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dfbe8b72-b37b-4667-94b3-43eef06ad621 | on-distributed-adaptive-optimization-with-1 | 2205.05632 | null | https://arxiv.org/abs/2205.05632v1 | https://arxiv.org/pdf/2205.05632v1.pdf | On Distributed Adaptive Optimization with Gradient Compression | We study COMP-AMS, a distributed optimization framework based on gradient averaging and adaptive AMSGrad algorithm. Gradient compression with error feedback is applied to reduce the communication cost in the gradient transmission process. Our convergence analysis of COMP-AMS shows that such compressed gradient averagin... | ['Ping Li', 'Belhal Karimi', 'Xiaoyun Li'] | 2022-05-11 | on-distributed-adaptive-optimization-with | https://openreview.net/forum?id=CI-xXX9dg9l | https://openreview.net/pdf?id=CI-xXX9dg9l | iclr-2022-4 | ['distributed-optimization'] | ['methodology'] | [-3.65637690e-01 -5.50420463e-01 -1.01820491e-01 -5.34058034e-01
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-4.34837401e-01 6.04250371e-01 1.23824246e-01 2.49484964... | [6.274902820587158, 4.955007076263428] |
915cbb06-a8ee-49f2-817f-04bf4eff940e | unexpected-novel-merbecovirus-discoveries-in | 2104.01533 | null | https://arxiv.org/abs/2104.01533v2 | https://arxiv.org/pdf/2104.01533v2.pdf | Unexpected novel Merbecovirus discoveries in agricultural sequencing datasets from Wuhan, China | In this study we document the unexpected discovery of multiple coronaviruses and a BSL-3 pathogen in agricultural cotton and rice sequencing datasets. In particular, we have identified a novel HKU5-related Merbecovirus in a cotton dataset sequenced by the Huazhong Agricultural University in 2017. We have also found an ... | ['Alejandro Sousa', 'Karl Sirotkin', 'Yuri Deigin', 'Adrian Jones', 'Daoyu Zhang'] | 2021-04-04 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 5.96096575e-01 -1.64491996e-01 1.53514132e-01 7.51473010e-02
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545d1052-03a1-4b3b-b7e4-da1b0aca3390 | is-pre-training-truly-better-than-meta | 2306.13841 | null | https://arxiv.org/abs/2306.13841v1 | https://arxiv.org/pdf/2306.13841v1.pdf | Is Pre-training Truly Better Than Meta-Learning? | In the context of few-shot learning, it is currently believed that a fixed pre-trained (PT) model, along with fine-tuning the final layer during evaluation, outperforms standard meta-learning algorithms. We re-evaluate these claims under an in-depth empirical examination of an extensive set of formally diverse datasets... | ['Sanmi Koyejo', 'Yu-Xiong Wang', 'Saumya Goyal', 'Patrick Yu', 'Brando Miranda'] | 2023-06-24 | null | null | null | null | ['meta-learning', 'few-shot-learning'] | ['methodology', 'methodology'] | [ 1.99674368e-01 6.42004833e-02 -3.37039262e-01 -1.30552694e-01
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4.75675575e-02 4.38787997e-01 3.88953924e-01 -3.98925364... | [9.839716911315918, 3.1177775859832764] |
1a1c10b7-c4ac-4298-a190-916f07c8d35d | connecting-surrogate-safety-measures-to-crash | 2210.01363 | null | https://arxiv.org/abs/2210.01363v1 | https://arxiv.org/pdf/2210.01363v1.pdf | Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction | Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying near misses. However, validating surrogate safety measures by connecting them to crashes is still an open question. This paper proposed a method to connect surroga... | ['Mark Hansen', 'Offer Grembek', 'Jiajian Lu'] | 2022-10-04 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.54095471e-01 -3.76841515e-01 -2.05585167e-01 -3.59233886e-01
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761029be-ac72-4fe4-aa5e-83dfd5226155 | prototypical-verbalizer-for-prompt-based-few-1 | 2203.09770 | null | https://arxiv.org/abs/2203.09770v1 | https://arxiv.org/pdf/2203.09770v1.pdf | Prototypical Verbalizer for Prompt-based Few-shot Tuning | Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze question. To make predictions, the model maps the output words to labels via a verbalizer, which is either manually designed or automatically buil... | ['Zhiyuan Liu', 'Longtao Huang', 'Ning Ding', 'Shengding Hu', 'Ganqu Cui'] | 2022-03-18 | null | https://aclanthology.org/2022.acl-long.483 | https://aclanthology.org/2022.acl-long.483.pdf | acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [-9.54468325e-02 1.88441023e-01 -6.33905113e-01 -4.72720981e-01
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1c15322b-f4b1-4232-b849-d5dcd6d8da5c | self-learning-eigenstates-with-a-quantum | 2006.13222 | null | https://arxiv.org/abs/2006.13222v2 | https://arxiv.org/pdf/2006.13222v2.pdf | Certified variational quantum algorithms for eigenstate preparation | Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form. In practice, representing quantum-mechanical states of a given Hamiltonian using available numerical methods, in particular those based on variational Monte Carlo simulat... | ['Dmitry Yudin', 'Jacob Biamonte', 'Andrey Kardashin', 'Alexey Uvarov'] | 2020-06-23 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.69613034e-01 -1.82444960e-01 2.90790141e-01 -1.40773073e-01
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1.06361965e-02 8.50314438e-01 6.38909340e-02 -5.25660753... | [5.6019062995910645, 4.889684677124023] |
0289ec98-956f-4a1b-a02b-6c2b3640384d | from-linguistic-resources-to-ontology-aware | null | null | https://aclanthology.org/2020.lrec-1.305 | https://aclanthology.org/2020.lrec-1.305.pdf | From Linguistic Resources to Ontology-Aware Terminologies: Minding the Representation Gap | Terminological resources have proven crucial in many applications ranging from Computer-Aided Translation tools to authoring softwares and multilingual and cross-lingual information retrieval systems. Nonetheless, with the exception of a few felicitous examples, such as the IATE (Interactive Terminology for Europe) Ter... | ['Federico Sangati', 'Giulia Speranza', 'Maria Pia di Buono', 'Johanna Monti'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-9.55601782e-02 3.06528091e-01 -2.28860423e-01 -2.41392348e-02
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2.49082565e-01 9.24419284e-01 3.67963850e-01 -6.85853302... | [9.376399040222168, 8.564809799194336] |
a6cf0532-1b8a-43e3-a11d-a5e7020f1509 | class-agnostic-few-shot-object-counting | null | null | https://ieeexplore.ieee.org/document/9423155 | https://openaccess.thecvf.com/content/WACV2021/papers/Yang_Class-Agnostic_Few-Shot_Object_Counting_WACV_2021_paper.pdf | Class-agnostic-Few-shot-Object-Counting | Object counting which aims to calculate the number of total instances of the given class is a classic but crucial task that can be applied to many applications. Most of the prior works only focus on counting certain classes of objects such as people, cars, animals, etc. However, in recent years, there are lots of appli... | ['Wen-Chin Chen', 'Winston H. Hsu', 'Hung-Ting Su', 'Shuo-Diao Yang'] | 2021-06-14 | null | null | null | wacv-2021-6 | ['object-counting'] | ['computer-vision'] | [ 2.17286304e-01 -7.75399804e-01 6.47258312e-02 -4.11645353e-01
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1.36454135e-01 6.46524310e-01 1.00254929e+00 -3.82464990... | [8.935623168945312, 0.4826538860797882] |
bb8131eb-b2d3-4549-96b3-0e98dee881f8 | treasure-in-distribution-a-domain | 2305.19949 | null | https://arxiv.org/abs/2305.19949v1 | https://arxiv.org/pdf/2305.19949v1.pdf | Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation | Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to... | ['Yong Xia', 'Hengfei Cui', 'Yiwen Ye', 'Yongsheng Pan', 'Ziyang Chen'] | 2023-05-31 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 4.15291339e-01 -3.08911949e-01 -3.55974078e-01 -4.17371482e-01
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2.17826217e-01 3.04264396e-01 2.43919402e-01 -1.90552697... | [14.54769229888916, -2.0180106163024902] |
361c08fa-84c9-4980-8035-eae922353e58 | dynaquant-compressing-deep-learning-training | 2306.11800 | null | https://arxiv.org/abs/2306.11800v1 | https://arxiv.org/pdf/2306.11800v1.pdf | DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization | With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage. Such failures are typically offset by a checkpointing mechanism, which comes at ... | ['Alexey Tumanov', 'Kexin Rong', 'Vidushi Vashishth', 'Venkata Prabhakara Sarath Nookala', 'Satwik Bhattamishra', 'Sameer Reddy', 'Amey Agrawal'] | 2023-06-20 | null | null | null | null | ['quantization', 'model-compression', 'transfer-learning'] | ['methodology', 'methodology', 'miscellaneous'] | [ 4.91579957e-02 -2.52066433e-01 -6.44805849e-01 -3.28894347e-01
-1.19009840e+00 -3.42923254e-01 3.08235675e-01 5.77241957e-01
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-2.30988413e-01 6.63630426e-01 2.13687673e-01 4.60761301... | [8.601835250854492, 3.2912981510162354] |
938c1e37-cb36-4d37-9aa4-0dc51a1d82d8 | how-to-train-unstable-looped-tensor-network | 2203.02617 | null | https://arxiv.org/abs/2203.02617v1 | https://arxiv.org/pdf/2203.02617v1.pdf | How to Train Unstable Looped Tensor Network | A rising problem in the compression of Deep Neural Networks is how to reduce the number of parameters in convolutional kernels and the complexity of these layers by low-rank tensor approximation. Canonical polyadic tensor decomposition (CPD) and Tucker tensor decomposition (TKD) are two solutions to this problem and pr... | ['Andrzej Cichocki', 'Petr Tichavsky', 'Nikolay Kozyrskiy', 'Igor Vorona', 'Dmitry Ermilov', 'Konstantin Sobolev', 'Anh-Huy Phan'] | 2022-03-05 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-2.34269840e-03 -7.52077103e-02 -2.04488505e-02 1.07461385e-01
1.29328340e-01 -5.91693044e-01 3.51681858e-01 -3.47199768e-01
-2.23017201e-01 4.24199700e-01 3.72328371e-01 -5.87082684e-01
-6.12735689e-01 -4.64184165e-01 -9.13788795e-01 -1.08760560e+00
-3.44909519e-01 6.81151152e-02 1.60830185e-01 -3.98642331... | [8.106209754943848, 3.500361204147339] |
b13cb5df-5ddc-46e1-8041-0c68644f157c | a-structured-learning-approach-to-temporal-1 | 1906.04943 | null | https://arxiv.org/abs/1906.04943v1 | https://arxiv.org/pdf/1906.04943v1.pdf | A Structured Learning Approach to Temporal Relation Extraction | Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challen... | ['Zhili Feng', 'Qiang Ning', 'Dan Roth'] | 2019-06-12 | a-structured-learning-approach-to-temporal | https://aclanthology.org/D17-1108 | https://aclanthology.org/D17-1108.pdf | emnlp-2017-9 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 2.06373751e-01 6.89484999e-02 -5.04905105e-01 -5.11168957e-01
-4.74610507e-01 -7.75295317e-01 8.98231804e-01 7.63761520e-01
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-4.18142468e-01 4.96385247e-01 4.65833247e-01 -1.62825599... | [9.077054023742676, 9.260272979736328] |
b6cacd5f-4ffe-44ee-ac9f-676c3cd63f76 | extracting-covid-19-events-from-twitter | 2006.02567 | null | https://arxiv.org/abs/2006.02567v4 | https://arxiv.org/pdf/2006.02567v4.pdf | Extracting a Knowledge Base of COVID-19 Events from Social Media | In this paper, we present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 31 fine-graine... | ['Wei Xu', 'Alan Ritter', 'Shi Zong', 'Ashutosh Baheti'] | 2020-06-03 | null | https://aclanthology.org/2022.coling-1.335 | https://aclanthology.org/2022.coling-1.335.pdf | coling-2022-10 | ['extracting-covid-19-events-from-twitter'] | ['natural-language-processing'] | [ 1.36871278e-01 3.49926949e-01 -6.25740886e-01 -3.95948946e-01
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-2.09069312e-01 8.25056791e-01 3.61106783e-01 -2.06091419... | [8.537625312805176, 9.388607025146484] |
cacd9601-2fb2-42f9-b6dd-82d18d6bf8c7 | a-novel-uncertainty-aware-collaborative | 2105.05496 | null | https://arxiv.org/abs/2105.05496v2 | https://arxiv.org/pdf/2105.05496v2.pdf | A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels | Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling... | ['Begum Demir', 'Tristan Kreuziger', 'Mahdyar Ravanbakhsh', 'Ahmet Kerem Aksoy'] | 2021-05-12 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.39814895e-01 6.31069094e-02 -5.22317598e-03 -6.20763302e-01
-1.25342917e+00 -6.12118006e-01 2.88130134e-01 1.87448755e-01
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7.69649893e-02 4.28809792e-01 5.87256849e-02 2.19475985... | [9.541625022888184, 3.857419490814209] |
