer-code', 'computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous', 'miscellaneous'] | [ 8.71278625e-03 -3.46492529e-01 3.50989223e-01 -6.04254961e-01
-6.13637686e-01 -1.03600073e+00 4.65838879e-01 -1.69990629e-01
-4.92178231e-01 4.57403183e-01 -2.11652771e-01 -3.59030217e-01
5.72577000e-01 -8.23001027e-01 -9.09013331e-01 -7.37634897e-01
-2.62335837e-01 3.73237729e-01 3.06418955e-01 -4.29409385... | [9.493273735046387, -1.5762608051300049] |
9d7b2b91-777a-44e4-aeff-db9b2494171c | probing-the-intra-component-correlations | 1604.04473 | null | http://arxiv.org/abs/1604.04473v1 | http://arxiv.org/pdf/1604.04473v1.pdf | Probing the Intra-Component Correlations within Fisher Vector for Material Classification | Fisher vector (FV) has become a popular image representation. One notable
underlying assumption of the FV framework is that local descriptors are well
decorrelated within each cluster so that the covariance matrix for each
Gaussian can be simplified to be diagonal. Though the FV usually relies on the
Principal Componen... | ['Matti Pietikäinen', 'Xiaopeng Hong', 'Xianbiao Qi', 'Guoying Zhao'] | 2016-04-15 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [-1.01281397e-01 -5.06244659e-01 6.26885891e-03 -1.73467949e-01
-4.14978445e-01 -5.72218478e-01 7.12029636e-01 -1.03122681e-01
-1.58199579e-01 2.27228835e-01 2.03990683e-01 7.33494312e-02
-3.49397480e-01 -3.45381618e-01 -4.54425067e-01 -1.27025652e+00
1.41544700e-01 3.68722552e-03 2.97606587e-01 1.63304389... | [7.92578125, 4.120667934417725] |
16955120-41dd-44d5-8790-f24a69f3534b | quantum-model-discovery | 2111.06376 | null | https://arxiv.org/abs/2111.06376v1 | https://arxiv.org/pdf/2111.06376v1.pdf | Quantum Model-Discovery | Quantum computing promises to speed up some of the most challenging problems in science and engineering. Quantum algorithms have been proposed showing theoretical advantages in applications ranging from chemistry to logistics optimization. Many problems appearing in science and engineering can be rewritten as a set of ... | ['Vincent E. Elfving', 'Oleksandr Kyriienko', 'Atiyo Ghosh', 'Niklas Heim'] | 2021-11-11 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.35446653e-01 -3.91961128e-01 1.21564776e-01 -2.73204297e-01
-8.41124952e-01 -8.47059906e-01 4.12594169e-01 -2.79116295e-02
-3.35737169e-01 8.07899177e-01 -6.48548901e-01 -4.57711339e-01
-4.59481835e-01 -1.02154934e+00 -7.06896365e-01 -1.16720581e+00
1.11623488e-01 7.99704075e-01 -1.94398314e-01 -6.98582411... | [5.6146321296691895, 4.913527011871338] |
0526c485-947f-4461-b6c6-e7c914622f55 | multi-class-versus-one-class-classifier-in | 2203.10837 | null | https://arxiv.org/abs/2203.10837v1 | https://arxiv.org/pdf/2203.10837v1.pdf | Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis | Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously unseen class or are not class data. In this case when there are not enough data to train two-class One-class classification appear ... | ['Pilar Calvo', 'Fernando Zelarin', 'Jordi Solé-Casals', 'Marcos Faundez-Zanuy', 'K. López-de-Ipiña'] | 2022-03-21 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 2.47063100e-01 2.53587961e-01 1.60888180e-01 -5.50449789e-01
-5.60343146e-01 -4.16491181e-02 4.15513217e-01 7.56271064e-01
-3.94456744e-01 1.01140058e+00 1.77656904e-01 1.10303916e-01
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-2.05970466e-01 7.75399864e-01 4.11753416e-01 -2.69379795... | [13.981186866760254, 3.335956573486328] |
44e22210-1880-4f01-a784-5724592786e8 | ensure-the-correctness-of-the-summary | null | null | https://aclanthology.org/C18-1121 | https://aclanthology.org/C18-1121.pdf | Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization | In this paper, we investigate the sentence summarization task that produces a summary from a source sentence. Neural sequence-to-sequence models have gained considerable success for this task, while most existing approaches only focus on improving the informativeness of the summary, which ignore the correctness, i.e., ... | ['Cheng-qing Zong', 'Junnan Zhu', 'Jiajun Zhang', 'Haoran Li'] | 2018-08-01 | ensure-the-correctness-of-the-summary-1 | https://aclanthology.org/C18-1121 | https://aclanthology.org/C18-1121.pdf | coling-2018-8 | ['summarization', 'abstractive-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.37023354e-01 6.61859453e-01 -2.68686205e-01 -6.57277524e-01
-1.29688811e+00 -5.51851213e-01 5.58710337e-01 4.14043844e-01
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5.16898513e-01 2.62236536e-01 -6.76927343e-02 -2.53163904... | [12.451476097106934, 9.45429515838623] |
e8148034-bbdb-40d0-a1d6-1e9e0e299b70 | audio-visual-character-profiles-for-detecting | 2203.11368 | null | https://arxiv.org/abs/2203.11368v1 | https://arxiv.org/pdf/2203.11368v1.pdf | Audio visual character profiles for detecting background characters in entertainment media | An essential goal of computational media intelligence is to support understanding how media stories -- be it news, commercial or entertainment media -- represent and reflect society and these portrayals are perceived. People are a central element of media stories. This paper focuses on understanding the representation ... | ['Shrikanth Narayanan', 'Rahul Sharma'] | 2022-03-21 | null | null | null | null | ['active-speaker-localization'] | ['audio'] | [ 4.97357816e-01 -1.29625350e-05 -1.54338881e-01 -4.58549410e-01
-8.47588241e-01 -6.83967352e-01 8.27674210e-01 5.31458743e-02
-8.08986798e-02 2.89616406e-01 6.40535772e-01 2.46494725e-01
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7.68532306e-02 4.20374393e-01 3.53771627e-01 -1.34733140... | [14.40358829498291, 5.06675386428833] |
fed79abc-4c28-4a63-9941-d36d97ce625a | improving-deep-learning-based-semi-supervised | 2102.08183 | null | https://arxiv.org/abs/2102.08183v2 | https://arxiv.org/pdf/2102.08183v2.pdf | Comparison of semi-supervised deep learning algorithms for audio classification | In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are single-model methods ... | ['Thomas Pellegrini', 'Etienne Labbé', 'Léo Cances'] | 2021-02-16 | null | null | null | null | ['audio-tagging', 'environmental-sound-classification', 'sound-classification'] | ['audio', 'audio', 'audio'] | [-1.35421485e-01 -2.27803111e-01 6.69662729e-02 -1.12793647e-01
-1.18008399e+00 -4.63754088e-01 4.99337167e-01 -1.70904323e-01
-5.83187819e-01 6.09935343e-01 1.70249850e-01 -4.80235636e-01
1.44162223e-01 -4.94368136e-01 -7.34288633e-01 -5.04836142e-01
-1.32057786e-01 1.63330883e-01 2.08945379e-01 -1.82886228... | [15.151320457458496, 5.205790996551514] |
dcf89f57-e140-4e95-866e-648bd5eb3ff7 | deep-depth-estimation-from-thermal-image | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shin_Deep_Depth_Estimation_From_Thermal_Image_CVPR_2023_paper.pdf | Deep Depth Estimation From Thermal Image | Robust and accurate geometric understanding against adverse weather conditions is one top prioritized conditions to achieve a high-level autonomy of self-driving cars. However, autonomous driving algorithms relying on the visible spectrum band are easily impacted by weather and lighting conditions. A long-wave infr... | ['In So Kweon', 'Jinsun Park', 'Ukcheol Shin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['stereo-depth-estimation', 'self-driving-cars'] | ['computer-vision', 'computer-vision'] | [ 3.57476920e-02 -5.61345279e-01 -1.59091845e-01 -7.10638821e-01
-6.90033853e-01 -5.47327161e-01 5.72444081e-01 -5.83937287e-01
-4.62995380e-01 7.45366096e-01 -1.30960479e-01 -3.40255022e-01
-7.23678526e-03 -1.12620032e+00 -5.49891710e-01 -9.45930183e-01
4.77089405e-01 1.08979397e-01 1.82209879e-01 -4.58257914... | [8.498821258544922, -2.335845470428467] |
827c7f96-cdda-4f45-aaee-e51e9a757c86 | mining-themes-in-clinical-notes-to-identify | 2305.19373 | null | https://arxiv.org/abs/2305.19373v1 | https://arxiv.org/pdf/2305.19373v1.pdf | Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure | Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and ... | ['Mia Cajita', 'Lingwei Chen', 'Krishnaprasad Thirunarayan', 'William L. Romine', 'Tanvi Banerjee', 'Ankita Agarwal'] | 2023-05-30 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 2.55844817e-02 1.87246680e-01 -1.97767600e-01 -2.76505560e-01
-6.03196502e-01 -4.07098383e-01 -2.49507487e-01 1.07705891e+00
3.54028791e-02 7.88622320e-01 7.33939886e-01 -5.49987495e-01
-9.29676771e-01 -7.18002200e-01 1.93673119e-01 -6.34548545e-01
-4.31899965e-01 1.06281781e+00 -3.42098832e-01 5.59676528... | [8.030925750732422, 6.35596227645874] |
7c22d574-b991-4afe-b498-e50105c59fce | 3dctn-3d-convolution-transformer-network-for | 2203.00828 | null | https://arxiv.org/abs/2203.00828v1 | https://arxiv.org/pdf/2203.00828v1.pdf | 3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification | Although accurate and fast point cloud classification is a fundamental task in 3D applications, it is difficult to achieve this purpose due to the irregularity and disorder of point clouds that make it challenging to achieve effective and efficient global discriminative feature learning. Lately, 3D Transformers have be... | ['Jonathan Li', 'Linlin Xu', 'Qian Xie', 'Dening Lu'] | 2022-03-02 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.32699677e-01 -8.65816176e-01 2.58186400e-01 -4.05011058e-01
-4.48195875e-01 -2.55520344e-01 5.53522587e-01 5.92221320e-02
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-2.69879609e-01 -1.25746930e+00 -5.84706962e-01 -7.13042915e-01
-8.49361569e-02 2.74758518e-01 6.03842199e-01 -1.37456700... | [7.91877555847168, -3.511536121368408] |
f48adae2-0807-440a-a2e6-fe4047b43390 | predicting-multiple-icd-10-codes-from | 2008.01515 | null | https://arxiv.org/abs/2008.01515v1 | https://arxiv.org/pdf/2008.01515v1.pdf | Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes | ICD coding from electronic clinical records is a manual, time-consuming and expensive process. Code assignment is, however, an important task for billing purposes and database organization. While many works have studied the problem of automated ICD coding from free text using machine learning techniques, most use recor... | ['Marcia M. de Souza e Sá', 'Saulo Pedro', 'Danilo Silva', 'Arthur D. Reys', 'Daniel Severo', 'Guilherme A. C. Salgado'] | 2020-07-29 | null | null | null | null | ['multi-label-classification-of-biomedical'] | ['medical'] | [-4.05126587e-02 1.43836588e-01 -2.45988458e-01 -4.98546988e-01
-1.14223921e+00 -2.73021251e-01 -1.87530071e-01 7.17192829e-01
-5.24914205e-01 7.37898767e-01 6.75551355e-01 -7.02266574e-01
-1.31840929e-01 -7.29851782e-01 -3.48180890e-01 -2.39702672e-01
