paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
183344d3-b337-4690-8874-a304c70bc25c | influence-of-asr-and-language-model-on | 2110.15704 | null | https://arxiv.org/abs/2110.15704v1 | https://arxiv.org/pdf/2110.15704v1.pdf | Influence of ASR and Language Model on Alzheimer's Disease Detection | Alzheimer's Disease is the most common form of dementia. Automatic detection from speech could help to identify symptoms at early stages, so that preventive actions can be carried out. This research is a contribution to the ADReSSo Challenge, we analyze the usage of a SotA ASR system to transcribe participant's spoken ... | ['Mireia Farrús', 'Jordi Luque', 'Guillermo Cámbara', 'Joan Codina-Filbà'] | 2021-09-20 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 5.08975238e-02 3.66438255e-02 3.77436548e-01 -4.17256117e-01
-1.20741177e+00 -3.36424746e-02 4.84364510e-01 1.50077671e-01
-9.17785048e-01 7.30537593e-01 8.30819726e-01 -1.60439610e-01
1.53311938e-01 -2.86896318e-01 -7.35769197e-02 -6.09902501e-01
-1.29897907e-01 5.01579762e-01 3.20928991e-01 -3.29499364... | [13.977234840393066, 5.390474796295166] |
f284db24-b42b-4677-b4f0-ba532d58ea49 | motion-matters-neural-motion-transfer-for | 2303.12059 | null | https://arxiv.org/abs/2303.12059v2 | https://arxiv.org/pdf/2303.12059v2.pdf | Motion Matters: Neural Motion Transfer for Better Camera Physiological Sensing | Machine learning models for camera-based physiological measurement can have weak generalization due to a lack of representative training data. Body motion is one of the most significant sources of noise when attempting to recover the subtle cardiac pulse from a video. We explore motion transfer as a form of data augmen... | ['Soumyadip Sengupta', 'Daniel McDuff', 'Shwetak Patel', 'Yulu Pan', 'Xin Liu', 'Akshay Paruchuri'] | 2023-03-21 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.96767825e-01 -1.67511910e-01 -2.42469355e-01 -2.69954145e-01
-6.38807774e-01 -3.73887181e-01 4.41952229e-01 -3.36242646e-01
-4.19780880e-01 5.77095151e-01 6.48193836e-01 -1.10335775e-01
4.84325528e-01 -1.53486058e-01 -8.67593944e-01 -8.18498969e-01
-8.62241983e-02 -2.88283378e-01 -1.20458685e-01 7.28292167... | [13.895999908447266, 2.8029603958129883] |
2a66f524-7dea-4a71-9935-78d524190e18 | optimizing-non-autoregressive-transformers | 2305.13667 | null | https://arxiv.org/abs/2305.13667v2 | https://arxiv.org/pdf/2305.13667v2.pdf | Optimizing Non-Autoregressive Transformers with Contrastive Learning | Non-autoregressive Transformers (NATs) reduce the inference latency of Autoregressive Transformers (ATs) by predicting words all at once rather than in sequential order. They have achieved remarkable progress in machine translation as well as many other applications. However, a long-standing challenge for NATs is the l... | ['Lingpeng Kong', 'Xipeng Qiu', 'Fei Huang', 'Jiangtao Feng', 'Chenxin An'] | 2023-05-23 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 5.70511460e-01 1.98638570e-02 -4.01652217e-01 -3.43932748e-01
-1.49202228e+00 -7.69056201e-01 1.01568115e+00 -2.76037812e-01
-1.49395078e-01 7.71266878e-01 8.16183269e-01 -8.04309845e-01
2.34927326e-01 -3.31483006e-01 -9.87801850e-01 -6.76316738e-01
6.11015499e-01 1.14325571e+00 6.23784512e-02 -3.40516090... | [11.868122100830078, 9.172807693481445] |
76d1c035-5d39-484b-bf3e-e1dcda1fd681 | towards-speech-enhancement-using-a | 2012.03594 | null | https://arxiv.org/abs/2012.03594v2 | https://arxiv.org/pdf/2012.03594v2.pdf | Towards speech enhancement using a variational U-Net architecture | We investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to work on the waveform signal, potentially neglecting relationships across higher dimensional spectro-temporal features. We ... | ['Jörn Anemüller', 'Eike J. Nustede'] | 2020-12-07 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 2.43050307e-01 -9.10381898e-02 4.44804788e-01 5.67895994e-02
-1.10514283e+00 -5.74818671e-01 2.69307882e-01 -9.49139670e-02
-4.47119445e-01 7.47456729e-01 5.79550564e-01 -3.49363536e-01
-3.26583147e-01 -3.21713537e-01 -6.10430956e-01 -8.41258109e-01
-2.00227857e-01 -3.28709394e-01 -1.91461965e-02 -3.70935768... | [15.135141372680664, 5.884401798248291] |
fea1bb56-7ade-45dd-ba36-0f0fe5225a5f | mapping-the-ictal-interictal-injury-continuum | 2211.05207 | null | https://arxiv.org/abs/2211.05207v4 | https://arxiv.org/pdf/2211.05207v4.pdf | Interpretable Machine Learning System to EEG Patterns on the Ictal-Interictal-Injury Continuum | In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored is constrained by the availability of trained physicians to read EEGs, and EEG interpretation can be subjective and prone to inter-observer... | ['M. Brandon Westover', 'Cynthia Rudin', 'Wendong Ge', 'Jin Jing', 'Zhicheng Guo', 'Alina Jade Barnett'] | 2022-11-09 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.68635745e-03 1.64733708e-01 2.22423911e-01 -5.38747430e-01
-3.73181045e-01 -6.26399338e-01 -8.48026499e-02 2.94512391e-01
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-5.48156738e-01 -2.88963377e-01 -3.77885491e-01 -6.31049514e-01
-4.29592103e-01 9.41200495e-01 -2.59042561e-01 1.97038770... | [13.271319389343262, 3.548218011856079] |
697c64a0-1fa0-4c0f-8d15-545ed6e3e33d | a-unified-bev-model-for-joint-learning-of-3d | 2302.14511 | null | https://arxiv.org/abs/2302.14511v2 | https://arxiv.org/pdf/2302.14511v2.pdf | A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation | Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, espe... | ['Guowei Wan', 'Yong liu', 'Yufei Liang', 'Yongkun Wen', 'Wendong Ding', 'Lin Li'] | 2023-02-28 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.74612805e-01 -2.95531660e-01 4.45758784e-03 -4.77093667e-01
-9.74995375e-01 -5.93351960e-01 4.71067905e-01 1.47216067e-01
-3.70976597e-01 -4.02764603e-02 -1.86430171e-01 1.03216276e-01
2.54076831e-02 -4.64726090e-01 -8.98008108e-01 -4.50667143e-01
-8.87292027e-02 7.73854673e-01 3.88350666e-01 -2.81213433... | [7.683271408081055, -3.0410983562469482] |
67db56d4-471a-4674-8ad1-f36f3a8a33f9 | 3d-a-nets-3d-deep-dense-descriptor-for | 1711.10108 | null | http://arxiv.org/abs/1711.10108v1 | http://arxiv.org/pdf/1711.10108v1.pdf | 3D-A-Nets: 3D Deep Dense Descriptor for Volumetric Shapes with Adversarial Networks | Recently researchers have been shifting their focus towards learned 3D shape
descriptors from hand-craft ones to better address challenging issues of the
deformation and structural variation inherently present in 3D objects. 3D
geometric data are often transformed to 3D Voxel grids with regular format in
order to be be... | ['Mengwei Ren', 'Liang Niu', 'Yi Fang'] | 2017-11-28 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 5.10879867e-02 1.01214923e-01 3.70246261e-01 -3.35991502e-01
-6.45673931e-01 -6.53221011e-01 5.68140805e-01 -9.80928317e-02
-3.66793759e-02 4.35740799e-01 4.38168682e-02 -3.26982468e-01
-1.45497814e-01 -1.29308045e+00 -7.22869396e-01 -7.51616061e-01
-1.94820374e-01 7.65070498e-01 -8.26136321e-02 -1.47295952... | [8.27568531036377, -3.7619924545288086] |
01681300-05e0-4b52-b140-cae7107971b9 | delta-descriptors-change-based-place | 2006.05700 | null | https://arxiv.org/abs/2006.05700v2 | https://arxiv.org/pdf/2006.05700v2.pdf | Delta Descriptors: Change-Based Place Representation for Robust Visual Localization | Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descript... | ['Gaurangi Anand', 'Sourav Garg', 'Michael Milford', 'Ben Harwood'] | 2020-06-10 | null | null | null | null | ['sequential-place-recognition'] | ['robots'] | [ 1.09683454e-01 -7.73068607e-01 -1.03527352e-01 -4.09978151e-01
-5.33460855e-01 -7.64975965e-01 1.09783220e+00 2.71348864e-01
-6.30405903e-01 5.97731769e-01 2.05736071e-01 3.74709219e-01
-3.05491924e-01 -7.15946674e-01 -5.38641989e-01 -7.20814407e-01
-2.54018158e-01 -6.04349449e-02 6.12782001e-01 -4.39927220... | [7.938803195953369, -1.7343196868896484] |
c0870a45-b6e9-4557-b436-88e44d4b6a94 | hashtag-guided-low-resource-tweet | 2302.10143 | null | https://arxiv.org/abs/2302.10143v1 | https://arxiv.org/pdf/2302.10143v1.pdf | Hashtag-Guided Low-Resource Tweet Classification | Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous. Thus, training on tweets is challenging and demands large-scale human-annotated labels, which are time-consuming and costly to obtain. In th... | ['Tong Zhang', 'Yan Song', 'Zhiliang Tian', 'Liangming Pan', 'Sedrick Scott Keh', 'Shizhe Diao'] | 2023-02-20 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-3.51587385e-02 3.89956176e-01 -4.33137208e-01 -7.56026745e-01
-1.04633379e+00 -6.40992045e-01 6.20581150e-01 6.79509461e-01
-3.72473180e-01 7.61597931e-01 4.73996371e-01 -5.28028123e-02
4.55043852e-01 -1.17163289e+00 -5.03422320e-01 -4.20994461e-01
9.22272429e-02 5.71262479e-01 1.89139187e-01 -6.46259904... | [10.773990631103516, 7.08544397354126] |
95b0da27-d807-4cf5-ad28-7c25eb45887f | r2-d2-a-modular-baseline-for-open-domain | 2109.03502 | null | https://arxiv.org/abs/2109.03502v1 | https://arxiv.org/pdf/2109.03502v1.pdf | R2-D2: A Modular Baseline for Open-Domain Question Answering | This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from all system's components. We demonstrate its strength across three open-d... | ['Pavel Smrz', 'Karel Ondrej', 'Martin Docekal', 'Martin Fajcik'] | 2021-09-08 | null | https://aclanthology.org/2021.findings-emnlp.73 | https://aclanthology.org/2021.findings-emnlp.73.pdf | findings-emnlp-2021-11 | ['triviaqa'] | ['miscellaneous'] | [-1.52928904e-01 3.53180975e-01 1.35475636e-01 -7.44274035e-02
-2.10202074e+00 -1.13533640e+00 1.01303732e+00 1.84688166e-01
-4.29272145e-01 1.01299727e+00 8.70996177e-01 -4.22834486e-01
-3.84791225e-01 -6.23776674e-01 -7.17410386e-01 -2.56769598e-01
2.81908482e-01 1.58337045e+00 6.95123494e-01 -8.70243609... | [11.32574462890625, 7.985174655914307] |
a21f328b-82ec-4163-a746-d9be2c1334a5 | doing-good-or-doing-right-exploring-the | 2107.01791 | null | https://arxiv.org/abs/2107.01791v1 | https://arxiv.org/pdf/2107.01791v1.pdf | Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models | Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current... | ['Yinglin Wang', 'Mingyue Han'] | 2021-07-05 | null | https://aclanthology.org/2021.acl-short.20 | https://aclanthology.org/2021.acl-short.20.pdf | acl-2021-5 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [ 6.30140528e-02 2.68499047e-01 -3.29971671e-01 -3.23697120e-01
-6.11023128e-01 -4.08535004e-01 8.73986602e-01 3.40463072e-01
-5.32600522e-01 9.23139334e-01 4.21061486e-01 -5.20347238e-01
-3.33266348e-01 -7.51214743e-01 -7.05542028e-01 -5.11607289e-01
1.35105718e-02 4.07922238e-01 1.31518051e-01 -3.27576697... | [9.877565383911133, 7.99576473236084] |
dd1fc2e6-b71e-49b0-8aa1-4cfb41f1bc12 | ph-sft-shape-from-template-with-a-physics | 2203.11938 | null | https://arxiv.org/abs/2203.11938v1 | https://arxiv.org/pdf/2203.11938v1.pdf | φ-SfT: Shape-from-Template with a Physics-Based Deformation Model | Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric an... | ['Vladislav Golyanik', 'Christian Theobalt', 'Mohamed Elgharib', 'Edith Tretschk', 'Navami Kairanda'] | 2022-03-22 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 4.43171740e-01 4.48391177e-02 4.36886311e-01 -9.78609174e-02
-4.12585706e-01 -5.61741471e-01 6.80543005e-01 -1.58037573e-01
-3.08875561e-01 5.96210420e-01 -1.89112082e-01 -7.69690005e-03
-1.12134509e-01 -7.22763658e-01 -1.04095042e+00 -8.36019218e-01
2.81157732e-01 9.07004356e-01 3.01974326e-01 -4.90016527... | [9.055054664611816, -2.985867500305176] |
f2ed84b3-9cd2-4c93-bee7-d2f4b2b1aebf | meta-learning-adversarial-bandit-algorithms | 2307.02295 | null | https://arxiv.org/abs/2307.02295v1 | https://arxiv.org/pdf/2307.02295v1.pdf | Meta-Learning Adversarial Bandit Algorithms | We study online meta-learning with bandit feedback, with the goal of improving performance across multiple tasks if they are similar according to some natural similarity measure. As the first to target the adversarial online-within-online partial-information setting, we design meta-algorithms that combine outer learner... | ['Zhiwei Steven Wu', 'Ron Meir', 'Kfir Y. Levy', 'Maria-Florina Balcan', 'Keegan Harris', 'Ilya Osadchiy', 'Mikhail Khodak'] | 2023-07-05 | null | null | null | null | ['meta-learning', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [-2.32971087e-01 4.62712109e-01 -6.04377866e-01 -2.56187975e-01
-1.51617491e+00 -9.34520781e-01 3.63625795e-01 1.46144077e-01
-7.74821341e-01 1.18977213e+00 3.32831681e-01 -4.27779138e-01
-6.66129053e-01 -4.87965703e-01 -1.38188708e+00 -9.59995151e-01
-3.32720816e-01 4.16822046e-01 -3.44613492e-01 -3.03734213... | [4.608808994293213, 3.369904041290283] |
61e147b7-a442-4fbf-8143-fd5ba2f50e30 | how-adults-understand-what-young-children-say | 2206.07807 | null | https://arxiv.org/abs/2206.07807v3 | https://arxiv.org/pdf/2206.07807v3.pdf | How Adults Understand What Young Children Say | Children's early speech often bears little resemblance to that of adults, and yet parents and other caregivers are able to interpret that speech and react accordingly. Here we investigate how these adult inferences as listeners reflect sophisticated beliefs about what children are trying to communicate, as well as how ... | ['Roger P. Levy', 'Elika Bergelson', 'Nicole H. Wong', 'Ruthe Foushee', 'Stephan C. Meylan'] | 2022-06-15 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 4.01375830e-01 6.90240085e-01 -1.06597513e-01 -8.03182483e-01
-3.35133523e-01 -3.43350887e-01 6.95227861e-01 5.67854106e-01
-8.65433156e-01 2.52078831e-01 1.09250271e+00 -3.81855726e-01
8.93005803e-02 -8.77439260e-01 -3.37513685e-01 -2.43335903e-01
1.41128555e-01 6.02713943e-01 2.26879478e-01 -5.08037396... | [10.374011039733887, 8.630864143371582] |
