paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
04e43d5e-b9ef-4924-aabe-cfa84dda29a4 | consistency-guided-scene-flow-estimation | 2006.11242 | null | https://arxiv.org/abs/2006.11242v2 | https://arxiv.org/pdf/2006.11242v2.pdf | Consistency Guided Scene Flow Estimation | Consistency Guided Scene Flow Estimation (CGSF) is a self-supervised framework for the joint reconstruction of 3D scene structure and motion from stereo video. The model takes two temporal stereo pairs as input, and predicts disparity and scene flow. The model self-adapts at test time by iteratively refining its predic... | ['Luc van Gool', 'Yuhua Chen', 'Cordelia Schmid', 'Cristian Sminchisescu'] | 2020-06-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/157_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520120.pdf | eccv-2020-8 | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.10199803e-01 -2.39346370e-01 -3.13787088e-02 -5.26918530e-01
-5.64961076e-01 -5.46137094e-01 3.76352310e-01 -1.52508423e-01
-3.25531989e-01 6.01417959e-01 2.98553616e-01 1.09038204e-01
2.24748299e-01 -5.39258122e-01 -7.14619339e-01 -4.52768534e-01
-1.12849988e-01 3.44676673e-01 6.11317873e-01 4.45558056... | [8.68388557434082, -2.048152446746826] |
ac1ff35b-5c0d-4b6c-9665-4fba48d40985 | beyond-information-exchange-an-approach-to | 2212.10805 | null | https://arxiv.org/abs/2212.10805v1 | https://arxiv.org/pdf/2212.10805v1.pdf | Beyond Information Exchange: An Approach to Deploy Network Properties for Information Diffusion | Information diffusion in Online Social Networks is a new and crucial problem in social network analysis field and requires significant research attention. Efficient diffusion of information are of critical importance in diverse situations such as; pandemic prevention, advertising, marketing etc. Although several mathem... | ['Ravi Kishore Devarapalli', 'Anupam Biswas', 'Soumita Das'] | 2022-12-21 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-1.54246330e-01 -1.86052784e-01 -4.50363398e-01 1.11026838e-01
4.11635667e-01 -6.67218506e-01 8.72422516e-01 7.84904182e-01
-6.36434555e-01 6.96916044e-01 3.74113649e-01 -7.06345439e-01
-9.00446951e-01 -1.03534746e+00 1.96080834e-01 -5.32425046e-01
-4.35097545e-01 2.45281681e-01 7.09920704e-01 -6.71708405... | [6.927112102508545, 5.355685234069824] |
fa1519c6-4654-45a9-a987-8d24c4736b45 | slendergnn-accurate-robust-and-interpretable | 2210.04081 | null | https://arxiv.org/abs/2210.04081v4 | https://arxiv.org/pdf/2210.04081v4.pdf | Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining | How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the s... | ['Christos Faloutsos', 'Shubhranshu Shekhar', 'Meng-Chieh Lee', 'Jaemin Yoo'] | 2022-10-08 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 6.29991293e-02 3.40009928e-01 -2.92482972e-01 -2.11307779e-01
1.46049678e-01 -5.41032851e-01 4.55932200e-01 3.87550443e-01
-1.33194774e-01 8.47075462e-01 3.69702256e-03 -7.48105764e-01
-6.86972260e-01 -1.04325056e+00 -5.75120866e-01 -6.85908556e-01
-7.33426273e-01 8.92106414e-01 2.19372049e-01 -4.82365131... | [6.861976623535156, 5.957032680511475] |
2263778f-3cc9-45f2-b7b3-cb3000b406c0 | evaluation-of-non-negative-matrix | 2110.00418 | null | https://arxiv.org/abs/2110.00418v1 | https://arxiv.org/pdf/2110.00418v1.pdf | Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets | With the development of technology, the use of social media has become quite common. Analyzing comments on social media in areas such as media and advertising plays an important role today. For this reason, new and traditional natural language processing methods are used to detect the emotion of these shares. In this p... | ['Tolgahan Cakaloglu', 'Banu Diri', 'Zekeriya Anil Guven'] | 2021-09-27 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-6.58571184e-01 -2.56186008e-01 -2.52022713e-01 -3.20725143e-01
-1.72604144e-01 -4.00731713e-01 5.19362032e-01 4.52064961e-01
-4.28260773e-01 5.99331975e-01 3.59041572e-01 -1.38494447e-01
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1.80196911e-01 4.00578558e-01 5.21003790e-02 -9.06603709... | [10.698607444763184, 7.056542873382568] |
e28bb039-ec20-4cf9-beb2-33dbc7112bd6 | accelerating-the-training-of-video-super | 2205.05069 | null | https://arxiv.org/abs/2205.05069v2 | https://arxiv.org/pdf/2205.05069v2.pdf | Accelerating the Training of Video Super-Resolution Models | Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to lon... | ['Ying Shan', 'Zhongang Qi', 'Xintao Wang', 'Lijian Lin'] | 2022-05-10 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [-1.64141413e-02 -2.97685564e-01 -1.57763716e-02 -1.85119018e-01
-8.51725757e-01 -4.21462715e-01 1.89492419e-01 -3.79645377e-01
-4.94545549e-01 7.38303185e-01 -2.59533674e-01 -5.09635091e-01
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-1.46572692e-02 2.92730361e-01 5.10550320e-01 -2.12761939... | [10.817943572998047, -1.6040434837341309] |
9aadc115-980e-494a-8c77-d0e7fba7c4ca | exploiting-prompt-learning-with-pre-trained | 2210.16539 | null | https://arxiv.org/abs/2210.16539v2 | https://arxiv.org/pdf/2210.16539v2.pdf | Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection | Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Textual embedding features produced by pre-trained language m... | ['Helen Meng', 'Xunying Liu', 'Shoukang Hu', 'Bo Zheng', 'Tianzi Wang', 'Jiajun Deng', 'Yi Wang'] | 2022-10-29 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 1.39448047e-01 3.06501895e-01 5.20629659e-02 -5.54449439e-01
-1.09213078e+00 -2.08058879e-01 6.80307984e-01 3.88760269e-01
-7.93628395e-01 9.43192720e-01 3.73352259e-01 -1.86332524e-01
-6.11203611e-02 -4.77474064e-01 7.97704086e-02 -3.91232520e-01
-3.92221391e-01 4.87802804e-01 3.55123878e-01 -7.26511925... | [13.933197021484375, 5.429246425628662] |
e282f3c4-1555-4510-9592-e41dc9a67e5c | improving-semantic-segmentation-via-video | 1812.01593 | null | https://arxiv.org/abs/1812.01593v3 | https://arxiv.org/pdf/1812.01593v3.pdf | Improving Semantic Segmentation via Video Propagation and Label Relaxation | Semantic segmentation requires large amounts of pixel-wise annotations to learn accurate models. In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks. We exploit video prediction m... | ['Bryan Catanzaro', 'Andrew Tao', 'Shawn Newsam', 'Kevin J. Shih', 'Karan Sapra', 'Yi Zhu', 'Fitsum A. Reda'] | 2018-12-04 | improving-semantic-segmentation-via-video-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Improving_Semantic_Segmentation_via_Video_Propagation_and_Label_Relaxation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Improving_Semantic_Segmentation_via_Video_Propagation_and_Label_Relaxation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-propagation'] | ['computer-vision'] | [ 5.85167885e-01 4.80706453e-01 -5.56467474e-01 -6.37925804e-01
-1.02375865e+00 -4.45521593e-01 3.18453968e-01 -3.30811709e-01
-4.50388730e-01 5.26266336e-01 -1.98496506e-01 -5.82421757e-02
5.08499622e-01 -5.78238249e-01 -1.07349706e+00 -3.24549645e-01
2.21976295e-01 6.30100608e-01 6.93814337e-01 1.68852836... | [9.233787536621094, 0.034350406378507614] |
8695ac1f-b85f-47f8-8658-d4c8d2bf383b | contactdb-analyzing-and-predicting-grasp | 1904.06830 | null | http://arxiv.org/abs/1904.06830v1 | http://arxiv.org/pdf/1904.06830v1.pdf | ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging | Grasping and manipulating objects is an important human skill. Since
hand-object contact is fundamental to grasping, capturing it can lead to
important insights. However, observing contact through external sensors is
challenging because of occlusion and the complexity of the human hand. We
present ContactDB, a novel da... | ['Samarth Brahmbhatt', 'James Hays', 'Cusuh Ham', 'Charles C. Kemp'] | 2019-04-15 | contactdb-analyzing-and-predicting-grasp-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Brahmbhatt_ContactDB_Analyzing_and_Predicting_Grasp_Contact_via_Thermal_Imaging_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Brahmbhatt_ContactDB_Analyzing_and_Predicting_Grasp_Contact_via_Thermal_Imaging_CVPR_2019_paper.pdf | cvpr-2019-6 | ['human-grasp-contact-prediction'] | ['miscellaneous'] | [ 1.10091127e-01 -3.62595171e-01 8.92637447e-02 -2.75447547e-01
-3.09241772e-01 -7.91966379e-01 3.05850267e-01 -1.45680398e-01
-8.68971348e-02 -2.29874942e-02 2.23328516e-01 1.36617020e-01
-2.71983683e-01 -6.34839237e-01 -1.00627446e+00 -4.59663659e-01
-1.96056500e-01 8.42490256e-01 1.97215691e-01 -1.15680017... | [5.962151527404785, -0.9153235554695129] |
cb7b8a3a-f652-488f-ac1c-055458d1ed24 | webal-1-workshop-on-artificial-life-and-the | 1406.2507 | null | http://arxiv.org/abs/1406.2507v4 | http://arxiv.org/pdf/1406.2507v4.pdf | WebAL-1: Workshop on Artificial Life and the Web 2014 Proceedings | Proceedings of WebAL-1: Workshop on Artificial Life and the Web 2014, held at
the 14th International Conference on the Synthesis and Simulation of Living
Systems (ALIFE 14), New York, NY, 31 July 2014. | ['Tim Taylor'] | 2014-06-10 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.57508492e-01 3.31702918e-01 3.67939383e-01 4.56862241e-01
7.01638699e-01 -6.63849413e-01 1.05490947e+00 6.51377499e-01
2.13840860e-03 7.28813469e-01 2.06646040e-01 -5.37635051e-02
2.49703094e-01 -1.02301681e+00 -4.35966522e-01 -2.48453841e-01
-7.74480045e-01 2.07247380e-02 3.99701446e-01 -3.75022501... | [5.602517604827881, 4.134853363037109] |
1981ef0e-9b35-4cd3-b119-74fbff20448b | what-are-you-anxious-about-examining-subjects | 2209.13595 | null | https://arxiv.org/abs/2209.13595v1 | https://arxiv.org/pdf/2209.13595v1.pdf | What Are You Anxious About? Examining Subjects of Anxiety during the COVID-19 Pandemic | COVID-19 poses disproportionate mental health consequences to the public during different phases of the pandemic. We use a computational approach to capture the specific aspects that trigger an online community's anxiety about the pandemic and investigate how these aspects change over time. First, we identified nine su... | ['Daniela V. Negraia', 'Sophie Lohmann', 'Steven R. Wilson', 'Lucia L. Chen'] | 2022-09-27 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.28617224e-02 4.02089506e-01 -2.24790514e-01 -1.21435158e-01
-8.66936505e-01 -7.24741817e-01 3.61881524e-01 1.05201840e+00
-3.98231447e-01 3.34112763e-01 8.54001045e-01 -5.23405015e-01
-2.23875970e-01 -7.53665686e-01 -9.33412686e-02 -2.76686221e-01
-2.98688143e-01 5.22051215e-01 -4.67172742e-01 -6.12750888... | [8.47744369506836, 9.745343208312988] |
93407aa6-b44f-4bd1-86f7-612c6f4cff13 | metaphorical-expressions-in-automatic-arabic | null | null | https://aclanthology.org/2020.lrec-1.604 | https://aclanthology.org/2020.lrec-1.604.pdf | Metaphorical Expressions in Automatic Arabic Sentiment Analysis | Over the recent years, Arabic language resources and NLP tools have been under rapid development. One of the important tasks for Arabic natural language processing is the sentiment analysis. While a significant improvement has been achieved in this research area, the existing computational models and tools still suffer... | ['Scott Piao', 'Israa Alsiyat'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.04230925e-01 -2.53897130e-01 1.21048845e-01 -4.64118659e-01
1.76802203e-01 -8.35032880e-01 8.12177181e-01 6.04740202e-01
-3.24287146e-01 4.59827274e-01 2.59262115e-01 -4.95028883e-01
5.88483028e-02 -9.04185355e-01 1.47197898e-02 -5.47487557e-01
2.92904805e-02 4.72136319e-01 6.63695261e-02 -1.31303275... | [11.042552947998047, 6.942001819610596] |
fb41bc8f-4f95-4952-b548-1c2926829e2b | a-comparative-study-on-deep-learning-methods | 2210.14031 | null | https://arxiv.org/abs/2210.14031v1 | https://arxiv.org/pdf/2210.14031v1.pdf | A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images | Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric a... | ['Rongjun Qin', 'Hessah Albanwan'] | 2022-10-25 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.04408428e-01 -4.90608305e-01 2.12626770e-01 -6.05727017e-01
-9.11766052e-01 -7.27064431e-01 7.56395698e-01 -1.44000500e-01
-3.75953615e-01 5.27430356e-01 2.69652009e-02 -3.40173274e-01
-3.48939151e-01 -1.22220290e+00 -6.36022866e-01 -5.60035288e-01
-3.50146770e-01 6.54425919e-01 2.52532244e-01 -6.57427847... | [8.7808198928833, -2.2708580493927] |