-8.20321590e-02 8.91612768e-01 -4.63248760e-01 7.32985809... | [8.004000663757324, 6.8070502281188965] |
be25d8f7-0bde-470a-a616-b5f8fdb6562f | alternating-gradient-descent-and-mixture-of | 2305.06324 | null | https://arxiv.org/abs/2305.06324v1 | https://arxiv.org/pdf/2305.06324v1.pdf | Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception | We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a novel design that combin... | ['Hartwig Adam', 'Huisheng Wang', 'Rachel Hornung', 'Yin Cui', 'Dan Kondratyuk', 'Hassan Akbari'] | 2023-05-10 | null | null | null | null | ['zero-shot-action-recognition', 'video-classification', 'video-text-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.06773654e-01 -4.24322188e-01 -4.26424474e-01 -2.45387420e-01
-1.44061542e+00 -3.92741770e-01 5.87449610e-01 -2.40547955e-02
-7.10052788e-01 4.78905499e-01 2.76513666e-01 6.61720261e-02
1.92890808e-01 -1.60025805e-01 -9.99730408e-01 -5.47820985e-01
1.04881287e-01 2.85076678e-01 1.44482851e-01 -1.24709524... | [10.247126579284668, 1.1880720853805542] |
a158837a-1cf5-4326-897e-9b244d379946 | learning-situation-hyper-graphs-for-video | 2304.08682 | null | https://arxiv.org/abs/2304.08682v2 | https://arxiv.org/pdf/2304.08682v2.pdf | Learning Situation Hyper-Graphs for Video Question Answering | Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for ... | ['Mubarak Shah', 'Niels Lobo', 'Chuang Gan', 'Walid Bousselham', 'Kim Chheu', 'Bo Wu', 'Hilde Kuehne', 'Aisha Urooj Khan'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Urooj_Learning_Situation_Hyper-Graphs_for_Video_Question_Answering_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.74913675e-01 2.71847516e-01 2.00132176e-01 -5.57831526e-01
-6.03579640e-01 -3.26007426e-01 4.60550129e-01 2.20632344e-01
-9.12455916e-02 4.30040583e-02 4.87702727e-01 -1.32228851e-01
-7.72518516e-02 -6.83201492e-01 -9.75510538e-01 -2.68286675e-01
-2.26552457e-01 2.79673696e-01 5.45908630e-01 -1.12797961... | [10.374966621398926, 1.0219171047210693] |
bfa131e2-99e0-4ca1-87a1-c3e6bf7b702a | multimodal-transformer-for-multimodal-machine | null | null | https://aclanthology.org/2020.acl-main.400 | https://aclanthology.org/2020.acl-main.400.pdf | Multimodal Transformer for Multimodal Machine Translation | Multimodal Machine Translation (MMT) aims to introduce information from other modality, generally static images, to improve the translation quality. Previous works propose various incorporation methods, but most of them do not consider the relative importance of multiple modalities. Equally treating all modalities may ... | ['Xiaojun Wan', 'Shaowei Yao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.24381316e-01 -2.58091241e-02 -4.97318119e-01 -1.09500974e-01
-8.90077770e-01 -4.56720412e-01 8.34960461e-01 -1.28269970e-01
-2.59804785e-01 7.36129522e-01 6.57783866e-01 -2.45237797e-01
4.58503276e-01 -3.99383754e-01 -8.44683349e-01 -6.95027888e-01
6.55016303e-01 2.30299324e-01 -7.47982115e-02 -3.00190568... | [11.47227668762207, 1.4754737615585327] |
2daa39e5-1868-4a4f-924d-93055755b713 | probing-pretrained-models-of-source-code | 2202.08975 | null | https://arxiv.org/abs/2202.08975v3 | https://arxiv.org/pdf/2202.08975v3.pdf | Probing Pretrained Models of Source Code | Deep learning models are widely used for solving challenging code processing tasks, such as code generation or code summarization. Traditionally, a specific model architecture was carefully built to solve a particular code processing task. However, recently general pretrained models such as CodeBERT or CodeT5 have been... | ['Nadezhda Chirkova', 'Sergey Troshin'] | 2022-02-16 | null | null | null | null | ['variable-misuse'] | ['computer-code'] | [ 1.32056415e-01 2.89202891e-02 -5.82417428e-01 -5.76115191e-01
-5.80003262e-01 -6.64541423e-01 4.16719019e-01 4.12880063e-01
3.74995545e-02 1.87280595e-01 3.60368162e-01 -7.66792893e-01
1.04270682e-01 -5.99129856e-01 -9.94680226e-01 3.68996225e-02
-4.28916931e-01 2.03101873e-01 1.12611935e-01 -2.44637113... | [7.6255059242248535, 7.815811634063721] |
21c784f5-13aa-4294-9a9b-4450a503b6f2 | rcaa-relational-context-aware-agents-for | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaojun_Chang_RCAA_Relational_Context-Aware_ECCV_2018_paper.pdf | RCAA: Relational Context-Aware Agents for Person Search | We aim to search for a target person from a gallery of whole scene images for which the annotations of pedestrian bounding boxes are unavailable. Previous approaches to this problem have relied on a pedestrian proposal net, which may generate redundant proposals and increase the computational burden. In this paper, we ... | ['Yi-Dong Shen', 'Po-Yao Huang', 'Xiaojun Chang', 'Xiaodan Liang', 'Alexander G. Hauptmann', 'Yi Yang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['person-search'] | ['computer-vision'] | [-2.90083438e-01 -2.56213337e-01 -8.39394182e-02 -4.29037631e-01
-6.21165395e-01 -3.11037153e-01 6.65788472e-01 6.59600794e-02
-9.87370551e-01 6.44913018e-01 2.01010093e-01 1.85766608e-01
2.38775209e-01 -8.47862482e-01 -5.32221198e-01 -6.52246535e-01
1.32104114e-01 4.99748319e-01 8.99351060e-01 4.37344611... | [14.765275955200195, 0.8310834169387817] |
25adeb47-fba1-451d-a096-67433da8fefa | college-student-retention-risk-analysis-from | 2109.05178 | null | https://arxiv.org/abs/2109.05178v1 | https://arxiv.org/pdf/2109.05178v1.pdf | College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion | We develop a Multimodal Spatiotemporal Neural Fusion network for Multi-Task Learning (MSNF-MTCL) to predict 5 important students' retention risks: future dropout, next semester dropout, type of dropout, duration of dropout and cause of dropout. First, we develop a general purpose multi-modal neural fusion network model... | ['Mohammad Arif Ul Alam'] | 2021-09-11 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-2.02261820e-01 -3.93709660e-01 -4.49613959e-01 -5.48066080e-01
-1.06513393e+00 -3.78797948e-01 2.72521585e-01 6.07800305e-01
-6.67997479e-01 6.56903148e-01 6.99005663e-01 -6.91218317e-01
-7.47675359e-01 -8.77777576e-01 -7.54182756e-01 -3.29855382e-01
9.39070657e-02 -8.03846493e-02 -1.67311132e-02 -2.47682676... | [10.098544120788574, 7.164192199707031] |
0fee637f-237a-43e3-b06f-a609f665aa3a | co-occurrence-based-texture-synthesis | 2005.08186 | null | https://arxiv.org/abs/2005.08186v2 | https://arxiv.org/pdf/2005.08186v2.pdf | Co-occurrence Based Texture Synthesis | As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate. In this work, we turn to co-occurrence statistics, which have long been used for texture analysis, to learn a controllable texture synthesis model. We propose a full... | ['Hadar Averbuch-Elor', 'Shai Avidan', 'Anna Darzi', 'Itai Lang', 'Ashutosh Taklikar'] | 2020-05-17 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 5.77205122e-01 3.92955571e-01 -2.12692246e-02 -4.15043771e-01
-6.52206004e-01 -7.60831416e-01 6.13812327e-01 -2.57825762e-01
3.24471831e-01 5.92246115e-01 3.90172042e-02 -2.10256800e-01
2.27901470e-02 -1.10577404e+00 -1.06820428e+00 -8.11986268e-01
8.12638327e-02 2.17279941e-01 -2.99635738e-01 -2.46257693... | [11.682534217834473, -0.48991966247558594] |
6650b719-85c7-4213-970a-e8d14937a3e4 | fast-monocular-scene-reconstruction-with-1 | 2305.13220 | null | https://arxiv.org/abs/2305.13220v1 | https://arxiv.org/pdf/2305.13220v1.pdf | Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids | Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions. The reliance on Multilayer Perceptrons (MLP), however, si... | ['Anima Anandkumar', 'Yuke Zhu', 'Or Litany', 'Charles Loop', 'Chris Choy', 'Wei Dong'] | 2023-05-22 | fast-monocular-scene-reconstruction-with | http://openaccess.thecvf.com//content/CVPR2023/html/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023_paper.pdf | cvpr-2023-1 | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 3.13435704e-01 -1.17820270e-01 1.12157263e-01 -7.63467312e-01
-7.09939063e-01 -4.21929598e-01 6.37475491e-01 -5.32990061e-02
-3.75246137e-01 9.01459396e-01 8.72234628e-02 -2.08686203e-01
1.43566802e-01 -1.10618210e+00 -1.04438972e+00 -2.35781908e-01
8.58225748e-02 5.97258449e-01 3.04929554e-01 1.86900839... | [8.868870735168457, -2.9965972900390625] |
d2d5b705-4b35-4911-b0bf-6fe346e09f62 | mot-masked-optimal-transport-for-partial | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.pdf | MOT: Masked Optimal Transport for Partial Domain Adaptation | As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models under changing environments. However, there are still limitations, including strict prior assumption and implicit alignment, for current OT modeling in challengi... | ['Chuan-Xian Ren', 'You-Wei Luo'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['partial-domain-adaptation'] | ['methodology'] | [ 5.38796186e-01 -1.89903662e-01 -3.41060817e-01 -5.10768056e-01
-2.76563972e-01 -3.31281483e-01 5.28797150e-01 -1.10878840e-01
-3.52310061e-01 9.74028766e-01 6.93321750e-02 -1.35708109e-01
-2.76048928e-01 -4.29256260e-01 -7.91070879e-01 -1.00893915e+00
6.37970045e-02 1.87015057e-01 3.28180790e-01 1.35106087... | [9.828239440917969, 1.9415026903152466] |
adb4bcc5-fc61-4a65-a153-36ed956eae8a | an-end-to-end-document-level-neural-discourse | 2012.11169 | null | https://arxiv.org/abs/2012.11169v1 | https://arxiv.org/pdf/2012.11169v1.pdf | An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations | Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging. Challenges include the deep structure of document-level discourse trees, the requirement of subtle semantic judgments, and the lack of large-scale training corpora. To address such challenges, we... | ['Nancy F. Chen', 'Zhengyuan Liu', 'Ke Shi'] | 2020-12-21 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 5.37337184e-01 7.45517373e-01 -3.80478173e-01 -6.01455033e-01
-1.01969171e+00 -6.52451754e-01 9.27059770e-01 4.57912356e-01
-2.90768325e-01 5.31370819e-01 1.08980715e+00 -6.14208281e-01
2.61659354e-01 -7.84817517e-01 -5.99108338e-01 -7.48254210e-02
-8.81229714e-03 2.41150290e-01 3.58075947e-01 -3.87503535... | [10.722044944763184, 9.371613502502441] |
5d88b3f6-a940-4282-9272-df860cb192ef | temporal-kernel-consistency-for-blind-video | 2108.08305 | null | https://arxiv.org/abs/2108.08305v1 | https://arxiv.org/pdf/2108.08305v1.pdf | Temporal Kernel Consistency for Blind Video Super-Resolution | Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation. These models are able to accurately estimate the unknown downscaling kernel from a given low-resolution (LR) image in order to leverage the kernel during restoration. Al... | ['Hongkai Wen', 'Nicholas D. Lane', 'Mohamed S. Abdelfattah', 'Royson Lee', 'Lichuan Xiang'] | 2021-08-18 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.86014235e-01 -5.53491831e-01 4.50599454e-02 5.00350185e-02
