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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6419c6c2-627e-4cd8-b5fd-0ddc0933e103 | stylizing-3d-scene-via-implicit | 2105.13016 | null | https://arxiv.org/abs/2105.13016v3 | https://arxiv.org/pdf/2105.13016v3.pdf | Stylizing 3D Scene via Implicit Representation and HyperNetwork | In this work, we aim to address the 3D scene stylization problem - generating stylized images of the scene at arbitrary novel view angles. A straightforward solution is to combine existing novel view synthesis and image/video style transfer approaches, which often leads to blurry results or inconsistent appearance. Ins... | ['Wei-Chen Chiu', 'Wei-Sheng Lai', 'Hung-Yu Tseng', 'Meng-Shiun Tsai', 'Pei-Ze Chiang'] | 2021-05-27 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 3.82326066e-01 6.02299683e-02 2.66994387e-01 -3.11589330e-01
-2.14028329e-01 -4.70571369e-01 4.84435797e-01 -7.58703172e-01
2.38007933e-01 7.24879861e-01 7.42475837e-02 -4.81202751e-02
2.32761249e-01 -9.69808519e-01 -8.57446432e-01 -7.42408395e-01
7.18410254e-01 1.05294384e-01 -1.97328150e-01 -1.01752758... | [9.340160369873047, -3.165867805480957] |
1d671a23-23bd-40e1-85aa-75eced087138 | scalable-approximate-inference-for-state | 1910.00879 | null | https://arxiv.org/abs/1910.00879v2 | https://arxiv.org/pdf/1910.00879v2.pdf | The Neural Moving Average Model for Scalable Variational Inference of State Space Models | Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training. To date, however, this strategy has been most applicable to models of independent data. We propose an extension to state space models of time series data based on a novel generative model... | ['Dennis Prangle', 'Tom Ryder', 'Andrew Golightly', 'Isaac Matthews'] | 2019-10-02 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-3.46671462e-01 -7.64007643e-02 -8.65435004e-02 -3.90164554e-01
-6.56319439e-01 -3.12889308e-01 8.89692307e-01 -4.20606852e-01
-4.02875215e-01 9.55501318e-01 5.01730852e-02 -5.39788187e-01
-3.11259836e-01 -9.42991734e-01 -5.73455155e-01 -7.36262858e-01
-2.01731384e-01 8.96332741e-01 1.37571722e-01 1.37902483... | [6.887795448303223, 3.730541467666626] |
b3af9ee5-973a-4fbd-b5df-12b2c1d304ee | tracking-instances-as-queries | 2106.11963 | null | https://arxiv.org/abs/2106.11963v2 | https://arxiv.org/pdf/2106.11963v2.pdf | Tracking Instances as Queries | Recently, query based deep networks catch lots of attention owing to their end-to-end pipeline and competitive results on several fundamental computer vision tasks, such as object detection, semantic segmentation, and instance segmentation. However, how to establish a query based video instance segmentation (VIS) frame... | ['Wenyu Liu', 'Bin Feng', 'Ying Shan', 'Yu Li', 'Xinggang Wang', 'Yuxin Fang', 'Shusheng Yang'] | 2021-06-22 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.33172452e-01 -5.40540181e-02 -5.43575943e-01 -6.43609941e-01
-1.18036246e+00 -4.38105881e-01 2.94339567e-01 -2.31872216e-01
-8.35235298e-01 3.48803729e-01 -3.06605399e-01 1.34942206e-02
1.08823225e-01 -5.24564147e-01 -1.09498131e+00 -2.20032677e-01
1.31252974e-01 5.85886061e-01 9.23943639e-01 -1.19798765... | [9.30655574798584, 0.028429890051484108] |
3f52be41-d506-400a-a2a4-7e855bc72000 | l-scaled-attention-a-novel-fast-attention | 2201.02912 | null | https://arxiv.org/abs/2201.02912v1 | https://arxiv.org/pdf/2201.02912v1.pdf | λ-Scaled-Attention: A Novel Fast Attention Mechanism for Efficient Modeling of Protein Sequences | Attention-based deep networks have been successfully applied on textual data in the field of NLP. However, their application on protein sequences poses additional challenges due to the weak semantics of the protein words, unlike the plain text words. These unexplored challenges faced by the standard attention technique... | ['Akshay Deepak', 'Md Shah Fahad', 'Ashish Ranjan'] | 2022-01-09 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 3.95726502e-01 1.73032388e-01 2.86992759e-01 -1.63934752e-01
-6.01782978e-01 -2.80248910e-01 3.76629412e-01 5.81215978e-01
-8.14611077e-01 9.28562820e-01 -9.25029442e-02 -3.95719498e-01
-5.82679873e-03 -4.68020082e-01 -9.29857671e-01 -9.51736093e-01
4.60016802e-02 5.50699830e-01 2.11246237e-01 -4.41919893... | [4.776734828948975, 5.601308822631836] |
368d6faf-a44f-4a1a-97e9-40b017f9641a | pytrial-a-comprehensive-platform-for | 2306.04018 | null | https://arxiv.org/abs/2306.04018v1 | https://arxiv.org/pdf/2306.04018v1.pdf | PyTrial: A Comprehensive Platform for Artificial Intelligence for Drug Development | Drug development is a complex process that aims to test the efficacy and safety of candidate drugs in the human body for regulatory approval via clinical trials. Recently, machine learning has emerged as a vital tool for drug development, offering new opportunities to improve the efficiency and success rates of the pro... | ['Jimeng Sun', 'Cao Xiao', 'Tianfan Fu', 'Brandon Theodorou', 'Zifeng Wang'] | 2023-06-06 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.59859046e-01 -2.38894165e-01 -6.01466954e-01 -2.46485904e-01
-7.34903514e-01 -5.14707327e-01 4.48856175e-01 5.06936908e-01
-1.02588460e-01 6.91186428e-01 -3.41982156e-01 -7.75928795e-01
-2.68492043e-01 -7.24478662e-01 -2.19657898e-01 -6.33553445e-01
-3.12243737e-02 9.06633377e-01 -1.83218390e-01 1.97844714... | [5.35670804977417, 5.739792823791504] |
37935041-ebf9-4219-b182-050c93effc47 | gazedirector-fully-articulated-eye-gaze | 1704.08763 | null | http://arxiv.org/abs/1704.08763v1 | http://arxiv.org/pdf/1704.08763v1.pdf | GazeDirector: Fully Articulated Eye Gaze Redirection in Video | We present GazeDirector, a new approach for eye gaze redirection that uses
model-fitting. Our method first tracks the eyes by fitting a multi-part eye
region model to video frames using analysis-by-synthesis, thereby recovering
eye region shape, texture, pose, and gaze simultaneously. It then redirects
gaze by 1) warpi... | ['Andreas Bulling', 'Peter Robinson', 'Louis-Philippe Morency', 'Tadas Baltrusaitis', 'Erroll Wood'] | 2017-04-27 | null | null | null | null | ['gaze-redirection'] | ['computer-vision'] | [ 1.82713883e-03 1.29392713e-01 -6.45026118e-02 -2.96285570e-01
-1.59590870e-01 -8.86656880e-01 4.19042319e-01 -7.56592751e-01
-2.16606751e-01 3.88571978e-01 4.58567202e-01 -1.43190667e-01
2.58263409e-01 5.76288626e-02 -7.83940315e-01 -5.46221375e-01
3.26092392e-01 -1.04589825e-02 7.28421062e-02 4.57537211... | [14.130725860595703, 0.064551942050457] |
8a3dc3f9-4695-42f0-a68a-b58d6a32e7ef | tetrahedral-diffusion-models-for-3d-shape | 2211.13220 | null | https://arxiv.org/abs/2211.13220v1 | https://arxiv.org/pdf/2211.13220v1.pdf | Tetrahedral Diffusion Models for 3D Shape Generation | Recently, probabilistic denoising diffusion models (DDMs) have greatly advanced the generative power of neural networks. DDMs, inspired by non-equilibrium thermodynamics, have not only been used for 2D image generation, but can also readily be applied to 3D point clouds. However, representing 3D shapes as point clouds ... | ['Konrad Schindler', 'Jan D. Wegner', 'Torben Peters', 'Nikolai Kalischek'] | 2022-11-23 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [-1.38754979e-01 5.15077263e-02 6.31411612e-01 -1.15486778e-01
-1.84753478e-01 -9.06603336e-01 1.05762601e+00 -3.53210010e-02
-2.27467954e-01 4.78748918e-01 -2.05793723e-01 -2.15051576e-01
-2.33066410e-01 -1.29936659e+00 -7.00463057e-01 -1.10244751e+00
-1.62399590e-01 9.71551597e-01 3.38908434e-01 -1.76374510... | [8.940215110778809, -3.616727352142334] |
ffd237f5-9f4f-40cc-9a1c-43fd64f57335 | drug-repurposing-for-sars-cov-2-using | 2206.01047 | null | https://arxiv.org/abs/2206.01047v2 | https://arxiv.org/pdf/2206.01047v2.pdf | Drug Repurposing For SARS-COV-2 Using Molecular Docking | Drug repurposing is an unconventional approach that is used to investigate new therapeutic aids of existing and shelved drugs. Recent advancement in technologies and the availability of the data of genomics, proteomics, transcriptomics, etc., and with the accessibility of large and reliable database resources, there ar... | ['Hina Bashir', 'Muhammad Ismail', 'Abdul Majid', 'Imra Aqeel'] | 2022-06-02 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.71200424e-01 -6.21650100e-01 -4.55948822e-02 4.69324477e-02
6.97564930e-02 -7.64053881e-01 3.11448127e-01 4.35064971e-01
-2.54626691e-01 1.57945454e+00 7.70660415e-02 -5.59704065e-01
-1.81687936e-01 -3.64602774e-01 -4.52384427e-02 -7.52073050e-01
-2.09162191e-01 8.53644848e-01 -5.77884093e-02 -3.27401042... | [4.6529130935668945, 5.0839738845825195] |
8659100a-8e7a-4a4b-a64f-17060f798897 | unsupervised-domain-adaptive-person-re | 1908.10359 | null | https://arxiv.org/abs/1908.10359v1 | https://arxiv.org/pdf/1908.10359v1.pdf | Unsupervised Domain-Adaptive Person Re-identification Based on Attributes | Pedestrian attributes, e.g., hair length, clothes type and color, locally describe the semantic appearance of a person. Training person re-identification (ReID) algorithms under the supervision of such attributes have proven to be effective in extracting local features which are important for ReID. Unlike person identi... | ['Vittorio Murino', 'Xiangping Zhu', 'Pietro Morerio'] | 2019-08-27 | null | null | null | null | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [ 1.11835070e-01 -2.99753040e-01 -3.02599426e-02 -9.26168323e-01
-3.13204587e-01 -6.54203236e-01 7.58009970e-01 3.23708445e-01
-4.28446114e-01 9.61051583e-01 2.39987850e-01 3.38661969e-01
-3.95704135e-02 -9.19222295e-01 -6.33030951e-01 -8.92611146e-01
3.30594659e-01 4.08511728e-01 -1.41135141e-01 -3.39160077... | [14.720922470092773, 1.0430488586425781] |
a9aa16b4-d755-406e-996b-f6744e04fa24 | low-level-online-control-of-the-formula-1 | 2303.00372 | null | https://arxiv.org/abs/2303.00372v1 | https://arxiv.org/pdf/2303.00372v1.pdf | Low-level Online Control of the Formula 1 Power Unit with Feedforward Cylinder Deactivation | Since 2014, the F\'ed\'eration Internationale de l'Automobile has prescribed a parallel hybrid powertrain for the Formula 1 race cars. The complex low-level interactions between the thermal and the electrical part represent a non-trivial and challenging system to be controlled online. We present a novel controller arch... | ['Christopher H. Onder', 'Alberto Cerofolini', 'Pol Duhr', 'Camillo Balerna', 'Giona Fieni', 'Marc-Philippe Neumann'] | 2023-03-01 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 9.24395584e-03 7.43475318e-01 -1.20487101e-01 3.31851333e-01
-6.48035930e-05 -6.70305073e-01 6.87446237e-01 7.58207142e-02
-1.87705621e-01 6.02971971e-01 -7.77097762e-01 -4.60967004e-01
-4.84809011e-01 -4.30401474e-01 -9.34245884e-01 -8.04996908e-01
4.12336364e-02 6.11534715e-01 3.55190039e-01 -2.88647324... | [5.434308052062988, 2.2043256759643555] |
3b5c49aa-6a72-4140-b8b4-efb16073b6c8 | question-answering-over-knowledge-base-using | null | null | https://aclanthology.org/N16-2016 | https://aclanthology.org/N16-2016.pdf | Question Answering over Knowledge Base using Factual Memory Networks | null | ['Sarthak Jain'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.288315773010254, 3.8666677474975586] |
e24f7a23-faae-46d4-9c75-9f67e7aedbbc | designing-novel-cognitive-diagnosis-models | 2307.04429 | null | https://arxiv.org/abs/2307.04429v1 | https://arxiv.org/pdf/2307.04429v1.pdf | Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search | Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to mee... | ['Xingyi Zhang', 'Yaochu Jin', 'Limiao Zhang', 'Ye Tian', 'Cheng Zhen', 'Haiping Ma', 'Shangshang Yang'] | 2023-07-10 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.54815280e-01 2.98312247e-01 -1.21776145e-02 -2.95016319e-01
-8.98252055e-02 -3.06214303e-01 1.32112861e-01 -1.08556405e-01
1.71130095e-02 4.96854067e-01 -2.64960229e-01 -4.79494810e-01
-9.01744068e-01 -1.00080228e+00 -3.00913632e-01 -7.13103592e-01
3.82164121e-01 6.96918070e-01 2.19553351e-01 -3.60433340... | [8.191364288330078, 3.4047327041625977] |
d208aec8-d6e9-48f5-b2d0-1f0e4b9e76c8 | robots-that-ask-for-help-uncertainty | 2307.01928 | null | https://arxiv.org/abs/2307.01928v1 | https://arxiv.org/pdf/2307.01928v1.pdf | Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners | Large language models (LLMs) exhibit a wide range of promising capabilities -- from step-by-step planning to commonsense reasoning -- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, which is a framework for measuring and aligning the uncert... | ['Anirudha Majumdar', 'Andy Zeng', 'Dorsa Sadigh', 'Zhenjia Xu', 'Jake Varley', 'Fei Xia', 'Leila Takayama', 'Peng Xu', 'Noah Brown', 'Stephen Tu', 'Sumeet Singh', 'Alexandra Bodrova', 'Anushri Dixit', 'Allen Z. Ren'] | 2023-07-04 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [-1.09033950e-01 9.51790750e-01 -1.55575484e-01 -6.10253513e-01
-1.00735116e+00 -5.72645605e-01 9.09368157e-01 1.37213439e-01
-4.15270180e-01 7.39261866e-01 4.31422263e-01 -3.52304220e-01
