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2797246d-55e0-4e02-828d-5ef158fc484d | a-divide-and-conquer-method-for-scalable-low | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Pan_A_Divide-and-Conquer_Method_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Pan_A_Divide-and-Conquer_Method_2013_CVPR_paper.pdf | A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit | Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a recent proposed method that fuses multiple output score lists from different models via pursuing a sh... | ['Yan Pan', 'Cong Liu', 'Shuicheng Yan', 'Hanjiang Lai'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['object-categorization'] | ['computer-vision'] | [ 3.27025414e-01 -4.66452330e-01 -1.11643828e-01 -2.25149557e-01
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1.60691328e-02 1.96568817e-01 4.19923693e-01 2.40365192... | [8.982688903808594, -0.8106698393821716] |
96c099d7-ce45-44a8-a1a4-f5f63fa0feed | emogator-a-new-open-source-vocal-burst | 2301.00508 | null | https://arxiv.org/abs/2301.00508v2 | https://arxiv.org/pdf/2301.00508v2.pdf | EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies | Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of speech emotion recognition, but an important aspect of human vocal communication. One barrier to study of these interesting vocalizations is a lack of large datase... | ['Fred W. Buhl'] | 2023-01-02 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-4.95642185e-01 -1.54127479e-01 -7.11073205e-02 -5.76464653e-01
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-2.48361349e-01 -9.17057469e-02 -2.77473360e-01 -3.05746764... | [13.6035737991333, 5.780977249145508] |
48e36b92-6b67-4b49-968c-74d840b68c12 | fairr-faithful-and-robust-deductive-reasoning-1 | 2203.10261 | null | https://arxiv.org/abs/2203.10261v1 | https://arxiv.org/pdf/2203.10261v1.pdf | FaiRR: Faithful and Robust Deductive Reasoning over Natural Language | Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model's logical reasoning process. Currently, these b... | ['Xiang Ren', 'Harman Singh', 'Soumya Sanyal'] | 2022-03-19 | null | https://aclanthology.org/2022.acl-long.77 | https://aclanthology.org/2022.acl-long.77.pdf | acl-2022-5 | ['fact-selection'] | ['natural-language-processing'] | [ 3.01948518e-01 1.01384687e+00 -2.48268303e-02 -9.24963132e-02
-2.94287711e-01 -8.47978771e-01 1.18967187e+00 8.84159580e-02
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8.07252973e-02 6.78325117e-01 5.96566617e-01 -3.09651434... | [9.206576347351074, 7.183178901672363] |
9c041c80-1bf4-467f-bdc2-5f94e166f0ad | prototypical-contrastive-learning-of | 2005.04966 | null | https://arxiv.org/abs/2005.04966v5 | https://arxiv.org/pdf/2005.04966v5.pdf | Prototypical Contrastive Learning of Unsupervised Representations | This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it implicitly encodes semant... | ['Steven C. H. Hoi', 'Pan Zhou', 'Junnan Li', 'Caiming Xiong'] | 2020-05-11 | null | https://openreview.net/forum?id=KmykpuSrjcq | https://openreview.net/pdf?id=KmykpuSrjcq | iclr-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.64820209e-01 1.83400542e-01 -7.07444072e-01 -5.58878958e-01
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-2.17718303e-01 7.41443813e-01 -3.04104686e-01 2.30433002... | [9.508312225341797, 2.977957248687744] |
d4c25d4b-e56a-42f2-9df6-5edde9f55815 | one-shot-key-information-extraction-from | 2109.13967 | null | https://arxiv.org/abs/2109.13967v1 | https://arxiv.org/pdf/2109.13967v1.pdf | One-shot Key Information Extraction from Document with Deep Partial Graph Matching | Automating the Key Information Extraction (KIE) from documents improves efficiency, productivity, and security in many industrial scenarios such as rapid indexing and archiving. Many existing supervised learning methods for the KIE task need to feed a large number of labeled samples and learn separate models for differ... | ['Liansheng Zhuang', 'Houqiang Li', 'Liangwei Wang', 'Zhiguang Liu', 'Minghong Yao'] | 2021-09-26 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 3.77035998e-02 -3.00674438e-01 -2.58930713e-01 -3.28578919e-01
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4.27638084e-01 8.08030546e-01 3.78927320e-01 -1.25732854... | [11.518860816955566, 2.244945764541626] |
79c64198-e852-4db6-bc4e-ec67c68a0d09 | scaling-laws-for-discriminative-speech | 2306.15815 | null | https://arxiv.org/abs/2306.15815v1 | https://arxiv.org/pdf/2306.15815v1.pdf | Scaling Laws for Discriminative Speech Recognition Rescoring Models | Recent studies have found that model performance has a smooth power-law relationship, or scaling laws, with training data and model size, for a wide range of problems. These scaling laws allow one to choose nearly optimal data and model sizes. We study whether this scaling property is also applicable to second-pass res... | ['Ivan Bulyko', 'Ariya Rastrow', 'Ankur Gandhe', 'Jari Kolehmainen', 'Prashanth Gurunath Shivakumar', 'Yile Gu'] | 2023-06-27 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 4.06023830e-01 1.42203584e-01 -1.84382483e-01 -5.60989439e-01
-8.37897182e-01 -3.85273725e-01 5.18564582e-01 -2.86355764e-01
-4.84296918e-01 7.88247049e-01 2.13460252e-01 -4.97857422e-01
-7.88019821e-02 -5.04243314e-01 -8.42230260e-01 -6.89692795e-01
2.01825947e-01 7.11573124e-01 5.90098262e-01 -3.31954002... | [14.160120964050293, 6.61926794052124] |
1b4da802-4165-4ece-b6a8-f54a1b41871b | differentiable-outlier-detection-enable | 2302.05608 | null | https://arxiv.org/abs/2302.05608v1 | https://arxiv.org/pdf/2302.05608v1.pdf | Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis | Often, deep network models are purely inductive during training and while performing inference on unseen data. Thus, when such models are used for predictions, it is well known that they often fail to capture the semantic information and implicit dependencies that exist among objects (or concepts) on a population level... | ['Sathya N. Ravi', 'Sourav Medya', 'Zhu Wang'] | 2023-02-11 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 1.30412459e-01 4.26098526e-01 -1.48451447e-01 -5.50199151e-01
-3.26091081e-01 -5.84638894e-01 7.26788700e-01 3.10571581e-01
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2.08874315e-01 7.09743500e-01 3.68719637e-01 2.46887729... | [10.68955135345459, 1.7449299097061157] |
21d84e4d-3c2a-4043-958e-c3f32317cff4 | window-size-selection-in-unsupervised-time | null | null | https://doi.org/10.1007/978-3-031-24378-3_6 | https://link.springer.com/content/pdf/10.1007/978-3-031-24378-3_6.pdf | Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark | Time series (TS) are sequences of values ordered in time. Such TS have in common, that important insights from the data can be drawn by inspecting local substructures, and not the recordings as a whole. ECG recordings, for instance, are characterized by normal or anomalous heartbeats that repeat themselves often within... | ['Ulf Leser', 'Patrick Schäfer', 'Arik Ermshaus'] | 2023-02-04 | null | null | null | advanced-analytics-and-learning-on-temporal | ['hyperparameter-optimization', 'change-point-detection', 'time-series-anomaly-detection'] | ['methodology', 'time-series', 'time-series'] | [ 5.26426315e-01 -2.59272218e-01 -2.39473522e-01 -1.92093655e-01
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-6.14262521e-01 4.17885810e-01 6.00611031e-01 -4.78014350... | [7.3278021812438965, 3.168457269668579] |
1099fa05-af22-499f-96a0-14c72a8afd82 | yolo-ret-towards-high-accuracy-real-time | 2110.13713 | null | https://arxiv.org/abs/2110.13713v1 | https://arxiv.org/pdf/2110.13713v1.pdf | YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs | Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency. However, in order to map deep neural network (DNN) based object detection models to edge devices, one typically needs to compress such models significantly, thus compromising the model accuracy. In this p... | ['Marianne Winslett', 'Deming Chen', 'Yin Yang', 'Yao Chen', 'Prakhar Ganesh'] | 2021-10-26 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.65015209e-01 -4.88329262e-01 2.00576186e-01 -1.09721839e-01
-3.34141910e-01 -5.20129025e-01 2.73449063e-01 -4.90262210e-02
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1.52960375e-01 3.43511641e-01 9.43918705e-01 -1.52604461... | [8.70868968963623, -0.3061772584915161] |
53f0b60d-d897-4d49-8a1a-dd1febb7659f | application-of-densenet-in-camera-model | 1809.00576 | null | http://arxiv.org/abs/1809.00576v2 | http://arxiv.org/pdf/1809.00576v2.pdf | Application of DenseNet in Camera Model Identification and Post-processing Detection | Camera model identification has earned paramount importance in the field of
image forensics with an upsurge of digitally altered images which are
constantly being shared through websites, media, and social applications. But,
the task of identification becomes quite challenging if metadata are absent
from the image and/... | [] | 2019-05-27 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.45662642e-01 -2.97727734e-01 2.41590127e-01 -7.60108083e-02
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-1.22001298e-01 2.13671446e-01 -1.17520532e-02 2.53781199... | [12.380775451660156, 0.9905688762664795] |
3a3c80b8-ba3d-463c-8304-997a22b0e574 | causal-estimation-for-text-data-with-apparent | 2210.00079 | null | https://arxiv.org/abs/2210.00079v3 | https://arxiv.org/pdf/2210.00079v3.pdf | Causal Estimation for Text Data with (Apparent) Overlap Violations | Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time? To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and ou... | ['Victor Veitch', 'Lin Gui'] | 2022-09-30 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 6.43557489e-01 3.90710086e-01 -9.95774150e-01 -3.52078617e-01
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-6.20655477e-01 -8.28254521e-01 -9.12924945e-01 -5.47927737e-01
1.96533605e-01 4.73304391e-01 -3.37238818e-01 2.18888178... | [8.012839317321777, 5.344010353088379] |
0f1427cb-de13-4b5a-bb4c-a39f82cfa3b1 | s2snet-a-pretrained-neural-network-for | 2306.1627 | null | https://arxiv.org/abs/2306.16270v1 | https://arxiv.org/pdf/2306.16270v1.pdf | S2SNet: A Pretrained Neural Network for Superconductivity Discovery | Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution to superconductivity research. Superconductors are valuable ... | ['Renjun Xu', 'Jiahong Zhang', 'Kaifan Yang', 'Ke Liu'] | 2023-06-28 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-1.76883250e-01 -4.85821128e-01 -4.91623610e-01 -3.32880646e-01
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3.67564559e-01 1.31795019e-01 3.82640153e-01 -2.49990985... | [5.185422897338867, 5.457492828369141] |
799afdfa-5a4c-47b7-95b0-2b7210627803 | using-virtual-edges-to-extract-keywords-from | 2205.02172 | null | https://arxiv.org/abs/2205.02172v1 | https://arxiv.org/pdf/2205.02172v1.pdf | Using virtual edges to extract keywords from texts modeled as complex networks | Detecting keywords in texts is important for many text mining applications. Graph-based methods have been commonly used to automatically find the key concepts in texts, however, relevant information provided by embeddings has not been widely used to enrich the graph structure. Here we modeled texts co-occurrence networ... | ['Diego R. Amancio', 'Thiago C. Silva', 'Jorge A. V. Tohalino'] | 2022-05-04 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [-3.11163306e-01 2.99679458e-01 -2.87063599e-01 3.31642121e-01
2.94200838e-01 -5.61505318e-01 1.08289337e+00 1.10226548e+00
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-6.22046173e-01 -1.11481988e+00 -1.28399044e-01 -4.37858343e-01
-5.52172124e-01 3.61940324e-01 4.43888217e-01 -5.00643849... | [10.163009643554688, 8.413750648498535] |
29650cd2-01b6-4a87-8368-5a277162042f | epilepsy-seizure-detection-anatomy-and | 2305.19347 | null | https://arxiv.org/abs/2305.19347v1 | https://arxiv.org/pdf/2305.19347v1.pdf | Epilepsy Seizure Detection: Anatomy and Analysis | A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many technique... | ['Nelly Elsayed', 'Murat Ozer', 'Zag ElSayed'] | 2023-05-30 | null | null | null | null | ['seizure-detection', 'anatomy'] | ['medical', 'miscellaneous'] | [-2.00278573e-05 -1.78884163e-01 -2.78181612e-01 -4.98227298e-01
