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
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f0488831-1ac4-4f13-b424-d4fcba8a9ed5 | chance-constrained-trajectory-optimization | 2005.04374 | null | https://arxiv.org/abs/2005.04374v3 | https://arxiv.org/pdf/2005.04374v3.pdf | Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems | Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available. We present a new approach for optimal motion planning with safe exploration that integrates chanc... | ['Soon-Jo Chung', 'Yisong Yue', 'Anima Anandkumar', 'Anqi Liu', 'Yashwanth Kumar Nakka', 'Guanya Shi'] | 2020-05-09 | null | null | null | null | ['optimal-motion-planning', 'safe-exploration'] | ['robots', 'robots'] | [ 4.84659262e-02 8.16603303e-01 -8.20884764e-01 1.31940812e-01
-1.19174874e+00 -5.89782476e-01 3.74899656e-01 1.70270249e-01
-7.90819466e-01 1.15946603e+00 -1.24557897e-01 -5.18317759e-01
-8.53774071e-01 -3.95099849e-01 -1.26741171e+00 -9.73418653e-01
-6.40711010e-01 4.56020057e-01 -8.80234167e-02 -5.60247293... | [4.685691833496094, 2.160020112991333] |
f1a73b32-2bf7-4ad6-9fae-f0021c3431fa | voxsim-a-visual-platform-for-modeling-motion | null | null | https://aclanthology.org/C16-2012 | https://aclanthology.org/C16-2012.pdf | VoxSim: A Visual Platform for Modeling Motion Language | Much existing work in text-to-scene generation focuses on generating static scenes. By introducing a focus on motion verbs, we integrate dynamic semantics into a rich formal model of events to generate animations in real time that correlate with human conceptions of the event described. This paper presents a working sy... | ['James Pustejovsky', 'Nikhil Krishnaswamy'] | 2016-12-01 | voxsim-a-visual-platform-for-modeling-motion-1 | https://aclanthology.org/C16-2012 | https://aclanthology.org/C16-2012.pdf | coling-2016-12 | ['scene-generation'] | ['computer-vision'] | [ 3.09714317e-01 5.05248725e-01 2.24883556e-01 -3.58234018e-01
-4.05482829e-01 -6.25732541e-01 1.44090891e+00 9.36783776e-02
-8.08799416e-02 8.46908927e-01 6.55451834e-01 -1.37339428e-01
3.60194504e-01 -1.02169824e+00 -2.23964453e-01 -8.94985422e-02
-2.32753545e-01 5.61596334e-01 6.67203248e-01 -6.82855964... | [11.250494003295898, 0.7991821765899658] |
cd661e39-9d72-4db1-afb9-0a91803062c2 | membership-inference-attack-in-face-of-data | null | null | https://openreview.net/forum?id=z_gX7gZe2cV | https://openreview.net/pdf?id=z_gX7gZe2cV | Membership Inference Attack in Face of Data Transformations | Membership inference attacks (MIAs) on machine learning models, which try to infer whether an example is in the training dataset of a target model, are widely studied in recent years as data privacy attracts increasing attention. One unignorable problem in the traditional MIA threat model is that it assumes the attacke... | ['Hao Chen', 'Yiwen Guo', 'Jiyu Chen'] | 2021-09-29 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.55649495e-01 2.21275032e-01 -1.00076526e-01 -2.45816275e-01
-6.74035430e-01 -9.11502600e-01 3.12186301e-01 4.25226718e-01
-5.36275804e-01 7.20320225e-01 -4.65012699e-01 -4.85925466e-01
5.13418987e-02 -1.10776019e+00 -1.19585752e+00 -8.87972951e-01
6.21110201e-02 2.33039021e-01 2.23924801e-01 1.33402571... | [5.87234354019165, 7.1700310707092285] |
3de3cd6f-98c6-4ef7-9ad8-91422892be17 | comments-on-fast-and-scalable-search-of-whole | 2304.08297 | null | https://arxiv.org/abs/2304.08297v4 | https://arxiv.org/pdf/2304.08297v4.pdf | Comments on 'Fast and scalable search of whole-slide images via self-supervised deep learning' | Chen et al. [Chen2022] recently published the article 'Fast and scalable search of whole-slide images via self-supervised deep learning' in Nature Biomedical Engineering. The authors call their method 'self-supervised image search for histology', short SISH. We express our concerns that SISH is an incremental modificat... | ['H. R. Tizhoosh', 'Shivam Kalra', 'Abubakr Shafique', 'Mehdi Afshari', 'Milad Sikaroudi'] | 2023-04-07 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.96280473e-01 4.06691879e-02 -3.96527082e-01 -6.03838146e-01
-1.16453767e+00 -3.72151524e-01 3.09163600e-01 6.30776942e-01
-7.99183130e-01 9.74635422e-01 1.47316992e-01 -1.88768461e-01
-3.00282240e-01 -3.77559602e-01 -5.91171384e-01 -1.12568474e+00
-4.47380207e-02 5.80250025e-01 3.97140831e-01 -1.03241131... | [15.090270042419434, -2.9483370780944824] |
c336e9f4-d6d4-4471-b784-4b1049be2c92 | flowgrad-controlling-the-output-of-generative | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_FlowGrad_Controlling_the_Output_of_Generative_ODEs_With_Gradients_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_FlowGrad_Controlling_the_Output_of_Generative_ODEs_With_Gradients_CVPR_2023_paper.pdf | FlowGrad: Controlling the Output of Generative ODEs With Gradients | Generative modeling with ordinary differential equations (ODEs) has achieved fantastic results on a variety of applications. Yet, few works have focused on controlling the generated content of a pre-trained ODE-based generative model. In this paper, we propose to optimize the output of ODE models according to a gui... | ['Qiang Liu', 'Wei Ping', 'Chengyue Gong', 'Shujian Zhang', 'Lemeng Wu', 'Xingchao Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-manipulation'] | ['computer-vision'] | [-8.57489035e-02 1.28214210e-01 2.88457811e-01 -1.67521253e-01
-4.07018006e-01 -6.94620371e-01 7.98825026e-01 -3.09792757e-01
-3.54748935e-01 5.22725940e-01 4.32664081e-02 -1.69135571e-01
1.22975074e-01 -9.50304508e-01 -9.64156747e-01 -7.34227121e-01
3.27682912e-01 5.54279029e-01 6.73702583e-02 -2.78569788... | [11.442907333374023, -0.49640223383903503] |
12788411-d167-410a-8c37-f76fe2def43b | robust-text-line-detection-in-historical | 2203.12346 | null | https://arxiv.org/abs/2203.12346v2 | https://arxiv.org/pdf/2203.12346v2.pdf | Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods | Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of documents that have degraded through the years. In this paper, we address the limitations that currently prevent people from building line segme... | ['Thierry Paquet', 'Christopher Kermorvant', 'Mélodie Boillet'] | 2022-03-23 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.26644412e-01 -2.29782715e-01 -2.88320575e-02 -3.09299082e-01
-5.47388911e-01 -9.94759381e-01 7.70967662e-01 2.46963829e-01
-3.44049603e-01 7.60597587e-01 4.40866686e-02 -2.80164331e-01
-2.06494167e-01 -5.73545992e-01 -4.94849056e-01 -3.12762111e-01
3.04206699e-01 6.84361219e-01 5.29576838e-01 -2.76500940... | [11.787986755371094, 2.6287829875946045] |
7912fbd5-775a-4fa0-a564-f1891e6b3d03 | privacy-preserving-tensor-factorization-for | 1908.09888 | null | https://arxiv.org/abs/1908.09888v2 | https://arxiv.org/pdf/1908.09888v2.pdf | Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis | Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires t... | ['Jian Lou', 'Xiaoqian Jiang', 'Jing Ma', 'Qiuchen Zhang', 'Li Xiong', 'Joyce C. Ho'] | 2019-08-26 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [-1.98141038e-01 2.24216923e-01 -1.39525384e-01 -4.36043620e-01
-8.13785851e-01 -9.39716935e-01 -3.71839076e-01 5.30265510e-01
-2.00983271e-01 7.13335872e-01 4.54531699e-01 -4.94272470e-01
-2.76169032e-01 -4.33979809e-01 -5.30313075e-01 -8.41813385e-01
-4.69050080e-01 6.24062061e-01 -6.27925217e-01 2.56552666... | [6.152403831481934, 6.431461334228516] |
cf05e287-158d-4836-a5c0-237ffc13a613 | s-2-contact-graph-based-network-for-3d-hand | 2208.00874 | null | https://arxiv.org/abs/2208.00874v1 | https://arxiv.org/pdf/2208.00874v1.pdf | S$^2$Contact: Graph-based Network for 3D Hand-Object Contact Estimation with Semi-Supervised Learning | Despite the recent efforts in accurate 3D annotations in hand and object datasets, there still exist gaps in 3D hand and object reconstructions. Existing works leverage contact maps to refine inaccurate hand-object pose estimations and generate grasps given object models. However, they require explicit 3D supervision w... | ['Hyung Jin Chang', 'Feng Zheng', 'Ales Leonardis', 'Kwang In Kim', 'Zhongqun Zhang', 'Tze Ho Elden Tse'] | 2022-08-01 | null | null | null | null | ['hand-object-pose'] | ['computer-vision'] | [ 1.36355445e-01 2.03896105e-01 -3.79008561e-01 -3.40584964e-01
-5.57206571e-01 -6.65480614e-01 3.89517933e-01 -2.75765806e-01
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-1.37155890e-01 -4.67995018e-01 -1.27123570e+00 -3.80460948e-01
2.82638252e-01 1.07767260e+00 3.04828405e-01 4.38168123... | [6.208086967468262, -1.0374020338058472] |
5ccb2f7a-c982-42dc-a612-d2c8e87df092 | industrial-anomaly-detection-with-domain | 2304.02216 | null | https://arxiv.org/abs/2304.02216v1 | https://arxiv.org/pdf/2304.02216v1.pdf | Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction | Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on the diversity of data categories, overlooking the diversity of domains within the same data category. In... | ['Xuefeng Chen', 'Chuang Sun', 'Xingwu Zhang', 'Zhibin Zhao', 'Zilong Zhang'] | 2023-04-05 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-7.87906498e-02 -5.97172737e-01 3.89718294e-01 -7.57457092e-02
-4.07465756e-01 -5.74433744e-01 4.44384515e-01 -4.52947281e-02
4.26389515e-01 2.16543525e-01 -2.26333112e-01 -3.51956367e-01
-2.28461683e-01 -7.49371529e-01 -3.59880894e-01 -1.06481504e+00
4.68361042e-02 8.08088407e-02 3.63331735e-01 -3.09308052... | [7.570939064025879, 2.0306143760681152] |
023eded4-dbd3-496e-b37b-137791c359e5 | heart-beat-characterization-from | 1605.04634 | null | http://arxiv.org/abs/1605.04634v1 | http://arxiv.org/pdf/1605.04634v1.pdf | Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances | A multiple instance learning (MIL) method, extended Function of Multiple
Instances ($e$FUMI), is applied to ballistocardiogram (BCG) signals produced by
a hydraulic bed sensor. The goal of this approach is to learn a personalized
heartbeat "concept" for an individual. This heartbeat concept is a prototype
(or "signatur... | ['Marjorie Skubic', 'Licet Rosales', 'Alina Zare', 'Princess Lyons', 'Changzhe Jiao'] | 2016-05-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 5.55480838e-01 3.72483075e-01 -2.33953092e-02 -4.74660069e-01
-8.23201418e-01 -1.96124494e-01 -8.32818449e-02 7.70381764e-02
1.69412598e-01 9.27240193e-01 -3.55216414e-01 1.18143521e-01
-3.88958216e-01 -6.46414042e-01 -6.79882228e-01 -9.43885446e-01
-3.37993294e-01 6.40595853e-01 -4.45119530e-01 2.40626380... | [14.199908256530762, 3.18831729888916] |
2529ac94-8cf6-4087-83e8-47c690ed03ff | a-comparative-analysis-of-techniques-and | 2305.13941 | null | https://arxiv.org/abs/2305.13941v2 | https://arxiv.org/pdf/2305.13941v2.pdf | A Comparative Analysis of Techniques and Algorithms for Recognising Sign Language | Sign language is a visual language that enhances communication between people and is frequently used as the primary form of communication by people with hearing loss. Even so, not many people with hearing loss use sign language, and they frequently experience social isolation. Therefore, it is necessary to create human... | ['S. K Singh', 'Ashutosh Bajpai', 'Ayush Sinha', 'Rupesh Kumar'] | 2023-05-05 | null | null | null | null | ['sign-language-recognition', 'sign-language-translation'] | ['computer-vision', 'computer-vision'] | [-1.60826460e-01 -2.27124274e-01 -4.24473166e-01 -1.17190003e-01
-6.02377057e-01 -1.60581514e-01 2.81861246e-01 -6.13607764e-01
-7.56809950e-01 7.20354497e-01 5.16197860e-01 -2.57016748e-01
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1.79747581e-01 2.14840919e-01 4.56978887e-01 -3.48006189... | [9.07785415649414, -6.384265422821045] |
f6fe5a4a-63bd-4495-8dd3-d9c038c4dd6e | a-probabilistic-time-evolving-approach-to | 2204.09404 | null | https://arxiv.org/abs/2204.09404v1 | https://arxiv.org/pdf/2204.09404v1.pdf | A Probabilistic Time-Evolving Approach to Scanpath Prediction | Human visual attention is a complex phenomenon that has been studied for decades. Within it, the particular problem of scanpath prediction poses a challenge, particularly due to the inter- and intra-observer variability, among other reasons. Besides, most existing approaches to scanpath prediction have focused on optim... | ['Belen Masia', 'Diego Gutierrez', 'Daniel Martin'] | 2022-04-20 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 7.26030171e-02 -1.64770961e-01 -3.62493157e-01 -5.14453173e-01
-5.42660117e-01 -4.08832759e-01 5.84258318e-01 -3.76718752e-02
-5.74356794e-01 4.25062925e-01 1.90299377e-01 -9.73591655e-02
-4.11316663e-01 -2.23114505e-01 -7.34302163e-01 -4.69433188e-01
-1.48447677e-01 5.55618882e-01 7.01043189e-01 -9.39528272... | [10.062260627746582, 1.2241708040237427] |