-3.75660777e-01 -3.99709761e-01 -8.29520881e-01 -1.83739305e-01
-3.91236365e-01 1.28648627e+00 1.27483830e-01 -5.24712145... | [4.3663010597229, 0.9578589200973511] |
e6aed30c-eb6f-4771-9863-30c5f957e747 | kidney-segmentation-in-neck-to-knee-body-mri | 2006.06996 | null | https://arxiv.org/abs/2006.06996v1 | https://arxiv.org/pdf/2006.06996v1.pdf | Kidney segmentation in neck-to-knee body MRI of 40,000 UK Biobank participants | The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained... | ['Joel Kullberg', 'Håkan Ahlström', 'Lowe Lundin', 'Andreas Wallin', 'Andreas Östling', 'Taro Langner', 'Robin Strand', 'Lukas Maldonis', 'Dag Lindgren', 'Albin Karlsson', 'Daniel Olmo'] | 2020-06-12 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 1.86298974e-02 3.58359426e-01 -7.77188092e-02 -6.09012365e-01
-6.36274636e-01 -5.23641527e-01 2.20624983e-01 8.33761513e-01
-5.66153586e-01 6.19850159e-01 2.72705674e-01 -2.27287859e-01
-1.04663335e-01 -7.07413971e-01 -4.76840049e-01 -6.08960092e-01
-5.87482333e-01 8.97569060e-01 -1.89028364e-02 7.02290356... | [14.131939888000488, -2.4211668968200684] |
2b6cced5-5e3a-46c0-b6d5-c95263ce952e | multi-layer-kernel-ridge-regression-for-one | 1805.07808 | null | http://arxiv.org/abs/1805.07808v2 | http://arxiv.org/pdf/1805.07808v2.pdf | Multi-layer Kernel Ridge Regression for One-class Classification | In this paper, a multi-layer architecture (in a hierarchical fashion) by
stacking various Kernel Ridge Regression (KRR) based Auto-Encoder for one-class
classification is proposed and is referred as MKOC. MKOC has many layers of
Auto-Encoders to project the input features into new feature space and the last
layer was r... | ['Chandan Gautam', 'Alexandros Iosifidis', 'Aruna Tiwari', 'Sundaram Suresh'] | 2018-05-20 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-1.02381609e-01 -9.77681726e-02 -3.56308728e-01 -5.30251265e-01
-4.73578274e-01 1.85132176e-02 6.31324291e-01 2.38104284e-01
-4.99554873e-01 9.26366985e-01 4.64089550e-02 -2.99359828e-01
-2.91516721e-01 -7.47277021e-01 -7.82631874e-01 -6.58517063e-01
-3.35980445e-01 -3.12598012e-02 2.52271801e-01 -9.54225883... | [8.234586715698242, 3.848489999771118] |
e4888a35-466c-4cfe-9e86-c0ec51548fa9 | tnpar-topological-neural-poisson-auto | 2306.14114 | null | https://arxiv.org/abs/2306.14114v1 | https://arxiv.org/pdf/2306.14114v1.pdf | TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences | Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i.d. assumption is often violated due to the inherent dependencies amon... | ['Zhifeng Hao', 'Keli Zhang', 'Zijian Li', 'Yuguang Yan', 'Jie Qiao', 'Wei Chen', 'Yuequn Liu', 'Ruichu Cai'] | 2023-06-25 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.32353351e-01 -2.13690996e-01 1.61066260e-02 -1.77042767e-01
-4.83189106e-01 -5.73956549e-01 9.85469520e-01 6.73913285e-02
-1.50565326e-01 8.76324236e-01 2.79878616e-01 -3.32068086e-01
-4.43197489e-01 -1.17495680e+00 -1.04248917e+00 -7.59753942e-01
-3.53955805e-01 7.18164861e-01 1.69795066e-01 2.36820400... | [7.002326011657715, 3.634523391723633] |
b1730035-9be8-468a-b9c1-15e4ac399bde | digeo-discriminative-geometry-aware-learning | 2303.09674 | null | https://arxiv.org/abs/2303.09674v1 | https://arxiv.org/pdf/2303.09674v1.pdf | DiGeo: Discriminative Geometry-Aware Learning for Generalized Few-Shot Object Detection | Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approaches enhance few-shot generalization with the sacrifice of base-class performance, or maintain high precision in base-class detection with l... | ['Shih-Fu Chang', 'Guangxing Han', 'Shiyuan Huang', 'Jincheng Xu', 'Yulei Niu', 'Jiawei Ma'] | 2023-03-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_DiGeo_Discriminative_Geometry-Aware_Learning_for_Generalized_Few-Shot_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_DiGeo_Discriminative_Geometry-Aware_Learning_for_Generalized_Few-Shot_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-object-detection'] | ['computer-vision'] | [ 1.80333138e-01 -1.15185589e-01 -4.40702766e-01 -3.96508276e-01
-9.84201074e-01 -4.34452891e-01 3.16791564e-01 2.37733468e-01
-3.75234634e-01 5.96320570e-01 -2.03536168e-01 2.09458858e-01
-2.97579944e-01 -6.95920944e-01 -6.43953383e-01 -8.77201259e-01
1.18068524e-01 2.51076192e-01 8.09551001e-01 -2.89281029... | [9.619672775268555, 2.142378568649292] |
711447a5-c0e2-4ff4-adf6-f5883bb1adb0 | learning-ensembles-of-potential-functions-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Hajimirsadeghi_Learning_Ensembles_of_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Hajimirsadeghi_Learning_Ensembles_of_ICCV_2015_paper.pdf | Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables | Many visual recognition tasks involve modeling variables which are structurally related. Hidden conditional random fields (HCRFs) are a powerful class of models for encoding structure in weakly supervised training examples. This paper presents HCRF-Boost, a novel and general framework for learning HCRFs in functional s... | ['Hossein Hajimirsadeghi', 'Greg Mori'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['group-activity-recognition'] | ['computer-vision'] | [ 5.15048683e-01 -8.35623592e-02 -4.82929438e-01 -7.12297916e-01
-6.40346646e-01 -2.10385025e-01 6.29335284e-01 -1.56739488e-01
-1.11770004e-01 9.55378234e-01 3.30220640e-01 4.08465527e-02
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-1.88552693e-01 3.51537794e-01 6.72103837e-02 1.88629165... | [8.52204704284668, 0.8417503833770752] |
b84e81eb-8991-4e7e-ae2d-44588d2ce1d9 | what-can-a-cook-in-italy-teach-a-mechanic-in | 2306.08713 | null | https://arxiv.org/abs/2306.08713v1 | https://arxiv.org/pdf/2306.08713v1.pdf | What can a cook in Italy teach a mechanic in India? Action Recognition Generalisation Over Scenarios and Locations | We propose and address a new generalisation problem: can a model trained for action recognition successfully classify actions when they are performed within a previously unseen scenario and in a previously unseen location? To answer this question, we introduce the Action Recognition Generalisation Over scenarios and lo... | ['Dima Damen', 'Barbara Caputo', 'Toby Perrett', 'Chiara Plizzari'] | 2023-06-14 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 4.73784059e-01 -2.98434850e-02 -2.28943124e-01 -3.46820414e-01
-9.16483819e-01 -8.32651019e-01 9.74008799e-01 -6.15583241e-01
-1.39726192e-01 6.37466192e-01 8.03041220e-01 2.98048351e-02
1.22812530e-02 -9.72438082e-02 -8.39886725e-01 -5.94662011e-01
-4.34904248e-02 5.01041591e-01 2.32650980e-01 2.61876106... | [8.31993579864502, 0.6658830046653748] |
6b251fa5-9e3e-4bc5-8a4a-14eca96a32f4 | emotional-speech-driven-animation-with | 2306.08990 | null | https://arxiv.org/abs/2306.08990v1 | https://arxiv.org/pdf/2306.08990v1.pdf | Emotional Speech-Driven Animation with Content-Emotion Disentanglement | To be widely adopted, 3D facial avatars need to be animated easily, realistically, and directly, from speech signals. While the best recent methods generate 3D animations that are synchronized with the input audio, they largely ignore the impact of emotions on facial expressions. Instead, their focus is on modeling the... | ['Timo Bolkart', 'Michael J. Black', 'Yandong Wen', 'Shashank Tripathi', 'Kiran Chhatre', 'Radek Daněček'] | 2023-06-15 | null | null | null | null | ['disentanglement'] | ['methodology'] | [-4.51964000e-03 2.70437121e-01 1.82597642e-03 -1.47220850e-01
-3.72192681e-01 -4.30889845e-01 5.07696807e-01 -8.32028389e-01
4.29042280e-02 2.95833260e-01 4.93481696e-01 2.97943950e-01
4.94045347e-01 -3.99492979e-01 -6.31099820e-01 -8.33449066e-01
-1.61564332e-02 -5.40975742e-02 -8.66186693e-02 -2.76511073... | [13.18896198272705, -0.4441170394420624] |
dd5f9e3a-a8c3-4589-90d2-9f641c294c2d | linguistic-cues-of-deception-in-a | 2111.03913 | null | https://arxiv.org/abs/2111.03913v3 | https://arxiv.org/pdf/2111.03913v3.pdf | Linguistic Cues of Deception in a Multilingual April Fools' Day Context | In this work we consider the collection of deceptive April Fools' Day(AFD) news articles as a useful addition in existing datasets for deception detection tasks. Such collections have an established ground truth and are relatively easy to construct across languages. As a result, we introduce a corpus that includes diac... | ['Dimitris Plexousakis', 'Giorgos Flouris', 'Panagiotis Papadakos', 'Katerina Papantoniou'] | 2021-11-06 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-2.89438516e-01 -1.89420164e-01 -3.96188587e-01 -5.81654787e-01
-8.96285772e-01 -1.00381327e+00 1.49923933e+00 3.39952558e-01
-4.69110370e-01 6.71965241e-01 8.20953369e-01 -2.60817289e-01
-6.18366338e-02 -2.64936447e-01 -3.49345893e-01 -3.80022258e-01
4.14732903e-01 2.21435994e-01 -2.83923179e-01 -4.41080749... | [8.179289817810059, 10.38051700592041] |
e582d2e8-fe50-4589-9e75-8234ae57b4b3 | a-two-stage-framework-with-self-supervised | 2304.09820 | null | https://arxiv.org/abs/2304.09820v1 | https://arxiv.org/pdf/2304.09820v1.pdf | A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification | Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-... | ['Wanxiang Che', 'Xiao Xu', 'Libo Qin', 'Bohan Li', 'Yunlong Feng'] | 2023-04-18 | null | null | null | null | ['cross-domain-text-classification'] | ['natural-language-processing'] | [ 1.69708654e-01 -8.16413090e-02 -4.10895258e-01 -7.12801635e-01
-9.38807607e-01 -7.98641741e-01 7.23465681e-01 3.75623375e-01
-7.02773392e-01 9.22820628e-01 1.56036988e-01 -1.26651630e-01
2.18678668e-01 -5.55958748e-01 -3.56388956e-01 -3.36224020e-01
4.50079054e-01 9.05176997e-01 4.09335345e-01 -5.01057804... | [10.67474365234375, 8.005663871765137] |
d0291c94-2fe6-4ed6-a486-ec840ecca956 | geoconv-geodesic-guided-convolution-for | 2003.03055 | null | https://arxiv.org/abs/2003.03055v1 | https://arxiv.org/pdf/2003.03055v1.pdf | GeoConv: Geodesic Guided Convolution for Facial Action Unit Recognition | Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroughly capture. Most existing AU recognition approaches leverage geometry information in a straightforward 2D or 3D manner, which either ignore... | ['Tat-Jen Cham', 'Yuedong Chen', 'Guoxian Song', 'Jianfei Cai', 'Zhiwen Shao', 'Jianming Zheng'] | 2020-03-06 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [-2.80900896e-02 -1.45710111e-01 -1.47170931e-01 -4.33281362e-01
-5.23959100e-01 -4.01667207e-01 5.94020903e-01 -5.69020987e-01
-2.78713077e-01 7.32838139e-02 4.45606977e-01 3.55205685e-02
1.17060877e-01 -6.32841766e-01 -5.82859576e-01 -8.03780973e-01
1.43030420e-01 1.95377544e-02 -3.49018544e-01 -1.18762396... | [13.70309066772461, 1.4013217687606812] |
a6dc4f8c-cc1a-4bc5-ba39-42221a5ea136 | generating-adjacency-matrix-for-video-query | 2008.08977 | null | https://arxiv.org/abs/2008.08977v2 | https://arxiv.org/pdf/2008.08977v2.pdf | Generating Adjacency Matrix for Video Relocalization | In this paper, we continue our work on video relocalization task. Based on using graph convolution to extract intra-video and inter-video frame features, we improve the method by using similarity-metric based graph convolution, whose weighted adjacency matrix is achieved by calculating similarity metric between feature... | ['Shuwei Huo', 'Yuan Zhou', 'Mingfei Wang', 'Ruolin Wang'] | 2020-08-19 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.00178503e-01 -2.39817888e-01 -4.09408152e-01 -2.21403360e-01
2.43896455e-01 -3.14918637e-01 5.00889361e-01 1.55501693e-01
-6.65535271e-01 6.08915210e-01 4.22180176e-01 -7.13484660e-02
-2.32325360e-01 -8.25213671e-01 -5.22221625e-01 -3.79788160e-01
-6.53919876e-01 -4.32610184e-01 7.45963097e-01 -1.33883236... | [9.047072410583496, 0.6159029603004456] |
76562407-532f-43c4-91a0-a91fc325c341 | extended-feature-pyramid-network-for-small | 2003.07021 | null | https://arxiv.org/abs/2003.07021v2 | https://arxiv.org/pdf/2003.07021v2.pdf | Extended Feature Pyramid Network for Small Object Detection | Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects.... | ['Yong liu', 'Chunfang Deng', 'Liang Liu', 'Mengmeng Wang'] | 2020-03-16 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.39871836e-01 -3.67512524e-01 -1.17673442e-01 -2.30887190e-01
-6.63326621e-01 -1.97537661e-01 9.53810960e-02 -3.65226537e-01
-3.25727433e-01 4.06750470e-01 -1.72873124e-01 6.50605783e-02
9.39631555e-03 -8.78779829e-01 -6.27216637e-01 -6.54548764e-01
1.21145815e-01 5.29129803e-03 1.31670368e+00 -9.10185128... | [8.807865142822266, -0.5077728033065796] |
4138a743-b97a-4457-9e88-40ef2229c7e6 | empathetic-response-generation-with-state | 2205.03676 | null | https://arxiv.org/abs/2205.03676v2 | https://arxiv.org/pdf/2205.03676v2.pdf | Empathetic Response Generation with State Management | A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either focus on improving the understanding of users' emotion or on proposing better responding strategies, and very few works consider both at the sa... | ['Ruifeng Xu', 'Lanjun Zhou', 'Jiachen Du', 'Jun Gao', 'YuHan Liu'] | 2022-05-07 | null | null | null | null | ['empathetic-response-generation', 'dialogue-management'] | ['natural-language-processing', 'natural-language-processing'] | [-1.73042700e-01 4.59336847e-01 -2.03472272e-01 -7.70970404e-01