9095e77a-86ef-421b-a729-a4d1b970ea5b | mutual-information-guided-knowledge-transfer | 2206.12063 | null | https://arxiv.org/abs/2206.12063v2 | https://arxiv.org/pdf/2206.12063v2.pdf | Mutual Information-guided Knowledge Transfer for Novel Class Discovery | We tackle the novel class discovery problem, aiming to discover novel classes in unlabeled data based on labeled data from seen classes. The main challenge is to transfer knowledge contained in the seen classes to unseen ones. Previous methods mostly transfer knowledge through sharing representation space or joint labe... | ['Xuming He', 'Qian He', 'Zhitong Gao', 'Ruijie Xu', 'Chuanyang Hu', 'Chuyu Zhang'] | 2022-06-24 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 3.05916458e-01 1.18905693e-01 -3.86610508e-01 -6.91129267e-01
-3.85781556e-01 -7.49126256e-01 5.13901114e-01 2.56348670e-01
-1.87588885e-01 1.01931489e+00 -1.08665898e-01 7.60406703e-02
-3.26860398e-01 -1.01862884e+00 -5.85899293e-01 -7.09386349e-01
3.30931127e-01 5.02806365e-01 1.69867039e-01 9.60474089... | [9.6411714553833, 2.9438462257385254] |
3816f2ee-990f-45fe-8f1d-6496d49c9ee8 | a-diachronic-analysis-of-the-nlp-research | 2305.12920 | null | https://arxiv.org/abs/2305.12920v1 | https://arxiv.org/pdf/2305.12920v1.pdf | A Diachronic Analysis of the NLP Research Paradigm Shift: When, How, and Why? | Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its ongoing development. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. By conducting extensiv... | ['Iryna Gurevych', 'Yufang Hou', 'Aniket Pramanick'] | 2023-05-22 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-5.44811338e-02 -1.99698538e-01 -7.73034334e-01 -1.09073773e-01
-8.93670619e-02 -4.34646338e-01 1.12765253e+00 4.41852272e-01
-1.95070103e-01 8.58304322e-01 5.60454071e-01 -7.07109749e-01
-3.93116593e-01 -6.33462787e-01 -5.90976894e-01 -2.79944956e-01
-3.85667771e-01 2.74518788e-01 2.13082954e-01 -2.41158204... | [9.671028137207031, 8.3146333694458] |
8f9c2a59-4599-4f5a-b2d7-d1ccd73d7149 | xvfi-extreme-video-frame-interpolation | 2103.16206 | null | https://arxiv.org/abs/2103.16206v2 | https://arxiv.org/pdf/2103.16206v2.pdf | XVFI: eXtreme Video Frame Interpolation | In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the extreme motion to the research community for video frame interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that first handles the VFI for 4K videos with large motion. The XVFI-Net is based on a recursive m... | ['Munchurl Kim', 'Jihyong Oh', 'Hyeonjun Sim'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sim_XVFI_eXtreme_Video_Frame_Interpolation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sim_XVFI_eXtreme_Video_Frame_Interpolation_ICCV_2021_paper.pdf | iccv-2021-1 | ['extreme-video-frame-interpolation'] | ['computer-vision'] | [-5.20634428e-02 -3.60848188e-01 -2.52667099e-01 3.30395587e-02
-1.68499514e-01 -1.94227844e-01 3.17768604e-01 -7.56749034e-01
-1.30807891e-01 7.33316720e-01 1.37428612e-01 -2.66304821e-01
8.18827301e-02 -6.28011405e-01 -1.04169858e+00 -6.00999892e-01
-2.19936758e-01 -1.39536545e-01 5.29407382e-01 -6.97380584... | [10.624603271484375, -1.359976053237915] |
84e9e5b4-676a-40e2-b486-a3a25792eba2 | generalizable-features-from-unsupervised | 1612.03809 | null | http://arxiv.org/abs/1612.03809v1 | http://arxiv.org/pdf/1612.03809v1.pdf | Generalizable Features From Unsupervised Learning | Humans learn a predictive model of the world and use this model to reason
about future events and the consequences of actions. In contrast to most
machine predictors, we exhibit an impressive ability to generalize to unseen
scenarios and reason intelligently in these settings. One important aspect of
this ability is ph... | ['Mehdi Mirza', 'Yoshua Bengio', 'Aaron Courville'] | 2016-12-12 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 3.57590586e-01 1.65081948e-01 -4.52445835e-01 -5.31826377e-01
8.72897450e-03 -6.00883305e-01 7.31234550e-01 2.59905845e-01
1.58416212e-01 8.20943534e-01 2.72452980e-01 -4.87552166e-01
-1.53687343e-01 -7.67275035e-01 -9.65390682e-01 -4.97707993e-01
-5.67476928e-01 2.04486847e-01 4.76759255e-01 -2.76935279... | [8.382649421691895, 0.6778398156166077] |
4daaea5a-6907-4e21-a276-a5d354b54872 | 3d-face-modeling-from-diverse-raw-scan-data | 1902.04943 | null | https://arxiv.org/abs/1902.04943v3 | https://arxiv.org/pdf/1902.04943v3.pdf | 3D Face Modeling From Diverse Raw Scan Data | Traditional 3D face models learn a latent representation of faces using linear subspaces from limited scans of a single database. The main roadblock of building a large-scale face model from diverse 3D databases lies in the lack of dense correspondence among raw scans. To address these problems, this paper proposes an ... | ['Luan Tran', 'Xiaoming Liu', 'Feng Liu'] | 2019-02-13 | 3d-face-modeling-from-diverse-raw-scan-data-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_3D_Face_Modeling_From_Diverse_Raw_Scan_Data_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_3D_Face_Modeling_From_Diverse_Raw_Scan_Data_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-face-modeling'] | ['computer-vision'] | [-7.70146474e-02 2.60589838e-01 -2.73295105e-01 -9.49223280e-01
-6.93970978e-01 -6.49093628e-01 6.54389799e-01 -7.29272842e-01
2.95087665e-01 1.54022515e-01 2.03491509e-01 2.15077013e-01
-5.72089143e-02 -7.06526518e-01 -1.04362810e+00 -4.17679250e-01
1.27242431e-01 9.42728639e-01 -5.14077485e-01 8.78313258... | [13.17330551147461, -0.0019794453401118517] |
ba9a3526-204c-4c26-9cc1-2ff869071629 | improving-sp-stock-prediction-with-time | 2002.05784 | null | https://arxiv.org/abs/2002.05784v1 | https://arxiv.org/pdf/2002.05784v1.pdf | Improving S&P stock prediction with time series stock similarity | Stock market prediction with forecasting algorithms is a popular topic these days where most of the forecasting algorithms train only on data collected on a particular stock. In this paper, we enriched the stock data with related stocks just as a professional trader would have done to improve the stock prediction model... | ['Lior Sidi'] | 2020-02-08 | null | null | null | null | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-9.53318834e-01 -3.03328365e-01 -4.19525981e-01 -2.05899194e-01
-5.36457971e-02 -7.82933593e-01 7.02091098e-01 -1.66429132e-01
-3.51393372e-01 1.00428426e+00 2.00405911e-01 -4.83062118e-01
1.17238583e-02 -1.08543277e+00 -4.48937297e-01 -4.10589725e-01
-2.43313491e-01 3.33893716e-01 6.11621857e-01 -8.78902256... | [4.522308349609375, 4.21636438369751] |
6a01a16f-8852-422e-aace-cbdad26bb4e6 | small-footprint-keyword-spotting-with-graph | 1912.05124 | null | https://arxiv.org/abs/1912.05124v1 | https://arxiv.org/pdf/1912.05124v1.pdf | Small-footprint Keyword Spotting with Graph Convolutional Network | Despite the recent successes of deep neural networks, it remains challenging to achieve high precision keyword spotting task (KWS) on resource-constrained devices. In this study, we propose a novel context-aware and compact architecture for keyword spotting task. Based on residual connection and bottleneck structure, w... | ['Leibo Liu', 'Dandan song', 'Shouyi Yin', 'Shaojun Wei', 'Peng Ouyang', 'Xi Chen'] | 2019-12-11 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 1.09042376e-02 -1.30216151e-01 -6.14817381e-01 -4.31091696e-01
-5.44205308e-01 -1.55556336e-01 2.86194116e-01 3.01469192e-02
-5.07888436e-01 5.14346302e-01 2.74388999e-01 -7.42335975e-01
-2.44351074e-01 -4.46021795e-01 -6.91105664e-01 -2.04727158e-01
1.20027110e-01 -2.54550744e-02 4.21565771e-01 -3.10930796... | [14.149051666259766, 6.342257022857666] |
ea60267d-dcd5-4efb-bb0e-41d31cb9d53d | naming-objects-for-vision-and-language | 2303.02871 | null | https://arxiv.org/abs/2303.02871v1 | https://arxiv.org/pdf/2303.02871v1.pdf | Naming Objects for Vision-and-Language Manipulation | Robot manipulation tasks by natural language instructions need common understanding of the target object between human and the robot. However, the instructions often have an interpretation ambiguity, because the instruction lacks important information, or does not express the target object correctly to complete the tas... | ['Jerry Jun Yokono', 'Tamaki Kojima', 'Yu Ishihara', 'Jianing Wu', 'Takayoshi Takayanagi', 'Shunichi Sekiguchi', 'Kazumi Aoyama', 'Tokuhiro Nishikawa'] | 2023-03-06 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 5.82358725e-02 1.74106106e-01 3.68610732e-02 -3.81461412e-01
-1.07324272e-01 -7.47057498e-01 3.47937316e-01 3.77788991e-02
-5.37628829e-01 5.97987831e-01 3.30979936e-02 -9.18833762e-02
-5.34287235e-03 -5.95827937e-01 -6.92680597e-01 -3.32083881e-01
9.55382586e-02 7.67929137e-01 3.61776054e-01 -3.87654662... | [4.558807849884033, 0.7929629683494568] |
947e6893-75c3-49fc-845d-ba8f0511502b | cnn-assisted-steganography-integrating | 2304.12503 | null | https://arxiv.org/abs/2304.12503v1 | https://arxiv.org/pdf/2304.12503v1.pdf | CNN-Assisted Steganography -- Integrating Machine Learning with Established Steganographic Techniques | We propose a method to improve steganography by increasing the resilience of stego-media to discovery through steganalysis. Our approach enhances a class of steganographic approaches through the inclusion of a steganographic assistant convolutional neural network (SA-CNN). Previous research showed success in discoverin... | ['Mitchell A. Thornton', 'Eric C. Larson', 'Theodore Manikas', 'Andrew Havard'] | 2023-04-25 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 8.77783895e-01 4.04405683e-01 2.31897041e-01 1.18316799e-01
-1.00548394e-01 -3.62398654e-01 6.93792403e-01 -5.55094540e-01
-2.05375940e-01 3.72195512e-01 -2.72474408e-01 -7.26866305e-01
2.18374327e-01 -1.41748571e+00 -9.37428534e-01 -7.27200747e-01
-4.52389300e-01 1.46932542e-01 4.66042280e-01 -7.80025542... | [4.312468528747559, 8.060715675354004] |
2034efa4-669a-4bcf-b756-fc90041705d0 | comparison-of-time-frequency-representations | 1706.07156 | null | http://arxiv.org/abs/1706.07156v1 | http://arxiv.org/pdf/1706.07156v1.pdf | Comparison of Time-Frequency Representations for Environmental Sound Classification using Convolutional Neural Networks | Recent successful applications of convolutional neural networks (CNNs) to
audio classification and speech recognition have motivated the search for
better input representations for more efficient training. Visual displays of an
audio signal, through various time-frequency representations such as
spectrograms offer a ri... | ['M. Huzaifah'] | 2017-06-22 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 2.85296053e-01 -3.88389111e-01 2.99418658e-01 -1.41585350e-01
-3.58199060e-01 -5.46830237e-01 4.76981968e-01 3.51801753e-01
-5.07995427e-01 4.68618870e-01 3.71842772e-01 -2.02327996e-01
-3.43854457e-01 -6.06977940e-01 -3.70620549e-01 -7.37152457e-01
-4.88653988e-01 -4.68173265e-01 4.03468087e-02 -2.46942088... | [15.186247825622559, 5.418130874633789] |
060b463f-28f5-4d10-a36f-1e901de6cd01 | cross-domain-joint-dictionary-learning-for | 2101.02362 | null | https://arxiv.org/abs/2101.02362v1 | https://arxiv.org/pdf/2101.02362v1.pdf | Cross-domain Joint Dictionary Learning for ECG Inference from PPG | The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis has limited fi... | ['Min Wu', 'Yuenan Li', 'Qiang Zhu', 'Xin Tian'] | 2021-01-07 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.44966015e-01 -1.81094810e-01 -9.79443081e-03 -3.13733310e-01
-9.52322483e-01 -5.80664396e-01 -1.45560250e-01 3.30451247e-03
2.64897376e-01 8.37172747e-01 4.55773562e-01 -2.90392339e-01
-5.55982292e-01 -4.41258430e-01 -2.89355636e-01 -8.31233442e-01
-3.34370017e-01 3.06054085e-01 -5.45895994e-01 2.05712661... | [14.269201278686523, 3.2438199520111084] |
6ca4c8fd-94ca-44bb-9bda-66682dd28b9d | sa2sl-from-aspect-based-sentiment-analysis-to | 2105.15079 | null | https://arxiv.org/abs/2105.15079v2 | https://arxiv.org/pdf/2105.15079v2.pdf | SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence | In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on a strict annotation sch... | ['Kiet Van Nguyen', 'Tin Van Huynh', 'Luan Thanh Nguyen', 'Sieu Khai Huynh', 'Tham Thi Nguyen', 'Kim Thi-Thanh Nguyen', 'Phuc Huynh Pham', 'Luong Luc Phan'] | 2021-05-31 | null | null | null | null | ['classification', 'vietnamese-aspect-based-sentiment-analysis', 'vietnamese-datasets', 'vietnamese-sentiment-analysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-9.82355624e-02 3.28666940e-02 -1.35746405e-01 -5.74708164e-01
-7.77576506e-01 -2.20255762e-01 5.51630199e-01 2.18172800e-02
-7.97064781e-01 3.10382873e-01 5.31680644e-01 -4.44250345e-01
5.32545269e-01 -8.13333333e-01 -2.26709723e-01 -5.64129293e-01
1.81224570e-01 2.12440789e-01 9.95390639e-02 -8.10446441... | [11.33250617980957, 6.800070285797119] |
d214d9b4-861b-4b31-86d7-40365bb882a1 | realistic-conversational-question-answering | 2302.05137 | null | https://arxiv.org/abs/2302.05137v1 | https://arxiv.org/pdf/2302.05137v1.pdf | Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement | Conversational Question Answering (ConvQA) models aim at answering a question with its relevant paragraph and previous question-answer pairs that occurred during conversation multiple times. To apply such models to a real-world scenario, some existing work uses predicted answers, instead of unavailable ground-truth ans... | ['Jong C. Park', 'Sung Ju Hwang', 'Jinheon Baek', 'Soyeong Jeong'] | 2023-02-10 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-1.11976445e-01 4.63260114e-01 3.81742746e-01 -9.65885341e-01
-1.29557753e+00 -6.88703060e-01 5.48317671e-01 4.06714752e-02
-1.82956353e-01 1.05080795e+00 6.40874565e-01 -3.70073378e-01
1.21425919e-01 -8.12327981e-01 -6.47422731e-01 -2.05033660e-01