ed81a457-eaea-4573-921b-12adf3d0874c | life-is-a-circus-and-we-are-the-clowns | 2210.12197 | null | https://arxiv.org/abs/2210.12197v2 | https://arxiv.org/pdf/2210.12197v2.pdf | Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes | Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference -- abilities lacking in even the best AI systems today. Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little a... | ['Dafna Shahaf', 'Oren Sultan'] | 2022-10-21 | null | null | null | null | ['textual-analogy-parsing', 'analogical-similarity'] | ['natural-language-processing', 'reasoning'] | [ 2.19679043e-01 2.49047279e-01 -9.71853361e-02 -2.84314364e-01
-1.32173747e-01 -8.49884391e-01 9.94369030e-01 8.04353058e-01
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-3.41455728e-01 -7.77451456e-01 -7.40750492e-01 1.21169902e-01
1.07769221e-01 8.29107285e-01 -5.64599261e-02 -4.30270135... | [10.5869722366333, 2.4113900661468506] |
4ff7685d-a47b-417a-bd53-c3048bd6ff81 | textual-representations-for-crosslingual | null | null | https://aclanthology.org/2021.ecnlp-1.14 | https://aclanthology.org/2021.ecnlp-1.14.pdf | Textual Representations for Crosslingual Information Retrieval | In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adopted the subword and character level representations for information retrieval that had shown to improve neural machine translation by reducing the out... | ['Liling Tan', 'Hang Zhang'] | null | null | null | null | acl-ecnlp-2021-8 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 1.56347185e-01 -2.19840378e-01 -6.80361032e-01 -1.32608684e-02
-1.80351186e+00 -8.04864109e-01 9.45495963e-01 4.73251522e-01
-6.77283764e-01 8.88673961e-01 6.63778186e-01 -5.39277673e-01
-2.34600171e-01 -5.62802613e-01 -4.95040417e-01 -9.50442627e-02
5.15038252e-01 6.64332151e-01 -1.89700529e-01 -6.28325999... | [11.417068481445312, 9.879083633422852] |
0de7ad92-5c87-4a08-aed6-e94f9cb01538 | lung-nodule-segmentation-via-level-set | 1910.03191 | null | https://arxiv.org/abs/1910.03191v3 | https://arxiv.org/pdf/1910.03191v3.pdf | Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation | Purpose: Lung nodule segmentation, i.e., the algorithmic delineation of the lung nodule surface, is a fundamental component of computational nodule analysis pipelines. We propose a new method for segmentation that is a machine learning based extension of current approaches, using labeled image examples to improve its a... | ['Matthew C Hancock', 'Jerry F Magnan'] | 2019-10-08 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [ 2.30136827e-01 3.95032972e-01 -3.69089842e-01 -1.92882776e-01
-1.00312471e+00 -7.49264061e-01 4.74196643e-01 1.34066314e-01
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-1.70501366e-01 -4.95482862e-01 -3.76733750e-01 -6.77132905e-01
1.32334686e-03 9.59068418e-01 8.09682488e-01 1.13349408... | [15.362509727478027, -2.1535327434539795] |
378906b6-1947-4f29-9ddf-ccbc6de8f047 | oriented-r-cnn-for-object-detection | 2108.05699 | null | https://arxiv.org/abs/2108.05699v1 | https://arxiv.org/pdf/2108.05699v1.pdf | Oriented R-CNN for Object Detection | Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes. This diminishes the detectors' speed, thereby becoming the computational bottleneck in advanced oriented object detection systems. This work proposes an effective and simple oriented object detection framework, term... | ['Junwei Han', 'Xiwen Yao', 'Jiabao Wang', 'Gong Cheng', 'Xingxing Xie'] | 2021-08-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.65039295e-01 1.86767772e-01 -2.49485269e-01 -4.83679287e-02
-7.68734932e-01 -3.14343154e-01 2.02873364e-01 -2.55461007e-01
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-1.47497490e-01 1.43962681e-01 1.04535246e+00 -3.43850136... | [8.742850303649902, -0.24156330525875092] |
666a2199-e816-4496-bc64-1654b7a95565 | automatic-features-for-essay-scoring-a-an | null | null | https://aclanthology.org/D16-1115 | https://aclanthology.org/D16-1115.pdf | Automatic Features for Essay Scoring -- An Empirical Study | null | ['Yue Zhang', 'Fei Dong'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.477737903594971, 3.8463637828826904] |
ebb021fa-0750-43eb-8ab6-f1ff1ba33e0a | multilingual-natural-language-processing | null | null | https://aclanthology.org/W12-0506 | https://aclanthology.org/W12-0506.pdf | Multilingual Natural Language Processing | null | ['Rada Mihalcea'] | 2012-04-01 | null | null | null | ws-2012-4 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.368608474731445, 3.664233922958374] |
e87f35a9-1c7d-476c-b29e-f5f8b8862418 | deep-autoencoder-like-nonnegative-matrix | null | null | https://dl.acm.org/citation.cfm?id=3271697 | https://smartyfh.com/Documents/18DANMF.pdf | Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection | Community structure is ubiquitous in real-world complex networks. The task of community detection over these networks is of paramount importance in a variety of applications. Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection due to its great interpretability and its natura... | ['Zibin Zheng', 'Fanghua Ye', 'Chuan Chen'] | 2018-10-22 | null | null | null | cikm-2018-10 | ['network-community-partition', 'local-community-detection'] | ['graphs', 'graphs'] | [-2.15764150e-01 -1.82220951e-01 9.99342576e-02 -2.81183925e-02
2.66536474e-01 -3.89186174e-01 6.07341766e-01 2.66501755e-01
-1.91545933e-01 4.76488113e-01 2.13383570e-01 -1.45064667e-01
-1.79356873e-01 -1.12829185e+00 -4.41702515e-01 -7.63909042e-01
-5.67454457e-01 7.26391792e-01 -3.40367062e-03 -1.77699625... | [7.282864093780518, 5.950841903686523] |
daf1366a-5ee8-445d-b8e4-72ace7d35c63 | multi-view-redescription-mining-using-tree | 2006.12227 | null | https://arxiv.org/abs/2006.12227v2 | https://arxiv.org/pdf/2006.12227v2.pdf | Approaches For Multi-View Redescription Mining | The task of redescription mining explores ways to re-describe different subsets of entities contained in a dataset and to reveal non-trivial associations between different subsets of attributes, called views. This interesting and challenging task is encountered in different scientific fields, and is addressed by a numb... | ['Tomislav Šmuc', 'Matej Mihelčić'] | 2020-06-22 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.44002867e-01 2.20519990e-01 -2.22108930e-01 -6.11750662e-01
-3.39014262e-01 -7.45982289e-01 5.74765861e-01 4.67376232e-01
-4.74434486e-03 8.90545070e-01 -1.12584442e-01 -3.08653921e-01
-7.72442341e-01 -1.20810604e+00 -3.51216972e-01 -6.06632054e-01
-1.67710900e-01 1.07986951e+00 2.97951698e-01 4.66573536... | [8.03699016571045, 4.815268516540527] |
c21170a9-b580-46ae-805a-3584e25a6566 | its-not-you-its-me-detecting-flirting-and-its | null | null | https://web.stanford.edu/~jurafsky/emnlp09.pdf | https://web.stanford.edu/~jurafsky/emnlp09.pdf | It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates | Automatically detecting human social intentions from spoken conversation is an
important task for dialogue understanding. Since the social intentions of the
speaker may differ from what is perceived
by the hearer, systems that analyze human
conversations need to be able to extract
both the perceived and the intend... | ['a'] | 2009-03-01 | null | null | null | emnlp09-2009-3 | ['dialogue-understanding'] | ['natural-language-processing'] | [ 1.68714002e-01 6.72502697e-01 -4.52854410e-02 -7.88732350e-01
-4.02202368e-01 -5.38367212e-01 7.85511553e-01 1.96857184e-01
-3.43511194e-01 3.32479775e-01 1.19900453e+00 5.18487170e-02
5.25703788e-01 -3.21824163e-01 -1.03812076e-01 -3.55214804e-01
2.58068480e-02 5.65188825e-01 -3.70740592e-01 -3.75779480... | [12.75313663482666, 7.73417854309082] |
19519152-3d49-4ba9-91fb-1f222280df6a | biconditional-generative-adversarial-networks | 1911.01861 | null | https://arxiv.org/abs/1911.01861v2 | https://arxiv.org/pdf/1911.01861v2.pdf | Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views | In this paper, we present a conditional GAN with two generators and a common discriminator for multiview learning problems where observations have two views, but one of them may be missing for some of the training samples. This is for example the case for multilingual collections where documents are not available in al... | ['Massih-Reza Amini', 'Anastasiia Doinychko'] | 2019-11-05 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 1.07391529e-01 4.12398905e-01 -4.10360605e-01 -4.68683869e-01
-9.68201578e-01 -8.21881771e-01 8.45651925e-01 -4.17097248e-02
-6.64064735e-02 9.60759103e-01 -1.50858894e-01 -1.58441765e-03
1.87058821e-01 -8.09552133e-01 -9.72799659e-01 -9.24441159e-01
1.59039199e-01 1.12024844e+00 -1.23734362e-01 1.14331864... | [9.876120567321777, 2.9388506412506104] |
eeab7a78-59fb-4d99-bdce-204dc4513b1b | an-analysis-of-categorical-distributional | 1802.08163 | null | http://arxiv.org/abs/1802.08163v1 | http://arxiv.org/pdf/1802.08163v1.pdf | An Analysis of Categorical Distributional Reinforcement Learning | Distributional approaches to value-based reinforcement learning model the
entire distribution of returns, rather than just their expected values, and
have recently been shown to yield state-of-the-art empirical performance. This
was demonstrated by the recently proposed C51 algorithm, based on categorical
distributiona... | ['Rémi Munos', 'Will Dabney', 'Mark Rowland', 'Yee Whye Teh', 'Marc G. Bellemare'] | 2018-02-22 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.69386977e-01 -3.33455615e-02 -4.64788109e-01 -3.24659675e-01
-1.06171799e+00 -9.32000220e-01 8.07766557e-01 2.81620085e-01
-6.77723110e-01 1.02703643e+00 1.31077647e-01 -6.87402129e-01
-7.24179447e-01 -8.59788299e-01 -6.50814354e-01 -8.42257559e-01
-4.49900925e-01 6.39603198e-01 -2.20681593e-01 -1.04061402... | [4.053205490112305, 2.5638010501861572] |
3114461c-e1cd-434c-ada6-102b228f4dc9 | field-of-junctions | 2011.13866 | null | https://arxiv.org/abs/2011.13866v3 | https://arxiv.org/pdf/2011.13866v3.pdf | Field of Junctions: Extracting Boundary Structure at Low SNR | We introduce a bottom-up model for simultaneously finding many boundary elements in an image, including contours, corners and junctions. The model explains boundary shape in each small patch using a 'generalized M-junction' comprising M angles and a freely-moving vertex. Images are analyzed using non-convex optimizatio... | ['Todd Zickler', 'Dor Verbin'] | 2020-11-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Verbin_Field_of_Junctions_Extracting_Boundary_Structure_at_Low_SNR_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Verbin_Field_of_Junctions_Extracting_Boundary_Structure_at_Low_SNR_ICCV_2021_paper.pdf | iccv-2021-1 | ['junction-detection', 'image-smoothing'] | ['computer-vision', 'computer-vision'] | [ 2.21042454e-01 3.18418056e-01 -1.75921395e-01 8.69163200e-02
-8.61566663e-01 -7.83422351e-01 2.82593071e-01 4.22882915e-01
-1.45383835e-01 2.57120639e-01 -1.48501098e-01 4.89316396e-02
1.55931637e-01 -5.87310970e-01 -5.29764414e-01 -7.63118923e-01
-1.85744345e-01 1.48345843e-01 6.91023409e-01 -1.24740824... | [11.565929412841797, -2.7298026084899902] |
1b5db403-0a1c-4714-92e7-37983f2ec32f | bright-channel-prior-attention-for | 2305.12845 | null | https://arxiv.org/abs/2305.12845v1 | https://arxiv.org/pdf/2305.12845v1.pdf | Bright Channel Prior Attention for Multispectral Pedestrian Detection | Multispectral methods have gained considerable attention due to their promising performance across various fields. However, most existing methods cannot effectively utilize information from two modalities while optimizing time efficiency. These methods often prioritize accuracy or time efficiency, leaving room for impr... | ['Yechenhao Yang', 'Jinyu Xie', 'Chenhang Cui'] | 2023-05-22 | null | null | null | null | ['pedestrian-detection', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 6.05807006e-01 -5.49999356e-01 2.02114984e-01 -4.70279753e-01
-4.15229648e-01 -1.25981763e-01 4.36202079e-01 -3.58444542e-01
-6.22606575e-01 6.46513581e-01 1.13606647e-01 1.85211509e-01
3.98184150e-01 -8.95445287e-01 -6.05684936e-01 -1.13405406e+00
6.46852314e-01 -5.38303673e-01 4.19274390e-01 -9.84528735... | [10.0680570602417, -1.5439214706420898] |
16ef7822-1386-4484-bc4b-3a7617b769eb | fast-likelihood-based-change-point-detection | 2301.08892 | null | https://arxiv.org/abs/2301.08892v1 | https://arxiv.org/pdf/2301.08892v1.pdf | Fast likelihood-based change point detection | Change point detection plays a fundamental role in many real-world applications, where the goal is to analyze and monitor the behaviour of a data stream. In this paper, we study change detection in binary streams. To this end, we use a likelihood ratio between two models as a measure for indicating change. The first mo... | ['Nikolaj Tatti'] | 2023-01-21 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.49355167e-01 -2.62729049e-01 -6.00327738e-02 -7.26574212e-02
