paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a2fdf4ab-a559-4320-b741-cf62ae293f42 | sequential-stochastic-optimization-in | 2108.09585 | null | https://arxiv.org/abs/2108.09585v1 | https://arxiv.org/pdf/2108.09585v1.pdf | Sequential Stochastic Optimization in Separable Learning Environments | We consider a class of sequential decision-making problems under uncertainty that can encompass various types of supervised learning concepts. These problems have a completely observed state process and a partially observed modulation process, where the state process is affected by the modulation process only through a... | ['Chelsea C. White III', 'R. Reid Bishop'] | 2021-08-21 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 4.42437440e-01 6.08003259e-01 -5.59049964e-01 -8.20314810e-02
-4.30539787e-01 -5.79896927e-01 8.77201796e-01 5.11398196e-01
-5.56276679e-01 1.12898135e+00 1.42339040e-02 -4.11626756e-01
-5.19636452e-01 -7.23818660e-01 -6.11300170e-01 -1.08563185e+00
-4.13383245e-01 1.26083565e+00 1.40835270e-01 1.27518937... | [4.362250804901123, 2.277177333831787] |
acc1381f-74f0-4182-bd10-346201ca89b6 | a-multi-hypothesis-classification-approach-to | 2002.12896 | null | https://arxiv.org/abs/2002.12896v2 | https://arxiv.org/pdf/2002.12896v2.pdf | A Multi-Hypothesis Approach to Color Constancy | Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models depend on camera spectral sensitivity and typically exhibit poor generalisation to new devices. Additionally, regression methods produce po... | ['Daniel Hernandez-Juarez', 'Steven McDonagh', 'Benjamin Busam', 'Ales Leonardis', 'Sarah Parisot', 'Gregory Slabaugh'] | 2020-02-28 | a-multi-hypothesis-approach-to-color | http://openaccess.thecvf.com/content_CVPR_2020/html/Hernandez-Juarez_A_Multi-Hypothesis_Approach_to_Color_Constancy_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hernandez-Juarez_A_Multi-Hypothesis_Approach_to_Color_Constancy_CVPR_2020_paper.pdf | cvpr-2020-6 | ['color-constancy'] | ['computer-vision'] | [ 6.40303791e-01 -4.65938389e-01 -4.85329665e-02 -4.83772308e-01
-1.27128887e+00 -9.53765333e-01 6.37592673e-01 -1.59197718e-01
-4.63131607e-01 6.98559403e-01 -1.76885314e-02 -1.36814892e-01
-7.18512684e-02 -6.17223978e-01 -1.11348164e+00 -9.67344522e-01
5.89178264e-01 2.50822335e-01 1.57934893e-02 3.25260252... | [10.20667552947998, -2.6251718997955322] |
daafb498-073f-481c-8e2e-4ded16f4f3c5 | music-representing-corpus-virtual-an-open | 2305.14948 | null | https://arxiv.org/abs/2305.14948v1 | https://arxiv.org/pdf/2305.14948v1.pdf | Music Representing Corpus Virtual: An Open Sourced Library for Explorative Music Generation, Sound Design, and Instrument Creation with Artificial Intelligence and Machine Learning | Music Representing Corpus Virtual (MRCV) is an open source software suite designed to explore the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) in Music Generation, Sound Design, and Virtual Instrument Creation (MGSDIC). The software is accessible to users of varying levels of experience, with ... | ['Christopher Johann Clarke'] | 2023-05-24 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.42875847e-02 1.64345831e-01 -2.49909181e-02 4.40995306e-01
-4.78700310e-01 -9.89215851e-01 2.00793356e-01 -2.16434360e-01
-4.10043821e-02 2.90322632e-01 5.16817808e-01 -3.65778089e-01
-3.29080611e-01 -5.44059932e-01 -6.03948571e-02 -3.82941753e-01
-1.86384432e-02 4.54046667e-01 -3.71556908e-01 -2.82855153... | [15.987963676452637, 5.464305400848389] |
70e7a5f1-0fda-4f28-9e10-ae3d3c764ff2 | target-free-text-guided-image-manipulation | 2211.14544 | null | https://arxiv.org/abs/2211.14544v2 | https://arxiv.org/pdf/2211.14544v2.pdf | Target-Free Text-guided Image Manipulation | We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training. To address this challenging task, we propose a Cyclic-Manipulation GAN (cManiGAN) in this paper... | ['Yu-Chiang Frank Wang', 'Chiao-An Yang', 'Cheng-Fu Yang', 'Wan-Cyuan Fan'] | 2022-11-26 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.38215780e-01 4.64970529e-01 -3.48902941e-01 -2.35855252e-01
-7.04653144e-01 -6.43669188e-01 7.49738634e-01 -5.05372941e-01
-3.11354995e-01 5.73998272e-01 -1.34051189e-01 -6.18636310e-01
3.20062727e-01 -8.30173135e-01 -1.37230361e+00 -7.18103945e-01
6.54567361e-01 2.88761169e-01 -8.06560665e-02 7.95076694... | [11.660076141357422, -0.33424249291419983] |
9e9bfa60-8511-47ec-beb9-cf56e13d40bb | compositional-sequence-labeling-models-for | 1607.06153 | null | http://arxiv.org/abs/1607.06153v1 | http://arxiv.org/pdf/1607.06153v1.pdf | Compositional Sequence Labeling Models for Error Detection in Learner Writing | In this paper, we present the first experiments using neural network models
for the task of error detection in learner writing. We perform a systematic
comparison of alternative compositional architectures and propose a framework
for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14
shared task ... | ['Helen Yannakoudakis', 'Marek Rei'] | 2016-07-20 | compositional-sequence-labeling-models-for-1 | https://aclanthology.org/P16-1112 | https://aclanthology.org/P16-1112.pdf | acl-2016-8 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 2.48371974e-01 2.03742087e-01 1.48488179e-01 -3.85568142e-01
-6.65040493e-01 -1.24147840e-01 4.54992563e-01 4.40424234e-01
-9.06700909e-01 6.39688492e-01 3.66298914e-01 -4.67195481e-01
-7.00777322e-02 -2.89557666e-01 -5.19746184e-01 4.18898493e-01
5.47227979e-01 5.86961448e-01 -6.72593042e-02 -2.57707506... | [11.234726905822754, 9.417295455932617] |
0469325b-d5e0-4594-bf0c-b1517faf9f52 | jitter-does-matter-adapting-gaze-estimation | 2210.02082 | null | https://arxiv.org/abs/2210.02082v1 | https://arxiv.org/pdf/2210.02082v1.pdf | Jitter Does Matter: Adapting Gaze Estimation to New Domains | Deep neural networks have demonstrated superior performance on appearance-based gaze estimation tasks. However, due to variations in person, illuminations, and background, performance degrades dramatically when applying the model to a new domain. In this paper, we discover an interesting gaze jitter phenomenon in cross... | ['Feng Lu', 'Yunfei Liu', 'Haofei Wang', 'Mingjie Xu', 'Yiwei Bao', 'Ruicong Liu'] | 2022-10-05 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 2.93495536e-01 -2.94555902e-01 1.66351199e-01 -3.99133772e-01
-1.37968376e-01 -3.91808569e-01 1.76307008e-01 -5.03616273e-01
-1.40339255e-01 7.37861633e-01 -1.30966812e-01 1.44470096e-01
-1.17314406e-01 5.17516804e-04 -8.68967414e-01 -8.75990033e-01
2.14842856e-01 -5.49218774e-01 1.96861386e-01 -1.10423580... | [14.077929496765137, 0.055266935378313065] |
44c386c1-4fd2-411d-bebb-10a7d0157154 | dkt-stdrl-spatial-and-temporal-representation | 2302.11569 | null | https://arxiv.org/abs/2302.11569v1 | https://arxiv.org/pdf/2302.11569v1.pdf | DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction | Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students' learning process, Deep Knowledge Tracing Based o... | ['Ya Li', 'Zexue Yang', 'Haihong Yun', 'Zhifeng Wang', 'Liting Lyu'] | 2023-02-15 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-3.22990566e-01 -3.33221197e-01 -4.36063230e-01 -2.85323292e-01
-2.24360406e-01 -3.87367994e-01 1.70328885e-01 2.05626041e-01
-5.37023842e-01 4.80104476e-01 8.06129798e-02 -4.51840460e-01
-4.96562332e-01 -1.15998840e+00 -4.86116827e-01 -4.81262684e-01
2.16913730e-01 -9.29372683e-02 7.13574409e-01 -3.26804459... | [10.14400577545166, 7.0787224769592285] |