-8.48716259e-01 -4.37892735e-01 4.62523252e-01 -5.52875578e-01
-2.58571923e-01 6.95420146e-01 4.25071180e-01 1.21449493e-02
-2.49737114e-01 -1.78480402e-01 -8.97026360e-01 -8.57928991e-01
-3.33395511e-01 -2.30257332e-01 4.30216253e-01 -7.16274679... | [11.223078727722168, -2.1880500316619873] |
ddd82d64-8aa0-4e16-acbf-ebce573de7fb | a-high-efficiency-model-indicating-the-role | 2304.00333 | null | https://arxiv.org/abs/2304.00333v1 | https://arxiv.org/pdf/2304.00333v1.pdf | A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury | Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical prop... | ['Stanislav M. Mintchev', 'Brian L. Frost'] | 2023-04-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 5.13407946e-01 -2.69402027e-01 7.10122287e-01 -6.85646012e-03
1.78338468e-01 -4.95537907e-01 5.80653250e-01 1.85934231e-01
-8.86280239e-01 1.00136721e+00 1.76372230e-02 -1.92421317e-01
-1.79309919e-01 -7.53167450e-01 -9.64221954e-01 -1.20602512e+00
-2.00547293e-01 3.55870485e-01 6.98005259e-01 -1.43756494... | [8.034697532653809, 2.81843900680542] |
1b5d4010-a35d-4678-88ce-1eecce736036 | discourse-aware-neural-extractive-model-for | 1910.14142 | null | https://arxiv.org/abs/1910.14142v2 | https://arxiv.org/pdf/1910.14142v2.pdf | Discourse-Aware Neural Extractive Text Summarization | Recently BERT has been adopted for document encoding in state-of-the-art text summarization models. However, sentence-based extractive models often result in redundant or uninformative phrases in the extracted summaries. Also, long-range dependencies throughout a document are not well captured by BERT, which is pre-tra... | ['Zhe Gan', 'Yu Cheng', 'Jingjing Liu', 'Jiacheng Xu'] | 2019-10-30 | discourse-aware-neural-extractive-text | https://aclanthology.org/2020.acl-main.451 | https://aclanthology.org/2020.acl-main.451.pdf | acl-2020-6 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.75601196e-01 7.50844300e-01 -5.02696991e-01 -3.43452543e-01
-1.05048037e+00 -5.75569510e-01 7.12270498e-01 9.06062365e-01
-3.23737562e-01 1.08884716e+00 1.40292490e+00 -1.01172067e-01
4.53218259e-02 -7.86961734e-01 -7.80461550e-01 -1.24512285e-01
-2.25386679e-01 6.44656777e-01 1.58466429e-01 -4.36304390... | [12.504447937011719, 9.495585441589355] |
ba1c60a0-98e2-439e-b3eb-af23b10cabb6 | hyfactor-hydrogen-count-labelled-graph-based | null | null | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/61aa38576d4e8f3bdba8aead/original/hy-factor-hydrogen-count-labelled-graph-based-defactorization-autoencoder.pdf | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/61aa38576d4e8f3bdba8aead/original/hy-factor-hydrogen-count-labelled-graph-based-defactorization-autoencoder.pdf | HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder | Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture and based on the hydrogen labeled graphs. Since the original DEFactor code was not available, its ne... | ['Alexandre Varnek', 'Timur Madzhidov', 'Evgenii Ziaikin', 'Daniyar Mazitov', 'Arkadii Lin', 'Tagir Akhmetshin'] | 2021-12-06 | null | null | null | chemrxiv-2021-12 | ['graph-sampling'] | ['graphs'] | [ 2.14940608e-01 1.40541524e-01 -8.89894441e-02 -1.43962830e-01
-7.76224673e-01 -6.56858087e-01 5.77074885e-01 6.16023302e-01
-2.76381016e-01 1.48532653e+00 1.72433898e-01 -5.74001133e-01
6.00149408e-02 -7.89973974e-01 -7.65271306e-01 -8.33341062e-01
2.06170082e-01 5.63477397e-01 3.22589338e-01 -3.63142431... | [4.765608310699463, 5.819337844848633] |
7bbcfaa8-fbf9-4b35-90b4-f2d7f01da5c4 | ad-pt-autonomous-driving-pre-training-with | 2306.00612 | null | https://arxiv.org/abs/2306.00612v2 | https://arxiv.org/pdf/2306.00612v2.pdf | AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset | It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks. Previous works mainly focus on the self-supervised pre-training pipeline, mea... | ['Yu Qiao', 'Yikang Li', 'Botian Shi', 'Tao Chen', 'Xiangchao Yan', 'Bo Zhang', 'Jiakang Yuan'] | 2023-06-01 | null | null | null | null | ['point-cloud-pre-training'] | ['computer-vision'] | [ 9.28124264e-02 -9.82763246e-02 -3.75619680e-01 -5.97837210e-01
-8.89856875e-01 -5.59253335e-01 6.67399645e-01 5.11441790e-02
-1.30324528e-01 3.39672208e-01 -1.17576867e-01 -2.88147479e-01
1.26736332e-02 -9.91752446e-01 -1.15789235e+00 -5.77225327e-01
1.05538636e-01 6.62238777e-01 7.11441994e-01 -6.35625124... | [8.179811477661133, -2.634601354598999] |
4366dfbd-8c3c-4d86-a1f0-76101632f1b1 | learning-relational-representations-by | null | null | https://aclanthology.org/N19-1327 | https://aclanthology.org/N19-1327.pdf | Learning Relational Representations by Analogy using Hierarchical Siamese Networks | We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. Following this idea, we collect a large set of analog... | ['Alfio Gliozzo', 'Michael Glass', 'Robert Farrell', 'Nicolas Fauceglia', 'Gaetano Rossiello'] | 2019-06-01 | null | https://openreview.net/forum?id=SkxE1b56TQ | https://openreview.net/pdf?id=SkxE1b56TQ | naacl-2019-6 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 1.78652778e-01 8.77126455e-01 -4.77691948e-01 -2.67066002e-01
-6.27774775e-01 -5.70018291e-01 8.14005554e-01 6.70280814e-01
-5.22505343e-01 9.05380607e-01 3.63702863e-01 -2.17295930e-01
-2.37007946e-01 -1.32482755e+00 -9.28865612e-01 -3.93889964e-01
-1.01708323e-01 8.91528487e-01 4.77836221e-01 -7.05440402... | [9.50476360321045, 8.598166465759277] |
7cc96841-ffe6-46b3-8c4e-85618e399dbd | evaluation-of-regularization-based-continual | 2304.13327 | null | https://arxiv.org/abs/2304.13327v1 | https://arxiv.org/pdf/2304.13327v1.pdf | Evaluation of Regularization-based Continual Learning Approaches: Application to HAR | Pervasive computing allows the provision of services in many important areas, including the relevant and dynamic field of health and well-being. In this domain, Human Activity Recognition (HAR) has gained a lot of attention in recent years. Current solutions rely on Machine Learning (ML) models and achieve impressive r... | ['Philippe Lalanda', 'Sandra Castellanos-Paez', 'Bonpagna Kann'] | 2023-04-26 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.46797419e-01 -1.50675341e-01 -3.83739978e-01 -1.53500706e-01
-6.40572131e-01 9.67124104e-02 6.43048108e-01 2.01813847e-01
-6.46870732e-01 1.07412291e+00 3.14462155e-01 2.30857804e-01
-4.16459233e-01 -4.28808749e-01 -4.01575387e-01 -8.27143192e-01
-5.01591504e-01 5.56454100e-02 2.90201008e-01 -5.96971512... | [7.581409931182861, 0.9606450796127319] |
a2d46f3b-8754-4077-80c3-353528579ee5 | neural-markov-logic-networks | 1905.13462 | null | https://arxiv.org/abs/1905.13462v3 | https://arxiv.org/pdf/1905.13462v3.pdf | Neural Markov Logic Networks | We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-ord... | ['Ondřej Kuželka', 'Giuseppe Marra'] | 2019-05-31 | neural-markov-logic-networks-1 | https://openreview.net/forum?id=SkeGvaEtPr | https://openreview.net/pdf?id=SkeGvaEtPr | iclr-2020-1 | ['triple-classification'] | ['graphs'] | [ 1.21912651e-01 1.06483781e+00 -6.81357861e-01 -4.27785814e-01
-1.92690939e-01 -4.58493561e-01 9.96656179e-01 4.01207268e-01
3.00686210e-02 9.88985181e-01 1.18580788e-01 -9.71451938e-01
-3.26666057e-01 -1.53691268e+00 -1.37633157e+00 -3.46879065e-01
-5.26700437e-01 8.27069581e-01 3.22990417e-01 -3.15788895... | [8.751395225524902, 7.234353065490723] |
316487f9-329e-47fa-a6b9-2c0e4a940578 | parallel-context-windows-improve-in-context | 2212.10947 | null | https://arxiv.org/abs/2212.10947v2 | https://arxiv.org/pdf/2212.10947v2.pdf | Parallel Context Windows for Large Language Models | When applied for processing long text, Large Language Models (LLMs) are limited by their context window. Existing efforts to address this limitation involve training specialized architectures, and cannot be easily applied to off-the-shelf LLMs. We present Parallel Context Windows (PCW), a method that alleviates the con... | ['Ehud Karpas', 'Omri Abend', 'Inbal Magar', 'Yoav Shoham', 'Kevin Leyton-Brown', 'Amnon Shashua', 'Ori Ram', 'Yonatan Belinkov', 'Yoav Levine', 'Nir Ratner'] | 2022-12-21 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.44111380e-01 -1.99558556e-01 -1.72842294e-01 -3.55271280e-01
-1.20731723e+00 -7.80459464e-01 6.52417064e-01 4.75687414e-01
-1.02426219e+00 3.55818123e-01 3.89478534e-01 -8.53448451e-01
-1.11236773e-01 -5.16072452e-01 -7.51823902e-01 -2.73357719e-01
-5.05034551e-02 4.93720740e-01 4.78430569e-01 -3.11992168... | [11.06722640991211, 8.150496482849121] |
b03040ba-9f88-48ff-88f6-f4ec436a93c1 | optical-non-line-of-sight-physics-based-3d | 2003.14414 | null | https://arxiv.org/abs/2003.14414v1 | https://arxiv.org/pdf/2003.14414v1.pdf | Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation | We describe a method for 3D human pose estimation from transient images (i.e., a 3D spatio-temporal histogram of photons) acquired by an optical non-line-of-sight (NLOS) imaging system. Our method can perceive 3D human pose by `looking around corners' through the use of light indirectly reflected by the environment. We... | ['Kris Kitani', "Matthew O'Toole", 'Ye Yuan', 'Mariko Isogawa'] | 2020-03-31 | optical-non-line-of-sight-physics-based-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Isogawa_Optical_Non-Line-of-Sight_Physics-Based_3D_Human_Pose_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Isogawa_Optical_Non-Line-of-Sight_Physics-Based_3D_Human_Pose_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['humanoid-control'] | ['robots'] | [ 1.49524704e-01 -1.65423681e-03 4.66054708e-01 -3.82562786e-01
-6.29532039e-01 -3.80726337e-01 3.98927331e-01 -6.50066584e-02
-6.98825121e-01 5.53084016e-01 1.65244281e-01 1.97109595e-01
2.59989142e-01 -6.08732104e-01 -1.10132241e+00 -4.02790874e-01
-7.65651241e-02 8.72517347e-01 2.31394157e-01 -2.11631253... | [6.936097621917725, -1.0464085340499878] |
c5c8c0bf-8d2d-4b4e-9d3a-66e7ce613fd1 | exploring-the-viability-of-synthetic-query | 2305.11944 | null | https://arxiv.org/abs/2305.11944v2 | https://arxiv.org/pdf/2305.11944v2.pdf | Exploring the Viability of Synthetic Query Generation for Relevance Prediction | Query-document relevance prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained) transformer-based models which are finetuned using large collections of labeled data. However, in specialized domains such as e-commerce and healthcare, the viability... | ['Marc Najork', 'Mike Bendersky', 'Kazuma Hashimoto', 'Krishna Srinivasan', 'Karthik Raman', 'Aditi Chaudhary'] | 2023-05-19 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.74484622e-01 2.07457334e-01 -7.28298068e-01 -4.23760325e-01