-6.57591701e-01 -7.52793789e-01 -9.50647071e-02 9.58006680e-01
-9.29999530e-01 4.68708664e-01 -5.96421182e-01 -5.42875707e-01
-1.38219342e-01 -1.13461399e+00 -8.26388776e-01 -5.87076664e-01
-5.83399773e-01 6.04809403e-01 8.42414737e-01 -6.38026297... | [6.711023807525635, 4.784191608428955] |
7b0b7286-b766-4091-aad9-9683fd7e8deb | attributes-and-categories-for-generic | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Tao_Attributes_and_Categories_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Tao_Attributes_and_Categories_2015_CVPR_paper.pdf | Attributes and Categories for Generic Instance Search From One Example | This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos. Firstly, we evaluate state-of-the-art instance search methods on this problem. We observe that what works for buildings loses i... | ['Arnold W. M. Smeulders', 'Shih-Fu Chang', 'Ran Tao'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['instance-search'] | ['computer-vision'] | [ 1.86313421e-01 -1.84057772e-01 -3.04345608e-01 -4.56904143e-01
-8.98102939e-01 -6.64567649e-01 4.85295057e-01 -1.79251414e-02
-2.34953269e-01 4.64671850e-01 -1.30813316e-01 1.05617367e-01
-8.75861466e-01 -6.26663148e-01 -7.40900695e-01 -5.35423815e-01
-1.46215498e-01 1.02443063e+00 5.47486007e-01 -2.97844470... | [7.892367362976074, -2.363372325897217] |
c2885a0b-5f12-4b85-9c2a-efcc1df21b14 | classifications-of-skull-fractures-using-ct | 2203.10786 | null | https://arxiv.org/abs/2203.10786v1 | https://arxiv.org/pdf/2203.10786v1.pdf | Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach | Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury types. So it is vital to detect and classify the fracture very early. In real world, often fractures occur at mu... | ['Moqsadur Rahman', 'Tareque Rahman Ornob', 'Md Moniruzzaman Emon'] | 2022-03-21 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-1.19863041e-01 -1.38705075e-01 1.98619500e-01 -2.78246492e-01
-5.61868429e-01 -1.74947202e-01 6.08923212e-02 7.43389428e-01
-6.27892852e-01 5.30350506e-01 -1.74854174e-01 -3.73023242e-01
-2.13172287e-01 -1.09469724e+00 -2.62715608e-01 -7.71441519e-01
-2.32578650e-01 5.64250946e-01 5.95828712e-01 -2.11506218... | [14.832159042358398, -2.445688486099243] |
c66bc924-9d15-43e0-a99b-8d745e6a2344 | understanding-membership-inferences-on-well | 1802.04889 | null | http://arxiv.org/abs/1802.04889v1 | http://arxiv.org/pdf/1802.04889v1.pdf | Understanding Membership Inferences on Well-Generalized Learning Models | Membership Inference Attack (MIA) determines the presence of a record in a
machine learning model's training data by querying the model. Prior work has
shown that the attack is feasible when the model is overfitted to its training
data or when the adversary controls the training algorithm. However, when the
model is no... | ['Xiao-Feng Wang', 'Vincent Bindschaedler', 'Kai Chen', 'Haixu Tang', 'Carl A. Gunter', 'Yunhui Long', 'Diyue Bu', 'Lei Wang'] | 2018-02-13 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 5.64332187e-01 2.10565016e-01 -3.76614571e-01 -2.53468454e-01
-9.17185068e-01 -1.05905092e+00 3.99824440e-01 3.99377435e-01
2.37737503e-02 6.17015541e-01 -4.58456397e-01 -7.63295829e-01
-4.06234175e-01 -1.10264122e+00 -1.37816894e+00 -7.39434659e-01
-2.83876419e-01 6.42654955e-01 1.56536043e-01 1.61632270... | [5.8367085456848145, 7.3339667320251465] |
d9410ffc-5b7d-426e-a791-87632daf2807 | denoising-autoencoder-based-defensive | 2303.15901 | null | https://arxiv.org/abs/2303.15901v1 | https://arxiv.org/pdf/2303.15901v1.pdf | Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm | Adversarial attacks significantly threaten the robustness of deep neural networks (DNNs). Despite the multiple defensive methods employed, they are nevertheless vulnerable to poison attacks, where attackers meddle with the initial training data. In order to defend DNNs against such adversarial attacks, this work propos... | ['António Casimiro', 'José Cecílio', 'Bakary Badjie'] | 2023-03-28 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.12043284e-01 -1.17988847e-01 6.55888438e-01 -2.92658191e-02
-3.83440435e-01 -1.22004116e+00 6.54891372e-01 -3.10561378e-02
-7.12426901e-01 7.51561344e-01 3.40531766e-02 -2.81282276e-01
2.64111906e-01 -1.07002544e+00 -9.19572353e-01 -1.24584460e+00
9.11295712e-02 -2.60183007e-01 2.70329595e-01 -4.01606888... | [5.517302513122559, 7.9446258544921875] |
5a491b7d-54db-486b-8e29-c66d9e41fcbd | marnet-backdoor-attacks-against-value | null | null | https://openreview.net/forum?id=-VsGCG_AQ69 | https://openreview.net/pdf?id=-VsGCG_AQ69 | MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning | Recent works have revealed that backdoor attacks against Deep Reinforcement Learning (DRL) could lead to abnormal action selection of the agent, which may result in failure or even catastrophe in crucial decision processes. However, existing attacks only consider single-agent RL systems, in which the only agent can obs... | ['Xueluan Gong', 'Zhicong Zheng', 'Yanjiao Chen'] | 2021-09-29 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.42129105e-01 2.78298020e-01 -1.79036722e-01 4.69911873e-01
-4.99140024e-01 -1.10814381e+00 9.21044230e-01 5.37482537e-02
-8.86754990e-01 9.31440651e-01 -7.59270862e-02 -3.09153855e-01
-1.33889332e-01 -7.59457707e-01 -7.52098978e-01 -1.00667143e+00
-4.62575257e-01 5.94945431e-01 3.01180810e-01 -4.72313643... | [3.986900806427002, 2.304965019226074] |
f7ba7079-d682-4610-81ff-2c0f230e5fd9 | synthtiger-synthetic-text-image-generator | 2107.09313 | null | https://arxiv.org/abs/2107.09313v1 | https://arxiv.org/pdf/2107.09313v1.pdf | SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models | For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world. Specifically, they generate multiple text images with diverse backgrounds, font styles, and text shapes and enable STR models to learn visual patterns that might not... | ['Sungrae Park', 'Han-Cheol Cho', 'Yoonsik Kim', 'Moonbin Yim'] | 2021-07-20 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.13612032e-01 -3.45830396e-02 2.18920350e-01 -1.71122581e-01
-7.21861601e-01 -8.13950837e-01 8.53095591e-01 -3.55858296e-01
-1.22704118e-01 5.53825438e-01 1.38689429e-01 -4.03971732e-01
3.90474230e-01 -4.88627881e-01 -8.63081455e-01 -3.41309547e-01
7.91251719e-01 4.09919769e-01 3.14803988e-01 6.32372946... | [11.467917442321777, 0.023084156215190887] |
4fdd17d0-40ea-4406-aa1e-b5c4e69cd589 | dish-detection-in-food-platters-a-framework | 2305.07552 | null | https://arxiv.org/abs/2305.07552v1 | https://arxiv.org/pdf/2305.07552v1.pdf | Dish detection in food platters: A framework for automated diet logging and nutrition management | Diet is central to the epidemic of lifestyle disorders. Accurate and effortless diet logging is one of the significant bottlenecks for effective diet management and calorie restriction. Dish detection from food platters is a challenging problem due to a visually complex food layout. We present an end-to-end computation... | ['Ganesh Bagler', 'Kirti Vashishtha', 'Abhuday Tiwari', 'Nikhilesh Verhwani', 'Nitesh Narwade', 'Samiksha Garg', 'Astha Jain', 'Meenal Jain', 'Ronak Chhajed', 'Ekta Gambhir', 'Hareesh Amuru', 'Nikhila Vishnumolakala', 'Anushka Gupta', 'Pratik Chauhan', 'Nidhi Verma', 'Shounak Ghatak', 'Shashank Dargar', 'Mansi Goel'] | 2023-05-12 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [ 1.34142801e-01 -2.54499286e-01 -2.61762589e-01 5.13290428e-02
-5.94509363e-01 -5.77983081e-01 -1.42329544e-01 9.62465286e-01
-3.24534953e-01 9.10040811e-02 2.04268530e-01 -2.19592210e-02
4.38915677e-02 -7.88568556e-01 -8.76733661e-01 -4.18764293e-01
-4.29112047e-01 5.53918958e-01 -2.25466101e-05 -2.49732465... | [11.563957214355469, 4.393401145935059] |
4f3f4388-ebff-4f86-bda6-4e6e401f6a87 | variational-monte-carlo-approach-to-partial | 2206.01927 | null | https://arxiv.org/abs/2206.01927v2 | https://arxiv.org/pdf/2206.01927v2.pdf | Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks | The accurate numerical solution of partial differential equations is a central task in numerical analysis allowing to model a wide range of natural phenomena by employing specialized solvers depending on the scenario of application. Here, we develop a variational approach for solving partial differential equations gove... | ['Martin Gärttner', 'Moritz Reh'] | 2022-06-04 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [-1.46592408e-01 -2.13982955e-01 3.79018515e-01 2.94667065e-01
-4.24139321e-01 -4.23047036e-01 6.11632586e-01 3.33395064e-01
-6.71082079e-01 1.11232007e+00 -4.16544318e-01 1.77583862e-02
-4.10065800e-01 -7.73970544e-01 -2.68016964e-01 -1.03494775e+00
-2.47487985e-02 9.75527465e-01 2.30381668e-01 -2.75026590... | [6.388342380523682, 3.709676504135132] |
545c48f1-3382-4e99-8307-19d3d45583d5 | 11k-hands-gender-recognition-and-biometric | 1711.04322 | null | http://arxiv.org/abs/1711.04322v9 | http://arxiv.org/pdf/1711.04322v9.pdf | 11K Hands: Gender recognition and biometric identification using a large dataset of hand images | The human hand possesses distinctive features which can reveal gender
information. In addition, the hand is considered one of the primary biometric
traits used to identify a person. In this work, we propose a large dataset of
human hand images (dorsal and palmar sides) with detailed ground-truth
information for gender ... | ['Mahmoud Afifi'] | 2017-11-12 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.94520384e-01 -2.22778141e-01 -2.19652161e-01 -5.36966324e-01
-2.66750157e-01 -7.36709595e-01 4.08484876e-01 -2.25951388e-01
-3.28429371e-01 4.35445428e-01 -1.92713514e-01 -2.33851090e-01
4.34771404e-02 -8.21117282e-01 -3.45215350e-01 -1.03220201e+00
1.23746127e-01 3.33390236e-01 -5.53980708e-01 9.00563374... | [13.329047203063965, 0.9680781364440918] |
5b13a600-7b1b-4e4c-82e9-d48e372660c4 | fingerflex-inferring-finger-trajectories-from | 2211.01960 | null | https://arxiv.org/abs/2211.01960v2 | https://arxiv.org/pdf/2211.01960v2.pdf | FingerFlex: Inferring Finger Trajectories from ECoG signals | Motor brain-computer interface (BCI) development relies critically on neural time series decoding algorithms. Recent advances in deep learning architectures allow for automatic feature selection to approximate higher-order dependencies in data. This article presents the FingerFlex model - a convolutional encoder-decode... | ['Alexey Timchenko', 'Alexander Kovalev', 'Vladislav Lomtev'] | 2022-10-23 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.70937806e-01 -2.81759530e-01 -1.43114120e-01 -1.07709244e-01
-5.27629077e-01 -4.65594083e-02 7.31544435e-01 -6.40858889e-01
-7.31604517e-01 8.44362736e-01 2.89570689e-01 -8.05202127e-02
-3.99673104e-01 -6.46748245e-02 -5.96807241e-01 -3.92103374e-01
-8.33914876e-01 2.93925047e-01 2.97417164e-01 -1.53903246... | [12.982634544372559, 3.407255172729492] |
b83ab35d-13a3-445b-aa72-4213d225376a | malafide-a-novel-adversarial-convolutive | 2306.07655 | null | https://arxiv.org/abs/2306.07655v1 | https://arxiv.org/pdf/2306.07655v1.pdf | Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems | We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs). By introducing convolutional noise using an optimised linear time-invariant filter, Malafide attacks can be used to compromise CM reliability while preserving other speech attributes such as ... | ['Nicholas Evans', 'Massimiliano Todisco', 'Hemlata Tak', 'Wanying Ge', 'Michele Panariello'] | 2023-06-13 | null | null | null | null | ['adversarial-attack', 'face-swapping', 'speaker-verification'] | ['adversarial', 'computer-vision', 'speech'] | [ 3.27479631e-01 2.98970819e-01 -1.54862218e-02 -5.61112054e-02
-8.73747587e-01 -1.22190499e+00 6.74319208e-01 -1.52558843e-02
-3.67424726e-01 2.16483146e-01 6.91892058e-02 -9.35182631e-01
6.86253160e-02 -2.06346542e-01 -4.68315333e-01 -7.85733879e-01
-4.30076629e-01 -9.01743844e-02 2.11322114e-01 -4.45522487... | [14.063446998596191, 5.870436668395996] |
3673859d-3260-416e-8a43-d6371d455215 | german-abusive-language-dataset-with-focus-on | null | null | https://aclanthology.org/2021.konvens-1.26 | https://aclanthology.org/2021.konvens-1.26.pdf | German Abusive Language Dataset with Focus on COVID-19 | null | ['Georg Groh', 'Svenja Räther', 'Maximilian Wich'] | null | null | null | null | konvens-ws-2021-9 | ['abusive-language'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.287423133850098, 3.77508807182312] |
04fa1ec8-b11d-4c99-9584-1608bd67ed25 | adaptive-noisy-matrix-completion | 2203.08340 | null | https://arxiv.org/abs/2203.08340v1 | https://arxiv.org/pdf/2203.08340v1.pdf | Adaptive Noisy Matrix Completion | Low-rank matrix completion has been studied extensively under various type of categories. The problem could be categorized as noisy completion or exact completion, also active or passive completion algorithms. In this paper we focus on adaptive matrix completion with bounded type of noise. We assume that the matrix $\m... | ['Ilqar Ramazanli'] | 2022-03-16 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 5.90643823e-01 3.33861321e-01 3.45243901e-01 2.29189500e-01