e9d8a86c-5494-4aab-831d-7b96ce7b4d56 | extractive-multi-document-summarization-using-2 | null | null | https://www.researchgate.net/publication/338101046_Extractive_Multi-document_Summarization_using_K-means_Centroid-based_Method_MMR_and_Sentence_Position | https://www.researchgate.net/publication/338101046_Extractive_Multi-document_Summarization_using_K-means_Centroid-based_Method_MMR_and_Sentence_Position | Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position | Multi-document summarization is more challenging than single-document summarization since it has to solve the problem of overlapping information among sentences from different documents. Also, since multi-document summarization dataset is rare, methods based on deep learning are difficult to be applied. In this paper, ... | ['Tuan Luu Minh', 'Huong Le Thanh', 'Hai Cao Manh'] | 2019-12-04 | null | null | null | the-tenth-international-symposium-2019-12 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.10361451e-01 -1.54196337e-01 -2.48454824e-01 -4.34793942e-02
-1.03430915e+00 -4.34729099e-01 2.71541744e-01 7.42221296e-01
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1.43714100e-01 2.99449086e-01 4.19210285e-01 -8.87295827... | [12.609309196472168, 9.548613548278809] |
0f515cb0-53ec-4ce5-9a90-9f7a613f0425 | ttui-at-semeval-2020-task-11-propaganda | null | null | https://aclanthology.org/2020.semeval-1.240 | https://aclanthology.org/2020.semeval-1.240.pdf | TTUI at SemEval-2020 Task 11: Propaganda Detection with Transfer Learning and Ensembles | In this paper, we describe our approaches and systems for the SemEval-2020 Task 11 on propaganda technique detection. We fine-tuned BERT and RoBERTa pre-trained models then merged them with an average ensemble. We conducted several experiments for input representations dealing with long texts and preserving context as ... | ['Steven Bethard', 'Moonsung Kim'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [-1.18420333e-01 7.26449415e-02 -4.63523924e-01 -1.44542232e-01
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-2.17297539e-01 4.01161283e-01 1.30312890e-01 -4.77965266... | [8.447781562805176, 10.68709659576416] |
16f597fc-2003-47f4-a6da-8dd218baf23a | diva-hisdb-a-precisely-annotated-large | null | null | https://diuf.unifr.ch/main/hisdoc/diva-hisdb | https://diuf.unifr.ch/main/hisdoc/sites/diuf.unifr.ch.main.hisdoc/files/uploads/hisdoc2.0-publications/2016-icfhr-divahisdb.pdf | DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts | This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The database consists of 150 annotated pages of three different medieval manuscripts with challenging layouts. Furthermore, we provide a layout analysis ground-truth wh... | ['Rolf Ingold', 'Marcus Liwicki', 'Angelika Garz', 'Nicole Eichenberger', 'Mathias Seuret', 'Fotini Simistira'] | 2016-10-23 | null | null | null | international-conference-on-frontiers-in | ['document-layout-analysis', 'text-line-extraction'] | ['computer-vision', 'computer-vision'] | [-1.47145823e-01 -3.68775338e-01 2.56347477e-01 -2.02211678e-01
-7.68876970e-01 -9.59862053e-01 7.70755768e-01 2.93277264e-01
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3.62244733e-02 8.92622828e-01 -1.46427408e-01 9.06677023... | [11.765739440917969, 2.636610746383667] |
46d4b54b-9916-44e0-839e-9165328ed426 | consistent-teacher-provides-better-1 | 2209.01589 | null | https://arxiv.org/abs/2209.01589v3 | https://arxiv.org/pdf/2209.01589v3.pdf | Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection | In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting problems. Therefore, we... | ['Wayne Zhang', 'Kai Chen', 'Chengqi Lyu', 'Shijie Fang', 'Litong Feng', 'Yijiang Li', 'Shilong Zhang', 'Xingyi Yang', 'Xinjiang Wang'] | 2022-09-04 | consistent-teacher-provides-better | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Consistent-Teacher_Towards_Reducing_Inconsistent_Pseudo-Targets_in_Semi-Supervised_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [-1.01674728e-01 2.23094761e-01 -3.24394166e-01 -4.96636063e-01
-1.17510211e+00 -7.28948593e-01 3.85092258e-01 -2.12046504e-01
-5.57462513e-01 5.17451525e-01 -1.27733588e-01 -2.76956588e-01
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2.31743574e-01 4.65545058e-01 7.06139624e-01 -1.59436718... | [9.184638977050781, 1.26859450340271] |
fdb586aa-dead-4a87-831f-3605cdc7b646 | asper-answer-set-programming-enhanced-neural | 2305.15374 | null | https://arxiv.org/abs/2305.15374v1 | https://arxiv.org/pdf/2305.15374v1.pdf | ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction | A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) a... | ['Tran Cao Son', 'Huiping Cao', 'Trung Hoang Le'] | 2023-05-24 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 6.25412539e-02 6.92632616e-01 -5.24195671e-01 -6.25000596e-01
-4.34728920e-01 -4.41526651e-01 4.04953510e-01 6.06285274e-01
-3.35669398e-01 1.14509368e+00 -9.78057832e-02 -3.72573167e-01
-2.92293429e-01 -1.31002986e+00 -6.48571789e-01 4.31763707e-03
-2.21942160e-02 8.10890973e-01 3.90241772e-01 -2.48431489... | [9.339573860168457, 8.315938949584961] |
9f8dc02f-c33c-43b6-a8d9-99e8947ee11c | identifiability-guaranteed-simplex-structured | 2106.09070 | null | https://arxiv.org/abs/2106.09070v1 | https://arxiv.org/pdf/2106.09070v1.pdf | Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder | This work focuses on the problem of unraveling nonlinearly mixed latent components in an unsupervised manner. The latent components are assumed to reside in the probability simplex, and are transformed by an unknown post-nonlinear mixing system. This problem finds various applications in signal and data analytics, e.g.... | ['Xiao Fu', 'Qi Lyu'] | 2021-06-16 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.75529343e-01 1.54663678e-02 -3.12513977e-01 1.18060745e-01
-4.91193295e-01 -5.67086339e-01 2.60865152e-01 -6.13723814e-01
1.33436024e-01 7.18765855e-01 -1.56931520e-01 -1.85685039e-01
-7.53396809e-01 -3.05829525e-01 -5.22489369e-01 -1.49919271e+00
8.69321898e-02 4.44025755e-01 -6.79253757e-01 7.48088732... | [10.069217681884766, -2.0088090896606445] |
b2cae581-cd50-4de8-b87c-6428f81ce0ec | single-source-one-shot-reenactment-using | 2104.03117 | null | https://arxiv.org/abs/2104.03117v1 | https://arxiv.org/pdf/2104.03117v1.pdf | Single Source One Shot Reenactment using Weighted motion From Paired Feature Points | Image reenactment is a task where the target object in the source image imitates the motion represented in the driving image. One of the most common reenactment tasks is face image animation. The major challenge in the current face reenactment approaches is to distinguish between facial motion and identity. For this re... | ['Esa Rahtu', 'Juho Kannala', 'Soumya Tripathy'] | 2021-04-07 | null | null | null | null | ['face-reenactment', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 2.70877779e-01 1.49305180e-01 -1.78783849e-01 -3.82406622e-01
-2.62625486e-01 -5.92983246e-01 1.05143332e+00 -8.11367512e-01
-1.24970367e-02 4.75976437e-01 6.96200803e-02 3.72746736e-01
3.18799496e-01 -4.64992702e-01 -7.59509206e-01 -8.25941622e-01
2.46714324e-01 5.82864463e-01 -1.12015709e-01 -4.83224034... | [12.70900821685791, -0.23303373157978058] |
4636224e-70b9-4e3d-970f-9c5f80cfcf1b | stoa-vlp-spatial-temporal-modeling-of-object | 2302.09736 | null | https://arxiv.org/abs/2302.09736v2 | https://arxiv.org/pdf/2302.09736v2.pdf | STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training | Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored. In this work, we propose STOA-V... | ['Bing Qin', 'Xiaocheng Feng', 'Heng Gong', 'Xuan Luo', 'Duyu Tang', 'Mao Zheng', 'Weihong Zhong'] | 2023-02-20 | null | null | null | null | ['video-question-answering', 'video-retrieval'] | ['computer-vision', 'computer-vision'] | [ 2.53465742e-01 -3.55308354e-01 -4.64509070e-01 -3.99377048e-01
-9.77839887e-01 -4.36704844e-01 7.06110835e-01 -2.78837644e-02
-4.98010546e-01 2.23810732e-01 6.71552479e-01 -5.95969297e-02
6.40692711e-02 -3.67459714e-01 -9.64364350e-01 -4.77686524e-01
1.94528133e-01 4.93857205e-01 5.26919782e-01 -6.44490775... | [10.10135555267334, 0.7603204250335693] |
26c34dc1-8381-4c9f-8f30-15886a7455b2 | improved-variational-neural-machine | 1909.09237 | null | https://arxiv.org/abs/1909.09237v1 | https://arxiv.org/pdf/1909.09237v1.pdf | Improved Variational Neural Machine Translation by Promoting Mutual Information | Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders. In this work, we address this problem in variational neural machine translation by explicitly promoting mutual information between the latent variables and the data. Our model extends the conditiona... | ['Xi-An Li', 'Jiatao Gu', 'Arya D. McCarthy', 'Ning Dong'] | 2019-09-19 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 9.80979353e-02 3.88266504e-01 -2.45888799e-01 -8.32309574e-03
-9.61905360e-01 -4.76866722e-01 9.32314754e-01 -3.97802174e-01
-1.59947112e-01 9.43783343e-01 6.68640912e-01 -4.16497082e-01
2.28419095e-01 -5.75561583e-01 -1.01891220e+00 -7.68787205e-01
6.25938356e-01 6.29898906e-01 -2.91759104e-01 -1.74551696... | [11.852632522583008, 9.166062355041504] |
6efcbd90-9675-4cab-8668-b3de8af54d9d | a-lightweight-graph-transformer-network-for | 2111.12696 | null | https://arxiv.org/abs/2111.12696v3 | https://arxiv.org/pdf/2111.12696v3.pdf | A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose | Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practical use of human mesh reconstruction models (e.g. virtual try-on system... | ['Chen Chen', 'Aidong Lu', 'Pu Wang', 'Matias Mendieta', 'Ce Zheng'] | 2021-11-24 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-3.59494597e-01 4.37998533e-01 1.77413791e-01 -2.90434033e-01
-4.91612434e-01 5.40158227e-02 1.43476084e-01 -7.96597358e-03
-3.57964426e-01 4.52832550e-01 -5.82424626e-02 1.52979806e-01
-6.49984181e-02 -1.08913243e+00 -9.41709161e-01 -1.75844282e-01
-1.83580786e-01 1.24729514e+00 5.00655770e-01 -4.47906792... | [6.97672176361084, -1.1911559104919434] |
fc63a5f6-2d23-41f2-bb18-e1ec261d8af0 | deep-learning-applications-for-lung-cancer | 2201.00227 | null | https://arxiv.org/abs/2201.00227v1 | https://arxiv.org/pdf/2201.00227v1.pdf | Deep Learning Applications for Lung Cancer Diagnosis: A systematic review | Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convo... | ['Shabnam Shadroo', 'Reza Monsefi', 'Hesamoddin Hosseini'] | 2022-01-01 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-1.57128707e-01 4.99226935e-02 -7.70839453e-01 5.09992778e-01
-3.35461229e-01 -4.67771245e-03 2.99852043e-01 8.10109153e-02
-4.80211586e-01 8.34246218e-01 2.90986121e-01 -6.56407952e-01
-1.65199801e-01 -9.91329849e-01 -5.49828000e-02 -7.67157674e-01
3.23613167e-01 4.03017849e-01 3.18191916e-01 1.25428796... | [15.342059135437012, -2.3851613998413086] |
ae0432eb-64a2-43aa-ac5b-7bf83b9709b4 | a-clustering-guided-contrastive-fusion-for | 2212.13726 | null | https://arxiv.org/abs/2212.13726v2 | https://arxiv.org/pdf/2212.13726v2.pdf | A Clustering-guided Contrastive Fusion for Multi-view Representation Learning | The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facilitate the development of multi-view applications. However, the community faces two challenges: i) how to learn robust representations from a ... | ['Yang Yu', 'Yongqi Zhu', 'Chang Xu', 'Chenyang Xu', 'Xiaoli Wang', 'Guoqing Chao', 'Guanzhou Ke'] | 2022-12-28 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.63162857e-01 -3.60779881e-01 -1.67133480e-01 -3.79594535e-01
-8.17478359e-01 -8.58261228e-01 6.53932095e-01 -1.67505771e-01
1.23749062e-01 3.11582834e-01 5.99467278e-01 7.86819831e-02
-1.77533001e-01 -4.86458182e-01 -3.66006434e-01 -9.19584930e-01
4.71557707e-01 1.25724480e-01 1.42540429e-02 -1.04163811... | [8.473071098327637, 4.551128387451172] |
c1fbb226-0577-425d-9767-d49c9519db35 | hierarchical-reinforcement-learning-for-5 | null | null | https://openreview.net/forum?id=6IqTG69Lkky | https://openreview.net/pdf?id=6IqTG69Lkky | Hierarchical reinforcement learning for efficent exploration and transfer | Sparse-reward domains are challenging for reinforcement learning algorithms since significant exploration is needed before encountering reward for the first time. Hierarchical reinforcement learning can facilitate exploration by reducing the number of decisions necessary before obtaining a reward. In this paper, we pre... | ['Anders Jonsson', 'Damien Allonsius', 'Simone Totaro', 'Lorenzo Steccanella'] | 2020-06-12 | null | null | null | icml-workshop-lifelongml-2020-7 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 3.51512641e-01 2.26037219e-01 -4.30699795e-01 -4.18126509e-02