26f9c82b-f252-4dde-9c83-43f3d7e09673 | supervised-dimensionality-reduction-by-a | 2006.12127 | null | https://arxiv.org/abs/2006.12127v1 | https://arxiv.org/pdf/2006.12127v1.pdf | Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features | We explore the application of linear discriminant analysis (LDA) to the features obtained in different layers of pretrained deep convolutional neural networks (CNNs). The advantage of LDA compared to other techniques in dimensionality reduction is that it reduces dimensions while preserving the global structure of data... | ['Gonzalo G. de Polavieja', 'Francisco J. H. Heras'] | 2020-06-22 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-4.43775266e-01 -1.79365993e-01 1.29209682e-01 -6.36130512e-01
-5.53331561e-02 -7.58892536e-01 6.17948294e-01 -3.93291842e-03
-5.59556603e-01 3.75308663e-01 1.89328700e-01 2.15181619e-01
-6.63419485e-01 -8.47539425e-01 -4.75440949e-01 -1.00290108e+00
-6.71148479e-01 5.65665960e-01 3.72654885e-01 -2.14141726... | [9.240310668945312, 2.7098381519317627] |
6a97694a-4dff-4abe-9390-6145b8776ef8 | investigating-efficiently-extending | 2208.04347 | null | https://arxiv.org/abs/2208.04347v1 | https://arxiv.org/pdf/2208.04347v1.pdf | Investigating Efficiently Extending Transformers for Long Input Summarization | While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs continues to be a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most pretrained models. Through an extensiv... | ['Peter J. Liu', 'Yao Zhao', 'Jason Phang'] | 2022-08-08 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 5.71451902e-01 3.29969257e-01 -4.61289227e-01 -3.36110324e-01
-1.16963041e+00 -7.52140462e-01 7.30522454e-01 2.78631210e-01
-5.78417301e-01 7.17499495e-01 1.01554763e+00 -6.60496712e-01
2.15371370e-01 -5.49599349e-01 -9.17949855e-01 -2.28754222e-01
2.84791082e-01 5.95396399e-01 3.77281606e-01 -4.76589471... | [11.658368110656738, 8.978447914123535] |
8dc84ad7-1c63-47d8-81be-de4529d85074 | network-aided-intelligent-traffic-steering-in | 2302.02711 | null | https://arxiv.org/abs/2302.02711v2 | https://arxiv.org/pdf/2302.02711v2.pdf | Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework | To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In thi... | ['Symeon Chatzinotas', 'Diep N. Nguyen', 'Dinh Thai Hoang', 'Nguyen Cong Luong', 'Markku Juntti', 'Dinh C. Nguyen', 'Nhan Thanh Nguyen', 'Thang X. Vu', 'Van-Dinh Nguyen'] | 2023-02-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-2.52269834e-01 -2.55213659e-02 -6.79091990e-01 -2.32222915e-01
-3.41560006e-01 -7.15391219e-01 -2.50848174e-01 -6.92329645e-01
1.89701438e-01 1.41095269e+00 -2.74707973e-01 -9.46657419e-01
-8.56854558e-01 -8.04659426e-01 1.31664589e-01 -7.25472569e-01
-6.78036869e-01 4.05911863e-01 -1.42626956e-01 -2.60734975... | [5.8861284255981445, 1.689986228942871] |
f81361dd-b900-4a9c-9fd0-035487993fef | weed-density-and-distribution-estimation-for | 2011.02193 | null | https://arxiv.org/abs/2011.02193v2 | https://arxiv.org/pdf/2011.02193v2.pdf | Weed Density and Distribution Estimation for Precision Agriculture using Semi-Supervised Learning | Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resu... | ['Ujjwal Verma', 'Sidharth R', 'Armaan Ashfaque', 'Shantam Shorewala'] | 2020-11-04 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 6.03611469e-01 4.05479819e-02 -2.91830778e-01 5.89566305e-02
2.03849196e-01 -9.74521399e-01 1.95960134e-01 6.46273017e-01
-4.31567520e-01 7.28928745e-01 -6.68385446e-01 -7.21556127e-01
-1.60128772e-02 -1.13295829e+00 -4.44469303e-01 -8.97057235e-01
-1.31680399e-01 2.67519265e-01 3.55087787e-01 -2.50448763... | [9.148111343383789, -1.559476375579834] |
1f4ed1fb-89df-4711-b261-3a5ff2cb94e0 | rmultinet-an-r-package-for-multilayer | 2302.04437 | null | https://arxiv.org/abs/2302.04437v1 | https://arxiv.org/pdf/2302.04437v1.pdf | rMultiNet: An R Package For Multilayer Networks Analysis | This paper develops an R package rMultiNet to analyze multilayer network data. We provide two general frameworks from recent literature, e.g. mixture multilayer stochastic block model(MMSBM) and mixture multilayer latent space model(MMLSM) to generate the multilayer network. We also provide several methods to reveal th... | ['Dong Xia', 'Chenyu Ren', 'Zhongyuan Lyu', 'Ting Li'] | 2023-02-09 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-3.68166447e-01 7.41288066e-02 -1.22551739e-01 -3.34934860e-01
-1.38407364e-01 -4.02588516e-01 5.58554113e-01 -4.04703230e-01
6.78886399e-02 5.24100900e-01 3.50323796e-01 -7.01150358e-01
-3.37265819e-01 -7.02977777e-01 -4.40744281e-01 -7.68645406e-01
-2.32056662e-01 3.64901088e-02 -1.38177555e-02 1.50136605... | [6.998011112213135, 5.335250377655029] |
f27ec2d7-3f2f-48a9-814b-5a4c5030e7ef | gpt3mix-leveraging-large-scale-language | 2104.08826 | null | https://arxiv.org/abs/2104.08826v2 | https://arxiv.org/pdf/2104.08826v2.pdf | GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation | Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts. Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. This paper proposes a novel data augmentation te... | ['Woomyeong Park', 'Sang-Woo Lee', 'Jaewook Kang', 'Dongju Park', 'Kang Min Yoo'] | 2021-04-18 | null | https://aclanthology.org/2021.findings-emnlp.192 | https://aclanthology.org/2021.findings-emnlp.192.pdf | findings-emnlp-2021-11 | ['text-augmentation'] | ['natural-language-processing'] | [ 2.07002923e-01 5.50359905e-01 -6.13432825e-01 -2.23330393e-01
-1.07087362e+00 -4.48936164e-01 8.63863766e-01 2.17054978e-01
-6.28741503e-01 9.92331982e-01 6.28793001e-01 -6.43172204e-01
3.11752588e-01 -8.33711565e-01 -6.79028809e-01 -2.47259855e-01
3.51061046e-01 1.01233721e+00 -5.84415495e-02 -4.54953313... | [10.818577766418457, 8.221076011657715] |
45d396f6-fed1-4b94-af24-b877817d4b67 | extending-the-use-of-mdl-for-high-dimensional | 2201.11171 | null | https://arxiv.org/abs/2201.11171v1 | https://arxiv.org/pdf/2201.11171v1.pdf | Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling | In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include signal denoising and variable selection in linear regression, for which the corresponding MDL solutions often enjoy consistent properties and pr... | ['Thomas C. M. Lee', 'Raymond K. W. Wong', 'Zhenyu Wei'] | 2022-01-26 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.81044453e-01 -2.20880300e-01 -3.70383374e-02 -2.78613687e-01
-1.13894069e+00 -1.96867302e-01 1.08057819e-01 2.64541805e-03
-3.84667724e-01 7.83180892e-01 -1.15597419e-01 -2.37018429e-02
-6.33956611e-01 -2.10889220e-01 -5.07491291e-01 -9.88417625e-01
-5.94891250e-01 -1.81960519e-02 -3.03329229e-01 8.75306651... | [7.054760456085205, 4.35951042175293] |
8c04ad26-7ffd-4113-85a6-957320a4e9e7 | all-for-one-and-one-for-all-improving-music | 2010.04228 | null | https://arxiv.org/abs/2010.04228v4 | https://arxiv.org/pdf/2010.04228v4.pdf | All for One and One for All: Improving Music Separation by Bridging Networks | This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. First, by using MDL we take advantage of the frequency and time domain representation of audio signals. Next, we utilize the relationship among instruments by joi... | ['Yuki Mitsufuji', 'Shusuke Takahashi', 'Stefan Uhlich', 'Ryosuke Sawata'] | 2020-10-08 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.09151039e-02 -4.53391463e-01 3.16473208e-02 -7.78904781e-02
-7.42558181e-01 -8.16884041e-01 2.74311781e-01 -1.53392434e-01
-5.93706071e-01 6.79382145e-01 5.45717776e-02 -6.82290941e-02
-4.91029114e-01 -4.40291196e-01 -5.99855363e-01 -7.91711926e-01
7.82664493e-02 1.81625247e-01 -2.41383840e-03 -1.38122842... | [15.534613609313965, 5.5019755363464355] |
2a9bbe29-d0b5-4a4a-a447-e008e9486007 | mongoose-path-wise-smooth-bayesian | 2302.11533 | null | https://arxiv.org/abs/2302.11533v1 | https://arxiv.org/pdf/2302.11533v1.pdf | MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning | In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepare the system for measurement. We consider a common scenario where prep... | ['Henry B. Moss', 'Laurence Aitchison', 'Adam X. Yang'] | 2023-02-22 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.81375965e-01 3.49801034e-01 1.03412934e-01 -1.49602979e-01
-1.12569249e+00 -3.82390380e-01 6.73368156e-01 -8.42696056e-02
-6.29067779e-01 1.07777631e+00 -2.59795815e-01 -3.60211223e-01
-8.65600765e-01 -4.79549527e-01 -6.09816313e-01 -1.16403985e+00
-3.53051454e-01 7.51622796e-01 5.69316521e-02 -1.61755338... | [6.247162818908691, 3.793543815612793] |
79107e54-55bb-4a5b-9db0-47194b611ab4 | metric-learning-with-adaptive-density | 1511.05939 | null | http://arxiv.org/abs/1511.05939v2 | http://arxiv.org/pdf/1511.05939v2.pdf | Metric Learning with Adaptive Density Discrimination | Distance metric learning (DML) approaches learn a transformation to a
representation space where distance is in correspondence with a predefined
notion of similarity. While such models offer a number of compelling benefits,
it has been difficult for these to compete with modern classification
algorithms in performance ... | ['Piotr Dollar', 'Manohar Paluri', 'Oren Rippel', 'Lubomir Bourdev'] | 2015-11-18 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 4.69913661e-01 1.61934756e-02 -3.45408350e-01 -7.21714437e-01
-1.04140842e+00 -5.94757020e-01 8.13790202e-01 5.14692008e-01
-6.24947309e-01 7.18910635e-01 3.38028371e-02 -2.01841578e-01
-5.23968041e-01 -5.64474404e-01 -4.39742744e-01 -6.46155238e-01
-8.23525637e-02 4.14200485e-01 6.61071315e-02 -1.55174732... | [9.431995391845703, 2.983315944671631] |
be63e3a9-8e18-4029-8ca1-3ee09e586e11 | safe-reinforcement-learning-for-multi-energy | 2207.03830 | null | https://arxiv.org/abs/2207.03830v4 | https://arxiv.org/pdf/2207.03830v4.pdf | Safe reinforcement learning for multi-energy management systems with known constraint functions | Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL d... | ['Maarten Messagie', 'Ann Nowé', 'Rüdiger Franke', 'Luis Ramirez Camargo', 'Glenn Ceusters'] | 2022-07-08 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.99993956e-02 4.29738969e-01 -3.55455786e-01 5.63861616e-02
-5.61142087e-01 -7.56249666e-01 5.96464038e-01 1.68203279e-01
-4.91982341e-01 1.28458226e+00 -4.42807943e-01 -4.01899368e-01
-5.98541856e-01 -8.25184822e-01 -6.05554760e-01 -9.49847817e-01
-3.44764769e-01 5.29242218e-01 7.27932260e-04 -1.46089897... | [5.180043697357178, 2.4274823665618896] |
1a102df1-e58f-457d-95d6-2f9e920a3e1f | revisiting-activation-regularization-for | 1708.01009 | null | http://arxiv.org/abs/1708.01009v1 | http://arxiv.org/pdf/1708.01009v1.pdf | Revisiting Activation Regularization for Language RNNs | Recurrent neural networks (RNNs) serve as a fundamental building block for
many sequence tasks across natural language processing. Recent research has
focused on recurrent dropout techniques or custom RNN cells in order to improve
performance. Both of these can require substantial modifications to the machine
learning ... | ['Richard Socher', 'Stephen Merity', 'Bryan McCann'] | 2017-08-03 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.81649253e-01 -1.50829628e-01 -7.57830665e-02 -2.46237636e-01
-2.80458450e-01 -2.84468859e-01 4.79122400e-01 -1.96498170e-01
-7.46013224e-01 6.22124255e-01 3.41764838e-01 -7.17383146e-01
6.28419757e-01 -5.67730367e-01 -5.01879752e-01 -5.38205743e-01
2.80711204e-01 1.23374544e-01 4.25594240e-01 -2.95425713... | [10.838618278503418, 6.437807083129883] |
714363ec-7f00-44fc-b5e8-7b7a0cff8d07 | neural-diffusion-processes | 2206.03992 | null | https://arxiv.org/abs/2206.03992v2 | https://arxiv.org/pdf/2206.03992v2.pdf | Neural Diffusion Processes | Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference. Building on the successes of denoising diffusion models for generative modelling, we propose Neural Diffusion Processes (NDPs), a novel approach that le... | ['Fergus Simpson', 'Zoubin Ghahramani', 'Alan Saul', 'Vincent Dutordoir'] | 2022-06-08 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.44990027e-01 4.83266383e-01 2.29377657e-01 -5.76728769e-02
-8.10294509e-01 -5.48004925e-01 1.27786255e+00 -1.93360224e-01
-1.02971114e-01 8.54930401e-01 3.38090122e-01 -2.12919638e-01
-5.39817154e-01 -8.90275836e-01 -8.72628033e-01 -9.65294182e-01
-2.36140952e-01 7.82597899e-01 4.39504087e-02 -1.68876387... | [6.944748401641846, 3.8769235610961914] |
4c5dd3f2-6980-4317-81b1-0d1c347c2b21 | performance-analysis-of-semi-supervised | 2002.12164 | null | https://arxiv.org/abs/2002.12164v1 | https://arxiv.org/pdf/2002.12164v1.pdf | Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs | Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data. In this work, we applied an existing algorithm named Variational Auto Encoder (VAE) that pre-train... | ['Varun Mannam', 'Arman Kazemi'] | 2020-02-26 | performance-analysis-of-semi-supervised-1 | https://arxiv.org/pdf/2002.12164.pdf | https://arxiv.org/pdf/2002.12164.pdf | null | ['small-data'] | ['computer-vision'] | [ 3.22602510e-01 -5.32618864e-03 -3.31034213e-02 -4.07675833e-01
