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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
277942d1-427a-4c50-b5b5-574bd0315946 | gravitational-laws-of-focus-of-attention | null | null | https://ieeexplore.ieee.org/document/8730418 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8730418 | Gravitational Laws of Focus of Attention | The understanding of the mechanisms behind focus of attention in a visual scene is a problem of great interest in visual perception and computer vision. In this paper, we describe a model of scanpath as a dynamic process which can be interpreted as a variational law somehow related to mechanics, where the focus of atte... | ['Stefano; Gori', 'Dario; Melacci', 'Zanca', 'Marco'] | 2019-06-04 | null | null | null | ieee-transactions-on-pattern-analysis-and-7 | ['scanpath-prediction'] | ['computer-vision'] | [ 1.66750520e-01 1.36351645e-01 -2.55843848e-02 -7.70468339e-02
5.00894845e-01 -1.28467217e-01 4.59781885e-01 -1.94181189e-01
-4.82292950e-01 4.69252020e-01 2.34446060e-02 -8.15764815e-02
-2.74202675e-01 -2.99733013e-01 -8.69281232e-01 -7.70423830e-01
1.49684504e-01 9.18332860e-02 8.58964741e-01 -3.86171579... | [9.99693775177002, 1.5428884029388428] |
99710274-741f-4cc6-a092-02ef66c6e532 | lstm-based-ecg-classification-for-continuous | 1812.04818 | null | https://arxiv.org/abs/1812.04818v3 | https://arxiv.org/pdf/1812.04818v3.pdf | LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices | Objective: A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods: The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks. Results: Experimental evaluations s... | ['Saeed Saadatnejad', 'Mohammadhosein Oveisi', 'Matin Hashemi'] | 2018-12-12 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.88671064e-01 -4.28223908e-01 -1.79217368e-01 -4.16654646e-01
-6.83201551e-01 4.69356738e-02 -4.97419596e-01 4.85920459e-01
-5.23214698e-01 6.33086503e-01 -1.27358556e-01 -5.05055845e-01
-1.93497077e-01 -4.50619966e-01 -6.50829822e-02 -5.28096199e-01
-3.81503552e-01 5.61051667e-02 -6.32085130e-02 7.20618367... | [14.234594345092773, 3.2509312629699707] |
ea152f72-1772-4c75-a904-d7bedabf41a0 | cuni-system-for-the-wmt18-multimodal-1 | null | null | https://aclanthology.org/W18-6441 | https://aclanthology.org/W18-6441.pdf | CUNI System for the WMT18 Multimodal Translation Task | We present our submission to the WMT18 Multimodal Translation Task. The main feature of our submission is applying a self-attentive network instead of a recurrent neural network. We evaluate two methods of incorporating the visual features in the model: first, we include the image representation as another input to the... | ['Du{\\v{s}}an Vari{\\v{s}}', "Jind{\\v{r}}ich Libovick{\\'y}", 'Jind{\\v{r}}ich Helcl'] | 2018-10-01 | null | null | null | ws-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.65477318e-01 2.26833895e-01 -2.38607496e-01 -4.31014955e-01
-9.72627580e-01 -6.20031595e-01 1.07792473e+00 -1.13322876e-01
-6.24827862e-01 5.96156597e-01 5.55068552e-01 -4.08162862e-01
6.72681153e-01 -4.14295524e-01 -9.55527008e-01 -3.88467222e-01
3.14651549e-01 5.36346614e-01 1.99150527e-03 -4.55252260... | [11.408249855041504, 1.5511177778244019] |
3de853e4-d598-4d60-a249-645bb204a404 | ttvos-lightweight-video-object-segmentation | 2011.04445 | null | https://arxiv.org/abs/2011.04445v3 | https://arxiv.org/pdf/2011.04445v3.pdf | TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss | Semi-supervised video object segmentation (semi-VOS) is widely used in many applications. This task is tracking class-agnostic objects from a given target mask. For doing this, various approaches have been developed based on online-learning, memory networks, and optical flow. These methods show high accuracy but are ha... | ['Nojun Kwak', 'Ganesh Venkatesh', 'Hyojin Park'] | 2020-11-09 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.45987640e-02 -3.31335694e-01 -2.65155226e-01 -3.05154234e-01
-3.88371170e-01 -1.18979082e-01 3.42763305e-01 -1.81613550e-01
-5.88344097e-01 5.06000102e-01 -2.39465833e-01 1.25484720e-01
5.41892573e-02 -6.65295780e-01 -7.12183535e-01 -7.14823246e-01
1.53599203e-01 1.50140867e-01 1.16652656e+00 5.87114990... | [9.200998306274414, -0.26618725061416626] |
de897c57-c5dc-4e0c-8e10-aa7bb27acf61 | learnable-path-in-neural-controlled | 2301.04333 | null | https://arxiv.org/abs/2301.04333v1 | https://arxiv.org/pdf/2301.04333v1.pdf | Learnable Path in Neural Controlled Differential Equations | Neural controlled differential equations (NCDEs), which are continuous analogues to recurrent neural networks (RNNs), are a specialized model in (irregular) time-series processing. In comparison with similar models, e.g., neural ordinary differential equations (NODEs), the key distinctive characteristics of NCDEs are i... | ['Sunhwan Lim', 'Sungpil Woo', 'Noseong Park', 'Seungji Kook', 'Minju Jo', 'Sheo Yon Jhin'] | 2023-01-11 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.32554278e-01 7.31873959e-02 -9.59026814e-02 -1.12929523e-01
-9.94461626e-02 -1.49173573e-01 6.42914057e-01 -3.31027925e-01
-2.26238832e-01 8.34288895e-01 2.33885460e-03 -6.01400733e-01
-1.84813980e-02 -7.40746260e-01 -7.67927170e-01 -5.33306301e-01
-3.89372706e-01 -9.90127474e-02 1.05734877e-01 -4.12880629... | [6.997147560119629, 3.2323763370513916] |
61e42bea-e36b-4c2d-906c-e3c250be9de2 | technical-report-auxiliary-tuning-and-its | 2006.16823 | null | https://arxiv.org/abs/2006.16823v1 | https://arxiv.org/pdf/2006.16823v1.pdf | Technical Report: Auxiliary Tuning and its Application to Conditional Text Generation | We introduce a simple and efficient method, called Auxiliary Tuning, for adapting a pre-trained Language Model to a novel task; we demonstrate this approach on the task of conditional text generation. Our approach supplements the original pre-trained model with an auxiliary model that shifts the output distribution acc... | ['Or Sharir', 'Yoel Zeldes', 'Dan Padnos', 'Barak Peleg'] | 2020-06-30 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 5.57532847e-01 7.53894746e-01 -1.92225397e-01 -4.57801223e-01
-9.75255132e-01 -6.16189420e-01 1.06441927e+00 -1.45606160e-01
-6.17119908e-01 1.05932903e+00 3.41464818e-01 -4.71138418e-01
4.09086704e-01 -7.76301444e-01 -1.02328849e+00 -5.38931012e-01
4.92179990e-01 9.66988802e-01 9.71668065e-02 -1.43541917... | [11.534234046936035, 8.983741760253906] |
820d09e0-81ff-408b-a0c2-a65247341ae7 | visibility-inspired-models-of-touch-sensors | 2203.04751 | null | https://arxiv.org/abs/2203.04751v2 | https://arxiv.org/pdf/2203.04751v2.pdf | Visibility-Inspired Models of Touch Sensors for Navigation | This paper introduces mathematical models of \sensors\ for mobile robots based on visibility. Serving a purpose similar to the pinhole camera model for computer vision, the introduced models are expected to provide a useful, idealized characterization of task-relevant information that can be inferred from their outputs... | ['Steven M. LaValle', 'Manivannan M.', 'Prasanna Routray', 'Basak Sakcak', 'Kshitij Tiwari'] | 2022-03-04 | null | null | null | null | ['contact-detection'] | ['robots'] | [ 5.01596093e-01 -3.31518091e-02 -1.26690164e-01 -2.49625623e-01
-3.37457247e-02 -6.65512025e-01 4.97447938e-01 9.94556397e-02
-4.53962356e-01 4.99411315e-01 -6.41547024e-01 -3.10437828e-01
-2.93127149e-01 -7.09750295e-01 -4.17548597e-01 -8.36348593e-01
1.39759749e-01 4.02051657e-01 3.44616324e-01 -3.24330091... | [7.288428783416748, -2.0604984760284424] |
38af3628-764a-41f3-9f56-a9ad586aee84 | a-semantic-backdoor-attack-against-graph | 2302.14353 | null | https://arxiv.org/abs/2302.14353v1 | https://arxiv.org/pdf/2302.14353v1.pdf | A semantic backdoor attack against Graph Convolutional Networks | Graph Convolutional Networks (GCNs) have been very effective in addressing the issue of various graph-structured related tasks, such as node classification and graph classification. However, extensive research has shown that GCNs are vulnerable to adversarial attacks. One of the security threats facing GCNs is the back... | ['Zhipeng Xiong', 'Jiazhu Dai'] | 2023-02-28 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.71787661e-01 2.87478447e-01 -1.77452117e-01 3.07024326e-02
1.14016213e-01 -1.04417956e+00 5.27115107e-01 4.08820599e-01
6.94046244e-02 3.85497600e-01 -5.04210174e-01 -6.59968376e-01
2.12643251e-01 -1.47413802e+00 -1.06896138e+00 -7.05837786e-01
-1.86199561e-01 3.82192037e-03 7.06921101e-01 -2.27135971... | [6.127984046936035, 7.381504535675049] |
9321ff68-8d1f-4853-91de-e60600664477 | gene-teams-are-on-the-field-evaluation-of | 2301.11763 | null | https://arxiv.org/abs/2301.11763v1 | https://arxiv.org/pdf/2301.11763v1.pdf | Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling | In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specific gene networks, rather than the presence of a particular single variant, predominates. In the case of complex diseases, disease status can... | ['Yelda Tarkan Arguden', 'Ayse Cirakoglu', 'Emrah Yucesan', 'Cagri Gulec', 'Suha Tuna'] | 2023-01-27 | null | null | null | null | ['medical-genetics'] | ['miscellaneous'] | [ 2.35872343e-01 5.33141568e-02 -1.10601433e-01 -1.67805597e-01
-2.71095991e-01 -4.19829160e-01 3.02010030e-01 2.08911613e-01
-2.39959508e-01 8.38678062e-01 1.06602542e-01 -1.58756331e-01
-6.28329933e-01 -8.25336516e-01 -3.97997141e-01 -1.02023971e+00
-4.42182660e-01 7.38343179e-01 -1.23173602e-01 -1.19070165... | [6.406226634979248, 5.468254089355469] |
84c61512-c4ee-4ba6-9713-ce4af4e22601 | learning-causal-effects-via-weighted | null | null | http://proceedings.neurips.cc/paper/2020/hash/95a6fc111fa11c3ab209a0ed1b9abeb6-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/95a6fc111fa11c3ab209a0ed1b9abeb6-Paper.pdf | Learning Causal Effects via Weighted Empirical Risk Minimization | Learning causal effects from data is a fundamental problem across the sciences. Determining the identifiability of a target effect from a combination of the observational distribution and the causal graph underlying a phenomenon is well-understood in theory. However, in practice, it remains a challenge to apply the ide... | ['Elias Bareinboim', 'Jin Tian', 'Yonghan Jung'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['causal-identification'] | ['reasoning'] | [ 6.13223195e-01 1.48205563e-01 -6.08931303e-01 -4.29945469e-01
-7.64382005e-01 -4.14863735e-01 5.72402000e-01 4.76081185e-02
-1.77276284e-02 1.09289300e+00 3.01719248e-01 -3.48998129e-01
-1.01048338e+00 -8.58507097e-01 -1.07293069e+00 -7.64098585e-01
-3.02138776e-01 1.17468707e-01 -2.62544066e-01 3.22351009... | [7.833114147186279, 5.296751499176025] |
5f489f91-7460-41af-8886-3dffd31e108e | real-time-cardiovascular-mr-with-spatio | 1803.05192 | null | http://arxiv.org/abs/1803.05192v3 | http://arxiv.org/pdf/1803.05192v3.pdf | Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease | PURPOSE: Real-time assessment of ventricular volumes requires high
acceleration factors. Residual convolutional neural networks (CNN) have shown
potential for removing artifacts caused by data undersampling. In this study we
investigated the effect of different radial sampling patterns on the accuracy
of a CNN. We also... | ['Vivek Muthurangu', 'Simon Arridge', 'Felix Lucka', 'Andreas Hauptmann', 'Jennifer A. Steeden'] | 2018-03-14 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [ 1.61714792e-01 2.11544454e-01 4.40587312e-01 -2.55412340e-01
-6.95988953e-01 -6.09858930e-01 -2.33084057e-02 -2.27536280e-02
-4.20381397e-01 6.45266294e-01 2.35671014e-01 -7.08645165e-01
-1.63296461e-01 -5.53932071e-01 -6.10962212e-01 -3.80714893e-01
-8.32303584e-01 8.01604271e-01 -8.10376778e-02 8.06903616... | [14.086670875549316, -2.4767367839813232] |
e6b769d6-d6f7-4164-83c3-c9747bc32b66 | rapid-learning-of-spatial-representations-for | 2206.02249 | null | https://arxiv.org/abs/2206.02249v3 | https://arxiv.org/pdf/2206.02249v3.pdf | Rapid Learning of Spatial Representations for Goal-Directed Navigation Based on a Novel Model of Hippocampal Place Fields | The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior potent... | ['Ali Minai', 'Dieter Vanderelst', 'Adedapo Alabi'] | 2022-06-05 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [-3.17444763e-04 -2.79536724e-01 9.04436707e-02 6.23583281e-03
9.35386792e-02 -6.95799768e-01 4.65964347e-01 3.89982730e-01
-9.30524409e-01 1.21915698e+00 -7.16279894e-02 -7.47524053e-02
-5.16281962e-01 -7.94701338e-01 -8.29495490e-01 -7.94145942e-01
-1.20292497e+00 1.74930245e-01 5.80870926e-01 -5.14937937... | [4.382242202758789, 1.1518956422805786] |
edc84333-fb86-4559-842d-42954ae2bf60 | stmt-a-spatial-temporal-mesh-transformer-for | 2303.18177 | null | https://arxiv.org/abs/2303.18177v1 | https://arxiv.org/pdf/2303.18177v1.pdf | STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition | We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model ... | ['Alexander Hauptmann', 'Celso M. de Melo', 'Junwei Liang', 'Po-Yao Huang', 'Xiaoyu Zhu'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_STMT_A_Spatial-Temporal_Mesh_Transformer_for_MoCap-Based_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_STMT_A_Spatial-Temporal_Mesh_Transformer_for_MoCap-Based_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.37091765e-01 1.30163446e-01 -2.49677882e-01 -4.32767928e-01
-8.24397326e-01 -4.26031575e-02 6.20278299e-01 -3.35159630e-01
-2.90203512e-01 4.18082923e-01 3.63814920e-01 1.40827835e-01
2.63075769e-01 -7.08266675e-01 -9.98418987e-01 -3.17575902e-01
5.98207042e-02 4.05504465e-01 7.32164502e-01 5.86865768... | [8.447073936462402, 0.3512386977672577] |
85d7bec4-bf34-4642-80d5-61be20ed73ee | monocular-expressive-body-regression-through | 2008.09062 | null | https://arxiv.org/abs/2008.09062v1 | https://arxiv.org/pdf/2008.09062v1.pdf | Monocular Expressive Body Regression through Body-Driven Attention | To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that inclu... | ['Michael J. Black', 'Dimitrios Tzionas', 'Vasileios Choutas', 'Georgios Pavlakos', 'Timo Bolkart'] | 2020-08-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/983_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550018.pdf | eccv-2020-8 | ['3d-human-reconstruction', '3d-multi-person-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [-2.12933812e-02 1.44040391e-01 -1.15605414e-01 -3.42085719e-01
-4.09670800e-01 -4.64044273e-01 3.41977865e-01 -5.37447691e-01
-3.26148570e-01 4.27376062e-01 3.09546739e-01 4.58934337e-01
3.42363775e-01 -4.58634049e-01 -6.86961949e-01 -3.66304904e-01
2.40985692e-01 8.83362889e-01 -1.60694957e-01 -1.31391600... | [7.123443603515625, -1.070980191230774] |
81ed42e5-444e-496f-869e-8b61f3e4ac79 | event-triggered-hybrid-energy-aware | 2302.00812 | null | https://arxiv.org/abs/2302.00812v1 | https://arxiv.org/pdf/2302.00812v1.pdf | Event-triggered Hybrid Energy-aware Scheduling in Manufacturing Systems | Incorporating renewable energy sources (RESs) into manufacturing systems has been an active research area in order to address many challenges originating from the unpredictable nature of RESs such as photovoltaics.In the energy-aware scheduling for manufacturing systems, the traditional off-line scheduling techniques c... | ['Ying Tan', 'Wen Li', 'Zhean Shao'] | 2023-02-02 | null | null | null | null | ['manufacturing-simulation'] | ['knowledge-base'] | [ 1.39960676e-01 -1.73750803e-01 5.42844925e-03 6.84594437e-02
2.96682660e-02 -5.58591783e-01 4.60901618e-01 2.77186692e-01
1.59835711e-01 1.02166176e+00 -5.30491710e-01 -8.35763589e-02
-8.35426807e-01 -1.07797515e+00 -4.78441626e-01 -1.09043252e+00
1.19723871e-01 5.24006009e-01 5.17042838e-02 -1.76378563... | [5.674221992492676, 2.5276670455932617] |
9c330423-544e-461c-81e0-2bb717928493 | optimising-the-input-image-to-improve-visual | 1903.11029 | null | http://arxiv.org/abs/1903.11029v1 | http://arxiv.org/pdf/1903.11029v1.pdf | Optimising the Input Image to Improve Visual Relationship Detection | Visual Relationship Detection is defined as, given an image composed of a
subject and an object, the correct relation is predicted. To improve the visual
part of this difficult problem, ten preprocessing methods were tested to
determine whether the widely used Union method yields the optimal results.