4.84330207e-01 7.83029854e-01 5.47653437e-01 -4.22508925... | [11.81196403503418, 8.019697189331055] |
4605ac99-e52c-4d37-a690-fd8982d3eb14 | a-diffusion-map-based-algorithm-for-gradient | 2108.06988 | null | https://arxiv.org/abs/2108.06988v5 | https://arxiv.org/pdf/2108.06988v5.pdf | A diffusion-map-based algorithm for gradient computation on manifolds and applications | We recover the Riemannian gradient of a given function defined on interior points of a Riemannian submanifold in the Euclidean space based on a sample of function evaluations at points in the submanifold. This approach is based on the estimates of the Laplace-Beltrami operator proposed in the diffusion-maps theory. The... | ['Jorge P. Zubelli', 'Antônio J. Silva Neto', 'Alvaro Almeida Gomez'] | 2021-08-16 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [-2.21958339e-01 2.85821348e-01 4.95517820e-01 -3.30875546e-01
-2.41874069e-01 -3.47894371e-01 2.04537004e-01 -2.76498199e-01
-6.03718221e-01 8.54401648e-01 -3.08724884e-02 -1.59693837e-01
-3.40965658e-01 -6.12963021e-01 -5.58897495e-01 -1.04561603e+00
-5.76015174e-01 4.40963358e-01 -3.81929353e-02 -2.85328239... | [7.471062183380127, 4.163649559020996] |
b0382bc9-7f61-4191-a991-b819fd63ad09 | altclip-altering-the-language-encoder-in-clip | 2211.06679 | null | https://arxiv.org/abs/2211.06679v2 | https://arxiv.org/pdf/2211.06679v2.pdf | AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities | In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model. Starting from the pre-trained multimodal representation model CLIP released by OpenAI, we altered its text encoder with a pre-trained multilingual text encoder XLM-R, and aligned... | ['Ledell Wu', 'Qinghong Yang', 'Fulong Ye', 'Bo-Wen Zhang', 'Guang Liu', 'Zhongzhi Chen'] | 2022-11-12 | null | null | null | null | ['zero-shot-transfer-image-classification', 'zero-shot-cross-modal-retrieval', 'xlm-r'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 3.00677330e-03 2.61494536e-02 -3.40776265e-01 -4.85224903e-01
-1.24977827e+00 -7.93203652e-01 1.03419197e+00 -2.59789348e-01
-6.82864964e-01 7.18338549e-01 3.45224261e-01 -4.39841092e-01
5.61927497e-01 -1.92134619e-01 -1.06340599e+00 -2.58250684e-01
1.38294473e-01 6.04424238e-01 -3.02558094e-01 -4.63746309... | [11.227875709533691, 1.5801827907562256] |
db545a66-fbb1-420f-937b-c477c5bc5be9 | color-constancy-by-learning-to-predict | 1506.02167 | null | http://arxiv.org/abs/1506.02167v2 | http://arxiv.org/pdf/1506.02167v2.pdf | Color Constancy by Learning to Predict Chromaticity from Luminance | Color constancy is the recovery of true surface color from observed color,
and requires estimating the chromaticity of scene illumination to correct for
the bias it induces. In this paper, we show that the per-pixel color statistics
of natural scenes---without any spatial or semantic context---can by themselves
be a po... | ['Ayan Chakrabarti'] | 2015-06-06 | color-constancy-by-learning-to-predict-1 | http://papers.nips.cc/paper/5864-color-constancy-by-learning-to-predict-chromaticity-from-luminance | http://papers.nips.cc/paper/5864-color-constancy-by-learning-to-predict-chromaticity-from-luminance.pdf | neurips-2015-12 | ['color-constancy'] | ['computer-vision'] | [ 5.47188878e-01 -4.93165553e-01 -7.62957633e-02 -5.69735825e-01
-9.07369971e-01 -8.11866701e-01 3.73832762e-01 -1.55518547e-01
-2.74813384e-01 7.39633560e-01 -7.32258260e-02 -2.10133046e-01
2.22779021e-01 -6.79448903e-01 -6.46253765e-01 -1.10877752e+00
1.52195275e-01 -7.61033893e-02 2.29542069e-02 1.18757479... | [10.420372009277344, -2.5974996089935303] |
1e5013b6-e988-493e-bae3-2909132a9315 | some-options-for-l1-subspace-signal | 1309.1194 | null | http://arxiv.org/abs/1309.1194v1 | http://arxiv.org/pdf/1309.1194v1.pdf | Some Options for L1-Subspace Signal Processing | We describe ways to define and calculate $L_1$-norm signal subspaces which
are less sensitive to outlying data than $L_2$-calculated subspaces. We focus
on the computation of the $L_1$ maximum-projection principal component of a
data matrix containing N signal samples of dimension D and conclude that the
general proble... | ['Panos P. Markopoulos', 'George N. Karystinos', 'Dimitris A. Pados'] | 2013-09-04 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 1.91990137e-01 -7.00613186e-02 1.57809719e-01 -3.16028297e-01
-1.01804972e+00 -5.83979070e-01 -6.23110086e-02 -3.22275043e-01
-5.56631029e-01 6.09466791e-01 3.40471715e-01 -3.16594183e-01
-7.08152831e-01 -3.59511584e-01 -3.59584033e-01 -9.25537944e-01
-8.82758260e-01 3.17583263e-01 -4.48391438e-01 1.28208742... | [7.066337585449219, 4.462714672088623] |
2c801bb0-62f7-4d33-8862-34f7bd575903 | pre-trained-embeddings-for-entity-resolution | 2304.12329 | null | https://arxiv.org/abs/2304.12329v1 | https://arxiv.org/pdf/2304.12329v1.pdf | Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark] | Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the most popular ones being fastText and variants of the BERT mod... | ['Manolis Koubarakis', 'Dimitrios Skoutas', 'George Papadakis', 'Alexandros Zeakis'] | 2023-04-24 | null | null | null | null | ['blocking', 'entity-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-2.84455597e-01 -7.71262916e-03 -7.56417811e-01 -2.42736578e-01
-9.18538153e-01 -4.08885300e-01 8.22917998e-01 5.89435101e-01
-9.30678725e-01 6.23805523e-01 5.25316298e-01 -4.12503004e-01
-2.00004280e-01 -1.03158164e+00 -7.49388099e-01 -2.25392982e-01
-3.65983456e-01 6.46029413e-01 3.19093764e-02 -4.09733355... | [9.452463150024414, 8.574385643005371] |
af46a9db-0109-4077-9b77-7996c312fd4c | low-precision-quantization-aware-training-in | 2305.19295 | null | https://arxiv.org/abs/2305.19295v1 | https://arxiv.org/pdf/2305.19295v1.pdf | Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function | Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limi... | ['Ahmed Eltawil', 'Mohammed E. Fouda', 'Ayan Shymyrbay'] | 2023-05-30 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 4.81218696e-01 -3.54087561e-01 -1.23079456e-01 -5.12596555e-02
-9.33246389e-02 -1.38485119e-01 4.94434953e-01 1.86759293e-01
-9.71933484e-01 1.04456043e+00 -6.07319474e-01 -2.52047956e-01
-1.37355939e-01 -7.96548128e-01 -5.40897250e-01 -1.06562018e+00
-4.69152965e-02 -9.19645280e-02 6.36798739e-01 -2.19885245... | [8.266236305236816, 2.5473713874816895] |
75ce0864-3e8b-4653-9fcc-62609f82aee8 | leveraging-non-dialogue-summaries-for-1 | 2210.09474 | null | https://arxiv.org/abs/2210.09474v1 | https://arxiv.org/pdf/2210.09474v1.pdf | Leveraging Non-dialogue Summaries for Dialogue Summarization | To mitigate the lack of diverse dialogue summarization datasets in academia, we present methods to utilize non-dialogue summarization data for enhancing dialogue summarization systems. We apply transformations to document summarization data pairs to create training data that better befit dialogue summarization. The sug... | ['Jihwa Lee', 'Dongchan Shin', 'Seongmin Park'] | 2022-10-17 | leveraging-non-dialogue-summaries-for | https://aclanthology.org/2022.tu-1.1 | https://aclanthology.org/2022.tu-1.1.pdf | tu-coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 2.92069614e-01 7.03145146e-01 -4.75192547e-01 -3.08423519e-01
-1.31342769e+00 -6.81378484e-01 8.51900280e-01 3.76282811e-01
-2.16243774e-01 1.31902254e+00 1.21782470e+00 -9.17490348e-02
2.00628519e-01 -5.18725753e-01 -7.07513765e-02 -1.25901431e-01
5.15287697e-01 5.28039873e-01 -1.65399052e-02 -7.96588063... | [12.45092487335205, 9.23597526550293] |
424f315e-1e84-4e6c-9cb6-ede11085af20 | segmentation-renormalized-deep-feature | 2102.06315 | null | https://arxiv.org/abs/2102.06315v2 | https://arxiv.org/pdf/2102.06315v2.pdf | Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization | Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of images across sites is contraindicated for downstream statistical and deep learning-based image analysis due to inconsistent contrast, resolution, and noise. To this end, in the absence of paired data, varia... | ['Guido Gerig', 'James Fishbaugh', 'Neel Dey', 'Mengwei Ren'] | 2021-02-11 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 7.85763562e-01 2.09412389e-02 -3.21705341e-02 -3.39063108e-01
-1.10814667e+00 -1.04493141e+00 5.94599962e-01 -1.51400000e-01
-4.56416279e-01 6.86560690e-01 2.52387762e-01 -3.41507047e-01
-1.75100252e-01 -5.46100557e-01 -7.72104681e-01 -7.94856012e-01
-3.46427299e-02 1.02824740e-01 5.32658473e-02 -5.40266708... | [13.970505714416504, -2.220198392868042] |
f4ca696f-6cdc-4c47-a4de-3066c7987b9c | inertia-guided-flow-completion-and-style | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.pdf | Inertia-Guided Flow Completion and Style Fusion for Video Inpainting | Physical objects have inertia, which resists changes in the velocity and motion direction. Inspired by this, we introduce inertia prior that optical flow, which reflects object motion in a local temporal window, keeps unchanged in the adjacent preceding or subsequent frame. We propose a flow completion network to a... | ['Dong Liu', 'Jingjing Fu', 'Kaidong Zhang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-inpainting'] | ['computer-vision'] | [ 8.01042840e-03 -5.01850426e-01 4.49298397e-02 -9.46746692e-02
2.69619618e-02 -3.42905164e-01 4.16251451e-01 -4.93424237e-01
-1.97868556e-01 7.81633139e-01 4.97633517e-01 1.59236357e-01
-2.14108229e-02 -7.53014326e-01 -4.95742559e-01 -5.67894697e-01
2.33040318e-01 -3.03984672e-01 5.50424516e-01 -1.11651726... | [10.749814987182617, -1.4422117471694946] |
8e0bb328-b7c5-4127-a2e6-0af35d8f87fa | mlrip-pre-training-a-military-language | 2207.13929 | null | https://arxiv.org/abs/2207.13929v1 | https://arxiv.org/pdf/2207.13929v1.pdf | MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base | Incorporating prior knowledge into pre-trained language models has proven to be effective for knowledge-driven NLP tasks, such as entity typing and relation extraction. Current pre-training procedures usually inject external knowledge into models by using knowledge masking, knowledge fusion and knowledge replacement. H... | ['Wei Sun', 'Jiping Zheng', 'Lin Yu', 'Xin Zhao', 'Xuekang Yang', 'Hui Li'] | 2022-07-28 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-4.24046703e-02 4.22197253e-01 -7.76967704e-01 -2.83766598e-01
-4.70252723e-01 -6.11594677e-01 5.46589792e-01 2.56185204e-01
-7.99130559e-01 1.38893330e+00 3.68814804e-02 -3.26714277e-01
5.93667431e-03 -9.51826811e-01 -5.37806809e-01 -2.29176641e-01
1.21564947e-01 4.34528291e-01 3.82491618e-01 -1.45019844... | [9.443830490112305, 8.545469284057617] |
25f71788-23f1-46b0-b80b-52aa46a77cff | timesnet-temporal-2d-variation-modeling-for | 2210.02186 | null | https://arxiv.org/abs/2210.02186v3 | https://arxiv.org/pdf/2210.02186v3.pdf | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis | Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis tasks. Previous methods attempt to accomplish this directly from the 1... | ['Mingsheng Long', 'Jianmin Wang', 'Hang Zhou', 'Yong liu', 'Tengge Hu', 'Haixu Wu'] | 2022-10-05 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-2.83778995e-01 -1.00010359e+00 -1.88636091e-02 -8.96513462e-02
-1.31472647e-01 -6.69435203e-01 6.15531266e-01 -1.54716283e-01
-4.09038477e-02 2.49339104e-01 1.87955335e-01 -4.45666999e-01
-5.26696324e-01 -4.48360771e-01 -3.23982120e-01 -9.29577649e-01
-6.51254714e-01 -2.36909464e-02 -8.12453553e-02 -3.35409492... | [7.116802215576172, 2.8963780403137207] |
93f95d87-4bda-4f82-87fb-5afbc6ef3b3c | tadse-template-aware-dialogue-sentence | 2305.14299 | null | https://arxiv.org/abs/2305.14299v1 | https://arxiv.org/pdf/2305.14299v1.pdf | TaDSE: Template-aware Dialogue Sentence Embeddings | Learning high quality sentence embeddings from dialogues has drawn increasing attentions as it is essential to solve a variety of dialogue-oriented tasks with low annotation cost. However, directly annotating and gathering utterance relationships in conversations are difficult, while token-level annotations, \eg, entit... | ['Guoyin Wang', 'Jiwei Li', 'Minsik Oh'] | 2023-05-23 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'intent-classification', 'slot-filling'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.16326731e-01 4.54470426e-01 -2.13134140e-01 -7.62071550e-01
-8.22340906e-01 -2.90762872e-01 6.52802169e-01 3.01343471e-01
-6.12851262e-01 8.18231583e-01 8.25005412e-01 -2.62634307e-01
1.68310180e-01 -6.17716253e-01 -2.62409896e-01 -4.55845147e-01
1.74515799e-01 4.30843830e-01 1.23416871e-01 -5.41206419... | [12.41801643371582, 7.655238628387451] |
f136f748-076a-47be-87eb-bebc52ae91cc | grammatical-analysis-of-pretrained-sentence-1 | null | null | https://openreview.net/forum?id=Hkx5cU26kN | https://openreview.net/pdf?id=Hkx5cU26kN | Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments | Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to ... | ['Anonymous'] | 2018-12-11 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [-3.45224403e-02 8.20550501e-01 1.82056531e-01 -6.81531787e-01
-8.98257017e-01 -9.69194055e-01 6.04606152e-01 4.25340414e-01
-6.32148147e-01 7.68963993e-01 4.02851522e-01 -7.12068915e-01
9.20118392e-02 -7.73376465e-01 -1.24118030e+00 -4.19193923e-01
-1.64027110e-01 7.75026441e-01 3.82321700e-02 -6.44653261... | [10.675360679626465, 9.384177207946777] |
83c587ae-1f00-46c7-939c-b015448512d0 | image-segmentation-based-on-multiscale-fast | 1812.04816 | null | http://arxiv.org/abs/1812.04816v1 | http://arxiv.org/pdf/1812.04816v1.pdf | Image Segmentation Based on Multiscale Fast Spectral Clustering | In recent years, spectral clustering has become one of the most popular
clustering algorithms for image segmentation. However, it has restricted
applicability to large-scale images due to its high computational complexity.