-1.17413616e+00 -8.13490570e-01 2.36759603e-01 -1.43967852e-01
-4.84977365e-01 7.18698859e-01 2.81057507e-01 -4.10387740e-02
-6.27222002e-01 -2.00926423e-01 -8.25434864e-01 -9.21414733e-01
-3.24200422e-01 2.05219045e-01 -3.92872542e-01 -2.60061562... | [6.976259231567383, 4.638597011566162] |
9acdf2b1-50d9-451d-98f6-ae84f2033595 | mmformer-multimodal-medical-transformer-for | 2206.02425 | null | https://arxiv.org/abs/2206.02425v2 | https://arxiv.org/pdf/2206.02425v2.pdf | mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation | Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segm... | ['Yefeng Zheng', 'Zhiqiang He', 'Yang Zhang', 'Yawen Huang', 'Dong Wei', 'Yuexiang Li', 'Jiawei Yang', 'Nanjun He', 'Yao Zhang'] | 2022-06-06 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.90851796e-01 6.76367506e-02 -3.30044150e-01 -4.93678778e-01
-1.64995623e+00 -3.56564760e-01 3.39003354e-01 -4.51987460e-02
-5.09699523e-01 6.10905051e-01 3.93040448e-01 -3.08928132e-01
-1.07870966e-01 -3.06732565e-01 -5.64092755e-01 -1.07326937e+00
1.45894855e-01 5.17960072e-01 2.69265343e-02 5.71869835... | [14.505309104919434, -2.310826063156128] |
efd55d4e-22fb-4b4b-8fab-cfda2acb23b1 | justices-for-information-bottleneck-theory | 2305.11387 | null | https://arxiv.org/abs/2305.11387v1 | https://arxiv.org/pdf/2305.11387v1.pdf | Justices for Information Bottleneck Theory | This study comes as a timely response to mounting criticism of the information bottleneck (IB) theory, injecting fresh perspectives to rectify misconceptions and reaffirm its validity. Firstly, we introduce an auxiliary function to reinterpret the maximal coding rate reduction method as a special yet local optimal case... | ['Zhijing Yang', 'Adil Mehmood Khan', 'Yongqiang Cheng', 'Faxian Cao'] | 2023-05-19 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 4.78188545e-01 7.76965678e-01 -4.16582346e-01 -1.73161477e-01
1.55993477e-01 -2.48399258e-01 6.20541513e-01 3.11561137e-01
-7.63105690e-01 7.16392577e-01 5.25533617e-01 -9.61665630e-01
-6.53883755e-01 -4.88809049e-01 -7.27090240e-01 -8.09830666e-01
-8.04984346e-02 -2.72978749e-02 -1.63956285e-02 -3.20699275... | [8.04636001586914, 3.531985282897949] |
7bdb9255-76a5-4556-9329-8045acb2bb35 | semantic-aware-transmission-for-robust-point | 2306.13296 | null | https://arxiv.org/abs/2306.13296v1 | https://arxiv.org/pdf/2306.13296v1.pdf | Semantic-aware Transmission for Robust Point Cloud Classification | As three-dimensional (3D) data acquisition devices become increasingly prevalent, the demand for 3D point cloud transmission is growing. In this study, we introduce a semantic-aware communication system for robust point cloud classification that capitalizes on the advantages of pre-trained Point-BERT models. Our propos... | ['Zhiguo Shi', 'Qianqian Yang', 'Kaiyi Chi', 'Tianxiao Han'] | 2023-06-23 | null | null | null | null | ['point-cloud-classification', 'scene-understanding', 'classification-1'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.00773978e-01 -1.46044672e-01 -1.03210472e-01 -9.85875055e-02
-1.19138432e+00 -2.92356461e-01 2.69969910e-01 2.23080844e-01
-1.08101323e-01 2.17207164e-01 -1.29009262e-01 -3.46646309e-01
-9.22623649e-02 -7.39078164e-01 -6.42444313e-01 -5.99619210e-01
-3.93380553e-01 1.02107473e-01 3.18889856e-01 7.64763951... | [7.834747791290283, -2.8643531799316406] |
915e4601-1734-45c2-85cb-6c0ac2327e6f | effectiveness-of-random-deep-feature | 1910.12392 | null | https://arxiv.org/abs/1910.12392v2 | https://arxiv.org/pdf/1910.12392v2.pdf | Effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples | We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation dete... | ['Bo-Wen Zhang', 'Ehsan Nowroozi', 'Benedetta Tondi', 'Mauro Barni'] | 2019-10-25 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 4.50125128e-01 2.74823934e-01 3.03122938e-01 1.84499070e-01
-2.40562931e-02 -1.15498567e+00 7.52959728e-01 4.63571772e-02
-4.88254040e-01 2.94448972e-01 -1.20480113e-01 -4.03181881e-01
-1.41952232e-01 -8.83210778e-01 -9.04409945e-01 -7.05034673e-01
-2.90735006e-01 7.09352270e-03 5.88920891e-01 -2.71876991... | [5.583834171295166, 7.829930782318115] |
9e6663f9-23ab-4ba1-90fb-eb92019b978e | natural-language-person-search-using-deep | 1809.00365 | null | http://arxiv.org/abs/1809.00365v1 | http://arxiv.org/pdf/1809.00365v1.pdf | Natural Language Person Search Using Deep Reinforcement Learning | Recent success in deep reinforcement learning is having an agent learn how to
play Go and beat the world champion without any prior knowledge of the game. In
that task, the agent has to make a decision on what action to take based on the
positions of the pieces. Person Search is recently explored using natural
language... | ['Ankit Shah', 'Tyler Vuong'] | 2018-09-02 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.49152696e-01 1.23682737e-01 2.40283608e-02 -1.87962666e-01
-6.25447690e-01 -5.04374743e-01 3.10343564e-01 4.16822642e-01
-9.26461041e-01 5.37589252e-01 1.44634277e-01 1.56122267e-01
-2.02071965e-01 -8.70792270e-01 -5.93501151e-01 -6.18284762e-01
-1.16969058e-02 1.09534729e+00 4.25026804e-01 -1.83399841... | [9.22881031036377, 0.5381669402122498] |
573c1492-ce75-4e42-aca0-f6263a858fef | keypoint-based-weakly-supervised-human | 1809.05285 | null | http://arxiv.org/abs/1809.05285v1 | http://arxiv.org/pdf/1809.05285v1.pdf | Keypoint Based Weakly Supervised Human Parsing | Fully convolutional networks (FCN) have achieved great success in human
parsing in recent years. In conventional human parsing tasks, pixel-level
labeling is required for guiding the training, which usually involves enormous
human labeling efforts. To ease the labeling efforts, we propose a novel weakly
supervised huma... | ['Guosheng Lin', 'Zhonghua Wu', 'Jianfei Cai'] | 2018-09-14 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 4.68404323e-01 6.07377946e-01 -1.85099587e-01 -6.77822351e-01
-8.92775059e-01 -5.26699007e-01 3.72990787e-01 5.86120337e-02
-6.57347739e-01 5.16746998e-01 -1.96041092e-01 -3.35668921e-01
6.11972809e-01 -7.91455507e-01 -9.22578096e-01 -3.31768423e-01
4.37058091e-01 5.15534401e-01 7.61655927e-01 7.97319710... | [9.410160064697266, 0.4702279567718506] |
59134d5b-5edd-4ff9-9c8a-7354a61b5b12 | fault-detection-via-occupation-kernel | 2303.11138 | null | https://arxiv.org/abs/2303.11138v2 | https://arxiv.org/pdf/2303.11138v2.pdf | Fault Detection via Occupation Kernel Principal Component Analysis | The reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamical system. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attention due to their ease of deployment and minimal need... | ['Rushikesh Kamalapurkar', 'Yingzhao Lian', 'Benjamin P. Russo', 'Zachary Morrison'] | 2023-03-20 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.35542469e-02 -3.90289903e-01 2.24728018e-01 1.31013989e-01
-5.41113377e-01 -2.84001857e-01 4.24516201e-01 3.27759743e-01
1.03268616e-01 5.60476303e-01 -4.27203685e-01 -2.77394056e-01
-7.95866787e-01 -5.31289697e-01 -2.96771228e-01 -9.64652002e-01
-2.47624069e-01 2.29541048e-01 3.13803613e-01 -2.77937859... | [6.520080089569092, 2.532090187072754] |
88c93277-277d-4570-bc3b-d153698409d3 | a-novel-approach-for-enhancing-sentiment | null | null | https://www.researchgate.net/publication/357150745_A_Novel_Approach_for_Enhancing_Sentiment_Classification_of_Persian_Reviews_Using_Convolutional_Neural_Network_and_Majority_Voting_Classifier | https://www.researchgate.net/publication/357150745_A_Novel_Approach_for_Enhancing_Sentiment_Classification_of_Persian_Reviews_Using_Convolutional_Neural_Network_and_Majority_Voting_Classifier | A Novel Approach for Enhancing Sentiment Classification of Persian Reviews Using Convolutional Neural Network and Majority Voting Classifier | Due to the rapid development of Internet-based applications such as social media, sentiment analysis has become one of the most widely used research areas of natural language processing and an important tool for extracting opinions from texts. Because a huge number of comments and reviews are generated today through so... | ['Milad Vazan'] | 2021-12-17 | null | null | null | digital-transformation-and-intelligent | ['persian-sentiment-anlysis'] | ['natural-language-processing'] | [-1.92647457e-01 -2.32724741e-01 -1.85974233e-03 -6.38788700e-01
-1.11037180e-01 -3.99863690e-01 5.69501162e-01 5.26624620e-01
-5.90254188e-01 8.02025616e-01 1.36479348e-01 -2.28934437e-01
2.09323674e-01 -8.86415720e-01 -7.05634281e-02 -5.26316524e-01
3.47943395e-01 6.55076578e-02 -5.09014539e-03 -6.01967275... | [11.112783432006836, 6.903971195220947] |
f03a3bd2-807c-4e81-b699-1fe4a6a8628f | coach-a-coarse-to-fine-approach-for-cross | 2004.11727 | null | https://arxiv.org/abs/2004.11727v1 | https://arxiv.org/pdf/2004.11727v1.pdf | Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling | As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-domain slot filling has naturally arisen to cope with this data scarcity problem. In this paper, we propose a Coarse-to-fine approach (Coach)... | ['Pascale Fung', 'Zihan Liu', 'Peng Xu', 'Genta Indra Winata'] | 2020-04-24 | coach-a-coarse-to-fine-approach-for-cross-1 | https://aclanthology.org/2020.acl-main.3 | https://aclanthology.org/2020.acl-main.3.pdf | acl-2020-6 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [-0.02446181 0.47969723 -0.45328107 -0.5970683 -0.84991425 -0.35838184
0.38560805 0.03354181 -0.46712124 0.96790195 0.34809935 -0.4277723
0.27856183 -0.75461984 -0.3415331 -0.22989385 0.44381 1.0093409
0.3766774 -0.42785665 -0.05487952 -0.15360434 -0.9975701 0.3158929
1.2895814 0.8791895 0.510... | [12.588020324707031, 7.406780242919922] |
6da4fdc6-c3a8-4b25-b1ad-61fed2d87473 | optimal-application-of-trajectory | 2303.00549 | null | https://arxiv.org/abs/2303.00549v1 | https://arxiv.org/pdf/2303.00549v1.pdf | Optimal Application of Trajectory Optimization by Travel Profile for Electric Train in an Electrical Transportation System | Increasing greenhouse gas (GHG) emission is one of the most important concerns of world decision-makers. The considerable part of it belongs to the transportation systems. As a solution, the world is going to use urban electrical transportation systems, and thus designing optimal methods and novel strategies is essenti... | ['Armin Mosavi'] | 2023-03-01 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.63739040e-01 -4.70764861e-02 -1.71342045e-01 -1.06286459e-01
-2.68078387e-01 -4.09166634e-01 4.01213914e-01 3.00057769e-01
-5.20253837e-01 9.35054064e-01 -3.28831732e-01 -4.86176193e-01
-7.24958837e-01 -1.42955792e+00 -2.60776103e-01 -8.68841290e-01
2.64015198e-01 4.22171533e-01 -1.10528560e-03 -2.71285743... | [5.594106674194336, 2.1510982513427734] |
707e1179-884c-4dba-8a6f-482a4c79e372 | long-history-short-term-memory-for-long-term | null | null | https://openreview.net/forum?id=HklmoRVYvr | https://openreview.net/pdf?id=HklmoRVYvr | Long History Short-Term Memory for Long-Term Video Prediction | While video prediction approaches have advanced considerably in recent years, learning to predict long-term future is challenging — ambiguous future or error propagation over time yield blurry predictions. To address this challenge, existing algorithms rely on extra supervision (e.g., action or object pose), motion flo... | ['Jan Kautz', 'Wonmin Byeon'] | 2019-09-25 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.71845579e-01 -9.74611193e-03 -5.71991682e-01 -4.81267542e-01
-3.65402699e-01 -2.23518476e-01 8.23948085e-01 -4.59995389e-01
-2.06889227e-01 8.60841215e-01 6.26652062e-01 -1.93458214e-01
1.70402870e-01 -5.73512316e-01 -1.04725063e+00 -3.28142136e-01
-3.64811659e-01 1.17343210e-01 6.91281438e-01 -6.57113045... | [8.09397029876709, 0.30766355991363525] |
a7e1be60-a3c7-4630-b7f1-c2f3f32d060c | a-cnn-regression-model-to-estimate-buildings | 2307.01378 | null | https://arxiv.org/abs/2307.01378v1 | https://arxiv.org/pdf/2307.01378v1.pdf | A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series | Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis. In this study, we propose a supervised Multimodal Building Height Regression Network (MBHR-Net) for estimating building heights at 10m spatial resolution using Sentinel-1 (S1) and Sentinel-2 (... | ['Yifang Ban', 'Andrea Nascetti', 'Ritu Yadav'] | 2023-07-03 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 2.19127417e-01 -4.12697569e-02 6.99196383e-03 -3.78333390e-01
-1.06672025e+00 -5.65314107e-02 5.68509877e-01 3.30234617e-01