Therefore, focusin... | ['Noel Mizzi', 'Adrian Muscat'] | 2019-03-26 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 3.11403304e-01 5.11651576e-01 2.34332159e-02 -3.50178331e-01
-1.36068448e-01 -5.26952207e-01 7.51027226e-01 5.72185159e-01
-4.08246011e-01 5.22696912e-01 -2.82319725e-01 -4.44773972e-01
-1.44438714e-01 -9.22756791e-01 -8.10160220e-01 -4.40862447e-01
8.80530924e-02 2.52106339e-01 6.06178343e-01 1.64988145... | [10.244171142578125, 1.664555549621582] |
e7aa7a80-80ba-40ac-bdd6-c4add9cfbd1e | how-many-labeled-license-plates-are-needed | 1808.08410 | null | http://arxiv.org/abs/1808.08410v1 | http://arxiv.org/pdf/1808.08410v1.pdf | How many labeled license plates are needed? | Training a good deep learning model often requires a lot of annotated data.
As a large amount of labeled data is typically difficult to collect and even
more difficult to annotate, data augmentation and data generation are widely
used in the process of training deep neural networks. However, there is no
clear common un... | ['Shugong Xu', 'Changhao Wu', 'Shunqing Zhang', 'Guocong Song'] | 2018-08-25 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 6.00174777e-02 -1.79556578e-01 2.09918544e-01 -4.84053791e-01
-5.66977620e-01 -7.52488613e-01 5.01122475e-01 -5.44772029e-01
-4.61686432e-01 9.02626336e-01 -4.06298518e-01 -8.62401575e-02
5.98603666e-01 -9.48039114e-01 -1.00604784e+00 -7.09831715e-01
5.20548761e-01 6.77822709e-01 2.00437561e-01 -2.73931414... | [9.935583114624023, -4.642817497253418] |
e4c5d5b0-f61e-46cb-b3a9-f2e756e46b1d | the-visual-centrifuge-model-free-layered | 1812.01461 | null | http://arxiv.org/abs/1812.01461v2 | http://arxiv.org/pdf/1812.01461v2.pdf | The Visual Centrifuge: Model-Free Layered Video Representations | True video understanding requires making sense of non-lambertian scenes where
the color of light arriving at the camera sensor encodes information about not
just the last object it collided with, but about multiple mediums -- colored
windows, dirty mirrors, smoke or rain. Layered video representations have the
potentia... | ['João Carreira', 'Jean-Baptiste Alayrac', 'Andrew Zisserman'] | 2018-12-04 | the-visual-centrifuge-model-free-layered-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Alayrac_The_Visual_Centrifuge_Model-Free_Layered_Video_Representations_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Alayrac_The_Visual_Centrifuge_Model-Free_Layered_Video_Representations_CVPR_2019_paper.pdf | cvpr-2019-6 | ['color-constancy'] | ['computer-vision'] | [ 2.20405012e-01 -1.83541164e-01 2.73284972e-01 -2.96648234e-01
-4.91925776e-01 -7.11937606e-01 6.61155164e-01 -4.23200101e-01
1.85702380e-03 5.40007949e-01 1.90571055e-01 -9.91461575e-02
7.31002688e-02 -4.55997288e-01 -1.01653326e+00 -7.45058239e-01
-5.86469829e-01 2.92105436e-01 4.65170771e-01 1.00996040... | [9.482486724853516, -2.884411334991455] |
cb765d48-89d9-476a-b341-08a003800c1b | sparse-illumination-learning-and-transfer-for | 1402.1879 | null | https://arxiv.org/abs/1402.1879v1 | https://arxiv.org/pdf/1402.1879v1.pdf | Sparse Illumination Learning and Transfer for Single-Sample Face Recognition with Image Corruption and Misalignment | Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required g... | ['S. Shankar Sastry', 'Tsung-Han Chan', 'Allen Y. Yang', 'Yi Ma', 'Liansheng Zhuang'] | 2014-02-08 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 7.49292195e-01 -1.59538642e-01 -2.42371783e-02 -5.86356759e-01
-9.90014315e-01 -4.77121413e-01 4.96395707e-01 -8.63527358e-01
-1.06920287e-01 6.82569265e-01 -1.51338562e-01 4.23983812e-01
-1.23387706e-02 -3.24447721e-01 -7.30014324e-01 -1.25225079e+00
5.66450238e-01 5.96061885e-01 -4.62649375e-01 -1.98930521... | [12.922341346740723, 0.3428160548210144] |
0cc73e79-46d0-4aa6-a71a-e466d5a366f8 | stereo-event-lifetime-and-disparity | 1907.07518 | null | https://arxiv.org/abs/1907.07518v1 | https://arxiv.org/pdf/1907.07518v1.pdf | Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors | Event-based cameras are biologically inspired sensors that output asynchronous pixel-wise brightness changes in the scene called events. They have a high dynamic range and temporal resolution of a microsecond, opposed to standard cameras that output frames at fixed frame rates and suffer from motion blur. Forming stere... | ['Ivan Petrović', 'Ivan Marković', 'Antea Hadviger'] | 2019-07-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 9.17074859e-01 -2.97289878e-01 5.10481060e-01 -3.04196775e-01
-5.01932263e-01 -5.99503577e-01 5.60785592e-01 2.74382234e-01
-9.57837224e-01 1.01425362e+00 -1.64027795e-01 2.35014558e-01
2.41025612e-01 -6.82181299e-01 -9.38655555e-01 -8.76208961e-01
-3.39272358e-02 -1.70220882e-02 9.68191087e-01 4.78828132... | [8.683606147766113, -1.3478641510009766] |
bca0da62-f8fb-480c-8b9c-bd008c157cbc | deep-reinforcement-learning-based-vehicle | 2304.02832 | null | https://arxiv.org/abs/2304.02832v1 | https://arxiv.org/pdf/2304.02832v1.pdf | Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing | In the traditional vehicular network, computing tasks generated by the vehicles are usually uploaded to the cloud for processing. However, since task offloading toward the cloud will cause a large delay, vehicular edge computing (VEC) is introduced to avoid such a problem and improve the whole system performance, where... | ['Qiang Fan', 'Pingyi Fan', 'Siyuan Wang', 'Qiong Wu'] | 2023-04-06 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-7.30473697e-01 -6.73302338e-02 -2.66757816e-01 -1.85275421e-01
-1.43305466e-01 -2.70524234e-01 7.37651885e-02 -1.39648125e-01
-3.07417423e-01 7.76277423e-01 -4.04530436e-01 -4.21993315e-01
-1.00300103e-01 -1.03492880e+00 -6.59971356e-01 -9.01789844e-01
-3.36125523e-01 2.57041723e-01 4.45910990e-01 -7.50507042... | [5.664848327636719, 1.487156867980957] |
e4b9a90d-cbe7-4f76-bc46-ac9f3e16b5d4 | productive-reproducible-workflows-for-dnns-a | 2206.09359 | null | https://arxiv.org/abs/2206.09359v1 | https://arxiv.org/pdf/2206.09359v1.pdf | Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection | As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely available under permissive licenses, and are accessible enough to enable even sma... | ['José Cano', 'Perry Gibson'] | 2022-06-19 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-2.13466778e-01 -1.81363106e-01 2.90885717e-01 -4.67540950e-01
-4.41594422e-01 -5.77625930e-01 3.84471156e-02 6.88718911e-03
-3.76309305e-01 4.82069284e-01 -3.65080118e-01 -4.59080786e-01
-3.05083632e-01 -8.16347361e-01 -6.52497411e-01 -5.31784534e-01
5.71949892e-02 8.08654130e-01 2.36100569e-01 4.06239182... | [8.423568725585938, 2.68607234954834] |
189506c6-4500-435e-9964-246b9b39cfcc | tailoring-machine-learning-for-process-mining | 2306.10341 | null | https://arxiv.org/abs/2306.10341v1 | https://arxiv.org/pdf/2306.10341v1.pdf | Tailoring Machine Learning for Process Mining | Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not n... | ['Wil van der Aalst', 'Ernesto Damiani', 'Sylvio Barbon Junior', 'Paolo Ceravolo'] | 2023-06-17 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 6.13394618e-01 3.32045019e-01 -1.32434592e-01 -3.88180465e-01
-2.46525817e-02 -5.31426847e-01 9.38537955e-01 8.60949397e-01
-1.77317888e-01 2.58212864e-01 5.35320444e-03 -6.46950245e-01
-4.97065872e-01 -1.08377087e+00 -3.02714974e-01 -3.62202376e-01
-2.58147903e-02 6.62339807e-01 1.04547374e-01 3.06639105... | [8.662229537963867, 6.004100322723389] |
fc50b77d-efaa-4a2d-aa2f-c3e16ca57a7b | repurposing-existing-deep-networks-for | 2201.02280 | null | https://arxiv.org/abs/2201.02280v1 | https://arxiv.org/pdf/2201.02280v1.pdf | Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping | We propose a novel optimization framework that crops a given image based on user description and aesthetics. Unlike existing image cropping methods, where one typically trains a deep network to regress to crop parameters or cropping actions, we propose to directly optimize for the cropping parameters by repurposing pre... | ['Hyung Jin Chang', 'Ales Leonardis', 'Abhishake Kumar Bojja', 'Kwang Moo Yi', 'Kedi Xia', 'Nora Horanyi'] | 2022-01-07 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 6.35564685e-01 3.13471347e-01 6.86106756e-02 -4.42934960e-01
-6.65168941e-01 -6.30824208e-01 3.27036947e-01 4.60749455e-02
-3.22698683e-01 4.37390327e-01 2.25262225e-01 -2.59968638e-01
2.26991862e-01 -8.48298371e-01 -9.15825307e-01 -5.93858540e-01
3.42101067e-01 -6.80010393e-02 -2.07031682e-01 -1.72698408... | [11.482085227966309, -0.955616295337677] |
a558749a-35a5-4fec-8295-6e00bf0e79e5 | robot-intent-recognition-method-based-on | null | null | https://openreview.net/forum?id=xREjEGUoY4c | https://openreview.net/pdf?id=xREjEGUoY4c | Robot Intent Recognition Method Based on State Grid Business Office | Artificial intelligence is currently in an era of change, not only changing the artificial intelligence technology itself, but also changing human society. It has become more and more common to use artificial intelligence as the core human-computer interaction technology to replace manpower. Intention recognition is an... | ['Hanchao Liu', 'Zhao Pu Hu', 'Lanfang Dong'] | 2021-09-29 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 3.01773876e-01 9.91652757e-02 -3.69665235e-01 -6.02099061e-01
1.22081749e-01 1.10354178e-01 4.70978230e-01 -1.98769584e-01
-6.94221258e-01 3.58528316e-01 1.91424131e-01 -3.77965897e-01
6.74181506e-02 -7.98754334e-01 3.12655754e-02 -3.71451020e-01
4.65758741e-01 6.07169688e-01 -1.43849134e-01 -1.79471731... | [9.41183853149414, 6.135878086090088] |
92c95bfb-ce69-4555-b199-c22c3aa87cae | poisoning-web-scale-training-datasets-is | 2302.10149 | null | https://arxiv.org/abs/2302.10149v1 | https://arxiv.org/pdf/2302.10149v1.pdf | Poisoning Web-Scale Training Datasets is Practical | Deep learning models are often trained on distributed, webscale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionally introduce malicious examples to a model's performance. Our attacks are immediately practical and could, today, poison 10 popular datasets. ... | ['Florian Tramèr', 'Kurt Thomas', 'Andreas Terzis', 'Hyrum Anderson', 'Will Pearce', 'Daniel Paleka', 'Christopher A. Choquette-Choo', 'Matthew Jagielski', 'Nicholas Carlini'] | 2023-02-20 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-5.31303287e-01 1.03150658e-01 -1.58035353e-01 -1.18656285e-01
-7.35016882e-01 -1.27504587e+00 6.94028020e-01 1.52033508e-01
-6.76332712e-01 7.08963990e-01 -9.27837864e-02 -4.56883937e-01
3.29037637e-01 -7.19430923e-01 -1.11660576e+00 -3.62140119e-01
-3.44133019e-01 5.75273812e-01 6.78569734e-01 -6.31567324... | [5.82712459564209, 7.6086931228637695] |
70edc54b-b63a-4924-b24f-6a2dd5bea838 | a-compact-embedding-for-facial-expression | 1811.11283 | null | http://arxiv.org/abs/1811.11283v2 | http://arxiv.org/pdf/1811.11283v2.pdf | A Compact Embedding for Facial Expression Similarity | Most of the existing work on automatic facial expression analysis focuses on
discrete emotion recognition, or facial action unit detection. However, facial
expressions do not always fall neatly into pre-defined semantic categories.