-6.78802609e-01 -2.54675925e-01 4.60922718e-01 -1.68526247e-01
-5.99800944e-01 9.30033565e-01 2.05742106e-01 1.36614040e-01
-2.93180853e-01 -6.72176838e-01 -6.47944868e-01 -1.09354711e+00
9.20638070e-02 5.36014855e-01 1.68852553e-01 2.28958443... | [9.057350158691406, 3.052260160446167] |
7013a4f4-aefd-4313-b3ea-605c1f80f5e7 | contrastive-learning-with-logic-driven-data | 2305.12599 | null | https://arxiv.org/abs/2305.12599v1 | https://arxiv.org/pdf/2305.12599v1.pdf | Contrastive Learning with Logic-driven Data Augmentation for Logical Reasoning over Text | Pre-trained large language model (LLM) is under exploration to perform NLP tasks that may require logical reasoning. Logic-driven data augmentation for representation learning has been shown to improve the performance of tasks requiring logical reasoning, but most of these data rely on designed templates and therefore ... | ['Jiamou Liu', 'Michael Witbrock', 'Yonghua Zhu', 'Yang Chen', 'Nathan Young', 'Neset Tan', 'Wanjun Zhong', 'Zhenyun Deng', 'Alex Yuxuan Peng', 'Qiming Bao'] | 2023-05-21 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.87113285e-01 3.20430040e-01 -2.41178185e-01 -6.26728415e-01
-8.15960348e-01 -6.39525056e-01 6.08620882e-01 3.05840790e-01
7.69820809e-03 6.77013695e-01 3.45775932e-01 -1.05913281e+00
-2.90005580e-02 -1.16755378e+00 -1.01433671e+00 5.79591244e-02
1.94751516e-01 7.76509404e-01 -4.08052295e-01 -6.10298216... | [9.609243392944336, 7.5694756507873535] |
04418d00-4482-4671-88a5-ff3a79855f42 | ensemble-based-fine-tuning-strategy-for | null | null | https://aclanthology.org/2022.clinicalnlp-1.11 | https://aclanthology.org/2022.clinicalnlp-1.11.pdf | Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative | In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative. Our experimental results on the THYME data show that ensembling as a fine-tuning strategy can further ... | ['Guergana Savova', 'Steven Bethard', 'Timothy Miller', 'Lijing Wang'] | null | null | null | null | naacl-clinicalnlp-2022-7 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 1.79270968e-01 3.75662357e-01 -4.93201554e-01 -4.93271619e-01
-1.20350575e+00 -6.08820021e-01 5.48049390e-01 4.23240125e-01
-6.36289358e-01 9.04853404e-01 4.46940929e-01 -5.05641282e-01
-3.89210701e-01 -3.21256578e-01 -3.54592353e-01 -3.90434891e-01
-4.90496784e-01 6.88077271e-01 1.94957495e-01 -3.55072320... | [8.537810325622559, 8.859450340270996] |
412d8a83-f783-41ec-8514-4448afbc5533 | right-to-be-forgotten-in-the-era-of-large | 2307.03941 | null | https://arxiv.org/abs/2307.03941v1 | https://arxiv.org/pdf/2307.03941v1.pdf | Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions | The Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja Gonz\'alez, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal dat... | ['Xiwei Xu', 'Mark Staples', 'Zhenchang Xing', 'Shidong Pan', 'Thong Hoang', 'Pamela Finckenberg-Broman', 'Dawen Zhang'] | 2023-07-08 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 2.70747729e-02 3.73489439e-01 -4.06848878e-01 4.01161611e-02
-4.80785429e-01 -7.14893758e-01 6.95798278e-01 2.36016944e-01
-6.19413972e-01 6.86260402e-01 7.26729482e-02 -7.63194025e-01
-1.19790442e-01 -8.58370006e-01 -4.28346992e-01 1.06624730e-01
5.65430999e-01 5.29817283e-01 2.21379682e-01 -1.49977863... | [9.2259521484375, 7.185349464416504] |
23c11f2d-4d9f-4a6c-b5a5-accc6c4da047 | revisiting-conversation-discourse-for | 2306.03975 | null | https://arxiv.org/abs/2306.03975v2 | https://arxiv.org/pdf/2306.03975v2.pdf | Revisiting Conversation Discourse for Dialogue Disentanglement | Dialogue disentanglement aims to detach the chronologically ordered utterances into several independent sessions. Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attri... | ['Donghong Ji', 'Tat-Seng Chua', 'Yinwei Wei', 'Lizi Liao', 'Shengqiong Wu', 'Fei Li', 'Hao Fei', 'Bobo Li'] | 2023-06-06 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 4.06540185e-01 6.15726531e-01 -3.88721973e-01 -6.88844919e-01
-7.95084417e-01 -6.45558655e-01 8.63022983e-01 3.08640581e-02
-2.77574435e-02 7.06245482e-01 9.01733816e-01 -4.22712922e-01
-8.69018491e-03 -6.09634340e-01 -2.45049372e-01 -7.16794968e-01
-9.27533507e-02 6.71064973e-01 -1.47131115e-01 -6.87599659... | [12.509480476379395, 7.844099044799805] |
e583c591-d45e-4d92-9754-74c981a36f96 | nearly-optimal-vc-dimension-and-pseudo | 2305.08466 | null | https://arxiv.org/abs/2305.08466v1 | https://arxiv.org/pdf/2305.08466v1.pdf | Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives | This paper addresses the problem of nearly optimal Vapnik--Chervonenkis dimension (VC-dimension) and pseudo-dimension estimations of the derivative functions of deep neural networks (DNNs). Two important applications of these estimations include: 1) Establishing a nearly tight approximation result of DNNs in the Sobole... | ['Yang Xiang', 'Haizhao Yang', 'Yahong Yang'] | 2023-05-15 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 2.07087189e-01 3.18416327e-01 1.52733311e-01 -2.16461003e-01
-5.53671896e-01 -2.61073053e-01 2.61264533e-01 1.44207859e-02
-5.72399318e-01 1.13159585e+00 -4.56579149e-01 -5.97115874e-01
-4.89085317e-01 -7.83791661e-01 -8.95853162e-01 -1.00722802e+00
-2.25800112e-01 7.79335618e-01 1.83519498e-01 -1.00922197... | [7.719717025756836, 3.5984854698181152] |
908f38b0-b5ce-48e6-9f5e-897b282ef5bf | a-multiple-kernel-testing-procedure-for-non | 2206.07239 | null | https://arxiv.org/abs/2206.07239v1 | https://arxiv.org/pdf/2206.07239v1.pdf | A Multiple kernel testing procedure for non-proportional hazards in factorial designs | In this paper we propose a Multiple kernel testing procedure to infer survival data when several factors (e.g. different treatment groups, gender, medical history) and their interaction are of interest simultaneously. Our method is able to deal with complex data and can be seen as an alternative to the omnipresent Cox ... | ['Nicolás Rivera', 'Tamara Fernández', 'Marc Ditzhaus'] | 2022-06-15 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 9.58878919e-02 -2.06762120e-01 -4.03218150e-01 -3.11213762e-01
-4.68585134e-01 -2.20388725e-01 3.03930193e-01 6.91972196e-01
-8.67015243e-01 1.09266448e+00 -2.32355312e-01 -7.35425472e-01
-4.76189375e-01 -8.48967433e-01 -4.02138948e-01 -8.61458719e-01
-5.81461728e-01 5.21874964e-01 2.16257244e-01 -1.62024237... | [7.726827621459961, 4.888603687286377] |
d00cf849-ad75-42bd-958a-084de1f59607 | anoonly-semi-supervised-anomaly-detection | 2305.18798 | null | https://arxiv.org/abs/2305.18798v1 | https://arxiv.org/pdf/2305.18798v1.pdf | AnoOnly: Semi-Supervised Anomaly Detection without Loss on Normal Data | Semi-supervised anomaly detection (SSAD) methods have demonstrated their effectiveness in enhancing unsupervised anomaly detection (UAD) by leveraging few-shot but instructive abnormal instances. However, the dominance of homogeneous normal data over anomalies biases the SSAD models against effectively perceiving anoma... | ['Heng Tao Shen', 'Fumin Shen', 'Xing Xu', 'Yi Qu', 'Peiyu Yang', 'Yixuan Zhou'] | 2023-05-30 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.83551595e-01 1.35663738e-02 -2.19478041e-01 -5.95065773e-01
-5.54177284e-01 -2.47758254e-01 8.62295032e-01 5.52130818e-01
-2.41084874e-01 2.42410079e-01 -3.82796749e-02 -3.78481239e-01
1.02092773e-01 -6.87914371e-01 -3.90876830e-01 -6.64674044e-01
-2.06888586e-01 2.38030210e-01 2.26332113e-01 -2.02439383... | [7.638351917266846, 2.380617380142212] |
79694382-8dea-4267-bef2-76ce1cfa6145 | seget-deep-neural-network-with-rich | 1811.11729 | null | http://arxiv.org/abs/1811.11729v1 | http://arxiv.org/pdf/1811.11729v1.pdf | SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image | Electron tomography (ET) allows high-resolution reconstructions of
macromolecular complexes at nearnative state. Cellular structures segmentation
in the reconstruction data from electron tomographic images is often required
for analyzing and visualizing biological structures, making it a powerful tool
for quantitative ... | ['Zhi-Yong Liu', 'Xiaohua Wan', 'Lifa Zhu', 'Fa Zhang', 'Enze Zhang'] | 2018-11-28 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.21047541e-01 -3.80596578e-01 1.06556304e-01 -3.38136345e-01
-9.70913529e-01 -6.22795999e-01 3.42307478e-01 1.45250767e-01
-7.77492762e-01 9.74702716e-01 -2.00229347e-01 -1.41669050e-01
3.05330843e-01 -6.06248558e-01 -8.15934598e-01 -8.92329156e-01
2.63829559e-01 1.26928341e+00 2.73817599e-01 2.57811219... | [14.015262603759766, -3.118941068649292] |
e266e013-6d34-4a3d-9a28-5935387b425e | control-a-video-controllable-text-to-video | 2305.13840 | null | https://arxiv.org/abs/2305.13840v1 | https://arxiv.org/pdf/2305.13840v1.pdf | Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models | This paper presents a controllable text-to-video (T2V) diffusion model, named Video-ControlNet, that generates videos conditioned on a sequence of control signals, such as edge or depth maps. Video-ControlNet is built on a pre-trained conditional text-to-image (T2I) diffusion model by incorporating a spatial-temporal s... | ['Liang Lin', 'Xuefeng Xiao', 'Xin Xia', 'Jiashi Li', 'Hefeng Wu', 'Pan Xie', 'Jie Wu', 'Weifeng Chen'] | 2023-05-23 | null | null | null | null | ['style-transfer', 'video-style-transfer', 'video-generation', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.98682421e-01 -1.81260929e-01 -8.21818337e-02 -1.67619258e-01
-5.85735679e-01 -3.13995481e-01 6.44561768e-01 -8.36067915e-01
-1.55311450e-01 7.66173482e-01 3.38629037e-01 1.01944640e-01
2.75766194e-01 -6.37491345e-01 -1.16623724e+00 -7.33071268e-01
2.87732899e-01 3.26683521e-02 8.98162276e-02 -3.51063572... | [10.87343978881836, -0.6375402808189392] |
d3b90019-a222-420e-9530-f20084a1f55a | modelling-customer-lifetime-value-in-the | 2304.03038 | null | https://arxiv.org/abs/2304.03038v1 | https://arxiv.org/pdf/2304.03038v1.pdf | Modelling customer lifetime-value in the retail banking industry | Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, commonly used approaches rely on simple heuristics and do not take advantage of the high predictive ability of modern machine learning technique... | ['Raad Khraishi', 'Salvatore Mercuri', 'Greig Cowan'] | 2023-04-06 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-2.43399099e-01 2.36152232e-01 -7.78157711e-01 -7.86153972e-01
-8.41762364e-01 -5.10159075e-01 4.04217601e-01 6.38451338e-01
-4.81095314e-01 7.00345278e-01 2.07929134e-01 -8.52544427e-01
-6.42803073e-01 -9.63638604e-01 -5.98851919e-01 -3.96630853e-01
-2.10690573e-01 1.24347854e+00 -2.81198889e-01 -2.01196328... | [9.284571647644043, 5.833154678344727] |
ddec5eef-5bab-45af-968e-5c594ed543f4 | spatiotemporal-modeling-of-multivariate | 2211.11176 | null | https://arxiv.org/abs/2211.11176v3 | https://arxiv.org/pdf/2211.11176v3.pdf | Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models | Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between the electrodes.... | ['Tina Baykaner', 'Daniel L. Rubin', 'Christopher Lee-Messer', 'Khaled K. Saab', 'Liangqiong Qu', 'Jared A. Dunnmon', 'Siyi Tang'] | 2022-11-21 | null | null | null | null | ['graph-structure-learning', 'seizure-detection', 'sleep-staging'] | ['graphs', 'medical', 'medical'] | [ 2.97320992e-01 -1.23641290e-01 1.26732826e-01 -7.69678950e-02
-2.90302366e-01 -4.36244845e-01 2.36473948e-01 3.16008389e-01
-2.38184690e-01 8.26582789e-01 2.45916575e-01 -4.33657557e-01
-4.54172403e-01 -2.01824337e-01 -5.36292493e-01 -3.95907521e-01
-1.05854297e+00 2.12345779e-01 2.04132274e-01 -2.67099440... | [13.28891658782959, 3.56632924079895] |
efce321a-10fc-40a4-bc4d-b9ae5a0d58a5 | editvae-unsupervised-part-aware-controllable | 2110.06679 | null | https://arxiv.org/abs/2110.06679v2 | https://arxiv.org/pdf/2110.06679v2.pdf | EditVAE: Unsupervised Part-Aware Controllable 3D Point Cloud Shape Generation | This paper tackles the problem of parts-aware point cloud generation. Unlike existing works which require the point cloud to be segmented into parts a priori, our parts-aware editing and generation are performed in an unsupervised manner. We achieve this with a simple modification of the Variational Auto-Encoder which ... | ['Christian Walder', 'Miaomiao Liu', 'Shidi Li'] | 2021-10-13 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.55814427e-01 5.68391263e-01 1.74372613e-01 -2.67460167e-01
-6.70311928e-01 -1.09333682e+00 1.20074034e+00 4.24058363e-02
2.59077847e-02 4.07362223e-01 2.15164214e-01 1.82810456e-01
-5.98163866e-02 -1.05861688e+00 -1.06460488e+00 -8.81536186e-01