-2.79182941e-01 8.73006582e-01 2.52406627e-01 -6.64042234e-01
-3.00291628e-01 -1.59431982e+00 -4.09830511e-01 -8.19593549e-01
-7.60807633e-01 3.95356379e-02 -1.30290940e-01 -6.67667091... | [9.383211135864258, -1.4673593044281006] |
99460561-361d-4781-af20-36f3e26575a9 | transferring-models-trained-on-natural-images | 2303.01491 | null | https://arxiv.org/abs/2303.01491v1 | https://arxiv.org/pdf/2303.01491v1.pdf | Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models | Transfer learning has remarkably improved computer vision. These advances also promise improvements in neuroimaging, where training set sizes are often small. However, various difficulties arise in directly applying models pretrained on natural images to radiologic images, such as MRIs. In particular, a mismatch in the... | ["the Alzheimer's Disease Neuroimaging Initiative", 'Greg Ver Steeg', 'Paul M. Thompson', 'Nikhil Dhinagar', 'Tamoghna Chattopadhyay', 'Umang Gupta'] | 2023-03-02 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 2.60189146e-01 5.00216186e-01 -5.79199456e-02 -7.44146109e-01
-5.42248905e-01 -3.22662473e-01 5.90446413e-01 -1.35521665e-01
-8.69349778e-01 6.15957737e-01 4.84663874e-01 -4.55656052e-01
8.41639638e-02 -7.20138013e-01 -9.77009416e-01 -4.54492867e-01
-5.68808973e-01 6.54768348e-01 1.69339538e-01 1.29263699... | [14.421257972717285, -1.9827381372451782] |
a610eea9-bb15-4469-b604-b54c9e2f9500 | multi-agent-cross-translated-diversification | 2006.02163 | null | https://arxiv.org/abs/2006.02163v4 | https://arxiv.org/pdf/2006.02163v4.pdf | Cross-model Back-translated Distillation for Unsupervised Machine Translation | Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently. Crucially, iterative back-translation and denoising auto-encoding for language modeling provide data diversity to train th... | ['Ai Ti Aw', 'Thanh-Tung Nguyen', 'Xuan-Phi Nguyen', 'Wu Kui', 'Shafiq Joty'] | 2020-06-03 | cross-model-back-translated-distillation-for | https://openreview.net/forum?id=K5a_QFEUzA1 | https://openreview.net/pdf?id=K5a_QFEUzA1 | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.62803009e-01 -2.04426393e-01 -5.15401602e-01 -2.56268531e-01
-1.27701044e+00 -6.73536718e-01 9.02302682e-01 -1.76350310e-01
-4.67897624e-01 1.01113188e+00 3.30012947e-01 -8.65715683e-01
3.24720889e-01 -2.66939282e-01 -7.33868361e-01 -5.97090840e-01
3.79393280e-01 9.45945084e-01 -2.41191000e-01 -6.15210593... | [11.601953506469727, 10.297959327697754] |
5b1fab77-1aa3-4e7b-90d8-60ed17a647c3 | ntnu-domain-semi-independent-short-message | null | null | https://aclanthology.org/S13-2071 | https://aclanthology.org/S13-2071.pdf | NTNU: Domain Semi-Independent Short Message Sentiment Classification | null | ['Bj{\\"o}rn Gamb{\\"a}ck', '{\\O}yvind Selmer', 'Lars Bungum', 'Mikael Brevik'] | 2013-06-01 | null | null | null | semeval-2013-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.346166133880615, 3.647867441177368] |
7f3371fe-b76f-4d26-9873-2498118b3cc1 | ecdans-efficient-temporal-causal-discovery | 2303.02833 | null | https://arxiv.org/abs/2303.02833v1 | https://arxiv.org/pdf/2303.02833v1.pdf | eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract) | Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations. In this study, we present a novel constraint-based CD approach for autocorrelated and non-stationary time series data (eCDANs) capable of detecting l... | ['Md Osman Gani', 'Uzma Hasan', 'Muhammad Hasan Ferdous'] | 2023-03-06 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-1.89650711e-02 -6.38365686e-01 -5.35922110e-01 -1.88690662e-01
-1.04336239e-01 -6.59570456e-01 1.10616851e+00 1.32898152e-01
8.29252750e-02 1.10161340e+00 6.55485809e-01 -6.64160013e-01
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65e41d7d-94e1-4f58-b283-46b7106d8936 | holo-dex-teaching-dexterity-with-immersive | 2210.06463 | null | https://arxiv.org/abs/2210.06463v1 | https://arxiv.org/pdf/2210.06463v1.pdf | Holo-Dex: Teaching Dexterity with Immersive Mixed Reality | A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional, contact-rich behaviors often require esoteric teleoperation tools. In this work, we presen... | ['Lerrel Pinto', 'Soumith Chintala', 'Irmak Güzey', 'Sridhar Pandian Arunachalam'] | 2022-10-12 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-4.73853767e-01 1.42706975e-01 -1.29710473e-02 -8.51243176e-03
-2.68831253e-01 -1.03756726e+00 1.89162493e-01 -5.74709892e-01
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-3.06504309e-01 -1.88417703e-01 -1.10234523e+00 -3.52926761e-01
-2.71071911e-01 6.83017969e-01 2.28063270e-01 -6.91244781... | [4.710915565490723, 0.6470305323600769] |
144a26c1-267f-4b5b-ab66-ad9c78b521d2 | modularity-based-linkage-model-for | 2306.01227 | null | https://arxiv.org/abs/2306.01227v1 | https://arxiv.org/pdf/2306.01227v1.pdf | Modularity based linkage model for neuroevolution | Crossover between neural networks is considered disruptive due to the strong functional dependency between connection weights. We propose a modularity-based linkage model at the weight level to preserve functionally dependent communities (building blocks) in neural networks during mixing. A proximity matrix is built by... | ['Marcus Gallagher', 'Yukai Qiao'] | 2023-06-02 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 5.64751148e-01 4.20334935e-01 -1.50146961e-01 -4.76908386e-02
3.09552193e-01 -7.01135635e-01 2.83548623e-01 2.17753559e-01
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-7.52549827e-01 -8.48879814e-01 -9.83758211e-01 -9.42004323e-01
-7.80954897e-01 4.40744102e-01 3.86847377e-01 -2.83641964... | [7.016350746154785, 5.528370380401611] |
64f76aa9-afcd-49dc-93c0-51d42f9d59fc | human-semantic-segmentation-using-millimeter | 2304.14132 | null | https://arxiv.org/abs/2304.14132v2 | https://arxiv.org/pdf/2304.14132v2.pdf | Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds | This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. Compared with cameras and lidars, millimeter-wave radars have the advantage of not revealing privacy, having a strong anti-interference ability, and having long detection distance. The sparsity and capt... | ['Han Cheng', 'Luoyu MEI', 'Pengfei Song'] | 2023-04-27 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 7.97470436e-02 -4.68648337e-02 1.63424745e-01 -4.02202606e-01
-6.97922707e-01 -4.11006629e-01 1.81947455e-01 -1.82674780e-01
-2.88584560e-01 4.89964306e-01 -3.74016970e-01 -3.16560000e-01
-4.39762414e-01 -1.11053610e+00 -5.66027641e-01 -7.53239095e-01
-3.86172861e-01 6.09921753e-01 2.91961819e-01 2.04489022... | [7.868849277496338, -3.1256461143493652] |
35108f9d-4ea5-41b8-9aab-45767f6d802b | equivariant-few-shot-learning-from-pretrained | 2305.09900 | null | https://arxiv.org/abs/2305.09900v1 | https://arxiv.org/pdf/2305.09900v1.pdf | Equivariant Few-Shot Learning from Pretrained Models | Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of \cite{basu2022equi} and \cite{kaba2022equivariance} propose group averaging (\textit{equitune}) and optimization-based methods, respectively, over features from group-tr... | ['Lav R. Varshney', 'Payel Das', 'Katherine Driggs-Campbell', 'Vijil Chenthamarakshan', 'Prasanna Sattigeri', 'Pulkit Katdare', 'Sourya Basu'] | 2023-05-17 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 2.00262129e-01 1.06646277e-01 -9.51367766e-02 -3.60784113e-01
-8.30195665e-01 -5.70491493e-01 6.34675205e-01 -2.83482462e-01
-5.63221574e-01 1.08630013e+00 8.64344314e-02 -3.21171761e-01
-3.79531294e-01 -1.10164261e+00 -1.20519090e+00 -5.40538251e-01
3.79050151e-02 3.02447736e-01 -1.74913079e-01 -7.27893233... | [10.55920124053955, 8.170063972473145] |
ba243020-c114-4271-a6a3-a6cb1ecdb160 | segeqa-video-segmentation-based-visual | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Luo_SegEQA_Video_Segmentation_Based_Visual_Attention_for_Embodied_Question_Answering_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_SegEQA_Video_Segmentation_Based_Visual_Attention_for_Embodied_Question_Answering_ICCV_2019_paper.pdf | SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering | Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user's questions by exploring the real world environment. It has attracted increasing research interests due to its broad applications in automatic driving system, in-home robots, and personal assistants. Most of... | [' Yazhou Yao', ' Zhenmin Tang', ' Fayao Liu', ' Zichuan Liu', ' Guosheng Lin', 'Haonan Luo'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['embodied-question-answering'] | ['computer-vision'] | [ 5.44877015e-02 1.42840207e-01 2.64000833e-01 -3.91225249e-01
-7.31333315e-01 -4.89885300e-01 5.27592123e-01 -5.13528399e-02
-7.31767356e-01 3.69580567e-01 2.98358407e-03 -3.56477171e-01
-3.70605253e-02 -8.32697868e-01 -6.40011191e-01 -4.68348593e-01
2.96844721e-01 2.84280390e-01 4.75539595e-01 -5.89711130... | [4.49120569229126, 0.38993972539901733] |
2d532c63-8a88-42e4-a6df-a0a8e8ae7817 | gradient-coherent-strong-regularization-for | 1811.08056 | null | https://arxiv.org/abs/1811.08056v2 | https://arxiv.org/pdf/1811.08056v2.pdf | Gradient-Coherent Strong Regularization for Deep Neural Networks | Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are common regularization tools in machine learning with their simplicity and effectiveness. However, we observe that imposing strong L1 or L2 reg... | ['Dae Hoon Park', 'Yi Chang', 'Huaqing Zhang', 'Chiu Man Ho'] | 2018-11-20 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.81731030e-01 -1.91005930e-01 -1.52445197e-01 -3.64961416e-01
-2.49148965e-01 -2.65356004e-01 3.07003200e-01 -6.62361756e-02
-7.20062256e-01 7.90874720e-01 1.60199590e-02 -3.07368726e-01
-1.65059060e-01 -6.06517375e-01 -9.08401251e-01 -9.56293523e-01
2.52478927e-01 -1.46674395e-01 1.42826498e-01 -4.16619191... | [8.543329238891602, 3.557657480239868] |
034422e9-429c-471c-83f5-57fd5f4546ec | can-we-trust-explainable-ai-methods-on-asr-an | 2305.18011 | null | https://arxiv.org/abs/2305.18011v1 | https://arxiv.org/pdf/2305.18011v1.pdf | Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition | Explainable AI (XAI) techniques have been widely used to help explain and understand the output of deep learning models in fields such as image classification and Natural Language Processing. Interest in using XAI techniques to explain deep learning-based automatic speech recognition (ASR) is emerging. but there is not... | ['Ajitha Rajan', 'Peter Bell', 'Xiaoliang Wu'] | 2023-05-29 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.21362132e-01 7.99847424e-01 -3.43684882e-01 -8.06695998e-01
-9.71941888e-01 -3.16335857e-01 9.17275071e-01 -7.17447996e-02
2.29574457e-01 6.47486210e-01 4.84219909e-01 -6.25046968e-01
-5.19158781e-01 -3.42652090e-02 -9.91760552e-01 -4.88172412e-01
3.18109877e-02 7.40776837e-01 -4.13716555e-01 9.09370109... | [8.969198226928711, 5.62040376663208] |
8b1eca46-fc86-4808-927b-1dd60cbc3b4b | uncertainty-driven-action-quality-assessment | 2207.14513 | null | https://arxiv.org/abs/2207.14513v1 | https://arxiv.org/pdf/2207.14513v1.pdf | Uncertainty-Driven Action Quality Assessment | Automatic action quality assessment (AQA) has attracted more interests due to its wide applications. However, existing AQA methods usually employ the multi-branch models to generate multiple scores, which is not flexible for dealing with a variable number of judges. In this paper, we propose a novel Uncertainty-Driven ... | ['Yaping Huang', 'Caixia Zhou'] | 2022-07-29 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 1.24880143e-01 2.81573176e-01 -2.83892065e-01 -5.27230918e-01
-1.39586544e+00 -5.12929037e-02 3.72764885e-01 1.04461730e-01
-4.06748325e-01 9.77919459e-01 5.77831507e-01 -4.99542169e-02
-4.10333514e-01 -7.42648304e-01 -5.47488391e-01 -8.56174946e-01
4.07849222e-01 5.73378384e-01 2.46061340e-01 -5.20251468... | [8.485690116882324, 0.8467597365379333] |
175717d5-0543-44b5-a92f-4b0ede9aa333 | incentive-allocation-in-vertical-federated | 2307.03515 | null | https://arxiv.org/abs/2307.03515v1 | https://arxiv.org/pdf/2307.03515v1.pdf | Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem | Vertical federated learning (VFL) is a promising approach for collaboratively training machine learning models using private data partitioned vertically across different parties. Ideally in a VFL setting, the active party (party possessing features of samples with labels) benefits by improving its machine learning mode... | ['Anna Wilbik', 'Frank Thuijsman', 'Marijn ten Thij', 'Afsana Khan'] | 2023-07-07 | null | null | null | null | ['fairness', 'federated-learning', 'fairness'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 5.27530350e-02 6.06671751e-01 -3.47054005e-01 -4.04327482e-01
-8.52164030e-01 -1.21141899e+00 2.00396881e-01 3.46451283e-01