Also, the similarity between expressions measured in the action unit space need
not corr... | ['Aseem Agarwala', 'Raviteja Vemulapalli'] | 2018-11-27 | a-compact-embedding-for-facial-expression-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Vemulapalli_A_Compact_Embedding_for_Facial_Expression_Similarity_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Vemulapalli_A_Compact_Embedding_for_Facial_Expression_Similarity_CVPR_2019_paper.pdf | cvpr-2019-6 | ['action-unit-detection'] | ['computer-vision'] | [ 3.45049441e-01 -3.82340439e-02 -3.05865824e-01 -9.29163456e-01
-3.17873061e-01 -3.53470802e-01 4.70831186e-01 -6.64679753e-03
-2.93969989e-01 4.03893709e-01 5.57222545e-01 3.80597204e-01
3.07842195e-01 -3.95382047e-01 -2.19686449e-01 -7.77874112e-01
-8.22030976e-02 -6.39613420e-02 -6.00507796e-01 -1.35355547... | [13.582874298095703, 1.795093297958374] |
e5f7ea30-8538-46f7-8e59-b32cae40aa76 | fishdreamer-towards-fisheye-semantic | 2303.13842 | null | https://arxiv.org/abs/2303.13842v2 | https://arxiv.org/pdf/2303.13842v2.pdf | FishDreamer: Towards Fisheye Semantic Completion via Unified Image Outpainting and Segmentation | This paper raises the new task of Fisheye Semantic Completion (FSC), where dense texture, structure, and semantics of a fisheye image are inferred even beyond the sensor field-of-view (FoV). Fisheye cameras have larger FoV than ordinary pinhole cameras, yet its unique special imaging model naturally leads to a blind ar... | ['Rainer Stiefelhagen', 'Kaiwei Wang', 'Huajian Ni', 'Yaozu Ye', 'Alina Roitberg', 'Kunyu Peng', 'Jiaming Zhang', 'Kailun Yang', 'Yu Li', 'Hao Shi'] | 2023-03-24 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 2.29068339e-01 8.98836106e-02 2.47102365e-01 -4.43376154e-01
-7.54670262e-01 -8.57197523e-01 5.36861897e-01 -6.13341987e-01
-2.98926771e-01 3.37037981e-01 5.41368961e-01 -7.06548914e-02
-2.17525840e-01 -3.90530258e-01 -9.56781149e-01 -7.21932113e-01
3.53909492e-01 -9.28019732e-02 3.93378228e-01 -6.82420731... | [8.843080520629883, -2.5760562419891357] |
fd74cdc3-0b4b-4f6d-a0f2-2f2f9195245c | a-speech-corpus-for-chronic-kidney-disease | 2211.01705 | null | https://arxiv.org/abs/2211.01705v1 | https://arxiv.org/pdf/2211.01705v1.pdf | A speech corpus for chronic kidney disease | In this study, we present a speech corpus of patients with chronic kidney disease (CKD) that will be used for research on pathological voice analysis, automatic illness identification, and severity prediction. This paper introduces the steps involved in creating this corpus, including the choice of speech-related param... | ['Minhwa Chung', 'Sejoong Kim', 'Jiwon Ryu', 'Myeong Ju Kim', 'Sunhee Kim', 'Jihyun Mun'] | 2022-11-03 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-2.25098059e-01 -2.88185924e-01 8.35773498e-02 -2.49451160e-01
-4.96746033e-01 -1.10172197e-01 6.97893053e-02 5.02107859e-01
-1.92267969e-01 4.78871971e-01 9.84317601e-01 -4.50599104e-01
-1.79222003e-01 -4.69238281e-01 3.45008850e-01 -4.18113619e-01
-2.86006361e-01 5.04402816e-01 -4.78348076e-01 1.79316089... | [14.059332847595215, 5.459327697753906] |
d3385574-307b-4778-91c6-c31990429cea | hope-hierarchical-object-prototype-encoding | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Yu_HOPE_Hierarchical_Object_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_HOPE_Hierarchical_Object_CVPR_2017_paper.pdf | HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos | This paper tackles the problem of efficient and effective object instance search in videos. To effectively capture the relevance between a query and video frames and precisely localize the particular object, we leverage the object proposals to improve the quality of object instance search in videos. However, hundreds o... | ['Junsong Yuan', 'Yuwei Wu', 'Tan Yu'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['instance-search'] | ['computer-vision'] | [-1.37535238e-03 -4.89456326e-01 -3.34864199e-01 -2.80292183e-01
-9.09028113e-01 -3.06946188e-01 3.07716757e-01 1.21980406e-01
-2.93434650e-01 2.33322233e-01 2.74543054e-02 4.44421142e-01
-2.88898647e-01 -4.11723405e-01 -7.50398099e-01 -7.04427600e-01
-9.23352614e-02 1.76213905e-01 7.06175864e-01 3.01350027... | [9.911163330078125, 0.4989762008190155] |
482e37c3-31c7-46e7-9175-776afcabe099 | single-image-deraining-from-model-based-to | 1912.07150 | null | https://arxiv.org/abs/1912.07150v2 | https://arxiv.org/pdf/1912.07150v2.pdf | Single Image Deraining: From Model-Based to Data-Driven and Beyond | The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation. The early single-image deraining methods employ a cost function, where various priors are developed to represent the properties of rain and background layers. Since 2017, single-i... | ['Yuming Fang', 'Shiqi Wang', 'Jiaying Liu', 'Robby T. Tan', 'Wenhan Yang'] | 2019-12-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.94600248e-01 -1.73931509e-01 3.10530931e-01 -4.46231812e-01
-6.98354006e-01 -2.21673682e-01 2.71786571e-01 -7.24537015e-01
2.92523503e-02 8.45625043e-01 -1.72546115e-02 -2.07382083e-01
2.31300041e-01 -6.64924800e-01 -8.34232450e-01 -1.14977121e+00
-8.81451964e-02 -1.00321166e-01 1.32325245e-02 -2.68106699... | [10.920541763305664, -3.2454254627227783] |
5ffc7c12-ce8e-4f4b-b9d3-cd325a2a9727 | two-stage-pipeline-for-multilingual-dialect | 2303.03487 | null | https://arxiv.org/abs/2303.03487v2 | https://arxiv.org/pdf/2303.03487v2.pdf | Two-stage Pipeline for Multilingual Dialect Detection | Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respe... | ['Aditya Kane', 'Ankit Vaidya'] | 2023-03-06 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-7.02063918e-01 -4.36538905e-01 -2.43488982e-01 -6.09178364e-01
-1.52863824e+00 -1.06619072e+00 8.75600219e-01 4.77786511e-02
-5.18897593e-01 8.74455750e-01 4.20026422e-01 -6.25597179e-01
2.53821760e-01 -4.98327732e-01 -3.36949170e-01 -4.94466573e-01
1.65463597e-01 8.82062972e-01 2.11113900e-01 -3.54105085... | [10.152641296386719, 10.739961624145508] |
a30aa448-2fb6-4311-a410-b73af93706f1 | robust-discovery-of-positive-and-negative | null | null | http://www.eurecom.fr/fr/publication/5469/detail/robust-discovery-of-positive-and-negative-rules-in-knowledge-bases-1 | https://www.dropbox.com/s/4hgcli75ccqe20t/Rudik_CR_ICDE.pdf?dl=0 | Robust Discovery of Positive and Negative Rules in Knowledge-Bases | We present RUDIK, a system for the discovery of declarative rules over knowledge-bases (KBs). RUDIK discovers rules that express positive relationships between entities, such as “if two persons have the same parent, they are siblings”, and negative rules, i.e., patterns that identify contradictions in the data, such as... | ['Paolo Papotti', 'Stefano Ortona', 'Venkata Vamsikrishna Meduri'] | 2018-04-16 | null | null | null | icde-2018-4 | ['knowledge-graphs-data-curation'] | ['knowledge-base'] | [-6.35629706e-03 6.70998454e-01 -4.58594471e-01 -2.52310902e-01
1.09942965e-01 -5.26283145e-01 7.41707906e-02 5.84295630e-01
-4.30214852e-02 1.26000071e+00 -2.84145683e-01 -6.73512459e-01
-6.75422251e-01 -1.49672365e+00 -8.64724338e-01 -2.34632701e-01
-5.37661254e-01 1.00000405e+00 7.35857844e-01 -9.23928246... | [8.867708206176758, 7.161899089813232] |
652ba512-6dee-41fa-acce-2155f7d0c436 | learning-to-personalize-for-web-search | 2009.08206 | null | https://arxiv.org/abs/2009.08206v1 | https://arxiv.org/pdf/2009.08206v1.pdf | Learning to Personalize for Web Search Sessions | The task of session search focuses on using interaction data to improve relevance for the user's next query at the session level. In this paper, we formulate session search as a personalization task under the framework of learning to rank. Personalization approaches re-rank results to match a user model. Such user mode... | ['Stephen Clark', 'Saad Aloteibi'] | 2020-09-17 | null | null | null | null | ['session-search'] | ['natural-language-processing'] | [ 3.55325431e-01 -5.88080585e-02 -5.23008704e-01 -7.38608420e-01
-8.25743556e-01 -5.54431677e-01 8.79065275e-01 5.66336036e-01
-8.94047022e-01 5.81561208e-01 6.79158330e-01 -1.08785771e-01
-7.28377998e-01 -3.55510592e-01 -2.03363672e-01 -3.33817378e-02
-4.41976875e-01 7.05508590e-01 5.15539765e-01 -3.59715641... | [11.757423400878906, 7.607293128967285] |
3b6b4295-3de3-443a-b2f2-c16d87498fdf | pivotal-prior-driven-supervision-for-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization | Weakly-supervised Temporal Action Localization (WTAL) attempts to localize the actions in untrimmed videos using only video-level supervision. Most recent works approach WTAL from a localization-by-classification perspective where these methods try to classify each video frame followed by a manually-designed post-p... | ['Mei Chen', 'Mubarak Shah', 'Sandra Sajeev', 'Matthew Hall', 'Ye Yu', 'Gaurav Mittal', 'Mamshad Nayeem Rizve'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['weakly-supervised-action-localization', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.43294966e-01 -8.21950089e-04 -7.66666293e-01 -1.91511959e-01
-9.44489360e-01 -5.49788058e-01 7.50446796e-01 -1.78937525e-01
-3.03550094e-01 4.67235953e-01 5.85857689e-01 6.49056509e-02
1.81119725e-01 -1.55758023e-01 -9.83038187e-01 -7.05052912e-01
-1.79023281e-01 5.18947542e-02 7.29405522e-01 2.16944039... | [8.456406593322754, 0.5996009111404419] |
1087b9fc-55b6-477f-95cf-e086094d3d37 | faircop-facial-image-retrieval-using | 2205.15870 | null | https://arxiv.org/abs/2205.15870v1 | https://arxiv.org/pdf/2205.15870v1.pdf | FaIRCoP: Facial Image Retrieval using Contrastive Personalization | Retrieving facial images from attributes plays a vital role in various systems such as face recognition and suspect identification. Compared to other image retrieval tasks, facial image retrieval is more challenging due to the high subjectivity involved in describing a person's facial features. Existing methods do so b... | ['Rajiv Ratn Shah', 'Ponnurangam Kumaraguru', 'Rishi Raj Jain', 'Shagun Uppal', 'Sarthak Bhagat', 'Drishti Bhasin', 'Aditya Saini', 'Devansh Gupta'] | 2022-05-28 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 4.26063150e-01 -1.64067224e-01 -4.23525661e-01 -8.72182667e-01
-5.89485765e-01 -3.81371886e-01 5.28445780e-01 3.87553200e-02
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-1.63252428e-01 -4.04930383e-01 -3.94557118e-01 -5.77235341e-01
-1.80291943e-02 1.14461601e-01 -1.00678019e-01 -1.42791942... | [13.2274169921875, 0.7681884169578552] |
cb0219f0-4510-4916-b8fa-b8dd391bc716 | document-level-entity-based-extraction-as | 2109.04901 | null | https://arxiv.org/abs/2109.04901v1 | https://arxiv.org/pdf/2109.04901v1.pdf | Document-level Entity-based Extraction as Template Generation | Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level EE systems build extractive models, which struggle to model long-term dependencies ... | ['Nanyun Peng', 'Sam Tang', 'Kung-Hsiang Huang'] | 2021-09-10 | null | https://aclanthology.org/2021.emnlp-main.426 | https://aclanthology.org/2021.emnlp-main.426.pdf | emnlp-2021-11 | ['role-filler-entity-extraction', '4-ary-relation-extraction', 'binary-relation-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.13342798e-01 4.05218303e-01 -4.80250478e-01 -2.71164805e-01
-1.04893196e+00 -8.68025243e-01 6.77262604e-01 3.99436802e-01
-6.77957654e-01 8.70643675e-01 2.54918993e-01 -3.55676115e-01
-5.14362752e-02 -7.53476918e-01 -8.08987260e-01 -2.00636268e-01
-6.78247586e-02 6.78763390e-01 3.12957197e-01 -2.08615944... | [9.363066673278809, 8.75308895111084] |
99ca78db-a4de-4c4a-93b3-f7e31c3e8543 | from-community-to-role-based-graph-embeddings | 1908.08572 | null | https://arxiv.org/abs/1908.08572v2 | https://arxiv.org/pdf/1908.08572v2.pdf | On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications | Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles based on structural similarity and communities based on proximity are fundamentally different but impor... | ['Sungchul Kim', 'Ryan A. Rossi', 'John Boaz Lee', 'Danai Koutra', 'Nesreen K. Ahmed', 'Di Jin'] | 2019-08-22 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 4.45531309e-02 2.96959370e-01 -2.62986988e-01 -1.14737175e-01
2.91614830e-01 -1.03322041e+00 9.15932298e-01 1.05722761e+00
-1.20484725e-01 3.71597618e-01 6.43380404e-01 -4.43821758e-01
-5.48809171e-01 -1.22144794e+00 -1.81829631e-01 -7.64171660e-01
-5.64592898e-01 3.70363116e-01 2.28830129e-01 -3.03212076... | [7.199984073638916, 6.16398811340332] |
