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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
eccfc125-42cc-4f71-9f18-c1b30b5eb58f | cross-network-social-user-embedding-with | 2209.01539 | null | https://arxiv.org/abs/2209.01539v1 | https://arxiv.org/pdf/2209.01539v1.pdf | Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees | Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy concerns about leaking sensitive user information. How to fully utili... | ['Philip S. Yu', 'Xu Bai', 'Jia Wu', 'Chaochao Chen', 'Zhiwei Liu', 'Lingjuan Lyu', 'Hao Peng', 'Lei Jiang', 'Jiaqian Ren'] | 2022-09-04 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 1.34420972e-02 2.27263033e-01 -4.55736667e-01 -4.63679940e-01
-1.57955185e-01 -8.68990362e-01 2.59747803e-01 3.95878613e-01
-1.60559580e-01 3.87772888e-01 4.01710391e-01 -2.07980767e-01
-5.27151704e-01 -1.11382568e+00 -2.59011775e-01 -4.68982399e-01
-1.40479654e-01 9.03275907e-02 4.05571461e-02 -1.80273235... | [6.0483832359313965, 6.937761306762695] |
151e4842-1dc2-474d-a6ff-a9699182d327 | dual-cross-attention-learning-for-fine | 2205.02151 | null | https://arxiv.org/abs/2205.02151v1 | https://arxiv.org/pdf/2205.02151v1.pdf | Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification | Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects... | ['Yi Shan', 'Lu Tian', 'Ji Liu', 'Dong Li', 'Wenjing Ke', 'Haowei Zhu'] | 2022-05-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-image-classification', 'fine-grained-visual-categorization'] | ['computer-vision', 'computer-vision'] | [ 4.70073670e-02 -4.32117075e-01 -1.22370133e-02 -5.27651310e-01
-6.01862073e-01 -6.02660716e-01 8.37028921e-01 1.50582641e-02
-5.53249836e-01 3.94287705e-01 4.52099770e-01 9.57208276e-02
-5.47542833e-02 -6.39720738e-01 -9.72697198e-01 -6.69840336e-01
1.85005784e-01 1.99340150e-01 5.59048913e-02 -5.32889180... | [9.572959899902344, 2.0625572204589844] |
54834823-28bd-43e6-aa7f-6f409b02f4b0 | evaluating-the-impact-of-bitcoin-on | 2205.00335 | null | https://arxiv.org/abs/2205.00335v1 | https://arxiv.org/pdf/2205.00335v1.pdf | Evaluating the Impact of Bitcoin on International Asset Allocation using Mean-Variance, Conditional Value-at-Risk (CVaR), and Markov Regime Switching Approaches | This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final ap... | ['Mohammadreza Mahmoudi'] | 2022-04-30 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-7.78281033e-01 -9.03284326e-02 -5.20115674e-01 1.04856610e-01
1.40745059e-01 -1.04675305e+00 8.07346523e-01 -1.75988480e-01
7.33715519e-02 8.25343847e-01 4.55323607e-01 -8.90339553e-01
-6.97432697e-01 -7.88343132e-01 -1.80661157e-01 -7.35468686e-01
-6.46034442e-03 3.98673505e-01 -1.50825664e-01 -1.04845025... | [4.762621879577637, 4.055124759674072] |
46c1888d-85f4-42a2-97c3-ba11c025cbd8 | transfer-learning-based-multi-objective | 2109.15136 | null | https://arxiv.org/abs/2109.15136v2 | https://arxiv.org/pdf/2109.15136v2.pdf | Transfer Learning Based Multi-Objective Genetic Algorithm for Dynamic Community Detection | Dynamic community detection is the hotspot and basic problem of complex network and artificial intelligence research in recent years. It is necessary to maximize the accuracy of clustering as the network structure changes, but also to minimize the two consecutive clustering differences between the two results. There is... | ['Gaoshan Deng', 'Siyu Gao', 'Gil Alterovitz', 'Wenhua Zeng', 'Fan Lin', 'Jungang Zou'] | 2021-09-30 | null | null | null | null | ['dynamic-community-detection'] | ['graphs'] | [ 9.83107602e-04 -7.10840523e-01 4.67053987e-02 7.25212321e-02
1.93303287e-01 -2.02909321e-01 4.53992840e-03 1.81525156e-01
-4.45969671e-01 5.54373682e-01 -2.36308649e-01 1.06995478e-01
-7.53457546e-01 -1.00452352e+00 -1.42966777e-01 -9.95601952e-01
-2.48975545e-01 5.50241649e-01 4.55224335e-01 -2.59213746... | [7.186348915100098, 5.200821876525879] |
8a82b7d4-f13b-424f-a81c-4b961b56296a | toward-training-at-imagenet-scale-with | 2201.12328 | null | https://arxiv.org/abs/2201.12328v2 | https://arxiv.org/pdf/2201.12328v2.pdf | Toward Training at ImageNet Scale with Differential Privacy | Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the training set. Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-li... | ['Abhradeep Thakurta', 'Andreas Terzis', 'Roxana Geambasu', 'Shuang Song', 'Steve Chien', 'Alexey Kurakin'] | 2022-01-28 | null | null | null | null | ['image-classification-with-dp'] | ['computer-vision'] | [ 9.39956978e-02 2.27750435e-01 2.43033632e-04 -6.86730266e-01
-7.26856530e-01 -6.62599206e-01 3.79203618e-01 -2.04015583e-01
-1.00841808e+00 7.52042770e-01 -7.96040744e-02 -7.36705422e-01
2.11826906e-01 -7.10443556e-01 -9.81050551e-01 -7.33156919e-01
-1.83544323e-01 1.56789213e-01 -2.05653191e-01 1.07340157... | [5.955568790435791, 6.915762901306152] |
f061c634-ad22-4af4-821b-a4c3b4f911ed | compressing-facial-makeup-transfer-networks | 2009.07604 | null | https://arxiv.org/abs/2009.07604v1 | https://arxiv.org/pdf/2009.07604v1.pdf | Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition | Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture. We address this issue by compressing facial makeup transfer networks with collaborat... | ['Lu Yu', 'Xinyi Hu', 'Zi Hui', 'Haoji Hu', 'Bianjiang Yang'] | 2020-09-16 | null | null | null | null | ['facial-makeup-transfer'] | ['computer-vision'] | [ 2.56758481e-01 3.10080230e-01 -3.47036242e-01 -4.75228906e-01
-1.76838458e-01 -2.55364209e-01 4.35719788e-01 -7.53115475e-01
-1.33722261e-01 4.22748148e-01 3.05343211e-01 -3.41000855e-01
-6.44402504e-02 -1.15733302e+00 -8.46303344e-01 -8.21525455e-01
-1.29700273e-01 -2.09332243e-01 -2.25505784e-01 -2.40666077... | [12.548384666442871, -0.10489228367805481] |
745e6315-c0df-4ab4-8a24-c640f461183c | do-deep-neural-networks-capture | 2302.07866 | null | https://arxiv.org/abs/2302.07866v1 | https://arxiv.org/pdf/2302.07866v1.pdf | Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? | Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks. This study empirically addresses this question by systematically examining recently published pre-trained seq2seq models with a carefully c... | ['Kentaro Inui', 'Keisuke Sakaguchi', 'Masashi Yoshikawa', 'Ana Brassard', 'Tatsuki Kuribayashi', 'Yoichi Aoki', 'Keito Kudo'] | 2023-02-15 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 4.54284996e-01 2.23917633e-01 -2.29856014e-01 -1.40029043e-01
-3.88573438e-01 -8.07239473e-01 6.91623032e-01 2.03229323e-01
-2.70996779e-01 6.86155796e-01 4.89462465e-01 -6.83438838e-01
-5.55580437e-01 -8.70011866e-01 -7.69922793e-01 -3.21412057e-01
-2.52413481e-01 5.72269320e-01 1.99321240e-01 -7.64411509... | [9.463016510009766, 7.268890857696533] |
b1451f60-7c92-4588-b17b-5e3f6d13f3ab | deeplogo-hitting-logo-recognition-with-the | 1510.02131 | null | http://arxiv.org/abs/1510.02131v1 | http://arxiv.org/pdf/1510.02131v1.pdf | DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer | Recently, there has been a flurry of industrial activity around logo
recognition, such as Ditto's service for marketers to track their brands in
user-generated images, and LogoGrab's mobile app platform for logo recognition.
However, relatively little academic or open-source logo recognition progress
has been made in t... | ['Forrest N. Iandola', 'Kurt Keutzer', 'Anting Shen', 'Peter Gao'] | 2015-10-07 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 9.41755157e-03 -7.23411918e-01 -7.98286796e-01 -3.28718424e-01
-2.20294952e-01 -3.92007679e-01 5.89410782e-01 -1.68726787e-01
-3.19588855e-02 9.97265354e-02 -4.78011295e-02 -4.68063235e-01
1.11103348e-01 -1.02225089e+00 -3.26765776e-01 -2.49723911e-01
-2.62387872e-01 3.66251945e-01 -1.24548376e-01 -1.50384739... | [9.36610221862793, 1.3557016849517822] |
a28a3bd3-f91a-4750-b9dd-e7b957f63bc2 | blind-image-quality-assessment-for-mri-with-a | 2107.06888 | null | https://arxiv.org/abs/2107.06888v1 | https://arxiv.org/pdf/2107.06888v1.pdf | Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network | Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-quality reference MRI images are usually not available. Recently, many efforts have been devoted to developing deep learning-based BIQA approac... | ['Shanshan Wang', 'Hairong Zheng', 'Cheng Li', 'Yu Gong', 'Chuyu Rong', 'Haoran Li', 'Kehan Qi'] | 2021-07-13 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-3.90565321e-02 -2.23657757e-01 2.89333779e-02 -3.00261647e-01
-1.19195855e+00 -1.69649869e-01 1.62152737e-01 7.32557327e-02
-5.70305347e-01 5.51932752e-01 3.65137190e-01 -2.31270611e-01
-5.08523583e-01 -6.54718041e-01 -3.36773396e-01 -1.03397393e+00
-3.78699601e-01 5.60757220e-01 1.95858970e-01 -1.58042610... | [13.934942245483398, -2.1854336261749268] |
eaab419d-411d-4c05-b910-7022879c8015 | generative-low-bitwidth-data-free | 2003.03603 | null | https://arxiv.org/abs/2003.03603v3 | https://arxiv.org/pdf/2003.03603v3.pdf | Generative Low-bitwidth Data Free Quantization | Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedded devices. Existing quantization methods require original data for calibration or fine-tuning to get better performance. However, in many re... | ['Mingkui Tan', 'JieZhang Cao', 'Chuangrun Liang', 'Jing Liu', 'Bohan Zhuang', 'Shoukai Xu', 'Haokun Li'] | 2020-03-07 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1469_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570001.pdf | eccv-2020-8 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.34013999e-01 6.74563646e-02 -6.28275752e-01 -2.75593579e-01
-1.01469386e+00 -4.79081064e-01 3.00496548e-01 -2.79771490e-03
-2.78549612e-01 1.01622057e+00 5.78234605e-02 -3.87904912e-01
2.74876118e-01 -1.16889024e+00 -1.05630422e+00 -7.93793023e-01
4.14270371e-01 1.15105890e-01 -7.24365190e-03 -4.92098406... | [8.774017333984375, 2.9618067741394043] |
b1b18fc0-5167-42d9-af00-28ce914c3b5e | ameli-enhancing-multimodal-entity-linking | 2305.14725 | null | https://arxiv.org/abs/2305.14725v1 | https://arxiv.org/pdf/2305.14725v1.pdf | AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes | We propose attribute-aware multimodal entity linking, where the input is a mention described with a text and image, and the goal is to predict the corresponding target entity from a multimodal knowledge base (KB) where each entity is also described with a text description, a visual image and a set of attributes and val... | ['Lifu Huang', 'Licheng Yu', 'Zhiyang Xu', 'Minqian Liu', 'Sijia Wang', 'Qifan Wang', 'Yu Chen', 'Barry Menglong Yao'] | 2023-05-24 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [ 1.02494724e-01 3.77471000e-01 -4.84449893e-01 -7.10924506e-01
-1.10436702e+00 -7.26462245e-01 7.57319510e-01 4.69354808e-01
-2.52539158e-01 8.12260032e-01 2.24185765e-01 1.37966245e-01
6.35989681e-02 -6.81644917e-01 -9.30013418e-01 7.16089010e-02
5.43501377e-02 1.10416627e+00 -1.31444097e-01 -9.40363854... | [10.882967948913574, 1.692028522491455] |
3fec2afa-35d3-4664-8c8d-1a7e1e5a4ac6 | learning-to-make-generalizable-and-diverse | 1910.09688 | null | https://arxiv.org/abs/1910.09688v1 | https://arxiv.org/pdf/1910.09688v1.pdf | Learning to Make Generalizable and Diverse Predictions for Retrosynthesis | We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.... | ['Benson Chen', 'Regina Barzilay', 'Tommi S. Jaakkola', 'Tianxiao Shen'] | 2019-10-21 | null | https://openreview.net/forum?id=BygfrANKvB | https://openreview.net/pdf?id=BygfrANKvB | null | ['retrosynthesis'] | ['medical'] | [ 8.72396469e-01 4.16981548e-01 -5.04399359e-01 -4.10956711e-01
-1.00269341e+00 -1.05213606e+00 1.03445649e+00 -7.37264156e-02
8.86727944e-02 1.23124719e+00 6.78228199e-01 -7.96304643e-01
3.25619042e-01 -6.28267169e-01 -8.95997047e-01 -8.64040017e-01
3.17635626e-01 5.68890214e-01 -2.07908064e-01 -2.75537789... | [4.543296813964844, 6.078108310699463] |
6b497118-ec6e-439b-bb5f-55ccff310012 | short-text-clustering-via-convolutional | null | null | https://aclanthology.org/W15-1509 | https://aclanthology.org/W15-1509.pdf | Short Text Clustering via Convolutional Neural Networks | null | ['Hong-Wei Hao', 'Peng Wang', 'Bo Xu', 'Jun Zhao', 'Jiaming Xu', 'Guanhua Tian', 'Fangyuan Wang'] | 2015-06-01 | null | null | null | ws-2015-6 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.330836772918701, 3.640049934387207] |