-6.08006299e-01 1.10761988e+00 1.59870118e-01 -3.93517077e-01
-4.34060156e-01 -1.02838171e+00 -6.61235988e-01 -1.10300183e+00
5.45176305e-02 5.11865079e-01 -2.43420288e-01 2.18502477... | [5.8807902336120605, 6.516333103179932] |
4e258202-2f99-4d6c-bafa-376014c6be6b | a-novel-self-knowledge-distillation-approach | 2209.01311 | null | https://arxiv.org/abs/2209.01311v1 | https://arxiv.org/pdf/2209.01311v1.pdf | A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition | Knowledge distillation is an effective transfer of knowledge from a heavy network (teacher) to a small network (student) to boost students' performance. Self-knowledge distillation, the special case of knowledge distillation, has been proposed to remove the large teacher network training process while preserving the st... | ['Jia-Ching Wang', 'Trang Phung', 'Duc-Quang Vu'] | 2022-09-03 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 2.12926537e-01 3.28520179e-01 -4.78884071e-01 -2.96184003e-01
-3.23784471e-01 -4.25378859e-01 4.87868547e-01 5.83274886e-02
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-3.90435934e-01 -8.95218790e-01 -6.29369676e-01 -8.15344036e-01
4.72966015e-01 3.83501709e-01 5.21560252e-01 -2.12308347... | [9.504691123962402, 3.3622353076934814] |
16e3954a-0526-4e91-8b93-0912f844ee3d | progressive-continual-learning-for-spoken | 2201.12546 | null | https://arxiv.org/abs/2201.12546v2 | https://arxiv.org/pdf/2201.12546v2.pdf | Progressive Continual Learning for Spoken Keyword Spotting | Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. To tackle such challenges, we propose a progressive continual learning strategy for small-footprint spoken keyword spotting (PCL-KWS). Specifically, the proposed PCL-KWS framework introduces a network instantiato... | ['Nancy F. Chen', 'Nana Hou', 'Yizheng Huang'] | 2022-01-29 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-1.89744290e-02 8.96888226e-03 -3.45917642e-01 -3.50972682e-01
-7.29742408e-01 -2.70752370e-01 3.69141221e-01 -1.90997079e-01
-7.76736200e-01 7.55809426e-01 1.82984337e-01 -5.38513601e-01
-1.45753413e-01 -1.88074842e-01 -7.81716228e-01 -3.41552466e-01
-5.36926836e-02 3.79216611e-01 8.10396075e-01 -2.50687242... | [9.968682289123535, 3.614481210708618] |
f1334180-5437-4b75-8fa6-3512a285e3c4 | text-to-image-diffusion-models-can-be-easily | 2305.04175 | null | https://arxiv.org/abs/2305.04175v1 | https://arxiv.org/pdf/2305.04175v1.pdf | Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning | With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis. To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigati... | ['Hang Su', 'Yuejian Fang', 'Shi Pu', 'Qingni Shen', 'Yinpeng Dong', 'Shengfang Zhai'] | 2023-05-07 | null | null | null | null | ['data-poisoning', 'backdoor-attack'] | ['adversarial', 'adversarial'] | [ 6.00248992e-01 5.12766279e-03 -3.32950085e-01 9.77860913e-02
-8.78488004e-01 -1.10044348e+00 1.06406665e+00 -3.02220792e-01
-1.01181261e-01 2.48199534e-02 1.28028661e-01 -8.69339406e-01
1.82753801e-01 -7.17615247e-01 -1.07755232e+00 -5.94207227e-01
7.63376132e-02 -1.39951736e-01 3.70353103e-01 -1.49240538... | [5.739577293395996, 7.845658779144287] |
9470da38-0ef6-43de-ac17-34ef18757070 | fusing-event-based-and-rgb-camera-for-robust | null | null | https://hal.archives-ouvertes.fr/hal-03591717/ | https://hal.archives-ouvertes.fr/hal-03591717/document | Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions | The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving. Traditional RGB-based detection fails under these conditions and it is thus important to design a sensor suite that is ... | ['Christian Laugier', 'Alessandro Renzaglia', 'Khushdeep Singh Mann', 'Anshul Paigwar', 'Abhishek Tomy'] | 2022-03-30 | null | null | null | icra-2022-3 | ['robust-object-detection', 'infrared-and-visible-image-fusion', 'stereo-lidar-fusion', 'event-based-vision'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.83009857e-01 -4.20490801e-01 4.14180458e-01 -4.72455382e-01
-6.21698976e-01 -4.40306753e-01 8.95837188e-01 8.80410969e-02
-9.19188201e-01 6.17198884e-01 -4.15745527e-01 -1.03535250e-01
1.78338364e-01 -8.19085658e-01 -1.06896377e+00 -8.13044190e-01
-1.57900639e-02 7.75368838e-03 1.05143356e+00 -1.66274175... | [8.309062004089355, -1.2304203510284424] |
8e30496a-f2ed-4153-aa9a-110bccabe372 | pytouch-a-machine-learning-library-for-touch | 2105.12791 | null | https://arxiv.org/abs/2105.12791v1 | https://arxiv.org/pdf/2105.12791v1.pdf | PyTouch: A Machine Learning Library for Touch Processing | With the increased availability of rich tactile sensors, there is an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch -- t... | ['Roberto Calandra', 'Trevor Darrell', 'Shaoxiong Wang', 'Po-Wei Chou', 'Jingwei Xu', 'Huazhe Xu', 'Mike Lambeta'] | 2021-05-26 | null | null | null | null | ['touch-detection'] | ['robots'] | [ 2.86726534e-01 -4.95484263e-01 -2.30936497e-01 -2.67811716e-01
-6.98959768e-01 -5.55915058e-01 3.71465422e-02 2.15959370e-01
-2.65029967e-01 1.94786012e-01 -1.80880681e-01 -4.38738614e-02
-6.27900288e-02 -7.94337809e-01 -5.31049371e-01 -3.85164440e-01
2.29273960e-02 2.88160950e-01 4.85248834e-01 -5.44122458... | [5.922492027282715, -0.7362760305404663] |
975bfb71-4aea-4702-b062-e4fbebfd4482 | videoinr-learning-video-implicit-neural-1 | 2206.04647 | null | https://arxiv.org/abs/2206.04647v1 | https://arxiv.org/pdf/2206.04647v1.pdf | VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution | Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate ... | ['Xiaolong Wang', 'Humphrey Shi', 'Zhangyang Wang', 'Vidit Goel', 'Xingqian Xu', 'Jingwen Liu', 'Yinbo Chen', 'Zeyuan Chen'] | 2022-06-09 | videoinr-learning-video-implicit-neural | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_VideoINR_Learning_Video_Implicit_Neural_Representation_for_Continuous_Space-Time_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['space-time-video-super-resolution', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.27156627e-01 -4.18484002e-01 -6.16299868e-01 -2.13132247e-01
-8.68973911e-01 -1.00974545e-01 3.24758470e-01 -6.21640980e-01
-1.58020005e-01 8.83858621e-01 3.92203122e-01 1.18623652e-01
4.44742590e-02 -8.18671942e-01 -8.74890089e-01 -3.73690486e-01
-9.08722878e-02 -1.75970688e-01 5.75974464e-01 -1.36206567... | [11.084028244018555, -1.8229509592056274] |
9eccd9c5-1b99-446f-a9a0-96325211b023 | iterative-self-knowledge-distillation-from | 2202.02265 | null | https://arxiv.org/abs/2202.02265v1 | https://arxiv.org/pdf/2202.02265v1.pdf | Iterative Self Knowledge Distillation -- From Pothole Classification to Fine-Grained and COVID Recognition | Pothole classification has become an important task for road inspection vehicles to save drivers from potential car accidents and repair bills. Given the limited computational power and fixed number of training epochs, we propose iterative self knowledge distillation (ISKD) to train lightweight pothole classifiers. Des... | ['Kuan-Chuan Peng'] | 2022-02-04 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 2.41697058e-02 6.49366200e-01 -3.20787340e-01 -5.19285560e-01
-6.16944671e-01 -2.31281012e-01 2.14757144e-01 3.57088536e-01
-7.86220491e-01 7.00013638e-01 -2.01748356e-01 -7.57725418e-01
-3.78011197e-01 -1.04412818e+00 -7.05390513e-01 -4.84196424e-01
3.15949112e-01 3.23874772e-01 6.95187569e-01 -1.80757549... | [9.478107452392578, 3.248389959335327] |
9ed72fe9-e48e-4639-8ba0-54b012b68e9c | big-little-adaptive-neural-networks-on-low | 2304.09695 | null | https://arxiv.org/abs/2304.09695v1 | https://arxiv.org/pdf/2304.09695v1.pdf | Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold Processors | This paper investigates the energy savings that near-subthreshold processors can obtain in edge AI applications and proposes strategies to improve them while maintaining the accuracy of the application. The selected processors deploy adaptive voltage scaling techniques in which the frequency and voltage levels of the p... | ['Jose Nunez-Yanez', 'Neil Howard', 'Zichao Shen'] | 2023-04-19 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 4.41426009e-01 -1.02293550e-03 -4.15093213e-01 -2.49252602e-01
-3.18852030e-02 -2.09283501e-01 1.54314175e-01 2.58875489e-01
-5.77783585e-01 8.76653552e-01 -5.22718608e-01 -4.11162615e-01
-3.18953484e-01 -8.03578019e-01 -5.98591805e-01 -7.32447982e-01
6.25648275e-02 2.19294026e-01 3.46825391e-01 1.04102947... | [8.22741985321045, 2.5273778438568115] |
e1032f49-d2c8-49ec-be28-79d182972b45 | meta-regression-analysis-of-errors-in-short | 2305.18550 | null | https://arxiv.org/abs/2305.18550v1 | https://arxiv.org/pdf/2305.18550v1.pdf | Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting | Forecasting electricity demand plays a critical role in ensuring reliable and cost-efficient operation of the electricity supply. With the global transition to distributed renewable energy sources and the electrification of heating and transportation, accurate load forecasts become even more important. While numerous e... | ['Thorsten Staake', 'Hannah Hartstang', 'Konstantin Hopf'] | 2023-05-29 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-3.35675955e-01 -3.38950723e-01 -7.43773162e-01 -1.99958026e-01
-1.79438785e-01 -5.92096627e-01 8.59992623e-01 5.77419363e-02
-9.93136093e-02 8.19291890e-01 3.95225346e-01 -9.90863502e-01
-4.91666555e-01 -9.81717229e-01 -1.82540402e-01 -7.80804574e-01
1.38360247e-01 4.05704767e-01 -5.17805398e-01 -2.34633684... | [6.119110107421875, 2.8624751567840576] |
4e21cda5-08a7-43ed-b003-a74b6e310eff | distanced-lstm-time-distanced-gates-in-long | 1909.05321 | null | https://arxiv.org/abs/1909.05321v1 | https://arxiv.org/pdf/1909.05321v1.pdf | Distanced LSTM: Time-Distanced Gates in Long Short-Term Memory Models for Lung Cancer Detection | The field of lung nodule detection and cancer prediction has been rapidly developing with the support of large public data archives. Previous studies have largely focused on cross-sectional (single) CT data. Herein, we consider longitudinal data. The Long Short-Term Memory (LSTM) model addresses learning with regularly... | ['Kim L. Sandler', 'Yuankai Huo', 'Sanja L. Antic', 'Steve Deppen', 'Riqiang Gao', 'Alexis B. Paulson', 'Yucheng Tang', 'Pierre P. Massion', 'Emily S. Epstein', 'Shunxing Bao', 'Bennett A. Landman', 'Aneri B. Balar'] | 2019-09-11 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 2.87851214e-01 -2.59180933e-01 -7.01807678e-01 -2.49278575e-01
-9.57495034e-01 -1.89090565e-01 5.52760184e-01 8.07446465e-02
-6.92414284e-01 7.37459660e-01 2.37531021e-01 -4.96239305e-01
-4.02000368e-01 -7.36927688e-01 -7.19061255e-01 -8.01395714e-01
-4.24923331e-01 3.95259470e-01 6.17023826e-01 2.84903318... | [15.21770191192627, -2.0536999702453613] |
5c489e2e-3c58-4ffd-8525-261ca8ba55e7 | on-the-importance-of-attention-in-meta | 1806.00852 | null | http://arxiv.org/abs/1806.00852v1 | http://arxiv.org/pdf/1806.00852v1.pdf | On the Importance of Attention in Meta-Learning for Few-Shot Text Classification | Current deep learning based text classification methods are limited by their
ability to achieve fast learning and generalization when the data is scarce. We
address this problem by integrating a meta-learning procedure that uses the
knowledge learned across many tasks as an inductive bias towards better natural
languag... | ['Hassan Chouaib', 'Mohammad Havaei', 'Stan Matwin', 'Nicolas Chapados', 'Xiang Jiang', 'Andrew Jesson', 'Thomas Vincent', 'Gabriel Chartrand'] | 2018-06-03 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 2.90623724e-01 1.92190677e-01 -4.19979870e-01 -7.02441573e-01
-5.83397031e-01 -2.96619266e-01 8.83212924e-01 4.88540858e-01
-6.62809253e-01 7.03056753e-01 4.19771999e-01 -2.18643427e-01
-3.04343611e-01 -5.49255908e-01 -4.59002763e-01 -5.67175806e-01
3.85576934e-01 6.62998319e-01 -2.31271058e-01 -3.16529691... | [10.761021614074707, 7.6909942626953125] |
63936196-bc55-4159-9fab-8f2b424c5280 | x-recosa-multi-scale-context-aggregation-for | 2303.07833 | null | https://arxiv.org/abs/2303.07833v1 | https://arxiv.org/pdf/2303.07833v1.pdf | X-ReCoSa: Multi-Scale Context Aggregation For Multi-Turn Dialogue Generation | In multi-turn dialogue generation, responses are not only related to the topic and background of the context but also related to words and phrases in the sentences of the context. However, currently widely used hierarchical dialog models solely rely on context representations from the utterance-level encoder, ignoring ... | ['Danqin Wu'] | 2023-03-14 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.51390135e-01 4.57448244e-01 9.26604569e-02 -6.55618668e-01
-8.83501649e-01 -2.66117036e-01 7.75524259e-01 1.23093374e-01
-2.88507372e-01 1.04703259e+00 9.67576563e-01 -9.20358002e-02
6.85185432e-01 -8.24265540e-01 -1.02378495e-01 -5.50427318e-01