-7.54299402e-01 -4.28467691e-01 2.68841803e-01 2.40545645e-01
-5.47538519e-01 1.29033017e+00 1.33072436e-01 -2.35926673e-01
-5.30208886e-01 -4.77271259e-01 -6.57467902e-01 -6.29117310e-01
-5.25053203e-01 7.32265115e-01 1.25871077e-01 -2.47644082... | [4.057211399078369, 1.7153586149215698] |
912e3628-d6d5-43a7-a00c-70a1db88640a | neural-batch-sampling-with-reinforcement | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5576_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710749.pdf | Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection | We are interested in the detection and segmentation of anomalies in images where the anomalies are typically small (i.e., a small tear in woven fabric, broken pin of an IC chip). From a statistical learning point of view, anomalies have low occurrence probability and are not from the main modes of a data distribution. ... | ['Wen-Hsuan Chu', 'Kris M. Kitani'] | null | null | null | null | eccv-2020-8 | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 4.46700931e-01 3.16866650e-03 6.73685363e-03 -5.01541913e-01
-4.10408854e-01 -6.02579415e-02 3.86486381e-01 3.23755711e-01
7.22328871e-02 3.12210500e-01 -2.73954481e-01 -9.71964374e-02
-1.21889345e-01 -8.53049099e-01 -1.12187099e+00 -9.80221927e-01
-2.21623793e-01 6.61428511e-01 3.44453335e-01 -9.60923880... | [7.6374897956848145, 2.2983906269073486] |
7b0df822-7ac8-45bf-bb87-06fa26686c21 | enhanced-graph-learning-schemes-driven-by | 2207.04747 | null | https://arxiv.org/abs/2207.04747v1 | https://arxiv.org/pdf/2207.04747v1.pdf | Enhanced graph-learning schemes driven by similar distributions of motifs | This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of prior information about the density of motifs of the unknown graph to enhance the inference of classical Gaussian graphi... | ['Antonio G. Marques', 'Santiago Segarra', 'T. Mitchell Roddenberry', 'Samuel Rey'] | 2022-07-11 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 3.85244548e-01 3.50052416e-01 -9.87889431e-03 -1.20236680e-01
-3.19118798e-01 -5.47269583e-01 5.29734313e-01 3.12825382e-01
-2.88782120e-01 7.67878473e-01 -1.81569830e-01 -3.52615416e-01
-5.38645208e-01 -6.90678716e-01 -9.74904656e-01 -9.27911162e-01
-1.74058616e-01 7.81115711e-01 -8.82884488e-02 1.89032868... | [6.972750186920166, 5.2098069190979] |
b3c8b90c-482f-4df2-ac8b-a4da6e36b854 | position-aware-tagging-for-aspect-sentiment | 2010.02609 | null | https://arxiv.org/abs/2010.02609v3 | https://arxiv.org/pdf/2010.02609v3.pdf | Position-Aware Tagging for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into seve... | ['Lidong Bing', 'Wei Lu', 'Hao Li', 'Lu Xu'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.183 | https://aclanthology.org/2020.emnlp-main.183.pdf | emnlp-2020-11 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.79051384e-01 -1.21447213e-01 -1.33583143e-01 -5.35123289e-01
-7.60759294e-01 -8.86325121e-01 5.05832374e-01 3.24005008e-01
-1.71827629e-01 5.42054832e-01 4.66680169e-01 -3.41339976e-01
2.19772995e-01 -5.41514695e-01 -6.39333367e-01 -5.02734005e-01
1.81393221e-01 1.43476024e-01 4.47883368e-01 -3.01516563... | [11.47482967376709, 6.651546955108643] |
f183da51-40fb-4092-8bea-15b90ba1c9dc | a-general-framework-for-revealing-human-mind | 2102.05236 | null | https://arxiv.org/abs/2102.05236v1 | https://arxiv.org/pdf/2102.05236v1.pdf | A General Framework for Revealing Human Mind with auto-encoding GANs | Addressing the question of visualising human mind could help us to find regions that are associated with observed cognition and responsible for expressing the elusive mental image, leading to a better understanding of cognitive function. The traditional approach treats brain decoding as a classification problem, readin... | ['Yike Guo', 'Peter Childs', 'Chunlin Li', 'Jialu Fan', 'Wenjia Bai', 'Ling Li', 'Shuo Wang', 'Rui Zhou', 'Pan Wang'] | 2021-02-10 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.47394300e-01 8.18600506e-02 3.53138655e-01 -5.70294976e-01
-2.23434754e-02 -3.91737163e-01 9.99982893e-01 -8.37178677e-02
-4.69259053e-01 3.97464573e-01 3.86383981e-01 -8.85153189e-02
-2.14688405e-01 -6.77994788e-01 -6.43119514e-01 -8.35821390e-01
-3.96519974e-02 1.45040289e-01 -2.98066616e-01 1.56991407... | [12.481012344360352, 3.319012403488159] |
04c67273-73de-40df-a702-66b27d6be858 | ostec-one-shot-texture-completion | 2012.15370 | null | https://arxiv.org/abs/2012.15370v1 | https://arxiv.org/pdf/2012.15370v1.pdf | OSTeC: One-Shot Texture Completion | The last few years have witnessed the great success of non-linear generative models in synthesizing high-quality photorealistic face images. Many recent 3D facial texture reconstruction and pose manipulation from a single image approaches still rely on large and clean face datasets to train image-to-image Generative Ad... | ['Stefanos Zafeiriou', 'Jiankang Deng', 'Baris Gecer'] | 2020-12-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gecer_OSTeC_One-Shot_Texture_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['robust-face-recognition'] | ['computer-vision'] | [ 4.98552591e-01 3.19561541e-01 1.77456334e-01 -3.29169303e-01
-7.35389650e-01 -5.27482688e-01 6.37717366e-01 -1.05581188e+00
1.09387107e-01 4.94278848e-01 -1.14911698e-01 1.13104880e-01
3.28663774e-02 -8.24124813e-01 -9.45946634e-01 -1.11599064e+00
6.06323659e-01 7.54256845e-01 -3.38114709e-01 -2.45595589... | [12.748886108398438, -0.29267311096191406] |
b9ccb583-31f5-4489-83b4-a6b0a0ede0db | a-generally-applicable-highly-scalable | 1711.05508 | null | http://arxiv.org/abs/1711.05508v1 | http://arxiv.org/pdf/1711.05508v1.pdf | A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis | Model-Based Diagnosis deals with the identification of the real cause of a
system's malfunction based on a formal system model and observations of the
system behavior. When a malfunction is detected, there is usually not enough
information available to pinpoint the real cause and one needs to discriminate
between multi... | ['Wolfgang Schmid', 'Konstantin Schekotihin', 'Patrick Rodler'] | 2017-11-15 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 4.05893892e-01 3.80697995e-01 8.31827521e-02 -7.14470968e-02
-9.93891597e-01 -6.25350893e-01 2.31030315e-01 6.87111080e-01
4.16836068e-02 5.71285963e-01 -8.08046162e-01 -6.45682871e-01
-8.09799373e-01 -7.30949879e-01 -5.24074912e-01 -7.39415765e-01
-1.71237826e-01 1.10962439e+00 4.98548597e-01 4.87262979... | [5.466128349304199, 2.739840269088745] |
ca63af98-bff6-4c9b-8955-9023769c97d0 | neuro-symbolic-sudoku-solver | 2307.00653 | null | https://arxiv.org/abs/2307.00653v1 | https://arxiv.org/pdf/2307.00653v1.pdf | Neuro-Symbolic Sudoku Solver | Deep Neural Networks have achieved great success in some of the complex tasks that humans can do with ease. These include image recognition/classification, natural language processing, game playing etc. However, modern Neural Networks fail or perform poorly when trained on tasks that can be solved easily using backtrac... | ['Lalit Pandey', 'Ashutosh Hathidara'] | 2023-07-02 | null | null | null | null | ['suduko'] | ['playing-games'] | [ 1.85726479e-01 2.52151072e-01 7.65239447e-02 9.94361704e-04
9.89548787e-02 -6.25091434e-01 3.46031368e-01 -4.44957130e-02
-6.04022205e-01 1.17038906e+00 -5.62272429e-01 -8.55188370e-01
-5.84814489e-01 -1.25511551e+00 -7.37839997e-01 -4.50215518e-01
-2.21627101e-01 8.08052182e-01 4.39610064e-01 -6.12696111... | [3.6301169395446777, 1.488662600517273] |
325d219e-fc84-494b-b592-44a6f446bfc2 | scalable-semi-supervised-dimensionality | 2201.00701 | null | https://arxiv.org/abs/2201.00701v1 | https://arxiv.org/pdf/2201.00701v1.pdf | Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOM | Dimensionality reduction methods have found vast application as visualization tools in diverse areas of science. Although many different methods exist, their performance is often insufficient for providing quick insight into many contemporary datasets, and the unsupervised mode of use prevents the users from utilizing ... | ['Jiří Vondrášek', 'Martin Kruliš', 'Jan Musil', 'Abhishek Koladiya', 'Miroslav Kratochvíl', 'Soňa Molnárová', 'Adam Šmelko'] | 2022-01-03 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-1.06666878e-01 -3.56686741e-01 1.95613325e-01 -3.56231123e-01
-1.58333153e-01 -7.05662429e-01 3.57331038e-01 6.56309843e-01
-1.81288883e-01 4.07571852e-01 -5.59868440e-02 -6.23126090e-01
-4.29912955e-01 -6.35888696e-01 8.80813748e-02 -1.08990896e+00
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4ea8eea0-ce3c-4825-890c-6eb7ebd2d501 | adversary-aware-rumor-detection | null | null | https://aclanthology.org/2021.findings-acl.118 | https://aclanthology.org/2021.findings-acl.118.pdf | Adversary-Aware Rumor Detection | null | ['Hong-Han Shuai', 'Shao-Yu Weng', 'Yi-Ting Chang', 'Yi-Syuan Chen', 'Yun-Zhu Song'] | null | null | null | null | findings-acl-2021-8 | ['rumour-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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ffa3a2e3-99e2-4294-ade8-27a169b55cd0 | tokencut-segmenting-objects-in-images-and | 2209.00383 | null | https://arxiv.org/abs/2209.00383v2 | https://arxiv.org/pdf/2209.00383v2.pdf | TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut | In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that compose an image or video are organised into a fully connected graph, where the edge between each pai... | ['Dominique Vaufreydaz', 'James L Crowley', 'Shell Xu Hu', 'Maomao Li', 'Yuming Du', 'Yuan Yuan', 'Xi Shen', 'Yangtao Wang'] | 2022-09-01 | null | null | null | null | ['object-discovery', 'saliency-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.31125605e-01 2.50858426e-01 -3.35434735e-01 -1.27688870e-01
-6.96693659e-01 -4.35252577e-01 4.39268202e-01 3.98146749e-01
-4.94827867e-01 3.28250945e-01 -2.55925924e-01 2.92204618e-01
6.76165149e-02 -6.08211577e-01 -7.26572275e-01 -5.51494241e-01
-1.62046149e-01 3.48348051e-01 9.90962803e-01 -4.60303063... | [9.346619606018066, 0.25597232580184937] |
7b1f4758-c4d4-498f-b1eb-8641bf7e101a | srnet-improving-generalization-in-3d-human | 2007.09389 | null | https://arxiv.org/abs/2007.09389v1 | https://arxiv.org/pdf/2007.09389v1.pdf | SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach | Human poses that are rare or unseen in a training set are challenging for a network to predict. Similar to the long-tailed distribution problem in visual recognition, the small number of examples for such poses limits the ability of networks to model them. Interestingly, local pose distributions suffer less from the lo... | ['Minhao Liu', 'Xiao Sun', 'Stephen Lin', 'Fuyang Huang', 'Ailing Zeng', 'Qiang Xu'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2201_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590494.pdf | eccv-2020-8 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-7.41012096e-02 1.08036913e-01 -5.43418467e-01 -3.17516059e-01
-3.66449356e-01 -3.58308285e-01 5.06188929e-01 -1.85347766e-01
-2.75413424e-01 8.69015694e-01 3.80938888e-01 4.98909593e-01
-3.01431745e-01 -5.92711449e-01 -8.37486327e-01 -9.01365280e-01
-2.25160912e-01 6.51268721e-01 1.71690926e-01 -3.42412703... | [7.157568454742432, -0.7018065452575684] |
451ff3a9-1ea0-40b3-bcf9-2f9995dcd503 | bifocal-neural-asr-exploiting-keyword | 2108.01704 | null | https://arxiv.org/abs/2108.01704v1 | https://arxiv.org/pdf/2108.01704v1.pdf | Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization | We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks. The architecture enables a dynamic pivot for its runtime compute pathway, namely taking advantage of keyword spotting to select which componen... | ['Ariya Rastrow', 'Grant P. Strimel', 'Jonathan Macoskey'] | 2021-08-03 | null | null | null | null | ['inference-optimization'] | ['audio'] | [ 4.59263682e-01 1.96350962e-01 -2.07333729e-01 -2.67807156e-01
-9.53324497e-01 -3.78566623e-01 1.88369423e-01 -5.29595912e-01
-5.94781756e-01 2.78666407e-01 2.33989954e-01 -1.05143428e+00
1.30962551e-01 -3.06508482e-01 -5.88236094e-01 -6.84570491e-01