-1.70729005e+00 -7.61075079e-01 8.30781579e-01 1.83129519e-01
-2.20433965e-01 1.03051567e+00 4.73808944e-01 -6.06754303e-01
-5.34366488e-01 -7.59252727e-01 -6.14210010e-01 -1.52352408e-01
3.52314562e-01 9.97668326e-01 1.57710597e-01 -7.27164865... | [11.447124481201172, 8.018543243408203] |
1bc02c06-5550-4fe9-a402-996b76442184 | a-neuromorphic-hardware-architecture-using | 1507.05695 | null | http://arxiv.org/abs/1507.05695v1 | http://arxiv.org/pdf/1507.05695v1.pdf | A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition | We present a hardware architecture that uses the Neural Engineering Framework
(NEF) to implement large-scale neural networks on Field Programmable Gate
Arrays (FPGAs) for performing pattern recognition in real time. NEF is a
framework that is capable of synthesising large-scale cognitive systems from
subnetworks. We wi... | ['Andre van Schaik', 'Runchun Wang', 'Tara Julia Hamilton', 'Jonathan Tapson', 'Chetan Singh Thakur'] | 2015-07-21 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 5.03998339e-01 -4.08140421e-01 3.29732776e-01 -2.73380131e-01
6.21419370e-01 -4.42879856e-01 5.48666537e-01 -4.18108404e-01
-7.42233872e-01 5.18866837e-01 -6.97892725e-01 -6.36157990e-01
-2.62717038e-01 -1.12728560e+00 -7.95384228e-01 -6.70104861e-01
9.25910398e-02 1.63334504e-01 4.36805189e-01 -3.03017318... | [8.329184532165527, 2.720287799835205] |
207c5b2e-96ee-4314-ac27-142dbacf8f7f | does-the-magic-of-bert-apply-to-medical-code | 2103.06511 | null | https://arxiv.org/abs/2103.06511v2 | https://arxiv.org/pdf/2103.06511v2.pdf | Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study | Unsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in many downstream tasks. In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes su... | ['Pekka Marttinen', 'Matti Hölttä', 'Shaoxiong Ji'] | 2021-03-11 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 4.15594667e-01 3.02156866e-01 -6.02670908e-01 -6.91871881e-01
-1.02029502e+00 -2.47253552e-01 4.63774279e-02 7.73982346e-01
-4.99727577e-01 4.67452914e-01 8.12260628e-01 -8.28723848e-01
-3.71232957e-01 -4.57451433e-01 -2.22586587e-01 -3.26574475e-01
-3.39148819e-01 7.58870006e-01 -3.97884697e-01 -1.16274640... | [8.011225700378418, 6.829153060913086] |
b0a5430a-e8de-4e59-b83f-10bf6c64639a | learning-review-representations-from-user-and | 1909.04455 | null | https://arxiv.org/abs/1909.04455v1 | https://arxiv.org/pdf/1909.04455v1.pdf | Learning review representations from user and product level information for spam detection | Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame. Existing works mainly focus on manually designing discrete textual or behavior features, which cannot capture complex semantics of reviews... | ['Shangwen Lv', 'Qianwen Ma', 'Chunyuan Yuan', 'Wei Zhou', 'Songlin Hu', 'Jizhong Han'] | 2019-09-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-1.95257906e-02 -4.12626937e-02 -4.49112386e-01 -7.03763902e-01
-3.20414424e-01 -1.98205918e-01 8.05800438e-01 7.90998340e-02
-2.90949047e-01 3.39836121e-01 2.41368100e-01 -2.36104712e-01
9.44485739e-02 -7.92719066e-01 -4.86944616e-01 -4.95301485e-01
6.05721533e-01 1.15703240e-01 -1.86819565e-02 -4.87353832... | [7.90991735458374, 9.936957359313965] |
8eeb140d-0b8e-4657-8e5c-a318c6a2a558 | dek-forecaster-a-novel-deep-learning-model | 2306.03412 | null | https://arxiv.org/abs/2306.03412v1 | https://arxiv.org/pdf/2306.03412v1.pdf | DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction | Internet traffic volume estimation has a significant impact on the business policies of the ISP (Internet Service Provider) industry and business successions. Forecasting the internet traffic demand helps to shed light on the future traffic trend, which is often helpful for ISPs decision-making in network planning acti... | ['Anwar Haque', 'Sudipto Baral', 'Sajal Saha'] | 2023-06-06 | null | null | null | null | ['outlier-detection', 'traffic-prediction'] | ['methodology', 'time-series'] | [-9.77953598e-02 -7.08480418e-01 -9.57228839e-02 7.64293130e-03
-3.77275407e-01 -1.92133084e-01 -5.20093627e-02 -8.29929039e-02
-2.16165155e-01 6.45515621e-01 -6.00942783e-02 -5.93183935e-01
-5.25818288e-01 -7.97901928e-01 -3.82050931e-01 -7.52988338e-01
-2.81512827e-01 5.83994389e-02 7.91158453e-02 -7.41856843... | [7.155219554901123, 2.715799570083618] |
b48c8f9b-f167-437d-908e-645445362d37 | practical-blind-membership-inference-attack | 2101.01341 | null | https://arxiv.org/abs/2101.01341v2 | https://arxiv.org/pdf/2101.01341v2.pdf | Practical Blind Membership Inference Attack via Differential Comparisons | Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. There are two types of MI attacks in the literature, i.e., these with and without shadow models. The success of the former heavily depends on the qu... | ['Yinzhi Cao', 'Neil Zhenqiang Gong', 'Philippe Burlina', 'Haolin Yuan', 'Yuchen Yang', 'Bo Hui'] | 2021-01-05 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 3.20006996e-01 1.22494753e-02 -2.07838669e-01 -2.63687313e-01
-7.15977430e-01 -1.15727735e+00 4.72973466e-01 -1.62783787e-01
-4.55337346e-01 6.22649908e-01 -1.42147154e-01 -6.71772301e-01
1.15139253e-01 -1.04105079e+00 -1.01082695e+00 -8.19552779e-01
1.37573197e-01 6.01478100e-01 1.58083379e-01 5.67825437... | [5.859760284423828, 7.277943134307861] |
6d6643bf-7bc4-4ec6-9672-126fa5cf5db2 | fairness-continual-learning-approach-to | 2305.15700 | null | https://arxiv.org/abs/2305.15700v1 | https://arxiv.org/pdf/2305.15700v1.pdf | Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments | Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the continual semantic segmentation needs to be better addressed. Meanwhile, fairness is one of the most v... | ['Khoa Luu', 'Bhiksha Raj', 'Hoang-Quan Nguyen', 'Thanh-Dat Truong'] | 2023-05-25 | null | null | null | null | ['continual-semantic-segmentation', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.48728788e-01 1.58119619e-01 -1.35494679e-01 -8.00118923e-01
-5.24422944e-01 -3.25052708e-04 3.75063181e-01 2.68396080e-01
-7.69084811e-01 8.19822311e-01 -3.55683118e-01 -8.60733613e-02
-3.02299500e-01 -6.24780476e-01 -6.54500186e-01 -7.23712623e-01
5.38544655e-01 3.51460576e-01 3.57155740e-01 4.80182432... | [9.587267875671387, 1.495603084564209] |
14cb0078-8555-4bd0-852d-aa063075fa4e | position-aware-structure-learning-for-graph | 2208.08302 | null | https://arxiv.org/abs/2208.08302v1 | https://arxiv.org/pdf/2208.08302v1.pdf | Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing | Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its impact on graph learning remain under-explored. In this paper, we provide a new understanding of to... | ['Philip S. Yu', 'Qian Li', 'Cheng Ji', 'Hao Peng', 'Xingcheng Fu', 'Haonan Yuan', 'JianXin Li', 'Qingyun Sun'] | 2022-08-17 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.56238163e-02 1.96858078e-01 -6.57682836e-01 -5.15921891e-01
-3.54288891e-02 -6.37076616e-01 1.65470362e-01 4.89099801e-01
2.74576450e-04 6.81708694e-01 1.53320149e-01 -2.40598276e-01
-5.01113117e-01 -1.02365470e+00 -5.15439987e-01 -8.27715456e-01
-2.43521661e-01 2.49986544e-01 4.28626299e-01 -2.22735479... | [7.350606441497803, 6.030884742736816] |
e76d9dd1-7e35-4563-b744-575fe4cba35d | swin2sr-swinv2-transformer-for-compressed | 2209.11345 | null | https://arxiv.org/abs/2209.11345v1 | https://arxiv.org/pdf/2209.11345v1.pdf | Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration | Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual q... | ['Radu Timofte', 'Maxime Burchi', 'Ui-Jin Choi', 'Marcos V. Conde'] | 2022-09-22 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 6.19797647e-01 -4.88996923e-01 -1.84808314e-01 -4.19689491e-02
-8.33440304e-01 -8.39506686e-02 1.36604711e-01 -3.95744294e-01
-1.40107736e-01 5.59660316e-01 4.08693701e-01 -1.63732380e-01
-1.43856168e-01 -7.06126988e-01 -1.01274490e+00 -5.52583218e-01
4.26562689e-02 -1.58835035e-02 2.96853840e-01 -5.59301734... | [11.13464069366455, -1.9733707904815674] |
64365054-9d0e-43fd-8c45-cba78dce2039 | czech-dataset-for-cross-lingual-subjectivity | 2204.13915 | null | https://arxiv.org/abs/2204.13915v1 | https://arxiv.org/pdf/2204.13915v1.pdf | Czech Dataset for Cross-lingual Subjectivity Classification | In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multil... | ['Josef Steinberger', 'Pavel Přibáň'] | 2022-04-29 | null | https://aclanthology.org/2022.lrec-1.148 | https://aclanthology.org/2022.lrec-1.148.pdf | lrec-2022-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-3.28127205e-01 2.16402620e-01 -1.74471036e-01 -5.35234928e-01
-1.19707382e+00 -1.01053703e+00 7.82725990e-01 3.47153276e-01
-8.94655526e-01 7.79378295e-01 4.75074619e-01 -1.00422263e-01
1.38601214e-01 -1.35914966e-01 -3.45505208e-01 -1.24393575e-01
3.65839064e-01 4.96010929e-01 3.23378414e-01 -5.68233371... | [10.943216323852539, 9.905285835266113] |
7d5f7172-d19f-4e89-9bfa-fd32765a5613 | aware-aspect-based-sentiment-analysis-dataset | null | null | https://ieeexplore.ieee.org/document/9679823 | https://ieeexplore.ieee.org/document/9679823 | AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation | The smartphone apps market is growing rapidly which challenges apps owners to continue improving their products and to compete in the market. The analysis of users feedback is a key enabler for improvements as stakeholders can utilize it to gain a broad understanding of the successes and failures of their products as w... | ['Malak Baslyman', 'Hamoud Aljamaan', 'Nouf Alturaief'] | 2021-11-19 | null | null | null | 2021-11-19-2021-11 | ['term-extraction', 'aspect-based-sentiment-analysis', 'aspect-category-polarity', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.04766470e-01 2.09201038e-01 -5.79894125e-01 -5.45018435e-01
-8.19885612e-01 -1.00109386e+00 4.32002336e-01 6.37047410e-01
-1.61084995e-01 4.90730077e-01 5.32020569e-01 -6.80503786e-01
1.49974942e-01 -6.64687216e-01 -5.37927210e-01 -9.06098038e-02
5.63508749e-01 1.72737688e-01 -1.70115590e-01 -3.49502027... | [11.338754653930664, 6.7862653732299805] |
e5129816-dc66-4107-b097-a43a0c42d883 | blindfold-baselines-for-embodied-qa | 1811.05013 | null | http://arxiv.org/abs/1811.05013v1 | http://arxiv.org/pdf/1811.05013v1.pdf | Blindfold Baselines for Embodied QA | We explore blindfold (question-only) baselines for Embodied Question
Answering. The EmbodiedQA task requires an agent to answer a question by
intelligently navigating in a simulated environment, gathering necessary visual
information only through first-person vision before finally answering.