6.67592406e-01 4.70058262e-01 2.46558815e-01 -6.30283177... | [12.58167839050293, 8.189515113830566] |
fd358a98-4271-40f5-ac37-c0ef42674316 | incrementality-bidding-and-attribution | 2208.12809 | null | https://arxiv.org/abs/2208.12809v1 | https://arxiv.org/pdf/2208.12809v1.pdf | Incrementality Bidding and Attribution | The causal effect of showing an ad to a potential customer versus not, commonly referred to as "incrementality", is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribu... | ['Jeffrey Wong', 'Randall Lewis'] | 2022-08-25 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 8.71185511e-02 2.74796665e-01 -1.02851129e+00 -6.97345316e-01
-7.81360924e-01 -6.22679055e-01 8.64625633e-01 1.69098407e-01
-2.85701960e-01 4.62256730e-01 6.93582475e-01 -1.13673878e+00
-4.41242546e-01 -6.37389481e-01 -8.94364953e-01 3.32825892e-02
-2.09404349e-01 3.45691949e-01 2.82761622e-02 4.35870588... | [9.130826950073242, 5.775877952575684] |
f5dcecef-9f63-4b7a-a5a1-f3f9bc179b56 | unsupervised-multilingual-word-embeddings | 1808.08933 | null | http://arxiv.org/abs/1808.08933v2 | http://arxiv.org/pdf/1808.08933v2.pdf | Unsupervised Multilingual Word Embeddings | Multilingual Word Embeddings (MWEs) represent words from multiple languages
in a single distributional vector space. Unsupervised MWE (UMWE) methods
acquire multilingual embeddings without cross-lingual supervision, which is a
significant advantage over traditional supervised approaches and opens many new
possibilities... | ['Claire Cardie', 'Xilun Chen'] | 2018-08-27 | unsupervised-multilingual-word-embeddings-1 | https://aclanthology.org/D18-1024 | https://aclanthology.org/D18-1024.pdf | emnlp-2018-10 | ['multilingual-word-embeddings'] | ['methodology'] | [-6.07499778e-01 -5.41575968e-01 -8.69745672e-01 -2.20215082e-01
-1.05895090e+00 -7.39301980e-01 7.40052581e-01 8.51108804e-02
-8.19481611e-01 7.87160456e-01 5.75906873e-01 -7.54323542e-01
2.26019442e-01 -6.55989707e-01 -5.49455404e-01 -3.99823129e-01
2.15215191e-01 5.03058493e-01 -2.74450004e-01 -4.87970531... | [11.067204475402832, 9.933663368225098] |
adf05f50-1db7-4672-8d7d-0a631b3c2f46 | bayesian-neural-ordinary-differential | 2012.07244 | null | https://arxiv.org/abs/2012.07244v4 | https://arxiv.org/pdf/2012.07244v4.pdf | Bayesian Neural Ordinary Differential Equations | Recently, Neural Ordinary Differential Equations has emerged as a powerful framework for modeling physical simulations without explicitly defining the ODEs governing the system, but instead learning them via machine learning. However, the question: "Can Bayesian learning frameworks be integrated with Neural ODE's to ro... | ['Krishna Vishal Vemula', 'Aslan Garcia-Valadez', 'Mohamed Tarek', 'Vaibhav Dixit', 'Karen Chung', 'Chris Rackauckas', 'Raj Dandekar'] | 2020-12-14 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-1.26249090e-01 -2.65306771e-01 1.90399885e-01 -4.98371348e-02
-7.25184023e-01 -4.63095397e-01 9.87152874e-01 -1.99290588e-01
-7.00156629e-01 1.02650046e+00 -3.10532570e-01 -5.66478252e-01
-4.30838495e-01 -7.92356372e-01 -7.11917460e-01 -1.19958103e+00
2.33791526e-02 8.02195668e-01 2.51357406e-01 2.25165635... | [6.969564914703369, 3.8650455474853516] |
92f22f3d-70c5-47b3-ae39-88657ebc15a9 | a-neural-network-multi-task-learning-approach | null | null | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1776-8 | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-017-1776-8.pdf | A neural network multi-task learning approach to biomedical named entity recognition | Background
Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it mig... | ['Anna Korhonen', 'Billy Chiu', 'Sampo Pyysalo', 'Gamal Crichton'] | 2017-08-15 | null | null | null | bmc-bioinformatics-2017-8 | ['multi-task-learning', 'anatomy', 'named-entity-recognition-ner'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 1.81606546e-01 1.14413776e-01 2.80589163e-01 -2.76219279e-01
-1.14491999e+00 -5.73142231e-01 3.43457758e-01 5.93967736e-01
-1.01240253e+00 8.65863144e-01 1.82239264e-01 -1.76937804e-01
-2.11081222e-01 -6.19644463e-01 -6.89360738e-01 -4.47172731e-01
8.47344846e-02 3.28483611e-01 9.45702791e-02 5.99825382... | [8.489190101623535, 8.78288459777832] |
c7abd68c-c102-44a7-ad44-dd9d014a52ea | reinforcement-learning-with-partial | 2304.13223 | null | https://arxiv.org/abs/2304.13223v1 | https://arxiv.org/pdf/2304.13223v1.pdf | Reinforcement Learning with Partial Parametric Model Knowledge | We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowledge Least Squares Policy Iteration (PLSPI), takes inspiration from both model-free RL and model-based control. It uses incomplete informati... | ['R. Bhushan Gopaluni', 'Michael G. Forbes', 'Nathan P. Lawrence', 'Philip D. Loewen', 'Shuyuan Wang'] | 2023-04-26 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.56605393e-01 5.03955364e-01 -7.94991970e-01 2.56079108e-01
-7.45551288e-01 -3.91567588e-01 4.70853031e-01 -3.62291753e-01
-5.06444454e-01 1.71088016e+00 -1.84112057e-01 -3.54296029e-01
-4.37430948e-01 -3.80418807e-01 -8.35381031e-01 -8.89833391e-01
-2.15088174e-01 8.00409093e-02 -3.17329690e-02 -5.90449274... | [4.262612342834473, 2.1694116592407227] |
cbd1ee25-bd85-4342-81d8-c2fa4d73a1ee | tempoqr-temporal-question-reasoning-over | 2112.05785 | null | https://arxiv.org/abs/2112.05785v1 | https://arxiv.org/pdf/2112.05785v1.pdf | TempoQR: Temporal Question Reasoning over Knowledge Graphs | Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve ... | ['George Karypis', 'Nagib Hakim', 'Tetiana Grinberg', 'Phillip R. Howard', 'Soji Adeshina', 'Vassilis N. Ioannidis', 'Prasanna Lakkur Subramanyam', 'Costas Mavromatis'] | 2021-12-10 | null | null | null | null | ['graph-question-answering'] | ['graphs'] | [-2.33815864e-01 5.48039496e-01 -3.17428917e-01 -3.50605667e-01
-7.50940323e-01 -7.50835061e-01 5.83501399e-01 6.93207979e-01
-1.12329811e-01 6.38214409e-01 4.96019006e-01 -5.25462031e-01
-5.20566344e-01 -1.43091178e+00 -6.08948112e-01 -1.68652266e-01
-2.40228564e-01 6.29650235e-01 8.76628518e-01 -6.81526780... | [10.392597198486328, 7.945568084716797] |
1c479a2e-9e8a-4a0b-b1b1-6243e72b8722 | jl-dcf-joint-learning-and-densely-cooperative | 2004.08515 | null | https://arxiv.org/abs/2004.08515v1 | https://arxiv.org/pdf/2004.08515v1.pdf | JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection | This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited am... | ['Ge-Peng Ji', 'Deng-Ping Fan', 'Qijun Zhao', 'Keren Fu'] | 2020-04-18 | jl-dcf-joint-learning-and-densely-cooperative-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fu_JL-DCF_Joint_Learning_and_Densely-Cooperative_Fusion_Framework_for_RGB-D_Salient_CVPR_2020_paper.pdf | cvpr-2020-6 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 2.61472519e-02 -6.40953556e-02 -3.84100199e-01 -3.69155049e-01
-9.13682163e-01 -4.15550649e-01 6.54596269e-01 1.59325242e-01
-4.82207537e-01 3.99052322e-01 5.37028238e-02 -4.45357822e-02
9.61273387e-02 -4.41623122e-01 -7.77907193e-01 -7.28209138e-01
1.68986246e-01 -6.41811341e-02 6.62632883e-01 -2.69347250... | [9.638601303100586, -0.7910177707672119] |
11013305-bb07-46f9-a2f8-c4219f04827b | starnet-style-aware-3d-point-cloud-generation | 2303.15805 | null | https://arxiv.org/abs/2303.15805v1 | https://arxiv.org/pdf/2303.15805v1.pdf | StarNet: Style-Aware 3D Point Cloud Generation | This paper investigates an open research task of reconstructing and generating 3D point clouds. Most existing works of 3D generative models directly take the Gaussian prior as input for the decoder to generate 3D point clouds, which fail to learn disentangled latent codes, leading noisy interpolated results. Most of th... | ['Chunyan Miao', 'Zhiqi Shen', 'Vun Chan Hua Nicholas', 'Guosheng Lin', 'Hao Wang', 'Yunfan Zhang'] | 2023-03-28 | null | null | null | null | ['point-cloud-reconstruction', 'point-cloud-generation', 'generating-3d-point-clouds'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.64694060e-02 1.57921955e-01 6.75982162e-02 -2.97077429e-02
-8.97663116e-01 -6.10893548e-01 7.36574411e-01 -4.71996814e-01
2.77191848e-01 7.98777640e-01 -8.76352787e-02 -1.84962943e-01
3.29587311e-01 -1.32375872e+00 -1.10309219e+00 -7.60354757e-01
3.08051646e-01 8.73864293e-01 -1.64658323e-01 -3.25270891... | [8.86362361907959, -3.6505799293518066] |
b9815043-9f97-49c8-81af-3c9d1285b9a3 | code-prompting-a-neural-symbolic-method-for | 2305.18507 | null | https://arxiv.org/abs/2305.18507v1 | https://arxiv.org/pdf/2305.18507v1.pdf | Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models | Large language models (LLMs) have scaled up to unlock a wide range of complex reasoning tasks with the aid of various prompting methods. However, current prompting methods generate natural language intermediate steps to help reasoning, which can cause imperfect task reduction and confusion. To mitigate such limitations... | ['Muhan Zhang', 'Zhouchen Lin', 'Haotong Yang', 'Yi Hu'] | 2023-05-29 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 3.29291910e-01 3.42877418e-01 -2.28823125e-01 -4.49612796e-01
-7.24484444e-01 -6.52733088e-01 8.77431393e-01 3.93826187e-01
-1.83656618e-01 3.51840734e-01 6.47653639e-01 -9.11064386e-01
-2.31355359e-03 -4.91001010e-01 -5.86413682e-01 7.88159482e-03
2.40563601e-02 -4.30416800e-02 1.30813152e-01 -3.86767894... | [8.070457458496094, 7.681636333465576] |
ea1aefa3-d77b-4168-8389-ce652f31fd03 | near-field-beam-management-for-extremely | 2306.16206 | null | https://arxiv.org/abs/2306.16206v1 | https://arxiv.org/pdf/2306.16206v1.pdf | Near-Field Beam Management for Extremely Large-Scale Array Communications | Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to achieve super-high spectral efficiency and spatial resolution in future wireless systems. The large aperture of XL-arrays means that spherical rather than planar wavefronts must be considered, and a paradigm shift from far-field to near-... | ['A. Lee Swindlehurst', 'Linglong Dai', 'Li Chen', 'Beixiong Zheng', 'Yong Zeng', 'Chenyu Wu', 'Yunpu Zhang', 'Changsheng You'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 2.84832984e-01 -2.56030381e-01 2.41596922e-01 -5.05997181e-01
-4.76798773e-01 -5.47973812e-01 1.67417333e-01 -4.61288124e-01
2.61017233e-02 9.17169333e-01 4.54248458e-01 -5.14091849e-01
-8.44618440e-01 -9.54960227e-01 -2.09546342e-01 -9.12257016e-01
-3.70104611e-01 -7.40880892e-02 7.51560777e-02 -3.15427572... | [6.385447025299072, 1.2423003911972046] |
01fa99f8-1d0c-4bac-8518-3806db71da10 | rethinking-surgical-instrument-segmentation-a | 2206.11804 | null | https://arxiv.org/abs/2206.11804v4 | https://arxiv.org/pdf/2206.11804v4.pdf | Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need | Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the ... | ['Hongliang Ren', 'Mengya Xu', 'Mobarakol Islam', 'An Wang'] | 2022-06-23 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.66820604e-02 2.22601235e-01 -4.85022277e-01 -2.93146431e-01
-6.92196131e-01 -6.47938788e-01 5.80620430e-02 -2.93926708e-02
-4.46077913e-01 5.59292853e-01 -6.56601116e-02 -6.30831420e-01
-2.09954217e-01 -4.81366009e-01 -6.70489132e-01 -8.38510275e-01
3.34006608e-01 4.38680500e-01 -1.40631884e-01 -1.12055756... | [14.553197860717773, -2.2471048831939697] |
f3e7ed18-21b5-421d-8c07-17c01c2ac0c5 | linking-convolutional-neural-networks-with | null | null | https://www.researchgate.net/publication/335620542_Linking_convolutional_neural_networks_with_graph_convolutional_networks_application_in_pulmonary_artery-vein_separation | https://bit.ly/2kNpbdv | Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation | Graph Convolutional Networks (GCNs) are a novel and powerful method for dealing with non-Euclidean data, while Convolutional Neural Networks (CNNs) can learn features from Euclidean data such as images. In this work, we propose a novel method to combine CNNs with GCNs (CNN-GCN), that can consider both Euclidean and non... | ['Boudewijn P. F. Lelieveldt', 'Berend C. Stoel', 'M. Els Bakker', 'Marius Staring', 'Lucia J. Kroft', 'Gudula J.A.M. Boon', 'Xiaojuan Xiao', 'Qiuxia Xie', 'Zhiwei Zhai', 'Xuhui Zhou', 'Frederikus A. Klok'] | 2019-09-01 | null | null | null | preprint-2019-9 | ['pulmonary-arteryvein-classification', 'pulmorary-vessel-segmentation', '3d-medical-imaging-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [-5.74324057e-02 4.68316019e-01 4.18536291e-02 -1.23370783e-02
-1.72771081e-01 -3.83736461e-01 2.95730054e-01 3.80552262e-01
-4.95289534e-01 3.08960229e-01 -5.43833971e-02 -4.50371742e-01
-3.83121103e-01 -1.15410721e+00 -2.59910196e-01 -6.89828932e-01
-3.14460337e-01 5.51972270e-01 4.93990511e-01 2.13359788... | [15.10130500793457, -2.2451276779174805] |