7331bcca-b205-4195-9698-f5a795a8a9f9 | blind-image-quality-assessment-via | 2305.09353 | null | https://arxiv.org/abs/2305.09353v1 | https://arxiv.org/pdf/2305.09353v1.pdf | Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token | Image quality assessment is a fundamental problem in the field of image processing, and due to the lack of reference images in most practical scenarios, no-reference image quality assessment (NR-IQA), has gained increasing attention recently. With the development of deep learning technology, many deep neural network-ba... | ['Aljosa Smolic', 'Pan Gao', 'Jinsong Shi'] | 2023-05-16 | null | null | null | null | ['blind-image-quality-assessment', 'image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.51278543e-01 -3.77157390e-01 2.84246773e-01 -2.97027677e-01
-6.61030233e-01 1.03804715e-01 3.75597894e-01 4.99325152e-03
-3.01615030e-01 4.53418225e-01 -5.29714152e-02 8.16036537e-02
-1.59626991e-01 -8.88257623e-01 -6.47077262e-01 -8.07175517e-01
2.62204498e-01 -2.00221673e-01 1.83505490e-01 -1.79038048... | [11.805098533630371, -1.8970340490341187] |
f6d91657-583b-41ef-8221-4d8e8b1fe965 | designing-biological-circuits-from-principles | 2111.04508 | null | https://arxiv.org/abs/2111.04508v1 | https://arxiv.org/pdf/2111.04508v1.pdf | Designing biological circuits: from principles to applications | Genetic circuit design is a well-studied problem in synthetic biology. Ever since the first genetic circuits -- the repressilator and the toggle switch -- were designed and implemented, many advances have been made in this area of research. The current review systematically organizes a number of key works in this domai... | ['Karthik Raman', 'Raghunathan Rengaswamy', 'Debomita Chakraborty'] | 2021-11-05 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 9.19135392e-01 6.41577542e-02 -1.19302854e-01 1.81478545e-01
2.15110645e-01 -1.07695842e+00 4.28468317e-01 2.87999749e-01
-2.03240126e-01 1.00632465e+00 -2.00205162e-01 -4.87756938e-01
-5.44857264e-01 -7.17303634e-01 -6.52252436e-01 -1.11501026e+00
-2.15387106e-01 2.90216625e-01 2.20999107e-01 -7.57350266... | [5.634951114654541, 4.203885555267334] |
21913acf-d982-46d0-aadf-c276af34b35c | pdformer-propagation-delay-aware-dynamic-long | 2301.07945 | null | https://arxiv.org/abs/2301.07945v2 | https://arxiv.org/pdf/2301.07945v2.pdf | PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction | As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN) models have emerged as ... | ['Jingyuan Wang', 'Wayne Xin Zhao', 'Chengkai Han', 'Jiawei Jiang'] | 2023-01-19 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.10560037e-01 -5.17146230e-01 -5.34075558e-01 -3.97728801e-01
4.26733587e-03 -2.85093606e-01 5.32341361e-01 -1.51275918e-01
-8.19526240e-02 4.36887622e-01 1.71125144e-01 -8.95888805e-01
-5.39540112e-01 -1.16686583e+00 -5.85683703e-01 -3.93373162e-01
-8.34234357e-02 2.06794411e-01 7.46655226e-01 -5.21239519... | [6.471134662628174, 2.050156354904175] |
b4b90d5c-316c-4b03-afa9-488bf48b5a68 | wasserstein-kelly-portfolios-a-robust-data | 2302.13979 | null | https://arxiv.org/abs/2302.13979v1 | https://arxiv.org/pdf/2302.13979v1.pdf | Wasserstein-Kelly Portfolios: A Robust Data-Driven Solution to Optimize Portfolio Growth | We introduce a robust variant of the Kelly portfolio optimization model, called the Wasserstein-Kelly portfolio optimization. Our model, taking a Wasserstein distributionally robust optimization (DRO) formulation, addresses the fundamental issue of estimation error in Kelly portfolio optimization by defining a ``ball" ... | ['Jonathan Yu-Meng Li'] | 2023-02-27 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 9.15316399e-03 8.94894302e-02 5.25469072e-02 -2.63108075e-01
-1.26239192e+00 -6.46336734e-01 1.04726955e-01 -5.58369868e-02
-3.69426101e-01 1.01555586e+00 -1.01661764e-01 -7.36198008e-01
-1.03517783e+00 -7.16352642e-01 -7.94363976e-01 -9.26566243e-01
-1.87614664e-01 2.96575159e-01 -1.88757345e-01 -5.85705936... | [5.113101959228516, 3.8997154235839844] |
68498880-8440-424a-aeb4-4effa533c64b | zero-shot-text-to-speech-synthesis | 2304.11976 | null | https://arxiv.org/abs/2304.11976v1 | https://arxiv.org/pdf/2304.11976v1.pdf | Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model | This paper proposes a zero-shot text-to-speech (TTS) conditioned by a self-supervised speech-representation model acquired through self-supervised learning (SSL). Conventional methods with embedding vectors from x-vector or global style tokens still have a gap in reproducing the speaker characteristics of unseen speake... | ['Yusuke Ijima', 'Takafumi Moriya', 'Hiroki Kanagawa', 'Takanori Ashihara', 'Kenichi Fujita'] | 2023-04-24 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 1.58304766e-01 3.45815881e-03 -1.83764711e-01 -4.22117084e-01
-8.05094659e-01 -1.92281738e-01 6.69532478e-01 -2.20562547e-01
2.25314070e-02 6.30964100e-01 6.37244344e-01 2.03637648e-02
1.39099583e-01 -5.19709826e-01 -3.07044387e-01 -9.59835470e-01
-9.70624238e-02 1.31984517e-01 -2.24750668e-01 -2.54389137... | [14.911758422851562, 6.5262651443481445] |
458bc163-b030-4b3d-9dd5-a79dcd1d90e9 | emospeech-guiding-fastspeech2-towards | 2307.00024 | null | https://arxiv.org/abs/2307.00024v1 | https://arxiv.org/pdf/2307.00024v1.pdf | EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech | State-of-the-art speech synthesis models try to get as close as possible to the human voice. Hence, modelling emotions is an essential part of Text-To-Speech (TTS) research. In our work, we selected FastSpeech2 as the starting point and proposed a series of modifications for synthesizing emotional speech. According to ... | ['Vitaly Shutov', 'Daria Diatlova'] | 2023-06-28 | null | null | null | null | ['emotion-recognition', 'speech-synthesis'] | ['computer-vision', 'speech'] | [-1.73222750e-01 5.21202147e-01 2.52654254e-01 -2.99215734e-01
-3.53581309e-01 -4.03358728e-01 5.55687606e-01 -1.18160516e-01
-5.28172702e-02 7.26267099e-01 4.57475364e-01 -1.43103644e-01
3.34396452e-01 -5.17077446e-01 -1.97216719e-01 -3.72124851e-01
2.71324784e-01 1.88930929e-01 1.02256671e-01 -6.31423175... | [14.754486083984375, 6.5207414627075195] |
f88b9956-af96-4a54-b9f3-20b96d0a7afb | proselflc-progressive-self-label-correction | 2005.03788 | null | https://arxiv.org/abs/2005.03788v6 | https://arxiv.org/pdf/2005.03788v6.pdf | ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks | To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction (LC). Two key issues are discovered: (1) Self LC is the most appealing as it exploits its own knowledge and requires no extra models. Howev... | ['David A. Clifton', 'Yang Hua', 'Xinshao Wang', 'Neil M. Robertson', 'Elyor Kodirov'] | 2020-05-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_ProSelfLC_Progressive_Self_Label_Correction_for_Training_Robust_Deep_Neural_CVPR_2021_paper.pdf | cvpr-2021-1 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 4.61890459e-01 7.58395731e-01 -2.97494233e-01 -5.75927258e-01
-5.18222272e-01 -5.42311847e-01 4.22373652e-01 3.18803310e-01
-8.16339076e-01 9.96444762e-01 9.24352407e-02 -2.09568202e-01
-4.39646691e-01 -4.64305580e-01 -7.88607419e-01 -9.42277968e-01
2.34379664e-01 1.77724257e-01 1.30874008e-01 5.49781695... | [9.254318237304688, 3.84490966796875] |
76b73b08-81a6-43a7-a978-fcb96acd31a4 | real-time-incremental-speech-to-speech | null | null | https://aclanthology.org/N12-1048 | https://aclanthology.org/N12-1048.pdf | Real-time Incremental Speech-to-Speech Translation of Dialogs | null | ['Srinivas Bangalore', 'Vivek Kumar Rangarajan Sridhar', 'Ladan Golipour', 'Prakash Kolan', 'Aura Jimenez'] | 2012-06-01 | null | null | null | naacl-2012-6 | ['speech-to-speech-translation'] | ['speech'] | [-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... | [-7.299546241760254, 3.723621129989624] |
bd9cb26e-68aa-41d5-a145-e6bd8e11dfbb | rethinking-audio-visual-synchronization-for | 2206.10421 | null | https://arxiv.org/abs/2206.10421v2 | https://arxiv.org/pdf/2206.10421v2.pdf | Rethinking Audio-visual Synchronization for Active Speaker Detection | Active speaker detection (ASD) systems are important modules for analyzing multi-talker conversations. They aim to detect which speakers or none are talking in a visual scene at any given time. Existing research on ASD does not agree on the definition of active speakers. We clarify the definition in this work and requi... | ['ChangShui Zhang', 'Zhiyao Duan', 'You Zhang', 'Abudukelimu Wuerkaixi'] | 2022-06-21 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 1.20944746e-01 1.65881217e-01 -2.45929137e-01 -3.22670162e-01
-7.21567869e-01 -6.00615621e-01 5.94032824e-01 -9.43167210e-02
3.13570462e-02 9.48930308e-02 6.47759378e-01 2.75635272e-01
5.94182201e-02 -5.90172447e-02 -3.04377943e-01 -8.73081326e-01
-1.16552375e-01 1.40546709e-01 1.66778788e-01 2.63256170... | [14.45088005065918, 5.124743461608887] |
3e3b1397-6904-4099-966a-3ac78f414a54 | direct-posenet-absolute-pose-regression-with | 2104.04073 | null | https://arxiv.org/abs/2104.04073v2 | https://arxiv.org/pdf/2104.04073v2.pdf | Direct-PoseNet: Absolute Pose Regression with Photometric Consistency | We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time. Our contribution is twofold: i) we design a direct matching module that supplies a photometric supervi... | ['Victor Prisacariu', 'ZiRui Wang', 'Shuai Chen'] | 2021-04-08 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 2.43764564e-01 2.14075178e-01 -1.44805968e-01 -5.32794654e-01
-7.67332673e-01 -5.03475904e-01 8.16920578e-01 -6.08002663e-01
-3.10465872e-01 5.63218176e-01 1.33973556e-02 -1.21892229e-01
1.71400130e-01 -6.62122190e-01 -1.17725778e+00 -3.82173896e-01
3.37322593e-01 5.52999020e-01 3.11647385e-01 -5.29940367... | [8.128260612487793, -2.3690221309661865] |
d4b7647d-3da3-4a9f-aca9-c476ced6f96e | pese-event-structure-extraction-using-pointer | 2211.12157 | null | https://arxiv.org/abs/2211.12157v1 | https://arxiv.org/pdf/2211.12157v1.pdf | PESE: Event Structure Extraction using Pointer Network based Encoder-Decoder Architecture | The task of event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format. Most previous works try to solve the problem by separately identifying multiple substructures and aggregating them to get the complete event structure. The problem wi... | ['Sudeshan Sarkar', 'Alapan Kuila'] | 2022-11-22 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.95989716e-01 2.12867916e-01 -3.03453386e-01 -5.39085329e-01
-1.00665843e+00 -6.10136271e-01 5.47406495e-01 8.37713480e-01
-4.84405607e-01 8.87522399e-01 8.39434564e-01 -8.34117234e-02
-2.24402070e-01 -8.69653881e-01 -7.76452184e-01 -7.50253052e-02
-4.65338558e-01 5.60944855e-01 4.62724805e-01 1.46856338... | [9.069357872009277, 9.198006629943848] |
ea70b1c4-97a3-498f-8522-c62e2bcbf0f3 | evaluating-explainable-methods-for-predictive | 2012.04218 | null | https://arxiv.org/abs/2012.04218v1 | https://arxiv.org/pdf/2012.04218v1.pdf | Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach | Predictive process analytics focuses on predicting the future states of running instances of a business process. While advanced machine learning techniques have been used to increase accuracy of predictions, the resulting predictive models lack transparency. Current explainable machine learning methods, such as LIME an... | ['Renuka Sindhgatta', 'Catarina Moreira', 'Chun Ouyang', 'Mythreyi Velmurugan'] | 2020-12-08 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.18034983e-01 8.55083823e-01 -3.19206089e-01 -3.45252961e-01
-1.23401247e-01 -3.88729066e-01 9.52721596e-01 5.96717894e-01
4.03918386e-01 3.40850651e-01 5.25063157e-01 -8.46241236e-01
-6.29039764e-01 -8.27181041e-01 -3.38050812e-01 -5.35907894e-02
1.58149004e-02 7.54555404e-01 -2.38352343e-01 2.49519840... | [8.605091094970703, 5.896371364593506] |
77e89609-048d-42ed-8981-c4d79acf2c3d | crop-mapping-from-image-time-series-deep | 2102.08820 | null | https://arxiv.org/abs/2102.08820v2 | https://arxiv.org/pdf/2102.08820v2.pdf | Crop mapping from image time series: deep learning with multi-scale label hierarchies | The aim of this paper is to map agricultural crops by classifying satellite image time series. Domain experts in agriculture work with crop type labels that are organised in a hierarchical tree structure, where coarse classes (like orchards) are subdivided into finer ones (like apples, pears, vines, etc.). We develop a... | ['Jan Dirk Wegner', 'Konrad Schindler', 'Constantin Streit', 'Frank Liebisch', 'Gregor Perich', "Stefano D'Aronco", 'Mehmet Ozgur Turkoglu'] | 2021-02-17 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 2.34360173e-01 2.86117136e-01 -4.16907638e-01 -5.59811831e-01
-3.92287582e-01 -9.16191757e-01 2.18293309e-01 6.68888032e-01
-5.37259839e-02 6.14842355e-01 -1.31243333e-01 -5.30582726e-01