f91892fd-42bc-4d62-8464-d1b65e6e09d2 | toward-fault-detection-in-industrial-welding | 2106.10160 | null | https://arxiv.org/abs/2106.10160v1 | https://arxiv.org/pdf/2106.10160v1.pdf | Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation | With the rise of deep learning models in the field of computer vision, new possibilities for their application in industrial processes proves to return great benefits. Nevertheless, the actual fit of machine learning for highly standardised industrial processes is still under debate. This paper addresses the challenges... | ['Prof. Dr. Kristof Van Laerhoven', 'Markus Schmitz', 'Georgij Safronov', 'Dr. Florian Schlather', 'Jibinraj Antony'] | 2021-06-18 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.18068236e-01 2.67475873e-01 1.19983718e-01 -2.17104301e-01
-3.74811202e-01 -6.29845440e-01 7.59837925e-01 1.46994531e-01
-3.85577977e-01 2.38966689e-01 -5.20642400e-01 -4.11680043e-01
-5.56102991e-01 -7.96691656e-01 -7.67417967e-01 -5.64399362e-01
-7.75416046e-02 6.67412043e-01 7.61037841e-02 -1.28264442... | [7.315260410308838, 1.9058246612548828] |
5addd9a6-1f77-4060-95a9-3ea053b0d978 | point2ssm-learning-morphological-variations | 2305.14486 | null | https://arxiv.org/abs/2305.14486v1 | https://arxiv.org/pdf/2305.14486v1.pdf | Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud | We introduce Point2SSM, a novel unsupervised learning approach that can accurately construct correspondence-based statistical shape models (SSMs) of anatomy directly from point clouds. SSMs are crucial in clinical research for analyzing the population-level morphological variation in bones and organs. However, traditio... | ['Shireen Elhabian', 'Jadie Adams'] | 2023-05-23 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.49367422e-01 2.14576185e-01 -2.78763503e-01 -4.32345331e-01
-1.10122716e+00 -3.54105622e-01 4.73443896e-01 6.71218514e-01
-3.15089434e-01 4.18831497e-01 -1.19424937e-02 -2.37070411e-01
-3.61580610e-01 -8.71478438e-01 -1.04061317e+00 -4.97702658e-01
-8.64393711e-02 1.05283296e+00 1.16033725e-01 -2.48672247... | [14.042756080627441, -2.517885446548462] |
9e55313b-f2e2-4381-a3c7-84ca9f460bb0 | learning-task-specific-strategies-for | 2304.12507 | null | https://arxiv.org/abs/2304.12507v1 | https://arxiv.org/pdf/2304.12507v1.pdf | Learning Task-Specific Strategies for Accelerated MRI | Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in suboptimal end-to-end performance. In this wo... | ['Katherine L. Bouman', 'Adrian V. Dalca', 'Andre van der Kouwe', 'Robert Frost', 'Yu Sun', 'Tianwei Yin', 'Zihui Wu'] | 2023-04-25 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 8.38327706e-01 4.68258299e-02 8.68185759e-02 -4.37640011e-01
-1.42578912e+00 -1.96073070e-01 3.20078433e-01 -6.19776733e-02
-2.72044003e-01 4.36407119e-01 4.87965822e-01 -2.22650900e-01
-2.46194378e-01 9.34385601e-03 -5.66385865e-01 -6.74199522e-01
-3.57600898e-01 3.78226131e-01 1.33292913e-01 3.09417129... | [13.534811019897461, -2.4025139808654785] |
d28a8b0e-8327-47c5-98c5-da13e763cf4b | dfanet-deep-feature-aggregation-for-real-time | 1904.02216 | null | http://arxiv.org/abs/1904.02216v1 | http://arxiv.org/pdf/1904.02216v1.pdf | DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation | This paper introduces an extremely efficient CNN architecture named DFANet
for semantic segmentation under resource constraints. Our proposed network
starts from a single lightweight backbone and aggregates discriminative
features through sub-network and sub-stage cascade respectively. Based on the
multi-scale feature ... | ['Pengfei Xiong', 'Hanchao Li', 'Jian Sun', 'Haoqiang Fan'] | 2019-04-03 | dfanet-deep-feature-aggregation-for-real-time-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Li_DFANet_Deep_Feature_Aggregation_for_Real-Time_Semantic_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_DFANet_Deep_Feature_Aggregation_for_Real-Time_Semantic_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['smac-1'] | ['playing-games'] | [-1.90587476e-01 -2.58826226e-01 -8.94429609e-02 -5.33264399e-01
-5.58893025e-01 -3.91122371e-01 2.38762405e-02 -1.64983615e-01
-1.02030671e+00 4.45219725e-01 -5.91238976e-01 -4.12222654e-01
2.57557601e-01 -9.75288987e-01 -7.40388393e-01 -5.45242012e-01
-1.04466714e-01 2.23202735e-01 6.83734357e-01 -2.02803798... | [9.264309883117676, -0.5074727535247803] |
6910a5f9-de2d-4d4c-82cd-d907d568cf18 | self-supervised-language-learning-from-raw | 2210.15759 | null | https://arxiv.org/abs/2210.15759v1 | https://arxiv.org/pdf/2210.15759v1.pdf | Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge | Recent progress in self-supervised or unsupervised machine learning has opened the possibility of building a full speech processing system from raw audio without using any textual representations or expert labels such as phonemes, dictionaries or parse trees. The contribution of the Zero Resource Speech Challenge serie... | ['Emmanuel Dupoux', 'Nicolas Hamilakis', 'Ewan Dunbar'] | 2022-10-27 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 4.37377125e-01 3.04805368e-01 -2.54801124e-01 -5.77820599e-01
-1.31661928e+00 -5.28161943e-01 5.58056653e-01 1.85315520e-01
-4.49105918e-01 5.64316690e-01 7.90481985e-01 -3.31764370e-01
1.64831057e-01 -8.65658894e-02 -3.88612360e-01 -4.36591238e-01
-5.40683448e-01 4.27616566e-01 -8.26297104e-02 -3.34042102... | [14.475871086120605, 6.688167095184326] |
b55f313a-5540-4cb7-9efc-51f57e6fb780 | fml-based-dynamic-assessment-agent-for-human | 1707.04828 | null | http://arxiv.org/abs/1707.04828v1 | http://arxiv.org/pdf/1707.04828v1.pdf | FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go | In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intellige... | ['Chia-Hsiu Kao', 'Ping-Chiang Chou', 'Chun-Hsun Chou', 'Su-Wei Lin', 'Pi-Hsia Hung', 'Sheng-Chi Yang', 'Mei-Hui Wang', 'Chang-Shing Lee', 'Nan Shuo', 'Naoyuki Kubota'] | 2017-07-16 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.18797278e-01 2.70732909e-01 2.18917038e-02 6.06349949e-03
-2.10687369e-01 -3.71018738e-01 5.52183509e-01 -6.92480803e-02
-4.46784437e-01 4.71222520e-01 -2.88291156e-01 -5.54731905e-01
-5.20537019e-01 -1.18924069e+00 -1.87051624e-01 -1.86328337e-01
-2.26613939e-01 5.59320986e-01 9.71578062e-01 -9.27567244... | [3.6501502990722656, 1.4179788827896118] |
32816177-a5dc-4735-a063-80ccb6e03160 | neural-360-circ-structured-light-with-learned | 2306.13361 | null | https://arxiv.org/abs/2306.13361v2 | https://arxiv.org/pdf/2306.13361v2.pdf | Neural 360$^\circ$ Structured Light with Learned Metasurfaces | Structured light has proven instrumental in 3D imaging, LiDAR, and holographic light projection. Metasurfaces, comprised of sub-wavelength-sized nanostructures, facilitate 180$^\circ$ field-of-view (FoV) structured light, circumventing the restricted FoV inherent in traditional optics like diffractive optical elements.... | ['Junsuk Rho', 'Seung-Hwan Baek', 'Yujin Jeon', 'Jooyeong Yun', 'Gyeongtae Kim', 'Eunsue Choi'] | 2023-06-23 | null | null | null | null | ['depth-estimation'] | ['computer-vision'] | [ 6.04720056e-01 2.94353604e-01 6.72006488e-01 -8.80023688e-02
-3.71363461e-01 -3.66882652e-01 2.24081144e-01 -7.31372178e-01
-5.24527669e-01 6.79507256e-01 -1.12581894e-01 -4.39247131e-01
-3.29144657e-01 -1.02195549e+00 -8.72109234e-01 -1.07959342e+00
-1.79998890e-01 -1.41771482e-02 -1.86576724e-01 -9.65411123... | [9.952178955078125, -2.723935604095459] |
9f401606-289e-402f-b41e-0d956163bc4a | compositional-sentence-representation-from | 1605.00482 | null | http://arxiv.org/abs/1605.00482v3 | http://arxiv.org/pdf/1605.00482v3.pdf | Compositional Sentence Representation from Character within Large Context Text | This paper describes a Hierarchical Composition Recurrent Network (HCRN)
consisting of a 3-level hierarchy of compositional models: character, word and
sentence. This model is designed to overcome two problems of representing a
sentence on the basis of a constituent word sequence. The first is a
data-sparsity problem i... | ['Soo-Young Lee', 'Geonmin Kim', 'Jisu Choi', 'Hwaran Lee'] | 2016-05-02 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 3.37289840e-01 4.42541033e-01 3.90362623e-03 -3.88293356e-01
-3.30240220e-01 -4.79219146e-02 4.51525509e-01 2.78071821e-01
-5.98401248e-01 4.66730148e-01 7.81176507e-01 -2.35689387e-01
2.93047756e-01 -6.96717978e-01 -6.78743124e-02 -7.08137333e-01
2.21312672e-01 2.48223618e-01 1.62708029e-01 -7.19329953... | [12.418726921081543, 7.685118675231934] |
198ad8cb-4621-4027-aae2-b73689c45c28 | self-supervised-spatiotemporal-learning-via | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_Self-Supervised_Spatiotemporal_Learning_via_Video_Clip_Order_Prediction_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Self-Supervised_Spatiotemporal_Learning_via_Video_Clip_Order_Prediction_CVPR_2019_paper.pdf | Self-Supervised Spatiotemporal Learning via Video Clip Order Prediction | We propose a self-supervised spatiotemporal learning technique which leverages the chronological order of videos. Our method can learn the spatiotemporal representation of the video by predicting the order of shuffled clips from the video. The category of the video is not required, which gives our technique the potenti... | [' Yueting Zhuang', ' Di Xie', ' Jian Shao', ' Zhou Zhao', ' Jun Xiao', 'Dejing Xu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['self-supervised-action-recognition'] | ['computer-vision'] | [-8.22589844e-02 -4.30449426e-01 -8.74797165e-01 -5.97018838e-01
-3.98277700e-01 -6.43475592e-01 5.07880867e-01 -3.69009942e-01
-3.08454543e-01 4.39934880e-01 7.91412652e-01 1.91558897e-01
-1.29534915e-01 -5.24052262e-01 -9.73375201e-01 -4.15194303e-01
-6.56889558e-01 -9.92564783e-02 4.31775331e-01 7.53800049... | [8.612643241882324, 0.7046021223068237] |
ede6e405-a392-4e40-a5b9-ea0561a9f130 | musiclm-generating-music-from-text | 2301.11325 | null | https://arxiv.org/abs/2301.11325v1 | https://arxiv.org/pdf/2301.11325v1.pdf | MusicLM: Generating Music From Text | We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff". MusicLM casts the process of conditional music generation as a hierarchical sequence-to-sequence modeling task, and it generates music at 24 kHz that remains consisten... | ['Christian Frank', 'Neil Zeghidour', 'Matt Sharifi', 'Marco Tagliasacchi', 'Adam Roberts', 'Aren Jansen', 'Qingqing Huang', 'Antoine Caillon', 'Mauro Verzetti', 'Jesse Engel', 'Zalán Borsos', 'Timo I. Denk', 'Andrea Agostinelli'] | 2023-01-26 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [ 3.06228310e-01 -1.37700766e-01 -2.31995489e-02 -2.32221484e-01
-1.32115686e+00 -1.06418169e+00 5.06809711e-01 -3.60526979e-01
1.32516727e-01 5.67719340e-01 8.52696657e-01 2.89458543e-01
-8.03954247e-03 -2.45732144e-01 -8.42530906e-01 -2.23343402e-01
2.45703563e-01 6.84969842e-01 -2.81241655e-01 -3.18090767... | [15.803168296813965, 5.667967796325684] |
631a262b-2ba4-4a9c-8f10-ae28cdd4d550 | aligning-artificial-neural-networks-to-the | null | null | https://openreview.net/forum?id=BJeY6sR9KX | https://openreview.net/pdf?id=BJeY6sR9KX | Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures | Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer a... | ['Daniel L. K. Yamins', 'Jonathan Prescott-Roy', 'Rishi Rajalingham', 'Najib J. Majaj', 'Jonas Kubilius', 'Daniel Bear', 'Pouya Bashivan', 'Kailyn Schmidt', 'Ha Hong', 'Elias B. Issa', 'Aran Nayebi', 'Martin Schrimpf', 'Kohitij Kar', 'James J. DiCarlo'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['object-categorization'] | ['computer-vision'] | [-3.22296917e-02 1.64932191e-01 1.00949734e-01 -2.23154858e-01
2.18063697e-01 -6.12240076e-01 5.83064795e-01 1.26547247e-01
-7.58091986e-01 2.14517161e-01 2.49034047e-01 -5.73723495e-01
-4.81177837e-01 -5.01341403e-01 -6.17040038e-01 -3.51266675e-02
-4.47958767e-01 1.55035108e-01 4.22053576e-01 -3.91286790... | [9.599494934082031, 2.4430971145629883] |
52a0d563-3d85-4bd7-b0d9-7213a71d266b | spaceyolo-a-human-inspired-model-for-real | 2302.00824 | null | https://arxiv.org/abs/2302.00824v1 | https://arxiv.org/pdf/2302.00824v1.pdf | SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection | The rapid proliferation of non-cooperative spacecraft and space debris in orbit has precipitated a surging demand for on-orbit servicing and space debris removal at a scale that only autonomous missions can address, but the prerequisite autonomous navigation and flightpath planning to safely capture an unknown, non-coo... | ['Madhur Tiwari', 'Markus Wilde', 'Ryan T. White', 'Trupti Mahendrakar'] | 2023-02-02 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [-2.13532805e-01 -3.40384841e-01 2.33900726e-01 1.54574811e-01