Consequently, a blindfold b... | ['Hugo Larochelle', 'Eugene Belilovsky', 'Kyle Kastner', 'Ankesh Anand', 'Aaron Courville'] | 2018-11-12 | null | null | null | null | ['embodied-question-answering'] | ['computer-vision'] | [-1.59048170e-01 4.33716267e-01 7.52924502e-01 -2.02801794e-01
-9.37633038e-01 -9.77338433e-01 5.66661119e-01 -2.58417964e-01
-7.72184968e-01 5.22739291e-01 3.90369773e-01 -6.27693236e-01
-6.62547303e-03 -5.42828381e-01 -8.12438667e-01 -3.29295933e-01
1.92074534e-02 5.98769069e-01 7.48226745e-03 -4.77485329... | [4.440549373626709, 0.550653338432312] |
f7b8cd71-7936-42d4-9051-acb63bad1406 | long-distance-dependencies-dont-have-to-be | null | null | https://aclanthology.org/P19-2012 | https://aclanthology.org/P19-2012.pdf | Long-Distance Dependencies Don't Have to Be Long: Simplifying through Provably (Approximately) Optimal Permutations | Neural models at the sentence level often operate on the constituent words/tokens in a way that encodes the inductive bias of processing the input in a similar fashion to how humans do. However, there is no guarantee that the standard ordering of words is computationally efficient or optimal. To help mitigate this, we ... | ['Rishi Bommasani'] | 2019-07-01 | null | null | null | acl-2019-7 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 5.36996901e-01 5.84440589e-01 -6.11900240e-02 -8.48801374e-01
-9.13325548e-01 -5.27738929e-01 6.30558848e-01 4.53257710e-01
-8.53128791e-01 6.19786859e-01 7.92311013e-01 -4.28746074e-01
-6.94959983e-02 -7.19658852e-01 -6.22939408e-01 -3.39193583e-01
-3.82906012e-02 2.08205611e-01 -9.72300917e-02 -2.85828024... | [10.659878730773926, 8.787598609924316] |
5f29cc75-d6cc-4d17-bf06-201724d68561 | propensity-scored-probabilistic-label-trees | 2110.10803 | null | https://arxiv.org/abs/2110.10803v1 | https://arxiv.org/pdf/2110.10803v1.pdf | Propensity-scored Probabilistic Label Trees | Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels. Recently, XMLC has been widely applied to diverse web applications such as automatic content labeling, online advertising, or recommendation ... | ['Krzysztof Dembczyński', 'Rohit Babbar', 'Kalina Jasinska-Kobus', 'Marek Wydmuch'] | 2021-10-20 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 5.08622944e-01 1.10601686e-01 -6.89625978e-01 -6.75026000e-01
-1.10913563e+00 -6.96482718e-01 3.69225472e-01 5.35452187e-01
-3.60785425e-01 7.85996974e-01 -4.85482924e-02 -3.42978001e-01
-4.79465514e-01 -5.54934859e-01 -5.21094382e-01 -7.62816846e-01
2.48376857e-02 8.82894933e-01 1.10365763e-01 2.53726631... | [9.323912620544434, 4.313809394836426] |
429d2711-312d-41e8-a7bf-aa93229b2215 | multi-channel-weighted-nuclear-norm | 1705.09912 | null | http://arxiv.org/abs/1705.09912v2 | http://arxiv.org/pdf/1705.09912v2.pdf | Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising | Most of the existing denoising algorithms are developed for grayscale images,
while it is not a trivial work to extend them for color image denoising because
the noise statistics in R, G, B channels can be very different for real noisy
images. In this paper, we propose a multi-channel (MC) optimization model for
real c... | ['David Zhang', 'Xiangchu Feng', 'Jun Xu', 'Lei Zhang'] | 2017-05-28 | multi-channel-weighted-nuclear-norm-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Xu_Multi-Channel_Weighted_Nuclear_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Xu_Multi-Channel_Weighted_Nuclear_ICCV_2017_paper.pdf | iccv-2017-10 | ['color-image-denoising'] | ['computer-vision'] | [ 2.96499997e-01 -6.47162378e-01 4.60955977e-01 -2.38139808e-01
-7.28032768e-01 -1.55400172e-01 1.01901265e-02 -3.51410627e-01
-7.97652423e-01 6.78737402e-01 7.29595199e-02 -4.17533778e-02
-1.23310260e-01 -6.04059994e-01 -4.00236726e-01 -1.31465840e+00
1.59352198e-01 -3.43784779e-01 1.46555947e-02 -2.51842082... | [11.278014183044434, -2.444622039794922] |
041ec1f3-2bcc-4842-b7ca-88b16a010ad1 | information-recovery-in-shuffled-graphs-via | 1605.02315 | null | http://arxiv.org/abs/1605.02315v2 | http://arxiv.org/pdf/1605.02315v2.pdf | Information Recovery in Shuffled Graphs via Graph Matching | While many multiple graph inference methodologies operate under the implicit
assumption that an explicit vertex correspondence is known across the vertex
sets of the graphs, in practice these correspondences may only be partially or
errorfully known. Herein, we provide an information theoretic foundation for
understand... | ['Vince Lyzinski'] | 2016-05-08 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 6.98363066e-01 4.84556854e-01 -2.46369913e-01 -2.57735610e-01
-7.72825480e-01 -7.55646229e-01 3.67350847e-01 3.95586699e-01
1.92929193e-01 6.58355176e-01 1.98365264e-02 -3.78893346e-01
-5.91538727e-01 -1.02748704e+00 -1.12285340e+00 -6.10922575e-01
-5.68587661e-01 7.88123131e-01 -1.81462318e-01 3.62438679... | [6.925954341888428, 5.166318893432617] |
adf07009-a048-49b7-b4c8-102fb455af96 | textworldexpress-simulating-text-games-at-one | 2208.01174 | null | https://arxiv.org/abs/2208.01174v2 | https://arxiv.org/pdf/2208.01174v2.pdf | TextWorldExpress: Simulating Text Games at One Million Steps Per Second | Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning. However, speed is a major limitation of current text-based games, capping at 300 steps per second, mainly due to the use of legacy tooling. In this work we present ... | ['Marc-Alexandre Côté', 'Peter A. Jansen'] | 2022-08-01 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-4.74932730e-01 7.38092288e-02 2.07434192e-01 9.93940830e-02
-7.60259986e-01 -7.55198181e-01 8.68575394e-01 2.84856290e-01
-7.17318773e-01 6.94707572e-01 1.45931825e-01 -9.13032949e-01
-1.07423461e-03 -1.06511605e+00 -1.35989279e-01 5.91908768e-02
2.31147185e-03 1.02694941e+00 5.50059199e-01 -8.53284359... | [3.523350954055786, 1.3698756694793701] |
ed9f4035-df8c-4c0b-8cba-10f58f75e3f8 | srel-severity-rating-ensemble-learning-for | 2304.10207 | null | https://arxiv.org/abs/2304.10207v2 | https://arxiv.org/pdf/2304.10207v2.pdf | Learning Graph Patterns of Reflection Coefficient for Non-destructive Diagnosis of Cu Interconnects | With the increasing operating frequencies and clock speeds in processors, interconnects affect both the reliability and performance of entire electronic systems. Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. However, traditional approaches usi... | ['Sungho Suh', 'Haebom Lee', 'Tae Yeob Kang'] | 2023-04-20 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.51277278e-02 -4.91159827e-01 1.51397884e-01 2.89073642e-02
-6.17413640e-01 -1.15948007e-01 -6.56293035e-02 5.13286233e-01
1.61816567e-01 6.79965556e-01 -3.93372893e-01 -4.28470701e-01
-6.33754909e-01 -7.99354672e-01 -1.32223189e-01 -1.01948905e+00
-3.85085553e-01 -7.90840015e-02 1.36511266e-01 1.27261132... | [6.916687488555908, 2.2669386863708496] |
033b59ed-f379-435c-a95f-c0e421b7e8c2 | savir-t-spatially-attentive-visual-reasoning | 2206.09265 | null | https://arxiv.org/abs/2206.09265v2 | https://arxiv.org/pdf/2206.09265v2.pdf | SAViR-T: Spatially Attentive Visual Reasoning with Transformers | We present a novel computational model, "SAViR-T", for the family of visual reasoning problems embodied in the Raven's Progressive Matrices (RPM). Our model considers explicit spatial semantics of visual elements within each image in the puzzle, encoded as spatio-visual tokens, and learns the intra-image as well as the... | ['Vladimir Pavlovic', 'Kalliopi Basioti', 'Pritish Sahu'] | 2022-06-18 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-1.23409882e-01 -8.48423690e-03 -3.92023511e-02 1.41474992e-01
-3.23848099e-01 -8.83094907e-01 6.25619888e-01 2.29448035e-01
-5.92290657e-03 2.31131852e-01 5.80640614e-01 -6.78458095e-01
-7.42220759e-01 -6.94189787e-01 -1.07356548e+00 -5.41877687e-01
-2.47850791e-01 3.47724527e-01 2.93466270e-01 -6.15302145... | [10.729042053222656, 2.0154621601104736] |
915c40f1-0275-4c7e-b18d-a08e87f8212a | a-survey-on-stance-detection-for-mis-and-1 | null | null | https://openreview.net/forum?id=eHMpE26Z2LC | https://openreview.net/pdf?id=eHMpE26Z2LC | A Survey on Stance Detection for Mis- and Disinformation Identification | Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a ... | ['Anonymous'] | 2021-12-17 | null | null | null | acl-arr-december-2022-12 | ['rumour-detection'] | ['natural-language-processing'] | [ 4.52227026e-01 8.91923726e-01 -5.24585843e-01 -2.02974036e-01
-5.21614373e-01 -9.14627194e-01 1.07438219e+00 1.16040361e+00
-2.90329158e-01 9.01734471e-01 9.24760699e-01 -8.53741229e-01
2.18253225e-01 -8.86350930e-01 -4.35167611e-01 -2.68022060e-01
3.67755920e-01 2.38789842e-01 4.12082195e-01 -5.19687414... | [8.446249008178711, 10.039057731628418] |
91b3a58a-2724-49db-83eb-81f230be5aba | similarity-based-label-smoothing-for-dialogue | 2107.11481 | null | https://arxiv.org/abs/2107.11481v1 | https://arxiv.org/pdf/2107.11481v1.pdf | Similarity Based Label Smoothing For Dialogue Generation | Generative neural conversational systems are generally trained with the objective of minimizing the entropy loss between the training "hard" targets and the predicted logits. Often, performance gains and improved generalization can be achieved by using regularization techniques like label smoothing, which converts the ... | ['Rohini Srihari', 'Souvik Das', 'Sougata Saha'] | 2021-07-23 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [ 3.74708802e-01 8.13102305e-01 -1.52234465e-01 -1.09443057e+00
-8.43500972e-01 -4.34372038e-01 7.56592333e-01 8.98726359e-02
-6.93321288e-01 1.10840595e+00 4.55523342e-01 -1.44692108e-01
1.27716511e-01 -7.90794134e-01 -3.51060033e-01 -6.59685373e-01
1.09600827e-01 1.07892025e+00 2.98100356e-02 -2.37126753... | [12.795100212097168, 8.001594543457031] |
19e9976f-6764-451c-a625-ed4f5ffad53f | shikeblcu-at-semeval-2020-task-2-an-external | null | null | https://aclanthology.org/2020.semeval-1.31 | https://aclanthology.org/2020.semeval-1.31.pdf | SHIKEBLCU at SemEval-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment | Lexical entailment recognition plays an important role in tasks like Question Answering and Machine Translation. As important branches of lexical entailment, predicting multilingual and cross-lingual lexical entailment (LE) are two subtasks of SemEval2020 Task2. In previous monolingual LE studies, researchers leverage ... | ['Dong Yu', 'Xiangying Luo', 'Yuchen Fan', 'Shike Wang'] | 2020-12-01 | null | null | null | semeval-2020 | ['multilingual-word-embeddings'] | ['methodology'] | [-1.88376814e-01 1.82038635e-01 -5.97581983e-01 -5.94923198e-01
-9.08702016e-01 -7.12571979e-01 6.71724141e-01 2.36369982e-01
-8.54690194e-01 9.13286865e-01 5.77626824e-01 -9.69815314e-01