-1.86750576e-01 -1.43259263e+00 -8.67165327e-01 -9.21721399e-01
-4.86903012e-01 2.72300899e-01 7.12961331e-02 -3.06944042... | [9.400534629821777, -1.5630769729614258] |
53b49537-1b2e-41d3-a658-e19fced09358 | learning-visible-connectivity-dynamics-for | 2105.10389 | null | https://arxiv.org/abs/2105.10389v4 | https://arxiv.org/pdf/2105.10389v4.pdf | Learning Visible Connectivity Dynamics for Cloth Smoothing | Robotic manipulation of cloth remains challenging for robotics due to the complex dynamics of the cloth, lack of a low-dimensional state representation, and self-occlusions. In contrast to previous model-based approaches that learn a pixel-based dynamics model or a compressed latent vector dynamics, we propose to learn... | ['David Held', 'Zixuan Huang', 'YuFei Wang', 'Xingyu Lin'] | 2021-05-21 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 6.63269758e-02 2.10662648e-01 -4.01955880e-02 2.03782186e-01
-3.45641583e-01 -5.60695529e-01 5.16623914e-01 -9.02064983e-03
8.59379768e-02 6.18467271e-01 -1.48607016e-01 4.89337407e-02
-1.35952652e-01 -8.28088284e-01 -1.26636839e+00 -7.73316026e-01
-3.88406128e-01 8.59590590e-01 4.00108159e-01 -4.35754150... | [5.178103923797607, 0.05848729610443115] |
ccfd0582-a11a-4912-9166-2023bb353ad9 | biformer-learning-bilateral-motion-estimation | 2304.02225 | null | https://arxiv.org/abs/2304.02225v1 | https://arxiv.org/pdf/2304.02225v1.pdf | BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation | A novel 4K video frame interpolator based on bilateral transformer (BiFormer) is proposed in this paper, which performs three steps: global motion estimation, local motion refinement, and frame synthesis. First, in global motion estimation, we predict symmetric bilateral motion fields at a coarse scale. To this end, we... | ['Chang-Su Kim', 'Jintae Kim', 'Junheum Park'] | 2023-04-05 | biformer-learning-bilateral-motion-estimation-1 | https://openaccess.thecvf.com/content/CVPR2023/html/Park_BiFormer_Learning_Bilateral_Motion_Estimation_via_Bilateral_Transformer_for_4K_CVPR_2023_paper.html | https://arxiv.org/pdf/2304.02225.pdf | cvpr-2023-6 | ['video-frame-interpolation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [-2.27202117e-01 -4.53034282e-01 -1.71010479e-01 -8.19247141e-02
-6.36562467e-01 -2.43946567e-01 5.24889529e-01 -3.48113656e-01
-1.47935823e-01 8.55133891e-01 4.95725691e-01 -4.92125489e-02
3.41196358e-01 -5.52163661e-01 -7.55657852e-01 -8.07265401e-01
3.33544075e-01 -6.47018999e-02 5.72203636e-01 6.24740086... | [10.75351333618164, -1.4666666984558105] |
d84315d1-7f00-41ea-b87b-82dee3ae6126 | a-feature-based-approach-for-video | 1605.08470 | null | http://arxiv.org/abs/1605.08470v1 | http://arxiv.org/pdf/1605.08470v1.pdf | A Feature based Approach for Video Compression | It is a high cost problem for panoramic image stitching via image matching
algorithm and not practical for real-time performance. In this paper, we take
full advantage ofHarris corner invariant characterization method light
intensity parallel meaning, translation and rotation, and made a realtime
panoramic image stitch... | ['Rajer Sindhu'] | 2016-05-26 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.95963836e-01 -8.99294734e-01 -3.57173324e-01 -5.05496468e-03
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-3.04491609e-01 -9.48677659e-01 -3.48198175e-01 -5.90036213e-01
3.92321825e-01 1.99501485e-01 2.95074642e-01 -1.34820238... | [9.054574012756348, -2.222778081893921] |
ed190b23-ff0c-43ae-a757-34e16a992042 | from-patches-to-pictures-paq-2-piq-mapping | 1912.10088 | null | https://arxiv.org/abs/1912.10088v1 | https://arxiv.org/pdf/1912.10088v1.pdf | From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality | Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily. Unfortunately, popular NR prediction models perform poorly on real-world distorted pictures. To advance progress on ... | ['Praful Gupta', 'Dhruv Mahajan', 'Deepti Ghadiyaram', 'Zhenqiang Ying', 'Haoran Niu', 'Alan Bovik'] | 2019-12-20 | from-patches-to-pictures-paq-2-piq-mapping-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Ying_From_Patches_to_Pictures_PaQ-2-PiQ_Mapping_the_Perceptual_Space_of_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ying_From_Patches_to_Pictures_PaQ-2-PiQ_Mapping_the_Perceptual_Space_of_CVPR_2020_paper.pdf | cvpr-2020-6 | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.12948574e-01 -2.62886137e-01 2.03218028e-01 -7.63448536e-01
-1.23633349e+00 -2.84336627e-01 2.39165470e-01 1.05713025e-01
-1.41312629e-01 4.89350885e-01 7.69734800e-01 -1.03329860e-01
7.32419044e-02 -5.89251280e-01 -6.60770357e-01 -3.74800056e-01
-2.48882905e-01 -4.72039282e-02 4.59584147e-01 -9.61332545... | [11.844594955444336, -1.7765096426010132] |
b66bcdab-15d2-4dff-b878-efe72fab06af | snap-efficient-extraction-of-private | 2208.12348 | null | https://arxiv.org/abs/2208.12348v2 | https://arxiv.org/pdf/2208.12348v2.pdf | SNAP: Efficient Extraction of Private Properties with Poisoning | Property inference attacks allow an adversary to extract global properties of the training dataset from a machine learning model. Such attacks have privacy implications for data owners sharing their datasets to train machine learning models. Several existing approaches for property inference attacks against deep neural... | ['Jonathan Ullman', 'Florian Tramèr', 'Matthew Jagielski', 'Alina Oprea', 'John Abascal', 'Harsh Chaudhari'] | 2022-08-25 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 9.95284542e-02 5.86875565e-02 -5.31161785e-01 -1.50423154e-01
-8.36236298e-01 -1.13135946e+00 2.07182422e-01 3.74439567e-01
-6.76890492e-01 9.16577220e-01 -2.86748201e-01 -8.10107112e-01
-2.35293925e-01 -1.28015316e+00 -1.28945029e+00 -6.44520700e-01
-3.59256357e-01 3.26589555e-01 3.24188173e-01 3.53241414... | [5.916811943054199, 7.255186080932617] |
a14cf7df-6c23-4ba9-b7c1-a52338c55002 | service-composition-in-the-chatgpt-era | 2305.15788 | null | https://arxiv.org/abs/2305.15788v1 | https://arxiv.org/pdf/2305.15788v1.pdf | Service Composition in the ChatGPT Era | The paper speculates about how ChatGPT-like systems can support the field of automated service composition and identifies new research areas to explore in order to take advantage of such tools in the field of service-oriented composition. | ['Ilche Georgievski', 'Marco Aiello'] | 2023-05-25 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 7.95280375e-03 5.49709499e-01 3.54652628e-02 -8.78624320e-01
-2.56785393e-01 -6.13273859e-01 8.20349336e-01 -4.18589920e-01
3.08837324e-01 1.11212522e-01 6.11559570e-01 -9.20636952e-01
-1.75937235e-01 -7.11118758e-01 2.09018916e-01 -5.54844677e-01
-4.09336574e-02 9.10891712e-01 8.64778101e-01 -7.98639059... | [8.634676933288574, 6.951788425445557] |
a68ae607-8c04-41ef-ad2f-207d5cdf768f | tips-text-induced-pose-synthesis | 2207.11718 | null | https://arxiv.org/abs/2207.11718v1 | https://arxiv.org/pdf/2207.11718v1.pdf | TIPS: Text-Induced Pose Synthesis | In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose dir... | ['Michael Blumenstein', 'Umapada Pal', 'Saumik Bhattacharya', 'Subhankar Ghosh', 'Prasun Roy'] | 2022-07-24 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 5.05217075e-01 2.27360994e-01 3.18000048e-01 -5.63225269e-01
-8.69889677e-01 -4.65137720e-01 9.34221268e-01 -1.87675301e-02
-5.48340142e-01 6.71462536e-01 2.81994849e-01 3.89210731e-01
-1.88761726e-02 -5.87093711e-01 -7.11911201e-01 -4.47851002e-01
3.70380342e-01 1.26330364e+00 4.44517612e-01 -1.58764631... | [7.120259761810303, -0.9948102235794067] |
03172ddd-771c-403c-aa05-cc041b0df89d | machine-translation-aided-bilingual-data-to | null | null | https://aclanthology.org/2020.webnlg-1.13 | https://aclanthology.org/2020.webnlg-1.13.pdf | Machine Translation Aided Bilingual Data-to-Text Generation and Semantic Parsing | We present a system for bilingual Data-ToText Generation and Semantic Parsing. We use a text-to-text generator to learn a single model that works for both languages on each of the tasks. The model is aided by machine translation during both pre-training and fine-tuning. We evaluate the system on WebNLG 2020 data 1 , wh... | ['Rami Al-Rfou', 'Siamak Shakeri', 'Heming Ge', 'Mihir Kale', 'Oshin Agarwal'] | null | null | null | null | acl-webnlg-inlg-2020-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [-2.78902292e-01 1.00513804e+00 -5.86892068e-01 -8.37144613e-01
-1.24656892e+00 -7.82371759e-01 9.31277215e-01 -7.01890439e-02
-4.82490540e-01 1.65444231e+00 6.75556719e-01 -6.57072604e-01
4.47323859e-01 -9.77056265e-01 -1.04455948e+00 2.95891196e-01
1.93152159e-01 1.59918582e+00 1.52176321e-01 -9.82653439... | [10.870941162109375, 9.569014549255371] |
5774911b-650c-49e0-96d8-65ff19890e5e | latentslam-unsupervised-multi-sensor | 2105.03265 | null | https://arxiv.org/abs/2105.03265v1 | https://arxiv.org/pdf/2105.03265v1.pdf | LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping | Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments. One drawback however is the sensitivity to perceptual aliasing due to the template matching of low-dimensional sensory... | ['Jan Steckel', 'Bart Dhoedt', 'Tim Verbelen', 'Wouter Jansen', 'Ozan Çatal'] | 2021-05-07 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 3.79457742e-01 -2.34874383e-01 2.87596196e-01 -3.71447861e-01
-4.31129187e-01 -7.51924336e-01 6.19830310e-01 2.81353027e-01
-7.59194970e-01 8.49797845e-01 -1.68042198e-01 8.61289427e-02
-5.60475409e-01 -7.94469059e-01 -6.82830036e-01 -7.01397538e-01
-3.14818859e-01 6.75114572e-01 4.57541734e-01 -2.75997758... | [7.317629814147949, -2.003607988357544] |
7161e5f6-5d35-47a2-b238-db2fa3c377ca | automl-systems-for-medical-imaging | 2306.04750 | null | https://arxiv.org/abs/2306.04750v2 | https://arxiv.org/pdf/2306.04750v2.pdf | AutoML Systems For Medical Imaging | The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recog... | ['Dr. Md Azim Ullah', 'Mofazzal Hossain', 'Sajedul Talukder', 'Md Jahangir Alam', 'Ismail Hossain', 'MD Abdullah Al Nasim', 'Angona Biswas', 'Tasmia Tahmida Jidney'] | 2023-06-07 | null | null | null | null | ['automl', 'architecture-search'] | ['methodology', 'methodology'] | [ 4.62068588e-01 1.95805013e-01 -3.03952008e-01 -4.45016116e-01
-5.29949903e-01 -3.62981170e-01 1.75255522e-01 1.03665449e-01
-4.07779008e-01 3.88219982e-01 -2.91265965e-01 -7.56559670e-01
-3.72817189e-01 -5.19924998e-01 -1.32081389e-01 -5.24944663e-01
-1.10427290e-01 8.10665071e-01 -3.14023316e-01 3.20650429... | [14.903812408447266, -2.4608302116394043] |
956ea3a7-4b5b-4787-8f04-24725c4a893b | lmbao-a-landmark-map-for-bundle-adjustment | 2209.08810 | null | https://arxiv.org/abs/2209.08810v1 | https://arxiv.org/pdf/2209.08810v1.pdf | LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM | LiDAR odometry is one of the essential parts of LiDAR simultaneous localization and mapping (SLAM). However, existing LiDAR odometry tends to match a new scan simply iteratively with previous fixed-pose scans, gradually accumulating errors. Furthermore, as an effective joint optimization mechanism, bundle adjustment (B... | ['Zhifei Duan', 'Xiaojun Tan', 'Nanjie Chen', 'Lu Jie', 'Jinping Wang', 'Letian Zhang'] | 2022-09-19 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.09575579e-01 -3.61416727e-01 -2.82756597e-01 -5.72411835e-01
-5.56749701e-01 -1.36918649e-01 2.59677052e-01 3.92233998e-01
-7.72627771e-01 8.80663991e-01 -3.75906616e-01 -2.77406424e-01
-2.96762049e-01 -1.10927594e+00 -6.48223698e-01 -5.79800546e-01
8.56342837e-02 8.71080577e-01 5.96037924e-01 -2.80656278... | [7.393918514251709, -2.2320444583892822] |
21576a6b-36e0-4b58-a7c1-466d00afcdc1 | constructing-code-mixed-universal-dependency | 2305.12258 | null | https://arxiv.org/abs/2305.12258v3 | https://arxiv.org/pdf/2305.12258v3.pdf | Constructing Code-mixed Universal Dependency Forest for Unbiased Cross-lingual Relation Extraction | Latest efforts on cross-lingual relation extraction (XRE) aggressively leverage the language-consistent structural features from the universal dependency (UD) resource, while they may largely suffer from biased transfer (e.g., either target-biased or source-biased) due to the inevitable linguistic disparity between lan... | ['Tat-Seng Chua', 'Min Zhang', 'Meishan Zhang', 'Hao Fei'] | 2023-05-20 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [-1.03441626e-01 -7.79392645e-02 -9.22560155e-01 -5.38339794e-01
-1.31210327e+00 -5.71345150e-01 4.90021348e-01 -2.90485501e-01
-5.84688373e-02 9.22242820e-01 3.85267228e-01 -9.15300548e-01
3.47719014e-01 -7.44250536e-01 -8.81167233e-01 -4.08100098e-01
1.64426118e-01 9.07262322e-03 9.20134410e-02 -3.59806418... | [10.763157844543457, 9.67033576965332] |