9.65336896e-03 -1.13360155e+00 6.39130175e-01 -3.79983276e-01
-3.14975530e-01 6.22594357e-01 -4.55281347e-01 -7.03667998e-01
-3.94414842e-01 -3.18033606e-01 -3.94772351e-01 -7.07460940e-01
-5.24235249e-01 9.67438340e-01 2.16988668e-01 -7.24044859... | [7.364480972290039, -1.8236219882965088] |
b6b01322-833f-4ba2-aab1-0d5ecdf41975 | end-to-end-learning-for-early-classification | 1901.10681 | null | https://arxiv.org/abs/1901.10681v2 | https://arxiv.org/pdf/1901.10681v2.pdf | End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping | Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classificati... | ['Devis Tuia', 'Sébastien Lefèvre', 'Rémi Emonet', 'Nicolas Courty', 'Marc Rußwurm', 'Romain Tavenard'] | 2019-01-30 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.94594972e-02 -2.37992451e-01 -3.02055955e-01 -6.17662311e-01
-5.64494014e-01 -6.74077272e-01 6.40868425e-01 5.83495438e-01
-2.17196792e-01 5.94106674e-01 -3.38333935e-01 -6.59173965e-01
-1.79673776e-01 -1.05964601e+00 -8.11330795e-01 -7.61446834e-01
-7.27281690e-01 3.73468757e-01 -5.14188595e-02 -3.52890015... | [9.467679977416992, -1.5663878917694092] |
bb694010-4610-4b59-85d0-b336ca4e295d | vtp-volumetric-transformer-for-multi-view | 2205.12602 | null | https://arxiv.org/abs/2205.12602v1 | https://arxiv.org/pdf/2205.12602v1.pdf | VTP: Volumetric Transformer for Multi-view Multi-person 3D Pose Estimation | This paper presents Volumetric Transformer Pose estimator (VTP), the first 3D volumetric transformer framework for multi-view multi-person 3D human pose estimation. VTP aggregates features from 2D keypoints in all camera views and directly learns the spatial relationships in the 3D voxel space in an end-to-end fashion.... | ['Gangyong Jia', 'Ouhan Huang', 'Renshu Gu', 'Yuxing Chen'] | 2022-05-25 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.11871672e-01 1.22150660e-01 -1.90056507e-02 -3.93280029e-01
-6.11105859e-01 -2.08900690e-01 4.67530161e-01 2.88134553e-02
-3.82880270e-01 4.60661799e-01 5.54764807e-01 2.25092188e-01
2.36184925e-01 -7.52692997e-01 -9.57757652e-01 -4.34361428e-01
-2.51662463e-01 6.18361294e-01 1.85354441e-01 -3.59968990... | [7.067235469818115, -0.9211204648017883] |
158382b3-a0f9-4d3a-a7c4-baca9991d249 | tampered-vae-for-improved-satellite-image | 2203.16149 | null | https://arxiv.org/abs/2203.16149v1 | https://arxiv.org/pdf/2203.16149v1.pdf | Tampered VAE for Improved Satellite Image Time Series Classification | The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising from both dimensions. Recent state-of-the-art deep learning models have shown promising results by stac... | ['Peter Nicholl', 'Yaxin Bi', 'Xin Cai'] | 2022-03-30 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.25162020e-01 -4.31255251e-01 -3.59098405e-01 -3.53393972e-01
-5.19216657e-01 -6.32984459e-01 5.67441761e-01 1.67468865e-03
-1.20099254e-01 2.71787435e-01 -1.16100296e-01 -4.90748703e-01
-2.62261868e-01 -1.01488745e+00 -6.98150814e-01 -1.06764245e+00
-3.92296940e-01 -1.54466957e-01 -9.11359265e-02 -2.81949461... | [9.48875904083252, -1.545661449432373] |
61d70982-4a04-4021-b481-7da8c56c4c65 | neural-network-inspired-analog-to-digital | 1911.12815 | null | https://arxiv.org/abs/1911.12815v1 | https://arxiv.org/pdf/1911.12815v1.pdf | Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices | Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to realize the NN operations and require high-precision RRAM cells (6~12-bit) to achiev... | ['Ayan Chakrabarti', 'Xuan Zhang', 'Weidong Cao', 'Liu Ke'] | 2019-11-28 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 8.54829013e-01 -3.99145305e-01 -2.48063877e-01 -3.81148279e-01
-4.75724101e-01 -3.13949049e-01 5.13443887e-01 1.44892380e-01
-5.58763444e-01 7.77135551e-01 -9.91206542e-02 -2.62371689e-01
-2.46271968e-01 -1.02728033e+00 -5.74884176e-01 -6.42277181e-01
1.49334759e-01 -5.40980250e-02 6.00005329e-01 -2.63884246... | [8.326347351074219, 2.58795428276062] |
a813438c-d957-4bc4-9b1f-a1deeafbc3b1 | an-efficient-edge-detection-approach-to | null | null | https://ieeexplore.ieee.org/document/8667063/authors#authors | https://ieeexplore.ieee.org/document/8667063/authors#authors | An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis | An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge ... | ['Mamta Mittal; Amit Verma; Iqbaldeep Kaur; Bhavneet Kaur; Meenakshi Sharma; Lalit Mohan Goyal; Sudipta Roy; TAI-HOON KIM'] | 2019-03-13 | null | null | null | ieee-2019-3 | ['face-detection', 'edge-detection'] | ['computer-vision', 'computer-vision'] | [ 1.84426069e-01 -3.27079415e-01 1.44054875e-01 -8.23762417e-02
-4.03486267e-02 -1.63427368e-01 2.89845824e-01 7.87052736e-02
-6.02149487e-01 5.15230060e-01 -2.68237621e-01 -3.68448019e-01
-1.44586697e-01 -5.90415776e-01 -1.36571169e-01 -6.66217089e-01
-2.92349219e-01 -2.15811297e-01 6.92108691e-01 -2.53691971... | [9.617547035217285, -1.5922220945358276] |
a6987ed9-5b98-4395-8bda-0edb644dafb3 | active-anomaly-detection-via-ensembles | 1809.06477 | null | http://arxiv.org/abs/1809.06477v1 | http://arxiv.org/pdf/1809.06477v1.pdf | Active Anomaly Detection via Ensembles | In critical applications of anomaly detection including computer security and
fraud prevention, the anomaly detector must be configurable by the analyst to
minimize the effort on false positives. One important way to configure the
anomaly detector is by providing true labels for a few instances. We study the
problem of... | ['Md. Rakibul Islam', 'Nitthilan Kannappan Jayakodi', 'Shubhomoy Das', 'Janardhan Rao Doppa'] | 2018-09-17 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.87800997e-01 3.66185270e-02 -1.90832198e-01 -6.06752217e-01
-8.58683884e-01 -6.94411874e-01 3.34911525e-01 7.46879935e-01
-3.97970796e-01 5.05474806e-01 -3.14058572e-01 -2.97428489e-01
-4.66830283e-01 -6.99814618e-01 -4.06054229e-01 -7.81176329e-01
-5.02983272e-01 8.59807730e-01 4.17522550e-01 8.15883055... | [7.5664544105529785, 2.519348621368408] |
3d546b72-b910-43c8-8b51-87b0ff5cd5de | polyglot-ner-massive-multilingual-named | 1410.3791 | null | http://arxiv.org/abs/1410.3791v1 | http://arxiv.org/pdf/1410.3791v1.pdf | POLYGLOT-NER: Massive Multilingual Named Entity Recognition | The increasing diversity of languages used on the web introduces a new level
of complexity to Information Retrieval (IR) systems. We can no longer assume
that textual content is written in one language or even the same language
family. In this paper, we demonstrate how to build massive multilingual
annotators with mini... | ['Steven Skiena', 'Rami Al-Rfou', 'Bryan Perozzi', 'Vivek Kulkarni'] | 2014-10-14 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.01705569e-01 7.94104561e-02 -3.59717548e-01 -2.54366457e-01
-1.08568788e+00 -9.82441723e-01 6.31310284e-01 4.37525898e-01
-9.96147394e-01 9.79390144e-01 1.86710119e-01 -3.81782591e-01
7.00535700e-02 -1.02649844e+00 -6.58071518e-01 -1.72505528e-02
1.96744561e-01 7.22372293e-01 3.80250335e-01 -6.77725017... | [9.855422973632812, 9.508953094482422] |
3029c7ce-effb-4812-94ce-6ad5ddf39129 | tackling-ambiguity-with-images-improved | 2212.10140 | null | https://arxiv.org/abs/2212.10140v2 | https://arxiv.org/pdf/2212.10140v2.pdf | Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation | One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images. However, recent work in multimodal MT (MMT) has shown that obtaining improvements from images is challenging, limited not only by the difficulty of building effective cross-m... | ['Rachel Bawden', 'Benoît Sagot', 'Ivan Laptev', 'Cordelia Schmid', 'Matthieu Futeral'] | 2022-12-20 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.39801598e-01 -7.12600276e-02 -5.28833382e-02 -2.40358844e-01
-1.70568562e+00 -8.35736275e-01 1.11306131e+00 -3.22666019e-01
-5.42895675e-01 8.53544295e-01 3.61461282e-01 -5.11077344e-01
3.35751116e-01 -6.85137957e-02 -1.07603467e+00 -5.43949842e-01
4.03964490e-01 9.42875206e-01 -4.28289026e-02 -4.22408313... | [11.439456939697266, 1.4938547611236572] |
0843198c-204b-4702-9276-4c4ff15b9ec4 | adversarial-examples-in-remote-sensing | 1805.10997 | null | http://arxiv.org/abs/1805.10997v1 | http://arxiv.org/pdf/1805.10997v1.pdf | Adversarial Examples in Remote Sensing | This paper considers attacks against machine learning algorithms used in
remote sensing applications, a domain that presents a suite of challenges that
are not fully addressed by current research focused on natural image data such
as ImageNet. In particular, we present a new study of adversarial examples in
the context... | ['I-Jeng Wang', 'Neil Fendley', 'Christopher Ratto', 'Wojciech Czaja', 'Michael Pekala'] | 2018-05-28 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 8.74072373e-01 7.50555396e-02 1.63895488e-01 -2.63269901e-01
-6.25389993e-01 -1.18321776e+00 8.45667899e-01 1.24876231e-01
-6.65222526e-01 6.98931813e-01 -3.27558756e-01 -1.00369346e+00
-1.94034174e-01 -1.07845008e+00 -7.97047734e-01 -9.27111685e-01
-8.67657185e-01 -3.12720165e-02 9.18549001e-02 -3.72862756... | [5.676064491271973, 7.770176887512207] |
41218b9a-d322-4cee-933e-b2b5b9890efc | leveraging-key-information-modeling-to | 2210.04473 | null | https://arxiv.org/abs/2210.04473v1 | https://arxiv.org/pdf/2210.04473v1.pdf | Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning | Recent language generative models are mostly trained on large-scale datasets, while in some real scenarios, the training datasets are often expensive to obtain and would be small-scale. In this paper we investigate the challenging task of less-data constrained generation, especially when the generated news headlines ar... | ['Bo Ren', 'Shanshan Feng', 'Di Yin', 'Lingfeng Qiao', 'Zhuoxuan Jiang'] | 2022-10-10 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 1.77773163e-01 3.57222348e-01 -4.71236140e-01 -4.28759784e-01
-1.19324601e+00 -4.33317542e-01 9.48687971e-01 -1.67001769e-01
-1.30106702e-01 1.14221430e+00 7.08765686e-01 -2.28557989e-01
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4.96700138e-01 7.77272165e-01 -2.45142486e-02 -2.88456321... | [11.930093765258789, 8.977411270141602] |
0af1e54f-ae76-462d-8334-462c21928e24 | controlled-natural-languages-and-default | 1905.04422 | null | https://arxiv.org/abs/1905.04422v1 | https://arxiv.org/pdf/1905.04422v1.pdf | Controlled Natural Languages and Default Reasoning | Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base languages. They preserve the expressiveness and coherence of natural ... | ['Tiantian Gao'] | 2019-05-11 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [-1.25544325e-01 9.80326056e-01 -8.04233015e-01 -5.90541601e-01
2.49468014e-01 -8.24536502e-01 9.10557985e-01 2.62688845e-01
-2.52006233e-01 1.18077087e+00 3.17745984e-01 -6.45890772e-01
-5.04505992e-01 -1.26081169e+00 -4.44596499e-01 -3.12189106e-02
-3.52104492e-02 5.92060268e-01 5.76444268e-01 -9.08822119... | [8.787256240844727, 6.867894172668457] |
c90700b0-86df-4b06-babe-e5d5e7b2aba6 | seefar-vehicle-speed-estimation-and-flow | null | null | https://link.springer.com/chapter/10.1007/978-3-031-06433-3_24 | https://www.researchgate.net/publication/360607227_SeeFar_Vehicle_Speed_Estimation_and_Flow_Analysis_from_a_Moving_UAV | SeeFar: Vehicle Speed Estimation and Flow Analysis from a Moving UAV | Visual perception from drones has been largely investigated for Intelligent Traffic Monitoring System (ITMS) recently. In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone. SeeFar differs from previous works in three key ways: ... | ['Rita Cucchiara', 'Simone Calderara', 'Yao Lu', 'Xiaoliang Ma', 'Mang Ning'] | 2022-05-15 | null | null | null | iciap-2022-5 | ['vehicle-speed-estimation'] | ['computer-vision'] | [-5.33293068e-01 -5.46870351e-01 -2.29017407e-01 -3.18613440e-01
-3.63820642e-01 -4.33564425e-01 4.92939681e-01 -4.81852084e-01
-4.94443715e-01 5.71161926e-01 -2.01528415e-01 -4.15290028e-01
-5.32898493e-02 -8.53830874e-01 -7.07847059e-01 -6.67613804e-01
-5.99685758e-02 2.31879964e-01 6.33287847e-01 -1.83186218... | [7.990015029907227, -1.1080236434936523] |