-4.53168809e-01 -4.13466364e-01 5.27301550e-01 2.30104774e-01
-4.15332496e-01 7.21585572e-01 8.31437230e-01 1.86841294e-01
3.81730407e-01 -6.45863175e-01 5.48190475e-01 -4.11440521e-01
3.83122325e-01 7.71163881e-01 2.95942370e-02 -9.30806637... | [13.147075653076172, 7.696939945220947] |
ad779b17-567b-4b5d-87f9-82fc75f70977 | towards-view-invariant-and-accurate-loop | 2305.14885 | null | https://arxiv.org/abs/2305.14885v1 | https://arxiv.org/pdf/2305.14885v1.pdf | Towards View-invariant and Accurate Loop Detection Based on Scene Graph | Loop detection plays a key role in visual Simultaneous Localization and Mapping (SLAM) by correcting the accumulated pose drift. In indoor scenarios, the richly distributed semantic landmarks are view-point invariant and hold strong descriptive power in loop detection. The current semantic-aided loop detection embeds t... | ['Shaojie Shen', 'Chuhao Liu'] | 2023-05-24 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 2.10682765e-01 -2.18298078e-01 2.57254974e-03 -4.90144283e-01
-3.65264893e-01 -8.77164900e-01 5.43865979e-01 3.15135717e-01
-1.85828768e-02 4.72566783e-01 -2.04979017e-01 -2.22850457e-01
-1.12513013e-01 -6.61473811e-01 -8.41270506e-01 -2.48414531e-01
-1.24936379e-01 6.43912613e-01 7.07573950e-01 -4.16385263... | [7.310600280761719, -2.2370786666870117] |
2c1b947a-c026-424a-93bd-f2e0272649a8 | channel-interaction-networks-for-fine-grained-1 | 2003.05235 | null | https://arxiv.org/abs/2003.05235v1 | https://arxiv.org/pdf/2003.05235v1.pdf | Channel Interaction Networks for Fine-Grained Image Categorization | Fine-grained image categorization is challenging due to the subtle inter-class differences.We posit that exploiting the rich relationships between channels can help capture such differences since different channels correspond to different semantics. In this paper, we propose a channel interaction network (CIN), which m... | ['Yu Gao', 'Weilin Huang', 'Xun Wang', 'Matthew R. Scott', 'Xintong Han'] | 2020-03-11 | channel-interaction-networks-for-fine-grained | https://www.semanticscholar.org/paper/Channel-Interaction-Networks-for-Fine-Grained-Image-Gao-Han/8585737f242285ad322acf23bc4943ddd50bf045 | https://pdfs.semanticscholar.org/8585/737f242285ad322acf23bc4943ddd50bf045.pdf | aaai-2020-2020-2 | ['image-categorization'] | ['computer-vision'] | [ 2.56521851e-01 -4.03428108e-01 -2.62584180e-01 -5.43082178e-01
-7.36292839e-01 -7.55999744e-01 8.35820436e-01 2.11134166e-01
-3.52869779e-01 3.01901758e-01 2.18354970e-01 -1.46262705e-01
-9.22765490e-03 -5.28033197e-01 -7.54997551e-01 -6.58850551e-01
-5.13397828e-02 -3.14928651e-01 7.48709589e-02 -2.12378576... | [9.59562873840332, 2.074218273162842] |
a953741d-ea63-4d90-a2df-c6e94c402b77 | entropy-driven-sampling-and-training-scheme | 2206.11474 | null | https://arxiv.org/abs/2206.11474v5 | https://arxiv.org/pdf/2206.11474v5.pdf | Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation | Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process. However, due to the ability of classifier to easily di... | ['Xi Li', 'Shoudong Ding', 'Yang Chen', 'Taiping Yao', 'Hui Wang', 'Guangcong Zheng', 'Shengming Li'] | 2022-06-23 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 3.35203350e-01 2.87341662e-02 1.80652320e-01 -5.24538994e-01
-9.02715862e-01 -1.39078230e-01 4.49503988e-01 -1.10836916e-01
-3.32233995e-01 5.59362471e-01 3.03375065e-01 -1.68973301e-02
-1.02243409e-01 -8.77876878e-01 -5.13646126e-01 -1.11958301e+00
-7.94723704e-02 -6.40737042e-02 2.94670314e-01 -4.54151854... | [11.464888572692871, -2.387092113494873] |
de221061-cd72-487d-8e22-f3611a18201e | cross-lingual-genqa-a-language-agnostic | 2110.07150 | null | https://arxiv.org/abs/2110.07150v3 | https://arxiv.org/pdf/2110.07150v3.pdf | Cross-Lingual Open-Domain Question Answering with Answer Sentence Generation | Open-Domain Generative Question Answering has achieved impressive performance in English by combining document-level retrieval with answer generation. These approaches, which we refer to as GenQA, can generate complete sentences, effectively answering both factoid and non-factoid questions. In this paper, we extend Gen... | ['Alessandro Moschitti', 'Eric Lind', 'Rik Koncel-Kedziorski', 'Luca Soldaini', 'Benjamin Muller'] | 2021-10-14 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [-3.77762645e-01 6.94252104e-02 3.17858338e-01 -4.69164789e-01
-2.06191921e+00 -1.15694606e+00 6.62345946e-01 -3.34869474e-01
-1.24654628e-01 1.12042749e+00 6.06857836e-01 -4.29957807e-01
1.19363956e-01 -1.14561462e+00 -6.00746810e-01 -1.90720111e-01
6.08659744e-01 1.27610135e+00 1.17219305e-02 -7.71750271... | [11.385845184326172, 8.355509757995605] |
f7410ef4-6261-464e-bda5-8e5c615fcf90 | gan-for-vision-kg-for-relation-a-two-stage | 2105.11789 | null | https://arxiv.org/abs/2105.11789v1 | https://arxiv.org/pdf/2105.11789v1.pdf | GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition | Zero-shot action recognition can recognize samples of unseen classes that are unavailable in training by exploring common latent semantic representation in samples. However, most methods neglected the connotative relation and extensional relation between the action classes, which leads to the poor generalization abilit... | ['Xiaonan Luo', 'BaoCai Yin', 'Jinghua Li', 'Shaofan Wang', 'Dehui Kong', 'Bin Sun'] | 2021-05-25 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.44879001e-01 1.07239902e-01 -2.77243286e-01 -2.60164827e-01
-1.24877371e-01 4.12523374e-02 5.80557346e-01 -3.89708430e-01
-1.12279974e-01 5.69229364e-01 2.27597520e-01 1.40603989e-01
1.83208678e-02 -1.48219252e+00 -4.72285360e-01 -8.76216233e-01
5.26602387e-01 2.71123290e-01 6.47516012e-01 -1.59399644... | [9.942127227783203, 2.5872435569763184] |
69ab957b-5bbc-479a-828c-da085a8f1d86 | screening-for-rem-sleep-behaviour-disorder | 1910.11702 | null | https://arxiv.org/abs/1910.11702v1 | https://arxiv.org/pdf/1910.11702v1.pdf | Screening for REM Sleep Behaviour Disorder with Minimal Sensors | Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) follo... | ['Maarten De Vos', 'Michele T. M. Hu', 'Mkael Symmonds', 'Christine Lo', 'Fernando Andreotti', 'Navin Cooray'] | 2019-10-24 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 1.30037084e-01 -1.20926075e-01 -1.43282086e-01 -1.70446590e-01
-6.03456557e-01 -2.06527099e-01 -2.52547655e-02 -6.34715036e-02
-9.13820326e-01 1.16389930e+00 -1.03808500e-01 -3.84187728e-01
-3.93112868e-01 -2.97106415e-01 1.54920980e-01 -4.55898732e-01
-4.15058732e-01 4.29563075e-01 2.21157163e-01 6.69951588... | [13.536670684814453, 3.388672113418579] |
c323d6de-bd1c-4c1d-8f55-225006f59f6d | on-the-role-of-supervision-in-unsupervised | 2010.02423 | null | https://arxiv.org/abs/2010.02423v2 | https://arxiv.org/pdf/2010.02423v2.pdf | On the Role of Supervision in Unsupervised Constituency Parsing | We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing $F_1$ score on the Wall Street Journal (WSJ) development set (1,700 sentences). We introduce strong baselines for them, by training an existing supervised parsing model (Kitaev and Klein, 2018) on the same la... | ['Kevin Gimpel', 'Karen Livescu', 'Haoyue Shi'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.614 | https://aclanthology.org/2020.emnlp-main.614.pdf | emnlp-2020-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 3.41933072e-01 7.72906721e-01 -2.63516694e-01 -7.51754344e-01
-1.30276239e+00 -7.89703786e-01 6.24510288e-01 3.96756202e-01
-6.90228045e-01 5.72599292e-01 3.77165616e-01 -5.63767016e-01
2.34355852e-01 -7.40460277e-01 -6.75767004e-01 -4.14293855e-01
5.97792007e-02 6.05699778e-01 3.62528056e-01 -3.53030376... | [10.377419471740723, 9.637932777404785] |
5f776a23-0bdc-4ebd-b17e-2936ea4062c0 | semeval-2012-task-5-chinese-semantic | null | null | https://aclanthology.org/S12-1050 | https://aclanthology.org/S12-1050.pdf | SemEval-2012 Task 5: Chinese Semantic Dependency Parsing | null | ['Meishan Zhang', 'Yanqiu Shao', 'Ting Liu', 'Wanxiang Che'] | 2012-07-01 | null | null | null | semeval-2012-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2076497077941895, 3.744049549102783] |
849edd92-c846-4f84-a94e-9873885a778c | learning-to-generate-explanation-from-e | null | null | https://aclanthology.org/2022.coling-1.260 | https://aclanthology.org/2022.coling-1.260.pdf | Learning to Generate Explanation from e-Hospital Services for Medical Suggestion | Explaining the reasoning of neural models has attracted attention in recent years. Providing highly-accessible and comprehensible explanations in natural language is useful for humans to understand model’s prediction results. In this work, we present a pilot study to investigate explanation generation with a narrative ... | ['Hsin-Hsi Chen', 'Hen-Hsen Huang', 'An-Zi Yen', 'Wei-Lin Chen'] | null | null | null | null | coling-2022-10 | ['explanation-generation'] | ['natural-language-processing'] | [ 4.06118810e-01 1.26509428e+00 -5.82539499e-01 -7.80595958e-01
-6.13009393e-01 -4.46191579e-02 8.17015707e-01 4.01992947e-01
1.73540562e-01 9.21986878e-01 1.15171278e+00 -6.39439642e-01
-2.83752382e-01 -5.28455138e-01 -5.84188998e-01 -1.99663937e-02
1.53482288e-01 5.22498667e-01 -1.35982022e-01 -1.75844133... | [9.55998706817627, 6.944816589355469] |
4216cb30-f523-48de-b158-3d43764eb27d | deep-learning-applications-for-covid-19 | null | null | https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00392-9 | https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-020-00392-9.pdf | Deep Learning applications for COVID-19 | This survey explores how Deep Learning has battled the COVID-19 pandemic and provides directions for future research on COVID-19. We cover Deep Learning applications in Natural Language Processing, Computer Vision, Life Sciences, and Epidemiology. We describe how each of these applications vary with the availability of... | ['Borko Furht', 'Taghi M. Khoshgoftaar', 'Connor Shorten'] | 2021-01-11 | null | null | null | journal-of-big-data-2021-1 | ['epidemiology'] | ['medical'] | [ 5.82592823e-02 -3.37384790e-01 -3.16774964e-01 -3.07631761e-01
-2.28766978e-01 -4.96531755e-01 3.50476265e-01 5.89932740e-01
-7.04011142e-01 6.11256540e-01 1.95066079e-01 -5.96492887e-01
-4.49082479e-02 -7.13400006e-01 -4.60915864e-01 -6.94938123e-01
-3.40516895e-01 8.11153233e-01 -5.92111051e-01 -3.60809773... | [15.56959056854248, -1.6666680574417114] |
ad54e3d8-745a-4fcd-a723-b2cafbd9f092 | benign-malignant-lung-nodule-classification | 1605.08350 | null | http://arxiv.org/abs/1605.08350v1 | http://arxiv.org/pdf/1605.08350v1.pdf | Benign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features | Lung cancer accounts for the highest number of cancer deaths globally. Early
diagnosis of lung nodules is very important to reduce the mortality rate of
patients by improving the diagnosis and treatment of lung cancer. This work
proposes an automated system to classify lung nodules as malignant and benign
in CT images.... | ['Tizita Nesibu Shewaye', 'Alhayat Ali Mekonnen'] | 2016-05-26 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-7.38980398e-02 2.51651227e-01 -4.86201227e-01 -9.12391394e-02
-9.27985787e-01 -5.05847275e-01 5.24975479e-01 -2.30830889e-02
-2.99834102e-01 5.42627871e-01 1.57040164e-01 -5.81710696e-01
-1.97970852e-01 -6.60715699e-01 6.76641017e-02 -7.50565469e-01
-7.18994439e-02 1.30862188e+00 7.97511041e-01 4.99467045... | [15.3897123336792, -2.1471614837646484] |
01b54a5c-b9c2-4a20-bad4-07869f251890 | spa-web-based-platform-for-easy-access-to | null | null | https://aclanthology.org/L16-1615 | https://aclanthology.org/L16-1615.pdf | SPA: Web-based Platform for easy Access to Speech Processing Modules | This paper presents SPA, a web-based Speech Analytics platform that integrates several speech processing modules and that makes it possible to use them through the web. It was developed with the aim of facilitating the usage of the modules, without the need to know about software dependencies and specific configuration... | ['Ricardo Ribeiro', "Eug{\\'e}nio Ribeiro", 'o', 'David Martins de Matos', 'Pedro Curto', 'Helena Moniz', 'Alberto Abad', 'Jaime Ferreira', 'Isabel Trancoso', 'Fern Batista'] | 2016-05-01 | spa-web-based-platform-for-easy-access-to-1 | https://aclanthology.org/L16-1615 | https://aclanthology.org/L16-1615.pdf | lrec-2016-5 | ['age-and-gender-classification'] | ['computer-vision'] | [-2.42707193e-01 3.05572927e-01 2.05937728e-01 -4.56424087e-01
-4.99098539e-01 -5.68732858e-01 6.05989456e-01 2.72794336e-01
-5.98939002e-01 3.46613735e-01 3.53108019e-01 -4.42219228e-01
-8.84213969e-02 -4.33323711e-01 4.88878995e-01 -5.58009744e-01
1.70599461e-01 7.93900132e-01 5.57513058e-01 -4.82537955... | [13.968788146972656, 6.271763324737549] |