3.04397225e-01 9.40165877e-01 -8.42169374e-02 -2.83560246... | [8.80625057220459, -3.530165195465088] |
ce84e8cd-a5d2-4a25-a0b6-a5e82958cffc | use-of-speaker-recognition-approaches-for | 2107.11506 | null | https://arxiv.org/abs/2107.11506v2 | https://arxiv.org/pdf/2107.11506v2.pdf | Use of speaker recognition approaches for learning and evaluating embedding representations of musical instrument sounds | Constructing an embedding space for musical instrument sounds that can meaningfully represent new and unseen instruments is important for downstream music generation tasks such as multi-instrument synthesis and timbre transfer. The framework of Automatic Speaker Verification (ASV) provides us with architectures and eva... | ['Junichi Yamagishi', 'Erica Cooper', 'Xuan Shi'] | 2021-07-24 | null | null | null | null | ['instrument-recognition', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [ 2.36532629e-01 2.54094243e-01 1.08838819e-01 -2.41929233e-01
-7.75887012e-01 -9.27765191e-01 2.46789366e-01 -3.00597161e-01
-4.58088160e-01 2.82310277e-01 6.28311098e-01 -2.21822307e-01
-1.21734969e-01 -3.24831188e-01 -4.20560300e-01 -4.95772719e-01
-2.63082236e-01 2.48957872e-02 -3.28678787e-01 -4.01537240... | [15.5222749710083, 5.998431205749512] |
74478ca9-8d40-4234-b85c-98dfebfbf18e | compressing-neural-networks-towards | 2107.11442 | null | https://arxiv.org/abs/2107.11442v2 | https://arxiv.org/pdf/2107.11442v2.pdf | Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition | We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultaneously achieving the desired overall compression. Our algorithm hinges on the idea of compressing each convolutional (or fully-connected) lay... | ['Daniela Rus', 'Dan Feldman', 'Oren Gal', 'Alaa Maalouf', 'Lucas Liebenwein'] | 2021-07-23 | null | http://proceedings.neurips.cc/paper/2021/hash/2adcfc3929e7c03fac3100d3ad51da26-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/2adcfc3929e7c03fac3100d3ad51da26-Paper.pdf | neurips-2021-12 | ['low-rank-compression'] | ['computer-code'] | [ 4.35879171e-01 2.24827752e-01 -4.97498363e-01 -4.61251557e-01
-7.93336451e-01 -4.93553191e-01 3.30833554e-01 -3.81574221e-02
-6.11128926e-01 2.61698037e-01 4.07908827e-01 -5.86005449e-01
-2.66240597e-01 -7.06166565e-01 -1.07175589e+00 -6.08557522e-01
-3.00608128e-01 9.07539576e-02 1.55236766e-01 3.50774437... | [8.536276817321777, 3.1956543922424316] |
5b507dc3-c9c2-4b95-9c63-236959dcc854 | random-forest-with-learned-representations | 1901.07828 | null | http://arxiv.org/abs/1901.07828v1 | http://arxiv.org/pdf/1901.07828v1.pdf | Random Forest with Learned Representations for Semantic Segmentation | In this work, we present a random forest framework that learns the weights,
shapes, and sparsities of feature representations for real-time semantic
segmentation. Typical filters (kernels) have predetermined shapes and
sparsities and learn only weights. A few feature extraction methods fix weights
and learn only shapes... | ['Truong Q. Nguyen', 'Byeongkeun Kang'] | 2019-01-23 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.82671082e-01 1.48036927e-01 -5.13350487e-01 -3.92491698e-01
-5.82069755e-02 -6.62379086e-01 2.54291266e-01 -4.10181344e-01
-4.76326197e-01 5.40066361e-01 -1.24989882e-01 4.05451730e-02
-5.54934442e-01 -9.41017091e-01 -5.20820498e-01 -6.31326914e-01
-1.49234161e-01 6.21829629e-01 5.80491960e-01 1.78228989... | [9.356389045715332, -0.004121270030736923] |
efedc614-db13-482a-a251-c3e65cefcee5 | squares-a-sql-synthesizer-using-query-reverse | null | null | https://dl.acm.org/doi/10.14778/3415478.3415492 | http://www.vldb.org/pvldb/vol13/p2853-orvalho.pdf | SQUARES: A SQL Synthesizer Using Query Reverse Engineering | Nowadays, many data analysts are domain experts, but they lack programming skills. As a result, many of them can provide examples of data transformations but are unable to produce the desired query. Hence, there is an increasing need for systems capable of solving the problem of Query Reverse Engineering (QRE). Given a... | ['Vasco Manquinho', 'Ruben Martins', 'Miguel Terra-Neves; Miguel Ventura', 'Pedro Orvalho'] | 2020-08-31 | null | null | null | null | ['enumerative-search', 'sql-synthesis'] | ['computer-code', 'computer-code'] | [ 4.71902974e-02 -1.33331423e-03 -7.58687630e-02 -7.39600897e-01
-4.92076844e-01 -9.14447367e-01 2.29601547e-01 6.75684631e-01
-4.45049033e-02 1.28194600e-01 -3.77113461e-01 -9.71854866e-01
5.19034006e-02 -1.66958368e+00 -7.21781313e-01 3.65597665e-01
3.64200920e-01 3.71888101e-01 4.50277120e-01 -5.29096723... | [9.207213401794434, 7.706489086151123] |
b8f6b1b3-0d6e-4ce9-97e9-ca59b3df711a | unsupervised-person-re-identification | 1705.10444 | null | http://arxiv.org/abs/1705.10444v2 | http://arxiv.org/pdf/1705.10444v2.pdf | Unsupervised Person Re-identification: Clustering and Fine-tuning | The superiority of deeply learned pedestrian representations has been
reported in very recent literature of person re-identification (re-ID). In this
paper, we consider the more pragmatic issue of learning a deep feature with no
or only a few labels. We propose a progressive unsupervised learning (PUL)
method to transf... | ['Yi Yang', 'Hehe Fan', 'Liang Zheng'] | 2017-05-30 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-3.21365707e-02 -1.29108995e-01 -1.41210556e-01 -5.89473724e-01
-3.71596605e-01 -3.13319564e-01 7.32491016e-01 2.18446508e-01
-8.54100645e-01 6.54360414e-01 4.30711836e-01 1.64788619e-01
1.47539183e-01 -7.08873808e-01 -6.05497122e-01 -7.91965127e-01
1.10733569e-01 6.58737838e-01 2.09146217e-01 8.66182968... | [14.801252365112305, 1.0922088623046875] |
36053604-52bd-4453-899a-ed971b9fd2ff | neural-combinatory-constituency-parsing | 2106.06689 | null | https://arxiv.org/abs/2106.06689v1 | https://arxiv.org/pdf/2106.06689v1.pdf | Neural Combinatory Constituency Parsing | We propose two fast neural combinatory models for constituency parsing: binary and multi-branching. Our models decompose the bottom-up parsing process into 1) classification of tags, labels, and binary orientations or chunks and 2) vector composition based on the computed orientations or chunks. These models have theor... | ['Mamoru Komachi', 'Aizhan Imankulova', 'Longtu Zhang', 'Zhousi Chen'] | 2021-06-12 | null | https://aclanthology.org/2021.findings-acl.194 | https://aclanthology.org/2021.findings-acl.194.pdf | findings-acl-2021-8 | ['constituency-parsing'] | ['natural-language-processing'] | [-1.82500109e-01 5.90804636e-01 -4.15965527e-01 -9.11070168e-01
-9.37119901e-01 -8.06702077e-01 1.15830870e-03 3.57139647e-01
-6.99245572e-01 7.11690605e-01 4.61354584e-01 -1.16664839e+00
5.08602321e-01 -8.54632497e-01 -6.28397882e-01 -4.06708360e-01
-3.37231666e-01 4.26952600e-01 4.43404764e-01 -2.73803622... | [10.375675201416016, 9.675494194030762] |
d64c42d2-cfec-40e7-a0ee-8cccb9df5259 | memory-augmented-sequential-paragraph | 2102.03741 | null | https://arxiv.org/abs/2102.03741v1 | https://arxiv.org/pdf/2102.03741v1.pdf | Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering | Retrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing works consider paragraphs as nodes in a graph and propose graph-based methods to retrieve them. However, in this paper, we point out the intrin... | ['Guoping Hu', 'Shijin Wang', 'Ting Liu', 'Yiming Cui', 'Nan Shao'] | 2021-02-07 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.12462327e-01 3.82982254e-01 -2.78485596e-01 -1.85037404e-01
-1.61775124e+00 -8.61689806e-01 4.76335227e-01 4.28311765e-01
-3.60677421e-01 9.04043138e-01 5.38352966e-01 -4.45012510e-01
-3.70763481e-01 -7.18072474e-01 -7.80682981e-01 -3.41120183e-01
4.60881025e-01 9.06681478e-01 8.34665835e-01 -6.70800447... | [11.07308292388916, 7.881235122680664] |
9f3e5b7a-1076-4a12-82e0-4b4aa02da6a2 | a-technique-to-create-weaker-abstract-board | 2209.00711 | null | https://arxiv.org/abs/2209.00711v1 | https://arxiv.org/pdf/2209.00711v1.pdf | A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning | Board games, with the exception of solo games, need at least one other player to play. Because of this, we created Artificial Intelligent (AI) agents to play against us when an opponent is missing. These AI agents are created in a number of ways, but one challenge with these agents is that an agent can have superior ab... | ['Indrima Upadhyay', 'Peter Jamieson'] | 2022-09-01 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.22789375e-01 4.58952218e-01 1.75779581e-01 2.23886460e-01
-5.82208753e-01 -7.67320037e-01 4.27831322e-01 -4.08234924e-01
-7.51675248e-01 1.51494467e+00 -3.29439700e-01 -3.63824666e-01
-2.33917728e-01 -1.14237177e+00 -4.81881797e-01 -5.99546134e-01
-2.83735991e-01 1.06312227e+00 7.87329435e-01 -9.89963293... | [3.4845573902130127, 1.5349806547164917] |
08c4847d-ce2b-491e-a888-087130592776 | orthogonal-features-based-eeg-signals | 2104.08120 | null | https://arxiv.org/abs/2104.08120v1 | https://arxiv.org/pdf/2104.08120v1.pdf | Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder | This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoisin... | ['Ahlad Kumar', 'Subham Nagar'] | 2021-04-16 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [ 2.10820526e-01 -1.86169460e-01 5.79852521e-01 -2.87254602e-01
8.05600956e-02 -9.43254773e-03 2.14863330e-01 -6.76195100e-02
-7.98415363e-01 8.69867682e-01 1.37369990e-01 1.54397205e-01
-4.44396526e-01 -5.57904601e-01 -8.46536994e-01 -9.64775503e-01
-2.80868441e-01 -3.02427649e-01 -4.45114106e-01 -3.91617090... | [13.157336235046387, 3.4074833393096924] |
d8d99680-30d7-4f6e-8a9d-c0fc7ab3e902 | emergent-and-predictable-memorization-in | 2304.11158 | null | https://arxiv.org/abs/2304.11158v2 | https://arxiv.org/pdf/2304.11158v2.pdf | Emergent and Predictable Memorization in Large Language Models | Memorization, or the tendency of large language models (LLMs) to output entire sequences from their training data verbatim, is a key concern for safely deploying language models. In particular, it is vital to minimize a model's memorization of sensitive datapoints such as those containing personal identifiable informat... | ['Edward Raff', 'Shivanshu Purohit', 'Quentin Anthony', 'Hailey Schoelkopf', 'Lintang Sutawika', 'USVSN Sai Prashanth', 'Stella Biderman'] | 2023-04-21 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 8.04287493e-02 -4.34012599e-02 -1.36894450e-01 -3.91526818e-01
-6.70454502e-01 -8.04628491e-01 5.18519878e-01 4.79239404e-01
-7.06493616e-01 7.33548522e-01 3.23240124e-02 -1.01916361e+00
-1.49788424e-01 -5.47185540e-01 -8.57753158e-01 -3.56288522e-01
-1.36654839e-01 2.92932838e-01 -1.22700445e-02 -4.87223230... | [10.680459022521973, 8.340264320373535] |
d327332c-62a3-4ad3-8847-c5c5910c0587 | improving-model-s-focus-improves-performance | 2303.00818 | null | https://arxiv.org/abs/2303.00818v1 | https://arxiv.org/pdf/2303.00818v1.pdf | Improving Model's Focus Improves Performance of Deep Learning-Based Synthetic Face Detectors | Deep learning-based models generalize better to unknown data samples after being guided "where to look" by incorporating human perception into training strategies. We made an observation that the entropy of the model's salience trained in that way is lower when compared to salience entropy computed for models training ... | ['Christopher Sweet', 'Adam Czajka', 'Jacob Piland'] | 2023-03-01 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 5.06795347e-01 7.34920084e-01 -3.22619677e-02 -4.95198309e-01
-6.00053787e-01 -2.03606874e-01 7.01669395e-01 8.30095634e-02
-5.31640828e-01 6.97715938e-01 1.49962440e-01 2.39875913e-01
-3.00467104e-01 -6.83911741e-01 -7.93161690e-01 -8.17953706e-01
1.74500361e-01 6.53573215e-01 2.26926446e-01 -2.52285033... | [10.013279914855957, 2.147045850753784] |
f3352e7c-8eb2-4722-9aae-1fe6d454e44a | prediction-interval-for-neural-network-models | 2210.04318 | null | https://arxiv.org/abs/2210.04318v4 | https://arxiv.org/pdf/2210.04318v4.pdf | Prediction intervals for neural network models using weighted asymmetric loss functions | We propose a simple and efficient approach to generate a prediction intervals (PI) for approximated and forecasted trends. Our method leverages a weighted asymmetric loss function to estimate the lower and upper bounds of the PI, with the weights determined by its coverage probability. We provide a concise mathematical... | ['Agnieszka Werpachowska', 'Yunpeng Han', 'Milo Grillo'] | 2022-10-09 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-4.49157767e-02 3.27856660e-01 -6.52690709e-01 -6.61756217e-01
-9.85419631e-01 -7.95235217e-01 7.61301100e-01 -9.98735614e-03
1.33571744e-01 1.21177328e+00 2.09613480e-02 -7.80270934e-01