24dea732-282f-4cfe-ae34-9c8be3ce6267 | the-adaptive-t-lasso-its-robustness-and | 2304.09310 | null | https://arxiv.org/abs/2304.09310v1 | https://arxiv.org/pdf/2304.09310v1.pdf | The Adaptive $τ$-Lasso: Its Robustness and Oracle Properties | This paper introduces a new regularized version of the robust $\tau$-regression estimator for analyzing high-dimensional data sets subject to gross contamination in the response variables and covariates. We call the resulting estimator adaptive $\tau$-Lasso that is robust to outliers and high-leverage points and simult... | ['Visa Koivunen', 'Emadaldin Mozafari-Majd'] | 2023-04-18 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.07701700e-02 -1.50247529e-01 -5.18581450e-01 -5.09423614e-01
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-6.26687884e-01 -7.25647628e-01 -9.50138330e-01 -9.76310611e-01
-5.31424880e-01 3.03984910e-01 -6.24115407e-01 1.92191511... | [7.249885082244873, 4.585195064544678] |
07dcfd7f-6287-4f21-8f9d-2037b7f687a2 | somesci-a-5-star-open-data-gold-standard | 2108.09070 | null | https://arxiv.org/abs/2108.09070v1 | https://arxiv.org/pdf/2108.09070v1.pdf | SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles | Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigatio... | ['Frank Krüger', 'Stefan Dietze', 'Felix Bensmann', 'David Schindler'] | 2021-08-20 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-2.61403695e-02 2.84562588e-01 -5.01587689e-01 1.08788386e-02
-6.81659400e-01 -1.00356376e+00 5.50837934e-01 1.15696037e+00
-4.50480819e-01 9.42702770e-01 2.44090036e-01 -6.56514049e-01
-2.77822137e-01 -5.84831178e-01 -9.88116086e-01 -3.11056852e-01
3.55831206e-01 1.09197572e-01 1.64618894e-01 2.30999768... | [9.041475296020508, 8.476205825805664] |
652b20a7-8e53-48dc-9cc5-e8abc3301508 | overcoming-limitations-of-mixture-density-1 | 1906.03631 | null | https://arxiv.org/abs/1906.03631v2 | https://arxiv.org/pdf/1906.03631v2.pdf | Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction | Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and multimodality of the future states is of great relevance. Existing approaches are rather limited in this regard and mostly yield a s... | ['Özgün Cicek', 'Osama Makansi', 'Thomas Brox', 'Eddy Ilg'] | 2019-06-09 | overcoming-limitations-of-mixture-density | http://openaccess.thecvf.com/content_CVPR_2019/html/Makansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Makansi_Overcoming_Limitations_of_Mixture_Density_Networks_A_Sampling_and_Fitting_CVPR_2019_paper.pdf | cvpr-2019-6 | ['probabilistic-deep-learning'] | ['computer-vision'] | [-4.73229364e-02 2.75073498e-01 -1.00471154e-01 -5.39974749e-01
-9.32641268e-01 -6.99384749e-01 8.40568006e-01 1.47338882e-01
-1.97367653e-01 9.62807715e-01 4.41144928e-02 -2.77433723e-01
-3.52825403e-01 -6.66160882e-01 -5.61028957e-01 -7.56019711e-01
-2.08080173e-01 8.37823749e-01 1.21156938e-01 -1.32384002... | [7.252106666564941, -0.5486910939216614] |
f0dfd728-a4e6-47aa-a5dd-8002ac5b4c4b | motion-artifact-reduction-in | 2109.02755 | null | https://arxiv.org/abs/2109.02755v1 | https://arxiv.org/pdf/2109.02755v1.pdf | Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection | Photoplethysmography (PPG) is a non-invasive and economical technique to extract vital signs of the human body. Although it has been widely used in consumer and research grade wrist devices to track a user's physiology, the PPG signal is very sensitive to motion which can corrupt the signal's quality. Existing Motion A... | ['Fengqing Zhu', 'George R. Wodicka', 'Craig J. Goergen', 'Stephan W. Wegerich', 'Mackenzie Tweardy', 'Runyu Mao'] | 2021-09-06 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.39033037e-01 -4.10447270e-01 2.21960500e-01 3.60492244e-02
-7.18399286e-01 -5.13877988e-01 -1.61220655e-02 -4.95588511e-01
-5.96963875e-02 8.26279104e-01 2.09880710e-01 9.87054780e-02
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-1.84124306e-01 -5.56985319e-01 1.51620328e-03 2.01344207... | [13.92375373840332, 2.9420199394226074] |
ca1a8749-b41b-469d-9e47-aacf55fef7fa | altiro3d-scene-representation-from-single | 2304.11161 | null | https://arxiv.org/abs/2304.11161v1 | https://arxiv.org/pdf/2304.11161v1.pdf | altiro3D: Scene representation from single image and novel view synthesis | We introduce altiro3D, a free extended library developed to represent reality starting from a given original RGB image or flat video. It allows to generate a light-field (or Native) image or video and get a realistic 3D experience. To synthesize N-number of virtual images and add them sequentially into a Quilt collage,... | ['L. Tenze', 'E. Canessa'] | 2023-04-02 | null | null | null | null | ['monocular-depth-estimation', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 4.46047038e-01 -1.19621344e-01 4.11589116e-01 -3.69509101e-01
-3.86155963e-01 -7.58803546e-01 3.72791231e-01 -7.20426202e-01
-3.18655223e-01 6.07357562e-01 -1.47244468e-01 -3.41367662e-01
5.59902847e-01 -8.01799655e-01 -7.38468409e-01 -5.57474971e-01
5.07082999e-01 2.51679599e-01 4.75382805e-01 -6.75410107... | [9.297365188598633, -2.663658618927002] |
6264efd7-f146-4c62-8f1d-7de52a9dd9e8 | density-ratio-estimation-and-neyman-pearson | 2302.10655 | null | https://arxiv.org/abs/2302.10655v1 | https://arxiv.org/pdf/2302.10655v1.pdf | Density Ratio Estimation and Neyman Pearson Classification with Missing Data | Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data. In this setting, we show that using standard DRE methods leads to biased results while our proposal (M-KLIEP), an adaptation of the popula... | ['Henry W J Reeve', 'Song Liu', 'Josh Givens'] | 2023-02-21 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 7.18302205e-02 -2.23225709e-02 -7.03283370e-01 -3.37531567e-01
-1.70181239e+00 -3.17518711e-01 4.04997617e-01 2.36183450e-01
-4.00888920e-01 1.37165356e+00 -1.45800903e-01 -6.18308783e-01
-5.89061260e-01 -7.64982879e-01 -8.95973027e-01 -8.23261261e-01
-3.25756401e-01 5.95315516e-01 -2.38711536e-01 3.82995039... | [7.656029224395752, 4.541189670562744] |
62323590-b0cf-46c2-b8b2-01bddeca29d4 | tafsir-dataset-a-novel-multi-task-benchmark | null | null | https://aclanthology.org/2022.coling-1.330 | https://aclanthology.org/2022.coling-1.330.pdf | Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature | Various historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research. In this work, we take the first data-driven steps towards this research line for Classical Arabic (CA) by addressing named entity recognition (NER) and topic modeling (TM) on the example of C... | ['Gemma Roig', 'Alexander Mehler', 'Ömer Özsoy', 'Carl Kruse', 'Misbahur Rehman', 'Rob van der Goot', 'Sajawel Ahmed'] | null | null | null | null | coling-2022-10 | ['topic-models'] | ['natural-language-processing'] | [-1.86193064e-01 -2.19920456e-01 -1.38316318e-01 -5.19807786e-02
-1.26111400e+00 -9.96217668e-01 7.47700274e-01 3.66155863e-01
-6.22532308e-01 5.48207402e-01 4.79647726e-01 -3.25057656e-01
-4.32920828e-02 -8.26281786e-01 -6.68570399e-01 -6.04728997e-01
-5.34472093e-02 8.84785056e-01 1.44351095e-01 -5.87841749... | [10.659993171691895, 10.018568992614746] |
67cf1c10-50f5-47dd-a203-16923de9b111 | audiogen-textually-guided-audio-generation | 2209.15352 | null | https://arxiv.org/abs/2209.15352v2 | https://arxiv.org/pdf/2209.15352v2.pdf | AudioGen: Textually Guided Audio Generation | We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates audio samples conditioned on text inputs. AudioGen operates on a learnt discrete audio representation. The task of text-to-audio generation p... | ['Yossi Adi', 'Yaniv Taigman', 'Devi Parikh', 'Jade Copet', 'Alexandre Défossez', 'Uriel Singer', 'Adam Polyak', 'Gabriel Synnaeve', 'Felix Kreuk'] | 2022-09-30 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.98449361e-01 -1.39565483e-01 1.94672585e-01 -1.95712388e-01
-1.43336320e+00 -7.19648004e-01 5.07142782e-01 -5.16628996e-02
-1.30190805e-01 7.00594008e-01 4.94104117e-01 -1.74089372e-02
1.86561555e-01 -5.28104842e-01 -8.97646785e-01 -6.34781241e-01
-1.21223353e-01 2.60988891e-01 -2.09914237e-01 9.35147777... | [15.358619689941406, 5.583165645599365] |
4afa69ca-ddfe-4ae7-bfbb-65e5266c55bd | on-the-expressivity-of-persistent-homology-in | 2302.09826 | null | https://arxiv.org/abs/2302.09826v2 | https://arxiv.org/pdf/2302.09826v2.pdf | On the Expressivity of Persistent Homology in Graph Learning | Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features, such as cycles of arbitrary length, in combination with multi-scale topological d... | ['Bastian Rieck'] | 2023-02-20 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 1.93987504e-01 3.80673319e-01 -4.40230668e-01 3.38153988e-02
-2.10230440e-01 -6.41657770e-01 7.83739448e-01 8.50002348e-01
-1.23350117e-02 7.61339247e-01 -3.24096680e-02 -6.34171784e-01
-7.57319033e-01 -1.11832619e+00 -3.45207691e-01 -7.20855236e-01
-9.28265572e-01 5.20500720e-01 2.43770659e-01 -4.79175389... | [7.018476963043213, 5.844665050506592] |
95f57baa-0d97-4ece-ae4a-97aada370003 | temporal-perceiving-video-language-pre | 2301.07463 | null | https://arxiv.org/abs/2301.07463v1 | https://arxiv.org/pdf/2301.07463v1.pdf | Temporal Perceiving Video-Language Pre-training | Video-Language Pre-training models have recently significantly improved various multi-modal downstream tasks. Previous dominant works mainly adopt contrastive learning to achieve global feature alignment across modalities. However, the local associations between videos and texts are not modeled, restricting the pre-tra... | ['Yi Yang', 'Jiashi Feng', 'Linchao Zhu', 'Jingjia Huang', 'Heng Wang', 'Xiaojie Jin', 'Fan Ma'] | 2023-01-18 | null | null | null | null | ['moment-retrieval', 'video-question-answering', 'video-retrieval', 'action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.93716580e-01 -5.60904562e-01 -7.29501605e-01 -3.40554625e-01
-1.14917696e+00 -6.49266720e-01 8.45632255e-01 -1.83779057e-02
-4.57546532e-01 2.36129671e-01 6.27456963e-01 2.39936352e-01
3.08534205e-02 -6.43954426e-02 -9.20609415e-01 -5.95505655e-01
-5.52045852e-02 1.77113324e-01 3.25027466e-01 2.30968714... | [10.066400527954102, 0.7738161087036133] |
4ca6e97c-f91c-4361-b4f8-c055cc8f19f4 | web-scale-academic-name-disambiguation-the | 2302.11848 | null | https://arxiv.org/abs/2302.11848v2 | https://arxiv.org/pdf/2302.11848v2.pdf | Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit | Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers. For example, on AMiner, an online academic search platform, about 10% of names own more than 100 authors. Such real-world challenging cases have not been effectively... | ['Jie Tang', 'Yuxiao Dong', 'Xiaoyan Li', 'Yuqing Cheng', 'Tianyi Han', 'Fanjin Zhang', 'Jing Zhang', 'Bo Chen'] | 2023-02-23 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-7.50662029e-01 -2.82638878e-01 -2.81044155e-01 -1.48157224e-01
-9.78127956e-01 -9.66249347e-01 8.10253620e-01 3.33503574e-01
-3.68345827e-01 9.70358849e-01 -8.16735327e-02 -4.20202136e-01
-3.23919058e-01 -8.05493772e-01 -5.51467359e-01 -3.16280335e-01
1.36486307e-01 1.01700485e+00 2.93129552e-02 -1.89788014... | [9.507208824157715, 8.210536003112793] |
f54e797b-fd07-4853-81eb-355676fba6ed | multi-granularity-interaction-simulation-for | 2303.13399 | null | https://arxiv.org/abs/2303.13399v1 | https://arxiv.org/pdf/2303.13399v1.pdf | Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation | Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis. Typically, massive and expansive pixel-level annotations are spent to train deep models by object-oriented interactions... | ['Jie Chen', 'Chang Liu', 'Li Yuan', 'Xiangyang Ji', 'Peng Jin', 'Zesen Cheng', 'Zhennan Wang', 'Yian Zhao', 'Kehan Li'] | 2023-03-23 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 3.64267379e-01 7.34306097e-01 -2.24261537e-01 -5.22455096e-01
-7.04227686e-01 -4.29795355e-01 3.71629953e-01 5.16000092e-01
-2.75746942e-01 5.74835539e-01 -1.19365072e-02 -1.37571871e-01
-4.45566960e-02 -8.81328762e-01 -1.01251042e+00 -7.54551828e-01
-1.11473233e-01 9.57651675e-01 7.19178140e-01 -7.91038424... | [9.581313133239746, 0.18329904973506927] |
73f32574-af36-4a29-afce-37416e381cf3 | unsupervised-shot-boundary-detection-for | 2110.09067 | null | https://arxiv.org/abs/2110.09067v1 | https://arxiv.org/pdf/2110.09067v1.pdf | Unsupervised Shot Boundary Detection for Temporal Segmentation of Long Capsule Endoscopy Videos | Physicians use Capsule Endoscopy (CE) as a non-invasive and non-surgical procedure to examine the entire gastrointestinal (GI) tract for diseases and abnormalities. A single CE examination could last between 8 to 11 hours generating up to 80,000 frames which is compiled as a video. Physicians have to review and analyze... | ['Donald Brown', 'Sana Syed', 'Michael Porter', 'Andrew Copland', 'James Jablonski', 'Philip Fernandes', 'Sodiq Adewole'] | 2021-10-18 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.88400835e-01 -2.82764919e-02 -7.09782541e-02 8.01787674e-02