50e1f93c-1d8f-46e4-bd4a-7f27e5df7401 | investigating-chain-of-thought-with-chatgpt | 2304.03087 | null | https://arxiv.org/abs/2304.03087v1 | https://arxiv.org/pdf/2304.03087v1.pdf | Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media | Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, early deep neural networks, and pre-trained fine-tuning models. However, with the evolution of very large pre-trained language models (VLPLMs... | ['Liwen Jing', 'Yangyang Li', 'Hu Huang', 'Daijun Ding', 'Xianghua Fu', 'BoWen Zhang'] | 2023-04-06 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-7.99869001e-02 2.59311944e-01 -6.05896592e-01 -5.95995784e-01
-5.80199718e-01 -4.63372231e-01 9.02100205e-01 5.18747747e-01
-7.56429970e-01 7.17944860e-01 5.26026785e-01 -7.08042026e-01
1.88102901e-01 -7.99251139e-01 -3.62259626e-01 -3.24349433e-01
-2.29650587e-02 7.15422392e-01 3.81958991e-01 -6.89149261... | [8.942431449890137, 10.055582046508789] |
3dab00ae-5746-4c2f-897a-ac37298a038a | c2f-tcn-a-framework-for-semi-and-fully | 2212.11078 | null | https://arxiv.org/abs/2212.11078v1 | https://arxiv.org/pdf/2212.11078v1.pdf | C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation | Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation, we propose an encoder-decoder-style architecture named C2F-TCN featuring a "coarse-to-fine" ensemble of decoder outputs. The C2F-TCN framew... | ['Angela Yao', 'Rahul Rahaman', 'Dipika Singhania'] | 2022-12-20 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 9.28095281e-01 3.32103103e-01 -6.49810374e-01 -5.12669444e-01
-1.10449743e+00 -5.67750692e-01 7.56370783e-01 -3.50196630e-01
-3.51234227e-01 5.30102074e-01 6.55646503e-01 1.49455234e-01
-1.35170831e-03 -3.01769018e-01 -8.32930326e-01 -7.55296588e-01
-7.18969554e-02 4.89538521e-01 5.60253799e-01 3.60311344... | [8.467888832092285, 0.5659775733947754] |
e9392c0a-3939-43f0-8242-c157e8565c60 | cheater-s-bowl-human-vs-computer-search | 2212.03296 | null | https://arxiv.org/abs/2212.03296v1 | https://arxiv.org/pdf/2212.03296v1.pdf | Cheater's Bowl: Human vs. Computer Search Strategies for Open-Domain Question Answering | For humans and computers, the first step in answering an open-domain question is retrieving a set of relevant documents from a large corpus. However, the strategies that computers use fundamentally differ from those of humans. To better understand these differences, we design a gamified interface for data collection --... | ['Jordan Boyd-Graber', 'Andrew Mao', 'Wanrong He'] | 2022-11-15 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-1.17992200e-01 1.76528454e-01 -8.80479738e-02 -3.14811707e-01
-1.30104685e+00 -9.33402538e-01 4.96801406e-01 1.38145894e-01
-5.81442058e-01 5.83834589e-01 3.30728650e-01 -8.44835579e-01
-5.27963459e-01 -8.03597391e-01 -1.23916134e-01 2.89511889e-01
3.82091880e-01 1.28373647e+00 7.33919919e-01 -9.63310540... | [11.572220802307129, 7.9421539306640625] |
0a5d8b6e-f7f8-43e6-b7f7-2c346c97d5f9 | ensembling-transformers-for-cross-domain | 2212.05696 | null | https://arxiv.org/abs/2212.05696v1 | https://arxiv.org/pdf/2212.05696v1.pdf | Ensembling Transformers for Cross-domain Automatic Term Extraction | Automatic term extraction plays an essential role in domain language understanding and several natural language processing downstream tasks. In this paper, we propose a comparative study on the predictive power of Transformers-based pretrained language models toward term extraction in a multi-language cross-domain sett... | ['Senja Pollak', 'Antoine Doucet', 'Andraz Pelicon', 'Matej Martinc', 'Hanh Thi Hong Tran'] | 2022-12-12 | null | null | null | null | ['term-extraction'] | ['natural-language-processing'] | [-9.48735625e-02 -7.92524889e-02 -3.49951774e-01 -4.91257161e-02
-9.67225194e-01 -8.32568407e-01 1.02737427e+00 5.84536612e-01
-8.55936110e-01 8.66440296e-01 2.64398634e-01 -6.87022328e-01
-2.25761190e-01 -4.76586133e-01 -3.76991004e-01 -3.56275916e-01
-2.58653492e-01 5.28260410e-01 -1.85178414e-01 -4.00275320... | [10.224411964416504, 9.551294326782227] |
3aea6d4a-d8ad-4dd9-ac26-4ab0e34182d7 | face-body-voice-video-person-clustering-with | 2105.09939 | null | https://arxiv.org/abs/2105.09939v1 | https://arxiv.org/pdf/2105.09939v1.pdf | Face, Body, Voice: Video Person-Clustering with Multiple Modalities | The objective of this work is person-clustering in videos -- grouping characters according to their identity. Previous methods focus on the narrower task of face-clustering, and for the most part ignore other cues such as the person's voice, their overall appearance (hair, clothes, posture), and the editing structure o... | ['Andrew Zisserman', 'Vicky Kalogeiton', 'Andrew Brown'] | 2021-05-20 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 7.20531270e-02 -1.38734967e-01 -1.51004612e-01 -4.66352403e-01
-7.18267918e-01 -7.33631253e-01 7.17828333e-01 -8.64081383e-02
9.63292941e-02 7.95933753e-02 8.23181272e-01 5.84011972e-01
-1.00531720e-01 -2.91254014e-01 -3.04873019e-01 -6.37271583e-01
3.76054794e-02 6.30849123e-01 -1.49849221e-01 -1.93777997... | [13.681641578674316, 1.0723369121551514] |
e0732033-b728-4821-af08-241a779a3b18 | case4sr-using-category-sequence-graph-to | null | null | https://www.sciencedirect.com/science/article/pii/S0950705120306870 | https://reader.elsevier.com/reader/sd/pii/S0950705120306870?token=FE79CE03D3E741C761C7E625D69F5A5A88DD0757634725107E20AA8E6CB7ADEA544CD7F8FC3476CE81EE9351078F0277&originRegion=us-east-1&originCreation=20230320035059 | CaSe4SR: Using category sequence graph to augment session-based recommendation | Session-based recommendation aims to predict next item based on users’ anonymous behavior sequence within a short time. Recent studies focus on modeling sequential dependencies or complex relations among items in a session via recurrent/convolutional/graph neural networks. However, the following problems still remain: ... | ['Tao Lian', 'Li Wang', 'Lin Liu'] | 2020-11-06 | null | null | null | knowledge-based-systems-2020-11 | ['session-based-recommendations'] | ['miscellaneous'] | [ 8.13304633e-03 -3.32990110e-01 -6.44252956e-01 -5.14629543e-01
3.00648585e-02 -6.22628868e-01 2.28883594e-01 2.25940749e-01
7.97868799e-03 4.87892777e-01 6.10209882e-01 -1.98783889e-01
-4.11253631e-01 -7.94597328e-01 -5.41153073e-01 -3.53318483e-01
-2.80004114e-01 -2.85694227e-02 -1.77133903e-02 -4.54770803... | [10.198661804199219, 5.613345623016357] |
22326299-1553-45c2-abef-2b2d3f2935c7 | gril-a-2-parameter-persistence-based | 2304.04970 | null | https://arxiv.org/abs/2304.04970v2 | https://arxiv.org/pdf/2304.04970v2.pdf | GRIL: A $2$-parameter Persistence Based Vectorization for Machine Learning | $1$-parameter persistent homology, a cornerstone in Topological Data Analysis (TDA), studies the evolution of topological features such as connected components and cycles hidden in data. It has been applied to enhance the representation power of deep learning models, such as Graph Neural Networks (GNNs). To enrich the ... | ['Tamal K. Dey', 'Shreyas N. Samaga', 'Soham Mukherjee', 'Cheng Xin'] | 2023-04-11 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-5.46107590e-02 7.30876550e-02 -2.11061910e-01 -2.04591639e-02
-1.69037551e-01 -7.54914343e-01 6.77022338e-01 4.24317569e-01
-7.95690119e-02 6.07909739e-01 1.39433980e-01 -5.07927239e-01
-5.79497576e-01 -1.22294593e+00 -1.03696489e+00 -6.28329337e-01
-9.88915265e-01 1.28581926e-01 2.92976022e-01 -5.98006725... | [6.932113170623779, 6.085634231567383] |
f791710b-9f7d-456d-955d-60a99cffcace | tsetlin-machine-embedding-representing-words | 2301.00709 | null | https://arxiv.org/abs/2301.00709v1 | https://arxiv.org/pdf/2301.00709v1.pdf | Tsetlin Machine Embedding: Representing Words Using Logical Expressions | Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that cap... | ['Jivitesh Sharma', 'Rohan Yadav', 'Lei Jiao', 'Ole-Christoffer Granmo', 'Bimal Bhattarai'] | 2023-01-02 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [-8.58069137e-02 8.64792019e-02 -4.92515385e-01 -7.80211985e-01
-1.48277804e-01 -5.73455989e-01 7.49322772e-01 8.21146309e-01
-5.08976936e-01 4.48663861e-01 5.95979989e-01 -5.72362125e-01
5.01766130e-02 -1.05444038e+00 -7.86727548e-01 -4.17548299e-01
5.94681054e-02 4.91264641e-01 -4.78939354e-01 -4.51099992... | [10.266483306884766, 8.630134582519531] |
70c5038f-1ede-436b-96f5-e3a4157315c4 | word-segmentation-on-discovered-phone-units | 2202.11929 | null | https://arxiv.org/abs/2202.11929v2 | https://arxiv.org/pdf/2202.11929v2.pdf | Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring | Recent work on unsupervised speech segmentation has used self-supervised models with phone and word segmentation modules that are trained jointly. This paper instead revisits an older approach to word segmentation: bottom-up phone-like unit discovery is performed first, and symbolic word segmentation is then performed ... | ['Herman Kamper'] | 2022-02-24 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 6.39882207e-01 6.36642754e-01 -5.25319278e-01 -4.74837691e-01
-7.66547024e-01 -5.70107281e-01 2.74968445e-01 1.28141657e-01
-8.11091006e-01 4.93472844e-01 1.54948696e-01 -6.54528022e-01
4.19061393e-01 -4.81496841e-01 -6.01480901e-01 -6.37998879e-01
2.13265225e-01 7.07665503e-01 7.11575687e-01 8.46965704... | [14.500436782836914, 6.673360824584961] |
4b2188c1-afcc-4e4a-9e8e-824324064e10 | refinedetlite-a-lightweight-one-stage-object | 1911.08855 | null | https://arxiv.org/abs/1911.08855v2 | https://arxiv.org/pdf/1911.08855v2.pdf | RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices | Previous state-of-the-art real-time object detectors have been reported on GPUs which are extremely expensive for processing massive data and in resource-restricted scenarios. Therefore, high efficiency object detectors on CPU-only devices are urgently-needed in industry. The floating-point operations (FLOPs) of networ... | ['Mengyuan Liu', 'Chen Chen', 'Qi Ju', 'Xiandong Meng', 'Wanpeng Xiao'] | 2019-11-20 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 1.19679131e-01 -3.61433983e-01 -1.79163426e-01 -1.92262009e-01
-3.23852569e-01 -2.50705071e-02 3.25281352e-01 3.19104362e-03
-9.27819848e-01 3.36831182e-01 -3.84647012e-01 -3.21005911e-01
1.10313492e-02 -9.12956715e-01 -5.94558716e-01 -8.74585688e-01
1.89560235e-01 2.03610107e-01 1.02561450e+00 -9.53545347... | [8.706923484802246, -0.35972973704338074] |
3cf1defe-e845-4aff-9d49-de65cd9a7353 | gm-mlic-graph-matching-based-multi-label | 2104.14762 | null | https://arxiv.org/abs/2104.14762v2 | https://arxiv.org/pdf/2104.14762v2.pdf | GM-MLIC: Graph Matching based Multi-Label Image Classification | Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assignments between images and their labels. In this paper, we treat each image as a bag of in... | ['Zizhang Wu', 'Gengyu Lyu', 'Yi Jin', 'Songhe Feng', 'He Liu', 'Yanan Wu'] | 2021-04-30 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.60013020e-01 1.26779839e-01 -2.94607580e-01 -6.61201298e-01
-6.38728142e-01 -2.34995633e-01 2.49682799e-01 5.51463664e-01
-8.41716677e-03 3.47274274e-01 -1.98704198e-01 1.17231488e-01
-3.62971455e-01 -9.82230186e-01 -5.45546651e-01 -6.98711276e-01
1.42123654e-01 2.42224634e-01 9.06924680e-02 5.02940953... | [9.791433334350586, 4.047863960266113] |
e6427afa-89a8-4dde-b784-15a1fee9c1e4 | graph-neural-networks-for-contextual-asr-with | 2305.18824 | null | https://arxiv.org/abs/2305.18824v1 | https://arxiv.org/pdf/2305.18824v1.pdf | Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator | The incorporation of biasing words obtained through contextual knowledge is of paramount importance in automatic speech recognition (ASR) applications. This paper proposes an innovative method for achieving end-to-end contextual ASR using graph neural network (GNN) encodings based on the tree-constrained pointer genera... | ['Phil Woodland', 'Chao Zhang', 'Guangzhi Sun'] | 2023-05-30 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 6.64726853e-01 4.26961571e-01 6.28448948e-02 -1.37218639e-01
-7.04450548e-01 -4.99550313e-01 7.44253337e-01 2.84371108e-01
-4.48547989e-01 4.75347966e-01 6.01693928e-01 -1.10964656e+00
1.21161424e-01 -7.46131897e-01 -4.64379996e-01 -4.72930402e-01
-1.15941875e-01 3.48244220e-01 -3.78896073e-02 -5.88567793... | [14.312844276428223, 6.8173418045043945] |
6023095f-a191-4511-b0af-d0e5073bc6c8 | an-event-driven-compressive-neuromorphic | 2205.13292 | null | https://arxiv.org/abs/2205.13292v1 | https://arxiv.org/pdf/2205.13292v1.pdf | An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection | Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an even... | ['Mohamad Sawan', 'Jie Yang', 'Fengshi Tian', 'Jinbo Chen'] | 2022-05-26 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 7.77673900e-01 -6.01020217e-01 1.79516464e-01 -6.15412109e-02