6dd0ac3b-0229-4aa6-89f5-8f6f9d32e7bc | a-multi-scale-multiple-instance-video | 1505.05914 | null | http://arxiv.org/abs/1505.05914v3 | http://arxiv.org/pdf/1505.05914v3.pdf | A Multi-scale Multiple Instance Video Description Network | Generating natural language descriptions for in-the-wild videos is a
challenging task. Most state-of-the-art methods for solving this problem borrow
existing deep convolutional neural network (CNN) architectures (AlexNet,
GoogLeNet) to extract a visual representation of the input video. However,
these deep CNN architec... | ['Kate Saenko', 'Vasili Ramanishka', 'Subhashini Venugopalan', 'Marcus Rohrbach', 'Huijuan Xu'] | 2015-05-21 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.95594597e-01 -1.00097749e-02 -2.10211016e-02 -3.95060956e-01
-8.61155391e-01 -6.20874107e-01 3.24407369e-01 -2.43176728e-01
-4.35932487e-01 4.65183377e-01 -1.46267593e-01 -2.04386637e-01
3.86090696e-01 -8.22218120e-01 -1.11906338e+00 -4.99472290e-01
2.54868418e-01 3.48398358e-01 9.14715886e-01 -3.58364344... | [9.281471252441406, 0.044471219182014465] |
40fa3400-1002-414d-a16a-613d0e74119c | visil-fine-grained-spatio-temporal-video | 1908.07410 | null | https://arxiv.org/abs/1908.07410v1 | https://arxiv.org/pdf/1908.07410v1.pdf | ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning | In this paper we introduce ViSiL, a Video Similarity Learning architecture that considers fine-grained Spatio-Temporal relations between pairs of videos -- such relations are typically lost in previous video retrieval approaches that embed the whole frame or even the whole video into a vector descriptor before the simi... | ['Ioannis Kompatsiaris', 'Giorgos Kordopatis-Zilos', 'Symeon Papadopoulos', 'Ioannis Patras'] | 2019-08-20 | visil-fine-grained-spatio-temporal-video-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Kordopatis-Zilos_ViSiL_Fine-Grained_Spatio-Temporal_Video_Similarity_Learning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Kordopatis-Zilos_ViSiL_Fine-Grained_Spatio-Temporal_Video_Similarity_Learning_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-similarity'] | ['computer-vision'] | [ 1.13846980e-01 -6.99571550e-01 -1.87817931e-01 -4.43414301e-01
-8.50288153e-01 -4.98461723e-01 8.15713644e-01 4.80824679e-01
-6.91030085e-01 3.26380223e-01 1.89975038e-01 2.22885758e-01
-4.05757517e-01 -6.34660840e-01 -7.98235655e-01 -5.62813699e-01
-3.26088011e-01 9.47519690e-02 4.24861878e-01 -2.03223944... | [9.91629409790039, 0.6412017345428467] |
74359535-c9aa-4a5d-bbaa-62b986d2397e | hierarchical-deep-learning-model-for | 2303.03386 | null | https://arxiv.org/abs/2303.03386v1 | https://arxiv.org/pdf/2303.03386v1.pdf | Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile | Batteries can effectively improve the security of energy systems and mitigate climate change by facilitating wind and solar power. The installed capacity of battery energy storage system (BESS), mainly the lithium ion batteries are increasing significantly in recent years. However, the battery degradation cannot be acc... | ['Xingpeng Li', 'Cunzhi Zhao'] | 2023-03-06 | null | null | null | null | ['energy-management'] | ['time-series'] | [-8.37544203e-01 -6.33650541e-01 -2.11247981e-01 -2.43240073e-01
-2.18385935e-01 -3.94290149e-01 2.58598179e-01 1.11306958e-01
-1.06049636e-02 1.37866187e+00 1.49660986e-02 -5.43853998e-01
-4.22241330e-01 -9.27610576e-01 -6.58030450e-01 -1.17966104e+00
-2.09654436e-01 5.82976878e-01 -2.36653298e-01 -3.05800885... | [5.669229030609131, 2.5439224243164062] |
76520016-dcb9-47fe-b5ba-de23ebdaf5c2 | occlusion-coherence-detecting-and-localizing | 1506.08347 | null | http://arxiv.org/abs/1506.08347v2 | http://arxiv.org/pdf/1506.08347v2.pdf | Occlusion Coherence: Detecting and Localizing Occluded Faces | The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face dete... | ['Charless C. Fowlkes', 'Golnaz Ghiasi'] | 2015-06-28 | null | null | null | null | ['occluded-face-detection'] | ['computer-vision'] | [ 6.63563162e-02 -6.28804490e-02 -3.00825208e-01 -5.54869294e-01
-9.06713545e-01 -5.88535726e-01 5.41797400e-01 -5.39361276e-02
-4.66873646e-02 4.02280152e-01 -1.55191831e-02 4.84404825e-02
3.52279752e-01 -4.62332845e-01 -6.27451658e-01 -6.95995033e-01
-2.30141595e-01 6.95728004e-01 3.02491903e-01 1.75236985... | [13.35744571685791, 0.3366940915584564] |
acee5a47-8fb9-4882-a154-b5fa86bd9d28 | dnnshield-dynamic-randomized-model | 2208.00498 | null | https://arxiv.org/abs/2208.00498v1 | https://arxiv.org/pdf/2208.00498v1.pdf | DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning | DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim. Recent works have proposed approximate computation as a defense mechanism against machine learning attacks. We show that these approaches, w... | ['Radu Teodorescu', 'Kristin Barber', 'Saikat Majumdar', 'Mohammad Hossein Samavatian'] | 2022-07-31 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 4.14685309e-02 -5.10832034e-02 -1.74561776e-02 -3.43924999e-01
-4.61761832e-01 -1.11510098e+00 5.18682241e-01 8.67190510e-02
-5.50431609e-01 5.78686476e-01 -7.78321028e-02 -6.77330673e-01
3.91047210e-01 -1.17288470e+00 -1.05176592e+00 -3.70958984e-01
-8.96521807e-02 3.22493494e-01 5.57488024e-01 -2.76326537... | [5.670760154724121, 7.71614933013916] |
abd8f278-fb58-43b4-a7a5-540b80409ae5 | latent-factor-guided-convolutional-neural | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Wen_Latent_Factor_Guided_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Wen_Latent_Factor_Guided_CVPR_2016_paper.pdf | Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition | While considerable progresses have been made on face recognition, age-invariant face recognition (AIFR) still remains a major challenge in real world applications of face recognition systems. The major difficulty of AIFR arises from the fact that the facial appearance is subject to significant intra-personal changes ca... | ['Yandong Wen', 'Yu Qiao', 'Zhifeng Li'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['age-invariant-face-recognition'] | ['computer-vision'] | [-1.21132255e-01 -1.80462018e-01 -4.74780723e-02 -7.09229767e-01
-6.53891712e-02 2.75056571e-01 3.26946288e-01 -4.80066419e-01
-2.04155445e-01 6.60695314e-01 -4.77639809e-02 1.11535914e-01
-5.55288941e-02 -7.29853749e-01 -5.35719097e-01 -6.45973802e-01
-3.88166428e-01 3.31959836e-02 -2.94318467e-01 -3.35280120... | [13.339977264404297, 0.6981818675994873] |
32842ff6-b38c-463f-ba9e-495a89e9c8ac | pl-unext-per-stage-edge-detail-and-line | 2303.04413 | null | https://arxiv.org/abs/2303.04413v1 | https://arxiv.org/pdf/2303.04413v1.pdf | PL-UNeXt: Per-stage Edge Detail and Line Feature Guided Segmentation for Power Line Detection | Power line detection is a critical inspection task for electricity companies and is also useful in avoiding drone obstacles. Accurately separating power lines from the surrounding area in the aerial image is still challenging due to the intricate background and low pixel ratio. In order to properly capture the guidance... | ['Daming Liu', 'Zhen Chen', 'Yang Cheng'] | 2023-03-08 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.06988001e-01 -2.28232279e-01 -3.89371701e-02 -1.96972966e-01
-6.79378629e-01 -9.43645120e-01 1.69417337e-01 -7.54429847e-02
-1.23439118e-01 5.80107450e-01 -7.54158020e-01 -5.31485498e-01
-9.71651375e-02 -1.00683451e+00 -5.34603596e-01 -8.38882923e-01
-3.58645052e-01 -4.97124232e-02 3.52721661e-01 -1.35183483... | [8.857515335083008, -0.9611779451370239] |
0c147ade-f3ca-4a3e-b0e0-47d2109e158b | asl-recognition-with-metric-learning-based-1 | 2004.05054 | null | https://arxiv.org/abs/2004.05054v1 | https://arxiv.org/pdf/2004.05054v1.pdf | ASL Recognition with Metric-Learning based Lightweight Network | In the past decades the set of human tasks that are solved by machines was extended dramatically. From simple image classification problems researchers now move towards solving more sophisticated and vital problems, like, autonomous driving and language translation. The case of language translation includes a challengi... | ['Evgeny Izutov'] | 2020-04-10 | asl-recognition-with-metric-learning-based | null | null | arxiv-org-2020-4 | ['sign-language-translation'] | ['computer-vision'] | [ 5.48333704e-01 -2.38974109e-01 -3.60884488e-01 -6.32857561e-01
-7.90459573e-01 -4.50571120e-01 7.62490392e-01 -7.35143304e-01
-9.20210183e-01 6.51577771e-01 1.37930840e-01 -5.31729162e-01
1.85660318e-01 -3.46286267e-01 -6.11025810e-01 -4.05628890e-01
2.63920844e-01 5.00640213e-01 4.05708164e-01 -3.78211379... | [9.165234565734863, -6.4789252281188965] |
2dcb72a2-f7f2-4550-ad85-a9d18e216c9b | evaluating-gesture-generation-in-a-large | 2303.08737 | null | https://arxiv.org/abs/2303.08737v1 | https://arxiv.org/pdf/2303.08737v1.pdf | Evaluating gesture-generation in a large-scale open challenge: The GENEA Challenge 2022 | This paper reports on the second GENEA Challenge to benchmark data-driven automatic co-speech gesture generation. Participating teams used the same speech and motion dataset to build gesture-generation systems. Motion generated by all these systems was rendered to video using a standardised visualisation pipeline and e... | ['Gustav Eje Henter', 'Mihail Tsakov', 'Teodor Nikolov', 'Carla Viegas', 'Youngwoo Yoon', 'Pieter Wolfert', 'Taras Kucherenko'] | 2023-03-15 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 3.63846570e-02 1.41064942e-01 1.73891291e-01 -1.98775977e-01
-1.01004016e+00 -8.49142611e-01 1.06894827e+00 -5.38908243e-01
-5.45233309e-01 5.10147333e-01 9.28156495e-01 5.14351465e-02
1.30313054e-01 3.36926244e-02 -2.26125896e-01 -5.06372690e-01
5.78364693e-02 4.14925545e-01 3.00265729e-01 -3.39322001... | [5.606679439544678, -0.0901964008808136] |
9c93ead3-eb06-42b3-bc85-9bd8212bc7b2 | neural-ode-and-dae-modules-for-power-system | 2110.12981 | null | https://arxiv.org/abs/2110.12981v5 | https://arxiv.org/pdf/2110.12981v5.pdf | Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling | In the context of high penetration of renewables, the need to build dynamic models of power system components based on accessible measurement data has become urgent. To address this challenge, firstly, a neural ordinary differential equations (ODE) module and a neural differential-algebraic equations (DAE) module are p... | ['Shaowei Huang', 'Huizhe Guan', 'Tirui He', 'Ying Chen', 'Tannan Xiao'] | 2021-10-25 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-5.90433657e-01 -3.19311470e-01 1.17689602e-01 3.72541398e-02
1.29663959e-01 -9.62886393e-01 2.55939484e-01 -1.52315587e-01
5.65371513e-01 8.58424664e-01 -4.75945473e-01 -4.59080130e-01
-6.10144794e-01 -7.45958924e-01 -2.94408262e-01 -8.36946905e-01
-3.07410210e-01 1.23151444e-01 -3.77036989e-01 -3.66942972... | [5.652680397033691, 2.5572242736816406] |
9b93cc70-901d-48b5-a40b-ccba457e018e | evopose2d-pushing-the-boundaries-of-2d-human | 2011.08446 | null | https://arxiv.org/abs/2011.08446v2 | https://arxiv.org/pdf/2011.08446v2.pdf | EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation using Accelerated Neuroevolution with Weight Transfer | Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks. Hypothesizing that neural architecture search holds great potential for human pose estimation, we explore the application of neu... | ['John McPhee', 'Alexander Wong', 'Kanav Vats', 'William McNally'] | 2020-11-17 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.60837144e-01 6.96692020e-02 -9.61115435e-02 7.18812197e-02
-2.61027753e-01 -5.31264424e-01 2.13605404e-01 -3.78710091e-01
-7.66452789e-01 7.02899456e-01 -3.27106453e-02 -3.69940102e-01
-1.78490534e-01 -7.34193563e-01 -1.01760423e+00 -2.81336784e-01
-2.90079206e-01 7.20357835e-01 1.77522436e-01 -5.66512764... | [7.164283275604248, -0.8075579404830933] |
d15ea8f1-80b0-4818-9fb0-4c3b77501a35 | stable-table-generation-framework-for-encoder | 2206.04045 | null | https://arxiv.org/abs/2206.04045v2 | https://arxiv.org/pdf/2206.04045v2.pdf | STable: Table Generation Framework for Encoder-Decoder Models | The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks. Following this constatation, we propose a framework for text-to-table neural models applicable to problems such as extraction of line items, jo... | ['Łukasz Garncarek', 'Dawid Jurkiewicz', 'Karolina Szyndler', 'Gabriela Pałka', 'Tomasz Dwojak', 'Łukasz Borchmann', 'Michał Turski', 'Michał Pietruszka'] | 2022-06-08 | null | null | null | null | ['knowledge-base-population', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.79731333e-01 6.11025214e-01 -5.84495902e-01 -3.13676715e-01
-8.64384234e-01 -8.60937476e-01 3.62590343e-01 5.28421879e-01
-1.81953236e-01 1.31105042e+00 3.50262940e-01 -6.26739502e-01
-2.30489314e-01 -1.28838468e+00 -1.37238896e+00 -1.97850987e-01
-1.76600546e-01 9.16279733e-01 9.44167096e-03 -1.54036105... | [9.626346588134766, 7.9447245597839355] |
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