-6.68774068e-01 -8.56037498e-01 -6.10614344e-02 4.43965942e-01
-4.91733283e-01 4.35502619e-01 -9.71278995e-02 -3.51377636e-01
-1.35847792e-01 -5.09911418e-01 -5.41802704e-01 -6.92254126e-01
-6.29477084e-01 2.69004758e-02 2.68142015e-01 3.78209978... | [14.035049438476562, -3.1752142906188965] |
eacf90eb-a781-46a3-8ec5-b93e6a8fd7a1 | learning-temporal-consistency-for-low-light | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Learning_Temporal_Consistency_for_Low_Light_Video_Enhancement_From_Single_CVPR_2021_paper.pdf | Learning Temporal Consistency for Low Light Video Enhancement From Single Images | Single image low light enhancement is an important task and it has many practical applications. Most existing methods adopt a single image approach. Although their performance is satisfying on a static single image, we found, however, they suffer serious temporal instability when handling low light videos. We notic... | ['Ying Fu', 'ShaoDi You', 'Yu Li', 'Fan Zhang'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['video-enhancement'] | ['computer-vision'] | [ 3.33348960e-01 -7.91543245e-01 -8.04343969e-02 -2.77621269e-01
-5.82245052e-01 -4.23407733e-01 2.80937076e-01 -6.78799808e-01
-4.14054632e-01 8.03302169e-01 -2.83015966e-02 6.24139979e-02
-6.95510507e-02 -5.03342032e-01 -7.16362000e-01 -1.00043619e+00
1.90911978e-01 -3.12977910e-01 7.06097782e-01 -2.08274305... | [10.702736854553223, -2.0637147426605225] |
13416882-b858-4dbd-aca3-c54e0ad58c77 | karaoker-alignment-free-singing-voice | 2204.04127 | null | https://arxiv.org/abs/2204.04127v2 | https://arxiv.org/pdf/2204.04127v2.pdf | Karaoker: Alignment-free singing voice synthesis with speech training data | Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a multispeaker Tacotron-based model conditioned on voice characteristic features that is traine... | ['Aimilios Chalamandaris', 'Pirros Tsiakoulis', 'Gunu Jho', 'June Sig Sung', 'Konstantinos Markopoulos', 'Georgios Vamvoukakis', 'Nikolaos Ellinas', 'Panos Kakoulidis'] | 2022-04-08 | null | null | null | null | ['speaker-identification', 'singing-voice-synthesis'] | ['speech', 'speech'] | [ 1.95100769e-01 -7.51683936e-02 3.35137039e-01 -3.71919870e-01
-1.31090689e+00 -8.70017886e-01 3.74530911e-01 -5.44009984e-01
-1.62877873e-01 3.70648265e-01 3.71576339e-01 2.02385746e-02
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1.93324015e-01 4.03314203e-01 -2.87727892e-01 -1.27634585... | [15.503649711608887, 6.115906238555908] |
e88bc2e6-9675-4957-b03e-9eb7b4cba894 | deepfakebench-a-comprehensive-benchmark-of | 2307.01426 | null | https://arxiv.org/abs/2307.01426v1 | https://arxiv.org/pdf/2307.01426v1.pdf | DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection | A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in... | ['Baoyuan Wu', 'Siwei Lyu', 'Xinhang Yuan', 'Yong Zhang', 'Zhiyuan Yan'] | 2023-07-04 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'management'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [-2.83750176e-01 -6.14364922e-01 -2.18755335e-01 -2.65242755e-01
-1.00127316e+00 -1.01603460e+00 4.85994965e-01 2.81232268e-01
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-8.29297975e-02 2.93104410e-01 7.20105827e-01 -4.22897898... | [8.924393653869629, 0.16087834537029266] |
1a00b5d1-800a-4ebd-a9d3-be11903fec6f | fine-grained-coordinated-cross-lingual-text | null | null | https://aclanthology.org/D18-1271 | https://aclanthology.org/D18-1271.pdf | Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition | This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the n... | ['Qing Dou', 'Heng Ji', 'Furu Wei', 'Tao Ge', 'Ming Zhou', 'Lei Cui', 'Baobao Chang', 'Zhifang Sui'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['decipherment'] | ['natural-language-processing'] | [ 9.06683058e-02 -2.62491405e-01 -7.49712884e-01 -2.09561631e-01
-7.29331613e-01 -6.46959960e-01 7.15675890e-01 6.24221146e-01
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-3.53736013e-01 1.03852320e+00 1.80244431e-01 -6.48386538... | [10.683725357055664, 9.140829086303711] |
14c15a9a-1366-4fbe-bb99-5d35c11e23ff | svma-a-gan-based-model-for-monocular-3d-human | 2106.05616 | null | https://arxiv.org/abs/2106.05616v4 | https://arxiv.org/pdf/2106.05616v4.pdf | SVMAC: Unsupervised 3D Human Pose Estimation from a Single Image with Single-view-multi-angle Consistency | Recovering 3D human pose from 2D joints is still a challenging problem, especially without any 3D annotation, video information, or multi-view information. In this paper, we present an unsupervised GAN-based model consisting of multiple weight-sharing generators to estimate a 3D human pose from a single image without 3... | ['Yongqi Sun', 'Cheng Sun', 'Jiahui Zhu', 'Yicheng Deng'] | 2021-06-10 | null | null | null | null | ['monocular-3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.66930869e-01 2.54384845e-01 -6.28801882e-02 -3.32665235e-01
-6.12150848e-01 -3.41427505e-01 3.04714978e-01 -7.72651136e-01
-4.70636964e-01 4.75494355e-01 2.35240966e-01 5.89517057e-01
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2.39374086e-01 1.06783044e+00 3.16056281e-01 -2.43884504... | [6.998142242431641, -1.0028839111328125] |
9b330df0-0046-4bda-b0b8-aa56c9054d49 | lung-infection-quantification-of-covid-19-in | 2003.04655 | null | https://arxiv.org/abs/2003.04655v3 | https://arxiv.org/pdf/2003.04655v3.pdf | Lung Infection Quantification of COVID-19 in CT Images with Deep Learning | CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients... | ['Yuxin Shi', 'Yaozong Gao', 'Nannan Shi', 'Miaofei Han', 'Dinggang Shen', 'Weiya Shi', 'Jun Wang', 'Fei Shan', 'Zhong Xue'] | 2020-03-10 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [-4.72334959e-02 -3.85808110e-01 -8.56628194e-02 8.70669540e-03
-4.77847517e-01 -5.30119181e-01 1.05834104e-01 3.30173075e-01
-7.14132726e-01 7.03475833e-01 -3.46419036e-01 -6.39483213e-01
-8.75285789e-02 -5.39385438e-01 -1.09457888e-01 -7.96676040e-01
-1.43207774e-01 1.38020563e+00 4.09354329e-01 5.76910973... | [15.281414985656738, -2.029709815979004] |
b9207e72-0391-45f8-a93b-174c67751949 | gmf-general-multimodal-fusion-framework-for | 2211.00207 | null | https://arxiv.org/abs/2211.00207v1 | https://arxiv.org/pdf/2211.00207v1.pdf | GMF: General Multimodal Fusion Framework for Correspondence Outlier Rejection | Rejecting correspondence outliers enables to boost the correspondence quality, which is a critical step in achieving high point cloud registration accuracy. The current state-of-the-art correspondence outlier rejection methods only utilize the structure features of the correspondences. However, texture information is c... | ['Xiaowei Zhao', 'Yuming Fang', 'Yifan Zuo', 'Wentao Qu', 'Xiaoshui Huang'] | 2022-11-01 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-3.38146716e-01 -3.87477070e-01 4.01895717e-02 -4.54147458e-01
-1.01643181e+00 -8.33309814e-02 5.23703277e-01 1.63898960e-01
-3.01963836e-01 1.46361291e-01 1.64632082e-01 9.51366648e-02
3.47980335e-02 -6.13095522e-01 -1.01390922e+00 -6.00093067e-01
3.24993223e-01 3.21603686e-01 2.63875365e-01 -2.73663908... | [7.717297554016113, -3.1351535320281982] |
24eb379b-980c-4734-95a5-7e850c3b371c | local-differential-privacy-for-sequential | 2301.00561 | null | https://arxiv.org/abs/2301.00561v1 | https://arxiv.org/pdf/2301.00561v1.pdf | Local Differential Privacy for Sequential Decision Making in a Changing Environment | We study the problem of preserving privacy while still providing high utility in sequential decision making scenarios in a changing environment. We consider abruptly changing environment: the environment remains constant during periods and it changes at unknown time instants. To formulate this problem, we propose a var... | ['Pratik Gajane'] | 2023-01-02 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.76090467e-01 7.28960931e-02 -4.74557817e-01 -3.13450843e-01
-1.13180923e+00 -1.13815188e+00 5.09378240e-02 1.88255548e-01
-7.21755683e-01 1.23510766e+00 -8.56138882e-04 -4.68989074e-01
-4.84580338e-01 -8.61487210e-01 -1.01555765e+00 -9.48773921e-01
-1.34004757e-01 4.41365898e-01 -2.17018779e-02 4.36000563... | [4.540175914764404, 3.421710968017578] |
2233c909-7fb1-43fb-bace-8060ffcbe032 | computer-vision-toolkit-for-non-invasive | 2005.06037 | null | https://arxiv.org/abs/2005.06037v1 | https://arxiv.org/pdf/2005.06037v1.pdf | Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts | Digitization has led to smart, connected technologies be an integral part of businesses, governments and communities. For manufacturing digitization, there has been active research and development with a focus on Cloud Manufacturing (CM) and the Industrial Internet of Things (IIoT). This work presents a computer vision... | ['David A. Wickelhaus', 'Sam Anand', 'Aditya M. Deshpande', 'Anil Kumar Telikicherla', 'Vinay Jakkali', 'Manish Kumar'] | 2020-05-12 | null | null | null | null | ['manufacturing-quality-control', 'curved-text-detection', 'contour-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.14680275e-01 -1.22078560e-01 4.15761828e-01 -5.32804467e-02
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-6.33490741e-01 -1.04102671e+00 -1.62380084e-01 -2.79437035e-01
1.67870402e-01 4.61253911e-01 -7.69513175e-02 -1.08528592... | [6.912036895751953, 2.2587084770202637] |
2a8e87eb-b52a-4234-a93e-3de3d2c45fa9 | energy-loss-prediction-in-iot-energy-services | 2305.10238 | null | https://arxiv.org/abs/2305.10238v1 | https://arxiv.org/pdf/2305.10238v1.pdf | Energy Loss Prediction in IoT Energy Services | We propose a novel Energy Loss Prediction(ELP) framework that estimates the energy loss in sharing crowdsourced energy services. Crowdsourcing wireless energy services is a novel and convenient solution to enable the ubiquitous charging of nearby IoT devices. Therefore, capturing the wireless energy sharing loss is ess... | ['Athman Bouguettaya', 'Abdallah Lakhdari', 'Amani Abusafia', 'Pengwei Yang'] | 2023-05-16 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [-4.01017547e-01 -2.80626953e-01 -3.77405703e-01 -4.22009856e-01
-6.56106174e-01 -4.09117073e-01 1.10948719e-01 1.93726182e-01
-3.19487154e-01 7.35187590e-01 1.81034565e-01 -2.44049411e-02
6.44447133e-02 -9.20340955e-01 -4.77386832e-01 -8.60467911e-01
-6.20798096e-02 1.34840995e-01 2.25369856e-01 -9.16918963... | [5.962747573852539, 1.8193378448486328] |
fa0174b2-5893-4763-9424-148a962c7ce9 | slice-connection-clustering-algorithm-for | 2203.03830 | null | https://arxiv.org/abs/2203.03830v1 | https://arxiv.org/pdf/2203.03830v1.pdf | Slice-Connection Clustering Algorithm for Tree Roots Recognition in Noisy 3D GPR Data | 3D mapping of tree roots is a popular ground-penetrating radar (GPR) application. In real field tests, the recognition of tree roots suffers due to noisey reflection patterns from subsurface targets that are not of interest, such as rocks, cavities, soil unevenness, etc. A Slice-Connection Clustering Algorithm (SCC) is... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Lai Fern Ow', 'Yee Hui Lee', 'Wenhao Luo'] | 2022-03-08 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.86798263e-01 -2.01465130e-01 4.70774472e-01 -1.56973064e-01
-4.18004811e-01 -2.16287121e-01 1.52388930e-01 3.78527716e-02
2.53030509e-01 2.76166499e-01 -9.92031693e-02 -2.53518015e-01
-4.44588095e-01 -1.01046371e+00 6.15730584e-02 -9.89921451e-01
-3.40825617e-01 6.05600178e-01 5.75564802e-01 -2.69617606... | [6.831818580627441, 1.3767892122268677] |
f00ce65e-f5c5-47cb-9908-c0b316ff4b8e | modeling-video-as-stochastic-processes-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Modeling_Video_As_Stochastic_Processes_for_Fine-Grained_Video_Representation_Learning_CVPR_2023_paper.pdf | Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning | A meaningful video is semantically coherent and changes smoothly. However, most existing fine-grained video representation learning methods learn frame-wise features by aligning frames across videos or exploring relevance between multiple views, neglecting the inherent dynamic process of each video. In this paper, ... | ['Bing Su', 'Qi Zheng', 'Daqing Liu', 'Heng Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 8.84885713e-02 -3.04030150e-01 -3.99988621e-01 -2.84648210e-01