-4.80456263e-01 -5.53353131e-01 -2.12201640e-01 4.12290215e-01
-5.18191338e-01 6.04404569e-01 -7.25225583e-02 4.91837747e-02
-7.69107789e-02 -4.16002929e-01 -3.29192072e-01 -6.33958101e-01
-1.56698212e-01 -6.03162467e-01 -2.09957585e-01 4.71270263... | [13.947436332702637, 3.149134874343872] |
cc327bf9-f2bb-4f78-94cb-18edfc0565a9 | cg-bert-conditional-text-generation-with-bert | 2004.01881 | null | https://arxiv.org/abs/2004.01881v1 | https://arxiv.org/pdf/2004.01881v1.pdf | CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection | In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a joint label space consisting of both existing intents which have enough labeled data and novel inte... | ['Chenwei Zhang', 'Philip Yu', 'Congying Xia', 'Hoang Nguyen', 'Jiawei Zhang'] | 2020-04-04 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 3.65559965e-01 2.76516944e-01 -9.29467380e-02 -6.28841162e-01
-1.11867869e+00 -6.19832836e-02 9.02459502e-01 -2.62315512e-01
-1.74732789e-01 6.62989855e-01 5.22231996e-01 -3.45912874e-02
5.05296767e-01 -5.54612100e-01 -2.40646571e-01 -4.79324460e-01
3.23912591e-01 9.58515406e-01 -1.01380050e-01 -2.19673827... | [12.090154647827148, 7.667134761810303] |
d4618911-e603-47df-8393-31af3a2f3b55 | learning-calibrated-guidance-for-object | 2103.11399 | null | https://arxiv.org/abs/2103.11399v4 | https://arxiv.org/pdf/2103.11399v4.pdf | Learning Calibrated-Guidance for Object Detection in Aerial Images | Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremendous progress. However, complex background and worse imaging quality are obvious problems in aerial object detection. Most state-of-the-art ... | ['Mingqiang Wei', 'Qixiang Geng', 'Dong Liang', 'Zongqi Wei', 'Huiyu Zhou', 'Liyan Zhang', 'Dong Zhang'] | 2021-03-21 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.03670755e-01 -6.25581801e-01 9.71560925e-02 -3.50622028e-01
-4.36228395e-01 -3.54396820e-01 1.99709982e-01 -1.64215505e-01
-3.79890680e-01 2.88262844e-01 -2.07697764e-01 -3.00015837e-01
-2.13660017e-01 -8.63149047e-01 -5.96195936e-01 -1.03822637e+00
-1.63437188e-01 -1.94427222e-01 6.40784562e-01 -1.71238169... | [8.831212043762207, -0.8222801089286804] |
29e3dfbb-cc4f-4b92-99ef-3095eb33673a | a-holistic-cascade-system-benchmark-and-human | 2301.10606 | null | https://arxiv.org/abs/2301.10606v1 | https://arxiv.org/pdf/2301.10606v1.pdf | A Holistic Cascade System, benchmark, and Human Evaluation Protocol for Expressive Speech-to-Speech Translation | Expressive speech-to-speech translation (S2ST) aims to transfer prosodic attributes of source speech to target speech while maintaining translation accuracy. Existing research in expressive S2ST is limited, typically focusing on a single expressivity aspect at a time. Likewise, this research area lacks standard evaluat... | ['Peng-Jen Chen', 'Ann Lee', 'Yossi Adi', 'Elizabeth Salesky', 'Hongyu Gong', 'Changhan Wang', 'Justine Kao', 'Benjamin Peloquin', 'Wen-Chin Huang'] | 2023-01-25 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [-3.59433666e-02 2.90116072e-01 -3.56221676e-01 -5.26041865e-01
-1.64331377e+00 -6.35922253e-01 4.17700052e-01 -5.28411448e-01
-2.24718116e-02 4.80379820e-01 6.07689798e-01 -1.11311367e-02
2.13972732e-01 -5.95637262e-02 -3.74856055e-01 -4.09569681e-01
4.36650485e-01 5.96673608e-01 1.42133445e-01 -5.27094364... | [14.72860336303711, 6.814136981964111] |
7738328f-7f4d-41d2-af3c-07fd8328728b | a-collaborative-ranking-model-with-multiple | 1807.04210 | null | http://arxiv.org/abs/1807.04210v2 | http://arxiv.org/pdf/1807.04210v2.pdf | A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion | Recommending venues plays a critical rule in satisfying users' needs on
location-based social networks. Recent studies have explored the idea of
adopting collaborative ranking (CR) for recommendation, combining the idea of
learning to rank and collaborative filtering. However, CR suffers from the
sparsity problem, main... | ['Crestani Fabio', 'Rafailidis Dimitrios', 'Aliannejadi Mohammad'] | 2018-07-13 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-0.34413484 -0.13673797 -0.4768377 -0.62631994 -0.323619 -0.41484648
0.8635267 0.32908255 -0.45075557 0.42930728 0.87175804 -0.20689593
-0.9305001 -0.9102382 -0.3096905 -0.5277272 -0.46690547 0.4567289
0.5819189 -0.4346539 0.8446847 0.05357393 -1.5477307 0.42048007
1.2166778 0.5951441 0.5... | [10.04401683807373, 5.6491923332214355] |
1452004c-78e1-4711-9959-b3d0bb6f475b | segmenting-unseen-industrial-components-in-a | 2002.03501 | null | https://arxiv.org/abs/2002.03501v3 | https://arxiv.org/pdf/2002.03501v3.pdf | Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data | Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered and unstructured environments with heavy occlusion, which makes it more challenging to deal with unseen objects. To tackle this problem, we pr... | ['Kyoobin Lee', 'Seungjun Choi', 'Seunghyeok Back', 'Jongwon Kim', 'Raeyoung Kang'] | 2020-02-10 | null | null | null | null | ['unseen-object-instance-segmentation'] | ['computer-vision'] | [ 5.51946282e-01 3.08011800e-01 3.04344654e-01 -2.58523673e-01
-7.05031574e-01 -9.21015263e-01 2.72926033e-01 -2.13460803e-01
7.55384937e-02 5.56383491e-01 -3.68744105e-01 -4.70131561e-02
1.85427666e-01 -7.81667054e-01 -8.61343086e-01 -7.28411853e-01
3.89165938e-01 5.75912595e-01 5.00468075e-01 2.16907766... | [6.382167816162109, -0.9982771277427673] |
70fe408e-551b-46ac-9c1b-9603af03b414 | compression-of-deep-neural-networks-for-image | 1701.04923 | null | http://arxiv.org/abs/1701.04923v1 | http://arxiv.org/pdf/1701.04923v1.pdf | Compression of Deep Neural Networks for Image Instance Retrieval | Image instance retrieval is the problem of retrieving images from a database
which contain the same object. Convolutional Neural Network (CNN) based
descriptors are becoming the dominant approach for generating {\it global image
descriptors} for the instance retrieval problem. One major drawback of
CNN-based {\it globa... | ['Ling-Yu Duan', 'Antoine Veillard', 'Olivier Morère', 'Jie Lin', 'Vijay Chandrasekhar', 'Tomaso Poggio', 'Qianli Liao'] | 2017-01-18 | null | null | null | null | ['image-instance-retrieval'] | ['computer-vision'] | [ 4.29684639e-01 -2.86861867e-01 -5.12669146e-01 -3.53142083e-01
-8.49777460e-01 -2.39091828e-01 3.94120157e-01 6.11309469e-01
-9.11881208e-01 3.82814050e-01 -4.01020259e-01 -1.15325131e-01
-5.24746716e-01 -1.02865326e+00 -9.08129930e-01 -5.14350176e-01
-1.36921585e-01 4.39575881e-01 3.40772837e-01 -9.00977175... | [8.560998916625977, 3.0139849185943604] |
5898ed09-a054-4ea5-b61f-3bf6a5a1550a | a-large-scale-study-on-unsupervised | 2104.14558 | null | https://arxiv.org/abs/2104.14558v1 | https://arxiv.org/pdf/2104.14558v1.pdf | A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning | We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same... | ['Kaiming He', 'Ross Girshick', 'Bo Xiong', 'Haoqi Fan', 'Christoph Feichtenhofer'] | 2021-04-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Feichtenhofer_A_Large-Scale_Study_on_Unsupervised_Spatiotemporal_Representation_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['self-supervised-action-recognition', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 2.66884774e-01 -3.75062436e-01 -6.67938948e-01 -2.91032284e-01
-7.24523902e-01 -6.53331935e-01 7.67281592e-01 -1.87400818e-01
-2.55243361e-01 5.64285636e-01 6.81798697e-01 -2.43802443e-01
-3.75249237e-01 -3.29745501e-01 -6.26503527e-01 -7.17208624e-01
-6.53321743e-01 -1.62682593e-01 3.33625525e-01 -2.59808481... | [8.754661560058594, 0.7314515113830566] |
aa3649a3-a147-446a-98f0-6b0de61dd4fe | distributed-coordination-of-multi-microgrids | 2305.04456 | null | https://arxiv.org/abs/2305.04456v1 | https://arxiv.org/pdf/2305.04456v1.pdf | Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services | We propose a distributed optimization framework that coordinates multiple microgrids in an Active Distribution Network (ADN) for provisioning passive voltage support based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the... | ['Prabodh Bajpai', 'Ashish R. Hota', 'Abhishek Mishra', 'Arghya Mallick'] | 2023-05-08 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-5.72781384e-01 -6.77792430e-02 -3.56324196e-01 -6.44899383e-02
-2.69840330e-01 -1.19708514e+00 1.58000246e-01 2.27516606e-01
2.22178310e-01 1.43623650e+00 -1.84596270e-01 -2.44303301e-01
-8.15984607e-01 -8.00294459e-01 8.56410563e-02 -1.28211164e+00
-6.15939200e-01 4.87185478e-01 -4.92607027e-01 -4.68889832... | [5.701694011688232, 2.587869167327881] |
0c1d2a33-e2e9-4a60-9e91-ef0c8943649f | a-robust-pose-transformational-gan-for-pose | 2001.01259 | null | https://arxiv.org/abs/2001.01259v1 | https://arxiv.org/pdf/2001.01259v1.pdf | A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis | Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. ... | ['Deepak Mishra', 'Arnab Karmakar'] | 2020-01-05 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 5.38833022e-01 1.88561931e-01 3.95667821e-01 -2.82583505e-01
-5.27732909e-01 -6.14706635e-01 5.71890056e-01 -4.94018644e-01
-1.26092374e-01 7.47901142e-01 -8.61596093e-02 1.36251673e-01
1.55550748e-01 -5.34126341e-01 -7.08769500e-01 -6.92349851e-01
5.57737589e-01 5.30051827e-01 2.57657349e-01 -1.92512408... | [11.6555814743042, -0.9100406765937805] |
c367f1e8-bf54-4ca6-bded-9ed8dda9660f | learning-relationships-for-multi-view-3d | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Learning_Relationships_for_Multi-View_3D_Object_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Learning_Relationships_for_Multi-View_3D_Object_Recognition_ICCV_2019_paper.pdf | Learning Relationships for Multi-View 3D Object Recognition | Recognizing 3D object has attracted plenty of attention recently, and view-based methods have achieved best results until now. However, previous view-based methods ignore the region-to-region and view-to-view relationships between different view images, which are crucial for multi-view 3D object representation. To tack... | [' Liwei Wang', 'Ze Yang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['3d-object-recognition'] | ['computer-vision'] | [-4.41290379e-01 -5.38828552e-01 -3.56745392e-01 -6.54956162e-01
-2.95074373e-01 -6.18826389e-01 5.89305222e-01 -1.83798194e-01
1.65481746e-01 9.03291926e-02 2.99221516e-01 2.97169298e-01
-3.28274071e-01 -6.47596240e-01 -3.57879132e-01 -4.86806571e-01
3.14452648e-01 1.78439662e-01 6.09548271e-01 2.90231705... | [8.141435623168945, -3.819246530532837] |
149c28af-3ca4-4593-a1a6-a2f06a7a40c1 | prediction-of-sea-surface-temperature-using | 1705.06861 | null | http://arxiv.org/abs/1705.06861v1 | http://arxiv.org/pdf/1705.06861v1.pdf | Prediction of Sea Surface Temperature using Long Short-Term Memory | This letter adopts long short-term memory(LSTM) to predict sea surface
temperature(SST), which is the first attempt, to our knowledge, to use
recurrent neural network to solve the problem of SST prediction, and to make
one week and one month daily prediction. We formulate the SST prediction
problem as a time series reg... | ['Junyu Dong', 'Qin Zhang', 'Hui Wang', 'Guoqiang Zhong', 'Xin Sun'] | 2017-05-19 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.89286843e-01 -5.32304525e-01 7.83736911e-03 -4.03707504e-01
-2.02206075e-01 -2.83437580e-01 4.88762259e-01 -6.23720348e-01
-4.14503366e-01 6.24229074e-01 1.46070898e-01 -9.11340892e-01
2.92746305e-01 -7.01956570e-01 -6.38224125e-01 -1.03652096e+00
-2.97770768e-01 -3.56677145e-01 6.64765909e-02 -4.83963132... | [6.634230136871338, 2.8899483680725098] |
a9288eaf-f012-4b1f-b11f-4b145d8672e7 | pre-trained-language-meaning-models-for | 2306.00124 | null | https://arxiv.org/abs/2306.00124v1 | https://arxiv.org/pdf/2306.00124v1.pdf | Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation | Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-... | ['Johan Bos', 'Malvina Nissim', 'Huiyuan Lai', 'Chunliu Wang'] | 2023-05-31 | null | null | null | null | ['cross-lingual-transfer', 'drs-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.30395377e-01 8.66256535e-01 -2.13583454e-01 -5.44642687e-01
-1.13277614e+00 -5.70052266e-01 9.88465130e-01 3.40157807e-01
-5.71924567e-01 8.99554908e-01 7.72865772e-01 -5.27367234e-01
2.03045696e-01 -7.83843219e-01 -6.76375985e-01 -3.66127521e-01
4.73454595e-01 4.78662640e-01 1.52578160e-01 -6.22751057... | [10.83299732208252, 9.283388137817383] |