4d3bbbdb-2210-4e17-983a-0ae8bec73b87 | birl-benchmark-on-image-registration-methods | 1912.13452 | null | https://arxiv.org/abs/1912.13452v2 | https://arxiv.org/pdf/1912.13452v2.pdf | BIRL: Benchmark on Image Registration methods with Landmark validation | This report presents a generic image registration benchmark with automatic evaluation using landmark annotations. The key features of the BIRL framework are: easily extendable, performance evaluation, parallel experimentation, simple visualisations, experiment's time-out limit, resuming unfinished experiments. From the... | ['Jiri Borovec'] | 2019-12-31 | null | null | null | null | ['birl-cima'] | ['medical'] | [ 1.72145039e-01 1.12143010e-01 5.79286851e-02 -1.91388130e-01
-9.49388504e-01 -4.43893135e-01 5.78395009e-01 4.36094910e-01
-6.51589811e-01 5.54492772e-01 -6.24185540e-02 -3.54015738e-01
-3.95663410e-01 -3.30716133e-01 -1.59616351e-01 -8.25183153e-01
-5.06373167e-01 7.81782568e-01 7.41719127e-01 -1.40260935... | [14.831329345703125, -2.9791982173919678] |
a3a8bf42-e9cd-46d6-87fa-38db009922d9 | dont-discuss-investigating-semantic-and | null | null | https://aclanthology.org/2021.ranlp-main.168 | https://aclanthology.org/2021.ranlp-main.168.pdf | “Don’t discuss”: Investigating Semantic and Argumentative Features for Supervised Propagandist Message Detection and Classification | One of the mechanisms through which disinformation is spreading online, in particular through social media, is by employing propaganda techniques. These include specific rhetorical and psychological strategies, ranging from leveraging on emotions to exploiting logical fallacies. In this paper, our goal is to push forwa... | ['Serena Villata', 'Elena Cabrio', 'Vorakit Vorakitphan'] | null | null | https://aclanthology.org/2021.ranlp-1.168 | https://aclanthology.org/2021.ranlp-1.168.pdf | ranlp-2021-9 | ['logical-fallacies', 'propaganda-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 2.20852476e-02 2.90434003e-01 -5.00064135e-01 -5.34269176e-02
-2.64134735e-01 -6.35950327e-01 1.56301475e+00 1.01520789e+00
-4.99491274e-01 7.68784285e-01 8.70117188e-01 -5.75009644e-01
-5.38674742e-02 -9.09720361e-01 -1.03318304e-01 -7.13302612e-01
1.60808474e-01 4.11161743e-02 -4.98590544e-02 -6.84610069... | [8.548059463500977, 10.473657608032227] |
bee38dc0-a99a-4ebb-b99c-93c4627fee44 | panoptic-partformer-learning-a-unified-model | 2204.04655 | null | https://arxiv.org/abs/2204.04655v2 | https://arxiv.org/pdf/2204.04655v2.pdf | Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation | Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part segmentation into one task. Previous work mainly utilizes separated approaches to handle thing, stuff, and part predictions individually without performing any shared computation and task association. In this work, we aim to unify these tasks... | ['DaCheng Tao', 'Yunhai Tong', 'Guangliang Cheng', 'Yibo Yang', 'Shilin Xu', 'Xiangtai Li'] | 2022-04-10 | null | null | null | null | ['part-level-panoptic-segmentation'] | ['computer-vision'] | [ 1.31115362e-01 2.08013281e-01 -1.69106528e-01 -5.10472298e-01
-9.02261972e-01 -4.86017644e-01 4.01725322e-01 -4.57844019e-01
-8.96799862e-02 2.94110030e-01 1.47416770e-01 -2.40142211e-01
2.28277713e-01 -6.34825170e-01 -9.87256587e-01 -5.98740876e-01
3.86101067e-01 5.60796916e-01 5.02622962e-01 -5.02136871... | [9.540616989135742, 0.2583373785018921] |
5965b15b-ceb2-456c-ab5d-47ab6fee8750 | data-summarization-via-bilevel-optimization | 2109.12534 | null | https://arxiv.org/abs/2109.12534v1 | https://arxiv.org/pdf/2109.12534v1.pdf | Data Summarization via Bilevel Optimization | The increasing availability of massive data sets poses a series of challenges for machine learning. Prominent among these is the need to learn models under hardware or human resource constraints. In such resource-constrained settings, a simple yet powerful approach is to operate on small subsets of the data. Coresets a... | ['Andreas Krause', 'Marco Tagliasacchi', 'Mojmír Mutný', 'Zalán Borsos'] | 2021-09-26 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.17327891e-01 -3.92753594e-02 -8.09047997e-01 -4.62014169e-01
-7.16970325e-01 -2.72891134e-01 4.95757647e-02 1.08482823e-01
-5.43979466e-01 6.64001226e-01 -1.15738235e-01 -1.33883357e-01
-5.42832196e-01 -6.15187585e-01 -8.89005303e-01 -6.82734430e-01
-1.69092566e-01 7.75636673e-01 -1.46430895e-01 2.43214890... | [8.41159439086914, 4.12514591217041] |
71489772-2f3d-4740-95f5-53c3514fb12e | applicaai-at-semeval-2020-task-11-on-roberta | 2005.07934 | null | https://arxiv.org/abs/2005.07934v2 | https://arxiv.org/pdf/2005.07934v2.pdf | ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them | This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task. The purpose of TC task was to identify an applied propaganda technique given propaganda text fragment. The goal of SI task was to find specific text... | ['Filip Graliński', 'Łukasz Borchmann', 'Dawid Jurkiewicz', 'Izabela Kosmala'] | 2020-05-16 | null | https://aclanthology.org/2020.semeval-1.187 | https://aclanthology.org/2020.semeval-1.187.pdf | semeval-2020 | ['propaganda-span-identification'] | ['natural-language-processing'] | [ 3.12548161e-01 2.10097402e-01 -1.13205798e-01 -1.54112056e-01
-6.88220084e-01 -4.66809154e-01 1.25257444e+00 2.54573345e-01
-4.38168883e-01 6.62982166e-01 4.11182195e-01 -5.81513941e-01
-9.06172693e-02 -7.83184409e-01 -2.31867388e-01 -6.38461649e-01
7.79134631e-02 5.99116802e-01 6.60899878e-02 -3.53389353... | [8.454607963562012, 10.767108917236328] |
8075ae2e-5524-4e47-935f-9e965e548b22 | mr4mr-mixed-reality-for-melody-reincarnation | 2209.07023 | null | https://arxiv.org/abs/2209.07023v1 | https://arxiv.org/pdf/2209.07023v1.pdf | MR4MR: Mixed Reality for Melody Reincarnation | There is a long history of an effort made to explore musical elements with the entities and spaces around us, such as musique concr\`ete and ambient music. In the context of computer music and digital art, interactive experiences that concentrate on the surrounding objects and physical spaces have also been designed. I... | ['Nao Tokui', 'Shoma Sawa', 'Keisuke Okazaki', 'Takumi Inoue', 'Ryuku Nobusue', 'Ryogo Ishino', 'Atsuya Kobayashi'] | 2022-09-15 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.28702432e-01 -1.05364099e-01 5.79507411e-01 1.56213209e-01
-5.44318974e-01 -6.18064165e-01 6.69493675e-01 -4.27919179e-01
1.94150373e-01 4.35338080e-01 6.04700208e-01 3.58688354e-01
1.04639810e-02 -9.38213825e-01 -3.26131970e-01 -3.82143915e-01
5.86081259e-02 -2.04131395e-01 1.91172078e-01 -6.28897309... | [16.03032112121582, 5.468379974365234] |
1d6d8213-8b86-4fec-ad85-9960df63d441 | efficient-data-driven-encoding-of-scene | 2103.02743 | null | https://arxiv.org/abs/2103.02743v1 | https://arxiv.org/pdf/2103.02743v1.pdf | Efficient data-driven encoding of scene motion using Eccentricity | This paper presents a novel approach of representing dynamic visual scenes with static maps generated from video/image streams. Such representation allows easy visual assessment of motion in dynamic environments. These maps are 2D matrices calculated recursively, in a pixel-wise manner, that is based on the recently in... | ['Dimitar Filev', 'Gint Puskorius', 'Mostafa Parchami', 'Enrique Corona', 'Bruno Costa'] | 2021-03-03 | null | null | null | null | ['video-description', 'intent-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 5.06344914e-01 -4.62884039e-01 2.01316625e-02 -1.09605610e-01
2.13222522e-02 -3.43072295e-01 7.52083719e-01 4.55806583e-01
-6.28846109e-01 3.67085069e-01 1.40911475e-01 -1.43716812e-01
-2.49857083e-01 -7.03032017e-01 -4.53297526e-01 -8.00825357e-01
-5.07843673e-01 2.34809723e-02 6.55708194e-01 1.45064950... | [8.208168029785156, 0.11335645616054535] |