-7.03413904e-01 -5.19491434e-01 8.51853192e-01 -5.79592511e-02
-4.97846194e-02 4.16216224e-01 3.97282302e-01 2.56096095e-01
-2.36651748e-01 -5.49716890e-01 -8.52393866e-01 -9.31992114e-01
-1.28928319e-01 2.71717131e-01 1.97075292e-01 4.01985884... | [8.708306312561035, 0.7351404428482056] |
98bdfbfc-b506-4223-9049-01d983ed255b | meranet-facial-micro-expression-recognition | 2012.04581 | null | https://arxiv.org/abs/2012.04581v2 | https://arxiv.org/pdf/2012.04581v2.pdf | MERANet: Facial Micro-Expression Recognition using 3D Residual Attention Network | Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still significant scope for improvements in micro-expression recognition techniques. The presence of micro... | ['Shiv Ram Dubey', 'Snehasis Mukherjee', 'Sai Prasanna Teja Reddy', 'Viswanatha Reddy Gajjala'] | 2020-12-07 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-4.30212431e-02 -3.27599347e-01 1.07557066e-01 -5.71609914e-01
-4.20608461e-01 -1.49464505e-02 4.15973961e-01 -3.24365258e-01
-4.34838176e-01 4.10189837e-01 -5.38539002e-03 4.64322001e-01
5.79710640e-02 -5.00026226e-01 -5.67537665e-01 -1.02911389e+00
-7.60325417e-02 -3.28781247e-01 -4.73354071e-01 -2.19678462... | [13.626602172851562, 1.726475715637207] |
57510f70-c682-4cf2-a1dc-3b1bd09716ee | neuroclip-neuromorphic-data-understanding-by | 2306.12073 | null | https://arxiv.org/abs/2306.12073v1 | https://arxiv.org/pdf/2306.12073v1.pdf | NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN | Recently, the neuromorphic vision sensor has received more and more interest. However, the neuromorphic data consists of asynchronous event spikes, which is not natural and difficult to construct a benchmark, thus limiting the neuromorphic data understanding for "unseen" objects by deep learning. Zero-shot and few-shot... | ['Yuanpei Chen', 'Yufei Guo'] | 2023-06-21 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [ 1.88568458e-01 -4.13033068e-01 4.41329747e-01 -4.17390674e-01
-4.69463468e-01 -3.61239761e-01 6.48219585e-01 -2.38025427e-01
-5.41609228e-01 6.52958393e-01 -4.86774184e-02 1.48330957e-01
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1.54841572e-01 -3.18443552e-02 6.26670122e-01 1.28416300... | [8.258538246154785, 2.293597936630249] |
f62c29d4-06be-4651-9392-1cdcab10a098 | mgpt-few-shot-learners-go-multilingual | 2204.07580 | null | https://arxiv.org/abs/2204.07580v1 | https://arxiv.org/pdf/2204.07580v1.pdf | mGPT: Few-Shot Learners Go Multilingual | Recent studies report that autoregressive language models can successfully solve many NLP tasks via zero- and few-shot learning paradigms, which opens up new possibilities for using the pre-trained language models. This paper introduces two autoregressive GPT-like models with 1.3 billion and 13 billion parameters train... | ['Tatiana Shavrina', 'Anastasia Kozlova', 'Vladislav Mikhailov', 'Maria Tikhonova', 'Alena Fenogenova', 'Oleh Shliazhko'] | 2022-04-15 | null | null | null | null | ['cross-lingual-natural-language-inference', 'few-shot-ner'] | ['natural-language-processing', 'natural-language-processing'] | [-4.57495511e-01 3.30574125e-01 -5.03251016e-01 -2.66785175e-01
-1.21178615e+00 -6.38482571e-01 1.00773883e+00 -3.15677375e-01
-6.43283904e-01 9.76618230e-01 2.63092399e-01 -7.01000750e-01
2.41383333e-02 -6.97025418e-01 -7.46742249e-01 -7.19761074e-01
-1.82184994e-01 1.38996887e+00 -2.77579844e-01 -4.21394587... | [10.924508094787598, 9.707917213439941] |
9563f369-b7d7-4948-8471-201578d63cf2 | bayesian-numerical-integration-with-neural | 2305.13248 | null | https://arxiv.org/abs/2305.13248v1 | https://arxiv.org/pdf/2305.13248v1.pdf | Bayesian Numerical Integration with Neural Networks | Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the integrand, and can quantify uncertainty over estimates of an integral. However, the most popular algorithm in this class, Bayesian quadrature,... | ['François-Xavier Briol', 'Philipp Hennig', 'Michael Tiemann', 'Katharina Ott'] | 2023-05-22 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.97635251e-01 -2.44405925e-01 1.50456101e-01 1.91071201e-02
-6.82926476e-01 -3.96451801e-01 4.36657220e-01 1.58593014e-01
-2.32162893e-01 8.36983800e-01 -2.03175977e-01 -4.40375358e-01
-5.48099518e-01 -1.00205982e+00 -6.69150352e-01 -9.79378462e-01
-2.69011617e-01 4.78696674e-01 9.67482775e-02 7.52061978... | [6.949198246002197, 3.7024734020233154] |
dc6bfd7f-8878-46b5-bf6c-161f3ba7b12c | efficient-large-scale-audio-tagging-via | 2211.04772 | null | https://arxiv.org/abs/2211.04772v3 | https://arxiv.org/pdf/2211.04772v3.pdf | Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation | Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computation... | ['Gerhard Widmer', 'Khaled Koutini', 'Florian Schmid'] | 2022-11-09 | null | null | null | null | ['audio-tagging'] | ['audio'] | [-9.37730446e-02 3.06292810e-02 3.90320388e-03 -2.78085560e-01
-9.43506956e-01 -6.24967933e-01 5.19908331e-02 1.22795105e-01
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-4.45011050e-01 3.52256268e-01 5.07546961e-01 -2.39295855... | [15.070103645324707, 5.222271919250488] |
2cc25fd5-ecf8-4421-8a91-15ebc11507ce | saliencycut-augmenting-plausible-anomalies | 2306.08366 | null | https://arxiv.org/abs/2306.08366v1 | https://arxiv.org/pdf/2306.08366v1.pdf | SaliencyCut: Augmenting Plausible Anomalies for Open-set Fine-Grained Anomaly Detection | Open-set fine-grained anomaly detection is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models. Re... | ['Kaizhu Huang', 'Chao Huang', 'Qiu-Feng Wang', 'Xi Yang', 'Yijie Hu', 'Jianan Ye'] | 2023-06-14 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 4.14003521e-01 6.98365793e-02 1.11622401e-02 -5.98767817e-01
-8.20518136e-01 -1.95254564e-01 5.73509812e-01 2.45874047e-01
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3.32971737e-02 -6.30252182e-01 -8.04505110e-01 -6.72635317e-01
-2.40931824e-01 4.28161383e-01 3.14018875e-01 -9.54851955... | [7.62280797958374, 2.334548234939575] |
99e95721-7f91-4e1d-8e87-4b79f8c05272 | rethinking-the-two-stage-framework-for | 2112.05375 | null | https://arxiv.org/abs/2112.05375v1 | https://arxiv.org/pdf/2112.05375v1.pdf | Rethinking the Two-Stage Framework for Grounded Situation Recognition | Grounded Situation Recognition (GSR), i.e., recognizing the salient activity (or verb) category in an image (e.g., buying) and detecting all corresponding semantic roles (e.g., agent and goods), is an essential step towards "human-like" event understanding. Since each verb is associated with a specific set of semantic ... | ['Tat-Seng Chua', 'Xiaoyu Yue', 'Wei Ji', 'Long Chen', 'Meng Wei'] | 2021-12-10 | null | null | null | null | ['grounded-situation-recognition', 'situation-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.03590190e-01 -4.38855179e-02 -3.37422609e-01 -4.98913914e-01
-7.97357023e-01 -2.90652335e-01 6.61564887e-01 2.47818157e-01
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-1.40549153e-01 -8.00053358e-01 -6.49271548e-01 -7.15461731e-01
1.84766531e-01 3.46262962e-01 2.73602396e-01 -4.35557187... | [10.012117385864258, 1.227681279182434] |
681755f4-e541-4579-9110-1f892aa6585f | mgtab-a-multi-relational-graph-based-twitter | 2301.01123 | null | https://arxiv.org/abs/2301.01123v2 | https://arxiv.org/pdf/2301.01123v2.pdf | MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark | The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these ... | ['Bin Yan', 'Linyuan Wang', 'Baojie Song', 'Jie Yang', 'Shuai Yang', 'Jian Chen', 'Kai Qiao', 'Shuhao Shi'] | 2023-01-03 | null | null | null | null | ['twitter-bot-detection', 'stance-detection'] | ['miscellaneous', 'natural-language-processing'] | [-2.71460712e-01 -2.15009786e-02 -9.19604897e-01 -1.16370723e-01
-4.99533653e-01 -6.11297965e-01 5.16432762e-01 6.17483556e-01
-2.53746688e-01 7.86544323e-01 4.16675329e-01 -3.20567757e-01
1.34799063e-01 -1.20316243e+00 5.12230173e-02 -2.88763866e-02
-1.01568900e-01 6.40609264e-01 5.01345396e-01 -3.34855497... | [8.152135848999023, 10.084451675415039] |
d517b126-a21f-4f2f-a04e-9424cbd9d9bf | native-tongues-lost-and-found-resources-and | null | null | https://aclanthology.info/papers/C12-1158/c12-1158 | https://www.aclweb.org/anthology/C12-1158v2 | Native Tongues, Lost and Found: Resources and Empirical Evaluations in Native Language Identification | null | ['Martin Chodorow', 'Joel Tetreault', 'Daniel Blanchard', 'Aoife Cahill'] | 2012-12-01 | native-tongues-lost-and-found-resources-and-1 | https://aclanthology.org/C12-1158 | https://aclanthology.org/C12-1158.pdf | coling-2012-12 | ['native-language-identification'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.539218783378601, 15.869214057922363] |
5242d735-cedb-4991-956d-e868cfe2a8ea | spatial-latent-representations-in-generative | 2303.14552 | null | https://arxiv.org/abs/2303.14552v1 | https://arxiv.org/pdf/2303.14552v1.pdf | Spatial Latent Representations in Generative Adversarial Networks for Image Generation | In the majority of GAN architectures, the latent space is defined as a set of vectors of given dimensionality. Such representations are not easily interpretable and do not capture spatial information of image content directly. In this work, we define a family of spatial latent spaces for StyleGAN2, capable of capturing... | ['Maciej Sypetkowski'] | 2023-03-25 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 4.53040183e-01 4.03243840e-01 8.99922177e-02 -3.71656984e-01
-5.54815650e-01 -8.57851088e-01 9.09902692e-01 -1.90113902e-01
-2.32923061e-01 6.55038297e-01 4.00688857e-01 -1.57941654e-01
1.35898530e-01 -1.27572668e+00 -1.02603126e+00 -7.56161273e-01
1.23223979e-02 4.80771214e-01 2.56580152e-02 -1.89982265... | [11.657167434692383, -0.4541776180267334] |
e656c687-4ca6-4dea-a2c3-3b707f0c1019 | lq-optimal-control-for-power-tracking | 2211.07690 | null | https://arxiv.org/abs/2211.07690v1 | https://arxiv.org/pdf/2211.07690v1.pdf | LQ Optimal Control for Power Tracking Operation of Wind Turbines | In this paper, an approach for active power control of individual wind turbines is presented. State-of-the-art controllers typically employ separate control loops for torque and pitch control. In contrast, we use a multivariable control approach. In detail, active power control is achieved by using reference trajectori... | ['Christian A. Hans', 'Jörg Raisch', 'Arnold Sterle', 'Aaron Grapentin'] | 2022-11-14 | null | null | null | null | ['pitch-control'] | ['audio'] | [-1.51736245e-01 6.72512054e-01 -2.45826051e-01 5.82051873e-01
-3.14071327e-02 -7.56234348e-01 6.85173750e-01 4.45279658e-01
-6.35218322e-02 9.11521614e-01 -4.47727919e-01 -2.26652220e-01
-5.79968154e-01 -7.29782581e-01 -3.58921170e-01 -9.02193546e-01
2.60794554e-02 1.56929553e-01 2.95255959e-01 -3.73746097... | [5.427847385406494, 2.5000016689300537] |
b8b600c8-3394-42c0-a3f0-e0d78f422e34 | skin-feature-point-tracking-using-deep | 2112.14159 | null | https://arxiv.org/abs/2112.14159v2 | https://arxiv.org/pdf/2112.14159v2.pdf | Skin feature point tracking using deep feature encodings | Facial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson's disease. Traditional computer visi... | ['Torbjörn E. M. Nordling', 'Jose Ramon Chang'] | 2021-12-28 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 1.74473614e-01 -2.44029284e-01 1.66572690e-01 -3.26762348e-01
-6.72468364e-01 -4.14496809e-01 2.77194738e-01 -1.49741590e-01
-5.96437871e-01 5.10052681e-01 -6.92706108e-02 3.12368870e-01
9.99657959e-02 -4.83295351e-01 -5.48872709e-01 -9.64800596e-01
-3.83143216e-01 -1.81022473e-02 7.23756701e-02 2.99921297... | [13.665066719055176, 2.0523951053619385] |
e5cac4ed-1fd9-4337-9bb6-2488f2d46179 | end-to-end-semi-supervised-learning-for-video | 2203.04251 | null | https://arxiv.org/abs/2203.04251v3 | https://arxiv.org/pdf/2203.04251v3.pdf | End-to-End Semi-Supervised Learning for Video Action Detection | In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-... | ['Yogesh Singh Rawat', 'Akash Kumar'] | 2022-03-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-classification-shift-consistency'] | ['computer-vision'] | [ 2.69884080e-01 -2.01653644e-01 -5.33163011e-01 -1.79409996e-01
-8.97566378e-01 -4.51156348e-01 3.71747881e-01 -7.22642522e-03
-6.39506638e-01 7.40290821e-01 1.14895649e-01 1.61451429e-01