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-4.34359193e-01 -1.87968254e-01 2.41194099e-01 -5.30526340e-01
-7.26065278e-01 2.29146853e-01 1.45509735e-01 1.62545741... | [13.227238655090332, 3.5149354934692383] |
09f8a8be-1bc4-4fcf-9cbe-1eaa32205367 | an-efficient-supervised-dictionary-learning | 1812.04748 | null | http://arxiv.org/abs/1812.04748v1 | http://arxiv.org/pdf/1812.04748v1.pdf | An efficient supervised dictionary learning method for audio signal recognition | Machine hearing or listening represents an emerging area. Conventional
approaches rely on the design of handcrafted features specialized to a specific
audio task and that can hardly generalized to other audio fields. For example,
Mel-Frequency Cepstral Coefficients (MFCCs) and its variants were successfully
applied to ... | ['Romain Hérault', 'Gilles Gasso', 'Imad Rida'] | 2018-12-12 | null | null | null | null | ['chord-recognition', 'audio-signal-recognition'] | ['audio', 'audio'] | [ 3.49979073e-01 -3.96337897e-01 8.76463111e-03 -1.93070382e-01
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-2.91988254e-01 2.50319928e-01 -3.02343871e-02 -4.07700866... | [15.577411651611328, 5.357540130615234] |
64e41451-c6ec-4a77-83ec-00e639268a31 | enhancing-transformer-backbone-for-egocentric | 2305.11365 | null | https://arxiv.org/abs/2305.11365v2 | https://arxiv.org/pdf/2305.11365v2.pdf | Enhancing Transformer Backbone for Egocentric Video Action Segmentation | Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation mode... | ['Octavia Camps', 'Mohsen Moghaddam', 'Balaji Sundareshan', 'Sakib Reza'] | 2023-05-19 | null | null | null | null | ['mixed-reality', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.35783657e-01 -2.47402536e-03 -4.32599485e-01 -3.15310180e-01
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1.33189142e-01 4.98318113e-02 5.19893229e-01 -1.45238116... | [8.424176216125488, 0.5752784013748169] |
26c73b58-966e-4ef8-8ac9-c6677708e9b2 | houghlanenet-lane-detection-with-deep-hough | 2307.03494 | null | https://arxiv.org/abs/2307.03494v1 | https://arxiv.org/pdf/2307.03494v1.pdf | HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic Convolution | The task of lane detection has garnered considerable attention in the field of autonomous driving due to its complexity. Lanes can present difficulties for detection, as they can be narrow, fragmented, and often obscured by heavy traffic. However, it has been observed that the lanes have a geometrical structure that re... | ['Miao Wang', 'Ariel Shamir', 'Jun-Long Chen', 'Hao-Bin Duan', 'Jia-Qi Zhang'] | 2023-07-07 | null | null | null | null | ['autonomous-driving', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [ 1.56625807e-02 -1.48493096e-01 5.78365289e-03 -4.81405884e-01
-3.14834893e-01 -3.91109079e-01 4.78721976e-01 -2.75171369e-01
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8.04846957e-02 -1.26738429e-01 8.11292589e-01 -3.86806369... | [8.01922607421875, -1.5144474506378174] |
f40f2337-ac35-446c-82ec-e8159b86ab7f | robust-reconstruction-of-indoor-scenes | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Choi_Robust_Reconstruction_of_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Choi_Robust_Reconstruction_of_2015_CVPR_paper.pdf | Robust Reconstruction of Indoor Scenes | We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to di... | ['Qian-Yi Zhou', 'Vladlen Koltun', 'Sungjoon Choi'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 5.24153888e-01 -2.33957022e-01 7.24292815e-01 -3.70395631e-01
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-4.00235742e-01 -1.00972414e+00 -6.98699653e-01 -3.71682405e-01
2.32824370e-01 5.70448220e-01 4.51243967e-01 -2.71507859... | [8.160934448242188, -2.531191349029541] |
872700b1-78f9-4663-ae91-c82bc4de915a | exploratory-analysis-of-news-sentiment-using | null | null | https://aclanthology.org/2021.bsnlp-1.7 | https://aclanthology.org/2021.bsnlp-1.7.pdf | Exploratory Analysis of News Sentiment Using Subgroup Discovery | In this study, we present an exploratory analysis of a Slovenian news corpus, in which we investigate the association between named entities and sentiment in the news. We propose a methodology that combines Named Entity Recognition and Subgroup Discovery - a descriptive rule learning technique for identifying groups of... | ['Senja Pollak', 'Elvys Linhares Pontes', 'Luis Adrián Cabrera-Diego', 'Anita Valmarska'] | null | null | null | null | eacl-bsnlp-2021-4 | ['subgroup-discovery'] | ['methodology'] | [-2.84999043e-01 3.54371458e-01 -7.93549299e-01 -7.19019771e-01
-1.31677076e-01 -8.95628214e-01 1.11150920e+00 8.45688283e-01
-3.62591058e-01 8.59167159e-01 1.06204164e+00 -2.95879394e-01
-3.90324831e-01 -8.46125126e-01 -2.26950154e-01 -4.31518167e-01
-4.03737396e-01 2.94272929e-01 1.52077422e-01 -2.77777940... | [10.973540306091309, 7.04805326461792] |
dde2f0a4-0624-48c4-bed2-b269b439cc41 | on-interpretability-of-deep-learning-based | 2005.02 | null | https://arxiv.org/abs/2005.02000v1 | https://arxiv.org/pdf/2005.02000v1.pdf | On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors | Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making ... | ['Stephan Alexander Braun', 'Sheraz Ahmed', 'Muhammad Naseer Bajwa', 'Adriano Lucieri', 'Muhammad Imran Malik', 'Andreas Dengel'] | 2020-05-05 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 5.66409588e-01 4.41961318e-01 -1.16946362e-01 -4.93873090e-01
-2.55941093e-01 -2.65857577e-01 4.74910825e-01 5.05660355e-01
-2.46886805e-01 5.36639869e-01 3.50241438e-02 -5.73642015e-01
-4.02476579e-01 -6.88893497e-01 -2.20300078e-01 -8.74272108e-01
-2.11615339e-02 7.37108946e-01 -2.10585058e-01 1.90429427... | [15.35865592956543, -2.639157772064209] |
e9c81a02-e82f-495c-b6d7-b1f4cf748705 | differentiable-parsing-and-visual-grounding | 2210.00215 | null | https://arxiv.org/abs/2210.00215v4 | https://arxiv.org/pdf/2210.00215v4.pdf | Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement | We present a new method, PARsing And visual GrOuNding (ParaGon), for grounding natural language in object placement tasks. Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding. For compositionality, ParaGon parses a l... | ['David Hsu', 'Wee Sun Lee', 'Zirui Zhao'] | 2022-10-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-1.04707114e-01 5.57270706e-01 -3.22786927e-01 -5.03658175e-01
-7.10790515e-01 -7.45450258e-01 2.96133190e-01 7.16926694e-01
-9.77250859e-02 3.34858030e-01 1.75632104e-01 -7.79965758e-01
-1.62379327e-03 -1.07047439e+00 -1.08652616e+00 -1.65529370e-01
-7.44874030e-02 9.70905125e-01 4.74599451e-01 -2.00590640... | [10.529550552368164, 1.7678478956222534] |
c1e91d10-353b-4399-8714-80da68b19644 | acquiring-frame-element-knowledge-with-deep | 2305.13944 | null | https://arxiv.org/abs/2305.13944v1 | https://arxiv.org/pdf/2305.13944v1.pdf | Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction | The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and pr... | ['Koichi Takeda', 'Ryohei Sasano', 'Kosuke Yamada'] | 2023-05-23 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.04460114e-01 4.53379363e-01 -5.76336324e-01 -6.63626432e-01
-7.46794939e-01 -7.33905971e-01 1.03534114e+00 2.78475255e-01
-5.97633779e-01 6.16945565e-01 8.62746298e-01 -3.63523126e-01
-1.98431034e-02 -8.88122678e-01 -5.96536815e-01 -5.33230782e-01
4.56017464e-01 6.46939337e-01 2.86160678e-01 -2.30290100... | [10.219840049743652, 9.240612030029297] |
e92cf467-5724-4b41-9b59-ba92ccfe8bfc | xlm-v-overcoming-the-vocabulary-bottleneck-in | 2301.10472 | null | https://arxiv.org/abs/2301.10472v1 | https://arxiv.org/pdf/2301.10472v1.pdf | XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models | Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged. This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R. In th... | ['Madian Khabsa', 'Luke Zettlemoyer', 'Marjan Ghazvininejad', 'Naman Goyal', 'Rui Hou', 'Yuning Mao', 'Hila Gonen', 'Davis Liang'] | 2023-01-25 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-6.66674256e-01 -7.00960457e-02 -6.00353658e-01 -1.92069903e-01
-1.07740641e+00 -9.65065956e-01 6.85876787e-01 4.16296184e-01
-9.21670198e-01 1.20905221e+00 5.49919546e-01 -7.99431145e-01
2.24567667e-01 -8.09021294e-01 -6.68077767e-01 2.64213294e-01
1.99889258e-01 8.01263452e-01 3.37210670e-02 -5.44596791... | [10.91313362121582, 9.855945587158203] |
b8d31620-4d75-4a90-b725-b6f712186aed | embodied-question-answering-in-photorealistic | 1904.03461 | null | http://arxiv.org/abs/1904.03461v1 | http://arxiv.org/pdf/1904.03461v1.pdf | Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | To help bridge the gap between internet vision-style problems and the goal of
vision for embodied perception we instantiate a large-scale navigation task --
Embodied Question Answering [1] in photo-realistic environments (Matterport
3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB
images, ... | ['Georgia Gkioxari', 'Erik Wijmans', 'Stefan Lee', 'Samyak Datta', 'Oleksandr Maksymets', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Abhishek Das'] | 2019-04-06 | embodied-question-answering-in-photorealistic-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wijmans_Embodied_Question_Answering_in_Photorealistic_Environments_With_Point_Cloud_Perception_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wijmans_Embodied_Question_Answering_in_Photorealistic_Environments_With_Point_Cloud_Perception_CVPR_2019_paper.pdf | cvpr-2019-6 | ['embodied-question-answering'] | ['computer-vision'] | [-1.83076840e-02 1.42736912e-01 2.95712799e-01 -2.11070135e-01
-7.54215717e-01 -8.47938061e-01 6.90127194e-01 -1.34116933e-01
-8.18289220e-01 3.84086967e-01 5.90263009e-01 -8.99619281e-01
-2.55606502e-01 -8.32386792e-01 -8.91435623e-01 -4.88477707e-01
-1.22948013e-01 6.09058328e-02 1.82561278e-02 -7.69084215... | [4.440381050109863, 0.6213662624359131] |
7428cf83-543d-46fb-8692-7a75e1df7925 | generative-colorization-of-structured-mobile | 2212.11541 | null | https://arxiv.org/abs/2212.11541v2 | https://arxiv.org/pdf/2212.11541v2.pdf | Generative Colorization of Structured Mobile Web Pages | Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative... | ['Kota Yamaguchi', 'Edgar Simo-Serra', 'Mayu Otani', 'Naoto Inoue', 'Kotaro Kikuchi'] | 2022-12-22 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.33135927e-02 -6.08979054e-02 5.09033538e-02 -3.84322673e-01
-5.76036334e-01 -1.03760386e+00 5.20829141e-01 -1.36562482e-01
-5.61615005e-02 3.37968975e-01 1.49761671e-02 -5.56346834e-01
-7.80433938e-02 -7.71108806e-01 -7.23087490e-01 -3.86146843e-01
-2.98465211e-02 2.64463633e-01 1.15916677e-01 -1.28878579... | [11.434292793273926, -0.9301757216453552] |
1e688178-10cc-42a8-8949-866d10db6ffb | espnet-st-all-in-one-speech-translation | 2004.10234 | null | https://arxiv.org/abs/2004.10234v2 | https://arxiv.org/pdf/2004.10234v2.pdf | ESPnet-ST: All-in-One Speech Translation Toolkit | We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech func... | ['Shinji Watanabe', 'Kevin Duh', 'Hirofumi Inaguma', 'Tomoki Hayashi', 'Nelson Enrique Yalta Soplin', 'Shun Kiyono', 'Shigeki Karita'] | 2020-04-21 | espnet-st-all-in-one-speech-translation-1 | https://aclanthology.org/2020.acl-demos.34 | https://aclanthology.org/2020.acl-demos.34.pdf | acl-2020-6 | ['speech-to-speech-translation'] | ['speech'] | [ 8.79342258e-02 -4.07648571e-02 -1.41576394e-01 -4.43111628e-01
-1.42586207e+00 -5.77764690e-01 7.10115314e-01 -3.78277868e-01
-1.18660979e-01 4.47901487e-01 5.42285621e-01 -9.01253104e-01
7.10442245e-01 -1.92111179e-01 -4.93545324e-01 -4.00777012e-01
4.16178048e-01 8.29376101e-01 9.38725770e-02 -4.69672531... | [14.484285354614258, 7.119692802429199] |
0851f81d-3151-40db-8e92-9b12f9d46780 | cross-domain-aspect-extraction-for-sentiment | null | null | https://www.sciencedirect.com/science/article/pii/S0167923618301386 | https://www.sciencedirect.com/science/article/pii/S0167923618301386 | Cross-domain aspect extraction for sentiment analysis: a transductive learning approach | Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze
consumer reviews at a high level of detail, where the opinion about each fea-
ture of the product or service is considered. ABSA usually explores supervised
inductive learning algorithms, which requires intense human effort for the la-