358304e5-d4a5-4f66-99a8-668689c08fc1 | elda-using-edges-to-have-an-edge-on-semantic | 2211.08888 | null | https://arxiv.org/abs/2211.08888v1 | https://arxiv.org/pdf/2211.08888v1.pdf | ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA | Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such information and achieved remarkable success. Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead t... | ['Chun-Yi Lee', 'Yi-Chen Lo', 'Chia-Che Chang', 'Chen-Hao Chao', 'Bo-Wun Cheng', 'Hsu-Shen Liu', 'Li-Yuan Tsao', 'Jie-En Yao', 'Shan-Ya Yang', 'Huang-Ru Liao', 'Ting-Hsuan Liao'] | 2022-11-16 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.94797659e-01 5.65921143e-02 -4.57472235e-01 -4.49551076e-01
-9.81497109e-01 -5.67346811e-01 8.16642404e-01 2.61156052e-01
-4.65530485e-01 6.78181887e-01 4.45530638e-02 -1.78981915e-01
-4.26266119e-02 -7.48871088e-01 -4.84352618e-01 -6.01748884e-01
3.02014530e-01 4.90870208e-01 5.49730957e-01 -6.74337745... | [9.733539581298828, 1.4484225511550903] |
7cc8ef85-633e-4a3d-964c-f0fac2674ff7 | ae-textspotter-learning-visual-and-linguistic | 2008.00714 | null | https://arxiv.org/abs/2008.00714v5 | https://arxiv.org/pdf/2008.00714v5.pdf | AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting | Scene text spotting aims to detect and recognize the entire word or sentence with multiple characters in natural images. It is still challenging because ambiguity often occurs when the spacing between characters is large or the characters are evenly spread in multiple rows and columns, making many visually plausible gr... | ['Tong Lu', 'Chunhua Shen', 'Zhibo Yang', 'Ping Luo', 'Xiaozhong Ji', 'Wenhai Wang', 'Ding Liang', 'Xuebo Liu', 'Enze Xie'] | 2020-08-03 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2183_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590443.pdf | eccv-2020-8 | ['text-spotting'] | ['computer-vision'] | [ 2.06833750e-01 -2.63422906e-01 6.98453560e-02 -1.49991676e-01
-6.48551047e-01 -6.24024868e-01 5.58156848e-01 2.16288000e-01
-4.49782312e-01 4.66322541e-01 6.55655488e-02 -5.31599879e-01
3.78659487e-01 -4.51121211e-01 -6.19184554e-01 -5.53307235e-01
5.14697969e-01 3.79679173e-01 4.37603861e-01 -1.21872611... | [11.923871040344238, 2.236082077026367] |
288a0d9e-ae2f-4a21-8da5-1bdfd07e726a | lexis-an-optimization-framework-for | 1602.05561 | null | http://arxiv.org/abs/1602.05561v3 | http://arxiv.org/pdf/1602.05561v3.pdf | Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data | Data represented as strings abounds in biology, linguistics, document mining,
web search and many other fields. Such data often have a hierarchical
structure, either because they were artificially designed and composed in a
hierarchical manner or because there is an underlying evolutionary process that
creates repeated... | ['Payam Siyari', 'Constantine Dovrolis', 'Bistra Dilkina'] | 2016-02-17 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 9.16575074e-01 1.57861814e-01 -2.54837483e-01 -2.95259506e-01
-3.50319922e-01 -8.83239090e-01 3.58643681e-02 8.84400487e-01
-2.38271877e-02 6.98534369e-01 2.74494559e-01 -4.81592000e-01
-5.52277386e-01 -1.22113800e+00 -9.59283769e-01 -7.83272326e-01
-4.24930304e-01 6.36884987e-01 5.72361536e-02 -5.87799549... | [7.287760257720947, 5.615289211273193] |
51cc5f84-074f-44bb-9cd9-9cafc4b4162a | zero-shot-style-transfer-for-gesture | 2208.01917 | null | https://arxiv.org/abs/2208.01917v1 | https://arxiv.org/pdf/2208.01917v1.pdf | Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding | Modeling virtual agents with behavior style is one factor for personalizing human agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different speakers including those unseen during training. Our model performs z... | ['Nicolas Obin', 'Catherine Pelachaud', 'Michele Grimaldi', 'Mireille Fares'] | 2022-08-03 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 3.74199152e-01 3.35934788e-01 3.88777032e-02 -5.99655271e-01
-4.94174808e-01 -7.93388486e-01 1.10357046e+00 -7.59295702e-01
-4.07962918e-01 3.18297356e-01 6.01611137e-01 1.40545264e-01
5.77406049e-01 -4.20891404e-01 -7.33127236e-01 -7.70706296e-01
1.60910115e-01 9.52433050e-01 -7.72856735e-03 -5.08126199... | [5.615528106689453, -0.1146547868847847] |
86f7720f-0569-47c7-b067-221b97556bd4 | neural-networks-and-spelling-features-for | null | null | https://aclanthology.org/W17-5025 | https://aclanthology.org/W17-5025.pdf | Neural Networks and Spelling Features for Native Language Identification | We present the RUG-SU team{'}s submission at the Native Language Identification Shared Task 2017. We combine several approaches into an ensemble, based on spelling error features, a simple neural network using word representations, a deep residual network using word and character features, and a system based on a recur... | ['Barbara Plank', 'Robert {\\"O}stling', 'Gintar{\\.e} Grigonyt{\\.e}', 'Johannes Bjerva'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 2.04148605e-01 -1.39204264e-01 -3.24059755e-01 -2.97826733e-02
-1.02168179e+00 -6.64835632e-01 7.94624627e-01 -7.08886981e-02
-9.89551187e-01 6.25242829e-01 5.47495484e-01 -8.27821970e-01
1.07224248e-01 -2.25441054e-01 -3.81393284e-01 -2.60761201e-01
3.05252641e-01 5.10943532e-01 1.18895277e-01 -4.38650966... | [10.438088417053223, 10.529435157775879] |
321fed62-e942-4b4d-9486-cfded59862ac | clique-graph-matching-by-preserving-global | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Nie_Clique-Graph_Matching_by_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Nie_Clique-Graph_Matching_by_2015_CVPR_paper.pdf | Clique-Graph Matching by Preserving Global & Local Structure | This paper originally proposes the clique-graph and further presents a clique-graph matching method by preserving global and local structures. Especially, we formulate the objective function of clique-graph matching with respective to two latent variables, the clique information in the original graph and the pairwise c... | ['Yu-Ting Su', 'An-An Liu', 'Wei-Zhi Nie', 'Zan Gao'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['graph-similarity'] | ['graphs'] | [ 2.77161986e-01 4.98395443e-01 -2.37313539e-01 -3.04282248e-01
-8.57440412e-01 -3.44005495e-01 3.47909629e-01 2.74240345e-01
1.55321077e-01 5.38827002e-01 -4.99903709e-02 2.79655367e-01
-5.14040649e-01 -8.89597893e-01 -5.56542993e-01 -5.96231222e-01
-3.25312763e-01 5.72631061e-01 1.91400036e-01 1.88754722... | [7.362357139587402, 5.0211591720581055] |
5e02c106-31be-45e1-9cc9-8b4632ff0b09 | generating-followup-questions-for | 2002.12344 | null | https://arxiv.org/abs/2002.12344v1 | https://arxiv.org/pdf/2002.12344v1.pdf | Generating Followup Questions for Interpretable Multi-hop Question Answering | We propose a framework for answering open domain multi-hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretrained single-hop answer extractor. This framework makes each hop interpretable, and makes the retrieval associated with later hops as flexib... | ['Christopher Malon', 'Bing Bai'] | 2020-02-27 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 4.36769396e-01 1.20731413e+00 -5.44040315e-02 -4.33616072e-01
-1.62617624e+00 -8.89505267e-01 7.44237840e-01 1.38322309e-01
-1.12857431e-01 1.25018167e+00 7.07036436e-01 -5.71035683e-01
-2.72073984e-01 -1.35697174e+00 -9.38758731e-01 1.63802147e-01
3.76773089e-01 1.28564095e+00 7.36850262e-01 -9.97889221... | [11.31721305847168, 8.080440521240234] |
6e5ec2f8-f6cf-4718-8b63-5c1b05e73eb2 | a-crowd-annotated-spanish-corpus-for-humor | 1710.00477 | null | http://arxiv.org/abs/1710.00477v4 | http://arxiv.org/pdf/1710.00477v4.pdf | A Crowd-Annotated Spanish Corpus for Humor Analysis | Computational Humor involves several tasks, such as humor recognition, humor