0c6dbbf6-942e-4527-ae65-8b38fa245d7f | cross-lingual-information-retrieval-with-bert | 2004.13005 | null | https://arxiv.org/abs/2004.13005v1 | https://arxiv.org/pdf/2004.13005v1.pdf | Cross-lingual Information Retrieval with BERT | Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the... | ['Amro El-Jaroudi', 'Damianos Karakos', 'Lingjun Zhao', 'Zhuolin Jiang', 'William Hartmann'] | 2020-04-24 | cross-lingual-information-retrieval-with-bert-1 | https://aclanthology.org/2020.clssts-1.5 | https://aclanthology.org/2020.clssts-1.5.pdf | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.11536014e-01 -3.63875836e-01 -4.52934921e-01 -6.85662389e-01
-1.63652515e+00 -3.68967146e-01 1.01346648e+00 3.87973189e-01
-1.12267804e+00 8.89929235e-01 5.57418108e-01 -4.81399238e-01
-3.89319181e-01 -5.61433911e-01 -7.06912100e-01 -6.49582669e-02
1.11425810e-01 1.02005041e+00 2.43112653e-01 -6.76888466... | [11.379855155944824, 9.77081298828125] |
dc6763a4-ef35-4a7e-9106-f0590a79aa62 | procedure-aware-pretraining-for-instructional | 2303.18230 | null | https://arxiv.org/abs/2303.18230v1 | https://arxiv.org/pdf/2303.18230v1.pdf | Procedure-Aware Pretraining for Instructional Video Understanding | Our goal is to learn a video representation that is useful for downstream procedure understanding tasks in instructional videos. Due to the small amount of available annotations, a key challenge in procedure understanding is to be able to extract from unlabeled videos the procedural knowledge such as the identity of th... | ['Juan Carlos Niebles', 'Silvio Savarese', 'Mubbasir Kapadia', 'Roberto Martín-Martín', 'Honglu Zhou'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Procedure-Aware_Pretraining_for_Instructional_Video_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Procedure-Aware_Pretraining_for_Instructional_Video_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 6.81562424e-01 1.01078458e-01 -5.72479248e-01 -4.43644226e-01
-7.40024328e-01 -1.06784320e+00 4.66741890e-01 2.96679527e-01
-1.56577557e-01 6.11789048e-01 5.20152450e-01 -4.54766959e-01
-1.73138484e-01 -5.44244468e-01 -1.33751678e+00 -2.96548218e-01
-5.87828495e-02 1.90953597e-01 1.45349026e-01 2.71511376... | [8.844502449035645, 0.6694371104240417] |
145d19f5-1567-41fb-b094-0a0edf565c9a | cluster-consistency-simple-yet-effect-robust | 2211.03333 | null | https://arxiv.org/abs/2211.03333v1 | https://arxiv.org/pdf/2211.03333v1.pdf | Cluster consistency: Simple yet effect robust learning algorithm on large-scale photoplethysmography for atrial fibrillation detection in the presence of real-world label noise | Obtaining large-scale well-annotated is always a daunting challenge, especially in the medical research domain because of the shortage of domain expert. Instead of human annotation, in this work, we use the alarm information generated from bed-side monitor to get the pseudo label for the co-current photoplethysmography... | ['Xiao Hu', 'Cynthia Rudin', 'Gari Clifford', 'Amit Shah', 'Zhicheng Guo', 'Cheng Ding'] | 2022-11-07 | null | null | null | null | ['photoplethysmography-ppg', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 1.23215236e-01 1.21864259e-01 7.02524511e-03 -4.91436988e-01
-7.19037831e-01 -2.46203095e-01 -2.82171220e-02 -1.82696417e-01
-1.69820964e-01 7.50273228e-01 -2.98145935e-02 1.16349712e-01
7.82847255e-02 -3.94637704e-01 -2.39236087e-01 -1.01761007e+00
1.42375708e-01 4.48034853e-01 1.54705495e-01 4.71821755... | [14.031670570373535, 2.705820083618164] |
27fecde8-2811-479d-b4f3-c1bcd91ac424 | cross-level-cross-scale-cross-attention | 2104.13053 | null | https://arxiv.org/abs/2104.13053v1 | https://arxiv.org/pdf/2104.13053v1.pdf | Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation | Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. And its permutation invariance property makes it ideally suitable for point cloud processing. Inspired by this remarkable success, we propose an end-to-end architecture, dubbed Cross-Leve... | ['Guo-Qiang Xiao', 'Jia Chen', 'Zhang-Yue He', 'Xian-Feng Han'] | 2021-04-27 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [ 3.00154258e-02 -3.61146241e-01 -7.78372958e-02 -5.36707342e-01
-9.48845148e-01 -3.03883761e-01 3.90084326e-01 2.51318544e-01
-1.62018970e-01 2.01417934e-02 -1.28039017e-01 -1.73487306e-01
-1.34262025e-01 -8.78485024e-01 -1.17773652e+00 -3.19672555e-01
-2.34744936e-01 1.20905049e-01 3.53197664e-01 -6.27942150... | [7.956623077392578, -3.5137083530426025] |
003ceffd-0890-410e-8f6b-fe156cd78902 | principled-multi-aspect-evaluation-measures | 2212.00492 | null | https://arxiv.org/abs/2212.00492v1 | https://arxiv.org/pdf/2212.00492v1.pdf | Principled Multi-Aspect Evaluation Measures of Rankings | Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query. Several methods extend this type of evaluation beyond relevance, making it possible to evaluate different aspects of a document ranking (e.g., relevanc... | ['Christina Lioma', 'Jakob Grue Simonsen', 'Lucas Chaves Lima', 'Maria Maistro'] | 2022-12-01 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-2.03792140e-01 8.26458633e-02 -2.86980540e-01 -4.56919700e-01
-1.19136882e+00 -9.72778857e-01 9.86427367e-01 7.45435834e-01
-6.78598166e-01 7.82753408e-01 3.96316856e-01 -4.09776896e-01
-7.76217759e-01 -6.39754176e-01 -2.73832858e-01 -5.25367200e-01
3.33089828e-02 9.58415508e-01 7.44598806e-01 -3.83300185... | [10.322091102600098, 7.694666862487793] |
b5c9a90c-27d1-4865-bad0-1ba22df91ba3 | learning-to-kindle-the-starlight | 2211.09206 | null | https://arxiv.org/abs/2211.09206v1 | https://arxiv.org/pdf/2211.09206v1.pdf | Learning to Kindle the Starlight | Capturing highly appreciated star field images is extremely challenging due to light pollution, the requirements of specialized hardware, and the high level of photographic skills needed. Deep learning-based techniques have achieved remarkable results in low-light image enhancement (LLIE) but have not been widely appli... | ['Han Pan', 'Shuyuan Zhu', 'Henry Leung', 'Zhongliang Jing', 'Lindong Wang', 'Jiaqi Wu', 'Yu Yuan'] | 2022-11-16 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 4.04853642e-01 -3.95131171e-01 4.07651424e-01 -2.34772101e-01
-5.25634706e-01 -2.42130771e-01 5.60088873e-01 -2.76987106e-01
-6.07882500e-01 6.64233685e-01 -1.11544706e-01 -6.68697804e-02
-2.19058007e-01 -8.38168681e-01 -7.49343514e-01 -8.21099997e-01
2.99968213e-01 -3.18807550e-02 7.15031266e-01 -4.27182406... | [10.741560935974121, -2.482313394546509] |
056ab5a7-5242-4f41-8929-8dd4927c1b28 | locating-and-editing-factual-knowledge-in-gpt | 2202.05262 | null | https://arxiv.org/abs/2202.05262v5 | https://arxiv.org/pdf/2202.05262v5.pdf | Locating and Editing Factual Associations in GPT | We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model's factual pred... | ['Yonatan Belinkov', 'Alex Andonian', 'David Bau', 'Kevin Meng'] | 2022-02-10 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 3.12218964e-01 3.79709363e-01 -1.27086148e-01 -4.29400891e-01
-4.44065779e-01 -5.33177376e-01 1.24126732e+00 4.94880825e-01
-4.27354544e-01 7.63306379e-01 6.74348235e-01 -5.08157372e-01
-1.51525185e-01 -1.01250780e+00 -8.70593071e-01 -2.14118376e-01
-2.13119417e-01 2.68385202e-01 -1.16000414e-01 -3.64998847... | [9.996282577514648, 7.938222885131836] |
f650e1e7-4dc5-41f4-8cba-2a707eceabd8 | genetic-multi-armed-bandits-a-reinforcement | 2302.07695 | null | https://arxiv.org/abs/2302.07695v1 | https://arxiv.org/pdf/2302.07695v1.pdf | Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation | This paper proposes a new algorithm, referred to as GMAB, that combines concepts from the reinforcement learning domain of multi-armed bandits and random search strategies from the domain of genetic algorithms to solve discrete stochastic optimization problems via simulation. In particular, the focus is on noisy large-... | ['Michael Krapp', 'Deniz Preil'] | 2023-02-15 | null | null | null | null | ['stochastic-optimization', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [-1.61481187e-01 -3.89784098e-01 -4.17648077e-01 1.00917198e-01
-1.02586389e+00 -6.19236887e-01 2.83560932e-01 -7.74914548e-02
-3.65505487e-01 1.40029442e+00 -2.67279863e-01 -5.90338290e-01
-7.08070278e-01 -1.03539169e+00 -6.96264625e-01 -1.08671665e+00
-3.23013306e-01 7.06551909e-01 -1.36685491e-01 -2.50455707... | [4.596925258636475, 3.2274274826049805] |
256a08f9-a8ed-4d0b-a252-e2a27d8dcfc6 | exploring-instance-level-uncertainty-for | 2012.12880 | null | https://arxiv.org/abs/2012.12880v3 | https://arxiv.org/pdf/2012.12880v3.pdf | Exploring Instance-Level Uncertainty for Medical Detection | The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines. Moreover, performance gain has been enabled by modelling uncertainty according to empirical evidence. While previous work has widely discussed the uncertainty estimation in segmentation and classificatio... | ['Lei He', 'Kun Wang', 'Weinan Song', 'Yao Zhang', 'Yuan Liang', 'Jiawei Yang'] | 2020-12-23 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 1.01966739e-01 6.13906503e-01 -1.28581092e-01 -5.33386707e-01
-1.22871244e+00 -2.31930286e-01 4.18639988e-01 2.62778819e-01
-3.08874100e-01 6.46640539e-01 -6.07305989e-02 -4.75915998e-01
-2.55909264e-01 -5.67698002e-01 -5.78763127e-01 -7.11773932e-01
-6.05909079e-02 5.39168537e-01 4.02764350e-01 4.28056657... | [14.411467552185059, -2.1201834678649902] |
f0230c48-82cc-42d1-8513-740d9d93bc47 | ifcnet-a-benchmark-dataset-for-ifc-entity | 2106.09712 | null | https://arxiv.org/abs/2106.09712v1 | https://arxiv.org/pdf/2106.09712v1.pdf | IFCNet: A Benchmark Dataset for IFC Entity Classification | Enhancing interoperability and information exchange between domain-specific software products for BIM is an important aspect in the Architecture, Engineering, Construction and Operations industry. Recent research started investigating methods from the areas of machine and deep learning for semantic enrichment of BIM mo... | ['Christoph van Treeck', 'Jérôme Frisch', 'Veronika Richter', 'Nicolas Pauen', 'Christoph Emunds'] | 2021-06-17 | null | null | null | null | ['ifc-entity-classification'] | ['computer-vision'] | [-1.76248506e-01 -3.12517248e-02 1.07969224e-01 -6.92622781e-01
-6.40242159e-01 -2.18651891e-01 5.07705033e-01 3.27677339e-01
1.18792370e-01 6.95433021e-01 5.40324636e-02 -2.91589171e-01
-6.08641148e-01 -1.36118793e+00 -8.71683419e-01 -3.29562843e-01
-8.01026076e-02 8.54828954e-01 9.12752599e-02 -4.68098819... | [7.381726264953613, 1.796416163444519] |
d62c2752-0415-49dd-bcc2-1bef9d99704c | model-based-safe-deep-reinforcement-learning | 2210.07573 | null | https://arxiv.org/abs/2210.07573v1 | https://arxiv.org/pdf/2210.07573v1.pdf | Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm | During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps. In the real world, this can limit the practicality of these algorithms as it can lead to potentially dangerous behavior. Hence safe exploration is a critical issue in ap... | ['Shalabh Bhatnagar', 'Ashish Kumar Jayant'] | 2022-10-14 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.49032354e-01 3.88782799e-01 -4.81479347e-01 6.34323061e-02
-8.49472106e-01 -5.40175378e-01 5.94541788e-01 4.19644445e-01
-9.89295721e-01 1.21002316e+00 -1.72108576e-01 -3.79449815e-01
-5.77757776e-01 -8.57011616e-01 -8.81672084e-01 -9.05273914e-01
-5.98003328e-01 8.35898757e-01 6.24905191e-02 -8.31405073... | [4.365190505981445, 2.2343578338623047] |
8411c316-6fe5-418f-b8a1-d728cca821f6 | optimize-the-co-expansion-of-generation-and | 2101.04048 | null | https://arxiv.org/abs/2101.04048v1 | https://arxiv.org/pdf/2101.04048v1.pdf | Optimize the Co-expansion of Generation and Transmission Considering Wind Power in the US Eastern Interconnection | This paper studies the generation and transmission expansion co-optimization problem with a high wind power penetration rate in large-scale power grids. In this paper, generation and transmission expansion co-optimization is modeled as a mixed-integer programming (MIP) problem. A scenario creation method is proposed to... | ['Shutang You'] | 2021-01-11 | null | null | null | null | ['paper-generation'] | ['natural-language-processing'] | [-5.45428336e-01 -3.34135324e-01 -3.36436719e-01 -1.22003118e-03
-2.98007410e-02 -8.64881158e-01 8.80659297e-02 8.36228356e-02
1.56358704e-01 1.48328125e+00 3.56478244e-01 -3.71770769e-01
-5.75698197e-01 -1.42125201e+00 2.10188299e-01 -9.05054152e-01
-4.67922896e-01 4.85600084e-01 -4.43240076e-01 -3.45706433... | [5.6439666748046875, 2.5525319576263428] |
8c9e1e26-9c9b-471a-929a-c2de1f023004 | block-coordinate-plug-and-play-methods-for | 2305.12672 | null | https://arxiv.org/abs/2305.12672v1 | https://arxiv.org/pdf/2305.12672v1.pdf | Block Coordinate Plug-and-Play Methods for Blind Inverse Problems | Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image denoisers. While PnP methods have been extensively used for image recovery with known measurement operators, there is little work... | ['Ulugbek S. Kamilov', 'Hongyu An', 'Jiaming Liu', 'Yuyang Hu', 'Shirin Shoushtari', 'Weijie Gan'] | 2023-05-22 | null | null | null | null | ['deblurring', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [ 5.17641962e-01 1.15451440e-01 2.89104313e-01 -1.35111675e-01