beling... | ['Solange Oliveira Rezende', 'Ricardo Marcondes Marcacini', 'Rafael Geraldeli Rossi', 'Ivone Penque Matsuno'] | 2018-10-21 | null | null | null | decision-support-system-2018-10 | ['aspect-extraction'] | ['natural-language-processing'] | [ 7.64253139e-02 3.93102527e-01 -6.48862898e-01 -6.71111882e-01
-6.90473735e-01 -7.01536775e-01 6.44183159e-01 5.86104691e-01
-1.20895617e-01 6.84949934e-01 -2.22574353e-01 -4.02437508e-01
6.76725209e-02 -1.33565915e+00 -6.17220640e-01 -4.97194767e-01
2.03826293e-01 8.06866288e-01 3.27112466e-01 -5.04066885... | [11.347989082336426, 6.714990139007568] |
50304da9-2405-4f3e-9503-f663ae9fbefb | jnd-based-perceptual-optimization-for-learned | 2302.13092 | null | https://arxiv.org/abs/2302.13092v2 | https://arxiv.org/pdf/2302.13092v2.pdf | JND-Based Perceptual Optimization For Learned Image Compression | Recently, learned image compression schemes have achieved remarkable improvements in image fidelity (e.g., PSNR and MS-SSIM) compared to conventional hybrid image coding ones due to their high-efficiency non-linear transform, end-to-end optimization frameworks, etc. However, few of them take the Just Noticeable Differe... | ['Weisi Lin', 'Lili Meng', 'Jian Jin', 'Feng Ding'] | 2023-02-25 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.36912209e-01 -4.38927025e-01 -3.07106018e-01 -4.10296112e-01
-5.23486853e-01 -1.27742887e-01 1.75711021e-01 -3.75773609e-02
-3.64025086e-01 4.35672015e-01 2.08116814e-01 -4.43507619e-02
-3.31386924e-01 -7.10166872e-01 -6.79602504e-01 -8.03136528e-01
-1.20166793e-01 -4.79285240e-01 2.01878473e-01 -1.09798618... | [11.31087589263916, -1.7295823097229004] |
f9faca90-1100-48b7-af5f-e157ec6c6b6e | rain-rate-estimation-with-sar-using-nexrad | 2207.07333 | null | https://arxiv.org/abs/2207.07333v2 | https://arxiv.org/pdf/2207.07333v2.pdf | Rainfall Estimation with SAR using NEXRAD collocations with Convolutional Neural Networks | Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA's Next-Generation Radar (NEXRAD), provide reflectivity and precipitation estimates of rainf... | ['Nicolas Longépé', 'Ronan Fablet', 'Romain Husson', 'Charles Peureux', 'Pierre Tandeo', 'Aurélien Colin'] | 2022-07-15 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 3.85156460e-02 -2.55323172e-01 1.28315091e-01 -6.47162378e-01
-5.53188443e-01 -4.66858447e-01 5.76881289e-01 1.51948497e-01
-7.06180811e-01 1.14649260e+00 -2.25786529e-02 -9.43345249e-01
-1.28602432e-02 -1.58267224e+00 -2.37767696e-01 -8.21675777e-01
-6.72079384e-01 1.15994580e-01 -7.21818432e-02 -7.80745208... | [9.499635696411133, -1.5397677421569824] |
91b63831-845a-40e1-afb5-01f3789fde20 | generalization-bounds-for-inductive-matrix | 2212.08339 | null | https://arxiv.org/abs/2212.08339v1 | https://arxiv.org/pdf/2212.08339v1.pdf | Generalization Bounds for Inductive Matrix Completion in Low-noise Settings | We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of t... | ['Marius Kloft', 'Yann Guermeur', 'Yunwen Lei', 'Rodrigo Alves', 'Antoine Ledent'] | 2022-12-16 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.14603269e-01 1.96856081e-01 -2.27492556e-01 3.66423994e-01
-9.04321671e-01 -6.41401291e-01 2.37066522e-01 5.36460221e-01
-5.53859591e-01 7.58354962e-01 5.06241977e-01 -1.88214600e-01
-2.34163716e-01 -7.81057715e-01 -1.16033340e+00 -9.30932343e-01
-3.39728862e-01 4.49944794e-01 3.12557593e-02 -3.27490330... | [6.935990810394287, 4.600698947906494] |
185e8fb2-64e0-4bb6-af5e-2023cb5d44be | knowledge-enhanced-personalized-review | 2010.0148 | null | https://arxiv.org/abs/2010.01480v1 | https://arxiv.org/pdf/2010.01480v1.pdf | Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network | Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on ... | ['Ji-Rong Wen', 'Nicholas Jing Yuan', 'Zhicheng Wei', 'Gaole He', 'Wayne Xin Zhao', 'Siqing Li', 'Junyi Li'] | 2020-10-04 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 2.53837079e-01 4.31903869e-01 -4.80879188e-01 -3.23709875e-01
-6.41858935e-01 -3.67284268e-01 4.50807750e-01 1.36590768e-02
2.25825310e-01 7.52879620e-01 5.44270396e-01 -2.04099253e-01
4.52396534e-02 -1.21912146e+00 -6.46425009e-01 -2.83255696e-01
3.76557618e-01 4.95653003e-01 -2.62179255e-01 -5.53562284... | [11.9232816696167, 8.906607627868652] |
6438c897-4ebd-4d71-94d4-90b89edf0de4 | object-centric-voxelization-of-dynamic-scenes | 2305.00393 | null | https://arxiv.org/abs/2305.00393v3 | https://arxiv.org/pdf/2305.00393v3.pdf | Unsupervised Object-Centric Voxelization for Dynamic Scene Understanding | Understanding the compositional dynamics of multiple objects in unsupervised visual environments is challenging, and existing object-centric representation learning methods often ignore 3D consistency in scene decomposition. We propose DynaVol, an inverse graphics approach that learns object-centric volumetric represen... | ['Xiaokang Yang', 'Yunbo Wang', 'Yanpeng Zhao', 'Siyu Gao'] | 2023-04-30 | null | null | null | null | ['neural-rendering', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [-1.18856144e-03 -1.44409403e-01 5.56449108e-02 -3.61584425e-01
-2.13193730e-01 -7.41523504e-01 8.78575921e-01 -8.18381310e-02
-6.45681247e-02 3.80662948e-01 5.46939909e-01 8.62902626e-02
-1.99682802e-01 -1.01271403e+00 -1.11516678e+00 -6.67243838e-01
2.55577005e-02 1.04404247e+00 1.64459765e-01 1.68048456... | [9.090901374816895, -3.138627767562866] |
a2b178ca-e3af-4229-8a08-24afe1d6887f | semiretro-semi-template-framework-boosts-deep-1 | 2202.08205 | null | https://arxiv.org/abs/2202.08205v1 | https://arxiv.org/pdf/2202.08205v1.pdf | SemiRetro: Semi-template framework boosts deep retrosynthesis prediction | Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded reaction templates, and TF methods are more scalable by decomposing retrosynthesis into subproblems, i.e., center identification and synthon... | ['Stan Z. Li', 'Lirong Wu', 'Cheng Tan', 'Zhangyang Gao'] | 2022-02-12 | semiretro-semi-template-framework-boosts-deep | https://openreview.net/forum?id=rMbLORc8oS | https://openreview.net/pdf?id=rMbLORc8oS | null | ['retrosynthesis'] | ['medical'] | [ 4.55112517e-01 -5.87825067e-02 -8.05626750e-01 9.00809690e-02
-8.13589871e-01 -1.00330758e+00 5.05817175e-01 4.60012518e-02
-1.49241447e-01 9.73405004e-01 -3.64667475e-02 -4.27789927e-01
1.44847721e-01 -9.40974712e-01 -7.56254911e-01 -1.01756847e+00
3.10776711e-01 2.63942301e-01 4.13791299e-01 -2.27932453... | [4.496354579925537, 6.113452434539795] |
c7feeecd-5bb5-47bd-8ff4-413ecdc94f07 | low-rank-compression-of-neural-nets-learning | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.pdf | Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer | Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix. The real difficulty in doing this is not in training the resulting neural net (made up of one low-rank matrix per layer), but in determining what the optimal rank of each layer is--effectively, an architecture searc... | [' Miguel A. Carreira-Perpinan', 'Yerlan Idelbayev'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['low-rank-compression'] | ['computer-code'] | [ 2.90467203e-01 4.88749146e-01 -1.76371232e-01 -1.45549133e-01
-7.34583676e-01 -4.34694618e-01 4.08842534e-01 -1.22762516e-01
-8.25928867e-01 7.02349722e-01 3.12101334e-01 -2.43770570e-01
-4.80999023e-01 -6.88130558e-01 -8.96770298e-01 -7.98057795e-01
-2.14226738e-01 9.04210985e-01 1.14013754e-01 3.39509100... | [8.409385681152344, 3.3951644897460938] |
21171961-85d4-4c89-937f-d94d09ab5f2b | forecasting-wireless-demand-with-extreme | 1905.06744 | null | https://arxiv.org/abs/1905.06744v2 | https://arxiv.org/pdf/1905.06744v2.pdf | Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes | Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on o... | ['Weisi Guo', 'Chengyao Sun'] | 2019-05-15 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-3.64230782e-01 3.77032697e-01 -2.04498842e-01 -2.01687366e-01
-5.27102411e-01 -2.50763118e-01 5.61154604e-01 -2.67748445e-01
5.17427623e-01 9.96798158e-01 2.01287404e-01 -1.08196950e+00
-6.14275932e-01 -1.08439064e+00 -3.20735812e-01 -9.23632979e-01
-7.16970384e-01 6.05357528e-01 -2.26695538e-01 -1.72256830... | [6.714344024658203, 2.8471901416778564] |
a84cb372-4b26-4c9d-8fec-572792f92e23 | graph-reinforcement-learning-for-operator | 2302.14678 | null | https://arxiv.org/abs/2302.14678v1 | https://arxiv.org/pdf/2302.14678v1.pdf | Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic | ALNS is a popular metaheuristic with renowned efficiency in solving combinatorial optimisation problems. However, despite 16 years of intensive research into ALNS, whether the embedded adaptive layer can efficiently select operators to improve the incumbent remains an open question. In this work, we formulate the choic... | ['Joerg Kalcsics', 'Julia Handl', 'Victor-Alexandru Darvariu', 'Syu-Ning Johnn'] | 2023-02-28 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 4.37244743e-01 2.79802307e-02 -3.15474600e-01 2.31972694e-01
-4.53715920e-01 -6.04618847e-01 1.24678545e-01 2.21531719e-01
-6.03307903e-01 7.30865955e-01 -1.50146529e-01 -6.71595752e-01
-7.03644156e-01 -1.07634795e+00 -6.09973788e-01 -8.72595131e-01
-2.44389862e-01 6.40165687e-01 -4.72684465e-02 -3.48073989... | [5.22815465927124, 3.0568618774414062] |
8d3ef10c-8e52-44a1-95e8-c93f3f548cba | shadow-removal-from-single-rgb-d-images | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Xiao_Shadow_Removal_from_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Xiao_Shadow_Removal_from_2014_CVPR_paper.pdf | Shadow Removal from Single RGB-D Images | We present the first automatic method to remove shadows from single RGB-D images. Using normal cues directly derived from depth, we can remove hard and soft shadows while preserving surface texture and shading. Our key assumption is: pixels with similar normals, spatial locations and chromaticity should have similar co... | ['Chi-Keung Tang', 'Efstratios Tsougenis', 'Yao Xiao'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['shadow-removal', 'intrinsic-image-decomposition'] | ['computer-vision', 'computer-vision'] | [ 8.53562236e-01 2.32664213e-01 5.07410824e-01 -4.27427888e-01
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2.32506648e-01 3.85956407e-01 1.08533776e+00 -1.25501394... | [10.811063766479492, -4.0694475173950195] |
0058cee4-f76d-40a7-b9a6-f30c892d9814 | codeexp-explanatory-code-document-generation | 2211.15395 | null | https://arxiv.org/abs/2211.15395v1 | https://arxiv.org/pdf/2211.15395v1.pdf | CodeExp: Explanatory Code Document Generation | Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education. However, existing code-to-text generation models often produce only high-level summaries of code that do not capture implementation-level choices essential for these scenarios.... | ['Nan Duan', 'Jianfeng Gao', 'Bo wang', 'Todd Mytkowicz', 'Jeevana Priya Inala', 'JunJie Huang', 'Chenglong Wang', 'Haotian Cui'] | 2022-11-25 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [-2.23119017e-02 5.64135790e-01 -2.71154851e-01 -5.28205693e-01
-1.00344908e+00 -7.45871663e-01 4.93573487e-01 2.56497324e-01
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2.30481476e-01 3.52504253e-01 -6.45075068e-02 -2.38607362... | [7.9040985107421875, 7.7307538986206055] |
e73fd0e7-b446-423c-bf2d-9fb5b59221c7 | non-stationary-contextual-bandits-and | 2302.07186 | null | https://arxiv.org/abs/2302.07186v2 | https://arxiv.org/pdf/2302.07186v2.pdf | Adversarial Rewards in Universal Learning for Contextual Bandits | We study the fundamental limits of learning in contextual bandits, where a learner's rewards depend on their actions and a known context, which extends the canonical multi-armed bandit to the case where side-information is available. We are interested in universally consistent algorithms, which achieve sublinear regret... | ['Patrick Jaillet', 'Steve Hanneke', 'Moise Blanchard'] | 2023-02-14 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.19454482e-01 1.79914042e-01 -8.02248418e-01 -6.26887679e-02
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-1.75979719e-01 6.76595151e-01 -1.64065182e-01 -3.35060544... | [4.533551216125488, 3.2907309532165527] |