-5.18722236e-01 -8.05977702e-01 -8.03953350e-01 -7.62069583e-01
-5.82562506e-01 6.25386000e-01 1.07215550e-02 2.69847661... | [7.165709495544434, 3.801043748855591] |
050d9fda-42cc-4f90-a1cc-53c8c59e2df1 | the-effects-of-super-resolution-on-object | 1812.04098 | null | http://arxiv.org/abs/1812.04098v3 | http://arxiv.org/pdf/1812.04098v3.pdf | The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery | We explore the application of super-resolution techniques to satellite
imagery, and the effects of these techniques on object detection algorithm
performance. Specifically, we enhance satellite imagery beyond its native
resolution, and test if we can identify various types of vehicles, planes, and
boats with greater ac... | ['Adam Van Etten', 'Jacob Shermeyer'] | 2018-12-10 | null | null | null | null | ['satellite-image-super-resolution'] | ['computer-vision'] | [ 4.53346521e-01 -3.51545990e-01 1.69906303e-01 -1.14825822e-01
-1.10939407e+00 -8.45567107e-01 6.19358480e-01 -1.30506262e-01
-5.44715524e-01 7.13289917e-01 1.02630056e-01 -3.08110952e-01
-2.30928168e-01 -1.22305739e+00 -4.37729031e-01 -7.47859418e-01
-7.24156559e-01 1.75759479e-01 8.03385377e-01 -3.71006459... | [9.358015060424805, -1.2659871578216553] |
c1911afb-8e63-4626-9e77-27b06f9c59c8 | automated-classification-of-stroke-blood-clot | 2304.13775 | null | https://arxiv.org/abs/2304.13775v1 | https://arxiv.org/pdf/2304.13775v1.pdf | Automated Classification of Stroke Blood Clot Origin using Whole-Slide Digital Pathology Images | The classification of the origin of blood clots is a crucial step in diagnosing and treating ischemic stroke. Various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound have been employed to detect and locate blood clots within the body. However, identifying the origin... | ['D. Elangovan', 'G. Senthilkumar', 'M. Logeshwaran', 'P. J. Joe Nikesh', 'Koushik Sivarama Krishnan'] | 2023-04-26 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [-2.01080754e-01 -3.54296535e-01 -4.73264843e-01 -5.54763479e-03
-9.38071430e-01 -7.25104570e-01 6.01462901e-01 6.13566816e-01
-5.14125526e-01 6.05226517e-01 3.49570096e-01 -6.56488895e-01
3.04234531e-02 -5.08906186e-01 -7.47655630e-02 -9.24069405e-01
-1.07057236e-01 8.09288383e-01 6.06946945e-01 3.57538939... | [14.226578712463379, -2.036107063293457] |
8f2c4fb4-a802-46f8-b394-8aa21aff2f23 | decentralized-machine-learning-for | 2207.14584 | null | https://arxiv.org/abs/2207.14584v2 | https://arxiv.org/pdf/2207.14584v2.pdf | Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum | The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal me... | ['Radu Prodan', 'Sasko Ristov', 'Dragi Kimovski'] | 2022-07-29 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-3.47672433e-01 5.89646041e-01 -1.54012278e-01 -3.04182023e-01
-6.78797007e-01 -5.58400333e-01 4.17636111e-02 8.29826713e-01
-3.13895375e-01 8.50277126e-01 -8.04321393e-02 -6.39787495e-01
-2.14512423e-01 -6.77596390e-01 -1.38936788e-01 -4.86015916e-01
-4.53260802e-02 6.40746415e-01 -3.88246953e-01 5.74159145... | [6.217926025390625, 6.430655002593994] |
9ee230ee-8a1f-4b80-a2b7-c2e231a3485b | a-framework-for-refining-text-classification | 2305.17401 | null | https://arxiv.org/abs/2305.17401v2 | https://arxiv.org/pdf/2305.17401v2.pdf | A Framework For Refining Text Classification and Object Recognition from Academic Articles | With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data mining for academic articles is challenging since it requires automatically ext... | ['Shinobu Hasegawa', 'Wen Gu', 'Koichi Ota', 'Jinghong Li'] | 2023-05-27 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-2.95773540e-02 -1.10058226e-01 -3.96201164e-01 -5.56795895e-02
-3.03390145e-01 -4.16252255e-01 3.37349236e-01 6.94491148e-01
-2.38647029e-01 9.48966444e-01 -3.50071907e-01 -7.38505721e-01
-5.04397571e-01 -8.89236152e-01 -4.76349920e-01 -1.87129155e-01
1.49991795e-01 5.09692371e-01 1.63595274e-01 1.71240568... | [9.611418724060059, 8.393172264099121] |
8ddd4af0-e77d-490d-a7c4-8435db408402 | eli5-long-form-question-answering | 1907.09190 | null | https://arxiv.org/abs/1907.09190v1 | https://arxiv.org/pdf/1907.09190v1.pdf | ELI5: Long Form Question Answering | We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum ``Explain Like I'm Five'' (ELI5) where an online community provides answers to questions which are comprehensible ... | ['Jason Weston', 'Yacine Jernite', 'Michael Auli', 'David Grangier', 'Angela Fan', 'Ethan Perez'] | 2019-07-22 | eli5-long-form-question-answering-1 | https://aclanthology.org/P19-1346 | https://aclanthology.org/P19-1346.pdf | acl-2019-7 | ['long-form-question-answering'] | ['natural-language-processing'] | [-1.32923067e-01 4.75631803e-01 1.26405358e-01 -5.55325449e-01
-1.72811341e+00 -1.01155198e+00 3.07369977e-01 1.17269121e-01
-6.39460087e-01 9.46434081e-01 8.08262646e-01 -6.41289532e-01
-1.67370483e-01 -3.93988639e-01 -5.70052326e-01 2.62641460e-01
3.79844666e-01 9.46617067e-01 3.59580368e-01 -8.00028026... | [11.477822303771973, 8.131074905395508] |
0473dffa-9b32-47ae-a481-8449fef54f87 | a-comparative-study-of-neural-network | 1910.11144 | null | https://arxiv.org/abs/1910.11144v1 | https://arxiv.org/pdf/1910.11144v1.pdf | A Comparative Study of Neural Network Compression | There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constit... | ['Hossein Baktash', 'Emanuele Natale', 'Laurent Viennot'] | 2019-10-24 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.11648881e-01 2.33004630e-01 1.72469784e-02 -3.67781132e-01
6.35563442e-03 -1.57563671e-01 5.71208715e-01 3.44486535e-01
-1.16052449e+00 8.05856943e-01 -1.51110500e-01 -4.70406651e-01
-5.80295801e-01 -1.00878620e+00 -6.36425078e-01 -7.51789749e-01
-3.70151252e-01 3.86950254e-01 6.72906339e-01 -4.50984657... | [8.504916191101074, 3.167708396911621] |
0bdca38e-925a-4e94-99ea-14ddd176f52d | object-centric-image-generation-from-layouts | 2003.07449 | null | https://arxiv.org/abs/2003.07449v2 | https://arxiv.org/pdf/2003.07449v2.pdf | Object-Centric Image Generation from Layouts | Despite recent impressive results on single-object and single-domain image generation, the generation of complex scenes with multiple objects remains challenging. In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate compl... | ['R. Devon Hjelm', 'Tristan Sylvain', 'Shikhar Sharma', 'Yoshua Bengio', 'Pengchuan Zhang'] | 2020-03-16 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 7.59984970e-01 2.02731863e-01 3.87951821e-01 -1.33654714e-01
-6.90835297e-01 -8.25643837e-01 7.08646595e-01 -1.92567289e-01
5.00367917e-02 6.79322779e-01 1.64724648e-01 -1.35507554e-01
-1.77110713e-02 -9.14480865e-01 -1.21576893e+00 -4.65626866e-01
1.72782481e-01 3.78346384e-01 4.23623919e-01 -2.95814008... | [11.412712097167969, -0.3827567398548126] |
c2fb1824-26f0-4a65-90c7-6b0778d7fd29 | test-time-batch-statistics-calibration-for | 2110.04065 | null | https://arxiv.org/abs/2110.04065v1 | https://arxiv.org/pdf/2110.04065v1.pdf | Test-time Batch Statistics Calibration for Covariate Shift | Deep neural networks have a clear degradation when applying to the unseen environment due to the covariate shift. Conventional approaches like domain adaptation requires the pre-collected target data for iterative training, which is impractical in real-world applications. In this paper, we propose to adapt the deep mod... | ['Zhou Zhao', 'Jingjing Li', 'Fuming You'] | 2021-10-06 | test-time-batch-statistics-calibration-for-1 | https://openreview.net/forum?id=9gz8qakpyhG | https://openreview.net/pdf?id=9gz8qakpyhG | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 1.55242562e-01 -5.07418990e-01 -1.42658487e-01 -8.77466619e-01
-9.58944678e-01 -4.99782324e-01 1.19342722e-01 -2.47763574e-01
-7.41347194e-01 9.25965369e-01 -3.23715538e-01 -2.01997772e-01
-3.75298768e-01 -7.35325098e-01 -8.01810205e-01 -1.05420029e+00
2.05512062e-01 4.81949121e-01 2.37563163e-01 -3.81891467... | [9.729986190795898, 1.733938455581665] |
c4578303-4564-429e-b03a-2565f824e57d | prime-probe-1-javascript-0-overcoming-browser | 2103.04952 | null | https://arxiv.org/abs/2103.04952v1 | https://arxiv.org/pdf/2103.04952v1.pdf | Prime+Probe 1, JavaScript 0: Overcoming Browser-based Side-Channel Defenses | The "eternal war in cache" has reached browsers, with multiple cache-based side-channel attacks and countermeasures being suggested. A common approach for countermeasures is to disable or restrict JavaScript features deemed essential for carrying out attacks. To assess the effectiveness of this approach, in this work w... | ['Yuval Yarom', 'Yossi Oren', 'Daniel Genkin', "Sioli O'Connell", 'Ayush Agarwal', 'Anatoly Shusterman'] | 2021-03-08 | null | null | null | null | ['website-fingerprinting-attacks'] | ['adversarial'] | [ 4.69099358e-03 -4.58874613e-01 -2.68848270e-01 9.80682820e-02
-6.98851109e-01 -1.39283562e+00 5.92321634e-01 -2.23908991e-01
-4.46119070e-01 1.06878281e-01 1.65514052e-01 -1.46303427e+00
2.90114343e-01 -6.58699036e-01 -6.55342221e-01 -2.69528925e-01
-2.41258860e-01 -5.32104611e-01 9.12452638e-01 -3.67659003... | [5.694497585296631, 7.479066371917725] |
850d7a3d-b489-4324-8fe1-466bdd0f74b1 | a-unified-framework-for-fast-large-scale | 2303.12751 | null | https://arxiv.org/abs/2303.12751v1 | https://arxiv.org/pdf/2303.12751v1.pdf | A Unified Framework for Fast Large-Scale Portfolio Optimization | We develop a unified framework for fast large-scale portfolio optimization with shrinkage and regularization for different objectives such as minimum variance, mean-variance, and maximum Sharpe ratio with various constraints on the portfolio weights. For all of the optimization problems, we derive the corresponding qua... | ['Abolfazl Safikhani', 'Pawel Polak', 'Ronakdilip Shah', 'Weichuan Deng'] | 2023-03-22 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.42829168e-01 -2.23953024e-01 -4.23295610e-02 -4.96990353e-01
-9.21416521e-01 -9.68074977e-01 2.93256670e-01 -3.74571741e-01
-2.38231778e-01 6.07783854e-01 2.92672217e-01 -6.77861750e-01
-7.32899129e-01 -7.35399902e-01 -4.99323040e-01 -6.95314586e-01
9.60501507e-02 4.39780951e-01 -3.93021762e-01 1.55272394... | [5.047155857086182, 3.955533266067505] |
49628913-dfbb-4b6e-bddc-13781ab5cea2 | prolificdreamer-high-fidelity-and-diverse | 2305.16213 | null | https://arxiv.org/abs/2305.16213v1 | https://arxiv.org/pdf/2305.16213v1.pdf | ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation | Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a const... | ['Jun Zhu', 'Hang Su', 'Chongxuan Li', 'Fan Bao', 'Yikai Wang', 'Cheng Lu', 'Zhengyi Wang'] | 2023-05-25 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.93409659e-02 -1.26641497e-01 1.75020799e-01 1.74050391e-01
-8.58042896e-01 -3.82647753e-01 9.17788088e-01 -3.44134569e-01
-5.09309173e-02 8.58393312e-01 3.22835565e-01 -3.67713541e-01
8.97540823e-02 -1.11047935e+00 -4.99296695e-01 -9.50780272e-01
5.94609156e-02 6.96612239e-01 4.86717492e-01 -2.41383001... | [11.241918563842773, -0.4436274766921997] |
1124290c-3d66-437b-8fb8-32a554e3b6c6 | neural-wavelet-domain-diffusion-for-3d-shape-1 | 2302.00190 | null | https://arxiv.org/abs/2302.00190v1 | https://arxiv.org/pdf/2302.00190v1.pdf | Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation | This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent ... | ['Chi-Wing Fu', 'Ruihui Li', 'Zhengzhe Liu', 'Ka-Hei Hui', 'Jingyu Hu'] | 2023-02-01 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 2.11708799e-01 3.41236353e-01 9.64751020e-02 -1.40787870e-01
-5.96616149e-01 -6.56423330e-01 8.18127751e-01 -3.12777907e-01
2.99279809e-01 7.30010629e-01 3.50726396e-01 -1.27310753e-01
-1.07066400e-01 -1.34280241e+00 -8.36863637e-01 -8.47725689e-01
9.44697764e-03 4.64676917e-01 -1.17167979e-01 -4.88317400... | [8.929086685180664, -3.6208791732788086] |
8a11fc95-6d77-49d4-8255-fdf70fd5177e | document-level-multi-event-extraction-with | 2305.18926 | null | https://arxiv.org/abs/2305.18926v1 | https://arxiv.org/pdf/2305.18926v1.pdf | Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization | Document-level multi-event extraction aims to extract the structural information from a given document automatically. Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events. However, such approaches ignore a global view of inter-dependency of mul... | ['Yulan He', 'Lin Gui', 'Xinyu Wang'] | 2023-05-30 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.75028944e-01 3.87021065e-01 -1.79483488e-01 -3.15242410e-01
-1.01335204e+00 -6.39515996e-01 8.67322862e-01 1.10159397e+00
-5.26503980e-01 8.02823603e-01 1.36465266e-01 -5.28837517e-02