In this paper, we first propose a novel algorithm called Fast Spectral
Clustering based on quad-t... | ['Chenjian Wu', 'Hong Chen', 'Minxin Chen', 'Guofeng Zhu', 'Chongyang Zhang'] | 2018-12-12 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 2.35353217e-01 -5.01284182e-01 -9.16338414e-02 1.14978291e-01
-6.63901627e-01 -5.88232458e-01 -1.34078175e-01 3.01520020e-01
-6.94118798e-01 9.60831642e-02 -2.97194839e-01 -3.56711388e-01
-2.14987509e-02 -8.94395709e-01 -2.71809459e-01 -8.13881636e-01
-1.53552219e-01 2.26211056e-01 9.22092736e-01 3.88318598... | [7.586995601654053, 4.714264869689941] |
2cbf309e-1aa0-487c-bd92-956f17b48fef | knowing-how-knowing-that-a-new-task-for | 2306.04187 | null | https://arxiv.org/abs/2306.04187v1 | https://arxiv.org/pdf/2306.04187v1.pdf | Knowing-how & Knowing-that: A New Task for Machine Reading Comprehension of User Manuals | The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However,current methods have trouble answering complex questions. Therefore, we introduce the Knowing-how & Knowing-that task that requires the model to answer factoid-style, procedure-style, and inconsistent questions about... | ['Jiancheng Lv', 'Zujie Wen', 'Wenqiang Lei', 'dingnan jin', 'Weihong Du', 'Jia Liu', 'Hongru Liang'] | 2023-06-07 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.44494522e-01 8.56336415e-01 1.16997510e-01 -7.61314034e-01
-9.10531044e-01 -8.24985445e-01 2.63138920e-01 3.63502413e-01
1.05408192e-01 3.06913257e-01 4.38045770e-01 -1.17647421e+00
-2.38870054e-01 -6.18618369e-01 -4.37416762e-01 3.71150494e-01
5.68191111e-01 5.94694674e-01 1.78847447e-01 -4.50371265... | [10.973759651184082, 7.923410415649414] |
56f2703b-57ea-4c82-85d3-c8011dd43276 | towards-antigenic-peptide-discovery-with | null | null | https://www.researchgate.net/publication/370528518_Towards_antigenic_peptide_discovery_with_better_MHC-I_binding_prediction_and_improved_benchmark_methodology | https://drive.google.com/file/d/1GH1t4pWf1cI9ivVrdOjS1o6tfk61DBCl/view | Towards antigenic peptide discovery with better MHC-I binding prediction and improved benchmark methodology | The Major Histocompatibility Complex (MHC) is a crucial component of the cellular immune system in vertebrates, responsible for, among others, presenting peptides derived from intracellular proteins. The MHC-I presentation is vital in the immune response and holds great promise in vaccine development and cancer immunot... | ['Anna Gambin', 'Piotr Grzegorczyk', 'Michał Rembalski', 'Michał Tyrolski', 'Piotr Kucharski', 'Grzegorz Preibisch', 'Stanisław Giziński'] | 2023-05-05 | null | null | null | machine-learning-for-drug-discovery-workshop | ['mhc-presentation-prediction'] | ['medical'] | [ 2.95069873e-01 -5.47206819e-01 -5.99381268e-01 -1.30804881e-01
-1.05823851e+00 -7.39733815e-01 6.59460902e-01 7.21872330e-01
-8.37518930e-01 1.22677648e+00 2.51641846e-03 -3.56862754e-01
7.32662603e-02 -5.66071868e-01 -5.96359134e-01 -1.07405770e+00
-2.72500694e-01 1.07551563e+00 4.06131536e-01 -5.67461312... | [4.760196208953857, 5.591445446014404] |
4e675ce2-443e-4929-9740-60e3de799ee7 | a-transformer-architecture-for-online-gesture | 2211.02643 | null | https://arxiv.org/abs/2211.02643v1 | https://arxiv.org/pdf/2211.02643v1.pdf | A Transformer Architecture for Online Gesture Recognition of Mathematical Expressions | The Transformer architecture is shown to provide a powerful framework as an end-to-end model for building expression trees from online handwritten gestures corresponding to glyph strokes. In particular, the attention mechanism was successfully used to encode, learn and enforce the underlying syntax of expressions creat... | ['Guénolé C. M. Silvestre', 'Mirco Ramo'] | 2022-11-04 | null | null | null | null | ['gesture-recognition', 'handwriting-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.48619080e-01 4.13799316e-01 -1.30508557e-01 -5.22624314e-01
-3.95451695e-01 -6.35646820e-01 6.50801897e-01 -3.05111200e-01
-3.21882218e-01 3.11275631e-01 1.12031482e-01 -3.82888675e-01
-1.12248681e-01 -6.56877279e-01 -6.18619025e-01 -6.45232856e-01
-1.25761583e-01 4.92794424e-01 3.38607165e-03 1.33910075... | [9.19549560546875, -6.4686174392700195] |
f88a15a1-e7c3-453a-9f29-5d1b7fed09cb | real-time-lip-sync-for-live-2d-animation | 1910.08685 | null | https://arxiv.org/abs/1910.08685v1 | https://arxiv.org/pdf/1910.08685v1.pdf | Real-Time Lip Sync for Live 2D Animation | The emergence of commercial tools for real-time performance-based 2D animation has enabled 2D characters to appear on live broadcasts and streaming platforms. A key requirement for live animation is fast and accurate lip sync that allows characters to respond naturally to other actors or the audience through the voice ... | ['Wilmot Li', 'Deepali Aneja'] | 2019-10-19 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 3.90478352e-04 4.25624922e-02 -1.65998966e-01 -2.08992615e-01
-1.09833324e+00 -3.69504601e-01 5.35418868e-01 -4.27317806e-02
-3.30009729e-01 4.33703780e-01 2.50400633e-01 -3.48889858e-01
6.76711500e-01 -2.86453426e-01 -5.48756957e-01 -3.71351570e-01
-3.95621002e-01 1.97314143e-01 3.92077208e-01 -1.86711192... | [13.245647430419922, -0.4466647803783417] |
2a1846e5-4f28-4cf0-98bb-4e2ae4a980da | efficient-joint-dimensional-search-with | 2208.05271 | null | https://arxiv.org/abs/2208.05271v1 | https://arxiv.org/pdf/2208.05271v1.pdf | Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation | Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal network structure that can run in real-time for this problem. Towards this goal, we jointly search the depth, channel, dilation rate and feat... | ['Wanli Ouyan', 'Qinghua Chi', 'Chongyan Zuo', 'Chen Lin', 'Zhen Mei', 'Jiayuan Fan', 'Tao Chen', 'Baopu Li', 'Peng Ye'] | 2022-08-10 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 1.52887851e-01 2.02319957e-02 -4.75416034e-02 -8.14923346e-02
-4.17255133e-01 -3.25870872e-01 -1.13302775e-01 -1.37949347e-01
-4.92263794e-01 4.38360780e-01 -3.67992103e-01 -2.41472185e-01
-2.90340871e-01 -6.81905627e-01 -3.67194176e-01 -8.30868363e-01
3.06037843e-01 5.69047555e-02 5.84493160e-01 3.74811552... | [9.651480674743652, -0.276358425617218] |
d129164b-7822-46e1-b054-376911d4b706 | ranking-aggregation-with-interactive-feedback | null | null | https://bmvc2022.mpi-inf.mpg.de/386/ | https://bmvc2022.mpi-inf.mpg.de/0386.pdf | Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification | Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-superv... | ['Chunjie Zhang', 'Zhongyuan Wang', 'Yue Zhang', 'Chao Liang', 'Ji Huang'] | 2022-11-21 | null | null | null | the-33rd-british-machine-vision-conference | ['person-re-identification'] | ['computer-vision'] | [-6.81765452e-02 -3.84763688e-01 -2.94025332e-01 -4.48811352e-01
-8.03489149e-01 -4.79573339e-01 7.34874666e-01 8.45479071e-02
-5.87189257e-01 5.81780493e-01 5.64117730e-01 3.37889224e-01
-2.47031674e-01 -5.67333579e-01 -2.16204688e-01 -5.17557740e-01
2.24435225e-01 8.48002255e-01 2.39264250e-01 -2.20969152... | [14.834145545959473, 1.0696346759796143] |
092568f5-f142-48da-9ec2-9db0e823b01e | non-monotonic-value-function-factorization | 2104.01939 | null | https://arxiv.org/abs/2104.01939v4 | https://arxiv.org/pdf/2104.01939v4.pdf | NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning | Multi-agent value-based approaches recently make great progress, especially value decomposition methods. However, there are still a lot of limitations in value function factorization. In VDN, the joint action-value function is the sum of per-agent action-value function while the joint action-value function of QMIX is t... | ['Quanlin Chen'] | 2021-04-05 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.05913860e-01 2.66282618e-01 -6.21858537e-01 2.52202693e-02
-6.43132508e-01 -4.94032085e-01 4.77170676e-01 -8.28192309e-02
-6.96594119e-01 1.29032540e+00 3.23786050e-01 -1.62544519e-01
-4.30273950e-01 -8.69054735e-01 -5.77495277e-01 -9.62268829e-01
-1.48138210e-01 5.61952531e-01 2.60200560e-01 -6.40081525... | [3.777921438217163, 2.0576374530792236] |
953a6078-fe5f-4624-b9d6-32ebca9782d5 | geometry-aligned-variational-transformer-for | 2209.00852 | null | https://arxiv.org/abs/2209.00852v1 | https://arxiv.org/pdf/2209.00852v1.pdf | Geometry Aligned Variational Transformer for Image-conditioned Layout Generation | Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant layout should consider both the intra-domain relationship within layout elements and the inter-doma... | ['Yuning Jiang', 'Tiezheng Ge', 'Hongtao Xie', 'Chuanbin Liu', 'Min Zhou', 'Ye Ma', 'Yunning Cao'] | 2022-09-02 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [ 4.17857580e-02 -2.25620344e-01 3.30720782e-01 -4.52909440e-01
-4.46967095e-01 -4.40813363e-01 2.52294451e-01 -5.03525622e-02
-1.90788787e-02 2.29867816e-01 3.74073237e-01 3.82899456e-02
-1.46096759e-02 -8.36516738e-01 -9.70983922e-01 -6.22352719e-01
6.82121336e-01 -6.41661137e-02 9.36634168e-02 -3.60470086... | [11.461195945739746, -0.7111929059028625] |
ff69fd26-7c98-4fd3-a8df-d3db3ee1726e | supervised-contrastive-learning-for-3 | 2210.16192 | null | https://arxiv.org/abs/2210.16192v2 | https://arxiv.org/pdf/2210.16192v2.pdf | Learning Audio Features with Metadata and Contrastive Learning | Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combi... | ['Nicolas Farrugia', 'Ilyass Moummad'] | 2022-10-27 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.59822041e-01 2.63838083e-01 -4.76591259e-01 -4.74529624e-01