9ff17d27-a22c-4a4c-b03e-277ac61e598e | completedt-point-cloud-completion-with-dense | 2205.14999 | null | https://arxiv.org/abs/2205.14999v2 | https://arxiv.org/pdf/2205.14999v2.pdf | CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers | Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, namely CompleteDT. Specifically, features are learned from point clouds with different resolutions, which is sampled ... | ['Shaokun Han', 'Shangwei Guo', 'Jun Li'] | 2022-05-30 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-2.97443364e-02 6.27102330e-02 2.32634023e-01 -1.93163559e-01
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1.95533797e-01 9.55501497e-01 2.03593299e-01 -1.58732504... | [8.296609878540039, -3.579843759536743] |
5f7b438d-9445-403f-95eb-0dedac809e3b | boosting-differentiable-causal-discovery-via | 2303.03187 | null | https://arxiv.org/abs/2303.03187v1 | https://arxiv.org/pdf/2303.03187v1.pdf | Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting | Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed ... | ['Tat-Seng Chua', 'Xiang Wang', 'Zhibo Cai', 'Wenchang Ma', 'Fangfu Liu', 'An Zhang'] | 2023-03-06 | null | null | null | null | ['causal-discovery', 'bilevel-optimization'] | ['knowledge-base', 'methodology'] | [ 2.03706831e-01 2.27833465e-01 -5.00924528e-01 -3.94713014e-01
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-6.49184883e-01 5.32878935e-01 1.22592457e-01 2.07001716... | [7.8201518058776855, 5.350645065307617] |
0a3f9e98-24f6-4af8-9dab-150a635800b4 | equivariant-single-view-pose-prediction-via | 2307.03704 | null | https://arxiv.org/abs/2307.03704v1 | https://arxiv.org/pdf/2307.03704v1.pdf | Equivariant Single View Pose Prediction Via Induced and Restricted Representations | Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would leverage the fact that objects can be rotated and translated in three dimensions to make predictions about novel images. However, imposing SO(3)-e... | ['Robin Walters', 'Linfeng Zhao', 'Ondrej Biza', 'David Klee', 'Owen Howell'] | 2023-07-07 | null | null | null | null | ['pose-prediction', 'pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.02818616e-01 4.15469021e-01 -2.18835816e-01 -6.03582144e-01
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-2.03085780e-01 9.61809516e-01 1.45848677e-01 -4.01152074... | [8.849030494689941, 2.384124517440796] |
493fd0a7-2082-4307-918d-8c00444f8ac3 | exploiting-global-and-local-hierarchies-for | 2205.02613 | null | https://arxiv.org/abs/2205.02613v3 | https://arxiv.org/pdf/2205.02613v3.pdf | Exploiting Global and Local Hierarchies for Hierarchical Text Classification | Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels. Since global hierarchy is static and irrelevant to text samples, it... | ['Qinghong Yang', 'Fuzhen Zhuang', 'Zhongzhi Chen', 'Leilei Sun', 'Deqing Wang', 'Ting Jiang'] | 2022-05-05 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 2.86519099e-02 2.66799122e-01 -5.72594345e-01 -4.37580526e-01
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4.37160820e-01 7.79577374e-01 7.58113861e-01 -1.96058318... | [9.652019500732422, 4.373481273651123] |
9d75b6c0-e1c8-4f31-adb8-056eafbda268 | variability-matters-evaluating-inter-rater | 2210.05175 | null | https://arxiv.org/abs/2210.05175v1 | https://arxiv.org/pdf/2210.05175v1.pdf | Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection | Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. In particular, annotating different types of cells in histopathology suffer from high inter- and intra-rater variability due to the ambiguity... | ['S ergio Pereira', 'Minuk Ma', 'Heon Song', 'Chunggi Lee', 'Cholmin Kang'] | 2022-10-11 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-6.17859177e-02 1.94785297e-01 -1.24524631e-01 -4.27764565e-01
-1.03508258e+00 -7.32965291e-01 1.80306464e-01 6.81579113e-01
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-4.66140397e-02 -4.11861181e-01 -4.50599343e-01 -9.66889322e-01
2.44153187e-01 6.68470144e-01 3.78402434e-02 2.74760127... | [15.020639419555664, -2.740983009338379] |
fedcc5f4-520b-4012-a2ae-c3087840dd55 | an-xai-approach-to-deep-learning-models-in | 2106.14186 | null | https://arxiv.org/abs/2106.14186v2 | https://arxiv.org/pdf/2106.14186v2.pdf | An XAI Approach to Deep Learning Models in the Detection of DCIS | The results showed that XAI could indeed be used as a proof of concept to begin discussions on the implementation of assistive AI systems within the clinical community. | ['Michele La Ferla'] | 2021-06-27 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 6.16032369e-02 1.02076638e+00 -2.52825350e-01 -4.74100173e-01
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-4.68539327e-01 8.93894315e-01 3.55672687e-01 -8.74353647e-01
-2.93144733e-01 -3.29749197e-01 9.57384426e-03 -2.25278541e-01
-4.07240599e-01 9.66088176e-01 -1.31560415e-01 -3.74530733... | [8.96122932434082, 6.213102340698242] |
48693a0b-bdd7-44e6-aff5-cbaf3ce66d52 | thermal-image-processing-via-physics-inspired | 2108.07973 | null | https://arxiv.org/abs/2108.07973v2 | https://arxiv.org/pdf/2108.07973v2.pdf | Thermal Image Processing via Physics-Inspired Deep Networks | We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (tha... | ['Richard Baraniuk', 'Ashok Veeraraghavan', 'Akshat Dave', 'Vishwanath Saragadam'] | 2021-08-18 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [ 6.55234039e-01 -4.88834172e-01 3.85273695e-01 -3.41861814e-01
-8.36556077e-01 -5.42270482e-01 3.86696696e-01 -5.06460905e-01
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6.04855604e-02 1.25413120e-01 1.73024833e-01 -1.21198259... | [10.835030555725098, -2.406712532043457] |
ac67c9b3-354b-4c2c-8792-3aac139e2d49 | a-survey-of-detection-methods-for-die | 2206.07481 | null | https://arxiv.org/abs/2206.07481v2 | https://arxiv.org/pdf/2206.07481v2.pdf | A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing | Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs). Die attachment and wire bonding are two steps of the manufacturing process that determine the power and signal transmission quality and dependability in an IC. This paper presents a survey or literature review of the methods ... | ['Nasser Kehtarnavaz', 'Lamia Alam'] | 2022-06-02 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.14358000e-01 -1.92334771e-01 -2.11820707e-01 -2.88003057e-01
-2.69551277e-01 -1.69798225e-01 -1.71655595e-01 1.19914733e-01
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-3.19589972e-01 -9.63370502e-01 -8.59499127e-02 -1.01554298e+00
1.96123093e-01 2.76836783e-01 1.40702292e-01 2.12636720... | [7.053093910217285, 2.112123489379883] |
0d10354b-a839-4baf-8db1-7fdcbc2c5693 | human-robot-skill-transfer-with-enhanced | 2304.05703 | null | https://arxiv.org/abs/2304.05703v1 | https://arxiv.org/pdf/2304.05703v1.pdf | Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives | Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like skills to robots by Learning from Demonstrations (LfD). The human demonstration is considered the target motion to mimic. However, human moti... | ['Homayoun Najjaran', 'Amir M. Soufi Enayati', 'Zengjie Zhang', 'Jayden Hong'] | 2023-04-12 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-2.54930049e-01 2.81719357e-01 -1.94734558e-01 1.06456771e-01
-1.36084169e-01 -3.17159444e-01 6.49167061e-01 -4.24349643e-02
-7.92744160e-01 9.13466334e-01 -2.24028125e-01 -1.27352819e-01
-5.23962796e-01 -6.82039201e-01 -7.29495347e-01 -6.63921297e-01
-3.90000403e-01 6.56769812e-01 4.06446904e-01 -5.62754154... | [4.834681987762451, 1.2862215042114258] |
db11c5a8-022c-48c3-902b-185b6f6f1a17 | deep-multiple-instance-learning-with-distance | 2305.10552 | null | https://arxiv.org/abs/2305.10552v2 | https://arxiv.org/pdf/2305.10552v2.pdf | Deep Multiple Instance Learning with Distance-Aware Self-Attention | Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags) of instances. This problem setting, known as multiple instance learning (MIL), is particularly relevant in the medical domain, where high-re... | ['Ognjen Arandjelović', 'David J. Harrison', 'Pietro Liò', 'Lucie Charlotte Magister', 'Georg Wölflein'] | 2023-05-17 | null | null | null | null | ['cancer-metastasis-detection', 'multiple-instance-learning'] | ['medical', 'methodology'] | [ 3.49066526e-01 2.30669901e-01 -3.08090895e-01 -2.96471566e-01
-9.98163640e-01 -3.59357715e-01 6.47432983e-01 8.38432372e-01
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3.55896018e-02 5.06393075e-01 2.23763242e-01 -6.17852397... | [15.031579971313477, -2.778726577758789] |
c7fbb96e-077f-4bfd-a7cc-7592548dd81a | learning-efficient-representations-for-3 | 2101.04792 | null | https://arxiv.org/abs/2101.04792v4 | https://arxiv.org/pdf/2101.04792v4.pdf | Learning Efficient Representations for Keyword Spotting with Triplet Loss | In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classifica... | ['Nikolay Mikhaylovskiy', 'Roman Vygon'] | 2021-01-12 | learning-efficient-representations-for-1 | https://arxiv.org/abs/2101.04792 | https://arxiv.org/ftp/arxiv/papers/2101/2101.04792.pdf | specom-2021 | ['keyword-spotting'] | ['speech'] | [-1.85667202e-01 1.92154869e-01 -3.06898415e-01 -6.79977417e-01
-9.43017721e-01 -5.09084702e-01 8.27157915e-01 4.59089935e-01
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-1.55766413e-01 6.05856180e-01 -1.52135985e-02 -1.07011534... | [9.508740425109863, 3.099494218826294] |
c30e9b81-e6b7-4acb-a5c8-ae203083f2f4 | multi-graph-fusion-networks-for-urban-region | 2201.0976 | null | https://arxiv.org/abs/2201.09760v2 | https://arxiv.org/pdf/2201.09760v2.pdf | Multi-Graph Fusion Networks for Urban Region Embedding | Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.... | ['Cheng Wang', 'Ming Cheng', 'Chuanpan Zheng', 'Shichao Zhu', 'Shirui Pan', 'Xiaoliang Fan', 'Xu Yan', 'Shangbin Wu'] | 2022-01-24 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-3.23386967e-01 -1.42631188e-01 -4.77001727e-01 -2.43451580e-01
-4.32097405e-01 5.25292382e-02 8.14028978e-01 1.56425804e-01
-5.21591008e-01 5.93649924e-01 7.95763850e-01 -3.34836334e-01
-3.91580284e-01 -1.05427408e+00 -6.10811591e-01 -5.54932475e-01
-4.76802230e-01 2.19232351e-01 6.91760480e-01 -4.96130228... | [6.546276569366455, 2.078890800476074] |
098c47e8-fa10-4a5a-9789-0d7a5cddddf0 | kast-knowledge-aware-adaptive-session-multi | 2210.03624 | null | https://arxiv.org/abs/2210.03624v1 | https://arxiv.org/pdf/2210.03624v1.pdf | KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction | Capturing the evolving trends of user interest is important for both recommendation systems and advertising systems, and user behavior sequences have been successfully used in Click-Through-Rate(CTR) prediction problems. However, if the user interest is learned on the basis of item-level behaviors, the performance may ... | ['ShengKai Yang', 'Kai Liu', 'Dike Sun'] | 2022-10-07 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 1.33174375e-01 -4.18553531e-01 -6.78872645e-01 -7.13864505e-01
-6.05090737e-01 -2.40994215e-01 1.02850638e-01 -2.28263922e-02
-3.24619740e-01 3.87180269e-01 3.59062940e-01 -2.08241418e-01
-4.20585632e-01 -5.99378169e-01 -5.94318628e-01 -5.47918022e-01
-2.13312477e-01 4.69922543e-01 4.19995695e-01 -1.92401975... | [10.092863082885742, 5.515351295471191] |
2c0fb5bf-21aa-48d1-a2cf-55cc228eaa70 | action-recognition-using-supervised-spiking | 1911.0363 | null | https://arxiv.org/abs/1911.03630v2 | https://arxiv.org/pdf/1911.03630v2.pdf | Action Recognition Using Supervised Spiking Neural Networks | Biological neurons use spikes to process and learn temporally dynamic inputs in an energy and computationally efficient way. However, applying the state-of-the-art gradient-based supervised algorithms to spiking neural networks (SNN) is a challenge due to the non-differentiability of the activation function of spiking ... | ['Saeed Reza Kheradpisheh', 'Hadi Farahani', 'Aref Moqadam Mehr'] | 2019-11-09 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 4.15157229e-01 -6.51865661e-01 3.67349945e-02 -1.97437465e-01
-3.27368751e-02 -4.31186527e-01 4.08564448e-01 -3.88344705e-01
-8.67106080e-01 9.74195123e-01 -5.32289743e-01 1.91240549e-01
-1.07665330e-01 -5.47989488e-01 -6.40594900e-01 -1.05865598e+00
1.93861455e-01 3.20832096e-02 6.62030041e-01 -1.25268906... | [8.225377082824707, 2.4513370990753174] |