2.28466779e-01 -6.58972800e-01 -8.53849411e-01 1.31518334e-01
2.75511414e-01 3.85060966e-01 2.01219708e-01 -4.94452387... | [10.96130084991455, 9.892548561096191] |
01727ede-d94b-49da-80c6-be0f0fcac032 | augmenting-passage-representations-with-query | 2305.03950 | null | https://arxiv.org/abs/2305.03950v1 | https://arxiv.org/pdf/2305.03950v1.pdf | Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval | Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task. However, cross-lingual data for training is often scarcely available. In this paper, rather than using more... | ['Guido Zuccon', 'Linjun Shou', 'Shengyao Zhuang'] | 2023-05-06 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.71322250e-01 -3.41604084e-01 -2.18360901e-01 -3.17427933e-01
-1.89459443e+00 -8.21545482e-01 8.80693793e-01 2.96087533e-01
-7.00476408e-01 7.64017701e-01 4.07042027e-01 -2.52096832e-01
4.40495349e-02 -8.83012593e-01 -8.33898544e-01 -3.99710834e-01
3.00558925e-01 1.11616826e+00 2.92895466e-01 -5.36004722... | [11.367645263671875, 9.788717269897461] |
ab4a828d-f0bb-4e63-909f-7cc6229f9c9c | visual-causal-scene-refinement-for-video | 2305.04224 | null | https://arxiv.org/abs/2305.04224v1 | https://arxiv.org/pdf/2305.04224v1.pdf | Visual Causal Scene Refinement for Video Question Answering | Existing methods for video question answering (VideoQA) often suffer from spurious correlations between different modalities, leading to a failure in identifying the dominant visual evidence and the intended question. Moreover, these methods function as black boxes, making it difficult to interpret the visual scene dur... | ['Liang Lin', 'Guanbin Li', 'Hong Yan', 'Yang Liu', 'Yushen Wei'] | 2023-05-07 | null | null | null | null | ['video-question-answering', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 2.57841229e-01 -5.16053699e-02 -2.81298518e-01 -2.87304968e-01
-8.47833693e-01 -5.80328226e-01 7.48877227e-01 1.01703271e-01
2.24413455e-01 3.86866897e-01 7.81892121e-01 -4.48496342e-01
-3.61541212e-01 -7.06435144e-01 -9.28838849e-01 -5.91711342e-01
-1.73895806e-02 -6.06090315e-02 5.84394932e-01 7.47676343... | [10.40755558013916, 1.166247844696045] |
51f66896-7fbb-47b8-958d-a8ed4b7d5b87 | the-2018-shared-task-on-extrinsic-parser | null | null | https://aclanthology.org/K18-2002 | https://aclanthology.org/K18-2002.pdf | The 2018 Shared Task on Extrinsic Parser Evaluation: On the Downstream Utility of English Universal Dependency Parsers | We summarize empirical results and tentative conclusions from the Second Extrinsic Parser Evaluation Initiative (EPE 2018). We review the basic task setup, downstream applications involved, and end-to-end results for seventeen participating teams. Based on in-depth quantitative and qualitative analysis, we correlate in... | ['Jari Bj{\\"o}rne', 'Lilja {\\O}vrelid', 'Stephan Oepen', 'Richard Johansson', 'Murhaf Fares'] | 2018-10-01 | null | null | null | conll-2018-10 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 2.28474457e-02 5.43218672e-01 5.74717969e-02 -8.83825362e-01
-1.65399003e+00 -1.02566493e+00 2.21807271e-01 4.89412427e-01
-9.10452664e-01 4.90695089e-01 8.43959272e-01 -5.75931370e-01
1.26075879e-01 -2.14335307e-01 -5.07937014e-01 -9.28684045e-03
4.85279784e-02 2.98948318e-01 3.75872329e-02 -2.60312080... | [10.411420822143555, 9.7951021194458] |
bc5ed7f5-0de9-44d3-ae05-ade0cfb3ddf3 | contextualized-embeddings-for-connective | null | null | https://aclanthology.org/2020.codi-1.7 | https://aclanthology.org/2020.codi-1.7.pdf | Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing | This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specifi... | ['Manfred Stede', 'René Knaebel'] | null | null | null | null | emnlp-codi-2020-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 6.88442960e-02 8.24905694e-01 -4.84892815e-01 -4.19216692e-01
-3.51777524e-01 -5.06693304e-01 9.41792548e-01 6.98084593e-01
-7.38196075e-01 9.18817461e-01 8.38771284e-01 -3.91648978e-01
-6.36479184e-02 -9.24688160e-01 -3.78063679e-01 -3.69829953e-01
-3.62353027e-01 5.07824004e-01 4.96478617e-01 -7.58792162... | [10.804051399230957, 9.303461074829102] |
75249087-41a9-4358-bd08-aafd08e6a830 | video-corpus-moment-retrieval-with | 2105.06247 | null | https://arxiv.org/abs/2105.06247v1 | https://arxiv.org/pdf/2105.06247v1.pdf | Video Corpus Moment Retrieval with Contrastive Learning | Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval (VCMR) is to retrieve a temporal moment (i.e., a fraction of a video) that semantically corresponds to a given text query. As video and text are from two distinct feature spaces, there are two general approaches to address VCMR: (i) t... | ['Rick Siow Mong Goh', 'Joey Tianyi Zhou', 'Liangli Zhen', 'Guoshun Nan', 'Wei Jing', 'Aixin Sun', 'Hao Zhang'] | 2021-05-13 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 2.14865044e-01 -3.26508462e-01 -5.70306182e-01 6.27808347e-02
-1.32248211e+00 -4.37572271e-01 7.74661362e-01 -4.62164916e-02
-5.52760541e-01 2.59842277e-01 5.66620052e-01 1.58122241e-01
-1.79018185e-01 -2.76594877e-01 -8.89161468e-01 -6.05964899e-01
-1.17285974e-01 1.19668074e-01 2.09987238e-01 1.68771923... | [10.189714431762695, 0.8203739523887634] |
0a1d911b-4fe1-44fe-8a53-5962d6fe0c41 | 3d-rotation-and-translation-for-hyperbolic | 2305.13015 | null | https://arxiv.org/abs/2305.13015v1 | https://arxiv.org/pdf/2305.13015v1.pdf | 3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding | The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant challenge in achieving better KG embeddings lies in capturing relation patterns, including symmetry, antisymmetry, inversion, commutative c... | ['Hidetoshi Shimodaira', 'Yihua Zhu'] | 2023-05-22 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [-6.27634108e-01 3.29778284e-01 -4.26777780e-01 -3.99468914e-02
2.43729308e-01 -3.89300883e-01 6.12650096e-01 6.60795510e-01
-6.58743903e-02 5.73898613e-01 5.65401852e-01 -2.76024759e-01
-6.04604959e-01 -1.13943255e+00 -2.79723942e-01 -4.71065223e-01
-5.83251655e-01 6.28079474e-01 1.01708367e-01 -4.79966074... | [8.710468292236328, 7.706488609313965] |
ede2e9a6-6495-4874-9a59-aa2e9c224b1e | delivering-inflated-explanations | 2306.15272 | null | https://arxiv.org/abs/2306.15272v1 | https://arxiv.org/pdf/2306.15272v1.pdf | Delivering Inflated Explanations | In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this way?'' Formal approaches to explainability build a formal model of the AI system and use this to reason about the properties of the system.... | ['Joao Marques-Silva', 'Peter Stuckey', 'Alexey Ignatiev', 'Yacine Izza'] | 2023-06-27 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 4.35635775e-01 1.03837776e+00 -2.65470207e-01 -6.26994610e-01
-7.10639432e-02 -7.60902703e-01 5.99477232e-01 1.59679532e-01
2.02236652e-01 8.42434227e-01 2.87568778e-01 -7.21346259e-01
-7.09684312e-01 -1.04056191e+00 -7.41006076e-01 -5.92872322e-01
-7.71522149e-02 7.38391399e-01 2.73257524e-01 -4.59567994... | [8.769692420959473, 5.968568325042725] |
1a5eb765-db34-41b5-b360-9e2173aee452 | weakly-supervised-amodal-instance-1 | 2010.13175 | null | https://arxiv.org/abs/2010.13175v4 | https://arxiv.org/pdf/2010.13175v4.pdf | Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model | Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Ther... | ['Alan Yuille', 'Adam Kortylewski', 'Yihong Sun'] | 2020-10-25 | weakly-supervised-amodal-instance | http://openaccess.thecvf.com//content/CVPR2022/html/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sun_Amodal_Segmentation_Through_Out-of-Task_and_Out-of-Distribution_Generalization_With_a_Bayesian_CVPR_2022_paper.pdf | cvpr-2022-1 | ['amodal-instance-segmentation'] | ['computer-vision'] | [ 3.61004084e-01 5.73727310e-01 -2.93290913e-01 -5.66167712e-01
-6.86976373e-01 -7.33327448e-01 4.68621761e-01 -1.54971048e-01
-2.64953434e-01 6.40604496e-01 -3.02651852e-01 -1.86679229e-01
3.12097520e-01 -4.58764076e-01 -1.28216124e+00 -7.85195947e-01
2.54999220e-01 8.85615349e-01 4.55041856e-01 4.95522946... | [9.514284133911133, 0.47357046604156494] |
074149dc-0d7f-40e0-a6b7-e280a6b19dd8 | frame-recurrent-video-inpainting-by-robust | 1905.02882 | null | https://arxiv.org/abs/1905.02882v1 | https://arxiv.org/pdf/1905.02882v1.pdf | Frame-Recurrent Video Inpainting by Robust Optical Flow Inference | In this paper, we present a new inpainting framework for recovering missing regions of video frames. Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fa... | ['Jiaming Liu', 'Chuan Wang', 'Yifan Ding', 'Haibin Huang', 'Jue Wang', 'Liqiang Wang'] | 2019-05-08 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-4.66106795e-02 -4.88119572e-01 -1.03371657e-01 -2.14616686e-01
-5.38801551e-01 -3.97903442e-01 2.05116317e-01 -4.39190447e-01
-3.33194137e-01 8.59572887e-01 2.35656530e-01 -7.36344084e-02
1.64524227e-01 -7.14426279e-01 -9.71340239e-01 -4.59840477e-01
-1.77435577e-01 -1.34991065e-01 3.18661034e-01 1.30676061... | [10.766607284545898, -1.4246138334274292] |
6a6a6cb3-f34e-4b69-9021-bfd11ed28d4a | learning-to-count-objects-in-images | null | null | http://papers.nips.cc/paper/4043-learning-to-count-objects-in-images | http://papers.nips.cc/paper/4043-learning-to-count-objects-in-images.pdf | Learning To Count Objects in Images | We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames. We focus on the practically-attractive case when the training images are annotated with dots (one dot per object). Our go... | ['Andrew Zisserman', 'Victor Lempitsky'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['object-counting'] | ['computer-vision'] | [ 2.29571730e-01 3.55573632e-02 -1.20450303e-01 -3.81438762e-01
-8.17085147e-01 -4.35977995e-01 5.50402761e-01 6.21938348e-01
-1.20073235e+00 6.59682810e-01 -7.06304610e-01 -1.75519377e-01
2.52763361e-01 -8.14580441e-01 -1.10779679e+00 -9.72637236e-01
-1.06331781e-01 8.67420256e-01 2.04903349e-01 5.50567091... | [9.007259368896484, 0.37411224842071533] |
c5137eaa-0ea9-4721-bc71-93e18313f85b | alzheimer-disease-classification-through-asr | 2306.03443 | null | https://arxiv.org/abs/2306.03443v1 | https://arxiv.org/pdf/2306.03443v1.pdf | Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses | Alzheimer's Disease (AD) is the world's leading neurodegenerative disease, which often results in communication difficulties. Analysing speech can serve as a diagnostic tool for identifying the condition. The recent ADReSS challenge provided a dataset for AD classification and highlighted the utility of manual transcri... | ['Helmer Strik', 'Mariano Alcañiz', 'Javier Marín-Morales', 'Cristian Tejedor-Garcia', 'Simone Wills', 'Lucía Gómez-Zaragozá'] | 2023-06-06 | null | null | null | null | ['word-embeddings', 'automatic-speech-recognition'] | ['methodology', 'speech'] | [ 1.97748303e-01 6.76688924e-02 2.17840493e-01 -2.68576503e-01