-1.45301616e+00 -4.10846114e-01 3.82613778e-01 5.02541721e-01
-4.53524977e-01 7.50138879e-01 5.14512420e-01 -4.91081327e-02
-5.45956671e-01 -3.84423822e-01 -5.19833386e-01 -8.38495016e-01
-1.64329670e-02 5.81873894e-01 -1.28792852e-01 2.29547709... | [9.335515022277832, 4.295867443084717] |
57b0ee8c-189b-4960-855d-5e0e30046c8b | low-light-image-enhancement-via-structure | 2305.05839 | null | https://arxiv.org/abs/2305.05839v1 | https://arxiv.org/pdf/2305.05839v1.pdf | Low-Light Image Enhancement via Structure Modeling and Guidance | This paper proposes a new framework for low-light image enhancement by simultaneously conducting the appearance as well as structure modeling. It employs the structural feature to guide the appearance enhancement, leading to sharp and realistic results. The structure modeling in our framework is implemented as the edge... | ['Jiangbo Lu', 'RuiXing Wang', 'Xiaogang Xu'] | 2023-05-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Low-Light_Image_Enhancement_via_Structure_Modeling_and_Guidance_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-enhancement', 'low-light-image-enhancement', 'edge-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.73459631e-01 -1.24915272e-01 2.45542452e-01 -2.90402770e-01
-3.01967293e-01 2.69050361e-03 4.00072783e-01 -4.80203152e-01
-1.88919619e-01 3.29482883e-01 7.84642547e-02 -1.85576007e-02
3.14772695e-01 -9.02204692e-01 -6.65042818e-01 -8.17434311e-01
3.50670666e-01 -3.83778185e-01 4.56283092e-01 -3.75213772... | [10.784859657287598, -2.3723878860473633] |
8e308c2f-f936-4953-808a-fdf0c4d03c17 | deep-learning-automated-quantification-of | 2303.11130 | null | https://arxiv.org/abs/2303.11130v1 | https://arxiv.org/pdf/2303.11130v1.pdf | Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation | Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical asse... | ['Andrew J. Swift', 'Samer Alabed', 'Krit Dwivedi', 'Michael J. Sharkey'] | 2023-03-20 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 7.29203783e-03 1.09333105e-01 -2.79588968e-01 -4.54068966e-02
-5.61821640e-01 -5.51502109e-01 3.83181721e-01 1.52908534e-01
-1.99340254e-01 5.82106113e-01 3.57443362e-01 -6.93756044e-01
-5.59276879e-01 -9.94319141e-01 1.62169915e-02 -7.77082920e-01
-9.91476178e-02 1.35771298e+00 5.97670436e-01 4.63939250... | [15.313026428222656, -2.1146209239959717] |
7f1edf17-3634-4838-bb22-3a65e857abfb | unifier-a-unified-retriever-for-large-scale | 2205.11194 | null | https://arxiv.org/abs/2205.11194v2 | https://arxiv.org/pdf/2205.11194v2.pdf | UnifieR: A Unified Retriever for Large-Scale Retrieval | Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely cate... | ['Kai Zhang', 'Guodong Long', 'Daxin Jiang', 'Can Xu', 'Chongyang Tao', 'Xiubo Geng', 'Tao Shen'] | 2022-05-23 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.93602845e-01 -5.82563519e-01 -6.74365163e-01 -8.96402001e-02
-1.53238404e+00 -6.12066865e-01 1.06549466e+00 5.65906286e-01
-3.76953125e-01 5.33457577e-01 7.09760487e-01 1.10261412e-02
-6.17608607e-01 -8.16161573e-01 -3.68687958e-01 -5.38664758e-01
1.89010516e-01 4.04289842e-01 2.93605536e-01 -5.32753170... | [11.40027141571045, 7.7765793800354] |
d52181e6-d25f-4943-a91b-50471539ec81 | making-invisible-visible-data-driven-seismic | 2106.11892 | null | https://arxiv.org/abs/2106.11892v3 | https://arxiv.org/pdf/2106.11892v3.pdf | Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation | Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, da... | ['Youzuo Lin', 'Qiang Guan', 'Xitong Zhang', 'Yuxin Yang'] | 2021-06-22 | null | null | null | null | ['seismic-imaging', 'seismic-inversion'] | ['miscellaneous', 'miscellaneous'] | [ 2.59985000e-01 -4.29769605e-02 4.25342679e-01 -1.55101970e-01
-8.98491979e-01 -1.44189239e-01 4.76126075e-01 2.77377069e-02
-1.66527808e-01 8.17481339e-01 1.25673383e-01 -4.53137130e-01
-3.92759651e-01 -1.09038877e+00 -9.61754262e-01 -9.49511349e-01
-3.93990248e-01 2.59211600e-01 -7.48985708e-02 -4.64139670... | [6.879220962524414, 2.5306782722473145] |
a740601b-499c-414f-a9f1-a162e15cac11 | similarity-preserving-representation-learning | 1702.03584 | null | https://arxiv.org/abs/1702.03584v3 | https://arxiv.org/pdf/1702.03584v3.pdf | Similarity Preserving Representation Learning for Time Series Clustering | A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and eas... | ['Jin-Feng Yi', 'Inderjit S. Dhillon', 'Lingfei Wu', 'Roman Vaculin', 'Qi Lei'] | 2017-02-12 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.15036629e-01 -7.10688591e-01 3.20183299e-02 -3.71118665e-01
-7.27484524e-01 -7.42518544e-01 1.42663002e-01 3.69137466e-01
-3.62510145e-01 3.64015043e-01 -2.83761919e-01 -8.75339806e-02
-8.42321754e-01 -9.05327320e-01 -4.83099759e-01 -8.87004852e-01
-7.46473074e-01 4.49804962e-01 -9.27510932e-02 -7.73719475... | [7.277429580688477, 3.3320744037628174] |
9e623db6-704d-4199-ba11-429707dcfff3 | a-constraint-programming-approach-for-mining | 1311.6907 | null | http://arxiv.org/abs/1311.6907v1 | http://arxiv.org/pdf/1311.6907v1.pdf | A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database | Constraint-based pattern discovery is at the core of numerous data mining
tasks. Patterns are extracted with respect to a given set of constraints
(frequency, closedness, size, etc). In the context of sequential pattern
mining, a large number of devoted techniques have been developed for solving
particular classes of c... | ['Jean-Philippe Métivier', 'Thierry Charnois', 'Samir Loudni'] | 2013-11-27 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.95046437e-01 -9.68016386e-02 -3.39642704e-01 -3.61817360e-01
4.24348563e-01 -4.80681330e-01 4.82101917e-01 3.55184138e-01
-3.44021350e-01 6.88063741e-01 -1.93042085e-01 -3.17025810e-01
-7.24463880e-01 -9.42469120e-01 -1.46784246e-01 -3.84350777e-01
-3.67273271e-01 5.00621319e-01 5.44007063e-01 -3.63887288... | [8.307347297668457, 6.32077169418335] |
2059c45e-33a8-4576-9547-5fc36f362571 | learning-to-measure-change-fully | 1810.09111 | null | http://arxiv.org/abs/1810.09111v3 | http://arxiv.org/pdf/1810.09111v3.pdf | Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection | A critical challenge problem of scene change detection is that noisy changes
generated by varying illumination, shadows and camera viewpoint make variances
of a scene difficult to define and measure since the noisy changes and semantic
ones are entangled. Following the intuitive idea of detecting changes by
directly co... | ['Min Deng', 'Yu Liu', 'Haifeng Li', 'Qing Zhu', 'Xinsha Fu', 'Jiawei Zhu', 'Enqiang Guo'] | 2018-10-22 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 3.92981768e-02 -8.40529919e-01 4.05111969e-01 -5.57735682e-01
-2.79319584e-01 -7.43965805e-01 5.65629005e-01 8.29073265e-02
-5.72230160e-01 5.87569296e-01 1.79388702e-01 1.66279525e-01
-2.56058034e-02 -6.91246212e-01 -6.15979433e-01 -6.83165133e-01
1.23835377e-01 -3.03021193e-01 6.56613588e-01 -3.06361884... | [9.554386138916016, -1.0794360637664795] |
ee27e813-d324-442b-be77-954d197d709e | a-classification-scheme-for-local-energy | 2210.15344 | null | https://arxiv.org/abs/2210.15344v1 | https://arxiv.org/pdf/2210.15344v1.pdf | A Classification Scheme for Local Energy Trading | The current trend towards more renewable and sustainable energy generation leads to an increased interest in new energy management systems and the concept of a smart grid. One important aspect of this is local energy trading, which is an extension of existing electricity markets by including prosumers, who are consumer... | ['Bert Zwart', 'Johann L. Hurink', 'Jens Hönen'] | 2022-10-27 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.72246718e-01 3.27273384e-02 -3.38159412e-01 -2.80543268e-02
-5.83218709e-02 -1.24375963e+00 7.57922888e-01 7.37283826e-02
2.33107675e-02 1.03443766e+00 -5.36770150e-02 -1.75169334e-01
-3.64199817e-01 -1.15468645e+00 -1.52446359e-01 -1.22757840e+00
-1.46907821e-01 3.95404845e-01 -1.42472178e-01 -2.96102941... | [5.683749198913574, 2.5730576515197754] |
885379f1-f286-4830-8cc0-6a21676b1bd8 | near-optimal-multiple-testing-in-bayesian | 2211.02778 | null | https://arxiv.org/abs/2211.02778v2 | https://arxiv.org/pdf/2211.02778v2.pdf | Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control | In high dimensional variable selection problems, statisticians often seek to design multiple testing procedures that control the False Discovery Rate (FDR), while concurrently identifying a greater number of relevant variables. Model-X methods, such as Knockoffs and conditional randomization tests, achieve the primary ... | ['Song Mei', 'Licong Lin', 'Taejoo Ahn'] | 2022-11-04 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 3.38887930e-01 -5.89257479e-02 -3.09868187e-01 -2.83188134e-01
-7.59599328e-01 -4.47925717e-01 3.84983063e-01 9.57220718e-02
-3.46805006e-01 1.30838430e+00 -1.19764775e-01 -5.67645311e-01
-5.90908170e-01 -7.98611581e-01 -6.80980206e-01 -9.56186175e-01
-3.86176825e-01 5.68828821e-01 4.43686768e-02 4.02627558... | [7.545971870422363, 4.765110015869141] |
297d5ab3-7967-4276-8e1a-fe0250177f8e | attentionmask-attentive-efficient-object | 1811.08728 | null | http://arxiv.org/abs/1811.08728v1 | http://arxiv.org/pdf/1811.08728v1.pdf | AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects | We propose a novel approach for class-agnostic object proposal generation,
which is efficient and especially well-suited to detect small objects.