-1.14409161e+00 -1.93545356e-01 2.36554846e-01 -7.10074067e-01
-4.34537739e-01 8.08877409e-01 6.26373351e-01 -7.81758726e-02
-5.87093711e-01 -2.41465017e-01 -8.29871893e-01 -1.11760676e+00
-2.43553184e-02 4.96461689e-01 -2.08215326e-01 -1.43168628... | [11.77672290802002, -2.4311134815216064] |
d8a97c95-de81-4df3-99ac-84d9e474391b | beyond-ann-exploiting-structural-knowledge | 2103.08366 | null | https://arxiv.org/abs/2103.08366v1 | https://arxiv.org/pdf/2103.08366v1.pdf | Beyond ANN: Exploiting Structural Knowledge for Efficient Place Recognition | Visual place recognition is the task of recognizing same places of query images in a set of database images, despite potential condition changes due to time of day, weather or seasons. It is important for loop closure detection in SLAM and candidate selection for global localization. Many approaches in the literature p... | ['Peter Protzel', 'Peer Neubert', 'Stefan Schubert'] | 2021-03-15 | null | null | null | null | ['loop-closure-detection', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.14966297e-01 -8.16015482e-01 -9.87039134e-02 -2.44127259e-01
-7.38941967e-01 -8.00104141e-01 4.72023070e-01 6.57915354e-01
-8.58188689e-01 7.00168252e-01 -4.26394731e-01 -1.34805560e-01
-2.78412938e-01 -6.51581347e-01 -8.32686067e-01 -5.87188065e-01
-3.86557400e-01 4.34122622e-01 8.78540099e-01 -1.88809171... | [7.5980377197265625, -1.9684937000274658] |
6d1a3d99-6d54-43ae-a675-dd48f9d6dd21 | aggression-and-misogyny-detection-using-bert | null | null | https://aclanthology.org/2020.trac-1.20 | https://aclanthology.org/2020.trac-1.20.pdf | Aggression and Misogyny Detection using BERT: A Multi-Task Approach | In recent times, the focus of the NLP community has increased towards offensive language, aggression, and hate-speech detection.This paper presents our system for TRAC-2 shared task on {``}Aggression Identification{''} (sub-task A) and {``}Misogynistic Aggression Identification{''} (sub-task B). The data for this share... | ['Prerana Mukherjee', 'Srinivas Pykl', 'Amitava Das', 'Parth Patwa', 'Niloofar Safi Samghabadi', 'Thamar Solorio'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['misogynistic-aggression-identification', 'aggression-identification'] | ['natural-language-processing', 'natural-language-processing'] | [-5.00003695e-01 -7.35937580e-02 3.01054031e-01 -2.84719527e-01
-8.66880119e-01 -4.75256383e-01 4.66544062e-01 3.47222760e-02
-8.51230383e-01 8.01965177e-01 2.72562146e-01 7.70675344e-03
-2.22703442e-01 -1.21336810e-01 4.28716354e-02 -6.38930023e-01
-1.14894314e-02 7.58694410e-01 -6.03939220e-02 -5.53690791... | [8.800849914550781, 10.751529693603516] |
fc37ade0-b7a2-4322-b9b2-e80ca9474c4c | a-geometric-model-for-polarization-imaging-on | 2211.16986 | null | https://arxiv.org/abs/2211.16986v1 | https://arxiv.org/pdf/2211.16986v1.pdf | A Geometric Model for Polarization Imaging on Projective Cameras | The vast majority of Shape-from-Polarization (SfP) methods work under the oversimplified assumption of using orthographic cameras. Indeed, it is still not well understood how to project the Stokes vectors when the incoming rays are not orthogonal to the image plane. We try to answer this question presenting a geometric... | ['Filippo Bergamasco', 'Mara Pistellato'] | 2022-11-29 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 3.41172338e-01 1.95043549e-01 2.60761499e-01 -2.51474857e-01
7.00149536e-02 -5.43820620e-01 7.63163149e-01 -7.11128831e-01
-4.78503287e-01 5.80704391e-01 6.51807636e-02 -1.06624737e-01
-2.29342766e-02 -9.47485566e-01 -7.19180107e-01 -9.58967686e-01
5.92975914e-01 8.50625873e-01 1.47182852e-01 -3.30258459... | [9.9786958694458, -2.7933340072631836] |
66142419-bc38-44a1-af57-540812af4ad4 | regularizing-end-to-end-speech-translation | 2112.10991 | null | https://arxiv.org/abs/2112.10991v2 | https://arxiv.org/pdf/2112.10991v2.pdf | Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement | End-to-end speech-to-text translation (E2E-ST) is becoming increasingly popular due to the potential of its less error propagation, lower latency, and fewer parameters. Given the triplet training corpus $\langle speech, transcription, translation\rangle$, the conventional high-quality E2E-ST system leverages the $\lang... | ['Tong Xu', 'Jun Xie', 'Boxing Chen', 'Weizhi Wang', 'Zhirui Zhang', 'Yichao Du'] | 2021-12-21 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.52767196e-01 -1.38038294e-02 -2.55566239e-01 -4.89593685e-01
-1.51269639e+00 -5.60813606e-01 4.02434230e-01 -3.16597492e-01
-2.47637734e-01 5.56582868e-01 3.16460997e-01 -8.92201424e-01
2.42783964e-01 -1.91518441e-01 -7.11113513e-01 -6.30689561e-01
3.37750167e-01 5.32802165e-01 -1.07419537e-02 -2.55949199... | [14.449492454528809, 7.144014358520508] |
0d142271-5c91-47fd-87d8-e859f55443d2 | applying-intelligent-reflector-surfaces-for | 2108.07834 | null | https://arxiv.org/abs/2108.07834v2 | https://arxiv.org/pdf/2108.07834v2.pdf | Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud using Terahertz Signals | The recent COVID-19 pandemic has driven researchers from different spectrum to develop novel solutions that can improve detection and understanding of SARS-CoV-2 virus. In this article we propose the use of Intelligent Reflector Surface (IRS) emitting terahertz signals to detect airborne respiratory aerosol cloud that ... | ['Sasitharan Balasubramaniam', 'Nicola Marchetti', 'Daniel Perez Martins', "Nathan D'Arcy", 'Michael Taynnan Barros', 'Harun Šiljak'] | 2021-08-17 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 4.33998555e-01 -3.88183624e-01 6.32284045e-01 -3.03848803e-01
-3.63168210e-01 -6.33480668e-01 3.13764900e-01 -1.52298331e-01
-9.04709697e-02 6.96312249e-01 -3.21106054e-02 -4.87954974e-01
-1.60736945e-02 -1.18463540e+00 -6.89192638e-02 -7.30651557e-01
-4.11277562e-02 7.96785474e-01 3.02701265e-01 -4.57036346... | [14.40982723236084, 3.8301804065704346] |
a4df9a94-7a8d-4c7b-83a7-1b1370ff511e | pre-emptive-learning-to-defer-for-sequential | 2109.06312 | null | https://arxiv.org/abs/2109.06312v2 | https://arxiv.org/pdf/2109.06312v2.pdf | Learning-to-defer for sequential medical decision-making under uncertainty | Learning-to-defer is a framework to automatically defer decision-making to a human expert when ML-based decisions are deemed unreliable. Existing learning-to-defer frameworks are not designed for sequential settings. That is, they defer at every instance independently, based on immediate predictions, while ignoring the... | ['Finale Doshi-Velez', 'Sonali Parbhoo', 'Shalmali Joshi'] | 2021-09-13 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.01027004e-01 1.07420638e-01 -4.23721403e-01 -3.83616000e-01
-5.94277084e-01 -6.82458878e-01 4.95362073e-01 4.30083215e-01
-6.39710546e-01 1.11887395e+00 -4.87719662e-02 -9.34370875e-01
-3.72587949e-01 -6.74774528e-01 -8.19417477e-01 -5.64353347e-01
-2.49110445e-01 5.00900328e-01 2.89277643e-01 9.45888758... | [4.141523361206055, 2.4477651119232178] |
98883a33-9625-4137-bc34-875a7e1cc64c | a-topic-based-sentence-representation-for | null | null | https://aclanthology.org/W19-8905 | https://aclanthology.org/W19-8905.pdf | A topic-based sentence representation for extractive text summarization | In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide... | ['Nikiforos Pittaras', 'Nikolaos Gialitsis', 'Panagiotis Stamatopoulos'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.90474385e-01 3.31353575e-01 -8.02390158e-01 -1.19947761e-01
-1.43452907e+00 -7.13491499e-01 1.10057366e+00 6.88361108e-01
-4.67969418e-01 9.32818532e-01 1.22740161e+00 -2.72086978e-01
-3.26854706e-01 -5.74810207e-01 -2.38659769e-01 -4.16652381e-01
-7.44837746e-02 4.70821381e-01 -2.05511954e-02 -3.50487024... | [12.438705444335938, 9.513208389282227] |
3ad7fc1d-d859-4726-bb59-7a1b19bffed8 | triviaqa-a-large-scale-distantly-supervised | 1705.03551 | null | http://arxiv.org/abs/1705.03551v2 | http://arxiv.org/pdf/1705.03551v2.pdf | TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension | We present TriviaQA, a challenging reading comprehension dataset containing
over 650K question-answer-evidence triples. TriviaQA includes 95K
question-answer pairs authored by trivia enthusiasts and independently gathered
evidence documents, six per question on average, that provide high quality
distant supervision for... | ['Luke Zettlemoyer', 'Mandar Joshi', 'Eunsol Choi', 'Daniel S. Weld'] | 2017-05-09 | triviaqa-a-large-scale-distantly-supervised-1 | https://aclanthology.org/P17-1147 | https://aclanthology.org/P17-1147.pdf | acl-2017-7 | ['triviaqa'] | ['miscellaneous'] | [-1.56878699e-02 3.87789994e-01 3.33361551e-02 -5.27585328e-01
-1.60675788e+00 -9.23904777e-01 3.99709225e-01 5.88433921e-01
-4.32804197e-01 8.40212822e-01 5.67023218e-01 -7.92312384e-01
-2.85380781e-01 -6.52232051e-01 -1.00223053e+00 4.10132296e-02
2.20490143e-01 1.07466114e+00 6.42463982e-01 -6.81990206... | [11.247316360473633, 8.047660827636719] |
85cf4885-f131-4bff-a594-b2135c767d9d | towards-explainable-dialogue-system | null | null | https://aclanthology.org/2021.icon-main.16 | https://aclanthology.org/2021.icon-main.16.pdf | Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques | Deep learning based methods have shown tremendous success in several Natural Language Processing (NLP) tasks. The recent trends in the usage of Deep Learning based models for natural language tasks have definitely produced incredible performance for several application areas. However, one major problem that most of the... | ['Asif Ekbal', 'Arindam Chatterjee', 'Ratnesh Joshi'] | null | null | null | null | icon-2021-12 | ['intent-detection', 'intent-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.88216585e-01 4.24937516e-01 -1.77099153e-01 -4.74698901e-01
-3.90549839e-01 -4.21733148e-02 6.59819186e-01 5.39762199e-01
-2.88889915e-01 6.08379602e-01 4.18030262e-01 -4.37653422e-01
-3.69579792e-01 -7.24280238e-01 -4.21771765e-01 -5.51546097e-01
2.47870982e-01 5.08331239e-01 5.59370816e-02 -3.19246531... | [9.428832054138184, 6.496097087860107] |
301443a3-d866-4c4e-a6df-19c72b0bad79 | compressing-features-for-learning-with-noisy | 2206.13140 | null | https://arxiv.org/abs/2206.13140v1 | https://arxiv.org/pdf/2206.13140v1.pdf | Compressing Features for Learning with Noisy Labels | Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the distilled information might not be relevant. In fact, recent research shows that networks can easily overfit all labels including those that ... | ['Johan A. K. Suykens', 'Chunrong Ai', 'Xi Shen', 'Shell Xu Hu', 'Yingyi Chen'] | 2022-06-27 | null | null | null | null | ['feature-compression', 'learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 4.66914296e-01 2.29597673e-01 -3.42377484e-01 -6.94023252e-01
-9.59398746e-01 -3.50489289e-01 1.39644101e-01 1.25817746e-01
-6.19011402e-01 7.63386071e-01 1.69203594e-01 -8.38467926e-02
-2.60049760e-01 -5.80466747e-01 -1.15231705e+00 -8.62951696e-01
2.20369935e-01 3.37335855e-01 -1.94101602e-01 1.85076967... | [9.273459434509277, 3.794708251953125] |
338e2e9e-4fa6-46b6-b498-d01b57a620a5 | shifting-more-attention-to-video-salient | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Fan_Shifting_More_Attention_to_Video_Salient_Object_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Fan_Shifting_More_Attention_to_Video_Salient_Object_Detection_CVPR_2019_paper.pdf | Shifting More Attention to Video Salient Object Detection | The last decade has witnessed a growing interest in video salient object detection (VSOD). However, the research community long-term lacked a well-established VSOD dataset representative of real dynamic scenes with high-quality annotations. To address this issue, we elaborately collected a visual-attention-consistent D... | [' Jianbing Shen', ' Ming-Ming Cheng', ' Wenguan Wang', 'Deng-Ping Fan'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.51323336e-01 -2.95059472e-01 -3.95316750e-01 -7.23529607e-02
-5.29356062e-01 -1.48039117e-01 3.48248303e-01 -1.88022122e-01
-2.70392865e-01 6.76607370e-01 4.49363500e-01 2.33897731e-01
3.14794749e-01 -6.52521104e-02 -8.74363363e-01 -6.34552598e-01
-4.12621386e-02 -3.01442266e-01 1.05538154e+00 -3.58195096... | [9.719754219055176, -0.28002670407295227] |
49fe2602-f44e-43b4-8e43-c9e9709f40c3 | a-survey-for-efficient-open-domain-question | 2211.07886 | null | https://arxiv.org/abs/2211.07886v1 | https://arxiv.org/pdf/2211.07886v1.pdf | A Survey for Efficient Open Domain Question Answering | Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language processing (NLP). Recent works have predominantly focused on improving the answering accuracy and achieved promising progress. However, higher... | ['Meng Fang', 'Trevor Cohn', 'Xiaojun Chen', 'Qingqing Cao', 'Dongkuan Xu', 'Shangsi Chen', 'Qin Zhang'] | 2022-11-15 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-1.33963048e-01 2.88039744e-01 -2.95823924e-02 -4.56368417e-01