generation, and humor scoring, for which it is useful to have human-curated
data. In this work we present a corpus of 27,000 tweets written in Spanish and
crowd-annotated by their humor value and funniness score, with about four
annotations pe... | ['Guillermo Moncecchi', 'Aiala Rosá', 'Luis Chiruzzo', 'Diego Garat', 'Santiago Castro'] | 2017-10-02 | a-crowd-annotated-spanish-corpus-for-humor-1 | https://aclanthology.org/W18-3502 | https://aclanthology.org/W18-3502.pdf | ws-2018-7 | ['humor-detection'] | ['natural-language-processing'] | [-6.22247756e-01 3.54723871e-01 -4.06647511e-02 1.10005289e-01
-1.93669885e-01 -5.66824317e-01 8.25301051e-01 6.01036370e-01
-3.97028416e-01 1.10510170e+00 8.33635569e-01 -9.46681127e-02
6.15780413e-01 -6.17701411e-01 3.34088318e-03 -3.61312389e-01
2.90726990e-01 4.87030298e-01 1.99857354e-01 -5.77298701... | [8.894294738769531, 11.009246826171875] |
b44a45f3-28d3-4b86-a198-7657961f640a | chameleon-plug-and-play-compositional | 2304.09842 | null | https://arxiv.org/abs/2304.09842v2 | https://arxiv.org/pdf/2304.09842v2.pdf | Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models | Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent limitations as they are incapable of accessing up-to-date information (stored on the Web or in task-specific knowledge bases), using extern... | ['Jianfeng Gao', 'Song-Chun Zhu', 'Ying Nian Wu', 'Kai-Wei Chang', 'Michel Galley', 'Hao Cheng', 'Baolin Peng', 'Pan Lu'] | 2023-04-19 | null | null | null | null | ['mathematical-reasoning', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [-1.46332771e-01 4.48048443e-01 -1.33698970e-01 2.97774673e-02
-8.62368464e-01 -7.49275208e-01 7.14107156e-01 -1.02525972e-01
-2.56625891e-01 4.63282585e-01 1.42282516e-01 -5.01610160e-01
-3.53369445e-01 -9.95110035e-01 -7.07363188e-01 -8.71188045e-02
2.35775188e-01 1.16991937e+00 3.57375681e-01 -5.47830939... | [9.010852813720703, 7.192659378051758] |
d23077b5-801a-4cc3-80a7-75478aa5b750 | a-layer-based-sequential-framework-for-scene | 1902.00671 | null | http://arxiv.org/abs/1902.00671v1 | http://arxiv.org/pdf/1902.00671v1.pdf | A Layer-Based Sequential Framework for Scene Generation with GANs | The visual world we sense, interpret and interact everyday is a complex
composition of interleaved physical entities. Therefore, it is a very
challenging task to generate vivid scenes of similar complexity using
computers. In this work, we present a scene generation framework based on
Generative Adversarial Networks (G... | ['Mehmet Ozgur Turkoglu', 'Berkay Kicanaoglu', 'William Thong', 'Luuk Spreeuwers'] | 2019-02-02 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 6.20210171e-01 1.32479191e-01 5.13973594e-01 -2.06036232e-02
-3.12772930e-01 -9.16384399e-01 1.00287771e+00 -3.20017576e-01
4.61010747e-02 8.31659257e-01 1.17528357e-01 -1.91102698e-01
3.82736832e-01 -1.07686937e+00 -8.56702924e-01 -6.70469522e-01
2.47164994e-01 2.31267422e-01 1.76587269e-01 -2.46542946... | [11.519639015197754, -0.47070378065109253] |
393c4d64-9d14-4a51-8832-fb44a724c0fd | deepnag-deep-non-adversarial-gesture | 2011.09149 | null | https://arxiv.org/abs/2011.09149v1 | https://arxiv.org/pdf/2011.09149v1.pdf | DeepNAG: Deep Non-Adversarial Gesture Generation | Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture synthesis has received inadequate attention. Further, GANs prohibitivel... | ['Joseph J. LaViola Jr', 'Eugene M. Taranta II', 'Mehran Maghoumi'] | 2020-11-18 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 3.77617896e-01 2.08408043e-01 -7.95019493e-02 -1.99089631e-01
-8.74688089e-01 -9.32952106e-01 9.44673061e-01 -6.48942232e-01
-4.34924006e-01 8.33990455e-01 2.21020788e-01 -3.34417045e-01
2.77664572e-01 -8.26107442e-01 -7.16833711e-01 -8.23171079e-01
1.40881419e-01 2.32122749e-01 -4.38727796e-01 -2.15629727... | [11.639032363891602, -0.2714634835720062] |
b3b7b4a9-3a11-4aa0-a36f-a6500cda8f2a | pwoc-3d-deep-occlusion-aware-end-to-end-scene | 1904.06116 | null | http://arxiv.org/abs/1904.06116v1 | http://arxiv.org/pdf/1904.06116v1.pdf | PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation | In the last few years, convolutional neural networks (CNNs) have demonstrated
increasing success at learning many computer vision tasks including dense
estimation problems such as optical flow and stereo matching. However, the
joint prediction of these tasks, called scene flow, has traditionally been
tackled using slow... | ['Oliver Wasenmüller', 'René Schuster', 'Rohan Saxena', 'Didier Stricker'] | 2019-04-12 | null | null | null | null | ['stereo-matching', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-9.50228646e-02 -2.98768818e-01 -2.43553832e-01 -4.60678935e-01
-2.72667319e-01 -2.40557730e-01 5.25400400e-01 -1.76740676e-01
-6.08159423e-01 6.85416698e-01 2.37224728e-01 -1.38709828e-01
3.89459878e-02 -5.27857006e-01 -8.36747050e-01 -4.82390732e-01
-1.09404489e-01 3.07456404e-01 1.97632253e-01 -8.82637277... | [8.678929328918457, -1.914023756980896] |
8b8d7ad0-879a-456c-9591-8ebae7e352bf | knowledge-driven-robot-program-synthesis-from | 2306.02739 | null | https://arxiv.org/abs/2306.02739v2 | https://arxiv.org/pdf/2306.02739v2.pdf | Knowledge-Driven Robot Program Synthesis from Human VR Demonstrations | Aging societies, labor shortages and increasing wage costs call for assistance robots capable of autonomously performing a wide array of real-world tasks. Such open-ended robotic manipulation requires not only powerful knowledge representations and reasoning (KR&R) algorithms, but also methods for humans to instruct ro... | ['Michael Beetz', 'Rainer Jäkel', 'Darko Katic', 'Andrei Haidu', 'Franklin Kenghagho Kenfack', 'Benjamin Alt'] | 2023-06-05 | null | null | null | null | ['code-generation', 'program-synthesis', 'common-sense-reasoning'] | ['computer-code', 'computer-code', 'reasoning'] | [-3.20528559e-02 4.96280640e-01 1.40669674e-01 -3.38512063e-01
-6.20724261e-02 -8.07430208e-01 4.84885871e-01 -1.87147886e-01
-1.19635843e-01 7.50080943e-01 -3.33398208e-02 -5.76468885e-01
-2.90514201e-01 -1.01279259e+00 -7.98985839e-01 1.11861818e-03
5.11750802e-02 7.93065667e-01 1.54674485e-01 -9.21503961... | [4.48160457611084, 0.8017279505729675] |
009e0f14-9cb5-43a8-85ef-54edb3677a15 | clustering-tree-structured-data-on-manifold | 1507.05532 | null | http://arxiv.org/abs/1507.05532v2 | http://arxiv.org/pdf/1507.05532v2.pdf | Clustering Tree-structured Data on Manifold | Tree-structured data usually contain both topological and geometrical
information, and are necessarily considered on manifold instead of Euclidean
space for appropriate data parameterization and analysis. In this study, we
propose a novel tree-structured data parameterization, called
Topology-Attribute matrix (T-A matr... | ['Na Lu', 'Hongyu Miao'] | 2015-07-20 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [ 1.95785612e-01 -1.21419981e-01 -6.80304875e-05 -3.25708568e-01
-1.32143199e-01 -5.11784673e-01 2.09175855e-01 1.86613835e-02
-2.17546746e-01 1.49033576e-01 1.48671851e-01 -3.20127606e-01
-8.79790783e-01 -4.17026252e-01 -1.42377630e-01 -1.00465417e+00
-2.80929595e-01 2.93530285e-01 -3.04462537e-02 1.83168277... | [7.966707706451416, 4.559545993804932] |
e876b44f-2231-4a65-a4e4-4750e4886280 | the-reactor-a-fast-and-sample-efficient-actor | 1704.04651 | null | http://arxiv.org/abs/1704.04651v2 | http://arxiv.org/pdf/1704.04651v2.pdf | The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning | In this work we present a new agent architecture, called Reactor, which
combines multiple algorithmic and architectural contributions to produce an
agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al.,
2016) and Categorical DQN (Bellemare et al., 2017), while giving better