2.98782866e-02 4.94889200e-01 1.17908590e-01 3.52963507... | [14.450271606445312, 6.65553092956543] |
e9027936-335c-4401-988c-4160449c5900 | da4ad-end-to-end-deep-attention-aware | 2003.03026 | null | https://arxiv.org/abs/2003.03026v2 | https://arxiv.org/pdf/2003.03026v2.pdf | DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving | We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy. Conventional approaches to the visual localization problem rely on handcrafted features or human-made objects on the road. They are known to be either pron... | ['Guowei Wan', 'Xiaofei Rui', 'Li Yu', 'Shiyu Song', 'Gang Wang', 'Shenhua Hou', 'Yao Zhou'] | 2020-03-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6091_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730273.pdf | eccv-2020-8 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.48983467e-01 -4.48532671e-01 -1.35750294e-01 -5.82878590e-01
-1.21238196e+00 -6.05403185e-01 6.00893021e-01 1.98741645e-01
-5.82583070e-01 4.47385997e-01 -2.34034389e-01 -6.80178851e-02
-1.77687958e-01 -4.33757752e-01 -1.07772207e+00 -4.44415629e-01
-1.79351136e-01 2.18884915e-01 2.46611163e-01 -1.90524757... | [7.590883731842041, -2.038874626159668] |
15868074-fc3e-48bd-9110-23cd42a3c087 | sparse-representation-classification-with | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Zhang_Sparse_Representation_Classification_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Zhang_Sparse_Representation_Classification_2015_CVPR_paper.pdf | Sparse Representation Classification With Manifold Constraints Transfer | The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems. In this paper we leverage the specific structure of data in order to improve recognition accuracies in general recognition tasks. In particular we propose a novel framework that allows to embed ma... | ['Alessio Del Bue', 'Alessandro Perina', 'Vittorio Murino', 'Baochang Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 2.86227763e-01 -1.90024897e-02 -2.47144759e-01 -5.22460938e-01
-3.89596671e-01 -2.82258928e-01 8.16286564e-01 -1.97756112e-01
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-4.99978304e-01 -4.38682646e-01 -6.36048019e-01 -9.00590539e-01
2.21268386e-01 3.37208122e-01 -4.04666632e-01 -5.00675626... | [8.021367073059082, 3.620786190032959] |
4085b317-537a-44d1-997c-d2ab38919fed | event-causality-identification-with-causal | 2211.12154 | null | https://arxiv.org/abs/2211.12154v1 | https://arxiv.org/pdf/2211.12154v1.pdf | Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022 | The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classification task. Subtask 2 required participants to identify the Cause, Effect ... | ['Nelleke Oostdijk', 'Farhana Ferdousi Liza', 'Onur Uca', 'Tommaso Caselli', 'Ali Hürriyetoğlu', 'Hansi Hettiarachchi', 'Fiona Anting Tan'] | 2022-11-22 | null | null | null | null | ['event-causality-identification'] | ['natural-language-processing'] | [ 5.41451633e-01 5.57900250e-01 -4.20702428e-01 -6.19315326e-01
-1.18436646e+00 -7.99376786e-01 1.09646142e+00 6.28074169e-01
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-2.52614707e-01 4.36362386e-01 4.19106811e-01 -3.39449942... | [9.110109329223633, 9.201799392700195] |
49ed0ebb-179d-4966-a6d4-d7ea91ff1be1 | you-only-demonstrate-once-category-level | 2201.12716 | null | https://arxiv.org/abs/2201.12716v2 | https://arxiv.org/pdf/2201.12716v2.pdf | You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration | Promising results have been achieved recently in category-level manipulation that generalizes across object instances. Nevertheless, it often requires expensive real-world data collection and manual specification of semantic keypoints for each object category and task. Additionally, coarse keypoint predictions and igno... | ['Stefan Schaal', 'Kostas Bekris', 'Wenzhao Lian', 'Bowen Wen'] | 2022-01-30 | null | null | null | null | ['3d-object-tracking', 'industrial-robots', 'robotic-grasping', 'robot-task-planning'] | ['computer-vision', 'robots', 'robots', 'robots'] | [-8.82530883e-02 -2.37466201e-01 -4.80738163e-01 -1.04532659e-01
-5.63770592e-01 -9.28346753e-01 5.73733687e-01 -3.17290798e-02
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-4.58110720e-01 -3.47811431e-01 -9.15970981e-01 -4.98401731e-01
-2.61266232e-01 5.28587401e-01 3.22171271e-01 -3.31766903... | [4.657526016235352, 0.7734889388084412] |
cb4ed976-b71f-4b03-8e90-55d26fbac8ec | uncertainty-aware-multimodal-activity | 1811.10811 | null | https://arxiv.org/abs/1811.10811v3 | https://arxiv.org/pdf/1811.10811v3.pdf | Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference | Deep neural networks (DNNs) provide state-of-the-art results for a multitude of applications, but the approaches using DNNs for multimodal audiovisual applications do not consider predictive uncertainty associated with individual modalities. Bayesian deep learning methods provide principled confidence and quantify pred... | ['Jonathan Huang', 'Paulo Lopez Meyer', 'Omesh Tickoo', 'Mahesh Subedar', 'Ranganath Krishnan'] | 2018-11-27 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [-1.16136044e-01 9.27367732e-02 -5.75845800e-02 -7.72146225e-01
-1.59468865e+00 -3.74983788e-01 9.28168297e-01 -1.29226640e-01
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-6.20307803e-01 -5.16811609e-01 -9.63706017e-01 -8.33334506e-01
4.88153426e-03 6.72360361e-01 1.90197736e-01 5.29729664... | [7.283899307250977, 3.840413808822632] |
abf085ca-b9d3-4f11-a9fc-398b52c6a684 | fine-tuning-multi-hop-question-answering-with | 2004.13821 | null | https://arxiv.org/abs/2004.13821v3 | https://arxiv.org/pdf/2004.13821v3.pdf | Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network | In this paper, we present a two stage model for multi-hop question answering. The first stage is a hierarchical graph network, which is used to reason over multi-hop question and is capable to capture different levels of granularity using the nature structure(i.e., paragraphs, questions, sentences and entities) of docu... | ['Guanming Xiong'] | 2020-04-20 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.17746647e-03 6.02588177e-01 -2.84785926e-01 -4.90328848e-01
-4.16963547e-01 -6.51187181e-01 3.48145097e-01 7.93393552e-01
-3.99214216e-02 7.13118553e-01 7.75271833e-01 -5.18359005e-01
-3.92249376e-01 -1.41240454e+00 -2.46595472e-01 1.69231027e-01
3.70982945e-01 6.55990958e-01 9.10705984e-01 -6.26648605... | [10.877216339111328, 7.927097797393799] |
319ad2fd-c307-428b-ac78-dd81bc8e0f02 | action2motion-conditioned-generation-of-3d | 2007.15240 | null | https://arxiv.org/abs/2007.15240v1 | https://arxiv.org/pdf/2007.15240v1.pdf | Action2Motion: Conditioned Generation of 3D Human Motions | Action recognition is a relatively established task, where givenan input sequence of human motion, the goal is to predict its ac-tion category. This paper, on the other hand, considers a relativelynew problem, which could be thought of as an inverse of actionrecognition: given a prescribed action type, we aim to genera... | ['Xinxin Zuo', 'Sen Wang', 'Chuan Guo', 'Shihao Zou', 'Li Cheng', 'Annan Deng', 'Minglun Gong', 'Qingyao Sun'] | 2020-07-30 | null | null | null | null | ['human-action-generation', 'action-generation'] | ['computer-vision', 'computer-vision'] | [ 8.85632075e-03 2.91619003e-01 -5.80047548e-01 8.57461467e-02
-4.71815526e-01 -3.47852230e-01 1.00363016e+00 -9.52611685e-01
-2.73718625e-01 7.68135011e-01 5.59073925e-01 -3.14014847e-03
-1.62467025e-02 -5.98764598e-01 -9.23293471e-01 -9.59189773e-01
5.11010876e-04 2.15948001e-01 -6.32178187e-02 -1.04009666... | [7.335896015167236, -0.1590886414051056] |
1c06deeb-e709-418c-9e04-472e45798fb5 | unsupervised-neural-adaptation-model-based-on | 2012.13152 | null | https://arxiv.org/abs/2012.13152v1 | https://arxiv.org/pdf/2012.13152v1.pdf | Unsupervised neural adaptation model based on optimal transport for spoken language identification | Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically degraded. In this paper, we propose an unsupervised neural adaptation model to deal with the distribution mismatch problem for SLID. In our... | ['Hisashi Kawai', 'Yu Tsao', 'Peng Shen', 'Xugang Lu'] | 2020-12-24 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 1.14422895e-01 1.23567628e-02 1.75662920e-01 -8.50969851e-01
-9.81447339e-01 -5.26446223e-01 4.71844941e-01 -1.33789584e-01
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1.53250903e-01 4.31182623e-01 1.83700576e-01 1.52350634... | [14.265451431274414, 6.42459774017334] |
9c14bfcc-d3f4-4d66-a8a6-7c397d46b272 | edge-data-based-trailer-inception | 2202.10236 | null | https://arxiv.org/abs/2202.10236v1 | https://arxiv.org/pdf/2202.10236v1.pdf | Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation | The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated b... | ['Feng Xia', 'Abdul Aziz', 'Ge Xu', 'Junjian Li', 'Zhichen Ni', 'Zhu Wang', 'Zhe Li', 'Honglong Chen'] | 2022-02-16 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.88997298e-01 -6.13482237e-01 -4.15326327e-01 -3.24923784e-01
-1.79434419e-01 -2.53288150e-01 1.39755055e-01 -3.76118124e-01
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2.34425291e-01 -4.63803113e-02 2.40435675e-02 -3.37568611... | [10.202909469604492, 5.563467025756836] |
bda7b8c0-5ee0-46bf-9e09-f30c4e1e7b36 | high-dimensional-bayesian-optimization-with-4 | 2204.13753 | null | https://arxiv.org/abs/2204.13753v2 | https://arxiv.org/pdf/2204.13753v2.pdf | High Dimensional Bayesian Optimization with Kernel Principal Component Analysis | Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points. It is well-known that BO does not scale well for high-dimensional problems because the GPR ... | ['Carola Doerr', 'Hao Wang', 'Elena Raponi', 'Kirill Antonov'] | 2022-04-28 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-4.98305351e-01 -3.57309461e-01 -1.31931901e-01 -8.14846978e-02
-1.23317695e+00 -3.04339796e-01 3.26042533e-01 -1.99867301e-02
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-6.41642928e-01 -6.48011088e-01 -7.00356066e-01 -1.07350111e+00
-2.44466454e-01 8.42801332e-01 -1.12120947e-02 2.84036487... | [6.717988014221191, 3.9912049770355225] |
b92d6978-809d-4de3-88e4-46f0550f09d9 | hit-and-lead-discovery-with-explorative-rl | 2110.01219 | null | https://arxiv.org/abs/2110.01219v3 | https://arxiv.org/pdf/2110.01219v3.pdf | Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation | Recently, utilizing reinforcement learning (RL) to generate molecules with desired properties has been highlighted as a promising strategy for drug design. A molecular docking program - a physical simulation that estimates protein-small molecule binding affinity - can be an ideal reward scoring function for RL, as it i... | ['Sung Ju Hwang', 'Seongok Ryu', 'Seul Lee', 'Doyeong Hwang', 'Soojung Yang'] | 2021-10-04 | null | http://proceedings.neurips.cc/paper/2021/hash/41da609c519d77b29be442f8c1105647-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/41da609c519d77b29be442f8c1105647-Paper.pdf | neurips-2021-12 | ['molecular-docking'] | ['medical'] | [ 1.18182868e-01 -4.46661143e-03 -3.01420420e-01 1.73503920e-01
-1.13008881e+00 -6.32469356e-01 3.27654511e-01 2.37609804e-01
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-9.40279588e-02 -8.77358019e-01 -1.06280911e+00 -9.19766724e-01
-2.29665518e-01 4.47253436e-01 5.00272252e-02 -5.93124866... | [4.936587333679199, 5.642829895019531] |
6812562e-aaac-4416-9b2e-406715510145 | ffpdg-fast-fair-and-private-data-generation | 2307.00161 | null | https://arxiv.org/abs/2307.00161v1 | https://arxiv.org/pdf/2307.00161v1.pdf | FFPDG: Fast, Fair and Private Data Generation | Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014generative}] based methods show good results in preserving privacy, the generated data may be more biased. At the same time, these methods req... | ['Bo wang', 'Francis Iannacci', 'Jinjin Zhao', 'Weijie Xu'] | 2023-06-30 | null | null | null | null | ['fairness', 'synthetic-data-generation', 'synthetic-data-generation', 'fairness'] | ['computer-vision', 'medical', 'miscellaneous', 'miscellaneous'] | [ 6.90656826e-02 3.96184653e-01 -1.89895347e-01 -5.58780253e-01
-8.01719248e-01 -5.38749516e-01 9.08911109e-01 -1.32470891e-01
-2.60739356e-01 1.45648313e+00 2.75417060e-01 -2.15787124e-02
4.50868130e-01 -1.13043225e+00 -6.16902411e-01 -5.98407328e-01