-3.20400894e-01 -1.16121161e+00 -8.76118481e-01 -5.41243434e-01
-2.86388606e-01 6.90583766e-01 5.70703566e-01 3.88268113... | [9.048918724060059, 9.165586471557617] |
56df1845-bbec-494a-bd13-ee86f1dbfe8b | q-malizing-flow-and-infinitesimal-density | 2305.11857 | null | https://arxiv.org/abs/2305.11857v2 | https://arxiv.org/pdf/2305.11857v2.pdf | Optimal transport flow and infinitesimal density ratio estimation | Continuous normalizing flows are widely used in generative tasks, where a flow network transports from a data distribution $P$ to a normal distribution. A flow model that transports from $P$ to an arbitrary $Q$, where both $P$ and $Q$ are accessible via finite samples, is of various application interests, particularly ... | ['Yao Xie', 'Xiuyuan Cheng', 'Chen Xu'] | 2023-05-19 | null | null | null | null | ['density-ratio-estimation', 'mutual-information-estimation'] | ['methodology', 'methodology'] | [-1.33554880e-02 3.94100957e-02 -7.88371935e-02 -3.76470864e-01
-7.61830091e-01 -1.25866696e-01 5.54811656e-01 -3.25168580e-01
-4.86085087e-01 1.01770711e+00 -4.10408765e-01 -1.41308472e-01
-5.53151309e-01 -1.39080954e+00 -8.58785510e-01 -9.34464872e-01
-2.26079807e-01 5.97251594e-01 -2.34635577e-01 8.85466766... | [7.200977802276611, 3.882577419281006] |
783d9358-1b98-43d9-a5eb-e8d45e98cfee | sill-net-feature-augmentation-with-separated | 2102.03539 | null | https://arxiv.org/abs/2102.03539v3 | https://arxiv.org/pdf/2102.03539v3.pdf | Sill-Net: Feature Augmentation with Separated Illumination Representation | For visual object recognition tasks, the illumination variations can cause distinct changes in object appearance and thus confuse the deep neural network based recognition models. Especially for some rare illumination conditions, collecting sufficient training samples could be time-consuming and expensive. To solve thi... | ['ChangShui Zhang', 'Ziang Yan', 'Zhong Cao', 'Haipeng Zhang'] | 2021-02-06 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 2.53122240e-01 -6.51682973e-01 -1.61922321e-01 -7.00673580e-01
-1.21495858e-01 -4.12092865e-01 2.60167807e-01 -6.39871955e-01
-3.74887526e-01 6.61757410e-01 -4.51134026e-01 1.26990471e-02
1.45062327e-01 -4.70733494e-01 -8.28509390e-01 -9.65221405e-01
2.43631884e-01 6.49253428e-02 1.82125196e-02 4.08760667... | [9.583718299865723, 1.9989383220672607] |
3584ba76-cea0-41f2-9d8f-cf230d5e7462 | coursera-corpus-mining-and-multistage-fine | 1912.11739 | null | https://arxiv.org/abs/1912.11739v2 | https://arxiv.org/pdf/1912.11739v2.pdf | Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation | Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures... | ['Sadao Kurohashi', 'Raj Dabre', 'Haiyue Song', 'Atsushi Fujita'] | 2019-12-26 | coursera-corpus-mining-and-multistage-fine-1 | https://aclanthology.org/2020.lrec-1.449 | https://aclanthology.org/2020.lrec-1.449.pdf | lrec-2020-5 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 2.72027880e-01 -2.83613116e-01 -1.30258620e-01 -5.92861176e-01
-1.80239034e+00 -9.84490812e-01 5.04868984e-01 8.96527842e-02
-3.63422066e-01 9.94004011e-01 4.37964231e-01 -7.00882614e-01
1.70798451e-01 -4.91061300e-01 -7.47873366e-01 -2.68343985e-01
4.34109122e-01 8.60871613e-01 -4.01103869e-03 -7.67833054... | [11.618317604064941, 10.321986198425293] |
fed69f8c-80d8-420c-bb81-642fce73755c | d2s-document-to-slide-generation-via-query | 2105.03664 | null | https://arxiv.org/abs/2105.03664v1 | https://arxiv.org/pdf/2105.03664v1.pdf | D2S: Document-to-Slide Generation Via Query-Based Text Summarization | Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benc... | ['Nancy X. R. Wang', 'Yunfeng Zhang', 'Dakuo Wang', 'Yufang Hou', 'Edward Sun'] | 2021-05-08 | null | https://aclanthology.org/2021.naacl-main.111 | https://aclanthology.org/2021.naacl-main.111.pdf | naacl-2021-4 | ['long-form-question-answering'] | ['natural-language-processing'] | [ 3.23764652e-01 1.85421005e-01 -3.36504132e-02 -2.25109115e-01
-1.97489250e+00 -1.02778518e+00 6.95574760e-01 6.32099152e-01
-2.40422815e-01 1.12779880e+00 7.67729640e-01 -2.14839339e-01
-8.77137333e-02 -2.94076771e-01 -6.87807798e-01 -1.26844987e-01
3.67577672e-01 7.49244750e-01 3.45660716e-01 -3.30343813... | [12.373164176940918, 9.466611862182617] |
1d6db917-09cb-40df-959d-32f751143abc | deep-nfa-a-deep-textit-a-contrario-framework | 2303.01363 | null | https://arxiv.org/abs/2303.01363v1 | https://arxiv.org/pdf/2303.01363v1.pdf | Deep-NFA: a Deep $\textit{a contrario}$ Framework for Small Object Detection | The detection of small objects is a challenging task in computer vision. Conventional object detection methods have difficulty in finding the balance between high detection and low false alarm rates. In the literature, some methods have addressed this issue by enhancing the feature map responses, but without guaranteei... | ['Arnaud Woiselle', 'Sidonie Lefebvre', 'Sylvie Le Hegarat-Mascle', 'Alina Ciocarlan'] | 2023-03-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 4.28217322e-01 -1.46271318e-01 2.34006077e-01 -4.21515286e-01
-4.28892493e-01 -2.50729620e-01 2.79071391e-01 5.24505556e-01
-6.65613055e-01 4.81699377e-01 -6.26623452e-01 -7.47665167e-02
-1.15154512e-01 -1.03496778e+00 -6.50776803e-01 -9.03868914e-01
2.80084699e-01 2.61503875e-01 1.01874650e+00 2.64492452... | [9.003761291503906, 1.241827130317688] |
c8592cde-1718-41c4-9863-9b5e07e660d6 | adversarial-style-augmentation-for-domain-1 | 2207.04892 | null | https://arxiv.org/abs/2207.04892v2 | https://arxiv.org/pdf/2207.04892v2.pdf | Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation | In this paper, we consider the problem of domain generalization in semantic segmentation, which aims to learn a robust model using only labeled synthetic (source) data. The model is expected to perform well on unseen real (target) domains. Our study finds that the image style variation can largely influence the model's... | ['Nicu Sebe', 'Gim Hee Lee', 'Yuyang Zhao', 'Zhun Zhong'] | 2022-07-11 | adversarial-style-augmentation-for-domain | https://openreview.net/forum?id=L_sHGieq1D | https://openreview.net/pdf?id=L_sHGieq1D | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.62619531e-01 3.06627065e-01 -6.21842267e-03 -4.56333011e-01
-7.17871904e-01 -9.06821907e-01 7.02785790e-01 -3.38180035e-01
-4.54635948e-01 7.67354429e-01 -6.45391405e-01 -3.98682728e-02
1.96080059e-01 -8.27681243e-01 -1.07701254e+00 -8.81353676e-01
3.58948439e-01 6.87284470e-01 3.47699255e-01 -3.51273328... | [9.766070365905762, 1.3121660947799683] |
d516a695-481b-4582-b652-aa2d85282286 | adaptive-conformal-regression-with-jackknife | 2305.19901 | null | https://arxiv.org/abs/2305.19901v1 | https://arxiv.org/pdf/2305.19901v1.pdf | Adaptive Conformal Regression with Jackknife+ Rescaled Scores | Conformal regression provides prediction intervals with global coverage guarantees, but often fails to capture local error distributions, leading to non-homogeneous coverage. We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution, inspired by the Jack... | ['Maria Rodriguez Martinez', 'Mattia Rigotti', 'Nicolas Deutschmann'] | 2023-05-31 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 5.77049315e-01 6.33741498e-01 -5.46248496e-01 -4.87137526e-01
-1.39215469e+00 -7.39740789e-01 3.70047271e-01 7.02852249e-01
-5.19013703e-02 1.13878810e+00 2.06245229e-01 -2.10259467e-01
-7.62676775e-01 -9.64960873e-01 -6.93237960e-01 -7.74113476e-01
7.17833042e-02 7.82483697e-01 3.84724289e-01 1.75788596... | [7.806207180023193, 4.522242546081543] |
11e00a18-f6ea-4302-b7b6-88010cbe7c8a | foresee-what-you-will-learn-data-augmentation | 2301.07845 | null | https://arxiv.org/abs/2301.07845v2 | https://arxiv.org/pdf/2301.07845v2.pdf | Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment | Existing domain generalization aims to learn a generalizable model to perform well even on unseen domains. For many real-world machine learning applications, the data distribution often shifts gradually along domain indices. For example, a self-driving car with a vision system drives from dawn to dusk, with the sky dar... | ['Boyu Wang', 'Charles Ling', 'Fan Zhou', 'Wei Wang', 'Qiuhao Zeng'] | 2023-01-19 | null | null | null | null | ['evolving-domain-generalization'] | ['computer-vision'] | [ 4.64726239e-01 -3.00654262e-01 -1.38368621e-01 -6.12720668e-01
-2.55495578e-01 -6.24860406e-01 6.85274363e-01 -2.28577241e-01
-1.47842690e-01 9.36670363e-01 -1.95439145e-01 -2.21543297e-01
-1.41704217e-01 -8.57151270e-01 -7.63780653e-01 -8.16039026e-01
1.82263091e-01 4.33047622e-01 3.00106913e-01 -5.05746365... | [10.177764892578125, 2.6465249061584473] |
51be1746-fd1b-46a1-b2e6-ee5bfd144fac | a-new-image-codec-paradigm-for-human-and | 2112.10071 | null | https://arxiv.org/abs/2112.10071v1 | https://arxiv.org/pdf/2112.10071v1.pdf | A New Image Codec Paradigm for Human and Machine Uses | With the AI of Things (AIoT) development, a huge amount of visual data, e.g., images and videos, are produced in our daily work and life. These visual data are not only used for human viewing or understanding but also for machine analysis or decision-making, e.g., intelligent surveillance, automated vehicles, and many ... | ['Huaxiang Zhang', 'Zhengguang Li', 'Tsui-Shan Chang', 'Zhuo Chen', 'Weisi Lin', 'Lili Meng', 'Jian Jin', 'Sien Chen'] | 2021-12-19 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.85403031e-01 -3.89367312e-01 -3.23246449e-01 -1.73233688e-01
-3.79978478e-01 3.58832814e-02 2.90796198e-02 1.55596152e-01
-2.87056148e-01 3.78282815e-01 -2.35995233e-01 -1.48543447e-01
1.60026237e-01 -9.71042871e-01 -4.89174098e-01 -8.40911210e-01
2.13720053e-01 -1.85196131e-01 5.72049141e-01 8.68574083... | [11.236496925354004, -1.71831476688385] |
f42ec4f6-a770-4002-bf51-1718c183222b | thinking-about-causation-a-causal-language | 2010.16217 | null | https://arxiv.org/abs/2010.16217v1 | https://arxiv.org/pdf/2010.16217v1.pdf | Thinking About Causation: A Causal Language with Epistemic Operators | This paper proposes a formal framework for modeling the interaction of causal and (qualitative) epistemic reasoning. To this purpose, we extend the notion of a causal model with a representation of the epistemic state of an agent. On the side of the object language, we add operators to express knowledge and the act of ... | ['Kaibo Xie', 'Fernando R. Velázquez-Quesada', 'Sonja Smets', 'Katrin Schulz', 'Fausto Barbero'] | 2020-10-30 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-1.31843552e-01 9.74568486e-01 -2.16866046e-01 -3.99463981e-01
3.21562499e-01 -4.62217122e-01 1.22967613e+00 2.25624770e-01
-1.75567329e-01 8.37139130e-01 7.49694586e-01 -3.90039057e-01
-5.23664713e-01 -1.30270600e+00 -6.59967065e-01 -3.97559524e-01
-3.90366673e-01 3.63018543e-01 6.00669503e-01 -2.86539167... | [8.573171615600586, 6.582921028137207] |
c30a8d09-2b92-45c6-af58-5dbb539c9df0 | rgb-infrared-cross-modality-person-re | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Wu_RGB-Infrared_Cross-Modality_Person_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wu_RGB-Infrared_Cross-Modality_Person_ICCV_2017_paper.pdf | RGB-Infrared Cross-Modality Person Re-Identification | Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessa... | ['Jian-Huang Lai', 'Shaogang Gong', 'Hong-Xing Yu', 'Wei-Shi Zheng', 'Ancong Wu'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['cross-view-person-re-identification'] | ['computer-vision'] | [ 2.35945582e-01 -6.76860154e-01 1.65265761e-02 -1.93839192e-01
-4.68602717e-01 -5.99974275e-01 5.65736353e-01 -2.99248397e-01
-7.42632568e-01 5.46346366e-01 9.64545086e-03 -2.59238005e-01
-1.64311435e-02 -8.27138305e-01 -7.60668218e-01 -6.90878272e-01
3.93484205e-01 5.64261675e-02 1.28306538e-01 -3.21278185... | [14.625280380249023, 0.9123075008392334] |
f8eb728e-5b3e-4faa-b9b2-8e543981e505 | k-means-on-a-log-cholesky-manifold-with | 2008.03454 | null | https://arxiv.org/abs/2008.03454v2 | https://arxiv.org/pdf/2008.03454v2.pdf | $k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences | We state theoretical properties for $k$-means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data. We then provide a novel application for this method, to time-series clustering of pixels in a sequence of Synthetic Apert... | ['Hien Nguyen', 'Daniel Fryer', 'Pascal Castellazzi'] | 2020-08-08 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 2.54388034e-01 -5.93817413e-01 4.40279752e-01 -5.84522426e-01
-4.42142099e-01 -6.36557460e-01 4.96192634e-01 -4.98973250e-01
-4.18997496e-01 2.37724647e-01 -4.07820642e-01 -4.67451841e-01
-1.12471676e+00 -3.63746136e-01 -1.30180806e-01 -1.15100002e+00