6d79be5d-f8df-437d-84ba-00eae3c19770 | triangle-net-towards-robustness-in-point | 2003.00856 | null | https://arxiv.org/abs/2003.00856v2 | https://arxiv.org/pdf/2003.00856v2.pdf | Triangle-Net: Towards Robustness in Point Cloud Learning | Three dimensional (3D) object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments. These real-time systems require effective classification methods that are robust t... | ['Juan Wachs', 'Chenxi Xiao'] | 2020-02-27 | null | null | null | null | ['3d-object-recognition', '3d-classification'] | ['computer-vision', 'computer-vision'] | [-5.97483804e-03 -4.08665150e-01 -2.81600475e-01 -3.71547580e-01
-5.55199325e-01 -5.14994800e-01 6.80019140e-01 -4.36344892e-02
-7.59187117e-02 2.87231147e-01 -2.00527281e-01 -1.31866455e-01
-3.85599107e-01 -6.74414396e-01 -8.08684409e-01 -6.33534908e-01
-2.39431128e-01 6.33588314e-01 2.69444138e-01 -1.37451097... | [7.824122428894043, -3.22392201423645] |
058c1be1-2565-48fc-b261-323358bcdadd | motion-guided-attention-for-video-salient | 1909.07061 | null | https://arxiv.org/abs/1909.07061v2 | https://arxiv.org/pdf/1909.07061v2.pdf | Motion Guided Attention for Video Salient Object Detection | Video salient object detection aims at discovering the most visually distinctive objects in a video. How to effectively take object motion into consideration during video salient object detection is a critical issue. Existing state-of-the-art methods either do not explicitly model and harvest motion cues or ignore spat... | ['Guanqi Chen', 'Haofeng Li', 'Yizhou Yu', 'Guanbin Li'] | 2019-09-16 | motion-guided-attention-for-video-salient-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Motion_Guided_Attention_for_Video_Salient_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Motion_Guided_Attention_for_Video_Salient_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.36445177e-01 -3.76928449e-01 -5.63267231e-01 -1.62958637e-01
-5.66488802e-01 -1.52622938e-01 2.59029925e-01 -2.65756428e-01
-4.04248118e-01 6.69631660e-01 4.65468585e-01 8.50076228e-02
2.62633950e-01 -1.79511487e-01 -7.59674609e-01 -6.92258298e-01
-3.04538816e-01 -3.78807753e-01 1.09485912e+00 -6.44072890... | [9.7184419631958, -0.3335892856121063] |
547344e1-fa24-4405-97d7-de0cdb8b4539 | using-anomaly-feature-vectors-for-detecting | 2107.00561 | null | https://arxiv.org/abs/2107.00561v1 | https://arxiv.org/pdf/2107.00561v1.pdf | Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples | We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. In contrast to current state-of-the-art methods that, given an input, detect whether an input is clean or adversarial, we aim to also identify the types of adversarial attack (e.g., PG... | ['Atul Prakash', 'Jiguo Song', 'Sahib Singh', 'Ryan Feng', 'Nelson Manohar-Alers'] | 2021-07-01 | null | https://openreview.net/forum?id=XDo0go2IJgT | https://openreview.net/pdf?id=XDo0go2IJgT | icml-workshop-aml-2021-7 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 1.16280392e-01 -3.47987831e-01 1.29453033e-01 -3.78676385e-01
-6.92049980e-01 -1.39165890e+00 9.40629184e-01 4.88489300e-01
-7.19169080e-02 3.24434847e-01 1.47726148e-01 -8.82734001e-01
-1.72704414e-01 -7.91540980e-01 -4.19809371e-01 -7.00743735e-01
-3.64644140e-01 9.84203741e-02 2.04453729e-02 -1.67889640... | [5.7032928466796875, 7.850957870483398] |
7eccd6bf-0699-4536-8fdb-02a6dd4a13bb | listwise-view-ranking-for-image-cropping | 1905.05352 | null | https://arxiv.org/abs/1905.05352v1 | https://arxiv.org/pdf/1905.05352v1.pdf | Listwise View Ranking for Image Cropping | Rank-based Learning with deep neural network has been widely used for image cropping. However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the defo... | ['Xiaofen Xing', 'Xiangmin Xu', 'Bolun Cai', 'Weirui Lu'] | 2019-05-14 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 4.14740413e-01 -3.00781190e-01 -2.27620110e-01 -4.44953352e-01
-1.02902329e+00 -3.30794871e-01 4.74505991e-01 -1.30842756e-02
-2.42005751e-01 5.28565526e-01 5.81754863e-01 2.47506365e-01
-3.75890493e-01 -6.74328864e-01 -7.11437702e-01 -7.77471483e-01
2.49869198e-01 2.45273098e-01 3.99319857e-01 8.35549831... | [11.300725936889648, -1.033552646636963] |
0aba42ef-e765-4124-ba62-42094671847e | locality-aware-inter-and-intra-video | 2203.14333 | null | https://arxiv.org/abs/2203.14333v2 | https://arxiv.org/pdf/2203.14333v2.pdf | Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning | Our target is to learn visual correspondence from unlabeled videos. We develop LIIR, a locality-aware inter-and intra-video reconstruction framework that fills in three missing pieces, i.e., instance discrimination, location awareness, and spatial compactness, of self-supervised correspondence learning puzzle. First, i... | ['Yi Yang', 'Jianwu Li', 'Lu Yang', 'Wenguan Wang', 'Tianfei Zhou', 'Liulei Li'] | 2022-03-27 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 1.29321203e-01 -6.26003221e-02 -6.40461981e-01 -5.06106794e-01
-7.50754237e-01 -7.18922496e-01 4.47112530e-01 1.01029813e-01
-3.75268489e-01 4.17278349e-01 5.19108653e-01 2.19796985e-01
-7.87738413e-02 -5.56305528e-01 -9.54269826e-01 -5.42703569e-01
1.68503419e-01 2.37526968e-01 4.12759036e-01 3.82862128... | [8.940773963928223, -0.28029531240463257] |
57853d4a-83eb-4efb-a7e6-0f4ab824f493 | physics-informed-machine-learning-of-redox | 2306.0101 | null | https://arxiv.org/abs/2306.01010v1 | https://arxiv.org/pdf/2306.01010v1.pdf | Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model | In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a ... | ['Panos Stinis', 'Yucheng Fu', 'Wenqian Chen'] | 2023-05-31 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.49121553e-01 -2.62378246e-01 -3.65386546e-01 1.02438219e-01
-2.59827256e-01 -5.13797283e-01 5.68655014e-01 3.27211231e-01
-4.59306180e-01 1.41327417e+00 -3.52189839e-01 -2.68148541e-01
-2.89577514e-01 -8.58648360e-01 -8.32132220e-01 -1.18500495e+00
-1.01323210e-01 4.26799834e-01 1.68557212e-01 -5.23434222... | [6.33090877532959, 3.060319423675537] |
06961d70-a9bb-455f-9db7-a13f48b28eeb | when-does-maml-work-the-best-an-empirical | 2005.117 | null | https://arxiv.org/abs/2005.11700v1 | https://arxiv.org/pdf/2005.11700v1.pdf | When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications | Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation. Many impacting factors, including data quantity, similarity among tasks, and the balance between general lang... | ['Yiping Song', 'Ming Zhang', 'Zequn Liu', 'Ruiyi Zhang'] | 2020-05-24 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [-1.02203735e-03 -5.70029497e-01 -6.35591924e-01 -1.97618276e-01
-6.18866742e-01 -1.06726889e-03 9.93943810e-01 1.93910599e-02
-6.11354351e-01 8.95700395e-01 4.72458631e-01 -8.83601513e-03
-1.82988241e-01 -5.82824528e-01 -1.73363388e-01 -3.80042493e-01
3.87136847e-01 5.29549837e-01 2.82013446e-01 -5.31547844... | [10.704620361328125, 7.781102657318115] |
7e639348-bce1-47e1-b799-425a6471e370 | real-time-emotion-classification-using-eeg | null | null | https://www.mdpi.com/1424-8220/21/5/1589 | https://www.mdpi.com/1424-8220/21/5/1589 | Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts | In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning out... | ['Santi Fort', 'Laia Subirats', 'Fatos Xhafa', 'Arijit Nandi'] | 2021-02-25 | null | null | null | mdpi-sensors-2021-2 | ['emotional-intelligence'] | ['natural-language-processing'] | [-3.53406996e-01 -2.39423171e-01 1.46761327e-03 -6.81227624e-01
8.27663690e-02 -1.75219446e-01 2.31368586e-01 4.17133808e-01
-6.87967360e-01 7.16791630e-01 -3.99515182e-01 -4.96486761e-02
-2.62079000e-01 -8.50085020e-01 -3.82428139e-01 -6.87948763e-01
-2.32054442e-01 5.56395464e-02 -3.43155891e-01 -4.64503646... | [13.302725791931152, 3.2610862255096436] |
4acf570a-7a59-432f-a022-1dd689c35b1c | frame-fast-and-robust-autonomous-3d-point | 2301.09213 | null | https://arxiv.org/abs/2301.09213v2 | https://arxiv.org/pdf/2301.09213v2.pdf | FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration | This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition lear... | ['George Nikolakopoulos', 'Ali-akbar Agha-mohammadi', 'Anton Koval', 'Nikolaos Stathoulopoulos'] | 2023-01-22 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 1.10744767e-01 -5.39571308e-02 3.29903275e-01 -2.52111375e-01
-5.12460947e-01 -8.05249274e-01 5.78184783e-01 7.13279486e-01
-7.23768353e-01 4.74707931e-01 -6.18931115e-01 1.35630239e-02
-6.95615947e-01 -8.31803024e-01 -7.02202797e-01 -5.16790628e-01
-3.28364849e-01 1.15029657e+00 3.28234941e-01 -4.27496165... | [7.331492900848389, -2.0621531009674072] |
e6a1873a-ac26-43ba-a5de-cd46863571d8 | analysis-of-tomographic-reconstruction-of-2d | 2304.06376 | null | https://arxiv.org/abs/2304.06376v1 | https://arxiv.org/pdf/2304.06376v1.pdf | Analysis of Tomographic Reconstruction of 2D Images using the Distribution of Unknown Projection Angles | It is well known that a band-limited signal can be reconstructed from its uniformly spaced samples if the sampling rate is sufficiently high. More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given just the distribution of the sample ... | ['Ajit Rajwade', 'Karthik S. Gurumoorthy', 'Sheel Shah'] | 2023-04-13 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 5.78742683e-01 -1.32702306e-01 1.42746940e-01 -2.56110936e-01
-9.60669458e-01 -3.19380462e-01 1.13981783e-01 -2.80197859e-01
-5.04038990e-01 9.07009542e-01 -1.78588018e-01 -2.94160515e-01
-2.71675795e-01 -4.61276442e-01 -8.44957292e-01 -9.72987831e-01
-2.00205684e-01 7.09818065e-01 1.13875769e-01 1.22508705... | [12.785025596618652, -2.7517738342285156] |
b3b32c79-95b7-41f4-a168-be3e3bee17a6 | grounding-of-textual-phrases-in-images-by | 1511.03745 | null | http://arxiv.org/abs/1511.03745v4 | http://arxiv.org/pdf/1511.03745v4.pdf | Grounding of Textual Phrases in Images by Reconstruction | Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual
content is a challenging problem with many applications for human-computer
interaction and image-text reference resolution. Few datasets provide the
ground truth spatial localization of phrases, thus it is desirable to learn
from data with no or... | ['Marcus Rohrbach', 'Anna Rohrbach', 'Trevor Darrell', 'Ronghang Hu', 'Bernt Schiele'] | 2015-11-12 | null | null | null | null | ['phrase-grounding', 'natural-language-visual-grounding'] | ['natural-language-processing', 'reasoning'] | [ 3.92012596e-01 3.15859050e-01 -4.08248156e-01 -3.56296092e-01
-1.14848578e+00 -7.08736956e-01 6.44201338e-01 1.39500737e-01
-4.80879843e-01 6.56808436e-01 2.77922481e-01 -2.83267349e-01
9.85707641e-02 -6.20707214e-01 -1.34644508e+00 -6.13646686e-01
2.95318544e-01 6.25067592e-01 3.31964225e-01 -3.53458300... | [10.512073516845703, 1.4123530387878418] |
b12e7aea-d3a2-40b8-b27d-390f43d75140 | compass-a-creative-support-system-that-alerts | 2202.13151 | null | https://arxiv.org/abs/2202.13151v1 | https://arxiv.org/pdf/2202.13151v1.pdf | COMPASS: a Creative Support System that Alerts Novelists to the Unnoticed Missing Contents | When humans write, they may unintentionally omit some information. Complementing the omitted information using a computer is helpful in providing writing support. Recently, in the field of story understanding and generation, story completion (SC) was proposed to generate the missing parts of an incomplete story. Althou... | ['Tatsuya Harada', 'Yusuke Mukuta', 'Ryohei Shimizu', 'Hiroaki Yamane', 'Yusuke Mori'] | 2022-02-26 | null | null | null | null | ['story-completion'] | ['natural-language-processing'] | [ 5.15973866e-01 3.22170258e-01 -1.04576372e-01 -2.08162174e-01
-2.98324853e-01 -3.89838964e-01 4.83178645e-01 7.96863344e-03
-1.69144794e-02 1.27691758e+00 5.49349964e-01 -9.34427828e-02
7.45174382e-03 -6.19465232e-01 -5.03103554e-01 -2.25204006e-01
8.71149063e-01 2.18460232e-01 2.12034225e-01 -4.50666875... | [11.850753784179688, 8.98405933380127] |
c05d0231-33f8-4541-8746-a94168aad2be | dictionary-learning-based-reconstruction | 1311.583 | null | http://arxiv.org/abs/1311.5830v1 | http://arxiv.org/pdf/1311.5830v1.pdf | Dictionary-Learning-Based Reconstruction Method for Electron Tomography | Electron tomography usually suffers from so called missing wedge artifacts
caused by limited tilt angle range. An equally sloped tomography (EST)
acquisition scheme (which should be called the linogram sampling scheme) was
recently applied to achieve 2.4-angstrom resolution. On the other hand, a