3.72659206e-01 3.84699643e-01 -1.48183465e-01 -4.13791835... | [6.219036102294922, 6.810904026031494] |
0ae62f8f-eddb-4579-8f36-6d29dd6a26be | carime-unpaired-caricature-generation-with | 2010.00246 | null | https://arxiv.org/abs/2010.00246v1 | https://arxiv.org/pdf/2010.00246v1.pdf | CariMe: Unpaired Caricature Generation with Multiple Exaggerations | Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the generic image-to-image translation, drawing a caricature automatically is a more challenging task due to the existence of various spacial de... | ['Zheng Gu', 'Yang Gao', 'Wenbin Li', 'Jing Huo', 'Chuanqi Dong'] | 2020-10-01 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.63715500e-01 3.08015883e-01 -1.59560479e-02 -2.78088540e-01
-4.59638536e-01 -3.65030974e-01 6.42791092e-01 -6.49954796e-01
2.58775860e-01 7.81786442e-01 2.95651972e-01 2.18396157e-01
1.18591994e-01 -7.94881403e-01 -9.33852017e-01 -5.28372169e-01
4.46327180e-01 3.60029876e-01 -2.55534589e-01 -5.18578708... | [12.155757904052734, -0.3704697787761688] |
ec2e8244-4343-49bf-9377-caf782eee8ea | artfacepoints-high-resolution-facial-landmark | 2210.09204 | null | https://arxiv.org/abs/2210.09204v1 | https://arxiv.org/pdf/2210.09204v1.pdf | ArtFacePoints: High-resolution Facial Landmark Detection in Paintings and Prints | Facial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists. With facial landmarks, portraits of different genres, such as paintings and prints, can be automatically aligned using control-point-based image registration. We propose a deep-... | ['Vincent Christlein', 'Andreas Maier', 'Aline Sindel'] | 2022-10-17 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 3.43143225e-01 -1.25019222e-01 -5.65773919e-02 -6.05521619e-01
-9.66114223e-01 -6.27072096e-01 8.62756252e-01 -1.09592155e-01
-1.28553703e-01 2.37725243e-01 7.13394061e-02 6.22263193e-01
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4.14868206e-01 9.12530065e-01 -1.75842866e-02 -9.65438560... | [13.318299293518066, 0.18376734852790833] |
2996f575-85bc-4aa2-974f-67c2584f4cb3 | towards-trustworthy-deception-detection | 2104.11761 | null | https://arxiv.org/abs/2104.11761v1 | https://arxiv.org/pdf/2104.11761v1.pdf | Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages | Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop future models that are generalizable and robust across expected environments a model may encounter in deployment. In this paper we present a ... | ['Svitlana Volkova', 'Dustin Arendt', 'Robin Cosbey', 'Ellyn Ayton', 'Maria Glenski'] | 2021-04-23 | null | https://aclanthology.org/2020.rdsm-1.1 | https://aclanthology.org/2020.rdsm-1.1.pdf | rdsm-coling-2020-12 | ['deception-detection'] | ['miscellaneous'] | [ 9.34457630e-02 -2.85838425e-01 8.10410157e-02 -4.23677176e-01
-9.41901028e-01 -8.17959666e-01 9.27515090e-01 1.68450788e-01
-6.92621171e-01 3.53592545e-01 2.47584775e-01 -6.22144043e-01
1.30867705e-01 -4.75095183e-01 -8.65948200e-01 -1.16076410e-01
2.56424487e-01 1.62620828e-01 1.56632334e-03 -3.25817257... | [6.090170860290527, 8.145794868469238] |
f6153877-0c7f-4ed1-a902-fbf788b248b9 | pyramid-dilated-deeper-convlstm-for-video | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hongmei_Song_Pseudo_Pyramid_Deeper_ECCV_2018_paper.pdf | Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection | This paper proposes a fast video salient object detection model, based on a novel recurrent network architecture, named Pyramid Dilated Bidirectional ConvLSTM (PDB-ConvLSTM). A Pyramid Dilated Convolution (PDC) module is first designed for simultaneously extracting spatial features at multiple scales. These spatial fea... | ['Kin-Man Lam', 'Jianbing Shen', 'Wenguan Wang', 'Sanyuan Zhao', 'Hongmei Song'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.79493982e-01 -1.33437425e-01 -3.60691905e-01 -2.62529135e-01
-4.79129732e-01 -1.78804342e-02 5.43312192e-01 6.00950979e-02
-6.43704891e-01 4.16044414e-01 4.67751682e-01 -1.45035356e-01
4.14202929e-01 -6.09246433e-01 -1.10857165e+00 -5.22728980e-01
-1.74608558e-01 -2.89545715e-01 1.15251637e+00 -1.45379871... | [9.676203727722168, -0.3796520531177521] |
1cd54ce4-2abf-4ced-abcd-0891d9db8a5d | deepdistance-a-multi-task-deep-regression | 1908.11211 | null | https://arxiv.org/abs/1908.11211v1 | https://arxiv.org/pdf/1908.11211v1.pdf | DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images | This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of lear... | ['Cigdem Gunduz-Demir', 'Rengul Cetin-Atalay', 'Gozde Nur Gunesli', 'Can Fahrettin Koyuncu'] | 2019-08-29 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.19668774e-01 4.23536673e-02 1.98151752e-01 -7.45715871e-02
-8.10684502e-01 -4.11571652e-01 7.66020656e-01 3.83733541e-01
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-1.00078784e-01 -6.26665652e-01 -7.24685788e-01 -1.26211905e+00
-5.38585261e-02 6.07954443e-01 3.05474222e-01 8.33448097... | [14.730199813842773, -3.207120656967163] |
6b2f1dc4-d80f-4b04-8907-9c4039dee3ca | keystroke-dynamics-as-signal-for-shallow | 1610.03321 | null | http://arxiv.org/abs/1610.03321v1 | http://arxiv.org/pdf/1610.03321v1.pdf | Keystroke dynamics as signal for shallow syntactic parsing | Keystroke dynamics have been extensively used in psycholinguistic and writing
research to gain insights into cognitive processing. But do keystroke logs
contain actual signal that can be used to learn better natural language
processing models?
We postulate that keystroke dynamics contain information about syntactic
s... | ['Barbara Plank'] | 2016-10-11 | keystroke-dynamics-as-signal-for-shallow-2 | https://aclanthology.org/C16-1059 | https://aclanthology.org/C16-1059.pdf | coling-2016-12 | ['ccg-supertagging'] | ['natural-language-processing'] | [-2.27468967e-01 1.38880372e-01 -4.23270732e-01 -6.75295234e-01
-6.80138946e-01 -6.01902187e-01 7.12457657e-01 4.62226868e-01
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-1.27474442e-01 3.21651489e-01 2.15251565e-01 -2.36609265... | [10.752079010009766, 8.986031532287598] |
9447fae0-f442-4f6c-9a32-a2ea158b54c8 | ocid-ref-a-3d-robotic-dataset-with-embodied | 2103.07679 | null | https://arxiv.org/abs/2103.07679v2 | https://arxiv.org/pdf/2103.07679v2.pdf | OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding | To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects. However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded d... | ['Wen-Chin Chen', 'Winston H. Hsu', 'Yu-Siang Wang', 'Jen-Wei Wang', 'Hung-Ting Su', 'Yun-Hsuan Liu', 'Ke-Jyun Wang'] | 2021-03-13 | null | https://aclanthology.org/2021.naacl-main.419 | https://aclanthology.org/2021.naacl-main.419.pdf | naacl-2021-4 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 5.75628690e-02 1.20007575e-01 1.23609625e-01 -4.44078028e-01
-2.55258322e-01 -4.29134607e-01 3.12265325e-02 -1.65765509e-01
-6.99350052e-03 5.51214337e-01 -1.03001565e-01 -6.45210370e-02
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3.13669115e-01 2.91931331e-01 1.36344030e-01 -4.16847348... | [6.242930889129639, -1.0187398195266724] |
0e0ef93e-3272-41cb-b1da-264acfb1f40a | pay-attention-accuracy-versus | 2303.17908 | null | https://arxiv.org/abs/2303.17908v1 | https://arxiv.org/pdf/2303.17908v1.pdf | Pay Attention: Accuracy Versus Interpretability Trade-off in Fine-tuned Diffusion Models | The recent progress of diffusion models in terms of image quality has led to a major shift in research related to generative models. Current approaches often fine-tune pre-trained foundation models using domain-specific text-to-image pairs. This approach is straightforward for X-ray image generation due to the high ava... | ['Bernhard Kainz', 'Matthew Baugh', 'Johanna P. Müller', 'Hadrien Reynaud', 'Mischa Dombrowski'] | 2023-03-31 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 5.79632580e-01 6.11662447e-01 -1.66184023e-01 -6.49249852e-01
-1.26945078e+00 -5.92139125e-01 5.04643559e-01 1.23154268e-01
-8.39140266e-02 7.66188681e-01 4.98423934e-01 -4.34371531e-01
-1.21500984e-01 -7.66266942e-01 -8.68699849e-01 -6.03805900e-01
3.03825617e-01 1.01337683e+00 -9.86192301e-02 -1.13906134... | [14.657205581665039, -1.7363115549087524] |
bb9564f4-4261-4d3e-9277-c46d12619206 | a-passage-based-approach-to-learning-to-rank | 1906.02083 | null | https://arxiv.org/abs/1906.02083v1 | https://arxiv.org/pdf/1906.02083v1.pdf | A Passage-Based Approach to Learning to Rank Documents | According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document'... | ['Oren Kurland', 'Eilon Sheetrit', 'Anna Shtok'] | 2019-06-05 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.64245152e-01 -6.12779975e-01 -4.95780081e-01 -2.40035713e-01
-1.92925215e+00 -9.29039001e-01 1.28983104e+00 9.33926761e-01
-7.08471119e-01 7.58090556e-01 1.05015361e+00 -2.56768644e-01
-7.15795517e-01 -6.74502671e-01 -5.06896973e-01 -3.60070556e-01
-2.67398745e-01 5.71870983e-01 5.54696143e-01 -7.48411834... | [11.47724723815918, 7.617893218994141] |
b5dca245-497c-48bc-92ae-69ccce05169a | abstractors-transformer-modules-for-symbolic | 2304.00195 | null | https://arxiv.org/abs/2304.00195v2 | https://arxiv.org/pdf/2304.00195v2.pdf | Abstractors: Transformer Modules for Symbolic Message Passing and Relational Reasoning | Reasoning in terms of relations, analogies, and abstraction is a hallmark of human intelligence. An active debate is whether this relies on the use of symbolic processing or can be achieved using the same forms of function approximation that have been used for tasks such as image, audio, and, most recently, language pr... | ['John Lafferty', 'Jonathan Cohen', 'Taylor Webb', 'Awni Altabaa'] | 2023-04-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.60150725e-01 6.14734471e-01 1.27864063e-01 -4.32603896e-01
-8.61883834e-02 -6.31253242e-01 1.05698562e+00 5.28091252e-01
-3.27694595e-01 3.57364476e-01 4.43902701e-01 -5.98206699e-01
-4.18549001e-01 -1.28868198e+00 -9.73052144e-01 -4.18435961e-01
-3.85406241e-02 6.16075099e-01 2.64639974e-01 -4.52510506... | [9.49337387084961, 7.159736156463623] |
f5fc71b4-7dd9-4753-9160-c30c20c9e553 | ssl-2-self-supervised-learning-meets-semi | 2303.05026 | null | https://arxiv.org/abs/2303.05026v1 | https://arxiv.org/pdf/2303.05026v1.pdf | SSL^2: Self-Supervised Learning meets Semi-Supervised Learning: Multiple Sclerosis Segmentation in 7T-MRI from large-scale 3T-MRI | Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with ... | ['Ipek Oguz', 'Francesca Bagnato', 'Kelsey Barter', 'Keejin Yoon', 'Daiwei Lu', 'Dewei Hu', 'Han Liu', 'Hao Li', 'Jiacheng Wang'] | 2023-03-09 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 5.71692169e-01 -7.47439126e-03 -6.82656348e-01 -6.70063138e-01
-1.26352572e+00 -4.04043347e-01 3.38254422e-01 1.48774639e-01
-7.36797750e-01 7.83338308e-01 9.99534577e-02 -7.23785013e-02
-2.75275204e-02 -3.02875370e-01 -5.65562725e-01 -6.15837216e-01
-1.01971254e-01 8.66939247e-01 6.29631996e-01 9.54263434... | [14.56110954284668, -2.201202869415283] |
89683275-99d3-4747-bcc6-34e868a40ad4 | contextual-affinity-distillation-for-image | 2307.03101 | null | https://arxiv.org/abs/2307.03101v1 | https://arxiv.org/pdf/2307.03101v1.pdf | Contextual Affinity Distillation for Image Anomaly Detection | Previous works on unsupervised industrial anomaly detection mainly focus on local structural anomalies such as cracks and color contamination. While achieving significantly high detection performance on this kind of anomaly, they are faced with logical anomalies that violate the long-range dependencies such as a normal... | ['Takayuki Okatani', 'Masanori Suganuma', 'Jie Zhang'] | 2023-07-06 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 1.54943705e-01 -8.07164982e-02 9.17703807e-02 -3.46099496e-01
-5.07384241e-01 -2.57688850e-01 4.01805729e-01 7.51997113e-01
4.01055254e-02 2.66822338e-01 -3.22604090e-01 -5.34233272e-01
-1.82635143e-01 -8.51562738e-01 -8.07726443e-01 -8.23536575e-01
1.34194270e-01 2.40147650e-01 8.32539737e-01 -5.81935234... | [7.556332588195801, 2.0745222568511963] |