Efficiency is achieved by scale-specific objectness attention maps which focus
the processing on promising parts of the image and reduce the amount of sampled
windows strongl... | ['Simone Frintrop', 'Christian Wilms'] | 2018-11-21 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 0.11038441 0.17748722 -0.04186622 -0.3082973 -0.91136336 -0.13438025
0.5906927 0.26218873 -0.7167974 0.44718647 -0.18392432 0.2318154
-0.04874433 -0.9426222 -0.83273274 -0.45777977 -0.19208731 0.7279084
1.1528661 -0.14182301 0.5583613 0.6193462 -1.9765705 0.49652177
0.6348926 1.0013981 0.59... | [9.134653091430664, 0.8892461061477661] |
10c2f1e5-27a0-4ebb-8074-68b52dfd772c | end-to-end-learning-with-multiple-modalities | 2304.07151 | null | https://arxiv.org/abs/2304.07151v1 | https://arxiv.org/pdf/2304.07151v1.pdf | End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting | With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. This paper investigates the multi-modal (MM) learning and end-to-end (E2E) learning for nowcasting renewable power as an intermediate to energ... | ['Jochen L. Cremer', 'Ali Rajaei', 'Rushil Vohra'] | 2023-04-14 | null | null | null | null | ['energy-management'] | ['time-series'] | [-8.84600282e-02 -7.62662962e-02 -9.32913497e-02 8.78135711e-02
-6.77706778e-01 -9.35844958e-01 9.74518716e-01 2.26243347e-01
3.83591652e-02 1.61715662e+00 1.91327348e-01 -3.85766268e-01
-6.23829007e-01 -1.03538692e+00 -4.58018005e-01 -9.32204664e-01
-3.16202790e-01 2.17152044e-01 -4.23982471e-01 -1.03176229... | [6.211390018463135, 2.7755162715911865] |
4bc1ff57-59e5-4e8b-a10d-68739ce5195e | reference-aware-language-models | 1611.01628 | null | http://arxiv.org/abs/1611.01628v5 | http://arxiv.org/pdf/1611.01628v5.pdf | Reference-Aware Language Models | We propose a general class of language models that treat reference as an
explicit stochastic latent variable. This architecture allows models to create
mentions of entities and their attributes by accessing external databases
(required by, e.g., dialogue generation and recipe generation) and internal
state (required by... | ['Wang Ling', 'Chris Dyer', 'Phil Blunsom', 'Zichao Yang'] | 2016-11-05 | reference-aware-language-models-1 | https://aclanthology.org/D17-1197 | https://aclanthology.org/D17-1197.pdf | emnlp-2017-9 | ['recipe-generation'] | ['miscellaneous'] | [-2.34108984e-01 9.66194212e-01 -3.72845203e-01 -3.25630605e-01
-1.00197256e+00 -8.93155694e-01 1.42311239e+00 2.96761781e-01
-3.55024636e-01 1.08255255e+00 7.08387852e-01 -6.70905709e-02
3.83093208e-01 -1.14163339e+00 -8.09434533e-01 -2.33240008e-01
1.90171644e-01 1.08642185e+00 3.21432352e-01 -3.53337407... | [11.30345344543457, 8.831893920898438] |
772ded2a-f013-4966-a35e-d68f72030454 | robust-design-of-power-minimizing-symbol | 1805.02395 | null | http://arxiv.org/abs/1805.02395v2 | http://arxiv.org/pdf/1805.02395v2.pdf | Robust Design of Power Minimizing Symbol-Level Precoder under Channel Uncertainty | In this paper, we investigate the downlink transmission of a multiuser
multiple-input single-output (MISO) channel under a symbol-level precoding
(SLP) scheme, having imperfect channel knowledge at the transmitter. In
defining the SLP problem, a general category of constructive interference
regions (CIR) called distanc... | [] | 2018-08-12 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.30211905e-01 2.64561266e-01 6.29366711e-02 -3.55845168e-02
-7.77992606e-01 -4.30817574e-01 1.06674671e-01 -1.61754221e-01
-1.64932773e-01 1.04037917e+00 1.93770826e-01 -5.44770420e-01
-4.70772207e-01 -6.77873552e-01 -4.61246908e-01 -1.12573409e+00
-3.41741174e-01 -1.33800417e-01 -2.44012043e-01 -1.44869968... | [6.140008926391602, 1.4368258714675903] |
f25665b3-ebdf-4208-a0bc-b222ca8ab2b7 | few-shot-learning-for-cross-target-stance | 2301.04535 | null | https://arxiv.org/abs/2301.04535v2 | https://arxiv.org/pdf/2301.04535v2.pdf | Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings | Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification sc... | ['Arkaitz Zubiaga', 'Parisa Jamadi Khiabani'] | 2023-01-11 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.96138710e-01 1.78117067e-01 -6.85452819e-01 -1.28071100e-01
-9.49184597e-01 -4.51623619e-01 1.18393707e+00 5.13578832e-01
-3.34658295e-01 4.34193552e-01 5.77047467e-01 1.59971379e-02
-2.48215701e-02 -9.59979117e-01 -1.83231667e-01 -3.54939342e-01
-3.65005434e-01 5.88662744e-01 7.80341148e-01 -7.40375102... | [8.859371185302734, 10.126546859741211] |
ce2f8d93-9157-4ba0-a6c6-ddc4b98ee77c | what-makes-for-effective-few-shot-point-cloud | 2304.00022 | null | https://arxiv.org/abs/2304.00022v1 | https://arxiv.org/pdf/2304.00022v1.pdf | What Makes for Effective Few-shot Point Cloud Classification? | Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods require extensive data collection and retraining when dealing with novel classes never seen before. On the other hand, we humans can quickly ... | ['Jiayuan Fan', 'Tao Chen', 'Yanggang Zhang', 'Yongbin Liao', 'Hongyuan Zhu', 'Chuangguan Ye'] | 2023-03-31 | null | null | null | null | ['few-shot-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 9.23972726e-02 -2.71520615e-01 -1.76397324e-01 -3.48601907e-01
-4.66868550e-01 -3.67899001e-01 5.66939890e-01 3.11192609e-02
-1.75605968e-01 4.62223917e-01 -2.34017611e-01 -4.97274809e-02
-1.69798762e-01 -8.26738536e-01 -6.79425955e-01 -5.14350593e-01
-1.13978006e-01 5.05179763e-01 8.62708926e-01 -2.62652129... | [7.96692419052124, -3.2432703971862793] |
49795470-ddcc-4604-9723-9771cbca4f3e | doctor-a-multi-disease-detection-continual | 2305.05738 | null | https://arxiv.org/abs/2305.05738v1 | https://arxiv.org/pdf/2305.05738v1.pdf | DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors | Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven disease detection methods rely on customizing individual models for each disease and its corresponding WMS data. However, such methods lack a... | ['Niraj K. Jha', 'Chia-Hao Li'] | 2023-05-09 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.30038217e-01 3.58972587e-02 -4.13192183e-01 -2.10080698e-01
-7.04409778e-01 -2.51670498e-02 8.72529373e-02 3.18251550e-01
-6.68745995e-01 8.39409232e-01 -4.79377545e-02 -1.78332359e-01
-9.81079713e-02 -7.73570597e-01 -7.23604858e-01 -7.61117697e-01
-2.63472665e-02 8.27198267e-01 4.88758050e-02 1.96147561... | [6.185147762298584, 6.29212760925293] |
a660febf-e78a-46dd-aa75-bec1006dd175 | fairness-in-face-presentation-attack | 2209.09035 | null | https://arxiv.org/abs/2209.09035v1 | https://arxiv.org/pdf/2209.09035v1.pdf | Fairness in Face Presentation Attack Detection | Face presentation attack detection (PAD) is critical to secure face recognition (FR) applications from presentation attacks. FR performance has been shown to be unfair to certain demographic and non-demographic groups. However, the fairness of face PAD is an understudied issue, mainly due to the lack of appropriately a... | ['Naser Damer', 'Vitomir Struc', 'Arjan Kuijper', 'Wufei Yang', 'Meiling Fang'] | 2022-09-19 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 5.11000678e-03 7.94267505e-02 -3.04329038e-01 -6.85249209e-01
-5.64657152e-01 -6.16683424e-01 5.11467755e-01 -4.25548889e-02
-2.82392539e-02 5.58862329e-01 2.99881399e-01 -2.45441973e-01
-1.70715615e-01 -6.34419560e-01 -1.39769197e-01 -6.04730487e-01
-1.24259755e-01 2.27394581e-01 -2.86727071e-01 -2.34173268... | [13.055694580078125, 1.1919002532958984] |
f5a0fe64-d610-4e13-b28c-41178f07cd31 | registration-free-face-ssd-single-shot | 1902.04042 | null | http://arxiv.org/abs/1902.04042v1 | http://arxiv.org/pdf/1902.04042v1.pdf | Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild | In this paper, we present a novel single shot face-related task analysis
method, called Face-SSD, for detecting faces and for performing various
face-related (classification/regression) tasks including smile recognition,
face attribute prediction and valence-arousal estimation in the wild. Face-SSD
uses a Fully Convolu... | ['Hatice Gunes', 'Youngkyoon Jang', 'Ioannis Patras'] | 2019-02-11 | null | null | null | null | ['smile-recognition'] | ['computer-vision'] | [ 5.13627648e-01 2.02735662e-01 2.89552838e-01 -7.59238124e-01
-1.85562804e-01 -2.06480116e-01 3.86251867e-01 -4.04744059e-01
-2.80122280e-01 2.11685643e-01 -3.13419789e-01 1.48767471e-01
2.78524131e-01 -3.63403231e-01 -3.08322400e-01 -8.05635989e-01
-1.42656103e-01 3.05978358e-01 -5.50063029e-02 8.51023272... | [13.414545059204102, 0.9787525534629822] |
7bfc772b-a257-4cf0-8913-da7191405f61 | prior-aware-synthetic-data-to-the-rescue | 2208.13944 | null | https://arxiv.org/abs/2208.13944v1 | https://arxiv.org/pdf/2208.13944v1.pdf | Prior-Aware Synthetic Data to the Rescue: Animal Pose Estimation with Very Limited Real Data | Accurately annotated image datasets are essential components for studying animal behaviors from their poses. Compared to the number of species we know and may exist, the existing labeled pose datasets cover only a small portion of them, while building comprehensive large-scale datasets is prohibitively expensive. Here,... | ['Sarah Ostadabbas', 'Xiangyu Bai', 'Shuangjun Liu', 'Le Jiang'] | 2022-08-30 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [ 1.43727392e-01 2.15157151e-01 1.65478766e-01 -4.51104581e-01
-6.64910078e-01 -5.96597672e-01 2.12666348e-01 -3.32719624e-01
-7.97696590e-01 7.41325617e-01 -4.00454521e-01 3.24018866e-01
1.63773701e-01 -6.89932942e-01 -1.27145088e+00 -5.13843477e-01
-1.41923754e-02 9.21625018e-01 4.81959194e-01 -2.49032840... | [7.5603461265563965, -1.0023808479309082] |
de8b029f-9529-4cfc-abe6-c5825eb095bd | reduce-reuse-recycle-modular-multi-object | 2304.03696 | null | https://arxiv.org/abs/2304.03696v1 | https://arxiv.org/pdf/2304.03696v1.pdf | Reduce, Reuse, Recycle: Modular Multi-Object Navigation | Our work focuses on the Multi-Object Navigation (MultiON) task, where an agent needs to navigate to multiple objects in a given sequence. We systematically investigate the inherent modularity of this task by dividing our approach to contain four modules: (a) an object detection module trained to identify objects from R... | ['Angel X. Chang', 'Manolis Savva', 'Unnat Jain', 'Tommaso Campari', 'Sonia Raychaudhuri'] | 2023-04-07 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [ 1.25818923e-01 8.53731558e-02 3.04613352e-01 -2.63335165e-02
-8.15855920e-01 -8.92750442e-01 7.86282003e-01 1.27137601e-01
-5.59614360e-01 5.00882447e-01 -1.78487077e-01 -4.24454719e-01
-2.19438285e-01 -7.82659352e-01 -7.52433121e-01 -6.31073713e-01
-4.98631775e-01 8.66749287e-01 8.02480459e-01 -2.89768755... | [4.600461483001709, 0.6715357899665833] |
fcacf1e8-ea69-40db-a059-437586a55ce4 | adversarially-masking-synthetic-to-mimic-real | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Adversarially_Masking_Synthetic_To_Mimic_Real_Adaptive_Noise_Injection_for_CVPR_2023_paper.pdf | Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation | This paper considers the synthetic-to-real adaptation of point cloud semantic segmentation, which aims to segment the real-world point clouds with only synthetic labels available. Contrary to synthetic data which is integral and clean, point clouds collected by real-world sensors typically contain unexpected and ir... | ['Yi Yang', 'Yunchao Wei', 'Xiaohan Wang', 'Guoliang Kang', 'Guangrui Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 5.14984012e-01 3.63472760e-01 1.13472052e-01 -4.37557369e-01
-8.06561708e-01 -5.62865674e-01 4.38757092e-01 -2.41837695e-01
-3.19363087e-01 5.73103487e-01 -5.13274193e-01 -1.95738330e-01
4.01869923e-01 -9.40790772e-01 -1.26390457e+00 -7.27150381e-01
1.70036539e-01 4.22126770e-01 3.91990453e-01 -5.90467192... | [9.68041706085205, 1.173060655593872] |
68614c89-5d51-4e19-8389-6e52cf991675 | neural-part-priors-learning-to-optimize-part | 2203.09375 | null | https://arxiv.org/abs/2203.09375v2 | https://arxiv.org/pdf/2203.09375v2.pdf | Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans | 3D object recognition has seen significant advances in recent years, showing impressive performance on real-world 3D scan benchmarks, but lacking in object part reasoning, which is fundamental to higher-level scene understanding such as inter-object similarities or object functionality. Thus, we propose to leverage lar... | ['Angela Dai', 'Alexey Bokhovkin'] | 2022-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bokhovkin_Neural_Part_Priors_Learning_To_Optimize_Part-Based_Object_Completion_in_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.71021575e-01 2.53445596e-01 1.46856353e-01 -5.08807182e-01
-7.62681842e-01 -7.01611936e-01 4.16842133e-01 2.14170083e-01
2.92819530e-01 -9.96127799e-02 9.88532677e-02 7.92674348e-02
-2.86897421e-01 -6.58864319e-01 -1.28195548e+00 -1.57776177e-01
-1.53003752e-01 1.22067904e+00 3.25154513e-01 7.34845176... | [8.078327178955078, -3.005382537841797] |
39d274f0-d318-4700-9529-0cc9a191c39b | adaptive-radial-projection-on-fourier | null | null | https://ieeexplore.ieee.org/document/9897910 | https://www.researchgate.net/publication/364320913_ADAPTIVE_RADIAL_PROJECTION_ON_FOURIER_MAGNITUDE_SPECTRUM_FOR_DOCUMENT_IMAGE_SKEW_ESTIMATION | Adaptive Radial Projection on Fourier Magnitude Spectrum for Document Image Skew Estimation | Skew estimation is one of the vital tasks in document processing systems, especially for scanned document images, because its performance impacts subsequent steps directly. Over the years, an enormous number of researches focus on this challenging problem in the rise of digitization age. In this research, we first prop... | ['Luan Pham; Phu Hao Hoang; Xuan Toan Mai; Tuan Anh Tran'] | 2022-10-18 | null | null | null | ieee-international-conference-on-image-8 | ['document-image-skew-estimation'] | ['computer-vision'] | [ 1.96281657e-01 -5.96861064e-01 -1.13590635e-01 -3.08601052e-01
-3.75754267e-01 -6.18291378e-01 5.82745016e-01 -1.09727956e-01
-2.09042430e-01 4.08190072e-01 1.73316956e-01 -1.39626622e-01
-2.92486131e-01 -6.27514839e-01 -2.34673247e-01 -5.66201150e-01
1.42106310e-01 3.04896832e-01 2.81473041e-01 -1.52369276... | [11.852361679077148, 2.587871551513672] |
f1e62a26-08c5-4db5-8581-4c51b177fbe6 | improved-modulation-spectrum-histogram | null | null | https://aclanthology.org/O13-1014 | https://aclanthology.org/O13-1014.pdf | 改良調變頻譜統計圖等化法於強健性語音辨識之研究 (Improved Modulation Spectrum Histogram Equalization for Robust Speech Recognition) [In Chinese] | null | ['Yu-Chen Kao', 'Berlin Chen'] | 2013-10-01 | improved-modulation-spectrum-histogram-1 | https://aclanthology.org/O13-1014 | https://aclanthology.org/O13-1014.pdf | roclingijclclp-2013-10 | ['robust-speech-recognition'] | ['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... | [-7.209540843963623, 3.835824728012085] |
e176efeb-bcce-4456-b7e9-3c84ff7ea9b7 | an-efficient-anchor-free-universal-lesion | 2203.16074 | null | https://arxiv.org/abs/2203.16074v1 | https://arxiv.org/pdf/2203.16074v1.pdf | An Efficient Anchor-free Universal Lesion Detection in CT-scans | Existing universal lesion detection (ULD) methods utilize compute-intensive anchor-based architectures which rely on predefined anchor boxes, resulting in unsatisfactory detection performance, especially in small and mid-sized lesions. Further, these default fixed anchor-sizes and ratios do not generalize well to diffe... | ['Lovekesh Vig', 'Monika Sharma', 'Meghal Dani', 'Manu Sheoran'] | 2022-03-30 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 2.95068204e-01 7.98012987e-02 -5.84085524e-01 -2.62168467e-01
-1.26035357e+00 -3.79712105e-01 4.91039574e-01 5.45501411e-01
-5.70423663e-01 3.07746202e-01 2.61783361e-01 -6.22191019e-02
1.59847796e-01 -6.05917692e-01 -5.43947399e-01 -7.48944879e-01
-2.95844495e-01 5.43673694e-01 9.43298757e-01 -4.80031110... | [15.057435035705566, -2.3648037910461426] |
0be20703-be64-4cb9-8e5e-2b6fbce65bbc | an-efficient-circuit-compilation-flow-for | null | null | https://ieeexplore.ieee.org/abstract/document/9218558 | https://ieeexplore.ieee.org/abstract/document/9218558/figures#figures | An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm | Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems. The two-qubits gates used in quantum circuit for QAOA are commutative i.e., the order of gates can be altered without changing the logical output. This re-ordering leads... | ['Swaroop Ghosh Authors Info & Claims', 'Abdullah Ash- Saki', 'Mahabubul Alam'] | 2020-10-09 | null | null | null | acm-ieee-design-automation-conference-dac | ['combinatorial-optimization'] | ['methodology'] | [ 6.30644783e-02 -6.50428981e-02 1.83246240e-01 -1.70012921e-01
-4.74445611e-01 -8.48415196e-01 -1.97444364e-01 3.54247719e-01
-4.09363002e-01 8.69702756e-01 -3.04414660e-01 -6.01311088e-01
2.77297229e-01 -1.62144089e+00 -6.40252709e-01 -6.62665486e-01
-3.16341147e-02 3.52024287e-01 2.95174301e-01 -5.61186612... | [5.587961196899414, 4.927291393280029] |
898737a7-49fc-425e-b6ee-9a79d6f08497 | deepsleep-fast-and-accurate-delineation-of | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3445559 | https://papers.ssrn.com/sol3/Delivery.cfm/TLDIGITALHEALTH-S-19-00424.pdf?abstractid=3445559&mirid=1 | Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning | Background: Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with many negative effects including daytime sleepiness and sleep disorders. High-quality annotation of polysomnographic recordings is crucial for the diagnosis of sleep arousal disorders. Curr... | ['Yuanfang Guan', 'Hongyang Li'] | 2019-09-07 | null | null | null | lancet-2019-9 | ['sleep-quality-prediction', 'sleep-micro-event-detection', 'sleep-arousal-detection'] | ['medical', 'medical', 'medical'] | [ 1.14789099e-01 -1.72857314e-01 2.59530041e-02 -6.85389638e-01
-6.49915397e-01 -6.58388436e-01 -6.42872155e-02 4.25322413e-01
-6.40157044e-01 1.08425665e+00 1.36535987e-01 -1.01084001e-02
-3.45879383e-02 -2.47248724e-01 -7.53663704e-02 -6.36291981e-01
-3.63068759e-01 4.58415359e-01 -1.14651574e-02 1.15941390... | [13.532449722290039, 3.4880926609039307] |
d526a8da-9e07-4f03-975b-3c2ef7b2dab9 | deep-learning-for-logo-recognition | 1701.02620 | null | http://arxiv.org/abs/1701.02620v2 | http://arxiv.org/pdf/1701.02620v2.pdf | Deep Learning for Logo Recognition | In this paper we propose a method for logo recognition using deep learning.