compressive sensing-ins... | ['Scott S. Verbridge', 'Hengyong Yu', 'Ge Wang', 'Baodong Liu', 'Lizhi Sun'] | 2013-11-22 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 5.16379178e-01 -2.98303932e-01 3.34431022e-01 -2.63930947e-01
-6.75834656e-01 7.90062770e-02 5.42282343e-01 -1.16793901e-01
-7.06957877e-01 9.53831673e-01 2.78593093e-01 -3.76012772e-01
-5.59745073e-01 -5.04485846e-01 -1.77501783e-01 -8.70538354e-01
3.14970553e-01 9.05832410e-01 2.97773153e-01 2.89784782... | [12.881592750549316, -2.735652446746826] |
10c33b9a-42bd-450f-a73c-06dc05170637 | bifnet-bidirectional-fusion-network-for-road | 2004.08582 | null | https://arxiv.org/abs/2004.08582v1 | https://arxiv.org/pdf/2004.08582v1.pdf | BiFNet: Bidirectional Fusion Network for Road Segmentation | Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the dis... | ['Yaran Chen', 'Haoran Li', 'Qichao Zhang', 'Dongbin Zhao'] | 2020-04-18 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 6.09835312e-02 -4.18488920e-01 1.80425391e-01 -4.86638963e-01
-2.53478914e-01 -2.93464392e-01 4.98487473e-01 -3.38255614e-01
-4.77409631e-01 3.86627376e-01 1.03740692e-02 -4.24157232e-01
-3.34737688e-01 -1.32181239e+00 -6.15050733e-01 -5.66237152e-01
7.80006289e-01 2.26275876e-01 8.11449766e-01 -6.98597133... | [8.18359088897705, -2.4679484367370605] |
815be30b-db20-4ee9-813d-b3ef3c7d1892 | leveraging-speech-separation-for | 2204.02306 | null | https://arxiv.org/abs/2204.02306v2 | https://arxiv.org/pdf/2204.02306v2.pdf | Low-Latency Speech Separation Guided Diarization for Telephone Conversations | In this paper, we carry out an analysis on the use of speech separation guided diarization (SSGD) in telephone conversations. SSGD performs diarization by separating the speakers signals and then applying voice activity detection on each estimated speaker signal. In particular, we compare two low-latency speech separat... | ['Enrico Zovato', 'Luca Serafini', 'Stefano Squartini', 'Alessio Brutti', 'Desh Raj', 'Samuele Cornell', 'Giovanni Morrone'] | 2022-04-05 | null | null | null | null | ['activity-detection', 'speech-separation'] | ['computer-vision', 'speech'] | [ 1.86209559e-01 4.62555736e-01 2.00032890e-01 -4.43336636e-01
-1.54732573e+00 -7.84447610e-01 5.29750705e-01 -1.30025953e-01
-3.92273098e-01 2.39627436e-01 3.27323854e-01 -6.65429831e-01
1.69274405e-01 1.42887086e-01 -2.62617201e-01 -5.69320083e-01
5.56605191e-05 7.77475238e-01 8.49805474e-02 1.80775017... | [14.698129653930664, 6.213558673858643] |
fd093d0b-b7a9-4660-b5b8-3c799ffd6925 | low-cost-lidar-based-vehicle-pose-estimation | 1910.01701 | null | https://arxiv.org/abs/1910.01701v1 | https://arxiv.org/pdf/1910.01701v1.pdf | Low-cost LIDAR based Vehicle Pose Estimation and Tracking | Detecting surrounding vehicles by low-cost LIDAR has been drawing enormous attention. In low-cost LIDAR, vehicles present a multi-layer L-Shape. Based on our previous optimization/criteria-based L-Shape fitting algorithm, we here propose a data-driven and model-based method for robust vehicle segmentation and tracking.... | ['John M. Dolan', 'Xiao Zhang', 'Chiyu Dong', 'Chen Fu'] | 2019-10-03 | null | null | null | null | ['vehicle-pose-estimation'] | ['computer-vision'] | [-2.59924054e-01 -3.78991365e-01 -9.88346264e-02 -5.05851090e-01
-8.43643725e-01 -6.08264148e-01 4.46287781e-01 -8.13273340e-02
-3.15407991e-01 5.23801088e-01 -7.47555315e-01 -3.89881790e-01
-1.48980156e-01 -8.64700198e-01 -8.10364187e-01 -6.15940869e-01
1.85950205e-01 1.00831282e+00 8.78422678e-01 1.09936722... | [7.143157005310059, -2.3470935821533203] |
979d6690-1e1f-440d-bf9a-fb4a9aa82d48 | zero-shot-learning-with-complementary | 1804.06505 | null | https://arxiv.org/abs/1804.06505v2 | https://arxiv.org/pdf/1804.06505v2.pdf | Complementary Attributes: A New Clue to Zero-Shot Learning | Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from seen classes to unseen classes. To take full advantage of the knowledge transferred by attributes, i... | ['Chuancai Liu', 'Ivor W. Tsang', 'Xiaofeng Xu'] | 2018-04-17 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [ 2.02638581e-01 2.17987373e-01 -2.83687830e-01 -6.29460096e-01
-7.67539322e-01 -3.03087473e-01 5.05603313e-01 3.89985621e-01
-3.04075629e-02 7.46331215e-01 1.18119746e-01 1.92749232e-01
-6.21982872e-01 -1.11322737e+00 -5.55633783e-01 -9.96309340e-01
1.21069260e-01 4.51463670e-01 4.49927777e-01 -2.61916637... | [9.982720375061035, 2.5267724990844727] |
2c8062ff-57d1-47b8-81c8-96cb2d98852b | customers-churn-prediction-in-financial | 1912.11346 | null | https://arxiv.org/abs/1912.11346v1 | https://arxiv.org/pdf/1912.11346v1.pdf | Customers Churn Prediction in Financial Institution Using Artificial Neural Network | In this study, a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in a financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest... | ['Kamorudeen A. Amuda', 'Adesesan B. Adeyemo'] | 2019-12-23 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-4.56470549e-01 -1.87147483e-01 -1.70285434e-01 -7.74081230e-01
2.34725535e-01 -4.05331939e-01 -4.04646188e-01 4.80703324e-01
-4.78457510e-01 6.84395313e-01 -1.20914735e-01 -8.85602534e-01
-3.18112910e-01 -8.12667131e-01 -2.33837396e-01 -4.20811296e-01
8.09995234e-02 5.29180050e-01 -1.82212114e-01 -8.89884159... | [8.36279010772705, 4.904707908630371] |
c4986370-f344-42e1-b08f-6bf3e229e4dd | squeezing-nnu-nets-with-knowledge | 2306.09886 | null | https://arxiv.org/abs/2306.09886v1 | https://arxiv.org/pdf/2306.09886v1.pdf | Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection | Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re... | ['Jakub Nalepa', 'Bertrand Le Saux', 'Nicolas Longépé', 'Piotr Bosowski', 'Michal Kawulok', 'Maciej Ziaja', 'Bartosz Grabowski'] | 2023-06-16 | null | null | null | null | ['cloud-detection', 'meta-learning'] | ['computer-vision', 'methodology'] | [ 2.51548022e-01 2.99442075e-02 -8.95601213e-02 -1.15139708e-01
-6.38726294e-01 -8.83144259e-01 2.19054952e-01 1.51773795e-01
-7.66515791e-01 5.61236322e-01 -2.61014849e-01 -5.30717254e-01
-3.78885269e-01 -1.10066104e+00 -8.61189663e-01 -7.77067244e-01
-6.41472340e-01 5.86785078e-01 4.80103910e-01 -1.28675774... | [9.538911819458008, -1.4814889430999756] |
fb472b42-d19b-4711-8ae5-5dcae404d1f6 | knowner-incremental-multilingual-knowledge-in | 1709.03544 | null | http://arxiv.org/abs/1709.03544v1 | http://arxiv.org/pdf/1709.03544v1.pdf | KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition | KnowNER is a multilingual Named Entity Recognition (NER) system that
leverages different degrees of external knowledge. A novel modular framework
divides the knowledge into four categories according to the depth of knowledge
they convey. Each category consists of a set of features automatically
generated from different... | ['Luciano del Corro', 'Johannes Hoffart', 'Gerhard Weikum', 'Dominic Seyler', 'Tatiana Dembelova'] | 2017-09-11 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.72878397e-01 3.46793002e-03 -6.61472499e-01 -3.34078342e-01
-1.01394749e+00 -1.13904059e+00 1.10114396e+00 4.12645221e-01
-8.40591252e-01 9.76779997e-01 5.15547752e-01 -2.38930270e-01
1.25961825e-01 -8.18501830e-01 -6.25275075e-01 2.39042006e-02
2.99620986e-01 5.18779159e-01 1.99175581e-01 -4.72257286... | [9.835872650146484, 9.677578926086426] |
493e57b1-9e2f-488b-8dad-b3ea9677ad8a | retrieval-based-layer-wise-adaptive | null | null | https://openreview.net/forum?id=zikTfLBMXAg | https://openreview.net/pdf?id=zikTfLBMXAg | Retrieval-based Layer-wise Adaptive Transformer for Source Code Summarization | We propose a model that learns both the sequential and the structural features of code for source code summarization. We adopt the Abstract Syntax Tree (AST) and graph convolution to model the structural information and the Transformer to model the sequential information. We convert code snippets into ASTs and apply gr... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-summarization'] | ['computer-code'] | [ 2.49814972e-01 3.35286796e-01 -2.72356182e-01 -5.47632694e-01
-3.66845548e-01 -5.62603772e-01 -3.64246592e-02 6.05605483e-01
1.56908497e-01 -1.57758780e-02 8.04140627e-01 -6.02405131e-01
3.05683196e-01 -9.25639689e-01 -8.29436660e-01 -1.39650553e-01
-3.27256739e-01 -3.55436355e-01 2.70097762e-01 1.83637179... | [7.557251453399658, 7.945128440856934] |
529be77a-c40d-4f91-b83e-d17957546089 | self-remixing-unsupervised-speech-separation | 2211.10194 | null | https://arxiv.org/abs/2211.10194v1 | https://arxiv.org/pdf/2211.10194v1.pdf | Self-Remixing: Unsupervised Speech Separation via Separation and Remixing | We present Self-Remixing, a novel self-supervised speech separation method, which refines a pre-trained separation model in an unsupervised manner. The proposed method consists of a shuffler module and a solver module, and they grow together through separation and remixing processes. Specifically, the shuffler first se... | ['Tetsuji Ogawa', 'Kohei Saijo'] | 2022-11-18 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.74669492e-01 5.62531985e-02 -3.05833906e-01 -5.39799929e-01
-9.22163844e-01 -5.53941488e-01 6.96402729e-01 -2.72937298e-01
-3.12854111e-01 5.56940615e-01 4.00688022e-01 -1.43806875e-01
-6.82057515e-02 7.66338184e-02 -6.69599414e-01 -8.66182089e-01
-8.89953002e-02 9.99001861e-01 1.76232293e-01 9.03432295... | [15.290121078491211, 5.720627784729004] |
4d0297f4-7cbd-445f-8389-12bd31ddfca9 | deep-learning-for-network-traffic | 2106.12693 | null | https://arxiv.org/abs/2106.12693v1 | https://arxiv.org/pdf/2106.12693v1.pdf | Deep Learning for Network Traffic Classification | Monitoring network traffic to identify content, services, and applications is an active research topic in network traffic control systems. While modern firewalls provide the capability to decrypt packets, this is not appealing for privacy advocates. Hence, identifying any information from encrypted traffic is a challen... | ['Derrick Liu', 'Weston Jackson', 'Niloofar Bayat'] | 2021-06-02 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.69939533e-01 -5.07376492e-01 -6.74535692e-01 -5.66255629e-01
-6.19345188e-01 -7.36541450e-01 4.64686155e-01 1.70263171e-01
-2.07561567e-01 5.89265943e-01 -3.87077242e-01 -1.19857585e+00
-1.76886678e-01 -7.24927366e-01 -3.16439450e-01 -4.08571243e-01
-5.73706813e-02 7.17483819e-01 1.72060683e-01 2.07301155... | [5.106464862823486, 7.25462532043457] |
d34b00d0-24b0-4144-b467-bf65d6fce932 | rethinking-rotation-invariance-with-point | 2301.00149 | null | https://arxiv.org/abs/2301.00149v1 | https://arxiv.org/pdf/2301.00149v1.pdf | Rethinking Rotation Invariance with Point Cloud Registration | Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation inv... | ['Weidong Cai', 'Chaoyi Zhang', 'Jianhui Yu'] | 2022-12-31 | null | null | null | null | ['3d-shape-retrieval', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [-3.65181416e-02 -4.73951787e-01 -3.54002148e-01 -5.69597840e-01
-6.05779886e-01 -8.83405626e-01 5.66317260e-01 -1.36061804e-02
-2.68331263e-02 -9.92649421e-02 1.60824060e-01 1.55965194e-01
-4.69499618e-01 -7.96375036e-01 -5.40923715e-01 -6.72520459e-01
2.15258420e-01 4.57979530e-01 -3.95649597e-02 -6.01164661... | [7.9550676345825195, -3.2377450466156006] |
d24cffe6-1f07-4a72-91f8-36a35b715bec | spectral-toolkit-of-algorithms-for-graphs | 2304.0317 | null | https://arxiv.org/abs/2304.03170v1 | https://arxiv.org/pdf/2304.03170v1.pdf | Spectral Toolkit of Algorithms for Graphs: Technical Report (1) | Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clustering, and this technical report presents a user's guide to STAG, showcase studies, and several tech... | ['He Sun', 'Peter Macgregor'] | 2023-04-05 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.37555152e-01 1.46701247e-01 -1.89083084e-01 -1.30388662e-01
-6.42404318e-01 -6.89771473e-01 3.73598278e-01 -1.83569156e-02
2.70042062e-01 5.42445302e-01 2.73242742e-01 -5.63621223e-01
-3.92070144e-01 -6.71673656e-01 -1.23102861e-02 -6.80553794e-01
-7.01354682e-01 6.64846957e-01 4.84479219e-01 1.58134490... | [7.055590629577637, 5.228991508483887] |
2383348d-dc53-4171-a9f3-2ac80d78898a | intent-mining-from-past-conversations-for | 2005.11014 | null | https://arxiv.org/abs/2005.11014v4 | https://arxiv.org/pdf/2005.11014v4.pdf | Intent Mining from past conversations for conversational agent | Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize... | ['Ajay Chatterjee', 'Shubhashis Sengupta'] | 2020-05-22 | null | https://aclanthology.org/2020.coling-main.366 | https://aclanthology.org/2020.coling-main.366.pdf | coling-2020-8 | ['intent-discovery', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.58952251e-01 3.96989703e-01 7.51241855e-03 -7.66660452e-01