run-time performance ... | ['Remi Munos', 'Will Dabney', 'Audrunas Gruslys', 'Mohammad Gheshlaghi Azar', 'Marc Bellemare', 'Bilal Piot'] | 2017-04-15 | the-reactor-a-fast-and-sample-efficient-actor-1 | https://openreview.net/forum?id=rkHVZWZAZ | https://openreview.net/pdf?id=rkHVZWZAZ | iclr-2018-1 | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.58516746e-02 -1.16388761e-01 -5.40128529e-01 -2.28169665e-01
-9.94840860e-01 -7.29817033e-01 9.97744620e-01 1.17478855e-01
-1.02928805e+00 9.87753510e-01 4.04684484e-01 -3.72152716e-01
-2.29739860e-01 -6.75248504e-01 -7.55015433e-01 -9.35731113e-01
-4.71615821e-01 5.88460267e-01 6.30412936e-01 -4.27761763... | [4.041455268859863, 2.0666840076446533] |
3e2ab14c-4135-494c-9a59-9f6b1a0980c6 | topics-in-contextualised-attention-embeddings | 2301.04339 | null | https://arxiv.org/abs/2301.04339v1 | https://arxiv.org/pdf/2301.04339v1.pdf | Topics in Contextualised Attention Embeddings | Contextualised word vectors obtained via pre-trained language models encode a variety of knowledge that has already been exploited in applications. Complementary to these language models are probabilistic topic models that learn thematic patterns from the text. Recent work has demonstrated that conducting clustering on... | ['Shoaib Jameel', 'Alba Garcia Seco de Herrera', 'Mozhgan Talebpour'] | 2023-01-11 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.03450008e-01 4.65450257e-01 -5.21203995e-01 -4.45385903e-01
-6.29032731e-01 -4.53876823e-01 1.13740277e+00 4.45697278e-01
-4.16287839e-01 2.49205291e-01 9.35319304e-01 -3.98191571e-01
-7.23656788e-02 -9.65517998e-01 -4.56861168e-01 -7.42401719e-01
-2.70732224e-01 8.10053945e-01 2.47769222e-01 9.59035754... | [10.389795303344727, 6.973628997802734] |
90fb3c25-0100-4c25-b766-449a281f345c | ezcoref-towards-unifying-annotation | 2210.07188 | null | https://arxiv.org/abs/2210.07188v1 | https://arxiv.org/pdf/2210.07188v1.pdf | ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution | Large-scale, high-quality corpora are critical for advancing research in coreference resolution. However, existing datasets vary in their definition of coreferences and have been collected via complex and lengthy guidelines that are curated for linguistic experts. These concerns have sparked a growing interest among re... | ["Brendan O'Connor", 'Mohit Iyyer', 'Luke Yeh', 'Jack Merullo', 'Kalpesh Krishna', 'Wenlong Zhao', 'Marzena Karpinska', 'Ankita Gupta'] | 2022-10-13 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.3529116e-02 3.7873384e-01 -1.4807767e-01 -3.9694321e-01
-1.3313395e+00 -1.3587763e+00 5.1815784e-01 3.9698246e-01
-5.6401461e-01 9.5843065e-01 8.5671979e-01 -3.1739664e-01
-2.0116642e-01 -4.8119009e-02 -4.5789739e-01 -3.5326114e-01
7.0182759e-01 9.1319972e-01 4.0307438e-01 -4.5474681e-01
2.8592470e-01... | [9.36636734008789, 9.349306106567383] |
c12653de-e131-445b-9a66-916279433008 | interactive-natural-language-based-person | 2002.08434 | null | https://arxiv.org/abs/2002.08434v1 | https://arxiv.org/pdf/2002.08434v1.pdf | Interactive Natural Language-based Person Search | In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions. Specifically, we study how to systematically design an algorithm to effectively acquire descriptions from humans. An algorithm is proposed by adapting models, used for visual and language under... | ['Wei-Lun Chao', 'Vikram Shree', 'Mark Campbell'] | 2020-02-19 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.57032624e-01 5.27092576e-01 3.32941979e-01 -3.94463211e-01
-3.77927423e-01 -4.38203752e-01 7.69559503e-01 3.02505612e-01
-6.19298458e-01 5.34143507e-01 6.10969588e-02 -2.01535691e-03
-3.03234577e-01 -6.04143441e-01 -3.26713592e-01 -2.75486648e-01
5.28381616e-02 1.24194741e+00 3.60287100e-01 -2.89063752... | [4.617668628692627, 0.8738678693771362] |
03271607-40ce-4e38-9c2a-6ed6ad48fe8c | differentially-private-hierarchical | 2302.00037 | null | https://arxiv.org/abs/2302.00037v2 | https://arxiv.org/pdf/2302.00037v2.pdf | Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees | Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approximation algorithms for hierarchical clustering under the rigorous framework introduced by (Dasgupta, 2016). We show strong lower bounds for t... | ['Vahab Mirrokni', 'Vincent Cohen-Addad', 'Mohammad Mahdian', 'Alessandro Epasto', 'Jacob Imola'] | 2023-01-31 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.17238089e-01 2.30589151e-01 -1.86646327e-01 -1.09913163e-01
-1.04768908e+00 -7.92157888e-01 -2.80060112e-01 5.82769990e-01
-5.14288008e-01 5.00955760e-01 -2.08215311e-01 -3.18743438e-01
-2.85876274e-01 -9.90635276e-01 -1.08093095e+00 -1.25827944e+00
-4.91245449e-01 6.04284406e-01 1.01040035e-01 3.38747889... | [6.704582691192627, 5.098775386810303] |
bf86c80d-5d36-472c-ba05-0b40aacae988 | fovea-foveated-image-magnification-for | 2108.12102 | null | https://arxiv.org/abs/2108.12102v2 | https://arxiv.org/pdf/2108.12102v2.pdf | FOVEA: Foveated Image Magnification for Autonomous Navigation | Efficient processing of high-res video streams is safety-critical for many robotics applications such as autonomous driving. To maintain real-time performance, many practical systems downsample the video stream. But this can hurt downstream tasks such as (small) object detection. Instead, we take inspiration from biolo... | ['Deva Ramanan', 'Nicolas Cebron', 'Mengtian Li', 'Chittesh Thavamani'] | 2021-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Thavamani_FOVEA_Foveated_Image_Magnification_for_Autonomous_Navigation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Thavamani_FOVEA_Foveated_Image_Magnification_for_Autonomous_Navigation_ICCV_2021_paper.pdf | iccv-2021-1 | ['real-time-object-detection', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.98685485e-01 8.42692032e-02 4.53028753e-02 -3.08582872e-01
-4.23740536e-01 -2.94480592e-01 4.09799367e-01 1.39567316e-01
-7.91490197e-01 4.19668853e-01 -3.43417712e-02 -2.18029141e-01
4.84723330e-01 -8.61625969e-01 -1.14592266e+00 -5.02005816e-01
-1.09406695e-01 -2.28902251e-01 1.00684190e+00 -2.96989948... | [9.583905220031738, -0.47187942266464233] |
db374ece-d588-4fe6-b2e1-9020a184b6e9 | scalable-graph-neural-networks-via | 2010.15421 | null | https://arxiv.org/abs/2010.15421v3 | https://arxiv.org/pdf/2010.15421v3.pdf | Scalable Graph Neural Networks via Bidirectional Propagation | Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or "layer-wise sampling" techniques to reduce training time. However, these methods still suffer from ... | ['Ji-Rong Wen', 'Xiaoyong Du', 'Ye Yuan', 'Yaliang Li', 'Bolin Ding', 'Zhewei Wei', 'Ming Chen'] | 2020-10-29 | null | http://proceedings.neurips.cc/paper/2020/hash/a7789ef88d599b8df86bbee632b2994d-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a7789ef88d599b8df86bbee632b2994d-Paper.pdf | neurips-2020-12 | ['graph-sampling'] | ['graphs'] | [-1.91213921e-01 2.01124221e-01 -3.92663419e-01 -2.24403456e-01
-4.33746189e-01 -3.19851011e-01 3.00034732e-01 2.30983570e-01
-2.87422806e-01 6.63298488e-01 -3.33996356e-01 -6.80236757e-01
-2.00678438e-01 -1.18727183e+00 -9.79556680e-01 -4.71466005e-01
-7.19200134e-01 4.67111588e-01 2.60956287e-01 -8.41371194... | [6.997605323791504, 6.007991790771484] |
33ca33f7-a7fb-4372-8ec1-26f274efc917 | ensemble-model-with-batch-spectral | 2006.04323 | null | https://arxiv.org/abs/2006.04323v2 | https://arxiv.org/pdf/2006.04323v2.pdf | Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data | In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge. We build a multi-branch ensemble framework by using diverse feature transformation matrices, while deploying batch spect... | ['Yuhong Guo', 'Jieping Ye', 'Zhen Zhao', 'Bingyu Liu'] | 2020-06-08 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 1.41563594e-01 -3.76313776e-01 -3.21751326e-01 -4.52154100e-01