7.82897770e-02 -9.37990248e-02 -7.37945557e-01 -6.50575876e-01
-1.44313321e-01 -1.27531722e-01 9.76801038e-01 3.21367979... | [8.535909652709961, 0.5111525058746338] |
b9550d68-21f6-4287-80f4-679e875528a5 | esad-end-to-end-deep-semi-supervised-anomaly | 2012.04905 | null | https://arxiv.org/abs/2012.04905v3 | https://arxiv.org/pdf/2012.04905v3.pdf | ESAD: End-to-end Deep Semi-supervised Anomaly Detection | This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new KL-divergence based objective function for semi-supervised anomaly detection, and show that two factors: the mutual information between the... | ['Peisen Zhao', 'Qi Tian', 'Yan-Feng Wang', 'Ya zhang', 'Fei Ye', 'Chaoqin Huang'] | 2020-12-09 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 2.11569846e-01 4.33082700e-01 -1.92013010e-01 -6.08288646e-01
-7.49150574e-01 -1.10059194e-01 3.26278478e-01 4.39529419e-01
-2.29177117e-01 3.24900329e-01 8.21422860e-02 -4.01373468e-02
-5.79128694e-03 -3.51210088e-01 -3.39419574e-01 -8.51469338e-01
-3.83316934e-01 4.47518855e-01 -6.95267469e-02 1.89151898... | [7.654982089996338, 2.308475971221924] |
7c8dd6d2-2d6e-4d1c-9301-45a66dced59e | deep-structured-feature-networks-for-table | 2102.10287 | null | https://arxiv.org/abs/2102.10287v2 | https://arxiv.org/pdf/2102.10287v2.pdf | Deep Structured Feature Networks for Table Detection and Tabular Data Extraction from Scanned Financial Document Images | Automatic table detection in PDF documents has achieved a great success but tabular data extraction are still challenging due to the integrity and noise issues in detected table areas. The accurate data extraction is extremely crucial in finance area. Inspired by this, the aim of this research is proposing an automated... | ['Josiah Poon', 'Wanying Zhou', 'Yiwen Gong', 'Mengting Wu', 'Siwen Luo'] | 2021-02-20 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 1.24692947e-01 -3.15467864e-02 1.22771725e-01 -3.06825101e-01
-4.05069798e-01 -8.68731201e-01 1.78944856e-01 5.43305516e-01
-8.92804097e-03 4.94867831e-01 1.44112974e-01 -3.35008830e-01
-3.45977187e-01 -1.12530637e+00 -7.13297248e-01 -1.48293123e-01
-9.83801410e-02 5.48105896e-01 4.18039560e-01 -1.91829577... | [11.701582908630371, 2.940671920776367] |
f68da2de-ddf0-4366-966d-a3589ecbd4c2 | incorporating-physical-constraints-in-a-deep | 1912.12976 | null | https://arxiv.org/abs/1912.12976v4 | https://arxiv.org/pdf/1912.12976v4.pdf | Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems | Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a data-based, probablistic perspective that enables the quantification of predictive u... | ['Phaedon-Stelios Koutsourelakis', 'Sebastian Kaltenbach'] | 2019-12-30 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 1.02547137e-02 -1.02078043e-01 2.96528757e-01 1.34228636e-02
-4.42928851e-01 -2.36963376e-01 9.90171671e-01 2.97427773e-01
-5.51490784e-01 1.19317687e+00 -3.42392951e-01 -1.04913421e-01
-7.46544659e-01 -9.55906749e-01 -5.59776843e-01 -1.19966066e+00
-7.27491900e-02 1.03600729e+00 1.97378024e-02 -1.21298343... | [6.421759605407715, 3.5263900756835938] |
5fcec48b-a1b8-49af-8836-3d376329d914 | learning-graph-edit-distance-by-graph-neural | 2008.07641 | null | https://arxiv.org/abs/2008.07641v1 | https://arxiv.org/pdf/2008.07641v1.pdf | Learning Graph Edit Distance by Graph Neural Networks | The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the gra... | ['Alicia Fornés', 'Josep Lladós', 'Andreas Fischer', 'Pau Riba'] | 2020-08-17 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 8.99538845e-02 8.40604827e-02 2.07684234e-01 -3.70469362e-01
-4.39295769e-01 -4.85132366e-01 8.22037101e-01 1.01442528e+00
-6.92912519e-01 2.53727525e-01 -6.03377931e-02 -3.14045519e-01
-5.77420175e-01 -1.12917888e+00 -6.14661515e-01 -5.71990252e-01
-3.16276878e-01 8.46436441e-01 -1.16648460e-02 -5.34058869... | [7.132585525512695, 6.055562973022461] |
64cb8a83-affb-4f8f-b13b-9790c9c99f70 | gradient-sparsification-for-efficient | 2304.04164 | null | https://arxiv.org/abs/2304.04164v1 | https://arxiv.org/pdf/2304.04164v1.pdf | Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy | Federated learning (FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to limited transmission b... | ['Hongbo Zhu', 'Wen Chen', 'Haitao Zhao', 'Ming Ding', 'Chuan Ma', 'Jun Li', 'Kang Wei'] | 2023-04-09 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 4.07254100e-02 8.30213726e-02 -3.57258737e-01 -3.05602819e-01
-9.20307398e-01 -5.66398680e-01 -1.77364349e-01 -7.37952664e-02
-3.44066679e-01 8.20554197e-01 -4.14335467e-02 -4.81910229e-01
-3.32451046e-01 -5.92403710e-01 -9.06028330e-01 -1.32175851e+00
-3.32830906e-01 -1.24472670e-01 -4.09071058e-01 3.82185400... | [5.90576171875, 5.963559627532959] |
f8c0802e-0a08-47c0-a4a8-13d34ee14f8d | quantile-based-fuzzy-c-means-clustering-of | 2109.11027 | null | https://arxiv.org/abs/2109.11027v1 | https://arxiv.org/pdf/2109.11027v1.pdf | Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques | Three robust methods for clustering multivariate time series from the point of view of generating processes are proposed. The procedures are robust versions of a fuzzy C-means model based on: (i) estimates of the quantile cross-spectral density and (ii) the classical principal component analysis. Robustness to the pres... | ['Borja Lafuente-Rego', 'José Antonio Vilar', "Pierpaolo D'Urso", 'Ángel López-Oriona'] | 2021-09-22 | null | null | null | null | ['clustering-multivariate-time-series'] | ['time-series'] | [-2.80328244e-02 -1.52271152e-01 3.86376351e-01 -1.07109793e-01
-6.67022109e-01 -4.99127954e-01 7.55764663e-01 6.45076334e-01
-3.07200551e-01 5.96982241e-01 9.29441117e-03 -3.23463231e-01
-7.77766287e-01 -6.36386633e-01 -3.52935106e-01 -1.12111628e+00
-4.74629819e-01 4.98201698e-01 -1.67763159e-02 -5.97294457... | [7.122663974761963, 3.4024245738983154] |
a9b6038f-afdd-443d-800d-78884ff00dba | bpnet-bezier-primitive-segmentation-on-3d | 2307.04013 | null | https://arxiv.org/abs/2307.04013v1 | https://arxiv.org/pdf/2307.04013v1.pdf | BPNet: Bézier Primitive Segmentation on 3D Point Clouds | This paper proposes BPNet, a novel end-to-end deep learning framework to learn B\'ezier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point cl... | ['Pierre Alliez', 'Xiao Xiao', 'Qian Li', 'Cheng Wen', 'Rao Fu'] | 2023-07-08 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.38637554e-02 -7.37058371e-02 2.73179915e-02 -6.23996377e-01
-7.22913921e-01 -3.60520333e-01 3.64146769e-01 -1.14677012e-01
-4.42621171e-01 6.40159324e-02 -3.86944056e-01 -2.44030073e-01
9.91535280e-03 -1.12242687e+00 -9.88922358e-01 -4.38485414e-01
1.59519926e-01 9.26402688e-01 3.61984044e-01 -4.71504740... | [8.004556655883789, -3.353414535522461] |
2738460d-4894-40eb-b9c2-91222e28b2be | interleaver-design-for-deep-neural-networks | 1711.06935 | null | http://arxiv.org/abs/1711.06935v3 | http://arxiv.org/pdf/1711.06935v3.pdf | Interleaver Design for Deep Neural Networks | We propose a class of interleavers for a novel deep neural network (DNN)
architecture that uses algorithmically pre-determined, structured sparsity to
significantly lower memory and computational requirements, and speed up
training. The interleavers guarantee clash-free memory accesses to eliminate
idle operational cyc... | ['Keith M. Chugg', 'Sourya Dey', 'Peter A. Beerel'] | 2017-11-18 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.30487406e-01 -2.47245401e-01 -3.46792012e-01 -3.76311481e-01
2.16736913e-01 -4.38874930e-01 2.79911131e-01 -2.74034739e-01
-7.65178561e-01 5.94829977e-01 -1.61768515e-02 -8.87733459e-01
-3.11179459e-01 -5.88230729e-01 -7.30195045e-01 -7.20769644e-01
-4.41526443e-01 -2.45048031e-01 3.06583434e-01 3.70022893... | [8.497527122497559, 3.024815082550049] |
ec237948-2080-44a1-a5c3-da22fb7fba51 | improving-data-augmentation-in-low-resource | null | null | https://openreview.net/forum?id=LINkFIdHsuZ | https://openreview.net/pdf?id=LINkFIdHsuZ | Improving Data Augmentation in Low-resource Question Answering with Active Learning in Multiple Stages | Neural approaches have become very popular in the domain of Question Answering, however they require a large amount of annotated data. Furthermore, they often yield very good performance but only in the domain they were trained on. In this work we propose a novel approach that combines data augmentation via question-an... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['question-answer-generation'] | ['natural-language-processing'] | [ 5.19410491e-01 4.88007814e-01 -1.88044593e-01 -4.76901352e-01
-1.21506238e+00 -6.95052683e-01 5.73922336e-01 3.06206852e-01
-8.86606872e-01 9.26538050e-01 2.03298274e-02 -3.90354306e-01
7.76647544e-03 -7.97734141e-01 -5.93969226e-01 -3.28533590e-01
4.32173461e-01 8.79630089e-01 5.19750535e-01 -5.75140893... | [11.083463668823242, 8.115372657775879] |
08149138-05bd-457f-8a87-e4fad2adc42e | always-on-674uw-4gop-s-error-resilient-binary | 2007.08952 | null | https://arxiv.org/abs/2007.08952v1 | https://arxiv.org/pdf/2007.08952v1.pdf | Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node | Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardwa... | ['Pasquale Davide Schiavone', 'Alfio Di Mauro', 'Luca Benini', 'Francesco Conti', 'Davide Rossi'] | 2020-07-17 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 3.94231915e-01 -8.81072059e-02 -5.94890356e-01 -2.97107309e-01
-2.03515127e-01 -5.74726820e-01 8.96663964e-02 4.16891605e-01
-7.91860282e-01 1.17152357e+00 -6.19586229e-01 -5.27673423e-01
1.93225771e-01 -8.67151320e-01 -9.56201851e-01 -7.53808439e-01
-3.27838629e-01 -1.75644513e-02 4.55465287e-01 1.74925640... | [8.275503158569336, 2.5507771968841553] |
ecf389b7-a86c-4821-925a-a37ea86d0127 | muslcat-multi-scale-multi-level-convolutional | 2104.02309 | null | https://arxiv.org/abs/2104.02309v1 | https://arxiv.org/pdf/2104.02309v1.pdf | MuSLCAT: Multi-Scale Multi-Level Convolutional Attention Transformer for Discriminative Music Modeling on Raw Waveforms | In this work, we aim to improve the expressive capacity of waveform-based discriminative music networks by modeling both sequential (temporal) and hierarchical information in an efficient end-to-end architecture. We present MuSLCAT, or Multi-scale and Multi-level Convolutional Attention Transformer, a novel architectur... | ['David Guy Brizan', 'Shyam Sudhakaran', 'Kai Middlebrook'] | 2021-04-06 | null | null | null | null | ['music-modeling'] | ['music'] | [ 3.42158452e-02 -5.08142769e-01 -1.19505348e-02 -1.19549222e-01
-1.09102345e+00 -8.67712021e-01 2.59207904e-01 -1.65131912e-01
-3.62427533e-01 2.90956795e-01 4.71766353e-01 7.74114132e-02
-4.04192984e-01 -4.18987244e-01 -5.50110996e-01 -4.86458272e-01
-4.62626576e-01 2.39852592e-01 1.91233054e-01 -1.41228572... | [15.696571350097656, 5.241477966308594] |
dd578d62-725f-454e-a2a3-6fb185ebb0d1 | knowledge-base-inference-for-regular | 2005.00480 | null | https://arxiv.org/abs/2005.00480v2 | https://arxiv.org/pdf/2005.00480v2.pdf | Regex Queries over Incomplete Knowledge Bases | We propose the novel task of answering regular expression queries (containing disjunction ($\vee$) and Kleene plus ($+$) operators) over incomplete KBs. The answer set of these queries potentially has a large number of entities, hence previous works for single-hop queries in KBC that model a query as a point in high-di... | ['Parth Shah', 'Mausam', 'Vaibhav Adlakha', 'Srikanta Bedathur'] | 2020-05-01 | null | https://openreview.net/forum?id=4YQVfA5vEJS | https://openreview.net/pdf?id=4YQVfA5vEJS | akbc-2021-10 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-5.27069390e-01 3.86325121e-01 -6.99278295e-01 -4.51300889e-01
-1.06368876e+00 -7.74631739e-01 4.33019668e-01 6.60151660e-01
-7.06014276e-01 5.57651877e-01 6.21222258e-01 -5.80653250e-01
-3.76431555e-01 -1.22187638e+00 -9.54850316e-01 1.98827773e-01
-5.12768388e-01 1.11901855e+00 5.44760406e-01 -6.32329464... | [9.098690032958984, 7.672320365905762] |
a487b9fb-884a-43ec-85bc-f1fbb04a618a | teaching-clip-to-count-to-ten | 2302.12066 | null | https://arxiv.org/abs/2302.12066v1 | https://arxiv.org/pdf/2302.12066v1.pdf | Teaching CLIP to Count to Ten | Large vision-language models (VLMs), such as CLIP, learn rich joint image-text representations, facilitating advances in numerous downstream tasks, including zero-shot classification and text-to-image generation. Nevertheless, existing VLMs exhibit a prominent well-documented limitation - they fail to encapsulate compo... | ['Tali Dekel', 'Michal Irani', 'Inbar Mosseri', 'Shiran Zada', 'Omer Tov', 'Ariel Ephrat', 'Roni Paiss'] | 2023-02-23 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 6.70313895e-01 -2.44364351e-01 -6.05845936e-02 -3.47295642e-01