Our recognition pipeline is composed of a logo region proposal followed by a
Convolutional Neural Network (CNN) specifically trained for logo
classification, even if they are not precisely localized. Experiments are
carried out on the FlickrLog... | ['Raimondo Schettini', 'Marco Buzzelli', 'Simone Bianco', 'Davide Mazzini'] | 2017-01-10 | null | null | null | null | ['logo-recognition'] | ['computer-vision'] | [ 3.72555941e-01 -1.72137111e-01 -3.54822487e-01 -2.01394558e-01
-3.74095708e-01 -2.69883573e-01 8.55599046e-01 -1.43158406e-01
-3.57962400e-01 2.38980904e-01 1.44107535e-01 -8.99543539e-02
1.16664067e-01 -7.58998215e-01 -8.54687631e-01 -7.53526509e-01
-2.62410581e-01 1.91641301e-01 2.86771148e-01 1.36606693... | [9.225594520568848, 1.1229444742202759] |
aeb841b7-dfaa-4212-add6-cfca0a641d09 | variational-relational-point-completion | 2104.10154 | null | https://arxiv.org/abs/2104.10154v1 | https://arxiv.org/pdf/2104.10154v1.pdf | Variational Relational Point Completion Network | Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-m... | ['Ziwei Liu', 'Shuai Yi', 'Haiyu Zhao', 'Junzhe Zhang', 'Zhongang Cai', 'Xinyi Chen', 'Liang Pan'] | 2021-04-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Pan_Variational_Relational_Point_Completion_Network_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [-1.77819118e-01 1.26554757e-01 -1.05032496e-01 -2.50893563e-01
-1.20755899e+00 -5.47550023e-01 6.45417035e-01 -3.88286859e-01
2.06160247e-01 2.73537815e-01 -1.55099332e-02 1.29107639e-01
-1.81502700e-01 -9.73183930e-01 -1.28852820e+00 -4.96828198e-01
3.59718114e-01 1.20235574e+00 1.49824202e-01 -3.82335298... | [8.449431419372559, -3.5897738933563232] |
ce87fa30-38a4-4b20-b4eb-74edc93dfe26 | hatebr-large-expert-annotated-corpus-of | null | null | https://openreview.net/forum?id=Nd1L1GfqOBS | https://openreview.net/pdf?id=Nd1L1GfqOBS | HateBR: Large expert annotated corpus of Brazilian Instagram comments for abusive language detection | Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection on the web and social media. The HateBR corpus was collected from Brazil... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['abusive-language'] | ['natural-language-processing'] | [-1.36943713e-01 5.17857194e-01 -1.72669023e-01 6.40361197e-03
-3.96770030e-01 -1.15668571e+00 8.88235569e-01 6.38840795e-01
-2.85980672e-01 6.63298845e-01 8.00646007e-01 -1.84474871e-01
2.15390176e-01 -3.64238828e-01 2.68728971e-01 -5.08537591e-01
1.91319197e-01 6.19430959e-01 1.84649497e-01 -3.57872576... | [8.725628852844238, 10.518516540527344] |
eac2f4f8-53f0-41a5-b293-4da2ebbc6b12 | genetic-algorithm-based-proportional-integral | 2304.10137 | null | https://arxiv.org/abs/2304.10137v1 | https://arxiv.org/pdf/2304.10137v1.pdf | Genetic-Algorithm-Based Proportional Integral Controller (GAPI) for ROV Steering Control | This article presents the design and real-time implementation of an optimal controller for precise steering control of a remotely operated underwater vehicle (ROV). A PI controller is investigated to achieve the desired steering performance. The gain parameters of the controller are tuned using the genetic algorithm (G... | ['Sarvat Mushtaq Ahmad', 'Ahsan Tanveer'] | 2023-04-20 | null | null | null | null | ['steering-control'] | ['computer-vision'] | [ 6.72539249e-02 3.50318141e-02 1.69663742e-01 -1.04390956e-01
4.60256562e-02 -5.57944775e-01 1.83628902e-01 -3.61774832e-01
-3.70870680e-01 8.33499610e-01 -3.26627493e-01 -3.72871280e-01
-7.75148094e-01 -5.59002638e-01 -3.09170395e-01 -1.20771742e+00
-1.29601926e-01 1.07745871e-01 3.25963020e-01 -8.48829627... | [5.2783966064453125, 2.2280843257904053] |
00f81ec0-bd47-4e7d-8a72-5f61d2b12254 | a-three-player-gan-for-super-resolution-in | 2303.13900 | null | https://arxiv.org/abs/2303.13900v1 | https://arxiv.org/pdf/2303.13900v1.pdf | A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging | Learning based single image super resolution (SISR) task is well investigated in 2D images. However, SISR for 3D Magnetics Resonance Images (MRI) is more challenging compared to 2D, mainly due to the increased number of neural network parameters, the larger memory requirement and the limited amount of available trainin... | ['Gabriele Lohmann', 'Klaus Scheffler', 'Florian Birk', 'Julius Steiglechner', 'Lucas Mahler', 'Qi Wang'] | 2023-03-24 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.78206700e-01 2.89339989e-01 2.09523812e-01 -1.76487893e-01
-1.05850744e+00 -2.40041777e-01 3.31091821e-01 -3.47632527e-01
-3.92994434e-01 9.98900235e-01 5.52623793e-02 1.09277843e-02
1.32937863e-01 -7.99009979e-01 -7.08474636e-01 -9.29416060e-01
2.42844656e-01 4.55532998e-01 2.96913743e-01 -5.23183309... | [13.810132026672363, -2.2097041606903076] |
9b86dc7e-669a-4116-8d66-d0012a40de9d | finding-coordinated-paths-for-multiple | 1402.3613 | null | http://arxiv.org/abs/1402.3613v1 | http://arxiv.org/pdf/1402.3613v1.pdf | Finding Coordinated Paths for Multiple Holonomic Agents in 2-d Polygonal Environment | Avoiding collisions is one of the vital tasks for systems of autonomous
mobile agents. We focus on the problem of finding continuous coordinated paths
for multiple mobile disc agents in a 2-d environment with polygonal obstacles.
The problem is PSPACE-hard, with the state space growing exponentially in the
number of ag... | ['Jiří Vokřínek', 'Michal Čáp', 'Pavel Janovský'] | 2014-02-14 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.17491284e-01 5.08328974e-01 -2.71727555e-02 5.66939414e-01
-7.17056632e-01 -8.35903406e-01 5.89672863e-01 4.26953405e-01
-7.56627142e-01 1.27662337e+00 -5.98369896e-01 -4.66540962e-01
-7.58913994e-01 -1.06233251e+00 -5.89138865e-01 -8.81080270e-01
-7.31264114e-01 1.43778467e+00 9.48164344e-01 -9.17784870... | [4.9694437980651855, 1.695407748222351] |
10693d7c-3bcd-4ea7-8ecd-ce6a186ff93d | csboundary-city-scale-road-boundary-detection | 2111.06020 | null | https://arxiv.org/abs/2111.06020v2 | https://arxiv.org/pdf/2111.06020v2.pdf | csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps | High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important information contained in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive within road ... | ['Ming Liu', 'Lujia Wang', 'Yuxiang Sun', 'Xiangcheng Hu', 'Lu Gan', 'Yuxuan Liu', 'Zhenhua Xu'] | 2021-11-11 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 3.58903348e-01 3.07237059e-01 -9.43588093e-02 -3.79107803e-01
-3.35775197e-01 -4.55991864e-01 3.91052604e-01 -1.74502984e-01
-3.31643783e-02 5.43823481e-01 -2.24692345e-01 -5.48664689e-01
6.64086491e-02 -1.39015067e+00 -5.76672077e-01 -4.35753196e-01
1.65874839e-01 3.68666083e-01 9.22443032e-01 -1.73042268... | [8.58311939239502, -1.6693329811096191] |
09a52d37-0546-49d8-baf5-3d4977368630 | higher-order-pooling-of-cnn-features-via | 1701.05432 | null | http://arxiv.org/abs/1701.05432v1 | http://arxiv.org/pdf/1701.05432v1.pdf | Higher-order Pooling of CNN Features via Kernel Linearization for Action Recognition | Most successful deep learning algorithms for action recognition extend models
designed for image-based tasks such as object recognition to video. Such
extensions are typically trained for actions on single video frames or very
short clips, and then their predictions from sliding-windows over the video
sequence are pool... | ['Piotr Koniusz', 'Stephen Gould', 'Anoop Cherian'] | 2017-01-19 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.77795607e-01 -5.20092964e-01 -3.60936105e-01 -4.53925103e-01
-7.93086052e-01 -2.78384686e-01 6.29780531e-01 6.87983334e-02
-5.80262601e-01 4.03722495e-01 3.70757312e-01 3.83099288e-01
-1.01740092e-01 -5.50852418e-01 -7.82337487e-01 -9.85126495e-01
-4.56446052e-01 -2.90782869e-01 6.58161283e-01 3.56314063... | [8.365890502929688, 0.643868625164032] |
2445a531-9c15-424b-a7b5-1ee19a4e36df | text-to-motion-retrieval-towards-joint | 2305.15842 | null | https://arxiv.org/abs/2305.15842v1 | https://arxiv.org/pdf/2305.15842v1.pdf | Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language | Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem.... | ['Tomáš Rebok', 'Fabrizio Falchi', 'Jan Sedmidubsky', 'Nicola Messina'] | 2023-05-25 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.48303264e-01 -5.48377216e-01 -2.86727786e-01 -1.18995883e-01
-1.37204623e+00 -5.09831786e-01 8.88151884e-01 -1.81507707e-01
-5.45210242e-01 3.16071481e-01 6.49316251e-01 2.37031356e-01
-2.23542541e-01 -3.96790087e-01 -6.44709826e-01 -5.08253753e-01
5.71984425e-02 5.21816492e-01 2.69960523e-01 1.07790584... | [10.059825897216797, 0.8536208271980286] |
3e138e37-b081-416a-9a99-bb9e9abddf24 | disc-differential-spectral-clustering-of | 2211.05314 | null | https://arxiv.org/abs/2211.05314v1 | https://arxiv.org/pdf/2211.05314v1.pdf | DiSC: Differential Spectral Clustering of Features | Selecting subsets of features that differentiate between two conditions is a key task in a broad range of scientific domains. In many applications, the features of interest form clusters with similar effects on the data at hand. To recover such clusters we develop DiSC, a data-driven approach for detecting groups of fe... | ['Ariel Jaffe', 'Gal Mishne', 'Ram Dyuthi Sristi'] | 2022-11-10 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 7.02074945e-01 -5.20549953e-01 -6.58721551e-02 -3.15756232e-01
-4.47850227e-01 -9.11670387e-01 4.93321776e-01 5.25744498e-01
-7.90884122e-02 3.96857589e-01 2.67206758e-01 -2.57155881e-03
-8.20568085e-01 -4.16553855e-01 -2.85375178e-01 -1.01245403e+00
-4.49715853e-01 2.61117190e-01 2.27742083e-02 -3.10212132... | [7.3645405769348145, 4.849296569824219] |
893b6483-0dc7-41a3-a7e6-159701221489 | sentence-embedding-leaks-more-information | 2305.03010 | null | https://arxiv.org/abs/2305.03010v1 | https://arxiv.org/pdf/2305.03010v1.pdf | Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence | Sentence-level representations are beneficial for various natural language processing tasks. It is commonly believed that vector representations can capture rich linguistic properties. Currently, large language models (LMs) achieve state-of-the-art performance on sentence embedding. However, some recent works suggest t... | ['Yangqiu Song', 'Mingshi Xu', 'Haoran Li'] | 2023-05-04 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 4.76324141e-01 2.22157612e-01 -3.17194700e-01 -2.79931843e-01
-9.18802738e-01 -6.87445343e-01 8.38120878e-01 3.21573615e-01
-5.22278905e-01 6.14366233e-01 7.75039852e-01 -8.11297178e-01
6.63293958e-01 -8.67960215e-01 -7.06776857e-01 -2.85680860e-01
9.99149084e-02 -2.46636406e-01 -1.14895217e-01 -1.88028201... | [10.740949630737305, 8.638459205627441] |
7f9f6e35-b47c-4415-bd62-0798e414f87e | adaptive-risk-tendency-nano-drone-navigation | 2203.14749 | null | https://arxiv.org/abs/2203.14749v2 | https://arxiv.org/pdf/2203.14749v2.pdf | Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning | Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones. In this paper, we investigate a specific case where a nano quadcopter robot learns to navigate an apriori-unknown cluttered environment under partial observabi... | ['Guido C. H. E. de Croon', 'Erik-Jan van Kampen', 'Cheng Liu'] | 2022-03-28 | null | null | null | null | ['drone-navigation', 'distributional-reinforcement-learning'] | ['computer-vision', 'methodology'] | [-3.27019215e-01 4.61704642e-01 -2.76352882e-01 -1.64569989e-01
-6.21453345e-01 -4.37933296e-01 4.93604869e-01 1.97426766e-01
-9.56238687e-01 1.26484084e+00 8.25374499e-02 -6.37860358e-01