b6dfff36-8580-47bf-afb3-82cb05404f40 | sequential-place-learning-heuristic-free-high | 2103.02074 | null | https://arxiv.org/abs/2103.02074v1 | https://arxiv.org/pdf/2103.02074v1.pdf | Sequential Place Learning: Heuristic-Free High-Performance Long-Term Place Recognition | Sequential matching using hand-crafted heuristics has been standard practice in route-based place recognition for enhancing pairwise similarity results for nearly a decade. However, precision-recall performance of these algorithms dramatically degrades when searching on short temporal window (TW) lengths, while demandi... | ['Michael Milford', 'Marvin Chancán'] | 2021-03-02 | null | null | null | null | ['sequential-image-classification', 'sequential-place-learning', 'sequential-place-recognition'] | ['computer-vision', 'robots', 'robots'] | [ 3.08370143e-01 -3.93735647e-01 -1.17384218e-01 -1.84127614e-01
-8.69044304e-01 -7.69931257e-01 6.15384340e-01 -1.82948131e-02
-1.00939929e+00 6.09798253e-01 -1.14473768e-01 -3.71390760e-01
-7.95412809e-02 -8.12408864e-01 -1.30453730e+00 -7.52379596e-01
-3.09492856e-01 1.16949946e-01 4.28598911e-01 -2.49753240... | [7.6562676429748535, -1.9969192743301392] |
0d24a501-a990-42eb-b89d-00e0b5d85c25 | dynamic-appearance-a-video-representation-for | 2211.12748 | null | https://arxiv.org/abs/2211.12748v2 | https://arxiv.org/pdf/2211.12748v2.pdf | Dynamic Appearance: A Video Representation for Action Recognition with Joint Training | Static appearance of video may impede the ability of a deep neural network to learn motion-relevant features in video action recognition. In this paper, we introduce a new concept, Dynamic Appearance (DA), summarizing the appearance information relating to movement in a video while filtering out the static information ... | ['Adrian G. Bors', 'Guoxi Huang'] | 2022-11-23 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 3.96540493e-01 -2.86536843e-01 -2.19283819e-01 -2.98289686e-01
-1.49265528e-01 -3.38881671e-01 5.82744479e-01 -6.01669073e-01
-4.73161399e-01 4.48650539e-01 4.23728436e-01 3.26425284e-02
7.57940933e-02 -2.98167288e-01 -9.06485319e-01 -9.77248728e-01
-8.98696780e-02 -9.62848663e-02 1.87941805e-01 1.46556303... | [8.577506065368652, 0.630423367023468] |
8691fd2e-06a3-4cab-aa11-c3263ae6ac3b | how-useful-are-gradients-for-ood-detection | 2205.10439 | null | https://arxiv.org/abs/2205.10439v1 | https://arxiv.org/pdf/2205.10439v1.pdf | How Useful are Gradients for OOD Detection Really? | One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is accurate on in distribution (ID) data, an OOD detection system will further equip the model with the option to defer prediction when the input... | ['Jeff Schneider', 'Ian Char', 'Youngseog Chung', 'Conor Igoe'] | 2022-05-20 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 6.63667023e-02 7.29057863e-02 -5.61357319e-01 -4.12879050e-01
-9.16664422e-01 -5.73620677e-01 7.10554540e-01 3.29551667e-01
9.95341092e-02 3.86290014e-01 7.39864558e-02 -9.28271413e-01
2.21342854e-02 -4.87165540e-01 -5.50629377e-01 -3.30191195e-01
-4.87271607e-01 6.25971794e-01 4.82666761e-01 1.10338509... | [9.158730506896973, 3.2672054767608643] |
0be5e562-d7f8-459d-9ebb-ee1d2e21b702 | enterprise-disk-drive-scrubbing-based-on | 2306.17169 | null | https://arxiv.org/abs/2306.17169v1 | https://arxiv.org/pdf/2306.17169v1.pdf | Enterprise Disk Drive Scrubbing Based on Mondrian Conformal Predictors | Disk scrubbing is a process aimed at resolving read errors on disks by reading data from the disk. However, scrubbing the entire storage array at once can adversely impact system performance, particularly during periods of high input/output operations. Additionally, the continuous reading of data from disks when scrubb... | ['Ava Hedayatipour', 'Soundouss Messoudi', 'Jinha Hwang', 'Rahul Vishwakarma'] | 2023-06-01 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [-2.04078212e-01 -7.55218565e-02 -5.54990530e-01 1.96815938e-01
-1.64966688e-01 -3.59448582e-01 4.95885871e-02 1.44694537e-01
1.99730545e-01 7.77938068e-01 -2.21083641e-01 -6.16062701e-01
-3.44440222e-01 -7.23755836e-01 -4.83311564e-01 -7.06774533e-01
6.74799979e-02 7.08876252e-01 4.76158857e-01 1.89386442... | [6.791932582855225, 2.7347073554992676] |
629eb3d8-355a-45f2-a9fd-4fff03b85b19 | semantics-preserved-distortion-for-personal | 2201.00965 | null | https://arxiv.org/abs/2201.00965v2 | https://arxiv.org/pdf/2201.00965v2.pdf | Semantics-Preserved Distortion for Personal Privacy Protection in Information Management | Although machine learning and especially deep learning methods have played an important role in the field of information management, privacy protection is an important and concerning topic in current machine learning models. In information management field, a large number of texts containing personal information are pr... | ['Xueyi Li', 'Zuchao Li', 'Ping Wang', 'Jiajia Li', 'Hai Zhao', 'Letian Peng'] | 2022-01-04 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.67398405e-01 5.55373549e-01 -9.30137709e-02 -6.45753264e-01
-6.05376601e-01 -4.61977661e-01 4.27644432e-01 6.16072893e-01
-5.72137535e-01 5.49212456e-01 6.77783549e-01 -3.24343801e-01
-1.40477955e-01 -8.73001516e-01 -6.61363065e-01 -5.56108654e-01
2.78584868e-01 8.51637721e-02 4.91424799e-02 -1.64862320... | [6.067238807678223, 6.914858341217041] |
d02029c5-46ed-468c-bad9-1a0349b13bad | a-theory-of-hypergames-on-graphs-for | 2008.03210 | null | https://arxiv.org/abs/2008.03210v1 | https://arxiv.org/pdf/2008.03210v1.pdf | A Theory of Hypergames on Graphs for Synthesizing Dynamic Cyber Defense with Deception | In this chapter, we present an approach using formal methods to synthesize reactive defense strategy in a cyber network, equipped with a set of decoy systems. We first generalize formal graphical security models--attack graphs--to incorporate defender's countermeasures in a game-theoretic model, called an attack-defend... | ['Jie Fu', 'Abhishek N. Kulkarni'] | 2020-08-07 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 1.89853817e-01 7.65568256e-01 6.81030527e-02 3.27922612e-01
-5.13401441e-02 -1.54780352e+00 6.57864749e-01 -2.10669786e-01
1.65361628e-01 4.40140784e-01 -4.27230671e-02 -1.04306626e+00
-5.14329612e-01 -1.34256613e+00 -1.97512776e-01 -3.68322909e-01
-4.44547057e-01 1.16073966e-01 5.33695698e-01 -7.74577498... | [4.838726997375488, 2.525693893432617] |
d18b102a-6f7e-4871-94a1-20d46246adcd | revisiting-inlier-and-outlier-specification | 2207.05286 | null | https://arxiv.org/abs/2207.05286v2 | https://arxiv.org/pdf/2207.05286v2.pdf | Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors | We focus on the problem of producing well-calibrated out-of-distribution (OOD) detectors, in order to enable safe deployment of medical image classifiers. Motivated by the difficulty of curating suitable calibration datasets, synthetic augmentations have become highly prevalent for inlier/outlier specification. While t... | ['Jayaraman J. Thiagarajan', 'Andreas Spanias', 'Deepta Rajan', 'Rushil Anirudh', 'Yamen Mubarka', 'Vivek Narayanaswamy'] | 2022-07-12 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 3.56193930e-01 -1.66851848e-01 -8.00220743e-02 -2.26255059e-01
-1.30168879e+00 -2.92156130e-01 4.20895547e-01 2.67007291e-01
-2.12512389e-01 3.68712515e-01 2.01770425e-01 -8.40669721e-02
5.45937829e-02 -9.33241323e-02 -6.59394741e-01 -8.10271263e-01
8.33022073e-02 3.70217919e-01 -2.03648195e-01 6.17778935... | [8.90772819519043, 1.6788651943206787] |
f8df13ac-da86-4734-8d8d-efa71a43f61e | interpretability-with-full-complexity-by | 2211.17264 | null | https://arxiv.org/abs/2211.17264v1 | https://arxiv.org/pdf/2211.17264v1.pdf | Interpretability with full complexity by constraining feature information | Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new ... | ['Dani S. Bassett', 'Kieran A. Murphy'] | 2022-11-30 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 4.85683054e-01 5.61305940e-01 -6.13078415e-01 -5.86764276e-01
-4.71586406e-01 -7.96592414e-01 5.67785144e-01 4.03496355e-01
-1.54052734e-01 4.88038301e-01 5.04242063e-01 -5.66398859e-01
-5.94674468e-01 -5.89484692e-01 -6.40370846e-01 -5.85780084e-01
-1.01439357e-01 6.54343367e-01 -3.40628147e-01 -4.12743762... | [8.78419017791748, 5.672595977783203] |
9635912d-4bff-4001-b68e-74a7b4eca593 | medcattrainer-a-biomedical-free-text | 1907.07322 | null | https://arxiv.org/abs/1907.07322v1 | https://arxiv.org/pdf/1907.07322v1.pdf | MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation | We present MedCATTrainer an interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model for biomedical domain text. NER+L is often used as a first step in deriving value from clinical text. Collecting labelled data for training models is difficult due to the need for spe... | ['Daniel Bean', 'Zeljko Kraljevic', 'Thomas Searle', 'Richard Dobson', 'Rebecca Bendayan'] | 2019-07-16 | medcattrainer-a-biomedical-free-text-1 | https://aclanthology.org/D19-3024 | https://aclanthology.org/D19-3024.pdf | ijcnlp-2019-11 | ['text-annotation'] | ['natural-language-processing'] | [ 1.88268006e-01 5.71769059e-01 -4.33690488e-01 -5.83900094e-01
-1.07277703e+00 -6.37080967e-01 3.03579092e-01 1.28475130e+00
-1.03238678e+00 1.01471782e+00 4.38458264e-01 -5.36135614e-01
-5.01647294e-01 -4.59373027e-01 -2.80944645e-01 -3.82675409e-01
3.17721032e-02 1.00097919e+00 1.35720745e-01 -1.13715054... | [8.445028305053711, 8.710925102233887] |
062a19c7-54ba-479f-839a-277d5d48e369 | transnetr-transformer-based-residual-network | 2303.07428 | null | https://arxiv.org/abs/2303.07428v1 | https://arxiv.org/pdf/2303.07428v1.pdf | TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing | Colonoscopy is considered the most effective screening test to detect colorectal cancer (CRC) and its precursor lesions, i.e., polyps. However, the procedure experiences high miss rates due to polyp heterogeneity and inter-observer dependency. Hence, several deep learning powered systems have been proposed considering ... | ['Ulas Bagci', 'Vanshali Sharma', 'Nikhil Kumar Tomar', 'Debesh Jha'] | 2023-03-13 | null | null | null | null | ['polyp-segmentation'] | ['computer-vision'] | [-2.68287417e-02 8.55604634e-02 -1.82727441e-01 -6.91743493e-02
-1.05559766e+00 -5.38677037e-01 3.84183973e-02 4.23073292e-01
-5.27761400e-01 3.76809061e-01 -7.58292228e-02 -8.60598385e-01
-1.77076682e-01 -8.09681177e-01 -8.10654223e-01 -6.11459076e-01
-3.32207382e-01 3.69551390e-01 3.94675344e-01 1.46174058... | [14.543816566467285, -2.847749948501587] |
2411ec58-ff02-4a4e-b7cb-f37cf21fed25 | multidepth-single-image-depth-estimation-via | 1907.11111 | null | https://arxiv.org/abs/1907.11111v1 | https://arxiv.org/pdf/1907.11111v1.pdf | MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification | We introduce MultiDepth, a novel training strategy and convolutional neural network (CNN) architecture that allows approaching single-image depth estimation (SIDE) as a multi-task problem. SIDE is an important part of road scene understanding. It, thus, plays a vital role in advanced driver assistance systems and auton... | ['Marco Körner', 'Lukas Liebel'] | 2019-07-25 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 2.49344036e-01 8.67837891e-02 -2.72634089e-01 -6.41005278e-01
-7.67811179e-01 -1.68268546e-01 4.92050439e-01 5.80193661e-02
-7.80859292e-01 6.93642616e-01 -5.37650883e-01 -3.92084509e-01
-1.58982780e-02 -8.90662551e-01 -7.61386573e-01 -6.94401741e-01
5.17429188e-02 7.29101717e-01 5.77153802e-01 -2.24228755... | [8.741202354431152, -1.9857349395751953] |
f2ee1c2d-a5d6-4bae-9f53-c62bd96cab72 | poseidon-face-from-depth-for-driver-pose | 1611.10195 | null | http://arxiv.org/abs/1611.10195v3 | http://arxiv.org/pdf/1611.10195v3.pdf | POSEidon: Face-from-Depth for Driver Pose Estimation | Fast and accurate upper-body and head pose estimation is a key task for
automatic monitoring of driver attention, a challenging context characterized
by severe illumination changes, occlusions and extreme poses. In this work, we
present a new deep learning framework for head localization and pose estimation
on depth im... | ['Roberto Vezzani', 'Guido Borghi', 'Rita Cucchiara', 'Marco Venturelli'] | 2016-11-30 | poseidon-face-from-depth-for-driver-pose-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Borghi_POSEidon_Face-From-Depth_for_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Borghi_POSEidon_Face-From-Depth_for_CVPR_2017_paper.pdf | cvpr-2017-7 | ['head-pose-estimation'] | ['computer-vision'] | [-3.21556211e-01 2.62510240e-01 2.12225094e-01 -9.15102720e-01
-8.60947669e-01 -1.80302635e-01 3.96510124e-01 -5.50893128e-01
-6.40346229e-01 3.67564529e-01 2.71315157e-01 2.51043707e-01
3.36180180e-01 -3.41850460e-01 -8.96314025e-01 -7.78395236e-01