-9.80126917e-01 4.68313962e-01 -5.38628511e-02 4.47749719... | [7.3052520751953125, 3.6144983768463135] |
de8e77c6-7d32-4135-8b09-5c8cbaf27050 | a-multimodal-translation-based-approach-for | null | null | https://aclanthology.org/S18-2027 | https://aclanthology.org/S18-2027.pdf | A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning | Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the KG entities. In this paper, we propose a multimodal translation-based approach that defines the ene... | ['Hatem Mousselly-Sergieh', 'Iryna Gurevych', 'Stefan Roth', 'Teresa Botschen'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['triple-classification'] | ['graphs'] | [-1.95785705e-02 2.50623465e-01 -5.87075055e-01 -3.10494959e-01
-1.03547168e+00 -7.63816237e-01 7.23873138e-01 5.64727783e-01
-3.19555521e-01 4.39613760e-01 4.46786195e-01 -1.24042869e-01
-5.60723525e-03 -9.21923757e-01 -1.12491047e+00 -4.53408152e-01
6.68371515e-03 6.27539575e-01 -1.66905507e-01 -2.84627736... | [8.811783790588379, 7.895482540130615] |
67338371-6968-4d7a-8a6a-7d30265539af | analysis-of-a-deep-learning-model-for-12-lead | 2211.01738 | null | https://arxiv.org/abs/2211.01738v2 | https://arxiv.org/pdf/2211.01738v2.pdf | Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria | Despite their remarkable performance, deep neural networks remain unadopted in clinical practice, which is considered to be partially due to their lack in explainability. In this work, we apply attribution methods to a pre-trained deep neural network (DNN) for 12-lead electrocardiography classification to open this "bl... | ['Anne-Christin Hauschild', 'Nicolai Spicher', 'Tim Seidler', 'Henning Dathe', 'Carolin Müller', 'Dagmar Krefting', 'Jacqueline Michelle Beinecke', 'Theresa Bender'] | 2022-11-03 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.25940749e-01 2.98991084e-01 -3.79484385e-01 -5.93188286e-01
-2.84437358e-01 -5.67775548e-01 2.79930294e-01 3.29623818e-01
-1.86935917e-01 1.09532583e+00 2.83191562e-01 -7.70792544e-01
-6.94236815e-01 -6.24786377e-01 -5.26846170e-01 -6.66239917e-01
-4.07880813e-01 4.63216335e-01 -5.80169201e-01 6.94425032... | [14.32495403289795, 3.2995400428771973] |
fd03ee20-1892-42b6-9534-0ad2d2d7ff77 | sagess-sampling-graph-denoising-diffusion | 2306.16827 | null | https://arxiv.org/abs/2306.16827v1 | https://arxiv.org/pdf/2306.16827v1.pdf | SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation | Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating images. These approaches have also proved successful in other applications such as tabular and graph data generation. However, due to comput... | ['Andrew Elliott', 'Gesine Reinert', 'Carsten Maple', 'Mihai Cucuringu', 'Praveen Selvaraj', 'Stratis Limnios'] | 2023-06-29 | null | null | null | null | ['link-prediction', 'graph-generation', 'synthetic-data-generation', 'synthetic-data-generation'] | ['graphs', 'graphs', 'medical', 'miscellaneous'] | [ 3.18605959e-01 5.61279058e-01 2.96949089e-01 1.47389978e-01
-4.97349322e-01 -5.28463185e-01 1.07317388e+00 4.53944623e-01
-2.14407921e-01 1.04091740e+00 -1.48715479e-02 -2.44555101e-01
-7.62254074e-02 -1.32663333e+00 -7.93578804e-01 -7.96784818e-01
-1.73650190e-01 9.68174934e-01 4.18232501e-01 -3.53760332... | [6.835711479187012, 6.0060834884643555] |
57d25616-25bd-42b4-bdac-895cb01f43d9 | coarse-to-fine-point-cloud-registration-with | 2210.02045 | null | https://arxiv.org/abs/2210.02045v2 | https://arxiv.org/pdf/2210.02045v2.pdf | Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations | Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose differences, or leverage global shapes, which leads to inconsistency when facing distribution variances such as partial overlapping. Combining... | ['Winston H. Hsu', 'Wen-Chin Chen', 'Hsin-Ying Lee', 'Tung-I Chen', 'Cheng-Wei Lin'] | 2022-10-05 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.98071885e-03 -2.30416402e-01 1.05382726e-01 -4.27924246e-01
-7.94353485e-01 -7.89034426e-01 7.75605321e-01 1.41331807e-01
-3.91452670e-01 1.55727655e-01 -8.47823024e-02 3.36467355e-01
-2.18822092e-01 -8.00337851e-01 -8.88294160e-01 -5.07684946e-01
1.69831380e-01 7.83703864e-01 6.52504086e-01 -3.20260227... | [7.752058506011963, -3.1021976470947266] |
677e363c-a5e2-4ee6-962a-7a258792547f | agnn-alternating-graph-regularized-neural | 2304.07014 | null | https://arxiv.org/abs/2304.07014v1 | https://arxiv.org/pdf/2304.07014v1.pdf | AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing | Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-based models suffer from the notorious over-smoothing issue, owing to which shallow networks are extensively adopted. This may be problematic... | ['Wenzhong Guo', 'Claudia Plant', 'Shiping Wang', 'Zhenghong Lin', 'Zhihao Wu', 'Zhaoliang Chen'] | 2023-04-14 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [ 1.35953382e-01 2.82679617e-01 -6.67852014e-02 -1.67820349e-01
-1.76359147e-01 -1.16960295e-02 4.73671168e-01 2.66184568e-01
-2.44759396e-01 4.43710536e-01 1.18749581e-01 -4.35644299e-01
-2.10186392e-01 -1.13550353e+00 -5.95346093e-01 -7.76396871e-01
-4.29106683e-01 6.43598214e-02 3.94862860e-01 -2.29254156... | [7.136491775512695, 6.234381675720215] |
4184e088-199c-4faf-a9ad-9c9954bb1249 | donet-dual-objective-networks-for-skin-lesion | 2008.08278 | null | https://arxiv.org/abs/2008.08278v1 | https://arxiv.org/pdf/2008.08278v1.pdf | DONet: Dual Objective Networks for Skin Lesion Segmentation | Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images. In the last few years, deep learning based semantic segmentation methods have significantly advanced the skin lesion segmentation results. However, the current performance is still unsatisfactory due to some challenging fa... | ['Xueming Qian', 'Yunchao Wei', 'Yi Yang', 'Yaxiong Wang', 'Li Zhu'] | 2020-08-19 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.55753589e-01 6.75144643e-02 -1.87836617e-01 -3.85460287e-01
-8.83310795e-01 -2.21672237e-01 1.65629342e-01 1.00317381e-01
-2.13514104e-01 4.68660593e-01 1.19023807e-01 -7.00914636e-02
-3.69823694e-01 -5.66719115e-01 -3.00907671e-01 -1.00777209e+00
5.07712126e-01 -9.33017433e-02 3.98608834e-01 -1.20600872... | [15.556282997131348, -2.8960089683532715] |
c2315cfe-3f82-4b1f-a8c2-d5925d11eaf8 | causal-language-model-for-zero-shot | null | null | https://openreview.net/forum?id=wFEl0shQ9F1 | https://openreview.net/pdf?id=wFEl0shQ9F1 | Causal Language Model for Zero-shot Constrained Keyphrase Generation | Recently, most of the state-of-the-art keyphrase prediction models are based on a supervised generative model.Although it shows noticeable improvement over statistical methods, it still struggles with low performance on out of the domain and low-resource data. To overcome these limitations, unsupervised methods have al... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.54839441e-01 -5.47666363e-02 -4.71215457e-01 1.05705135e-01
-9.46739078e-01 -8.24240088e-01 9.19400156e-01 3.62177044e-01
-3.64130318e-01 1.04408813e+00 4.14506018e-01 -3.25876892e-01
-9.11264867e-03 -8.38833094e-01 -5.64574480e-01 -3.52358311e-01
2.96346217e-01 7.52670527e-01 6.21668518e-01 -5.89044452... | [12.235579490661621, 8.874590873718262] |
c73f7f13-157c-442f-ba67-59a1a3134af4 | faithfulness-aware-decoding-strategies-for | 2303.03278 | null | https://arxiv.org/abs/2303.03278v1 | https://arxiv.org/pdf/2303.03278v1.pdf | Faithfulness-Aware Decoding Strategies for Abstractive Summarization | Despite significant progress in understanding and improving faithfulness in abstractive summarization, the question of how decoding strategies affect faithfulness is less studied. We present a systematic study of the effect of generation techniques such as beam search and nucleus sampling on faithfulness in abstractive... | ['Mohit Bansal', 'Markus Dreyer', 'Kathleen McKeown', 'Mengwen Liu', 'David Wan'] | 2023-03-06 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.79846838e-01 2.76455462e-01 -6.21362805e-01 -1.63510993e-01
-1.24970329e+00 -7.95206964e-01 1.02021170e+00 5.77843130e-01
-1.98582053e-01 9.49135959e-01 1.25293255e+00 -2.63205349e-01
1.80190727e-01 -8.28535855e-01 -4.62896913e-01 -2.13872835e-01
2.53518283e-01 6.50513172e-01 1.11785106e-01 -3.81418288... | [12.300827026367188, 9.312620162963867] |
102e1a67-c596-4895-ad6c-e5c87430407c | gnn-sl-sequence-labeling-based-on-nearest | 2212.02017 | null | https://arxiv.org/abs/2212.02017v2 | https://arxiv.org/pdf/2212.02017v2.pdf | GNN-SL: Sequence Labeling Based on Nearest Examples via GNN | To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. Since not all the retrieved tagging examples benefit the model... | ['Guoyin Wang', 'Lingjuan Lyu', 'Tianwei Zhang', 'Jiwei Li', 'Rongbin Ouyang', 'Yuxian Meng', 'Shuhe Wang'] | 2022-12-05 | null | null | null | null | ['part-of-speech-tagging', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.05916250e-01 4.01279837e-01 -4.12057281e-01 -2.75122702e-01
-8.41541588e-01 -9.33237433e-01 2.66751409e-01 2.22120881e-01
-5.49609363e-01 8.42092931e-01 1.65982366e-01 -7.22441018e-01
3.86140168e-01 -7.12344110e-01 -6.32394850e-01 -5.08588672e-01
-6.18176013e-02 4.77706522e-01 5.03058016e-01 -6.97965547... | [9.787453651428223, 9.56202220916748] |
1cad684a-76b2-43e0-a38a-91d091adcce9 | discovering-topics-with-neural-topic-models-1 | 1911.10924 | null | https://arxiv.org/abs/1911.10924v1 | https://arxiv.org/pdf/1911.10924v1.pdf | Discovering topics with neural topic models built from PLSA assumptions | In this paper we present a model for unsupervised topic discovery in texts corpora. The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents. These probabilities are used to re... | ['Sileye 0. Ba'] | 2019-11-25 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-1.49874881e-01 5.40895343e-01 -4.94569957e-01 -5.45913696e-01
-8.71790707e-01 -1.64048046e-01 1.18267000e+00 3.26158643e-01
-2.86031634e-01 6.40270829e-01 7.63738573e-01 -1.38971388e-01
-1.18697388e-03 -1.18465054e+00 -6.95328951e-01 -6.30708516e-01
-2.22398683e-01 1.07192600e+00 5.61977662e-02 8.08779970... | [10.41259765625, 6.939775466918945] |
1198d83f-4d45-4a3a-9dfd-79bbda480b27 | mining-non-redundant-local-process-models | 1712.04159 | null | http://arxiv.org/abs/1712.04159v2 | http://arxiv.org/pdf/1712.04159v2.pdf | Mining Non-Redundant Local Process Models From Sequence Databases | Sequential pattern mining techniques extract patterns corresponding to
frequent subsequences from a sequence database. A practical limitation of these
techniques is that they overload the user with too many patterns. Local Process
Model (LPM) mining is an alternative approach coming from the field of process
mining. Wh... | ['Niek Tax', 'Marlon Dumas'] | 2017-12-12 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 9.08298314e-01 -1.12080730e-01 -3.30217987e-01 -1.15023762e-01
9.22720209e-02 -4.70029771e-01 3.74544263e-01 8.49895716e-01
-1.16730675e-01 6.08677685e-01 -2.42831167e-02 -3.31559062e-01
-5.45591056e-01 -1.16745806e+00 -3.27513486e-01 -3.07052672e-01
-5.05053759e-01 6.23349905e-01 8.91056895e-01 1.75206184... | [8.344443321228027, 6.256791591644287] |
f09ebe32-a95c-42bd-9238-b011793cac39 | lifting-uniform-learners-via-distributional | 2303.16208 | null | https://arxiv.org/abs/2303.16208v2 | https://arxiv.org/pdf/2303.16208v2.pdf | Lifting uniform learners via distributional decomposition | We show how any PAC learning algorithm that works under the uniform distribution can be transformed, in a blackbox fashion, into one that works under an arbitrary and unknown distribution $\mathcal{D}$. The efficiency of our transformation scales with the inherent complexity of $\mathcal{D}$, running in $\mathrm{poly}(... | ['Li-Yang Tan', 'Ali Malik', 'Jane Lange', 'Guy Blanc'] | 2023-03-27 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-9.52274427e-02 3.61701220e-01 -3.79553199e-01 -2.98052907e-01
-1.40025759e+00 -1.03362310e+00 -1.07512876e-01 3.87260169e-01
-5.11009037e-01 1.00476837e+00 -5.67746460e-01 -6.51633561e-01
-4.77239937e-01 -1.37353873e+00 -9.92529690e-01 -1.35980737e+00
-2.36733258e-01 1.27508330e+00 1.67808264e-01 4.11337852... | [6.3458733558654785, 4.493159294128418] |
e49557f7-1953-4516-9a8a-5ee279413786 | multiplicative-tree-structured-long-short | null | null | https://aclanthology.org/S18-2032 | https://aclanthology.org/S18-2032.pdf | Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations | Tree-structured LSTMs have shown advantages in learning semantic representations by exploiting syntactic information. Most existing methods model tree structures by bottom-up combinations of constituent nodes using the same shared compositional function and often making use of input word information only. The inability... | ['Nam Khanh Tran', 'Weiwei Cheng'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['learning-semantic-representations'] | ['methodology'] | [ 2.78756112e-01 4.66934681e-01 -3.55443120e-01 -5.18500268e-01