-6.36344016e-01 -7.80746162e-01 6.37730420e-01 8.57927874e-02
-2.32963890e-01 6.25549436e-01 3.73165935e-01 -3.82686734e-01
1.51921928e-01 -5.04529059e-01 1.80125199e-02 -6.70671046e-01
1.53986439e-01 1.38661575e+00 2.73569614e-01 -2.68296152... | [12.650794982910156, 7.717530727386475] |
c14499fb-cfc5-43c9-af4c-349a043dc9f7 | audio-visual-contrastive-learning-for-self | 2204.13386 | null | https://arxiv.org/abs/2204.13386v2 | https://arxiv.org/pdf/2204.13386v2.pdf | Self-supervised Contrastive Learning for Audio-Visual Action Recognition | The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. In this paper, we propose an end-to-end self-supervised framework named Audio-Visual Contrastive Learning (AVCL), to learn discriminative audio-visual representations for action recogniti... | ['Haoyuan Lan', 'Ying Tan', 'Yang Liu'] | 2022-04-28 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 3.26022178e-01 -5.08937418e-01 -3.36824507e-01 -3.80835384e-01
-1.23373759e+00 -2.12133810e-01 6.43299699e-01 -2.27497041e-01
-2.67129779e-01 3.47764581e-01 7.37395763e-01 4.57610548e-01
4.96535338e-02 -1.98726952e-02 -6.11060560e-01 -7.63650179e-01
2.75850352e-02 4.33112755e-02 -1.33922906e-03 2.31788948... | [8.942839622497559, 0.854483962059021] |
c0efcdce-3717-44e4-92a0-c09c90c51082 | l2cs-net-fine-grained-gaze-estimation-in | 2203.03339 | null | https://arxiv.org/abs/2203.03339v1 | https://arxiv.org/pdf/2203.03339v1.pdf | L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments | Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, estimating gaze in-the-wild is still a challenging problem due to the uniqueness of eye ap... | ['Ayoub Al-Hamadi', 'Aly Khalifa', 'Thorsten Hempel', 'Ahmed A. Abdelrahman'] | 2022-03-07 | null | null | null | null | ['gaze-estimation', 'eye-tracking'] | ['computer-vision', 'computer-vision'] | [-2.28003249e-01 -1.85495213e-01 -3.54853570e-02 -6.49713695e-01
-1.35412961e-01 -2.68365771e-01 5.22236116e-02 -3.61433327e-01
-4.07740593e-01 5.02260208e-01 -1.33965448e-01 -2.54790813e-01
1.24728046e-01 -1.07532017e-01 -6.72885239e-01 -7.50131726e-01
2.46851340e-01 -2.94634789e-01 -1.55272810e-02 -1.56463549... | [14.140106201171875, 0.09435758739709854] |
849ce658-70c2-492f-b9f6-b22b0b7fea77 | high-impedance-non-linear-fault-detection-via | 2301.04123 | null | https://arxiv.org/abs/2301.04123v1 | https://arxiv.org/pdf/2301.04123v1.pdf | High-Impedance Non-Linear Fault Detection via Eigenvalue Analysis with low PMU Sampling Rates | This technique holds several advantages over contemporary techniques: It utilizes technology that is already deployed in the field, it offers a significant degree of generality, and so far it has displayed a very high-level of sensitivity without sacrificing accuracy. Validation is performed in the form of simulations ... | ['Sean Meyn', 'Arturo Bretas', 'Gian Paramo'] | 2023-01-10 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.20175026e-01 -1.79730073e-01 -3.05731177e-01 -2.08078250e-01
-3.73407513e-01 -1.48557350e-01 4.91944879e-01 2.65821129e-01
-1.22748569e-01 1.15790129e+00 -4.55943018e-01 -7.33637512e-01
-6.30441785e-01 -7.37513542e-01 -9.98490080e-02 -6.39843643e-01
-9.52303529e-01 3.33796948e-01 5.84415853e-01 -3.94955128... | [6.37514066696167, 2.5951907634735107] |
a082249a-ed7a-40f9-842c-08b41e571009 | joint-level-generation-and-translation-using | 2306.16662 | null | https://arxiv.org/abs/2306.16662v1 | https://arxiv.org/pdf/2306.16662v1.pdf | Joint Level Generation and Translation Using Gameplay Videos | Procedural Content Generation via Machine Learning (PCGML) faces a significant hurdle that sets it apart from other fields, such as image or text generation, which is limited annotated data. Many existing methods for procedural level generation via machine learning require a secondary representation besides level image... | ['Matthew Guzdial', 'Negar Mirgati'] | 2023-06-29 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 6.80450797e-01 3.90971631e-01 1.09442314e-02 -6.34177821e-03
-1.23755193e+00 -6.40233815e-01 9.05210316e-01 -1.54519096e-01
-2.59885401e-01 6.48913622e-01 3.76272976e-01 -1.35810584e-01
4.12647754e-01 -1.10825408e+00 -8.93265307e-01 -2.85987079e-01
1.56117976e-01 3.59478176e-01 4.71931040e-01 -4.79450911... | [11.057918548583984, -0.2096801996231079] |
f4749c6a-2283-40da-9475-b480636661c6 | can-current-task-oriented-dialogue-models | 2212.10504 | null | https://arxiv.org/abs/2212.10504v2 | https://arxiv.org/pdf/2212.10504v2.pdf | Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild? | Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. Howe... | ['WooMyoung Park', 'Hyungsuk Noh', 'Donghyun Kwak', 'Kyunghyun Cho', 'Wangkyo Jung', 'Hyunhoon Jung', 'Shin Ah Oh', 'Youngki Hong', 'Donghoon Ham', 'Donghyeon Ko', 'Sungdong Kim', 'Sang-Woo Lee'] | 2022-12-20 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-1.79502711e-01 7.34192252e-01 -2.20778927e-01 -4.06075656e-01
-7.17424572e-01 -7.44643688e-01 8.59547138e-01 -2.46014670e-01
-3.42018127e-01 1.04548144e+00 4.64029253e-01 -6.99238300e-01
1.18325144e-01 -6.71329558e-01 1.40997991e-01 -1.77476957e-01
1.90040946e-01 1.12890959e+00 5.59090376e-01 -8.95903885... | [12.857820510864258, 7.874277591705322] |
af5cda29-2fd0-435e-a15a-6e11f73cfa15 | joint-turn-and-dialogue-level-user | 2010.02495 | null | https://arxiv.org/abs/2010.02495v2 | https://arxiv.org/pdf/2010.02495v2.pdf | Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations | Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user... | ['Josep Valls Vargas', 'Spyros Matsoukas', 'Lazaros Polymenakos', 'Aditya Tiwari', 'Praveen Kumar Bodigutla'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.347 | https://aclanthology.org/2020.findings-emnlp.347.pdf | findings-of-the-association-for-computational | ['dialogue-management'] | ['natural-language-processing'] | [-1.11689210e-01 4.75776881e-01 -5.38474545e-02 -1.15783238e+00
-1.04332435e+00 -4.06872481e-01 6.21458173e-01 2.27600902e-01
-6.65469527e-01 8.84074330e-01 5.85590601e-01 -1.82695881e-01
3.19951087e-01 -4.99150455e-01 -6.48668408e-02 -2.34859481e-01
2.42693350e-01 5.82892239e-01 -2.77403444e-01 -8.07697833... | [12.836946487426758, 7.995329856872559] |
65e667d6-a042-4110-a643-eb0e38b31931 | 3dsgrasp-3d-shape-completion-for-robotic | 2301.00866 | null | https://arxiv.org/abs/2301.00866v1 | https://arxiv.org/pdf/2301.00866v1.pdf | 3DSGrasp: 3D Shape-Completion for Robotic Grasp | Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose ... | ['Jose Santos-Victor', 'Alessio Del Bue', 'Alexandre Bernardino', 'Plinio Moreno', 'Atabak Dehban', 'Pietro Morerio', 'Matteo Taiana', 'Yiming Wang', 'Dimitris Dimou', 'Nuno F. Duarte', 'Seyed S. Mohammadi'] | 2023-01-02 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-8.95582289e-02 -1.24906793e-01 -8.93069655e-02 -4.29570109e-01
-7.68135369e-01 -4.99481112e-01 1.49702460e-01 -1.09227441e-01
9.74851400e-02 2.94138253e-01 -6.35064244e-02 5.77751324e-02
-1.17354073e-01 -6.62538230e-01 -1.41018176e+00 -5.49092889e-01
-7.65439570e-02 8.82854164e-01 2.50228971e-01 -7.80743286... | [5.763518333435059, -0.8774271607398987] |
4d4fa507-5c62-4231-8434-756a32750ee3 | contrastive-multi-view-framework-for-customer | 2306.144 | null | https://arxiv.org/abs/2306.14400v1 | https://arxiv.org/pdf/2306.14400v1.pdf | Contrastive Multi-view Framework for Customer Lifetime Value Prediction | Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of consumption events and the interference of data variance and noise obstruct LTV estimation. Many existing LTV prediction methods directly train ... | ['Ruiming Tang', 'Yuan Fang', 'Hong Zhu', 'Qinglin Jia', 'Jingjie Li', 'Chuhan Wu'] | 2023-06-26 | null | null | null | null | ['value-prediction', 'contrastive-learning', 'contrastive-learning', 'marketing'] | ['computer-code', 'computer-vision', 'methodology', 'miscellaneous'] | [-3.06241602e-01 -3.16521376e-01 -9.20825839e-01 -7.08832145e-01
-9.07113552e-01 -3.57417554e-01 7.55499229e-02 6.70300722e-02
-3.87190431e-02 5.30016899e-01 4.36442971e-01 -1.20347343e-01
-1.34884819e-01 -9.72559869e-01 -5.39062917e-01 -7.55035162e-01
3.14352036e-01 5.51365376e-01 -2.13044390e-01 -5.52714288... | [10.106123924255371, 5.5602216720581055] |
9b940173-0532-41d4-aa85-03aa1adca5e5 | safe-exploration-of-nonlinear-dynamical | 1812.05506 | null | https://arxiv.org/abs/1812.05506v4 | https://arxiv.org/pdf/1812.05506v4.pdf | A predictive safety filter for learning-based control of constrained nonlinear dynamical systems | The transfer of reinforcement learning (RL) techniques into real-world applications is challenged by safety requirements in the presence of physical limitations. Most RL methods, in particular the most popular algorithms, do not support explicit consideration of state and input constraints. In this paper, we address th... | ['Melanie N. Zeilinger', 'Kim P. Wabersich'] | 2018-12-13 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.99149364e-01 5.28332889e-01 -2.10136145e-01 2.36462384e-01
-1.73376068e-01 -5.92373013e-01 7.70285070e-01 5.29492378e-01
-6.89472318e-01 1.10851085e+00 -4.29554224e-01 -5.47591865e-01
-6.32738829e-01 -9.33646619e-01 -5.34441471e-01 -8.67843151e-01
7.78589696e-02 3.62532467e-01 4.51270849e-01 -3.58399838... | [4.893315315246582, 2.1897082328796387] |
42dc4310-a1a1-4edb-bbaf-c2dab081d20d | a-deep-learning-system-for-domain-specific | 2303.1051 | null | https://arxiv.org/abs/2303.10510v1 | https://arxiv.org/pdf/2303.10510v1.pdf | A Deep Learning System for Domain-specific speech Recognition | As human-machine voice interfaces provide easy access to increasingly intelligent machines, many state-of-the-art automatic speech recognition (ASR) systems are proposed. However, commercial ASR systems usually have poor performance on domain-specific speech especially under low-resource settings. The author works with... | ['Yanan Jia'] | 2023-03-18 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-4.08089459e-02 1.30805299e-01 2.03810990e-01 -6.06167674e-01
-1.38674760e+00 -3.76887262e-01 4.43433493e-01 -4.12813365e-01
-6.83369815e-01 3.76450270e-01 5.77297330e-01 -7.27167904e-01
4.41331387e-01 -1.55729458e-01 -3.85743707e-01 -3.03331167e-01
5.36541343e-01 7.86554039e-01 3.40188202e-03 -6.68927729... | [14.290719032287598, 6.75852632522583] |
1fcd7aad-6066-49af-a8c0-330e0ef8fc16 | pay-attention-to-your-tone-introducing-a-new | 2212.1019 | null | https://arxiv.org/abs/2212.10190v1 | https://arxiv.org/pdf/2212.10190v1.pdf | Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite | We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edit... | ['Si-Qing Chen', 'Furu Wei', 'Yuki Li', 'Allen Mao', 'Tao Ge', 'Xun Wang'] | 2022-12-20 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.96718627e-01 6.56691313e-01 4.58790101e-02 -4.45792973e-01
-8.66286576e-01 -9.43357170e-01 5.84173381e-01 -1.87930644e-01
-4.63634610e-01 9.90126729e-01 5.23147285e-01 -4.89431143e-01
3.48297387e-01 -5.60206711e-01 -5.09688795e-01 -6.83570430e-02
8.16106975e-01 8.59510362e-01 -1.31802499e-01 -9.32338119... | [11.621671676635742, 9.620414733886719] |
9cbc7695-db84-41e8-8325-dfcd0cba32bb | orthographic-transliteration-for-kabyle | null | null | https://aclanthology.org/2021.icnlsp-1.3 | https://aclanthology.org/2021.icnlsp-1.3.pdf | Orthographic Transliteration for Kabyle Speech Recognition | null | ['Ni Lao', 'Christopher Haberland'] | null | null | null | null | icnlsp-2021-11 | ['transliteration'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.268579959869385, 3.7361953258514404] |
1a910c4e-9e36-4ba2-bb3c-95761ad1b7c5 | lightweight-pyramid-networks-for-image | 1805.06173 | null | http://arxiv.org/abs/1805.06173v1 | http://arxiv.org/pdf/1805.06173v1.pdf | Lightweight Pyramid Networks for Image Deraining | Existing deep convolutional neural networks have found major success in image
deraining, but at the expense of an enormous number of parameters. This limits
their potential application, for example in mobile devices. In this paper, we
propose a lightweight pyramid of networks (LPNet) for single image deraining.