3.42219591e-01 8.15004349e-01 3.72209176e-02 -1.34127617... | [13.666793823242188, 0.28201407194137573] |
dd655d1b-6019-4a29-9c07-584808c8cb45 | multi-pass-q-networks-for-deep-reinforcement | 1905.04388 | null | https://arxiv.org/abs/1905.04388v1 | https://arxiv.org/pdf/1905.04388v1.pdf | Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces | Parameterised actions in reinforcement learning are composed of discrete actions with continuous action-parameters. This provides a framework for solving complex domains that require combining high-level actions with flexible control. The recent P-DQN algorithm extends deep Q-networks to learn over such action spaces. ... | ['George D. Konidaris', 'Steven D. James', 'Craig J. Bester'] | 2019-05-10 | null | null | null | null | ['control-with-prametrised-actions'] | ['playing-games'] | [-1.94595501e-01 1.33465916e-01 -5.72697997e-01 1.02753662e-01
-8.01986277e-01 -6.11936688e-01 5.88791013e-01 -3.83003056e-01
-7.27433145e-01 1.50232184e+00 1.11788042e-01 -4.29205090e-01
-8.12483251e-01 -8.29026818e-01 -7.81058967e-01 -9.11258042e-01
-4.27558303e-01 6.21366620e-01 3.88220429e-01 -6.22388721... | [4.098951816558838, 1.9867446422576904] |
6bb7d16a-915f-434e-a1a9-f478cf056bc3 | a-fine-to-coarse-convolutional-neural-network | 1805.11790 | null | http://arxiv.org/abs/1805.11790v2 | http://arxiv.org/pdf/1805.11790v2.pdf | A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition | This paper presents a new framework for human action recognition from a 3D
skeleton sequence. Previous studies do not fully utilize the temporal
relationships between video segments in a human action. Some studies
successfully used very deep Convolutional Neural Network (CNN) models but often
suffer from the data insuf... | ['Nakamasa Inoue', 'Koichi Shinoda', 'Thao Minh Le'] | 2018-05-30 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.10370690e-01 -4.32543427e-01 -3.23614955e-01 -4.27623093e-01
-2.98802525e-01 -8.11239779e-02 2.80483365e-01 -4.50422078e-01
-8.43922794e-01 5.47464550e-01 3.91919196e-01 2.35843793e-01
1.63633212e-01 -5.86248636e-01 -6.40446484e-01 -4.32085544e-01
-2.36161090e-02 1.58230156e-01 5.12648046e-01 -1.77198455... | [7.849247455596924, 0.386807382106781] |
abd6cc4f-afba-4777-ad38-c342ff1ba0e4 | designing-optimal-mortality-risk-prediction | 1411.5086 | null | http://arxiv.org/abs/1411.5086v2 | http://arxiv.org/pdf/1411.5086v2.pdf | Designing Optimal Mortality Risk Prediction Scores that Preserve Clinical Knowledge | Many in-hospital mortality risk prediction scores dichotomize predictive
variables to simplify the score calculation. However, hard thresholding in
these additive stepwise scores of the form "add x points if variable v is
above/below threshold t" may lead to critical failures. In this paper, we seek
to develop risk pre... | ['Karla A. Lawson', 'Natalia M. Arzeno', 'Haris Vikalo', 'Sarah V. Duzinski'] | 2014-11-19 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.23566441e-01 -2.90929496e-01 -1.34566113e-01 -4.55583692e-01
-7.47977197e-01 -2.81197459e-01 -6.15558289e-02 6.88000858e-01
-5.35688698e-01 8.93564165e-01 4.43088114e-02 -7.12482631e-01
-7.62039661e-01 -5.44884384e-01 5.17879194e-03 -7.84337103e-01
-4.52134728e-01 7.31112719e-01 -3.60308327e-02 -5.59322871... | [8.040899276733398, 5.994631767272949] |
74eecb30-4393-49da-bb79-8c08a899ec3c | rumour-detection-using-graph-neural-network | 2212.10080 | null | https://arxiv.org/abs/2212.10080v1 | https://arxiv.org/pdf/2212.10080v1.pdf | Rumour detection using graph neural network and oversampling in benchmark Twitter dataset | Recently, online social media has become a primary source for new information and misinformation or rumours. In the absence of an automatic rumour detection system the propagation of rumours has increased manifold leading to serious societal damages. In this work, we propose a novel method for building automatic rumour... | ['Preeti Kaur', 'Prince Bansal', 'Shaswat Patel'] | 2022-12-20 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 2.36705080e-01 3.81612867e-01 -8.64490420e-02 -8.62341672e-02
-2.86592543e-01 -3.83019028e-03 1.00249052e+00 5.56171894e-01
-9.85399038e-02 8.34347606e-01 5.16712546e-01 -2.56783903e-01
1.53925031e-01 -1.15455532e+00 -5.35415292e-01 -1.80838525e-01
-2.50768185e-01 3.44444901e-01 3.57322603e-01 -8.27967405... | [8.207063674926758, 10.128119468688965] |
65c482f2-442a-430e-9c7e-4dd925562cb1 | social-sensor-composition-for-tapestry-scenes | 2003.13684 | null | https://arxiv.org/abs/2003.13684v1 | https://arxiv.org/pdf/2003.13684v1.pdf | Social-Sensor Composition for Tapestry Scenes | The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated loc... | ['Tooba Aamir', 'Hai Dong', 'Athman Bouguettaya'] | 2020-03-28 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 3.62484246e-01 -3.22016895e-01 2.92250574e-01 -6.57953024e-01
-3.28711718e-01 -6.27974093e-01 8.59004974e-01 5.30724049e-01
-3.09137106e-01 4.11495477e-01 3.05721164e-01 2.46346593e-01
-5.22889912e-01 -9.74204004e-01 -4.44971859e-01 -7.88988650e-01
-2.14364350e-01 1.64174035e-01 7.58612394e-01 -2.53478795... | [8.025069236755371, -1.54420804977417] |
7c4b3d0e-17d0-4509-87c6-929e60057874 | robust-domain-adaptation-for-machine-reading | 2209.11615 | null | https://arxiv.org/abs/2209.11615v1 | https://arxiv.org/pdf/2209.11615v1.pdf | Robust Domain Adaptation for Machine Reading Comprehension | Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., noisy correspondence) due to i) the unavailable QA pairs in target documents, an... | ['Xi Peng', 'Zujie Wen', 'Jia Liu', 'Zhenyu Huang', 'Liang Jiang'] | 2022-09-23 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.14535785e-01 3.34680587e-01 4.49968636e-01 -2.44270593e-01
-1.11295092e+00 -6.66840017e-01 5.68142056e-01 -1.78528484e-02
-3.30763727e-01 7.34776497e-01 4.57230419e-01 -3.80780876e-01
-1.12748839e-01 -8.53265584e-01 -7.72442997e-01 -6.05956912e-01
5.28907180e-01 6.21817946e-01 5.65972209e-01 -7.69691169... | [11.620872497558594, 8.07483959197998] |
d9c13ef8-ac2d-45e3-889f-0b8ce8f47209 | androshield-automated-android-applications | null | null | https://www.mdpi.com/2078-2489/10/10/326 | https://www.mdpi.com/2078-2489/10/10/326/pdf | AndroShield: Automated Android Applications Vulnerability Detection, a Hybrid Static and Dynamic Analysis Approach | The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is con... | ['Menna Tullah Magdy', 'Nouran Abdeen', 'Hanan Hindy', 'Amr Amin', 'Islam Hegazy', 'Amgad Eldessouki'] | 2019-10-22 | null | null | null | mdpi-information-2019-10 | ['vulnerability-detection', 'mobile-security'] | ['miscellaneous', 'miscellaneous'] | [ 1.05621710e-01 -5.87649830e-03 -1.75239667e-01 2.11458206e-01
-2.35880762e-01 -1.07986832e+00 2.51739055e-01 4.84475702e-01
-1.57739773e-01 3.25291395e-01 -2.82097071e-01 -9.78393137e-01
-1.20616466e-01 -8.43579292e-01 -4.35203999e-01 -2.16234639e-01
9.02950093e-02 -2.38063604e-01 7.87220478e-01 -1.17581762... | [14.392706871032715, 9.66324520111084] |
8c1b5e93-1792-43b1-bd9a-78901f747594 | dynamic-video-frame-interpolation-with | 2304.12664 | null | https://arxiv.org/abs/2304.12664v1 | https://arxiv.org/pdf/2304.12664v1.pdf | Dynamic Video Frame Interpolation with integrated Difficulty Pre-Assessment | Video frame interpolation(VFI) has witnessed great progress in recent years. While existing VFI models still struggle to achieve a good trade-off between accuracy and efficiency: fast models often have inferior accuracy; accurate models typically run slowly. However, easy samples with small motion or clear texture can ... | ['Cheul-hee Hahm', 'Jayoon Koo', 'Jie Chen', 'Longhai Wu', 'Youxin Chen', 'Xin Jin', 'Ban Chen'] | 2023-04-25 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 8.68004784e-02 -4.57315624e-01 -3.83612931e-01 -4.90858614e-01
-8.26172113e-01 -2.55752772e-01 3.52216899e-01 -1.42973259e-01
-2.13301510e-01 4.96031106e-01 2.40831003e-01 -9.69581604e-02
1.09596193e-01 -7.25217998e-01 -6.61146104e-01 -2.84302741e-01
1.86400563e-01 3.12119067e-01 5.10078669e-01 1.65448353... | [10.768559455871582, -1.4147365093231201] |
3cba2ca0-3844-4421-adda-28b30cfa7703 | you-talking-to-me-a-corpus-and-algorithm-for | null | null | https://aclanthology.org/P08-1095 | https://aclanthology.org/P08-1095.pdf | You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement | When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been ma... | ['Eugene Charniak', 'Micha Elsner'] | 2008-06-01 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 4.12903577e-01 7.44141757e-01 1.07405655e-01 -3.38635206e-01
-8.85749340e-01 -1.00868809e+00 1.13725877e+00 1.89442724e-01
-2.30044439e-01 8.90933871e-01 7.60499299e-01 -5.14428437e-01
-1.09189317e-01 -2.81103879e-01 1.47450224e-01 -3.69040757e-01
-1.48872957e-01 9.67145979e-01 1.36877611e-01 -3.29242080... | [12.571585655212402, 7.99116849899292] |
f92740eb-c05c-4e99-9264-fd744c598772 | transfer-learning-and-local-interpretable | 2211.05633 | null | https://arxiv.org/abs/2211.05633v2 | https://arxiv.org/pdf/2211.05633v2.pdf | Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights | The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) ... | ['Kishor Datta Gupta', 'Amin G. Alhashim', 'Md Khairul Islam', 'Fatematuj Jahora', 'Md Shahin Ali', 'Tareque Abu Abdullah', 'Md Manjurul Ahsan'] | 2022-11-01 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-3.34150791e-01 -1.13923736e-02 -6.41292810e-01 -2.10112691e-01
-5.62934093e-02 -1.93859786e-01 5.46893418e-01 1.30525783e-01
-4.35435064e-02 1.01242661e+00 -4.61265184e-02 -9.11806345e-01
-4.47942913e-01 -6.06732488e-01 -5.15379846e-01 -3.95712882e-01
-6.04841173e-01 1.02155364e+00 -2.51508474e-01 -1.70043692... | [15.596699714660645, -1.669425368309021] |
c39570f6-4a0b-4d80-9377-5902aae88555 | xomivae-an-interpretable-deep-learning-model | 2105.12807 | null | https://arxiv.org/abs/2105.12807v2 | https://arxiv.org/pdf/2105.12807v2.pdf | XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data | The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable dee... | ['Yike Guo', 'Kai Sun', 'XiaoYu Zhang', 'Eloise Withnell'] | 2021-05-26 | null | null | null | null | ['tumour-classification'] | ['medical'] | [-2.28898942e-01 6.92807674e-01 -1.69738755e-01 -3.30940753e-01
-2.88524508e-01 -3.00338984e-01 4.46560472e-01 2.09136382e-01
2.96567455e-02 8.88288736e-01 4.10933852e-01 -3.01296055e-01
-5.59029877e-01 -5.51377356e-01 -8.55217338e-01 -1.10512972e+00
6.42747954e-02 9.16690230e-01 -8.16366136e-01 2.51879752... | [6.038267135620117, 5.714881896972656] |
e2ba3b2e-e695-4e05-8a66-ab8cb8aeb6f5 | membership-inference-attack-susceptibility-of | 2104.08305 | null | https://arxiv.org/abs/2104.08305v1 | https://arxiv.org/pdf/2104.08305v1.pdf | Membership Inference Attack Susceptibility of Clinical Language Models | Deep Neural Network (DNN) models have been shown to have high empirical privacy leakages. Clinical language models (CLMs) trained on clinical data have been used to improve performance in biomedical natural language processing tasks. In this work, we investigate the risks of training-data leakage through white-box or b... | ['Hong Yu', 'Bhanu Pratap Singh Rawat', 'Abhyuday Jagannatha'] | 2021-04-16 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.58665344e-01 6.84056580e-01 -1.92783669e-01 -6.36235595e-01
-1.00667787e+00 -6.92009747e-01 2.61513412e-01 6.63423181e-01
-7.09579885e-01 8.16696644e-01 2.22290196e-02 -1.06505048e+00
1.37599826e-01 -5.21332562e-01 -8.70523393e-01 -6.27562284e-01
-5.24198532e-01 1.08307665e-02 -3.26449871e-01 6.01141453... | [6.032906532287598, 6.8853583335876465] |
7b01be5a-94f9-40b8-9d5e-a33ebdc95293 | generation-of-artificial-facial-drug-abuse | 2304.06106 | null | https://arxiv.org/abs/2304.06106v1 | https://arxiv.org/pdf/2304.06106v1.pdf | Generation of artificial facial drug abuse images using Deep De-identified anonymous Dataset augmentation through Genetics Algorithm (3DG-GA) | In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized faci... | ['Amine Nait-ali', 'Régis Fournier', 'Lou Laurent', 'Hazem Zein'] | 2023-04-12 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 5.29031992e-01 3.94037068e-01 -2.82614343e-02 -3.40141445e-01
-3.97156894e-01 -4.04047489e-01 2.54950285e-01 -3.13399255e-01
-1.39091602e-02 1.12210166e+00 -1.04917876e-01 -5.28032752e-03
1.47474751e-01 -1.06751370e+00 -5.43325186e-01 -8.56999338e-01