-1.25726330e+00 -7.98373997e-01 4.46166366e-01 4.32007700e-01
-5.41879058e-01 1.07038605e+00 2.08372369e-01 -6.04302466e-01
-3.79591823e-01 -1.20723093e+00 -3.67435783e-01 -2.01347262e-01
2.17280611e-01 1.01315320e+00 6.02159917e-01 -5.61933994... | [11.235349655151367, 7.9423346519470215] |
eb710abb-d344-4a0f-954e-f14de8cbbde5 | a-skip-connection-architecture-for | null | null | http://openaccess.thecvf.com/content_CVPRW_2019/html/Media_Forensics/Mazaheri_A_Skip_Connection_Architecture_for_Localization_of_Image_Manipulations_CVPRW_2019_paper.html | http://openaccess.thecvf.com/content_CVPRW_2019/papers/Media%20Forensics/Mazaheri_A_Skip_Connection_Architecture_for_Localization_of_Image_Manipulations_CVPRW_2019_paper.pdf | A Skip Connection Architecture for Localization of Image Manipulations | Detection and localization of image manipulations are becoming of increasing interest to researchers in recent years due to the significant rise of malicious content-changing image tampering on the web. One of the major challenges for an image manipulation detection method is to discriminate between the tampered region... | ['Amit K. Roy-Chowdhury', 'Niluthpol Chowdhury Mithun', 'Ghazal Mazaheri', 'Jawadul H. Bappy'] | 2019-06-01 | null | null | null | ieee-conference-on-computer-vision-and-2 | ['image-manipulation-detection'] | ['computer-vision'] | [ 8.32271934e-01 -5.60487866e-01 -1.82478294e-01 9.53945145e-02
-6.68386877e-01 -5.59423268e-01 5.24607420e-01 -3.45409811e-02
-3.05680782e-01 1.37200311e-01 2.32296571e-01 9.27439183e-02
3.77627581e-01 -6.47306085e-01 -1.13453591e+00 -7.71132886e-01
-1.61720797e-01 -7.82595277e-01 5.05804300e-01 2.27003787... | [12.30740737915039, 0.9365178346633911] |
cecafb23-0e4a-4cd2-9d96-39cf97ce407e | imageeye-batch-image-processing-using-program | 2304.03253 | null | https://arxiv.org/abs/2304.03253v3 | https://arxiv.org/pdf/2304.03253v3.pdf | ImageEye: Batch Image Processing Using Program Synthesis | This paper presents a new synthesis-based approach for batch image processing. Unlike existing tools that can only apply global edits to the entire image, our method can apply fine-grained edits to individual objects within the image. For example, our method can selectively blur or crop specific objects that have a cer... | ['Isil Dillig', 'Roopsha Samanta', 'Qiaochu Chen', 'Celeste Barnaby'] | 2023-04-06 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 6.38307810e-01 3.16911727e-01 1.22740448e-01 -6.05091870e-01
-1.92697823e-01 -6.53800547e-01 6.84648037e-01 -4.69201058e-02
-4.59987074e-01 5.51217556e-01 -4.46798742e-01 -4.69650894e-01
2.67131180e-02 -7.26426482e-01 -1.15179646e+00 -7.30240643e-02
2.67277539e-01 1.73942685e-01 5.27781367e-01 -6.02849126... | [11.368924140930176, -0.25948551297187805] |
738aade1-cdbe-4e5e-8d8c-913f5a727f5a | quartile-based-seasonality-decomposition-for | 2306.05989 | null | https://arxiv.org/abs/2306.05989v1 | https://arxiv.org/pdf/2306.05989v1.pdf | Quartile-Based Seasonality Decomposition for Time Series Forecasting and Anomaly Detection | The timely detection of anomalies is essential in the telecom domain as it facilitates the identification and characterization of irregular patterns, abnormal behaviors, and network anomalies, contributing to enhanced service quality and operational efficiency. Precisely forecasting and eliminating predictable time ser... | ['Bulbul Singh', 'Ebenezer RHP Isaac'] | 2023-06-09 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [ 4.33795266e-02 -5.62267125e-01 2.81340271e-01 -2.19542757e-01
-2.99424171e-01 -4.23043698e-01 4.36759830e-01 6.22130692e-01
-9.13491622e-02 4.93563145e-01 -2.30274081e-01 -6.82386577e-01
-7.68223882e-01 -7.25524902e-01 -1.61360539e-02 -8.40071678e-01
-3.96841019e-01 4.21750993e-01 -6.38701841e-02 -2.64812022... | [7.208133220672607, 2.7911462783813477] |
da293f86-95d5-477e-ad3d-20a7b1625c27 | unsupervised-classification-in-hyperspectral | 1604.08182 | null | http://arxiv.org/abs/1604.08182v2 | http://arxiv.org/pdf/1604.08182v2.pdf | Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm | In this paper, a graph-based nonlocal total variation method (NLTV) is
proposed for unsupervised classification of hyperspectral images (HSI). The
variational problem is solved by the primal-dual hybrid gradient (PDHG)
algorithm. By squaring the labeling function and using a stable simplex
clustering routine, an unsupe... | ['Andrea L. Bertozzi', 'Wei Zhu', 'Da Kuang', 'Dominique Zosso', 'Devin Dahlberg', 'Alexandre Tiard', 'Victoria Chayes', 'Stephanie Sanchez', 'Stanley Osher'] | 2016-04-27 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 3.22710574e-01 -3.58453244e-01 -1.09621152e-01 -7.55110160e-02
-6.72871530e-01 -6.14059925e-01 3.46323609e-01 -2.78915673e-01
-2.32482314e-01 9.05699670e-01 -2.78378785e-01 -2.67388463e-01
-5.91993690e-01 -6.29329085e-01 1.11371102e-02 -1.45922315e+00
-5.76509628e-04 4.60807323e-01 -1.92563936e-01 -1.18858136... | [10.074843406677246, -2.009645462036133] |
488ec3e8-80f5-4648-be1e-a483a7e2e042 | salesforce-causalai-library-a-fast-and | 2301.10859 | null | https://arxiv.org/abs/2301.10859v1 | https://arxiv.org/pdf/2301.10859v1.pdf | Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data | We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and time series data, of both discrete and continuous types. This library includes algorithms that handle linear and non-linear causal relation... | ['Juan Carlos Niebles', 'Kun Zhang', 'Caiming Xiong', 'Stephen Hoi', 'Huan Wang', 'Eric Hu', 'Shelby Heinecke', 'Paul Josel', 'Wenzhuo Yang', 'Weiran Yao', 'Chenghao Liu', 'Matthew Fernandez', 'Devansh Arpit'] | 2023-01-25 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-1.60243988e-01 1.17337964e-01 -6.66506767e-01 -4.63862151e-01
-4.46018934e-01 -6.23521566e-01 8.64802539e-01 2.86510020e-01
2.21971497e-01 1.05987895e+00 6.49402678e-01 -8.10484886e-01
-6.23795867e-01 -1.17487419e+00 -6.27889514e-01 -5.61248124e-01
-8.84266376e-01 5.95009804e-01 -8.68568122e-02 2.16110945... | [7.828372955322266, 5.3903584480285645] |
06d2761d-5f0b-4399-909a-2d6ec510132d | the-algonauts-project-2021-challenge-how-the | 2104.13714 | null | https://arxiv.org/abs/2104.13714v1 | https://arxiv.org/pdf/2104.13714v1.pdf | The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion | The sciences of natural and artificial intelligence are fundamentally connected. Brain-inspired human-engineered AI are now the standard for predicting human brain responses during vision, and conversely, the brain continues to inspire invention in AI. To promote even deeper connections between these fields, we here re... | ['A. Oliva', 'G. Roig', 'K. Kay', 'N. A. R. Murty', 'A. Andonian', 'M. Graumann', 'P. Iamshchinina', 'A. Lascelles', 'B. Lahner', 'K. Dwivedi', 'R. M. Cichy'] | 2021-04-28 | null | null | null | null | ['human-fmri-response-prediction'] | ['computer-vision'] | [ 1.89284936e-01 -1.09701961e-01 4.15064186e-01 -3.24848086e-01
9.49377716e-02 -5.31880200e-01 7.81690538e-01 -4.32189792e-01
-5.83433509e-01 6.75073802e-01 1.54880509e-01 -2.42290758e-02
3.65693606e-02 -3.26059222e-01 -5.64399242e-01 -4.23924655e-01
-2.00152308e-01 1.88071802e-02 1.60698533e-01 -2.55357265... | [10.490528106689453, 2.4560608863830566] |
2d8f09c4-840d-40f6-9183-341583fe2328 | stylization-based-architecture-for-fast-deep | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Stylization-Based_Architecture_for_Fast_Deep_Exemplar_Colorization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Stylization-Based_Architecture_for_Fast_Deep_Exemplar_Colorization_CVPR_2020_paper.pdf | Stylization-Based Architecture for Fast Deep Exemplar Colorization | Exemplar-based colorization aims to add colors to a grayscale image guided by a content related reference im- age. Existing methods are either sensitive to the selection of reference images (content, position) or extremely time and resource consuming, which limits their practical applica- tion. To tackle these problems... | [' Guixu Zhang', ' Yun Sheng', ' Faming Fang', ' Tingting Wang', 'Zhongyou Xu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['image-stylization'] | ['computer-vision'] | [ 2.14500099e-01 -3.55933756e-01 6.24747090e-02 -3.13057125e-01
-7.31098711e-01 -4.05097246e-01 5.91467440e-01 -1.43092990e-01
-4.39282149e-01 6.72941148e-01 -3.94231752e-02 1.01429775e-01
1.02630399e-01 -7.92410553e-01 -9.54208374e-01 -5.90096772e-01
4.38247740e-01 3.04455608e-01 1.28164232e-01 -2.37560540... | [11.468721389770508, -0.9932047724723816] |
0aa81ba5-cc22-48ed-9d93-143a755fcd4d | a-multi-size-neural-network-with-attention | 2105.03278 | null | https://arxiv.org/abs/2105.03278v1 | https://arxiv.org/pdf/2105.03278v1.pdf | A Multi-Size Neural Network with Attention Mechanism for Answer Selection | Semantic matching is of central significance to the answer selection task which aims to select correct answers for a given question from a candidate answer pool. A useful method is to employ neural networks with attention to generate sentences representations in a way that information from pair sentences can mutually i... | ['Jie Huang'] | 2021-04-24 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [ 6.80386182e-03 -5.29336371e-02 1.57413229e-01 -4.39818650e-01
-5.07499218e-01 8.74326304e-02 4.66024250e-01 1.77999258e-01
-4.90759164e-01 6.46746933e-01 6.63876057e-01 -5.41937053e-02
-3.17035586e-01 -1.31719792e+00 -3.90896171e-01 -2.05781162e-01
4.48758423e-01 3.23594719e-01 6.62213564e-01 -8.26969624... | [11.003519058227539, 8.15639591217041] |
c5623d5e-21fa-4258-b6a4-855461f5b510 | contrastive-cross-domain-sequential | 2304.03891 | null | https://arxiv.org/abs/2304.03891v1 | https://arxiv.org/pdf/2304.03891v1.pdf | Contrastive Cross-Domain Sequential Recommendation | Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing wo... | ['Bin Wang', 'Tingwen Liu', 'Jiawei Sheng', 'Xin Cong', 'Jiangxia Cao'] | 2023-04-08 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 3.15544009e-01 -5.37469089e-01 -4.84792352e-01 -5.80697417e-01
-5.16270280e-01 -6.77154064e-01 2.85348445e-01 -2.86129452e-02
-3.30458045e-01 6.41537309e-01 4.90128607e-01 -1.48558363e-01
-5.28260767e-01 -6.29266679e-01 -5.49677968e-01 -1.31662458e-01
-1.24344990e-01 3.83633643e-01 8.09363090e-03 -3.97751272... | [10.168294906616211, 5.593576908111572] |
4a8622f2-8af6-4034-bbcd-34069ad3ef86 | efficient-and-robust-question-answering-from | 1805.08092 | null | http://arxiv.org/abs/1805.08092v1 | http://arxiv.org/pdf/1805.08092v1.pdf | Efficient and Robust Question Answering from Minimal Context over Documents | Neural models for question answering (QA) over documents have achieved
significant performance improvements. Although effective, these models do not
scale to large corpora due to their complex modeling of interactions between
the document and the question. Moreover, recent work has shown that such models
are sensitive ... | ['Sewon Min', 'Richard Socher', 'Victor Zhong', 'Caiming Xiong'] | 2018-05-21 | efficient-and-robust-question-answering-from-1 | https://aclanthology.org/P18-1160 | https://aclanthology.org/P18-1160.pdf | acl-2018-7 | ['triviaqa'] | ['miscellaneous'] | [ 2.78316230e-01 2.88763791e-01 4.34165955e-01 -6.74890578e-01
-1.40640974e+00 -1.07941711e+00 6.15264833e-01 1.75988674e-01
-5.54881096e-01 8.28444302e-01 2.93014377e-01 -6.51885569e-01
-4.30135019e-02 -9.30583417e-01 -9.49085236e-01 -1.39054403e-01
2.30759919e-01 7.64019668e-01 6.69149458e-01 -7.49871314... | [11.242973327636719, 8.072137832641602] |
54219d37-7441-4af8-b514-6e8ada97c905 | beyond-toxic-toxicity-detection-datasets-are | 2303.15110 | null | https://arxiv.org/abs/2303.15110v1 | https://arxiv.org/pdf/2303.15110v1.pdf | Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety | The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these ta... | ['Isaac Kwan Yin Chung', 'Elizaveta Korotkova'] | 2023-03-27 | null | null | null | null | ['hate-speech-detection', 'toxic-comment-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.38981563e-01 -2.23025531e-02 -2.47265846e-01 -2.29522854e-01
-8.75379682e-01 -7.98824608e-01 7.51329541e-01 9.11055982e-01
-2.62957096e-01 4.50013727e-01 5.77089727e-01 -2.32619986e-01
2.10976340e-02 -6.58262789e-01 -2.80729085e-01 -4.42388922e-01
1.25353307e-01 -9.01144668e-02 3.32495630e-01 -2.71970123... | [8.849251747131348, 10.562220573425293] |
fd0b2efa-7e03-47b4-a77b-594b9ca13ecd | refining-a-k-nearest-neighbor-graph-for-a | 2302.11296 | null | https://arxiv.org/abs/2302.11296v1 | https://arxiv.org/pdf/2302.11296v1.pdf | Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering | Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational demands. One of the effective ways to bypass these computational demands is to perform spectral clusteri... | ['Masahiro Takatsuka', 'John Stavrakakis', 'Mashaan Alshammari'] | 2023-02-22 | null | null | null | null | ['graph-clustering', 'spectral-graph-clustering', 'graph-partitioning'] | ['graphs', 'graphs', 'graphs'] | [-4.66063386e-03 -2.27790400e-01 6.09203577e-02 -3.17115515e-01