-1.50834054e-01 -5.37146211e-01 5.33585608e-01 2.00511605e-01
-5.22510886e-01 6.22794509e-01 6.38834417e-01 -5.18206596e-01
7.52383471e-03 -1.16778028e+00 -5.09537697e-01 -4.24343377e-01
-1.45069032e-03 2.99229473e-01 1.28037870e-01 -3.40269297... | [10.527334213256836, 9.124380111694336] |
427f9949-19a9-4c5e-b3c5-fd02b1536fa1 | a-learning-based-trajectory-planning-of | 2209.09206 | null | https://arxiv.org/abs/2209.09206v1 | https://arxiv.org/pdf/2209.09206v1.pdf | A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks | Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This wor... | ['Matti Latva-aho', 'Hirley Alves', 'Nurul Huda Mahmood', 'Mohammad Shehab', "Jean Michel de Souza Sant'Ana", 'Dian Echevarría Pérez', 'Eslam Eldeeb'] | 2022-09-13 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-1.07612357e-01 3.88881564e-01 -2.71982223e-01 -1.59068003e-01
-3.61280769e-01 -6.07236147e-01 2.51450211e-01 4.95364577e-01
-7.03369379e-01 9.71685231e-01 -3.71499330e-01 -3.74976903e-01
-5.15145302e-01 -1.25910079e+00 -6.41450047e-01 -1.06871867e+00
-4.69731003e-01 1.59974143e-01 1.65482804e-01 7.84109626... | [5.850461006164551, 1.6038376092910767] |
869dd2a6-8cae-4301-ae6d-13e0dd670c43 | a-topological-classifier-to-characterize | 2303.04231 | null | https://arxiv.org/abs/2303.04231v1 | https://arxiv.org/pdf/2303.04231v1.pdf | A topological classifier to characterize brain states: When shape matters more than variance | Despite the remarkable accuracies attained by machine learning classifiers to separate complex datasets in a supervised fashion, most of their operation falls short to provide an informed intuition about the structure of data, and, what is more important, about the phenomena being characterized by the given datasets. B... | ['Ignasi Cos', 'Carles Casacuberta', 'Fritz-Pere Nobbe Fisas', 'Gloria Cecchini', 'Aina Ferrà'] | 2023-03-07 | null | null | null | null | ['topological-data-analysis', 'eeg', 'eeg'] | ['graphs', 'methodology', 'time-series'] | [ 2.78814137e-01 -1.53336162e-02 2.77495205e-01 -3.52993220e-01
-9.62293372e-02 -7.36578703e-01 8.98569047e-01 6.64052188e-01
-3.54886383e-01 5.61226964e-01 1.71366408e-01 -1.56149223e-01
-9.31504190e-01 -8.60329151e-01 -3.86929721e-01 -9.94876087e-01
-4.63287145e-01 5.57807148e-01 1.03112802e-01 -1.96822256... | [7.713146209716797, 3.9564218521118164] |
084de56d-3a43-48cb-b406-fb1063a44971 | can-question-rewriting-help-conversational | 2204.06239 | null | https://arxiv.org/abs/2204.06239v1 | https://arxiv.org/pdf/2204.06239v1.pdf | Can Question Rewriting Help Conversational Question Answering? | Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To... | ['Bryan Wilie', 'Samuel Cahyawijaya', 'Yan Xu', 'Etsuko Ishii'] | 2022-04-13 | null | https://aclanthology.org/2022.insights-1.13 | https://aclanthology.org/2022.insights-1.13.pdf | insights-acl-2022-5 | ['question-rewriting'] | ['natural-language-processing'] | [ 8.11460614e-02 7.26219952e-01 3.66094768e-01 -2.69451231e-01
-1.37697852e+00 -9.17982638e-01 9.25549865e-01 1.54271469e-01
-3.52592707e-01 6.88857257e-01 8.95385623e-01 -8.32634091e-01
-6.54100627e-02 -4.83349532e-01 -4.34158146e-01 -1.48004338e-01
2.00008810e-01 3.49380314e-01 1.41571447e-01 -9.44222391... | [11.937817573547363, 8.01059341430664] |
6aab6b3c-36ec-46ff-b8bd-0dbcafdb48ca | monash-university-uea-ucr-time-series | 2006.10996 | null | https://arxiv.org/abs/2006.10996v3 | https://arxiv.org/pdf/2006.10996v3.pdf | Monash University, UEA, UCR Time Series Extrinsic Regression Archive | Time series research has gathered lots of interests in the last decade, especially for Time Series Classification (TSC) and Time Series Forecasting (TSF). Research in TSC has greatly benefited from the University of California Riverside and University of East Anglia (UCR/UEA) Time Series Archives. On the other hand, th... | ['Geoffrey I. Webb', 'Francois Petitjean', 'Christoph Bergmeir', 'Chang Wei Tan'] | 2020-06-19 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-7.07184225e-02 -6.17184043e-01 -1.89718351e-01 -3.98510873e-01
-2.33108565e-01 -3.99472415e-01 4.32980537e-01 9.54533741e-02
-2.47349441e-01 7.10649192e-01 2.23686616e-03 -3.79682481e-01
-3.72160524e-01 -6.73702776e-01 -2.94375092e-01 -6.07277930e-01
-5.62653124e-01 1.49333715e-01 -2.77449489e-01 -3.08377892... | [7.198606967926025, 3.067883014678955] |
b6e30ea1-9ba5-4ad1-9617-0e724c88497d | ctrgan-cycle-transformers-gan-for-gait | 2206.15248 | null | https://arxiv.org/abs/2206.15248v4 | https://arxiv.org/pdf/2206.15248v4.pdf | CTrGAN: Cycle Transformers GAN for Gait Transfer | We introduce a novel approach for gait transfer from unconstrained videos in-the-wild. In contrast to motion transfer, the objective here is not to imitate the source's motions by the target, but rather to replace the walking source with the target, while transferring the target's typical gait. Our approach can be trai... | ['Gil Ben-Artzi', 'Hay Hoffman', 'Noam Gaash', 'Shahar Mahpod'] | 2022-06-30 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 5.17318785e-01 1.39289081e-01 1.58121213e-01 1.59187749e-01
-8.81922007e-01 -5.82883656e-01 4.57667351e-01 -4.89388138e-01
-2.27755189e-01 7.90671647e-01 2.35274777e-01 3.09643865e-01
3.31628889e-01 -9.09160852e-01 -1.10844183e+00 -8.50824594e-01
-2.13314921e-01 4.42160755e-01 5.38146615e-01 -1.94520652... | [10.787787437438965, -0.6375570893287659] |
ac6b420e-e3f2-4db2-9878-28174d7ec6e3 | codi-co-evolving-contrastive-diffusion-models | 2304.12654 | null | https://arxiv.org/abs/2304.12654v1 | https://arxiv.org/pdf/2304.12654v1.pdf | CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis | With growing attention to tabular data these days, the attempt to apply a synthetic table to various tasks has been expanded toward various scenarios. Owing to the recent advances in generative modeling, fake data generated by tabular data synthesis models become sophisticated and realistic. However, there still exists... | ['Noseong Park', 'Jayoung Kim', 'Chaejeong Lee'] | 2023-04-25 | null | null | null | null | ['type'] | ['speech'] | [ 1.56202167e-01 1.92888170e-01 -2.02928424e-01 -1.57027751e-01
-4.32063550e-01 -5.77930152e-01 8.95733058e-01 -1.05928011e-01
5.42348176e-02 1.20553613e+00 -2.09294260e-02 -1.66951846e-02
1.41199648e-01 -1.14776468e+00 -8.21596026e-01 -7.29893148e-01
3.68632078e-01 8.92080307e-01 -3.33154611e-02 -2.15441346... | [11.651156425476074, 9.2030029296875] |
50167a5c-6f9a-40de-8267-ed02e0e2f257 | what-is-where-by-looking-weakly-supervised | 2206.09358 | null | https://arxiv.org/abs/2206.09358v2 | https://arxiv.org/pdf/2206.09358v2.pdf | What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs | Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been encountered during the training of the localization mechanism. Moreover, ... | ['Lior Wolf', 'Yoad Tewel', 'Tal Shaharabany'] | 2022-06-19 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 3.23942214e-01 3.18656415e-01 -1.50469124e-01 -2.35307395e-01
-1.16857278e+00 -8.47609282e-01 6.70896888e-01 2.49076977e-01
-4.82945293e-01 5.34946084e-01 -8.97718966e-02 -3.08706671e-01
2.49621272e-01 -6.91760838e-01 -1.33806217e+00 -6.70687497e-01
1.06145829e-01 8.24171245e-01 5.53771496e-01 -5.07836528... | [10.4222993850708, 1.3174545764923096] |
ddfb90ca-ac13-4df1-bb37-22c8c7411813 | simplified-continuous-high-dimensional-belief | 2302.06697 | null | https://arxiv.org/abs/2302.06697v1 | https://arxiv.org/pdf/2302.06697v1.pdf | Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints | Online decision making under uncertainty in partially observable domains, also known as Belief Space Planning, is a fundamental problem in robotics and Artificial Intelligence. Due to an abundance of plausible future unravelings, calculating an optimal course of action inflicts an enormous computational burden on the a... | ['Vadim Indelman', 'Andrey Zhitnikov'] | 2023-02-13 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.37717652e-01 4.60472256e-01 -1.57853186e-01 -2.22333744e-01
-7.83796489e-01 -5.72688460e-01 5.52774668e-01 5.77737331e-01
-9.08532619e-01 1.13463664e+00 -8.87556840e-03 -2.87081480e-01
-7.29007185e-01 -1.11868477e+00 -8.72460842e-01 -7.84281492e-01
-3.42658758e-01 1.05709887e+00 3.93205136e-01 -2.58418977... | [4.7699360847473145, 1.9870073795318604] |
92cec554-0893-4699-9ebb-55a30d5e97b9 | a-semi-supervised-model-for-persian-rumor | null | null | https://doi.org/10.1007/s11042-020-10077-3 | https://link.springer.com/article/10.1007/s11042-020-10077-3 | A semi-supervised model for Persian rumor verification based on content information | Rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. In social networks, false-rumors may have significantly different contextual characteristics from true-rumors at lexical, syntactic, semantic levels. Therefore, this study presents the BERT-SAWS semi-supervised lear... | ['Arash Sharifi', 'Mohammad-Reza Feizi-Derakhshi', 'Zoleikha Jahanbakhsh-Nagadeh'] | 2020-11-20 | null | null | null | multimedia-tools-and-applications-2020-11 | ['rumour-detection'] | ['natural-language-processing'] | [-3.65036041e-01 2.34091327e-01 -3.36454481e-01 -5.64717352e-01
-2.06700534e-01 -1.93675518e-01 9.57535326e-01 2.70204008e-01
-2.81843662e-01 4.05202955e-01 1.06875217e+00 -4.50920463e-01
8.93185213e-02 -6.83317423e-01 -2.36233994e-01 -1.98196039e-01
-3.32152545e-02 4.92649555e-01 -4.89908941e-02 -7.82234848... | [8.242209434509277, 10.204100608825684] |
c51902d6-5f8f-4f28-92fa-1657962a8cf7 | uncertainty-aware-self-training-for-low | 2302.08659 | null | https://arxiv.org/abs/2302.08659v1 | https://arxiv.org/pdf/2302.08659v1.pdf | Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling | Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc. However, the satisfying results achieved by traditional supervised-based approaches heavily depend on the large amounts of human an... | ['Aoying Zhou', 'Ming Gao', 'Jun Huang', 'Chengyu Wang', 'Jianing Wang'] | 2023-02-17 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 2.68347681e-01 1.50797352e-01 -5.16712427e-01 -7.58457661e-01
-1.28014600e+00 -4.80204165e-01 2.54262447e-01 2.05108915e-02
-7.12715209e-01 1.18108678e+00 1.48044035e-01 -4.10822093e-01
1.79806530e-01 -4.59155470e-01 -9.83544052e-01 -9.30940270e-01
4.80946422e-01 5.32146037e-01 -2.46038418e-02 3.54754776... | [9.477416038513184, 3.8967397212982178] |
9cbaa560-cb12-44f1-80ff-e86d8dc679da | contrastive-clustering-to-mine-pseudo | null | null | https://openreview.net/forum?id=pN1JOdrSY9 | https://openreview.net/pdf?id=pN1JOdrSY9 | Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation | Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means, such as iterative back-translation. However, there may exist small amount of actual parallel data hidden in the ... | ['Shafiq Joty', 'Philipp Koehn', 'Changhan Wang', 'Yun Tang', 'Hongyu Gong', 'Xuan-Phi Nguyen'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.01882854e-01 -1.23676471e-01 -5.03597677e-01 -5.39857745e-01
-1.34603882e+00 -8.51646304e-01 8.49045873e-01 -3.65743309e-01
-6.95265830e-01 1.37816072e+00 1.77775174e-01 -6.85860157e-01
6.13925755e-01 -3.95808458e-01 -9.19174254e-01 -5.73622465e-01
4.58592862e-01 1.09169722e+00 -3.59208167e-01 -4.86208081... | [11.623319625854492, 10.340692520141602] |
924a9544-37f6-4083-83c3-515323e12a9d | wman-weakly-supervised-moment-alignment-1 | 1909.13784 | null | https://arxiv.org/abs/1909.13784v2 | https://arxiv.org/pdf/1909.13784v2.pdf | LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval | The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to the given natural language query without access to temporal annotations during training. Prior strongly- and weakly-supervised approaches often leverage co-attention mechanisms to learn visual-semantic representations... | ['Reuben Tan', 'Kate Saenko', 'Bryan A. Plummer', 'Huijuan Xu'] | 2019-09-27 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-1.28544077e-01 -2.42624179e-01 -8.10864627e-01 -4.17810738e-01
-8.30654860e-01 -3.87267888e-01 9.16777790e-01 3.50767642e-01
-4.53006774e-01 3.59728038e-01 6.33455813e-01 1.69906439e-03
1.64369375e-01 -4.77154016e-01 -8.31183314e-01 -4.64914232e-01
-9.21086520e-02 1.23467281e-01 2.60155082e-01 1.03760593... | [10.06683349609375, 0.7848902344703674] |
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