1.74135134e-01 3.49471092e-01 -5.76775670e-01 4.44811471... | [12.824206352233887, 0.5539568066596985] |
90d4c919-2b27-40b0-89ba-9d5bf4b566b0 | multi-source-domain-adaptation-for-text-1 | 2211.09913 | null | https://arxiv.org/abs/2211.09913v1 | https://arxiv.org/pdf/2211.09913v1.pdf | Multi-source Domain Adaptation for Text-independent Forensic Speaker Recognition | Adapting speaker recognition systems to new environments is a widely-used technique to improve a well-performing model learned from large-scale data towards a task-specific small-scale data scenarios. However, previous studies focus on single domain adaptation, which neglects a more practical scenario where training da... | ['John H. L. Hansen', 'Zhenyu Wang'] | 2022-11-17 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 3.27948928e-01 -5.16650558e-01 2.53341824e-01 -4.35937524e-01
-1.27812243e+00 -6.25998318e-01 2.48762116e-01 -2.38045782e-01
-4.42106038e-01 6.21454835e-01 2.66009897e-01 -5.12471125e-02
-1.02609150e-01 -3.49085629e-01 -5.56340396e-01 -8.34360719e-01
-2.49753613e-02 4.64130938e-01 2.16281697e-01 -1.99159041... | [14.527610778808594, 6.159206390380859] |
0a97d4b7-500e-4672-b4a9-002988d177ee | efficient-on-device-session-based | 2209.13422 | null | https://arxiv.org/abs/2209.13422v4 | https://arxiv.org/pdf/2209.13422v4.pdf | Efficient On-Device Session-Based Recommendation | On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-based recommendation models in resource-constrained mobile devices, te... | ['Hongzhi Yin', 'Quoc Viet Hung Nguyen', 'Chaoqun Yang', 'Qinyong Wang', 'Junliang Yu', 'Xin Xia'] | 2022-09-27 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-2.42634676e-02 -4.78786469e-01 -6.28033459e-01 -2.33230010e-01
-2.49072924e-01 -2.92237639e-01 5.12186438e-02 -5.00806347e-02
-1.66241154e-01 1.91153735e-01 4.18281943e-01 -5.02506196e-01
-3.75494957e-01 -8.12330961e-01 -5.00889063e-01 -8.19653153e-01
-1.79539453e-02 1.52569771e-01 -5.14286608e-02 -6.43947050... | [10.083020210266113, 5.5194196701049805] |
66eb6611-8ee9-4b1d-9cfc-6e5f9216bff5 | what-is-the-reward-for-handwriting | 2009.10962 | null | https://arxiv.org/abs/2009.10962v1 | https://arxiv.org/pdf/2009.10962v1.pdf | What is the Reward for Handwriting? -- Handwriting Generation by Imitation Learning | Analyzing the handwriting generation process is an important issue and has been tackled by various generation models, such as kinematics based models and stochastic models. In this study, we use a reinforcement learning (RL) framework to realize handwriting generation with the careful future planning ability. In fact, ... | ['Seiichi Uchida', 'Keisuke Kanda', 'Brian Kenji Iwana'] | 2020-09-23 | null | null | null | null | ['handwriting-generation'] | ['computer-vision'] | [-1.46969199e-01 3.18007797e-01 -8.26085582e-02 4.74584214e-02
-2.29814462e-02 -8.09051931e-01 7.27373600e-01 -3.68413895e-01
-1.13653496e-01 9.25145328e-01 -2.73469312e-04 -2.70184129e-01
-6.33572862e-02 -9.19395387e-01 -7.84610629e-01 -7.70762622e-01
4.15981621e-01 4.32964116e-01 -1.54974582e-02 -6.46620393... | [4.424548149108887, 1.9152264595031738] |
056de8cb-3360-4245-9455-62df1f74769b | point-of-interest-type-prediction-using-text | 2109.00602 | null | https://arxiv.org/abs/2109.00602v1 | https://arxiv.org/pdf/2109.00602v1.pdf | Point-of-Interest Type Prediction using Text and Images | Point-of-interest (POI) type prediction is the task of inferring the type of a place from where a social media post was shared. Inferring a POI's type is useful for studies in computational social science including sociolinguistics, geosemiotics, and cultural geography, and has applications in geosocial networking tech... | ['Nikolaos Aletras', 'Danae Sánchez Villegas'] | 2021-09-01 | null | https://aclanthology.org/2021.emnlp-main.614 | https://aclanthology.org/2021.emnlp-main.614.pdf | emnlp-2021-11 | ['type-prediction'] | ['computer-code'] | [ 1.21103078e-01 -6.30985498e-02 -2.48671770e-01 -2.37998232e-01
4.53452987e-04 -3.10880870e-01 1.20016778e+00 4.75146472e-01
-1.57896653e-01 3.85113150e-01 8.37618053e-01 -6.80898726e-02
-3.01309526e-01 -1.13560641e+00 -5.20587206e-01 -4.77652192e-01
-4.67545837e-01 2.03105897e-01 -7.66836479e-03 -2.36445725... | [10.028351783752441, 6.646862983703613] |
63380f67-cbed-4394-b1f1-686bb94fd870 | what-do-we-need-to-build-explainable-ai | 1712.09923 | null | http://arxiv.org/abs/1712.09923v1 | http://arxiv.org/pdf/1712.09923v1.pdf | What do we need to build explainable AI systems for the medical domain? | Artificial intelligence (AI) generally and machine learning (ML) specifically
demonstrate impressive practical success in many different application domains,
e.g. in autonomous driving, speech recognition, or recommender systems. Deep
learning approaches, trained on extremely large data sets or using
reinforcement lear... | ['Constantinos S. Pattichis', 'Chris Biemann', 'Andreas Holzinger', 'Douglas B. Kell'] | 2017-12-28 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.22051731e-01 1.01116753e+00 -2.51244575e-01 -4.99459028e-01
-2.61704594e-01 -3.55953127e-01 5.80870986e-01 2.63770342e-01
-3.23006481e-01 9.32967007e-01 -1.37748355e-02 -7.15815306e-01
-4.92368639e-01 -8.54152560e-01 -7.06929803e-01 -4.74986196e-01
1.12863198e-01 7.89510548e-01 -2.88366795e-01 -3.54082555... | [8.802984237670898, 5.716146945953369] |
febe80e2-4c06-48bf-b594-298ca48c5f08 | benchmark-for-generic-product-detection-a | 1912.09476 | null | https://arxiv.org/abs/1912.09476v2 | https://arxiv.org/pdf/1912.09476v2.pdf | Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection | Object detection in densely packed scenes is a new area where standard object detectors fail to train well. Dense object detectors like RetinaNet trained on large and dense datasets show great performance. We train a standard object detector on a small, normally packed dataset with data augmentation techniques. This da... | ['Srikrishna Varadarajan', 'Muktabh Mayank Srivastava', 'Sonaal Kant'] | 2019-12-19 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [-2.05709934e-01 -7.25970864e-02 -4.67135687e-04 -2.82722116e-01
-6.82731211e-01 -4.57512051e-01 5.35254478e-01 1.58602208e-01
-5.49254477e-01 2.04207122e-01 7.80448643e-03 1.87949464e-02
6.29933298e-01 -6.84782386e-01 -1.04420424e+00 -4.55314964e-01
-1.44784838e-01 5.25465429e-01 9.47480381e-01 -1.52861044... | [9.149383544921875, 0.8721863627433777] |
04fcfa67-3213-495a-ae4c-7883fed5c0f9 | chinese-ner-using-lattice-lstm | 1805.02023 | null | http://arxiv.org/abs/1805.02023v4 | http://arxiv.org/pdf/1805.02023v4.pdf | Chinese NER Using Lattice LSTM | We investigate a lattice-structured LSTM model for Chinese NER, which encodes
a sequence of input characters as well as all potential words that match a
lexicon. Compared with character-based methods, our model explicitly leverages
word and word sequence information. Compared with word-based methods, lattice
LSTM does ... | ['Yue Zhang', 'Jie Yang'] | 2018-05-05 | chinese-ner-using-lattice-lstm-1 | https://aclanthology.org/P18-1144 | https://aclanthology.org/P18-1144.pdf | acl-2018-7 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 1.36563433e-02 -2.61562705e-01 -2.40973786e-01 -1.96499392e-01
-1.03702986e+00 -6.91064417e-01 8.39799047e-02 3.47787887e-01
-1.19739938e+00 8.98456097e-01 6.43402338e-01 -4.56057668e-01
6.84525073e-01 -1.02524304e+00 -4.14070636e-01 -4.50613678e-01
3.19119319e-02 3.38332355e-01 7.65025690e-02 -1.77164048... | [9.967954635620117, 9.816767692565918] |
021d0b29-550c-4444-810d-8af0dd2dc0ec | window-detection-in-facade-imagery-a-deep | 2107.10006 | null | https://arxiv.org/abs/2107.10006v1 | https://arxiv.org/pdf/2107.10006v1.pdf | Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN | The parsing of windows in building facades is a long-desired but challenging task in computer vision. It is crucial to urban analysis, semantic reconstruction, lifecycle analysis, digital twins, and scene parsing amongst other building-related tasks that require high-quality semantic data. This article investigates the... | ['Mola Ayenew', 'Nils Nordmark'] | 2021-07-21 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 5.96624732e-01 6.65103048e-02 2.95335680e-01 -5.96306264e-01
-9.29547310e-01 -5.07190228e-01 5.82832694e-01 1.26932949e-01
-4.75441843e-01 2.68505096e-01 -1.69139981e-01 -7.37761915e-01
1.10229447e-01 -1.32718205e+00 -9.52919960e-01 -1.93003625e-01
-3.98590565e-01 4.32885557e-01 8.84173512e-01 -2.52140611... | [9.292375564575195, -1.1809372901916504] |
df3db08c-f02a-413b-beb6-afa260479830 | fast-optimal-transport-through-sliced | 2307.01770 | null | https://arxiv.org/abs/2307.01770v1 | https://arxiv.org/pdf/2307.01770v1.pdf | Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics | Wasserstein distance (WD) and the associated optimal transport plan have been proven useful in many applications where probability measures are at stake. In this paper, we propose a new proxy of the squared WD, coined min-SWGG, that is based on the transport map induced by an optimal one-dimensional projection of the t... | ['Nicolas Courty', 'Clément Bonet', 'Gilles Gasso', 'Laetitia Chapel', 'Guillaume Mahey'] | 2023-07-04 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.44001648e-01 1.27814427e-01 1.63949475e-01 -2.90512532e-01
-4.71835077e-01 -6.71419561e-01 4.30426598e-01 2.00079218e-01
-7.07970977e-01 6.63490713e-01 7.50588952e-03 -3.81687552e-01
-7.16547072e-01 -8.67904007e-01 -6.56808436e-01 -9.12085176e-01
-1.63556203e-01 3.63327175e-01 2.09597468e-01 -2.01202363... | [7.349875450134277, 4.000860691070557] |
eb149e0c-6de3-4804-a4d1-936b53c012d7 | stacked-convolutional-deep-encoding-network | 2004.04959 | null | https://arxiv.org/abs/2004.04959v1 | https://arxiv.org/pdf/2004.04959v1.pdf | Stacked Convolutional Deep Encoding Network for Video-Text Retrieval | Existing dominant approaches for cross-modal video-text retrieval task are to learn a joint embedding space to measure the cross-modal similarity. However, these methods rarely explore long-range dependency inside video frames or textual words leading to insufficient textual and visual details. In this paper, we propos... | ['Zheng-Jun Zha', 'Rui Zhao', 'Kecheng Zheng'] | 2020-04-10 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-1.21126086e-01 -8.90111148e-01 -2.30718330e-01 -3.61321092e-01
-9.05989647e-01 -5.91554284e-01 8.55257690e-01 1.06671110e-01
-6.35840356e-01 1.72626048e-01 4.42383498e-01 -6.70600161e-02
-2.96348453e-01 -4.91647929e-01 -6.21251106e-01 -7.06950426e-01
8.04352537e-02 -1.33320883e-01 3.63122314e-01 -4.87586074... | [10.412640571594238, 0.9692619442939758] |
eaa14728-7cfd-462b-a0c1-1e5d197bbdba | the-story-of-qos-prediction-in-vehicular | 2302.11966 | null | https://arxiv.org/abs/2302.11966v1 | https://arxiv.org/pdf/2302.11966v1.pdf | The Story of QoS Prediction in Vehicular Communication: From Radio Environment Statistics to Network-Access Throughput Prediction | As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leverage... | ['Slawomir Stanczak', 'Hans D. Schotten', 'Frank H. P. Fitzek', 'Gerhard Fettweis', 'Martin Kasparick', 'Raja Sattiraju', 'Anton Krause', 'Philipp Geuer', 'Sanket Partani', 'Rodrigo Hernangomez', 'Cara Watermann', 'Daniel F. Külzer', 'Christian L. Vielhaus', 'Alexandros Palaios'] | 2023-02-23 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.05883656e-03 3.30277979e-01 -4.62903172e-01 -3.46883923e-01
-2.72660613e-01 -4.36967760e-01 4.94577855e-01 -8.33279118e-02
1.40359119e-01 1.14691186e+00 -1.46129385e-01 -7.87981510e-01
-7.05009282e-01 -7.87249327e-01 -4.93447721e-01 -8.15398991e-01
-6.01202548e-01 6.02615058e-01 -4.31803986e-02 -3.51908386... | [6.107729434967041, 1.636047124862671] |
bbc482f9-0315-43d6-b33b-432b6f683e0c | analysis-of-word-embeddings-and-sequence | null | null | https://aclanthology.org/U15-1003 | https://aclanthology.org/U15-1003.pdf | Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction | null | ['Laurianne Sitbon', 'Mahnoosh Kholghi', 'Lance De Vine', 'Guido Zuccon', 'Anthony Nguyen'] | 2015-12-01 | analysis-of-word-embeddings-and-sequence-1 | https://aclanthology.org/U15-1003 | https://aclanthology.org/U15-1003.pdf | alta-2015-12 | ['clinical-concept-extraction'] | ['medical'] | [-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... | [-7.35734224319458, 3.5743727684020996] |
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