-9.01791871e-01 -4.53455865e-01 5.62137425e-01 -2.65578091e-01
-2.66474932e-01 7.44875133e-01 3.06341439e-01 5.78029361e-03
-2.16222093e-01 -4.02266026e-01 -4.06269610e-01 -6.09839618e-01
1.52731940e-01 3.80497903e-01 1.30855739e-01 -3.92058074... | [10.014922142028809, 3.03242826461792] |
cc98dc40-1320-4db5-9053-db4b220114da | one-shot-video-inpainting | 2302.14362 | null | https://arxiv.org/abs/2302.14362v1 | https://arxiv.org/pdf/2302.14362v1.pdf | One-Shot Video Inpainting | Recently, removing objects from videos and filling in the erased regions using deep video inpainting (VI) algorithms has attracted considerable attention. Usually, a video sequence and object segmentation masks for all frames are required as the input for this task. However, in real-world applications, providing segmen... | ['Sangyoun Lee', 'Suhwan Cho', 'Sangjin Lee'] | 2023-02-28 | null | null | null | null | ['video-inpainting', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.53685975e-01 8.82351473e-02 -2.04235032e-01 -2.66661972e-01
-5.92520237e-01 -4.76940453e-01 2.18456343e-01 -2.88737267e-01
-3.70723009e-01 5.74092507e-01 -7.67990872e-02 -5.42516671e-02
4.22646224e-01 -5.79498827e-01 -9.84223187e-01 -4.85957295e-01
4.20864165e-01 2.06135616e-01 6.94231510e-01 -2.95912251... | [9.369644165039062, -0.23502781987190247] |
52957f4e-a624-4387-ae53-bb13655805c0 | constructing-high-frequency-economic | 2303.01863 | null | https://arxiv.org/abs/2303.01863v1 | https://arxiv.org/pdf/2303.01863v1.pdf | Constructing High Frequency Economic Indicators by Imputation | Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high ... | ['Susannah Scanlan', 'Serena Ng'] | 2023-03-03 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [-5.58183268e-02 -6.39097989e-02 -4.90455776e-01 -4.90591079e-02
-7.06597269e-01 -7.62265205e-01 8.95372629e-01 9.18892026e-02
-6.46306276e-01 1.04013801e+00 9.71484900e-01 -5.95843315e-01
-3.82613838e-01 -9.05955434e-01 -5.25530040e-01 -6.61181569e-01
4.85095754e-02 2.18180954e-01 -6.62715733e-01 -4.27372232... | [6.029023170471191, 4.037415981292725] |
eaee78d4-f0e1-4e75-b165-9eb3f6b17d98 | fakeswarm-improving-fake-news-detection-with | 2305.19194 | null | https://arxiv.org/abs/2305.19194v1 | https://arxiv.org/pdf/2305.19194v1.pdf | FakeSwarm: Improving Fake News Detection with Swarming Characteristics | The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. To ex... | ['Xuesong Ye', 'Jun Wu'] | 2023-05-30 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-2.33961463e-01 -7.62075558e-02 -1.50464147e-01 1.59129947e-01
-1.27202898e-01 -8.81648123e-01 1.26458383e+00 5.43931603e-01
-1.38593405e-01 4.98136044e-01 4.53948855e-01 -3.31904858e-01
-3.97678912e-02 -8.59227717e-01 -6.33898735e-01 -4.51629072e-01
-2.44265184e-01 4.34449881e-01 2.76739150e-01 -7.65332103... | [8.131845474243164, 10.267253875732422] |
0cf6868d-4fa9-4c3e-9035-f2dadb48c4b5 | asl-skeleton3d-and-asl-phono-two-novel | 2201.02065 | null | https://arxiv.org/abs/2201.02065v1 | https://arxiv.org/pdf/2201.02065v1.pdf | ASL-Skeleton3D and ASL-Phono: Two Novel Datasets for the American Sign Language | Sign language is an essential resource enabling access to communication and proper socioemotional development for individuals suffering from disabling hearing loss. As this population is expected to reach 700 million by 2050, the importance of the language becomes even more essential as it plays a critical role to ensu... | ['Cleber Zanchettin', 'Cleison Correia de Amorim'] | 2022-01-06 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 2.55017728e-02 -4.55104373e-02 -1.72801644e-01 -1.12085775e-01
-5.49782097e-01 -2.02154487e-01 4.59934980e-01 -9.46649164e-02
-7.00192392e-01 6.50289059e-01 7.64222145e-01 -1.10205457e-01
-2.22006887e-01 -5.58714390e-01 7.49990046e-02 -6.57603323e-01
2.19816014e-01 3.10631782e-01 -1.48469564e-02 -4.46844101... | [9.035935401916504, -6.342881679534912] |
aa23a1d8-89e5-473e-9bcc-a0d3879b0776 | center-feature-fusion-selective-multi-sensor | 2209.12880 | null | https://arxiv.org/abs/2209.12880v2 | https://arxiv.org/pdf/2209.12880v2.pdf | Center Feature Fusion: Selective Multi-Sensor Fusion of Center-based Objects | Leveraging multi-modal fusion, especially between camera and LiDAR, has become essential for building accurate and robust 3D object detection systems for autonomous vehicles. Until recently, point decorating approaches, in which point clouds are augmented with camera features, have been the dominant approach in the fie... | ['Ming C. Wu', 'Masayoshi Tomizuka', 'Wei Zhan', 'Yiyang Zhou', 'Philip Jacobson'] | 2022-09-26 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [ 1.86817031e-02 -6.25772715e-01 4.38494943e-02 -3.76226634e-01
-8.50325167e-01 -9.70367610e-01 7.75769770e-01 8.18063319e-02
-4.96257126e-01 2.59224921e-01 -2.45389357e-01 -6.76532760e-02
3.28459859e-01 -1.01764703e+00 -9.04500484e-01 -6.18031979e-01
4.65344846e-01 2.51120836e-01 8.61049891e-01 -1.40008181... | [7.721103191375732, -2.4248061180114746] |
a9e90d2d-cfa7-4da1-a015-d5ac0b7a51cc | a-functional-regression-approach-to-facial | 1612.02203 | null | http://arxiv.org/abs/1612.02203v2 | http://arxiv.org/pdf/1612.02203v2.pdf | A Functional Regression approach to Facial Landmark Tracking | Linear regression is a fundamental building block in many face detection and
tracking algorithms, typically used to predict shape displacements from image
features through a linear mapping. This paper presents a Functional Regression
solution to the least squares problem, which we coin Continuous Regression,
resulting ... | ['Fernando de la Torre', 'Enrique Sánchez-Lozano', 'Michel Valstar', 'Georgios Tzimiropoulos', 'Brais Martinez'] | 2016-12-07 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 6.47248998e-02 -1.85581505e-01 -2.28693619e-01 -1.27828270e-01
-8.82481098e-01 -3.24939996e-01 5.48373640e-01 -5.64982593e-01
-2.06751168e-01 5.13640881e-01 -2.94025630e-01 -3.40561807e-01
-2.90054344e-02 -1.04105525e-01 -1.04606700e+00 -6.97547615e-01
-3.22030187e-01 3.86973798e-01 2.22185656e-01 -9.57794785... | [13.459795951843262, 0.18885652720928192] |
3b0f7bc0-019e-4258-9dba-c73f083e167a | stan-stage-adaptive-network-for-multi-task | 2306.12232 | null | https://arxiv.org/abs/2306.12232v1 | https://arxiv.org/pdf/2306.12232v1.pdf | STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation | Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challenging, multiple evaluation metrics are often employed. Existing methods generally formulate the optimization of these evaluation metrics as a ... | ['Suhang Wang', 'Xuanji Xiao', 'Wenhao Zheng', 'Wanda Li'] | 2023-06-21 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 1.53179482e-01 -6.94157720e-01 -7.12027192e-01 -4.95781928e-01
-4.87891704e-01 -4.09254253e-01 1.77007914e-01 1.73852280e-01
-3.94859999e-01 3.53210419e-01 2.63151079e-01 -4.23951060e-01
-6.65981710e-01 -3.48164737e-01 -2.18112901e-01 -4.62854117e-01
6.49579838e-02 3.84142667e-01 9.28983688e-02 -2.96321929... | [10.13988971710205, 5.610818386077881] |
da6863d2-169a-4f41-80d9-f23c8a5d97f4 | minimax-risk-classifiers-with-0-1-loss | 2201.06487 | null | https://arxiv.org/abs/2201.06487v5 | https://arxiv.org/pdf/2201.06487v5.pdf | Minimax risk classifiers with 0-1 loss | Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability). Conventional methods enable tractable learning and provide out-of-sample generalization by using surrogate losses instead of the 0-1 loss and considering specific families of rules ... | ['Peter Grünwald', 'Mauricio Romero', 'Santiago Mazuelas'] | 2022-01-17 | null | null | null | null | ['classification'] | ['methodology'] | [ 9.73262936e-02 5.31516790e-01 -7.52759457e-01 -7.73183346e-01