0531b940-9c47-4a10-982d-e9a44807d4f5 | toward-personalized-medicine-in-connectomic | 2109.12327 | null | https://arxiv.org/abs/2109.12327v1 | https://arxiv.org/pdf/2109.12327v1.pdf | Toward Personalized Medicine in Connectomic Deep Brain Stimulation | At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained... | ['Andreas Horn', 'Clemens Neudorfer', 'Michael D. Fox', 'Helen S. Mayberg', 'Andrea A. Kühn', 'Carsten Finke', 'Shan H. Siddiqi', 'Nanditha Rajamani', 'Barbara Hollunder'] | 2021-09-25 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 4.02220339e-01 6.69821575e-02 -5.01520932e-01 -2.19549045e-01
-6.30247235e-01 -9.09446418e-01 2.89308667e-01 4.90281701e-01
-1.84234649e-01 8.36711407e-01 5.41496634e-01 -7.58541152e-02
-1.06059802e+00 -4.07842398e-01 -1.13595128e-01 -4.27918583e-01
-1.54242173e-01 8.43911648e-01 -6.31544366e-02 -1.21204279... | [13.034975051879883, 3.4074392318725586] |
1a871528-f263-4712-8e33-271f8b63fc69 | amodal-instance-segmentation-with-kins | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Qi_Amodal_Instance_Segmentation_With_KINS_Dataset_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Qi_Amodal_Instance_Segmentation_With_KINS_Dataset_CVPR_2019_paper.pdf | Amodal Instance Segmentation With KINS Dataset | Amodal instance segmentation, a new direction of instance segmentation, aims to segment each object instance involving its invisible, occluded parts to imitate human ability. This task requires to reason objects' complex structure. Despite important and futuristic, this task lacks data with large-scale and detailed ann... | [' Jiaya Jia', ' Xiaoyong Shen', ' Shu Liu', ' Li Jiang', 'Lu Qi'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['amodal-instance-segmentation'] | ['computer-vision'] | [ 5.38205385e-01 3.04067254e-01 -3.71772289e-01 -2.61643231e-01
-5.71595252e-01 -6.92281485e-01 3.26105475e-01 -5.54113925e-01
4.07481976e-02 6.47368312e-01 7.65552595e-02 -1.27874255e-01
-2.93840077e-02 -3.18556339e-01 -7.29422569e-01 -6.82182550e-01
5.00226080e-01 4.70725536e-01 3.74762803e-01 6.65326416... | [9.694302558898926, 0.3177073299884796] |
83a7a280-1439-423d-88e3-5df89d2da98c | timestamp-supervised-action-segmentation-with | 2206.15031 | null | https://arxiv.org/abs/2206.15031v4 | https://arxiv.org/pdf/2206.15031v4.pdf | Timestamp-Supervised Action Segmentation with Graph Convolutional Networks | We introduce a novel approach for temporal activity segmentation with timestamp supervision. Our main contribution is a graph convolutional network, which is learned in an end-to-end manner to exploit both frame features and connections between neighboring frames to generate dense framewise labels from sparse timestamp... | ['Quoc-Huy Tran', 'M. Zeeshan Zia', 'Andrey Konin', 'Shakeeb Siddiqui', 'Awais Ahmed', 'Sanjay Haresh', 'Hamza Khan'] | 2022-06-30 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 5.47997236e-01 2.85353214e-01 -3.66209626e-01 -5.85057020e-01
-7.01648533e-01 -6.78988695e-01 7.07442164e-01 5.94255999e-02
-5.78797579e-01 6.31612897e-01 5.16478777e-01 -1.72615111e-01
3.69035453e-01 -4.76595193e-01 -1.02072489e+00 -6.49622262e-01
-3.09649408e-01 4.88354951e-01 4.18769658e-01 2.67901748... | [8.579451560974121, 0.494861364364624] |
64dc04eb-1476-4ddf-8e03-56120c7bd142 | monte-carlo-q-learning-for-general-game | 1802.05944 | null | http://arxiv.org/abs/1802.05944v2 | http://arxiv.org/pdf/1802.05944v2.pdf | Monte Carlo Q-learning for General Game Playing | After the recent groundbreaking results of AlphaGo, we have seen a strong
interest in reinforcement learning in game playing. General Game Playing (GGP)
provides a good testbed for reinforcement learning. In GGP, a specification of
games rules is given. GGP problems can be solved by reinforcement learning.
Q-learning i... | ['Aske Plaat', 'Hui Wang', 'Michael Emmerich'] | 2018-02-16 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.17616248e-01 1.38633296e-01 -1.06185839e-01 2.47366577e-01
-8.27653408e-01 -7.30028808e-01 4.42562133e-01 -1.02755344e-02
-6.25382364e-01 1.23536301e+00 7.20195249e-02 -7.32184708e-01
-3.27159941e-01 -1.17384899e+00 -5.68216205e-01 -6.06768787e-01
-5.38134217e-01 3.85325372e-01 6.66070282e-01 -8.32044601... | [3.573046922683716, 1.5335273742675781] |
f4756435-3ad4-4f93-abb3-35f925944885 | broad-linguistic-complexity-analysis-for | null | null | https://aclanthology.org/2021.bea-1.5 | https://aclanthology.org/2021.bea-1.5.pdf | Broad Linguistic Complexity Analysis for Greek Readability Classification | This paper explores the linguistic complexity of Greek textbooks as a readability classification task. We analyze textbook corpora for different school subjects and textbooks for Greek as a Second Language, covering a very wide spectrum of school age groups and proficiency levels. A broad range of quantifiable linguist... | ['Detmar Meurers', 'Maria Giagkou', 'Savvas Chatzipanagiotidis'] | null | null | null | null | eacl-bea-2021-4 | ['cross-corpus'] | ['computer-vision'] | [-1.34251997e-01 6.71037957e-02 -9.41447765e-02 -1.75166056e-01
-1.10118604e+00 -8.79326880e-01 7.63338089e-01 7.59428740e-01
-5.68153799e-01 5.28560996e-01 6.10550880e-01 -2.94464886e-01
-7.28831887e-01 -9.13004518e-01 -1.76349327e-01 -2.26995066e-01
4.47148353e-01 2.91380882e-01 2.95751840e-01 -3.77662003... | [11.006858825683594, 10.121245384216309] |
42a817d4-1d97-448a-8731-ea64cdc56138 | formality-style-transfer-for-noisy-user | null | null | https://aclanthology.org/D19-5502 | https://aclanthology.org/D19-5502.pdf | Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora | Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. As each existing dataset is sourced from a specific domain and context, most use cases will have a sizable mismatch from the vocabulary and sentence structures of any dataset available. This reduces the performanc... | ['Alan W. black', 'Isak Czeresnia Etinger'] | 2019-11-01 | null | null | null | ws-2019-11 | ['formality-style-transfer'] | ['natural-language-processing'] | [ 3.14245045e-01 1.27638131e-01 1.26466781e-01 -8.50598872e-01
-9.58326399e-01 -1.18529499e+00 5.52255690e-01 -3.86093736e-01
-5.49228728e-01 1.14759946e+00 5.65899432e-01 -1.49641484e-01
2.49336988e-01 -4.39950436e-01 -6.87903225e-01 -2.37814099e-01
5.15150905e-01 9.04906988e-01 -4.61827256e-02 -6.27973258... | [11.528836250305176, 9.634202003479004] |
730d9369-b1f1-454e-89d0-3b2c68cbdab3 | unsupervised-learning-of-probabilistic | 1903.03545 | null | https://arxiv.org/abs/1903.03545v2 | https://arxiv.org/pdf/1903.03545v2.pdf | Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces | Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation function... | ['Mert R. Sabuncu', 'Guha Balakrishnan', 'Adrian V. Dalca', 'John Guttag'] | 2019-03-08 | null | null | null | null | ['constrained-diffeomorphic-image-registration', 'deformable-medical-image-registration', 'diffeomorphic-medical-image-registration'] | ['computer-vision', 'medical', 'medical'] | [ 1.24554597e-01 1.39409110e-01 -1.08662926e-01 -6.42399549e-01
-1.15433872e+00 -3.64007950e-01 6.21895671e-01 1.82645738e-01
-5.12156904e-01 5.86986363e-01 1.19033143e-01 -4.65784513e-04
-4.32510227e-01 -7.04204381e-01 -6.05457664e-01 -7.29713976e-01
-4.35073435e-01 7.61507392e-01 2.98557371e-01 3.09966914... | [13.968268394470215, -2.5367794036865234] |
95d04527-51a5-4dc3-9a2b-afd6aa7d320f | mimamo-net-integrating-micro-and-macro-motion | 1911.09784 | null | https://arxiv.org/abs/1911.09784v1 | https://arxiv.org/pdf/1911.09784v1.pdf | MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition | Spatial-temporal feature learning is of vital importance for video emotion recognition. Previous deep network structures often focused on macro-motion which extends over long time scales, e.g., on the order of seconds. We believe integrating structures capturing information about both micro- and macro-motion will benef... | ['Zhaokang Chen', 'Didan Deng', 'Bertram Shi', 'Yuqian Zhou'] | 2019-11-21 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [-1.26111448e-01 -4.06318009e-01 -2.23381400e-01 -3.54777575e-01
-1.19773842e-01 -4.98818338e-01 4.22448963e-01 -1.93445422e-02
-3.76291394e-01 4.59598839e-01 3.83439451e-01 1.14404134e-01
8.48906264e-02 -5.00800550e-01 -3.93716067e-01 -7.34471142e-01
-3.18630219e-01 -3.54379714e-01 3.69913988e-02 -3.29714984... | [13.529342651367188, 1.8160613775253296] |
d4ce851b-e9d2-465f-8ee5-b2347fd68b0c | a-novel-two-level-causal-inference-framework | 2304.04755 | null | https://arxiv.org/abs/2304.04755v1 | https://arxiv.org/pdf/2304.04755v1.pdf | A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis | In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and implementing appropriate treatments, and refining treatments if needed. The main pain-point is the lack of a systematic method to identify causal r... | ['Anqi He', 'Kamran Paynabar', 'Devesh Upadhyay', 'Milad Zafar Nezhad', 'Thi Tu Trinh Tran', 'Huanyi Shui', 'Qian Wang'] | 2023-03-31 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.28313828e-01 3.05317640e-02 -1.02178228e+00 -3.55981141e-01
-8.10253561e-01 -2.33331352e-01 5.21649957e-01 1.70471475e-01
-2.72794254e-02 7.66065240e-01 4.88401115e-01 -7.98564553e-01
-6.28740847e-01 -8.70794058e-01 -4.88791198e-01 -4.96701777e-01
9.26717743e-02 1.97847441e-01 -1.68003604e-01 -5.61947487... | [7.849998950958252, 5.3227152824401855] |
64fb67db-e3b1-4aff-95de-df64615ea9ef | a-unified-implicit-dialog-framework-for | 1802.04358 | null | http://arxiv.org/abs/1802.04358v1 | http://arxiv.org/pdf/1802.04358v1.pdf | A Unified Implicit Dialog Framework for Conversational Search | We propose a unified Implicit Dialog framework for goal-oriented, information
seeking tasks of Conversational Search applications. It aims to enable dialog
interactions with domain data without replying on explicitly encoded the rules
but utilizing the underlying data representation to build the components
required for... | ['Song Feng', 'Kshitij P. Fadnis', 'Sunil Shashidhara', 'R. Chulaka Gunasekara', 'Lazaros C. Polymenakos'] | 2018-02-12 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-5.51252849e-02 8.56092691e-01 2.34508708e-01 -6.16091311e-01
-5.05143881e-01 -8.70085716e-01 9.61588919e-01 6.14124276e-02
-2.58898795e-01 6.01575851e-01 4.41220939e-01 -3.87427002e-01
-3.70545954e-01 -5.62759757e-01 1.40778124e-01 -1.08562939e-01
2.54773587e-01 1.13982880e+00 4.48451996e-01 -8.03046227... | [12.7999267578125, 7.93665885925293] |
7d7ba734-72e7-447e-bc88-5c5d078c74ab | probing-neural-representations-of-scene | 2303.06367 | null | https://arxiv.org/abs/2303.06367v1 | https://arxiv.org/pdf/2303.06367v1.pdf | Probing neural representations of scene perception in a hippocampally dependent task using artificial neural networks | Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to inferior temporal cortex (IT). However, the ability of these networks to explain representations in high... | ['Caswell Barry', 'Christian F. Doeller', 'Markus Frey'] | 2023-03-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Frey_Probing_Neural_Representations_of_Scene_Perception_in_a_Hippocampally_Dependent_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Frey_Probing_Neural_Representations_of_Scene_Perception_in_a_Hippocampally_Dependent_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 3.57005805e-01 2.80555338e-01 2.12421566e-01 -4.61026490e-01
1.43512636e-01 -5.15412688e-01 9.85013306e-01 -2.27152511e-01
-6.10630035e-01 3.18457544e-01 3.09346080e-01 -5.40537946e-02
-1.22667082e-01 -7.88007200e-01 -9.55097377e-01 -7.42630363e-01
2.03344017e-01 6.11866295e-01 1.02709040e-01 -1.51505426... | [10.10061264038086, 2.538435220718384] |
35c69435-0ad2-4b3f-92ea-f8e1cbc0fc18 | ldsa-learning-dynamic-subtask-assignment-in | 2205.02561 | null | https://arxiv.org/abs/2205.02561v3 | https://arxiv.org/pdf/2205.02561v3.pdf | LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning | Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all agents share the same policy or value network. However, in many complex multi-agent tasks, different agents are expected to possess specific ab... | ['Houqiang Li', 'Jiangcheng Zhu', 'Wengang Zhou', 'Xunhan Hu', 'Jian Zhao', 'Mingyu Yang'] | 2022-05-05 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-2.21533895e-01 -1.44003153e-01 -3.64218354e-01 -9.26294029e-02
-2.36295715e-01 -6.37497008e-01 4.17841256e-01 -7.10124895e-02
-5.19127846e-01 9.73005652e-01 2.18079150e-01 1.00891767e-02
-3.68413985e-01 -5.31918824e-01 -4.94164884e-01 -1.04991663e+00
-9.52638090e-02 5.70748091e-01 4.59112704e-01 -5.05417883... | [3.7860519886016846, 1.9293774366378784] |
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