-6.09688640e-01 -6.93729520e-01 2.99399853e-01 4.61816072e-01
-4.98244673e-01 5.74487329e-01 -9.56628695e-02 3.10610458e-02
-6.57782972e-01 -8.58681560e-01 -2.90377140e-01 -1.13566518e+00
-3.90428752e-01 5.34157038e-01 4.98927921e-01 3.09729308... | [7.551070690155029, 4.632050514221191] |
5227ebf0-6821-4fa3-b0d7-08cc1be6557a | one-model-is-not-enough-ensembles-for | null | null | https://www.mdpi.com/1424-8220/22/13/5043 | https://www.mdpi.com/1424-8220/22/13/5043 | One Model is Not Enough: Ensembles for Isolated Sign Language Recognition | In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and o... | ['Zdeněk Krňoul', 'Miroslav Hlaváč', 'Matyáš Boháček', 'Jakub Kanis', 'Ivan Gruber', 'Marek Hrúz'] | 2022-07-04 | null | null | null | sensors-2022-7 | ['sign-language-recognition'] | ['computer-vision'] | [ 3.01615179e-01 -6.05713308e-01 1.71168093e-02 -4.18223500e-01
-7.23316014e-01 -3.88730466e-01 6.61521018e-01 -9.50046003e-01
-5.35009682e-01 3.01843196e-01 2.07572833e-01 5.20112626e-02
-1.06162004e-01 9.57385600e-02 -3.51757169e-01 -1.24381065e+00
9.62627232e-02 2.93738425e-01 3.25425565e-01 -1.26387551... | [9.160294532775879, -6.47140645980835] |
f8d57e7b-25fa-406a-b702-1f6ecea273b2 | information-structure-syntax-and-pragmatics | null | null | https://aclanthology.org/W16-3801 | https://aclanthology.org/W16-3801.pdf | Information structure, syntax, and pragmatics and other factors in resolving scope ambiguity | The paper is a corpus study of the factors involved in disambiguating potential scope ambiguity in sentences with negation and universal quantifier, such as {``}I don{'}t want talk to all these people{''}, which can alternatively mean {`}I don{'}t want to talk to any of these people{'} and {`}I don{'}t want to talk to ... | ['Valentina Apresjan'] | 2016-12-01 | null | null | null | ws-2016-12 | ['implicatures'] | ['natural-language-processing'] | [ 2.18423575e-01 5.56705475e-01 1.28240675e-01 -5.41596055e-01
-7.14227974e-01 -6.49107158e-01 4.65456277e-01 6.63795352e-01
-7.72500992e-01 8.09633374e-01 8.53201687e-01 -5.16354620e-01
-6.30489290e-01 -5.57424247e-01 -2.19716996e-01 -5.43572843e-01
-5.56357913e-02 8.31319690e-01 6.71891212e-01 -7.14855492... | [10.161881446838379, 9.285039901733398] |
3c8e92d3-caeb-4721-824d-100c8604153b | local-energy-distribution-based | 2304.11839 | null | https://arxiv.org/abs/2304.11839v3 | https://arxiv.org/pdf/2304.11839v3.pdf | Local Energy Distribution Based Hyperparameter Determination for Stochastic Simulated Annealing | This paper presents a local energy distribution based hyperparameter determination for stochastic simulated annealing (SSA). SSA is capable of solving combinatorial optimization problems faster than typical simulated annealing (SA), but requires a time-consuming hyperparameter search. The proposed method determines hyp... | ['Takahiro Hanyu', 'Duckgyu Shin', 'Kyo Kuroki', 'Naoya Onizawa'] | 2023-04-24 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 5.70868179e-02 -4.26950067e-01 -4.92453158e-01 -3.11190784e-01
-7.91473627e-01 -6.15814805e-01 4.73329902e-01 2.30683506e-01
-5.94759285e-01 1.10352898e+00 -2.97498971e-01 -3.15308511e-01
-3.52540225e-01 -8.73504817e-01 -4.53695655e-01 -1.43908811e+00
7.02936947e-02 1.00221968e+00 4.88748312e-01 -8.70882124... | [6.188405990600586, 4.102367401123047] |
14050aa1-84f4-408e-9768-a7f075a48872 | towards-open-scenario-semi-supervised-medical | 2304.04059 | null | https://arxiv.org/abs/2304.04059v1 | https://arxiv.org/pdf/2304.04059v1.pdf | Towards Open-Scenario Semi-supervised Medical Image Classification | Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classe... | ['ZongYuan Ge', 'Zhuoting Zhu', 'Lin Wang', 'Zhen Yu', 'Wei Feng', 'Yicheng Wu', 'Lie Ju'] | 2023-04-08 | null | null | null | null | ['semi-supervised-medical-image-classification'] | ['medical'] | [ 1.44183353e-01 6.60125762e-02 -3.94422263e-01 -6.10323131e-01
-1.04185772e+00 -3.18334788e-01 1.05879731e-01 1.12584546e-01
-2.96663702e-01 8.01532507e-01 1.03684686e-01 -7.97062069e-02
8.80147442e-02 -4.03661102e-01 -6.13310516e-01 -9.73437607e-01
7.36774683e-01 5.61748564e-01 5.75529449e-02 3.18183124... | [14.725704193115234, -1.9796679019927979] |
445d9927-2206-41e0-9f98-ae5e9d8e8ea4 | deberta-decoding-enhanced-bert-with | 2006.03654 | null | https://arxiv.org/abs/2006.03654v6 | https://arxiv.org/pdf/2006.03654v6.pdf | DeBERTa: Decoding-enhanced BERT with Disentangled Attention | Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techn... | ['Weizhu Chen', 'Xiaodong Liu', 'Jianfeng Gao', 'Pengcheng He'] | 2020-06-05 | null | https://openreview.net/forum?id=XPZIaotutsD | https://openreview.net/pdf?id=XPZIaotutsD | iclr-2021-1 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.98697612e-01 4.01842147e-01 -2.94829402e-02 -3.13424736e-01
-1.18756998e+00 -7.89685667e-01 9.42731202e-01 -2.40970686e-01
-6.46543622e-01 7.75471032e-01 4.91717130e-01 -5.66785097e-01
4.12964791e-01 -7.09751725e-01 -1.01151049e+00 -6.19309604e-01
7.52808526e-02 5.93603790e-01 -2.52425849e-01 -5.05937338... | [10.972209930419922, 7.682807922363281] |
497947e2-dc7a-474d-b1bc-225526463b25 | graph-anomaly-detection-with-graph-neural | 2209.14930 | null | https://arxiv.org/abs/2209.14930v2 | https://arxiv.org/pdf/2209.14930v2.pdf | Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges | Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (G... | ['Sungsu Lim', 'Won-Yong Shin', 'Byung Suk Lee', 'Hwan Kim'] | 2022-09-29 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 8.03242475e-02 2.65098572e-01 2.36670226e-02 -5.06784283e-02
6.08952880e-01 -4.13177609e-01 5.06672680e-01 8.84969652e-01
3.19343567e-01 2.62969673e-01 -3.02785426e-01 -4.37465787e-01
-3.34887475e-01 -1.26340759e+00 -6.91137493e-01 -5.61665595e-01
-7.75921047e-01 4.56768721e-01 2.24233285e-01 -4.09393817... | [6.655348777770996, 5.8279619216918945] |
ee646f8e-b95c-46fe-8393-ce3582d27c70 | unsupervised-explanation-generation-via | 2211.11160 | null | https://arxiv.org/abs/2211.11160v1 | https://arxiv.org/pdf/2211.11160v1.pdf | Unsupervised Explanation Generation via Correct Instantiations | While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-related tasks. Among them, explaining the reason why a statement is wrong (e.g., against commonsense) is incredibly challenging. The major diff... | ['Lingpeng Kong', 'Yang Liu', 'Zhixing Li', 'Jiangjie Chen', 'Zhiyong Wu', 'Sijie Cheng'] | 2022-11-21 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 5.48168898e-01 6.76822662e-01 -2.74677217e-01 -4.53845471e-01
-8.98078620e-01 -5.54761887e-01 6.73639655e-01 3.70040894e-01
4.55865301e-02 8.64101648e-01 4.55203086e-01 -8.57425869e-01
1.46704406e-01 -3.13492835e-01 -5.51797152e-01 -7.58827701e-02
4.12043750e-01 8.54705215e-01 -1.96074903e-01 -1.47439674... | [9.58715534210205, 6.943080425262451] |
49107e5a-0f75-4973-8531-8371adbb2e4d | general-adversarial-defense-against-black-box | 2212.05387 | null | https://arxiv.org/abs/2212.05387v1 | https://arxiv.org/pdf/2212.05387v1.pdf | General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments | Deep Neural Networks (DNNs) are vulnerable to the black-box adversarial attack that is highly transferable. This threat comes from the distribution gap between adversarial and clean samples in feature space of the target DNNs. In this paper, we use Deep Generative Networks (DGNs) with a novel training mechanism to elim... | ['Jiaya Jia', 'Philip Torr', 'Hengshuang Zhao', 'Xiaogang Xu'] | 2022-12-11 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 5.78271508e-01 1.93285808e-01 -6.37046471e-02 -2.67060220e-01
-7.15815425e-01 -1.10203731e+00 5.22215605e-01 -4.66432810e-01
-4.53904122e-01 7.50062048e-01 -3.52449149e-01 -2.80926079e-01
2.62595952e-01 -1.14573514e+00 -1.16137457e+00 -1.07258642e+00
3.57692510e-01 -5.73504381e-02 4.60160464e-01 -7.97419101... | [5.5108866691589355, 7.974135875701904] |
5efd8b57-7354-4ca5-85aa-cf3c1d7fe75f | who-sides-with-whom-towards-computational | null | null | https://aclanthology.org/P19-1273 | https://aclanthology.org/P19-1273.pdf | Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates | Understanding the structures of political debates (which actors make what claims) is essential for understanding democratic political decision making. The vision of computational construction of such discourse networks from newspaper reports brings together political science and natural language processing. This paper ... | ["Sebastian Pad{\\'o}", 'Jonas Kuhn', 'Sebastian Haunss', 'Erenay Dayanik', 'Andre Blessing', 'Nico Blokker'] | 2019-07-01 | null | null | null | acl-2019-7 | ['knowledge-base-population'] | ['natural-language-processing'] | [ 4.41426903e-01 1.33068597e+00 -7.94664264e-01 -5.16364813e-01
-8.14056873e-01 -1.08104885e+00 1.48199379e+00 7.03791499e-01
-4.20890331e-01 1.08574998e+00 1.60868537e+00 -1.33028936e+00
-1.65187091e-01 -9.51225460e-01 -5.60446858e-01 1.40335798e-01
1.39276460e-01 6.54741645e-01 1.19374551e-01 -6.60007179... | [9.082672119140625, 9.817211151123047] |
2ec38969-501d-48d2-b248-c8f6cd36d916 | trigger-free-event-detection-via-derangement | 2208.09659 | null | https://arxiv.org/abs/2208.09659v1 | https://arxiv.org/pdf/2208.09659v1.pdf | Trigger-free Event Detection via Derangement Reading Comprehension | Event detection (ED), aiming to detect events from texts and categorize them, is vital to understanding actual happenings in real life. However, mainstream event detection models require high-quality expert human annotations of triggers, which are often costly and thus deter the application of ED to new domains. Theref... | ['Haiqin Yang', 'Jiachen Zhao'] | 2022-08-20 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 0.36089382 0.02801965 -0.02338756 -0.48894516 -0.9566152 -0.5059604
0.67351115 0.73692644 -0.68880576 0.42927012 0.54657817 -0.34520498
0.08530479 -0.81397253 -0.65738136 -0.31246743 0.3979181 0.07919145
0.418652 -0.15475759 0.11333106 -0.05640946 -1.6768565 0.5774322
1.208531 0.90920794 0.35... | [9.113868713378906, 9.174711227416992] |
c9c50180-7ff7-444b-a979-b21df764266a | fast-online-object-tracking-and-segmentation | 1812.05050 | null | https://arxiv.org/abs/1812.05050v2 | https://arxiv.org/pdf/1812.05050v2.pdf | Fast Online Object Tracking and Segmentation: A Unifying Approach | In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting thei... | ['Qiang Wang', 'Li Zhang', 'Philip H. S. Torr', 'Weiming Hu', 'Luca Bertinetto'] | 2018-12-12 | fast-online-object-tracking-and-segmentation-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Fast_Online_Object_Tracking_and_Segmentation_A_Unifying_Approach_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Fast_Online_Object_Tracking_and_Segmentation_A_Unifying_Approach_CVPR_2019_paper.pdf | cvpr-2019-6 | ['real-time-visual-tracking'] | ['computer-vision'] | [ 1.73783563e-02 -9.48030129e-02 -3.46331149e-01 -1.74483702e-01
-7.65839398e-01 -1.03828621e+00 6.70133591e-01 -2.66571462e-01
-8.65633011e-01 5.38475871e-01 -4.88928258e-01 -1.96024224e-01
2.65386701e-01 -3.03904247e-02 -1.09631312e+00 -5.27624190e-01
-2.97336876e-01 7.17361212e-01 6.95713937e-01 2.07341388... | [6.511534690856934, -1.873499870300293] |
040f9838-7f97-4c33-86c0-a00073be4e6c | deep-amended-gradient-descent-for-efficient | 2108.05547 | null | https://arxiv.org/abs/2108.05547v1 | https://arxiv.org/pdf/2108.05547v1.pdf | Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images | This paper investigates the problem of recovering hyperspectral (HS) images from single RGB images. To tackle such a severely ill-posed problem, we propose a physically-interpretable, compact, efficient, and end-to-end learning-based framework, namely AGD-Net. Precisely, by taking advantage of the imaging process, we f... | ['Qingfu Zhang', 'Sen Jia', 'Junhui Hou', 'Hui Liu', 'Zhiyu Zhu'] | 2021-08-12 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 3.06260586e-01 -3.80558223e-01 2.55484253e-01 -4.34305489e-01
-7.62751877e-01 -4.11883555e-02 7.30384588e-02 -3.53079408e-01
-4.25041080e-01 5.71556389e-01 -6.20182417e-03 -3.22684973e-01
-4.43304628e-01 -8.05408180e-01 -7.00561285e-01 -1.09437180e+00
1.44920781e-01 -1.91989377e-01 -9.88669470e-02 -1.66772172... | [10.343132019042969, -2.052926540374756] |
2ad97161-ef13-4b21-b380-d6818d588f77 | improving-model-based-reinforcement-learning | 2102.05599 | null | https://arxiv.org/abs/2102.05599v1 | https://arxiv.org/pdf/2102.05599v1.pdf | Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision | Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizi... | ['Stefan Wermter', 'Muhammad Burhan Hafez', 'Cornelius Weber', 'Julien Scholz'] | 2021-02-10 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.89247847e-01 3.13367933e-01 -2.89544910e-01 -2.03384206e-01