-1.35366738e+00 -5.22012413e-01 2.51749784e-01 4.78569478e-01
-5.09931386e-01 1.17327845e+00 -7.02859104e-01 -4.59397107e-01
-7.21741140e-01 -9.01147902e-01 -1.01927924e+00 -7.19413042e-01
-5.20658731e-01 4.57893968e-01 8.49943012e-02 3.74955326... | [7.998234272003174, 4.198162078857422] |
571a2f57-d036-4637-b0dc-6dc6b2f52838 | dna-teq-an-adaptive-exponential-quantization | 2306.16430 | null | https://arxiv.org/abs/2306.16430v1 | https://arxiv.org/pdf/2306.16430v1.pdf | DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference | Quantization is commonly used in Deep Neural Networks (DNNs) to reduce the storage and computational complexity by decreasing the arithmetical precision of activations and weights, a.k.a. tensors. Efficient hardware architectures employ linear quantization to enable the deployment of recent DNNs onto embedded systems a... | ['Antonio González', 'Marc Riera', 'Bahareh Khabbazan'] | 2023-06-28 | null | null | null | null | ['quantization'] | ['methodology'] | [ 7.12323980e-03 -2.06407338e-01 -3.95148620e-02 -4.62514251e-01
-3.68425965e-01 -3.43703091e-01 1.89491555e-01 2.82691509e-01
-1.14260495e+00 4.83604014e-01 -2.75856525e-01 -5.51545560e-01
4.36538495e-02 -9.70951259e-01 -7.42939711e-01 -7.97830522e-01
-4.00010403e-03 1.50779530e-01 5.19948006e-01 5.47959178... | [8.543625831604004, 3.0354163646698] |
3041b162-efe5-49bc-a6e4-abd967290c34 | procontext-exploring-progressive-context | 2210.15511 | null | https://arxiv.org/abs/2210.15511v4 | https://arxiv.org/pdf/2210.15511v4.pdf | ProContEXT: Exploring Progressive Context Transformer for Tracking | Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it cannot account for changes in object appearance between frames. To this end, we revamped the tracking framework with Progressive Context E... | ['Xuansong Xie', 'Yifeng Geng', 'Wangmeng Xiang', 'Xu Bao', 'Bin Luo', 'Chenyang Li', 'Jun-Yan He', 'Zhi-Qi Cheng', 'Jin-Peng Lan'] | 2022-10-27 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-1.10372566e-01 -5.46678007e-01 -4.37357187e-01 -1.99867919e-01
-2.83596992e-01 -7.01626360e-01 6.54737830e-01 -1.62071347e-01
-3.80721092e-01 4.89395231e-01 2.44985506e-01 -1.75122947e-01
2.94580221e-01 -3.20412904e-01 -7.12106645e-01 -4.59404558e-01
-1.98998541e-01 6.12843968e-03 1.07827353e+00 1.93989798... | [6.304574489593506, -2.0792808532714844] |
50e98043-6c83-44ae-ae28-042390b1bbfd | sim-to-real-transfer-for-miniature-autonomous | 2011.05617 | null | https://arxiv.org/abs/2011.05617v1 | https://arxiv.org/pdf/2011.05617v1.pdf | Sim-To-Real Transfer for Miniature Autonomous Car Racing | Sim-to-real, a term that describes where a model is trained in a simulator then transferred to the real world, is a technique that enables faster deep reinforcement learning (DRL) training. However, differences between the simulator and the real world often cause the model to perform poorly in the real world. Domain ra... | ['I-Chen Wu', 'Yuan-Hao Chen', 'Jin-Bo Huang', 'Ting-Han Wei', 'Yeong-Jia Roger Chu'] | 2020-11-11 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-6.37848228e-02 3.95760238e-02 -1.73557267e-01 -1.92292854e-01
-7.26187408e-01 -8.89204979e-01 2.55346000e-01 -3.05849351e-02
-5.26534677e-01 8.38212013e-01 -4.83583927e-01 -8.26241374e-01
1.24042109e-01 -9.59133148e-01 -1.28864896e+00 -3.90551865e-01
7.27662668e-02 3.38378757e-01 4.53478903e-01 -6.15251660... | [4.840635299682617, 1.272264838218689] |
bdffecfe-5044-4a6f-9363-4e954b426360 | accurate-3d-facial-geometry-prediction-by | 2104.08403 | null | https://arxiv.org/abs/2104.08403v1 | https://arxiv.org/pdf/2104.08403v1.pdf | Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network | This work focuses on complete 3D facial geometry prediction, including 3D facial alignment via 3D face modeling and face orientation estimation using the proposed multi-task, multi-modal, and multi-representation landmark refinement network (M$^3$-LRN). Our focus is on the important facial attributes, 3D landmarks, and... | ['Ulrich Neumann', 'Qiangeng Xu', 'Cho-Ying Wu'] | 2021-04-16 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [-2.33890638e-01 3.22997510e-01 -6.15412556e-02 -5.76265633e-01
-9.62852180e-01 -2.09330454e-01 1.42729536e-01 -4.06785578e-01
-3.03207114e-02 7.73176625e-02 2.87805021e-01 5.36238179e-02
-1.60493925e-01 -4.29086626e-01 -7.78240621e-01 -4.17746216e-01
-3.73533010e-01 5.67026317e-01 -4.62494999e-01 -2.65298605... | [13.318603515625, 0.1651419848203659] |
527a7cbe-9561-471e-8710-4a69ab96c3a3 | a-plug-and-play-priors-framework-for | 2012.13074 | null | https://arxiv.org/abs/2012.13074v3 | https://arxiv.org/pdf/2012.13074v3.pdf | A Plug-and-Play Priors Framework for Hyperspectral Unmixing | Spectral unmixing is a widely used technique in hyperspectral image processing and analysis. It aims to separate mixed pixels into the component materials and their corresponding abundances. Early solutions to spectral unmixing are performed independently on each pixel. Nowadays, investigating proper priors into the un... | ['Wei Chen', 'Jie Chen', 'Xiuheng Wang', 'Min Zhao'] | 2020-12-24 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 7.93213129e-01 -5.44232905e-01 7.39304423e-02 7.23096728e-02
-4.15074527e-01 -4.05200094e-01 4.42326993e-01 -2.15115711e-01
-3.00217986e-01 7.14274704e-01 -4.05535363e-02 -1.34391516e-01
-3.73542905e-01 -7.37203419e-01 -3.56841981e-01 -1.42695868e+00
5.46896219e-01 1.24912463e-01 -3.43505740e-01 -2.75505781... | [10.10165786743164, -2.0761470794677734] |
c8d2c5f6-964c-4ac4-add4-4c4c4627f824 | towards-carbon-neutral-edge-computing | 2304.11374 | null | https://arxiv.org/abs/2304.11374v1 | https://arxiv.org/pdf/2304.11374v1.pdf | Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets | Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs. Besides, many governments are launching carbon emission rights (CER) for operator... | ['Xu Chen', 'Xiaoxi Zhang', 'Zhi Zhou', 'Huirong Ma'] | 2023-04-22 | null | null | null | null | ['edge-computing'] | ['time-series'] | [ 2.44174123e-01 8.37868527e-02 -4.29386914e-01 3.77890207e-02
-6.59828067e-01 -5.10781527e-01 -1.95124701e-01 -1.42912254e-01
-3.31951052e-01 1.00331628e+00 -4.94850546e-01 -6.06496453e-01
-5.05700707e-01 -6.22007787e-01 -7.84591794e-01 -8.72995794e-01
-4.99233156e-02 6.03038192e-01 -4.17880625e-01 3.07925850... | [5.818693161010742, 1.814211130142212] |
2c670ef7-4b1b-4bb9-a448-3c1c19100cf0 | gesture-recognition-in-robotic-surgery-a | 2102.00027 | null | https://arxiv.org/abs/2102.00027v1 | https://arxiv.org/pdf/2102.00027v1.pdf | Gesture Recognition in Robotic Surgery: a Review | Objective: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research direct... | ['Danail Stoyanov', 'Matthew J. Clarkson', 'Beatrice van Amsterdam'] | 2021-01-29 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 2.58650392e-01 -1.07391894e-01 -9.97515976e-01 -2.96056122e-01
-9.60793495e-01 -5.53397179e-01 3.73173058e-01 1.29582956e-01
-8.12582254e-01 3.04616779e-01 6.54336989e-01 -2.75337040e-01
-7.41193652e-01 1.32217675e-01 -3.03645153e-02 -1.11841667e+00
-3.96483153e-01 4.75695282e-01 6.02418110e-02 1.57402307... | [14.04604721069336, -3.397076368331909] |
42aee254-c71b-4151-baaf-aff10cb5dff4 | fine-grained-representation-learning-and | 1808.04505 | null | http://arxiv.org/abs/1808.04505v1 | http://arxiv.org/pdf/1808.04505v1.pdf | Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding | Object categories inherently form a hierarchy with different levels of