Instead... | ['Yue Huang', 'Xueyang Fu', 'Borong Liang', 'Xinghao Ding', 'John Paisley'] | 2018-05-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.28871098e-01 1.25934586e-01 3.82070214e-01 -2.40939885e-01
-2.81949520e-01 -3.70962024e-02 -3.23950499e-02 -4.64520335e-01
-5.49689710e-01 4.73326325e-01 -1.00667708e-01 -4.83638942e-01
2.48142332e-01 -7.80888200e-01 -6.60428166e-01 -8.63703847e-01
9.75766256e-02 -4.36043382e-01 5.34464777e-01 -2.67715007... | [11.066550254821777, -2.6299221515655518] |
ef07540c-c4c9-4a54-bdab-e4d44a891c25 | a-machine-learning-model-of-the-combination | 2102.0941 | null | https://arxiv.org/abs/2102.09410v1 | https://arxiv.org/pdf/2102.09410v1.pdf | A Machine Learning model of the combination of normalized SD1 and SD2 indexes from 24h-Heart Rate Variability as a predictor of myocardial infarction | Aim: to evaluate the ability of the nonlinear 24-HRV as a predictor of MI using Machine Learning Methods: The sample was composed of 218 patients divided into two groups (Healthy, n=128; MI n=90). The sample dataset is part of the Telemetric and Holter Electrocardiogram Warehouse (THEW) database, from the University of... | ['Cristiano Mostarda', 'Adeilson Serra Mendes Vieira', 'Sara Raquel Dutra-Macedo', 'Antonio Carlos Silva-Filho'] | 2021-02-18 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-7.31606036e-02 -4.03149307e-01 -4.34552908e-01 -3.95459205e-01
-2.91177928e-01 -2.15575993e-01 5.88005148e-02 3.63296866e-01
-5.31548321e-01 1.08592498e+00 2.64336526e-01 -7.30620325e-01
-6.46288931e-01 -7.08622456e-01 1.06131174e-01 -6.43355966e-01
-6.72884047e-01 6.50564730e-01 -2.03541324e-01 -2.30451658... | [14.120706558227539, 3.147688627243042] |
9b9d074b-d6b4-45bc-92ac-b3bfff664cca | seeing-through-noise-visually-driven-speaker | 1708.06767 | null | http://arxiv.org/abs/1708.06767v3 | http://arxiv.org/pdf/1708.06767v3.pdf | Seeing Through Noise: Visually Driven Speaker Separation and Enhancement | Isolating the voice of a specific person while filtering out other voices or
background noises is challenging when video is shot in noisy environments. We
propose audio-visual methods to isolate the voice of a single speaker and
eliminate unrelated sounds. First, face motions captured in the video are used
to estimate ... | ['Shmuel Peleg', 'Ariel Ephrat', 'Tavi Halperin', 'Aviv Gabbay'] | 2017-08-22 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 3.03137839e-01 -2.18961135e-01 9.92944166e-02 -2.59376550e-03
-1.13174164e+00 -4.33326632e-01 2.02389002e-01 -5.48061967e-01
-3.05280119e-01 5.46290934e-01 5.62025309e-01 9.85946059e-02
3.45727175e-01 -2.59825978e-02 -6.38096154e-01 -7.81823516e-01
1.70678243e-01 1.56072136e-02 2.99645811e-01 3.27706397... | [14.496175765991211, 5.142880439758301] |
a832b919-710b-483c-be8d-193821e0ad6f | treegcn-ed-encoding-point-cloud-using-a-tree | 2110.0317 | null | https://arxiv.org/abs/2110.03170v3 | https://arxiv.org/pdf/2110.03170v3.pdf | TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network | Point cloud is one of the widely used techniques for representing and storing 3D geometric data. In the past several methods have been proposed for processing point clouds. Methods such as PointNet and FoldingNet have shown promising results for tasks like 3D shape classification and segmentation. This work proposes a ... | ['Shanmuganathan Raman', 'Kaustubh Sadekar', 'Prajwal Singh'] | 2021-10-07 | null | null | null | null | ['3d-shape-retrieval', 'point-cloud-completion'] | ['computer-vision', 'computer-vision'] | [-2.92019546e-01 8.64583161e-03 3.20299864e-01 -5.97691476e-01
-2.93848574e-01 -3.80164832e-01 5.66145062e-01 4.05211598e-01
-2.19816282e-01 -3.33195217e-02 1.39346560e-02 -4.29772675e-01
-8.18936825e-02 -8.74066353e-01 -9.72174585e-01 -2.52525032e-01
-3.65186095e-01 5.39304495e-01 1.93474256e-02 -5.23770936... | [7.968454837799072, -3.645448684692383] |
d4c30aae-334d-418d-af80-53168e3b6eea | rong-he-ti-shi-xue-xi-de-gu-shi-sheng-cheng | null | null | https://aclanthology.org/2022.ccl-1.16 | https://aclanthology.org/2022.ccl-1.16.pdf | 融合提示学习的故事生成方法(A Story Generation Method Incorporating Prompt Learning) | “开放式自动故事生成通过输入故事的开头、大纲、主线等,得到具有一致性、连贯性和逻辑性的故事。现有的方法想要提升生成故事的质量,往往需要大量训练数据和更多参数的模型。针对以上问题,该文利用提示学习在零样本与少样本场景下的优势,同时使用外部常识推理知识,提出了一种故事生成方法。该方法将故事生成分为三个阶段:输入故事的开头,常识推理模型生成可能的事件;根据类型不同,将事件填入问题模板中,构建引导模型生成合理回答的问题;问答模型产生对应问题的答案,并选择困惑度最小的作为故事下文。重复上述过程,最终生成完整的故事。自动评测与人工评测指标表明,与基线模型相比,该文提出的方法能够生成更连贯、具体和合乎逻辑的故事。” | ['Piji Li', 'Xuanfan Ni'] | null | null | null | null | ccl-2022-10 | ['story-generation'] | ['natural-language-processing'] | [-0.8836528 -0.90223825 0.7115538 0.41826648 0.43298185 -1.4358604
0.03549371 0.5313022 0.27940723 1.3035522 0.24480593 -0.23701435
-0.32959497 -1.142533 -0.11744367 -1.2117032 -0.30272025 1.4253384
0.5230325 -0.21200477 0.61858505 0.8573796 -1.2266926 0.22741711
0.9992249 1.222598 0.89... | [-3.316173553466797, 6.907741069793701] |
e7477347-e09d-4ca5-9ef7-82f675cfb987 | lung-cancer-screening-using-adaptive-memory | 1710.05719 | null | http://arxiv.org/abs/1710.05719v2 | http://arxiv.org/pdf/1710.05719v2.pdf | Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks | In this paper, we investigate the effectiveness of deep learning techniques
for lung nodule classification in computed tomography scans. Using less than
10,000 training examples, our deep networks perform two times better than a
standard radiology software. Visualization of the networks' neurons reveals
semantically me... | ['Supratik Moulik', 'Aryan Mobiny', 'Hien Van Nguyen'] | 2017-10-11 | null | null | null | null | ['lung-nodule-classification', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [ 1.83426023e-01 6.52044415e-01 6.52829185e-02 -5.28080106e-01
-6.07960105e-01 -3.33766192e-01 4.73278500e-02 2.22062781e-01
-4.33771312e-01 5.67675412e-01 2.83083230e-01 -6.33288860e-01
-3.12605679e-01 -5.68583906e-01 -5.75063884e-01 -5.86602449e-01
-1.80340216e-01 4.13352907e-01 3.75743151e-01 2.05605313... | [15.20455551147461, -2.227426767349243] |
ed636c6f-8f0a-46f4-875f-da93b56317ce | pms-net-robust-haze-removal-based-on-patch | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.pdf | PMS-Net: Robust Haze Removal Based on Patch Map for Single Images | In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation an... | [' Sy-Yen Kuo', ' Jian-Jiun Ding', 'Wei-Ting Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['single-image-haze-removal', 'single-image-deraining', 'computational-phenotyping'] | ['computer-vision', 'computer-vision', 'medical'] | [ 1.31364450e-01 -4.90443677e-01 5.93610466e-01 -1.88733917e-02
-1.99917126e-02 1.38384253e-01 2.81000614e-01 -2.15809777e-01
-1.72146350e-01 6.15315437e-01 -5.39592654e-02 -4.80991639e-02
9.10214037e-02 -1.26092565e+00 -4.99888301e-01 -1.28451002e+00
3.33746374e-01 -2.88073421e-01 7.47798860e-01 -4.40159082... | [10.883035659790039, -3.1501641273498535] |
7ae42fe1-dfb6-4934-b0fe-83ff7a0374df | global-trajectory-helps-person-retrieval-in-a | 2204.129 | null | https://arxiv.org/abs/2204.12900v3 | https://arxiv.org/pdf/2204.12900v3.pdf | Cross-Camera Trajectories Help Person Retrieval in a Camera Network | We are concerned with retrieving a query person from multiple videos captured by a non-overlapping camera network. Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. To address this issue, we propose a pedestrian retrieval fra... | ['Wei-Shi Zheng', 'JianHuang Lai', 'Xiaohua Xie', 'Xin Zhang'] | 2022-04-27 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-2.53441185e-01 -9.94612873e-01 -2.78961331e-01 -3.57117832e-01
-5.91459751e-01 -7.47121096e-01 6.25886440e-01 -5.70063926e-02
-3.48703623e-01 4.20607537e-01 4.10011321e-01 -4.26922143e-02
-2.78081894e-01 -7.99961984e-01 -6.53357744e-01 -5.91701806e-01
1.15005620e-01 -3.18182446e-02 4.54809308e-01 2.57761151... | [14.767066955566406, 1.0372008085250854] |
5df14826-16e2-4cc2-85a0-f7323a6c83fe | void-distributions-reveal-structural-link | 1811.00077 | null | http://arxiv.org/abs/1811.00077v1 | http://arxiv.org/pdf/1811.00077v1.pdf | Void distributions reveal structural link between jammed packings and protein cores | Dense packing of hydrophobic residues in the cores of globular proteins
determines their stability. Recently, we have shown that protein cores possess
packing fraction $\phi \approx 0.56$, which is the same as dense, random
packing of amino acid-shaped particles. In this article, we compare the
structural properties of... | [] | 2018-10-31 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.18903923e-02 1.56632125e-01 1.15061626e-01 -1.49809420e-01
9.81982276e-02 -6.38554335e-01 2.56658614e-01 5.70707023e-01
-5.00110805e-01 9.68322337e-01 2.66564023e-02 -6.29857004e-01
1.19685650e-01 -7.34446108e-01 -7.66853333e-01 -1.22435331e+00
-3.21150273e-01 1.11681759e+00 6.37200415e-01 -2.17181966... | [4.783280372619629, 5.245797634124756] |
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