-3.82679760e-01 -4.36660528e-01 4.83490556e-01 2.79081494e-01
-8.16237450e-01 9.00059164e-01 4.37016599e-02 -3.21930391e-03
-1.00459464e-01 -7.95797586e-01 -7.99165606e-01 -6.19626939e-01
-4.76969898e-01 6.17397964e-01 4.99986857e-01 -5.45866013... | [4.180840492248535, 1.5734302997589111] |
ad490ff1-c224-4e07-b598-e8b8961cc56e | the-dlcc-node-classification-benchmark-for | 2207.06014 | null | https://arxiv.org/abs/2207.06014v1 | https://arxiv.org/pdf/2207.06014v1.pdf | The DLCC Node Classification Benchmark for Analyzing Knowledge Graph Embeddings | Knowledge graph embedding is a representation learning technique that projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot of uptake and have been heavily used in link prediction and other downstream prediction tasks. Most approaches are evaluated on a single ta... | ['Heiko Paulheim', 'Jan Portisch'] | 2022-07-13 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.46304572e-01 6.03477776e-01 -5.36235452e-01 -2.55733520e-01
-9.81773213e-02 -6.86150193e-01 8.03237557e-01 7.61546075e-01
-2.82898247e-01 7.39093781e-01 3.32297593e-01 -3.20084453e-01
-5.07763386e-01 -1.34011304e+00 -6.32722139e-01 -2.57337868e-01
-5.46441376e-01 8.13757837e-01 3.93703729e-01 -5.44841468... | [8.853761672973633, 7.742548942565918] |
c6ae109e-28bc-49aa-9965-d43b6644ae9a | tracking-objects-with-3d-representation-from | 2306.05416 | null | https://arxiv.org/abs/2306.05416v1 | https://arxiv.org/pdf/2306.05416v1.pdf | Tracking Objects with 3D Representation from Videos | Data association is a knotty problem for 2D Multiple Object Tracking due to the object occlusion. However, in 3D space, data association is not so hard. Only with a 3D Kalman Filter, the online object tracker can associate the detections from LiDAR. In this paper, we rethink the data association in 2D MOT and utilize t... | ['Zhaoxiang Zhang', 'Naiyan Wang', 'Zehao Huang', 'Yuntao Chen', 'Yuqi Wang', 'Lue Fan', 'JiaWei He'] | 2023-06-08 | null | null | null | null | ['object-tracking', 'multiple-object-tracking'] | ['computer-vision', 'computer-vision'] | [-2.62790889e-01 -1.03515573e-01 -4.60213155e-01 -2.21682638e-01
-5.43238580e-01 -5.05380332e-01 3.73045444e-01 -1.59267247e-01
-4.33930188e-01 5.81496298e-01 -3.86399895e-01 6.25536144e-02
-9.12627429e-02 -4.32369500e-01 -9.46097076e-01 -6.25149608e-01
-6.76157251e-02 8.96032989e-01 9.49313879e-01 4.28381622... | [6.61021614074707, -2.2677292823791504] |
d602205b-ec11-4d66-ad1b-148a3d58fcc3 | bayesflow-can-reliably-detect-model | 2112.08866 | null | https://arxiv.org/abs/2112.08866v5 | https://arxiv.org/pdf/2112.08866v5.pdf | Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks | Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow framework uses a two-step approach to enable amortized parameter estimation in settings where the likelihood function is implicitly defined by a... | ['Stefan T. Radev', 'Ullrich Köthe', 'Paul-Christian Bürkner', 'Marvin Schmitt'] | 2021-12-16 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 1.6098145e-01 -1.1718173e-01 -5.4377887e-02 -2.7205935e-01
-5.8303040e-01 -4.4529685e-01 8.1899476e-01 -1.1168682e-01
-5.1760685e-01 1.0894004e+00 -1.7437932e-01 -7.1693623e-01
-3.9240524e-01 -8.4558678e-01 -9.2076516e-01 -8.1554472e-01
-3.0900878e-01 7.4803412e-01 -8.2089260e-02 4.2635494e-01
1.9438265e-01... | [6.807300567626953, 3.9544498920440674] |
3e6c4b61-77c1-4da1-9008-47d3e5d11bde | bridging-the-gap-between-f-gans-and-1 | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5361-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5361-Paper.pdf | Bridging the Gap Between f-GANs and Wasserstein GANs | Generative adversarial networks (GANs) variants approximately minimize divergences between the model and the data distribution using a discriminator. Wasserstein GANs (WGANs) enjoy superior empirical performance, however, unlike in f-GANs, the discriminator does not provide an estimate for the ratio between model and d... | ['Jiaming Song', 'Stefano Ermon'] | null | null | https://proceedings.icml.cc/static/paper_files/icml/2020/5361-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/5361-Paper.pdf | icml-2020-1 | ['density-ratio-estimation'] | ['methodology'] | [ 2.97501624e-01 2.34686658e-01 -1.36012614e-01 -2.64731526e-01
-1.04633844e+00 -6.00641072e-01 8.83428216e-01 -3.98010671e-01
-2.83293486e-01 1.20704651e+00 1.42197251e-01 -1.59472108e-01
-8.27150196e-02 -9.61662352e-01 -8.90361607e-01 -8.55388463e-01
1.26751378e-01 8.33860338e-01 -4.26730961e-01 -3.28493677... | [11.557414054870605, -0.08200128376483917] |
0513ba78-621d-48b1-8780-4d62cb6438b3 | injecting-word-embeddings-with-another | null | null | https://aclanthology.org/I17-2020 | https://aclanthology.org/I17-2020.pdf | Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings | Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness. These knowledge resources (like WordNet, Paraphrase Database) may not exist for all languages. In this work we introduce a method to injec... | ['P', 'Manish Shrivastava', 'Vikram Pudi', 'Prakhar ey'] | 2017-11-01 | injecting-word-embeddings-with-another-1 | https://aclanthology.org/I17-2020 | https://aclanthology.org/I17-2020.pdf | ijcnlp-2017-11 | ['learning-word-embeddings'] | ['methodology'] | [-6.50603712e-01 -1.94852039e-01 -3.88470054e-01 -2.88496703e-01
-5.44381738e-01 -1.03501546e+00 7.19886541e-01 2.85091281e-01
-1.11410177e+00 9.40412223e-01 8.07626963e-01 -4.22492385e-01
2.61993021e-01 -1.02156126e+00 -3.34660828e-01 -9.96851847e-02
2.71159738e-01 5.85875034e-01 1.09468915e-01 -6.88508689... | [10.946320533752441, 9.907103538513184] |
6e4ed74b-b442-4110-b562-ea8506a8428a | self-supervised-image-representation-learning-1 | 2301.09299 | null | https://arxiv.org/abs/2301.09299v1 | https://arxiv.org/pdf/2301.09299v1.pdf | Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay | Self-supervised learning has become a popular approach in recent years for its ability to learn meaningful representations without the need for data annotation. This paper proposes a novel image augmentation technique, overlaying images, which has not been widely applied in self-supervised learning. This method is desi... | ['Shaofei Wang', 'Han Ding', 'Yinheng Li'] | 2023-01-23 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 5.67418337e-01 5.15564382e-01 -4.08264846e-01 -4.20709133e-01
-2.26271927e-01 -7.56736696e-02 8.90810370e-01 4.32612032e-01
-4.11879897e-01 7.04104185e-01 3.15443397e-01 -4.96978238e-02
1.83994070e-01 -4.59402740e-01 -3.52175981e-01 -6.27857029e-01
3.17148585e-03 1.31296441e-01 2.05263376e-01 -1.86967328... | [9.460387229919434, 2.464905023574829] |
04bb6247-c20b-4138-bc38-18fac5f78d25 | lapnet-automatic-balanced-loss-and-optimal | 1911.01149 | null | https://arxiv.org/abs/1911.01149v2 | https://arxiv.org/pdf/1911.01149v2.pdf | LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection | Real-time single-stage object detectors based on deep learning still remain less accurate than more complex ones. The trade-off between model performance and computational speed is a major challenge. In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise betw... | ['Mohamed Chaouch', 'Quoc-Cuong Pham', 'Florian Chabot'] | 2019-11-04 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [ 1.18515015e-01 2.02930838e-01 -2.21137211e-01 -4.92693871e-01
-5.81286728e-01 -6.32441193e-02 3.56038421e-01 5.26786208e-01
-5.81481695e-01 6.62019908e-01 -4.43478584e-01 2.38924697e-01
-2.11910725e-01 -8.25611055e-01 -7.19799638e-01 -6.92756951e-01
2.20160764e-02 6.45034015e-01 8.46178710e-01 2.19012555... | [9.082897186279297, 1.1942384243011475] |
4260ed8b-7bc4-440b-8dae-764b8dfafb00 | efficient-algorithms-for-sparse-moment | 2207.13008 | null | https://arxiv.org/abs/2207.13008v1 | https://arxiv.org/pdf/2207.13008v1.pdf | Efficient Algorithms for Sparse Moment Problems without Separation | We consider the sparse moment problem of learning a $k$-spike mixture in high dimensional space from its noisy moment information in any dimension. We measure the accuracy of the learned mixtures using transportation distance. Previous algorithms either assume certain separation assumptions, use more recovery moments, ... | ['Jian Li', 'Zhiyuan Fan'] | 2022-07-26 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.22506864e-01 -1.39962863e-02 1.33462682e-01 -5.03662713e-02
-1.24424338e+00 -6.21127307e-01 4.29712415e-01 -6.89941570e-02
-5.04194736e-01 5.55228531e-01 -3.09503190e-02 -8.93194377e-02
-5.10397136e-01 -2.98230022e-01 -8.02211404e-01 -1.24547589e+00
-4.00664777e-01 7.98991442e-01 1.45818228e-02 8.51959065... | [7.193763256072998, 4.1971306800842285] |
d701cd03-ddc9-42d2-8c82-86180742aec9 | gfie-a-dataset-and-baseline-for-gaze | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_GFIE_A_Dataset_and_Baseline_for_Gaze-Following_From_2D_to_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_GFIE_A_Dataset_and_Baseline_for_Gaze-Following_From_2D_to_CVPR_2023_paper.pdf | GFIE: A Dataset and Baseline for Gaze-Following From 2D to 3D in Indoor Environments | Gaze-following is a kind of research that requires locating where the person in the scene is looking automatically under the topic of gaze estimation. It is an important clue for understanding human intention, such as identifying objects or regions of interest to humans. However, a survey of datasets used for gaze-... | ['Jingtai Liu', 'Bohan Zhou', 'Dingye Yang', 'Xiaolin Zhai', 'Yuxue Yang', 'Zhengxi Hu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['gaze-estimation'] | ['computer-vision'] | [ 1.01650052e-01 6.19574599e-02 -1.29623398e-01 -6.05229139e-01
-6.84915334e-02 -5.77209294e-01 3.25081944e-01 -2.76026964e-01
-5.44257283e-01 3.72092307e-01 5.45174778e-02 -9.80272964e-02
1.99567363e-01 -2.06608504e-01 -6.27104998e-01 -8.14381421e-01
4.63503063e-01 8.61300826e-02 1.34234980e-01 -1.96352210... | [14.110374450683594, 0.11671550571918488] |
d1682a4b-7b2a-49ad-9ac6-2fef8bc64e54 | stereo-matching-with-color-weighted | 1708.07987 | null | http://arxiv.org/abs/1708.07987v2 | http://arxiv.org/pdf/1708.07987v2.pdf | Stereo Matching With Color-Weighted Correlation, Hierarchical Belief Propagation And Occlusion Handling | In this paper, we contrive a stereo matching algorithm with careful handling
of disparity, discontinuity and occlusion. This algorithm works a worldwide
matching stereo model which is based on minimization of energy. The global
energy comprises two terms, firstly the data term and secondly the smoothness
term. The data... | ['Vamshhi Pavan Kumar Varma Vegeshna'] | 2017-08-26 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.08102486e-01 -3.21887314e-01 -1.36022661e-02 -6.07104182e-01
-2.58658171e-01 5.81593849e-02 7.44784534e-01 -1.08047398e-02
-6.61407888e-01 8.37426484e-01 2.93490320e-01 9.34440494e-02
-7.07301944e-02 -7.83157229e-01 -3.64119112e-01 -7.89602339e-01
-2.09198566e-03 6.65711999e-01 8.00421894e-01 -3.40163559... | [9.092700958251953, -2.4267141819000244] |
a1982bbe-46c2-45ee-8a1f-63032c4c9695 | 190807816 | 1908.07816 | null | https://arxiv.org/abs/1908.07816v3 | https://arxiv.org/pdf/1908.07816v3.pdf | A Multi-Turn Emotionally Engaging Dialog Model | Open-domain dialog systems (also known as chatbots) have increasingly drawn attention in natural language processing. Some of the recent work aims at incorporating affect information into sequence-to-sequence neural dialog modeling, making the response emotionally richer, while others use hand-crafted rules to determin... | ['Yubo Xie', 'Pearl Pu', 'Ekaterina Svikhnushina'] | 2019-08-15 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-2.44093835e-01 6.12979472e-01 1.49697915e-01 -7.66268015e-01
-4.12461162e-01 -6.98366106e-01 5.69263756e-01 -1.47831067e-01
-2.54476756e-01 9.22530949e-01 4.91239756e-01 -4.11272943e-02
7.07667053e-01 -6.68641269e-01 1.23003628e-02 -3.10262918e-01
2.92816460e-01 7.70372927e-01 -3.04202527e-01 -9.67139423... | [13.086043357849121, 7.701837539672852] |
5636bf14-bb60-4c02-8ff3-28bd97295c22 | efficient-localness-transformer-for-smart | 2203.16537 | null | https://arxiv.org/abs/2203.16537v1 | https://arxiv.org/pdf/2203.16537v1.pdf | Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation | Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity usage conditioned on the aggregated power signals (i.e., smart sensor on the main... | ['Dong Wang', 'Lanyu Shang', 'Ziyi Kou', 'Huimin Zeng', 'Zhenrui Yue'] | 2022-03-29 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.71629685e-01 -2.45153040e-01 -5.52945435e-01 -3.39095533e-01
-9.67294812e-01 -5.13254583e-01 4.95510578e-01 8.93364772e-02
1.63276456e-02 4.64540809e-01 5.42793751e-01 -1.04977451e-01
-2.58051157e-01 -8.03620756e-01 -6.87126577e-01 -7.63189435e-01
-6.49546972e-03 1.59005076e-01 -7.16997981e-01 1.92562919... | [16.069480895996094, 7.582119464874268] |
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