-1.35646141e+00 -4.10854191e-01 7.18326211e-01 4.60721374e-01
-7.67329693e-01 8.32803071e-01 7.07783639e-01 -2.58202165e-01
6.54145628e-02 -3.77837926e-01 -1.96188297e-02 -5.40076256e-01
-2.13495910e-01 3.93791080e-01 1.96058422e-01 -5.77212349... | [13.939201354980469, 5.380517959594727] |
d9b542e4-2e88-47ca-947a-9340e65549e7 | parallel-and-flexible-sampling-from | 2105.08164 | null | https://arxiv.org/abs/2105.08164v2 | https://arxiv.org/pdf/2105.08164v2.pdf | Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics | This paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defi... | ['John Thickstun', 'Vivek Jayaram'] | 2021-05-17 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.42739332e-01 7.61597753e-02 -1.14798851e-01 -3.71882886e-01
-1.09971344e+00 -4.27414834e-01 8.77243936e-01 -6.80875838e-01
-1.30714044e-01 6.44976616e-01 5.42772651e-01 4.42683846e-02
4.24260944e-02 -6.99595034e-01 -5.12798429e-01 -7.17340171e-01
-4.22895811e-02 5.81748962e-01 2.48273477e-01 3.44500481... | [15.40218734741211, 5.839367389678955] |
30b22e61-7e09-4465-a9e2-6ec40accc5d4 | an-open-web-platform-for-rule-based-speech-to | null | null | https://aclanthology.org/P16-2027 | https://aclanthology.org/P16-2027.pdf | An Open Web Platform for Rule-Based Speech-to-Sign Translation | null | ['Nikos Tsourakis', 'Manny Rayner', 'Irene Strasly', 'Johanna Gerlach', 'Sarah Ebling', 'Pierrette Bouillon'] | 2016-08-01 | null | null | null | acl-2016-8 | ['sign-language-translation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-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... | [-7.276164531707764, 3.832803964614868] |
53468341-02de-46b7-a41d-fd6fddd5eea5 | graph-attention-transformer-network-for-multi | 2203.04049 | null | https://arxiv.org/abs/2203.04049v1 | https://arxiv.org/pdf/2203.04049v1.pdf | Graph Attention Transformer Network for Multi-Label Image Classification | Multi-label classification aims to recognize multiple objects or attributes from images. However, it is challenging to learn from proper label graphs to effectively characterize such inter-label correlations or dependencies. Current methods often use the co-occurrence probability of labels based on the training set as ... | ['Yong Rui', 'Jianping Fan', 'Xin Geng', 'Zhongchao shi', 'Yao Zhang', 'Shikai Chen', 'Jin Yuan'] | 2022-03-08 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.02597338e-01 -9.90399048e-02 -3.63223791e-01 -6.84149444e-01
-3.76448512e-01 -3.65203887e-01 3.31962764e-01 2.54681557e-01
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-4.10182983e-01 -5.94322979e-01 -3.30644011e-01 -6.42325342e-01
2.99158186e-01 4.17655438e-01 4.81897965e-02 1.21922888... | [9.727176666259766, 4.1189470291137695] |
fa5c6906-a409-4af4-935e-af5120fe518b | revisiting-temporal-modeling-for-clip-based | 2301.11116 | null | https://arxiv.org/abs/2301.11116v1 | https://arxiv.org/pdf/2301.11116v1.pdf | Revisiting Temporal Modeling for CLIP-based Image-to-Video Knowledge Transferring | Image-text pretrained models, e.g., CLIP, have shown impressive general multi-modal knowledge learned from large-scale image-text data pairs, thus attracting increasing attention for their potential to improve visual representation learning in the video domain. In this paper, based on the CLIP model, we revisit tempora... | ['Thomas H. Li', 'Xinglong Wu', 'Jiashi Feng', 'Ge Li', 'Jingjia Huang', 'Ruyang Liu'] | 2023-01-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Revisiting_Temporal_Modeling_for_CLIP-Based_Image-to-Video_Knowledge_Transferring_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition', 'video-text-retrieval', 'video-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.77548363e-03 -7.32968748e-01 -4.80377108e-01 -2.54896045e-01
-7.07152069e-01 -5.20353079e-01 6.10923231e-01 -3.49938124e-01
-4.43557829e-01 3.50503296e-01 1.22866325e-01 -3.59827206e-02
-1.36508182e-01 -4.49255109e-01 -8.55376124e-01 -6.32171988e-01
9.38237831e-02 3.98685895e-02 5.20413637e-01 2.13991441... | [10.20455265045166, 0.9235958456993103] |
93dffa8d-eacb-427f-ab75-9ca426a8cabb | n-grams-textrank-a-novel-domain-keyword | null | null | https://aclanthology.org/2020.icon-termtraction.3 | https://aclanthology.org/2020.icon-termtraction.3.pdf | N-Grams TextRank A Novel Domain Keyword Extraction Technique | The rapid growth of the internet has given us a wealth of information and data spread across the web. However, as the data begins to grow we simultaneously face the grave problem of an Information Explosion. An abundance of data can lead to large scale data management problems as well as the loss of the true meaning of... | ['Dipti Mishra Sharma', 'Manvith Reddy', 'Akshat Gahoi', 'Saransh Rajput'] | null | null | null | null | icon-2020-12 | ['keyword-extraction'] | ['natural-language-processing'] | [ 1.41437098e-01 -6.95173442e-02 -3.65063131e-01 -1.17581710e-01
-7.49726534e-01 -6.93538010e-01 8.40093374e-01 5.94522595e-01
-7.57622421e-01 8.94770920e-01 3.66305411e-01 -3.92623425e-01
-4.48075086e-01 -9.36219096e-01 -2.39118367e-01 -2.74732828e-01
-5.19345328e-02 8.77280951e-01 8.58805954e-01 -3.52288097... | [10.319461822509766, 7.636564254760742] |
e83e53fd-64c8-49e7-a7b4-ad5db54a103d | does-data-repair-lead-to-fair-models-curating | 2110.10389 | null | https://arxiv.org/abs/2110.10389v1 | https://arxiv.org/pdf/2110.10389v1.pdf | Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias | Contextual information is a valuable cue for Deep Neural Networks (DNNs) to learn better representations and improve accuracy. However, co-occurrence bias in the training dataset may hamper a DNN model's generalizability to unseen scenarios in the real world. For example, in COCO, many object categories have a much hig... | ['Chetan Arora', 'Saket Anand', 'Sumanyu Muku', 'Sharat Agarwal'] | 2021-10-20 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.12186295e-01 1.81831509e-01 -4.77651060e-01 -9.08375144e-01
-3.69045734e-01 -3.83630455e-01 5.05712748e-01 3.79839629e-01
-6.46524251e-01 8.10048699e-01 -1.97089151e-01 -2.15406537e-01
-2.92859703e-01 -9.43915665e-01 -9.10137475e-01 -1.02781284e+00
2.14210704e-01 3.49014670e-01 6.64696097e-02 1.36334434... | [9.127540588378906, 4.011073589324951] |
26fa5576-425b-4253-8da0-d45e6d4d4e7f | automatic-glossary-of-clinical-terminology-a | 2306.00665 | null | https://arxiv.org/abs/2306.00665v1 | https://arxiv.org/pdf/2306.00665v1.pdf | Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge | Background: More than 400,000 biomedical concepts and some of their relationships are contained in SnomedCT, a comprehensive biomedical ontology. However, their concept names are not always readily interpretable by non-experts, or patients looking at their own electronic health records (EHR). Clear definitions or descr... | ['Thomas Demeester', 'François Remy'] | 2023-06-01 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.22219168e-01 4.17650282e-01 -6.24095559e-01 -3.19661081e-01
-8.15217555e-01 -9.49178517e-01 8.76282305e-02 9.44083750e-01
-5.40355027e-01 1.10612929e+00 6.94168389e-01 -5.49484134e-01
-5.67747295e-01 -6.44147754e-01 -1.57662615e-01 -2.82015204e-01
7.73003176e-02 8.85225415e-01 -4.89247054e-01 -1.96313292... | [8.479768753051758, 8.653773307800293] |
c157dacd-33cb-4bb6-a495-8955db1c09f0 | autonomous-intelligent-software-development | 2208.06393 | null | https://arxiv.org/abs/2208.06393v1 | https://arxiv.org/pdf/2208.06393v1.pdf | Autonomous Intelligent Software Development | We present an overview of the design and first proof-of-concept implementation for AIDA, an autonomous intelligent developer agent that develops software from scratch. AIDA takes a software requirements specification and uses reasoning over a semantic knowledge graph to interpret the requirements, then designs and writ... | ['Mark Alan Matties'] | 2022-08-12 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-8.10470507e-02 9.29250479e-01 -1.38099179e-01 -4.91102099e-01
-9.66901109e-02 -8.69347215e-01 3.11456591e-01 3.29106450e-01
4.06044513e-01 2.40620196e-01 2.43552461e-01 -6.38450503e-01
-5.24463534e-01 -9.69652474e-01 -5.93734860e-01 4.01433945e-01
6.34843335e-02 4.50167656e-01 5.95807672e-01 -4.29736048... | [8.311513900756836, 7.442638397216797] |
4569fe8a-1e34-4f65-b379-9c63fb3048e5 | reinforcement-learning-with-action-free-pre | 2203.13880 | null | https://arxiv.org/abs/2203.13880v2 | https://arxiv.org/pdf/2203.13880v2.pdf | Reinforcement Learning with Action-Free Pre-Training from Videos | Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper, we investigate if such unsupervised pre-training methods can also be effective for vision-based reinforcement learning (RL). To this end, we... | ['Pieter Abbeel', 'Stephen James', 'Kimin Lee', 'Younggyo Seo'] | 2022-03-25 | null | null | null | null | ['video-prediction', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 1.34067714e-01 1.17768399e-01 -2.67920136e-01 -2.78812140e-01
-6.46797776e-01 -4.34908390e-01 8.31995487e-01 -3.14456910e-01
-2.50943244e-01 6.27188385e-01 4.97819811e-01 -2.33905409e-02
1.38848290e-01 -8.11455190e-01 -1.30311680e+00 -6.15474701e-01
-1.76914975e-01 2.68840402e-01 2.09362313e-01 -2.05789030... | [4.5321946144104, 0.9462528228759766] |
37cfeea1-203f-4581-9efb-e5510205a881 | advanced-local-motion-patterns-for-macro-and | 1805.01951 | null | http://arxiv.org/abs/1805.01951v1 | http://arxiv.org/pdf/1805.01951v1.pdf | Advanced local motion patterns for macro and micro facial expression recognition | In this paper, we develop a new method that recognizes facial expressions, on
the basis of an innovative local motion patterns feature, with three main
contributions. The first one is the analysis of the face skin temporal
elasticity and face deformations during expression. The second one is a unified
approach for both... | ['C. Djeraba', 'B. Allaert', 'IM. Bilasco'] | 2018-05-04 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.01511911e-01 -2.29353756e-01 -2.38171205e-01 -6.69275641e-01
-5.31463444e-01 -3.13606858e-01 5.19424915e-01 -6.04657590e-01
-2.10105911e-01 4.62027192e-01 5.70002049e-02 5.52916586e-01
4.48596999e-02 -1.39167577e-01 -2.19362542e-01 -1.26682413e+00
-1.67060912e-01 1.81508243e-01 -2.67343432e-01 -5.25003016... | [13.609602928161621, 1.8599430322647095] |
bf97bfcd-cf4c-4489-8613-ac1cbf2f215a | reasoning-over-the-air-a-reasoning-based | 2306.11229 | null | https://arxiv.org/abs/2306.11229v1 | https://arxiv.org/pdf/2306.11229v1.pdf | Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework | Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users' QoE. Most existing works... | ['Mehdi Bennis', 'Merouane Debbah', 'Walid Saad', 'H. Vincent Poor', 'Guangming Shi', 'Yingyu Li', 'Yiwei Liao', 'Yong Xiao'] | 2023-06-20 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [ 6.73027992e-01 6.50577605e-01 -1.29083112e-01 -4.64141518e-01