-9.77357149e-01 -4.90650833e-01 1.12114537e+00 1.38167202e-01
-8.95989239e-01 5.05219340e-01 1.49119303e-01 -2.35449165e-01
6.40120208e-01 -6.80907369e-01 -1.21734643e+00 -4.75558043e-01
2.12905779e-01 6.32668972e-01 -6.38651550e-02 9.91526470... | [10.339632987976074, 1.4141595363616943] |
99dd2fe7-d7a4-4342-a936-b96ce0135b44 | evi-multilingual-spoken-dialogue-tasks-and | null | null | https://openreview.net/forum?id=p5jgs957DXh | https://openreview.net/pdf?id=p5jgs957DXh | EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification | Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date-of-birth. In this wo... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['spoken-dialogue-systems'] | ['speech'] | [-3.53950858e-01 1.88537166e-01 -1.77258160e-02 -5.67982793e-01
-9.39121246e-01 -9.19673920e-01 1.02283537e+00 5.40941775e-01
-8.72440875e-01 1.01436198e+00 4.82134283e-01 -5.40658355e-01
1.11859910e-01 -3.91597629e-01 1.13645740e-01 -3.59236777e-01
-3.58907193e-01 8.51570368e-01 1.88981686e-02 -3.93116683... | [12.852577209472656, 7.862083911895752] |
c5bd5884-da79-42d0-af0b-72a483cd431f | part-of-speech-tagging-of-odia-language-using | 2207.03256 | null | https://arxiv.org/abs/2207.03256v1 | https://arxiv.org/pdf/2207.03256v1.pdf | Part-of-Speech Tagging of Odia Language Using statistical and Deep Learning-Based Approaches | Automatic Part-of-speech (POS) tagging is a preprocessing step of many natural language processing (NLP) tasks such as name entity recognition (NER), speech processing, information extraction, word sense disambiguation, and machine translation. It has already gained a promising result in English and European languages,... | ['Pankaj K Sa', 'Tapas Kumar Mishra', 'Tusarkanta Dalai'] | 2022-07-07 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-1.43256597e-02 2.80100405e-02 -2.04838768e-01 -3.36434513e-01
-5.12083232e-01 -6.24525666e-01 6.43490434e-01 3.85437667e-01
-8.72456014e-01 9.24692988e-01 4.30885911e-01 -5.50793588e-01
1.94243371e-01 -9.34845030e-01 -3.35448414e-01 -4.84185249e-01
-8.54513422e-02 7.57760406e-01 2.00362995e-01 -1.83157459... | [9.865303993225098, 9.719889640808105] |
d606bc79-aee2-4bc6-9de2-376b44f477d6 | decision-transformer-under-random-frame | 2303.03391 | null | https://arxiv.org/abs/2303.03391v1 | https://arxiv.org/pdf/2303.03391v1.pdf | Decision Transformer under Random Frame Dropping | Controlling agents remotely with deep reinforcement learning~(DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning sensors. Typical RL methods usually require considerable online int... | ['Huazhe Xu', 'Yang Gao', 'Ray Chen Zheng', 'Kaizhe Hu'] | 2023-03-03 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 7.12755369e-03 -3.07820350e-01 -1.57759711e-01 -3.33863527e-01
-7.12647974e-01 -7.66029537e-01 6.10969484e-01 -1.05384797e-01
-8.47568631e-01 1.04621708e+00 -1.63539290e-01 -4.26927626e-01
8.23324323e-02 -6.12085938e-01 -7.48490691e-01 -8.34147155e-01
-5.57335079e-01 4.95291471e-01 4.80022192e-01 -3.54350984... | [4.088668346405029, 1.6918017864227295] |
e107a780-dd34-488d-ae72-16f1c2d7e687 | topic-relevant-response-generation-using | null | null | https://aclanthology.org/2020.coling-main.359 | https://aclanthology.org/2020.coling-main.359.pdf | Topic-relevant Response Generation using Optimal Transport for an Open-domain Dialog System | Conventional neural generative models tend to generate safe and generic responses which have little connection with previous utterances semantically and would disengage users in a dialog system. To generate relevant responses, we propose a method that employs two types of constraints - topical constraint and semantic c... | ['Tatsuya Kawahara', 'Tianyu Zhao', 'Shuying Zhang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['open-domain-dialog'] | ['natural-language-processing'] | [ 3.47273022e-01 3.89728367e-01 4.08415198e-02 -8.56206656e-01
-6.01942301e-01 -4.30880874e-01 1.04065239e+00 1.19257636e-01
-5.65138698e-01 8.02294552e-01 1.02877259e+00 9.08528641e-02
1.15369283e-01 -9.62986887e-01 -1.15748055e-01 -5.14891267e-01
3.35509479e-01 5.47935784e-01 6.79048151e-02 -5.94171345... | [12.612913131713867, 8.279994010925293] |
8cd2b73c-5dbf-43da-b71b-639b95b65274 | slam-simultaneous-localisation-and-mapping-at | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Salas-Moreno_SLAM_Simultaneous_Localisation_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Salas-Moreno_SLAM_Simultaneous_Localisation_2013_CVPR_paper.pdf | SLAM++: Simultaneous Localisation and Mapping at the Level of Objects | We present the major advantages of a new 'object oriented' 3D SLAM paradigm, which takes full advantage in the loop of prior knowledge that many scenes consist of repeated, domain-specific objects and structures. As a hand-held depth camera browses a cluttered scene, realtime 3D object recognition and tracking provides... | ['Paul H. J. Kelly', 'Andrew J. Davison', 'Renato F. Salas-Moreno', 'Hauke Strasdat', 'Richard A. Newcombe'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['3d-object-recognition'] | ['computer-vision'] | [ 5.26830375e-01 9.70061403e-03 2.27191061e-01 -3.53441983e-01
-5.81076205e-01 -5.72960794e-01 6.65034533e-01 3.61896187e-01
-5.35605431e-01 3.09460670e-01 -2.43616059e-01 -2.16148514e-02
-2.32954800e-01 -4.55687702e-01 -6.17214322e-01 -2.69225091e-01
-4.75858092e-01 1.26582897e+00 8.18892658e-01 -1.95836931... | [7.32609224319458, -2.3508501052856445] |
a85a8a61-0b5d-487e-9962-1fa8a90ac353 | multi-temporal-lip-audio-memory-for-visual | 2305.04542 | null | https://arxiv.org/abs/2305.04542v1 | https://arxiv.org/pdf/2305.04542v1.pdf | Multi-Temporal Lip-Audio Memory for Visual Speech Recognition | Visual Speech Recognition (VSR) is a task to predict a sentence or word from lip movements. Some works have been recently presented which use audio signals to supplement visual information. However, existing methods utilize only limited information such as phoneme-level features and soft labels of Automatic Speech Reco... | ['Yong Man Ro', 'Minsu Kim', 'Jeong Hun Yeo'] | 2023-05-08 | null | null | null | null | ['visual-speech-recognition'] | ['speech'] | [ 1.72058985e-01 -4.51509029e-01 -3.81037623e-01 -3.38127524e-01
-9.61028695e-01 -2.07653269e-01 4.31232631e-01 -1.45018056e-01
-2.07142964e-01 4.11780208e-01 4.32122886e-01 -1.82042569e-01
2.18739256e-01 -4.34524924e-01 -5.66374838e-01 -5.55609941e-01
1.64361551e-01 -2.29774863e-01 4.10526037e-01 4.69280407... | [14.346670150756836, 5.074512481689453] |
3f766e40-57cd-4775-adaa-f8fc2c24c5b6 | transrev-modeling-reviews-as-translations | 1801.10095 | null | http://arxiv.org/abs/1801.10095v2 | http://arxiv.org/pdf/1801.10095v2.pdf | TransRev: Modeling Reviews as Translations from Users to Items | The text of a review expresses the sentiment a customer has towards a
particular product. This is exploited in sentiment analysis where machine
learning models are used to predict the review score from the text of the
review. Furthermore, the products costumers have purchased in the past are
indicative of the products ... | ['Daniel Onoro-Rubio', 'Alberto Garcia-Duran', 'Hui Li', 'Roberto Gonzalez', 'Mathias Niepert'] | 2018-01-30 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-3.79813284e-01 5.66169210e-02 -7.31617153e-01 -5.62269866e-01
-4.53697562e-01 -4.32112813e-01 5.77777684e-01 6.11103058e-01
-2.81112105e-01 5.36684506e-02 4.95871127e-01 -1.21008299e-01
-1.75497547e-01 -9.87557471e-01 -5.01180112e-01 -4.95826751e-01
4.56695378e-01 5.04261672e-01 -2.27800295e-01 -4.97718841... | [10.729722023010254, 6.249797344207764] |
29ec6df4-d15e-44d6-b078-ac7aa39d950d | deep-dependency-networks-for-multi-label | 2302.00633 | null | https://arxiv.org/abs/2302.00633v2 | https://arxiv.org/pdf/2302.00633v2.pdf | Deep Dependency Networks for Multi-Label Classification | We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data. First, we show that the performance of previous approaches that combine Markov Random Fields with ne... | ['Vibhav Gogate', 'Yu Xiang', 'Shivvrat Arya'] | 2023-02-01 | null | null | null | null | ['action-classification', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 2.23610118e-01 9.13066044e-02 -5.17386615e-01 -7.00801909e-01
-8.15813005e-01 -3.74131680e-01 6.57026947e-01 3.22711356e-02
-3.66288006e-01 7.71571457e-01 1.31166935e-01 -2.54725933e-01
3.96914780e-02 -5.47765315e-01 -1.12492275e+00 -6.37738168e-01
-2.83624828e-01 4.94218528e-01 2.36595526e-01 3.30293030... | [8.586382865905762, 0.9167747497558594] |
4395ef7c-a880-4667-b953-a5bcbf4e9bbc | few-shot-table-to-text-generation-with-prompt | 2302.04415 | null | https://arxiv.org/abs/2302.04415v2 | https://arxiv.org/pdf/2302.04415v2.pdf | Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization | Pre-trained language models (PLM) have achieved remarkable advancement in table-to-text generation tasks. However, the lack of labeled domain-specific knowledge and the topology gap between tabular data and text make it difficult for PLMs to yield faithful text. Low-resource generation likewise faces unique challenges ... | ['Xinbing Wang', 'Guanjie Zheng', 'Zhouhan Lin', 'Ziwei He', 'Jianping Zhou', 'Jiexing Qi', 'Minyxuan Yan', 'Zhixin Guo'] | 2023-02-09 | null | null | null | null | ['memorization', 'table-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.66042757e-01 6.09442472e-01 -2.86670119e-01 -9.23624858e-02
-1.32758570e+00 -5.99365771e-01 1.09242070e+00 1.84531555e-01
-2.65637296e-03 1.21294439e+00 4.72458631e-01 1.37246959e-02
1.22239992e-01 -1.10942149e+00 -5.90154231e-01 -2.44135842e-01
5.88414431e-01 1.26626194e+00 1.06713682e-01 -6.13171399... | [11.669618606567383, 8.871084213256836] |
45c4f59c-fd08-4cf8-9afe-17f33416a043 | evolvegcn-evolving-graph-convolutional | 1902.10191 | null | https://arxiv.org/abs/1902.10191v3 | https://arxiv.org/pdf/1902.10191v3.pdf | EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs | Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, ... | ['Charles E. Leiserson', 'Tao B. Schardl', 'Jie Chen', 'Giacomo Domeniconi', 'Toyotaro Suzumura', 'Tim Kaler', 'Tengfei Ma', 'Hiroki Kanezashi', 'Aldo Pareja'] | 2019-02-26 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-1.75970554e-01 -1.65741891e-02 -2.49264352e-02 -3.85840982e-02
3.16649318e-01 -5.03661036e-01 6.25625551e-01 -2.32267454e-02
-2.69224763e-01 3.63863438e-01 -4.82084230e-02 -5.16260564e-01
-1.14368506e-01 -1.06272042e+00 -5.26379287e-01 -8.04816961e-01
-4.02074277e-01 3.49919707e-01 1.37242049e-01 -5.78130841... | [7.174453258514404, 6.066494941711426] |
aeae39f9-b5a7-4d40-8d6e-a0c49a1bd163 | road-segmentation-using-cnn-with-gru | 1804.05164 | null | http://arxiv.org/abs/1804.05164v1 | http://arxiv.org/pdf/1804.05164v1.pdf | Road Segmentation Using CNN with GRU | This paper presents an accurate and fast algorithm for road segmentation
using convolutional neural network (CNN) and gated recurrent units (GRU). For
autonomous vehicles, road segmentation is a fundamental task that can provide
the drivable area for path planning. The existing deep neural network based
segmentation al... | ['Yecheng Lyu', 'Xinming Huang'] | 2018-04-14 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 3.65283281e-01 1.54573232e-01 -3.80847633e-01 -5.15786827e-01
-5.60689688e-01 -1.18908390e-01 2.94919640e-01 -4.16261107e-01
-5.91510296e-01 4.37096715e-01 -1.48453549e-01 -8.39146078e-01
3.94285858e-01 -1.29112411e+00 -9.00171041e-01 -3.78662586e-01
1.96466669e-01 3.78031820e-01 5.27156591e-01 -1.83950514... | [8.993851661682129, -0.8691067695617676] |
3a4d63a9-5289-498e-b1b3-12a54e1c0391 | shi-he-jian-dong-ren-shi-yong-zhi-yu-yin | null | null | https://aclanthology.org/2019.ijclclp-2.3 | https://aclanthology.org/2019.ijclclp-2.3.pdf | 適合漸凍人使用之語音轉換系統初步研究 (Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients) | null | ['Daniel Hládek', 'Matúš Pleva', 'Guang-Feng Deng', 'Yuan-Fu Liao', 'Bai-Hong Huang'] | null | null | null | null | ijclclp-2019-12 | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.159788131713867, 3.5886831283569336] |
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