-6.78734422e-01 -8.66292298e-01 -7.44996667e-01 -7.60516822e-01
-7.98667967e-01 3.09792876e-01 -5.66768870e-02 -2.28568017... | [4.499425411224365, 2.3975071907043457] |
d9b2c823-6eeb-4af3-bd13-a9abb722428b | feature-combination-meets-attention-baidu | 2106.14447 | null | https://arxiv.org/abs/2106.14447v1 | https://arxiv.org/pdf/2106.14447v1.pdf | Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection | With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace. To automate sports video editing/highlight generation process, a key task is to precisely recognize and locate the events in the long untrimmed videos. In this tech repo... | ['Jingyu Xin', 'Bo He', 'Zhiyu Cheng', 'Le Kang', 'Xin Zhou'] | 2021-06-28 | null | null | null | null | ['action-spotting', 'replay-grounding'] | ['computer-vision', 'computer-vision'] | [ 2.49412596e-01 -6.26551449e-01 -6.23080492e-01 -8.79468396e-02
-8.77656162e-01 -7.33466566e-01 3.67601395e-01 8.98292735e-02
-4.70811218e-01 4.90945518e-01 7.53251851e-01 4.74689424e-01
1.99015826e-01 -4.99110132e-01 -6.45000100e-01 -3.80376250e-01
-2.80233502e-01 -6.69426024e-02 6.69659257e-01 -2.57308453... | [7.982781887054443, 0.21034570038318634] |
56c790b6-3f58-4a18-ba1a-1073edda2b7c | graph-neural-network-surrogates-of-fair-graph | 2303.08157 | null | https://arxiv.org/abs/2303.08157v2 | https://arxiv.org/pdf/2303.08157v2.pdf | Graph Neural Network Surrogates of Fair Graph Filtering | Graph filters that transform prior node values to posterior scores via edge propagation often support graph mining tasks affecting humans, such as recommendation and ranking. Thus, it is important to make them fair in terms of satisfying statistical parity constraints between groups of nodes (e.g., distribute score mas... | ['Symeon Papadopoulos', 'Emmanouil Krasanakis'] | 2023-03-14 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.84489861e-01 6.27132952e-01 -4.63393986e-01 -5.42156518e-01
-2.49040440e-01 -5.95101237e-01 6.43754482e-01 7.38687217e-01
-3.63831401e-01 6.73493147e-01 4.57521290e-01 -4.24384803e-01
-4.75627363e-01 -1.38665891e+00 -6.39065802e-01 -1.56005681e-01
-4.53673601e-01 7.77342141e-01 2.89961725e-01 -3.57629135... | [8.623262405395508, 5.456225395202637] |
4e3b964b-4506-4906-a231-2a1bf21fa092 | multi-level-multiple-instance-learning-with | 2306.05029 | null | https://arxiv.org/abs/2306.05029v1 | https://arxiv.org/pdf/2306.05029v1.pdf | Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification | Whole slide image (WSI) refers to a type of high-resolution scanned tissue image, which is extensively employed in computer-assisted diagnosis (CAD). The extremely high resolution and limited availability of region-level annotations make it challenging to employ deep learning methods for WSI-based digital diagnosis. Mu... | ['Xinggang Wang', 'Yan Liu', 'Hao Xin', 'Yingzhuang Liu', 'Qiaozhe Zhang', 'Ruijie Zhang'] | 2023-06-08 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.37350363e-01 -1.00522421e-01 -5.38446128e-01 -9.95989665e-02
-1.48300815e+00 -3.13770533e-01 3.74556392e-01 2.81601697e-01
-5.02975047e-01 9.21881676e-01 -1.35337338e-01 -5.12049139e-01
-1.71775818e-01 -6.21603549e-01 -4.76098746e-01 -1.19389653e+00
2.24708095e-01 4.15580124e-01 3.38245898e-01 2.46376753... | [15.074305534362793, -2.8770899772644043] |
1aea74df-b319-48d5-9293-ea725ffa91e0 | physnet-a-neural-network-for-predicting | 1902.08408 | null | http://arxiv.org/abs/1902.08408v2 | http://arxiv.org/pdf/1902.08408v2.pdf | PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges | In recent years, machine learning (ML) methods have become increasingly
popular in computational chemistry. After being trained on appropriate ab
initio reference data, these methods allow to accurately predict the properties
of chemical systems, circumventing the need for explicitly solving the
electronic Schr\"odinge... | ['Oliver T. Unke', 'Markus Meuwly'] | 2019-02-22 | physnet-a-neural-network-for-predicting-1 | null | null | j-chem-theory-comput-2019-2 | ['formation-energy'] | ['miscellaneous'] | [ 4.44518365e-02 9.52456594e-02 -1.73607975e-01 -2.97091901e-01
-5.91770947e-01 -3.30721468e-01 3.11020076e-01 7.11642802e-01
-5.72204411e-01 1.35171676e+00 -2.57222742e-01 -6.47514045e-01
1.36029571e-01 -9.21216011e-01 -1.14804697e+00 -1.18915629e+00
-4.95839745e-01 5.46924174e-01 3.76014933e-02 -4.08802480... | [5.15622091293335, 5.34404182434082] |
e3d79c6a-72cf-409d-ac7b-6634dad9c745 | a-deep-learning-approach-for-real-time-3d | 1907.03520 | null | https://arxiv.org/abs/1907.03520v2 | https://arxiv.org/pdf/1907.03520v2.pdf | A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data | We present a new deep learning approach for real-time 3D human action recognition from skeletal data and apply it to develop a vision-based intelligent surveillance system. Given a skeleton sequence, we propose to encode skeleton poses and their motions into a single RGB image. An Adaptive Histogram Equalization (AHE) ... | ['Sergio A. Velastin', 'Houssam Salmane', 'Alain Crouzil', 'Pablo Zegers', 'Louahdi Khoudour', 'Huy Hieu Pham'] | 2019-07-08 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 5.79354703e-01 -8.18597302e-02 -1.48065895e-01 -5.08299053e-01
-4.02907282e-01 -8.29100162e-02 5.73727608e-01 -4.82316762e-01
-7.58280873e-01 1.55028746e-01 2.27788359e-01 -1.86970979e-01
7.97235891e-02 -6.79981470e-01 -7.63717175e-01 -7.84670770e-01
-2.85711497e-01 2.13946640e-01 4.72010016e-01 -2.18973428... | [7.769983768463135, 0.4293111264705658] |
45e71b26-b68f-49d9-a9f2-f80e2cc65b9f | global-counterfactual-explanations | 2204.06917 | null | https://arxiv.org/abs/2204.06917v1 | https://arxiv.org/pdf/2204.06917v1.pdf | Global Counterfactual Explanations: Investigations, Implementations and Improvements | Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While ... | ['Daniele Magazzeni', 'Saumitra Mishra', 'Dan Ley'] | 2022-04-14 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.57031988e-02 6.47847831e-01 -1.04959512e+00 -4.11631197e-01
-6.57992899e-01 -5.01752496e-01 8.94683242e-01 2.16228768e-01
1.47321522e-01 1.17758548e+00 7.09200084e-01 -1.00559497e+00
-9.65050578e-01 -5.96748471e-01 -2.80020475e-01 -2.29078785e-01
-4.19189222e-02 4.04897183e-01 -4.84553039e-01 -3.86982076... | [8.678763389587402, 5.647788047790527] |
73259a1e-82f1-4fef-9d08-eca335e6acc5 | knowledge-representation-learning-a | 1812.10901 | null | http://arxiv.org/abs/1812.10901v1 | http://arxiv.org/pdf/1812.10901v1.pdf | Knowledge Representation Learning: A Quantitative Review | Knowledge representation learning (KRL) aims to represent entities and
relations in knowledge graph in low-dimensional semantic space, which have been
widely used in massive knowledge-driven tasks. In this article, we introduce
the reader to the motivations for KRL, and overview existing approaches for
KRL. Afterwards,... | ['Maosong Sun', 'Ruobing Xie', 'Yankai Lin', 'Zhiyuan Liu', 'Xu Han'] | 2018-12-28 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-3.23663414e-01 3.11552376e-01 -5.63651800e-01 -2.14569911e-01
-5.20147383e-01 -6.30211055e-01 4.34461534e-01 5.83999157e-01
-9.57373083e-02 8.62564266e-01 2.84604669e-01 -3.43935043e-01
-8.80177557e-01 -1.06158841e+00 -4.12799358e-01 -1.62828088e-01
-2.63505995e-01 6.97774172e-01 1.60000101e-01 -3.51701140... | [8.846288681030273, 7.928858280181885] |
1c2bb955-5ef9-45dc-934f-11f8cea1d95c | disentangled-representation-learning-gan-for | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Tran_Disentangled_Representation_Learning_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tran_Disentangled_Representation_Learning_CVPR_2017_paper.pdf | Disentangled Representation Learning GAN for Pose-Invariant Face Recognition | The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform b... | ['Xiaoming Liu', 'Luan Tran', 'Xi Yin'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['robust-face-recognition'] | ['computer-vision'] | [ 4.41070288e-01 3.27807426e-01 -8.89127515e-03 -5.77658176e-01
-9.08330798e-01 -8.49087179e-01 5.96087337e-01 -1.02561069e+00
2.26423025e-01 6.72530770e-01 2.69585103e-01 1.70611188e-01
1.69770285e-01 -6.22767687e-01 -7.92246997e-01 -9.50031817e-01
2.80983269e-01 5.98316550e-01 -6.15799606e-01 -1.54519632... | [12.889564514160156, 0.07611364871263504] |
54f4d4b0-0160-462d-a27b-5f0943be1b2c | late-breaking-results-scalable-and-efficient | 2304.06728 | null | https://arxiv.org/abs/2304.06728v1 | https://arxiv.org/pdf/2304.06728v1.pdf | Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection | Cybersecurity has emerged as a critical challenge for the industry. With the large complexity of the security landscape, sophisticated and costly deep learning models often fail to provide timely detection of cyber threats on edge devices. Brain-inspired hyperdimensional computing (HDC) has been introduced as a promisi... | ['Mohsen Imani', 'Sitao Huang', 'Mariam Issa', 'Hanning Chen', 'Junyao Wang'] | 2023-04-11 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-3.88914347e-02 -4.45000529e-01 7.65149817e-02 1.78571478e-01
-5.01813173e-01 -6.91302299e-01 7.29107201e-01 1.07306994e-01
-3.08564276e-01 3.52636218e-01 -6.36922196e-02 -6.40059471e-01
-3.26918960e-01 -8.41288626e-01 -5.30214608e-01 -7.06961930e-01
-2.08318457e-01 -4.51094657e-02 1.94883481e-01 -1.26614466... | [5.601268291473389, 7.783019542694092] |
5aef9a20-8974-4a78-b6a3-78e2952c9fa5 | mask-textspotter-v3-segmentation-proposal | 2007.09482 | null | https://arxiv.org/abs/2007.09482v1 | https://arxiv.org/pdf/2007.09482v1.pdf | Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting | Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress. However, most of the current arbitrary-shape scene text spotters use region proposal networks (RPN) to produce proposals. RPN relies heavily on manually designed anchors and its proposals are repres... | ['Xiang Bai', 'Tal Hassner', 'Minghui Liao', 'Jing Huang', 'Guan Pang'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1436_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560681.pdf | eccv-2020-8 | ['text-spotting'] | ['computer-vision'] | [ 2.41005599e-01 -1.65346377e-02 -4.22377251e-02 -2.74101824e-01
-1.10407305e+00 -5.25870025e-01 5.78931332e-01 -2.07078919e-01
-2.72838771e-01 1.48760065e-01 7.38626346e-02 -4.17172849e-01
2.64286548e-01 -5.55623889e-01 -6.63770497e-01 -6.10472262e-01
4.59287256e-01 9.54017818e-01 6.30871952e-01 -5.78762032... | [12.07950496673584, 2.292877435684204] |
e0b3e8d5-e2f5-454e-a59a-2a24f831da95 | incremental-online-learning-algorithms | 2209.00591 | null | https://arxiv.org/abs/2209.00591v1 | https://arxiv.org/pdf/2209.00591v1.pdf | Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors | Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing. However, traditional TinyML methods can only perform inference, limited to static environments or classes. Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model ... | ['Davide Brunelli', 'Andrea Albanese', 'Alessandro Avi'] | 2022-09-01 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.93796065e-02 1.12019435e-01 -4.37528074e-01 -3.86959225e-01
-2.44717583e-01 -2.75011599e-01 4.07022208e-01 4.52307649e-02
-5.84384561e-01 8.33644748e-01 -2.79466897e-01 -2.38978550e-01
1.55140638e-01 -9.32595670e-01 -1.07054436e+00 -6.73127949e-01
-1.36268958e-01 6.59382641e-01 4.65690315e-01 3.41064781... | [8.038177490234375, 2.539698362350464] |
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