-4.99278665e-01 -4.06069279e-01 6.57229424e-01 -1.65310964e-01
1.08027585e-01 7.65976608e-01 3.68291885e-01 -3.56120020e-01
-3.45288903e-01 -9.87334132e-01 -4.30179596e-01 -6.26667559e-01
-2.36370891e-01 1.84013411e-01 -1.12460077e-01 -5.02894819... | [6.385897159576416, 1.68131422996521] |
551d3f9d-2935-4da0-b5e8-51149a04085d | ueca-prompt-universal-prompt-for-emotion | null | null | https://aclanthology.org/2022.coling-1.613 | https://aclanthology.org/2022.coling-1.613.pdf | UECA-Prompt: Universal Prompt for Emotion Cause Analysis | Emotion cause analysis (ECA) aims to extract emotion clauses and find the corresponding cause of the emotion. Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. These task-specific methods have a deficiency of universality. And the relations among multiple objectives in one task are not ex... | ['Jiahai Wang', 'Zhaoyang Wang', 'Zizhen Zhang', 'Zhiyue Liu', 'Xiaopeng Zheng'] | null | null | null | null | coling-2022-10 | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.68632083e-02 -2.05506265e-01 -4.62742627e-01 -4.97381479e-01
-5.09433031e-01 -2.18958765e-01 2.11801946e-01 -4.04637635e-01
-3.15204293e-01 6.10814035e-01 5.28445020e-02 1.01196095e-01
-3.33445728e-01 -3.77395928e-01 -2.89653569e-01 -6.02366865e-01
2.66879916e-01 4.20545600e-02 -2.03116670e-01 -3.92766386... | [12.634276390075684, 6.186312675476074] |
00df8cc1-04a5-431c-858d-42e8c136724f | how-much-does-input-data-type-impact-final | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Luo_How_Much_Does_Input_Data_Type_Impact_Final_Face_Model_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Luo_How_Much_Does_Input_Data_Type_Impact_Final_Face_Model_CVPR_2022_paper.pdf | How Much Does Input Data Type Impact Final Face Model Accuracy? | Face models are widely used in image processing and other domains. The input data to create a 3D face model ranges from accurate laser scans to simple 2D RGB photographs. These input data types are typically deficient either due to missing regions, or because they are under-constrained. As a result, reconstruction ... | ['James Davis', 'Alex Pang', 'Minghao Liu', 'Eric Sandoval Ruezga', 'Akila de Silva', 'Issei Mori', 'Fahim Hasan Khan', 'Jiahao Luo'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['face-model'] | ['computer-vision'] | [ 1.51532546e-01 2.09391087e-01 7.97469448e-03 -8.16808641e-01
-3.72597724e-01 -2.65788674e-01 3.49827647e-01 -6.12217844e-01
-7.86098465e-02 5.41188836e-01 -1.53871864e-01 -6.54840618e-02
-1.31705822e-02 -1.04279578e+00 -5.21988332e-01 -3.48145664e-01
2.38606244e-01 8.79347146e-01 9.26282033e-02 -1.27940506... | [13.059958457946777, -0.033079296350479126] |
8dd93199-9179-46dd-ad8d-fd520facae2e | knowledge-driven-encode-retrieve-paraphrase | 1903.10122 | null | http://arxiv.org/abs/1903.10122v1 | http://arxiv.org/pdf/1903.10122v1.pdf | Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation | Generating long and semantic-coherent reports to describe medical images
poses great challenges towards bridging visual and linguistic modalities,
incorporating medical domain knowledge, and generating realistic and accurate
descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase
(KERP) approach ... | ['Zhiting Hu', 'Xiaodan Liang', 'Christy Y. Li', 'Eric P. Xing'] | 2019-03-25 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 3.68590534e-01 4.20101553e-01 -1.71859935e-01 -4.39029872e-01
-1.09632814e+00 -3.21821183e-01 5.44048905e-01 6.51697934e-01
3.83935422e-01 4.30726528e-01 5.61855257e-01 -1.42317787e-01
-1.28321871e-01 -8.59894156e-01 -4.40544248e-01 -2.28864431e-01
6.09442554e-02 6.25953019e-01 4.20103222e-01 4.26322855... | [15.039667129516602, -1.394852638244629] |
3f6d2503-1e77-4079-a51d-4fefcf24e2b1 | i2dformer-learning-image-to-document | 2209.10304 | null | https://arxiv.org/abs/2209.10304v1 | https://arxiv.org/pdf/2209.10304v1.pdf | I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification | Despite the tremendous progress in zero-shot learning(ZSL), the majority of existing methods still rely on human-annotated attributes, which are difficult to annotate and scale. An unsupervised alternative is to represent each class using the word embedding associated with its semantic class name. However, word embeddi... | ['Federico Tombari', 'Luc van Gool', 'Yongqin Xian', 'Muhammad Ferjad Naeem'] | 2022-09-21 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [-3.61790298e-03 -2.21347716e-02 -4.28984791e-01 -3.05804521e-01
-8.80218148e-01 -6.36683166e-01 9.48453367e-01 3.30459684e-01
-2.87056118e-01 2.92482913e-01 6.56729341e-01 1.86513469e-01
3.44953239e-02 -9.01264906e-01 -7.95175195e-01 -7.39467919e-01
4.31801796e-01 1.82707876e-01 1.38826296e-01 9.18687321... | [10.159383773803711, 2.0493674278259277] |
24326ece-fe6e-4190-b129-e7f66badf79a | an-aggregation-of-aggregation-methods-in | 2211.01256 | null | https://arxiv.org/abs/2211.01256v1 | https://arxiv.org/pdf/2211.01256v1.pdf | An Aggregation of Aggregation Methods in Computational Pathology | Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of ... | ['Nasir Rajpoot', 'Mark Eastwood', 'Amina Asif', 'Hammam M. AlGhamdi', 'Ruoyu Wang', 'Robert Jewsbury', 'Mohsin Bilal'] | 2022-11-02 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 9.39860702e-01 2.52848476e-01 -3.74511212e-01 -1.44233674e-01
-1.40958345e+00 -3.11175138e-01 5.73320150e-01 7.76624501e-01
-3.05917621e-01 6.59429491e-01 2.79764086e-01 -1.13248311e-01
-6.50552154e-01 -7.67248333e-01 -5.07391274e-01 -1.30502117e+00
-4.95732650e-02 6.52811110e-01 3.93048823e-01 2.26035491... | [15.10126781463623, -2.990962028503418] |
b348282b-aef9-4599-816b-9c93156eca95 | paced-curriculum-distillation-with-prediction | 2302.01049 | null | https://arxiv.org/abs/2302.01049v1 | https://arxiv.org/pdf/2302.01049v1.pdf | Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation | Purpose: In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scorin... | ['Hongliang Ren', 'Ben Glocker', 'Bhavesh Gupta', 'V. K. Viekash', 'S. P. Sharan', 'Lalithkumar Seenivasan', 'Mobarakol Islam'] | 2023-02-02 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.88107359e-01 3.99696141e-01 -3.41593474e-01 -4.38342273e-01
-8.84743810e-01 -5.66072822e-01 3.11744094e-01 3.06960076e-01
-5.69891095e-01 7.11257994e-01 -4.84577008e-02 -3.87739211e-01
-4.95513171e-01 -5.95923245e-01 -7.62519419e-01 -9.94308591e-01
5.97240515e-02 4.03931677e-01 4.08635557e-01 -1.55683253... | [14.565452575683594, -1.9796340465545654] |
489a2fd6-16a3-4473-9526-3f069b689259 | relative-velocity-based-reward-functions-for | 2112.13984 | null | https://arxiv.org/abs/2112.13984v2 | https://arxiv.org/pdf/2112.13984v2.pdf | Relative velocity-based reward functions for crowd navigation of robots | The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment, but while the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power con... | ['Fei Li', 'Xiaoqing Yang'] | 2021-12-28 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-5.04937887e-01 4.38078120e-02 -5.88766694e-01 3.46591502e-01
7.29184389e-01 -4.41850036e-01 2.22080722e-01 -4.61373121e-01
-2.32658848e-01 5.93642235e-01 -5.58172703e-01 -5.62525868e-01
-4.45055723e-01 -8.51219058e-01 -2.35781655e-01 -9.51421082e-01
2.97183469e-02 2.39147514e-01 2.15089545e-01 -5.84141076... | [5.270007133483887, 1.7484543323516846] |
8a323983-5609-4fda-b34b-1ba824b498d4 | deep-attentive-survival-analysis-in-limit | 2306.05479 | null | https://arxiv.org/abs/2306.05479v1 | https://arxiv.org/pdf/2306.05479v1.pdf | Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers | One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learnin... | ['Stefan Zohren', 'Fernando Moreno-Pino', 'Alvaro Cartea', 'Alvaro Arroyo'] | 2023-06-08 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.87456286e-01 -3.22888464e-01 -2.65189439e-01 -4.87540990e-01
-7.82568812e-01 -9.59645212e-01 6.73797190e-01 5.43929815e-01
-4.83774811e-01 4.71081853e-01 5.45230031e-01 -7.76991308e-01
-3.25648546e-01 -1.02312982e+00 -7.83968747e-01 -4.78057235e-01
-2.64301628e-01 1.10238457e+00 4.21057045e-02 -2.39976466... | [4.533705711364746, 4.1162919998168945] |
e8d06ff6-0985-496f-b643-894a79c7956f | utilizing-lexical-similarity-to-enable-zero | 2305.05214 | null | https://arxiv.org/abs/2305.05214v1 | https://arxiv.org/pdf/2305.05214v1.pdf | Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages | We address the task of machine translation from an extremely low-resource language (LRL) to English using cross-lingual transfer from a closely related high-resource language (HRL). For many of these languages, no parallel corpora are available, even monolingual corpora are limited and representations in pre-trained se... | ['Anoop Kunchukuttan', 'Maunendra Sankar Desarkar', 'Rahul Kejriwal', 'Kaushal Kumar Maurya'] | 2023-05-09 | null | null | null | null | ['zero-shot-machine-translation', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.71735319e-02 -2.79017031e-01 -6.11282110e-01 -3.75328183e-01
-1.53275800e+00 -9.18073237e-01 5.27697027e-01 1.07420819e-05
-7.23111093e-01 1.08166909e+00 2.83404499e-01 -6.40622258e-01
5.03870904e-01 -5.84766805e-01 -1.01558697e+00 -3.64354312e-01
2.88810015e-01 4.91362154e-01 1.06518529e-02 -5.18395364... | [11.116044998168945, 10.051677703857422] |
fdd6b35e-f0ab-400d-a813-f09812a7762e | the-robotic-surgery-procedural-framebank | null | null | https://aclanthology.org/2022.lrec-1.420 | https://aclanthology.org/2022.lrec-1.420.pdf | The Robotic Surgery Procedural Framebank | Robot-Assisted minimally invasive robotic surgery is the gold standard for the surgical treatment of many pathological conditions, and several manuals and academic papers describe how to perform these interventions. These high-quality, often peer-reviewed texts are the main study resource for medical personnel and cons... | ['Paolo Fiorini', 'Simone Paolo Ponzetto', 'Marco Rospocher', 'Marco Bombieri'] | null | null | null | null | lrec-2022-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 5.02995908e-01 9.81049359e-01 -9.72640574e-01 -3.42518985e-01
-8.35956454e-01 -9.85326767e-01 2.82278657e-01 7.13251233e-01
-6.80250585e-01 9.24232781e-01 6.96075320e-01 -3.89191091e-01
-3.53724271e-01 -6.50691271e-01 -5.89596212e-01 -4.95309412e-01
4.00321960e-01 6.84282780e-01 1.66102454e-01 -3.76560628... | [14.186646461486816, -3.2104828357696533] |
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