concept abstraction, especially for fine-grained categories. For example, birds
(Aves) can be categorized according to a four-level hierarchy of order, family,
genus, and species. This hierarchy encodes rich correlations among various
categories acr... | ['Wenxi Wu', 'Liang Lin', 'Yuefang Gao', 'Xiaonan Luo', 'Tianshui Chen', 'Le Dong'] | 2018-08-14 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [-5.29665835e-02 -2.78521895e-01 -2.12313652e-01 -7.29910553e-01
1.79616407e-01 -4.97026891e-01 2.76409179e-01 3.03284734e-01
-3.14409256e-01 3.40150625e-01 4.68778312e-01 1.33846283e-01
-2.86533892e-01 -1.03036010e+00 -3.22343618e-01 -5.22706866e-01
-1.44568428e-01 1.56079382e-01 4.89374816e-01 -3.36418711... | [9.673028945922852, 2.060192346572876] |
b616b533-52bc-453c-9b8c-a817ee959373 | unifying-neural-learning-and-symbolic | 2004.13577 | null | https://arxiv.org/abs/2004.13577v1 | https://arxiv.org/pdf/2004.13577v1.pdf | Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation | Automated medical report generation in spine radiology, i.e., given spinal medical images and directly create radiologist-level diagnosis reports to support clinical decision making, is a novel yet fundamental study in the domain of artificial intelligence in healthcare. However, it is incredibly challenging because it... | ['Benzheng Wei', 'Zhongyi Han', 'Yilong Yin', 'Shuo Li'] | 2020-04-28 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 5.80758691e-01 9.80286896e-01 -1.76002875e-01 -2.14240640e-01
-8.09147179e-01 -3.00560117e-01 5.28160214e-01 3.12639862e-01
1.91625252e-01 7.23604798e-01 3.16874772e-01 -7.70401895e-01
-2.54639059e-01 -1.09813631e+00 -9.08675671e-01 -5.75699210e-01
9.87565368e-02 7.68162847e-01 4.25560512e-02 -1.35801420... | [15.026083946228027, -1.4274706840515137] |
35c9b6a6-1ddc-41cf-83d0-c3d52d9b0aed | out-of-distribution-detection-with-energy | 2302.12002 | null | https://arxiv.org/abs/2302.12002v2 | https://arxiv.org/pdf/2302.12002v2.pdf | Master's Thesis: Out-of-distribution Detection with Energy-based Models | Today, deep learning is increasingly applied in security-critical situations such as autonomous driving and medical diagnosis. Despite its success, the behavior and robustness of deep networks are not fully understood yet, posing a significant risk. In particular, researchers recently found that neural networks are ove... | ['Sven Elflein'] | 2023-01-28 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 5.15034422e-02 2.36555219e-01 -2.16795560e-02 -2.07116246e-01
-5.14991581e-01 -4.14973617e-01 4.93865609e-01 2.26065382e-01
-4.83344138e-01 8.40191066e-01 -2.46162310e-01 -4.99995142e-01
-1.89966023e-01 -8.87102783e-01 -9.83756065e-01 -1.01612663e+00
1.16038471e-02 4.09386396e-01 2.64414430e-01 2.47942865... | [7.585549354553223, 3.6221511363983154] |
8a3e3f74-7f10-42ea-a30a-c4736be9c947 | model-based-monitoring-and-state-estimation | 2305.00252 | null | https://arxiv.org/abs/2305.00252v1 | https://arxiv.org/pdf/2305.00252v1.pdf | Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter | A digital twin (DT) monitors states of the physical twin (PT) counterpart and provides a number of benefits such as advanced visualizations, fault detection capabilities, and reduced maintenance cost. It is the ability to be able to detect the states inside the DT that enable such benefits. In order to estimate the des... | ['Peter Gorm Larsen', 'Cláudio Gomes', 'Hao Feng'] | 2023-04-29 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.13679200e-01 -1.52772695e-01 2.86125720e-01 2.93565065e-01
3.59466672e-01 -3.77685249e-01 2.51743168e-01 1.94599037e-03
3.52766126e-01 7.00120747e-01 -6.69379056e-01 -5.09641469e-01
-1.98065504e-01 -7.00257003e-01 -5.07329583e-01 -7.69514441e-01
-3.93538445e-01 -4.64989960e-01 5.76337218e-01 6.90228716... | [6.483114719390869, 2.489853858947754] |
9514edcf-a530-495e-911e-c7f215c9622d | the-whole-is-greater-than-the-sum-of-its-3 | 2305.07855 | null | https://arxiv.org/abs/2305.07855v1 | https://arxiv.org/pdf/2305.07855v1.pdf | The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation | This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) without increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, ... | ['Yuki Mitsufuji', 'Shusuke Takahashi', 'Stefan Uhlich', 'Naoya Takahashi', 'Ryosuke Sawata'] | 2023-05-13 | null | null | null | null | ['music-source-separation'] | ['music'] | [-1.50093019e-01 -2.10427731e-01 1.67237431e-01 4.78172041e-02
-8.00030410e-01 -6.12128913e-01 3.18187982e-01 -2.54869133e-01
-4.99961585e-01 7.78540909e-01 4.92028892e-02 -1.69311136e-01
-6.19019687e-01 -4.92329478e-01 -7.04847217e-01 -8.31620336e-01
-1.60155222e-01 2.44223684e-01 1.53481558e-01 -1.63314655... | [15.468769073486328, 5.468409538269043] |
28d0e110-9383-4e12-9aa4-b41c98c0409f | exploring-the-robustness-of-distributional-1 | null | null | https://openreview.net/forum?id=z2zmSDKONK | https://openreview.net/pdf?id=z2zmSDKONK | Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations | In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning~(RL), a class of state-of-the-art m... | ['Linglong Kong', 'Shangling Jui', 'Hengshuai Yao', 'Yingnan Zhao', 'Yi Liu', 'Ke Sun'] | 2021-09-29 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-7.74977803e-02 8.70564431e-02 -7.34805241e-02 -1.07998222e-01
-1.13889968e+00 -8.92659783e-01 4.48559076e-01 -8.45232308e-02
-7.66184092e-01 9.76410568e-01 -3.89526375e-02 -7.35235989e-01
-3.68414432e-01 -6.19087815e-01 -8.14729512e-01 -1.28345609e+00
-3.82897198e-01 2.71854281e-01 -2.71762572e-02 -2.59958468... | [4.269867897033691, 2.5972611904144287] |
c09bff26-7e79-43b6-885f-9a20bd28ebfe | fairness-in-streaming-submodular-maximization-1 | 2305.15118 | null | https://arxiv.org/abs/2305.15118v1 | https://arxiv.org/pdf/2305.15118v1.pdf | Fairness in Streaming Submodular Maximization over a Matroid Constraint | Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developin... | ['Jakub Tarnawski', 'Jakab Tardos', 'Ashkan Norouzi-Fard', 'Federico Fusco', 'Marwa El Halabi'] | 2023-05-24 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 3.20415229e-01 2.97592372e-01 -7.90764570e-01 -8.20712864e-01
-7.01524675e-01 -5.35192788e-01 2.13347599e-01 5.67001224e-01
-4.95699197e-01 1.01876581e+00 1.42752126e-01 9.67610180e-02
-4.43131775e-01 -8.02952409e-01 -6.36228085e-01 -6.31475031e-01
-3.96245271e-01 6.43033504e-01 -2.44799197e-01 -5.05179055... | [6.609245300292969, 4.972165584564209] |
e5bb81b0-6f64-41aa-88f3-1bc3120b0cac | towards-massively-multi-domain-multilingual | 2305.14463 | null | https://arxiv.org/abs/2305.14463v1 | https://arxiv.org/pdf/2305.14463v1.pdf | Towards Massively Multi-domain Multilingual Readability Assessment | We present ReadMe++, a massively multi-domain multilingual dataset for automatic readability assessment. Prior work on readability assessment has been mostly restricted to the English language and one or two text domains. Additionally, the readability levels of sentences used in many previous datasets are assumed on th... | ['Wei Xu', 'Mohit Chandra', 'Michael J. Ryan', 'Tarek Naous'] | 2023-05-23 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-3.24817118e-03 2.91168034e-01 -3.49427247e-03 -3.84114563e-01
-1.42682183e+00 -9.83660936e-01 5.62238753e-01 5.71568727e-01
-6.40490592e-01 9.27903771e-01 8.41299057e-01 -2.11713284e-01
-2.20401451e-01 -7.08055794e-01 -3.95374447e-01 4.32922207e-02
5.86934745e-01 8.06170166e-01 6.28967807e-02 -6.25552416... | [11.045701026916504, 10.160038948059082] |
7505f62c-2704-4ba4-87d7-b3e393cf254d | composite-force-learning-of-chaotic-echo | 2207.02420 | null | https://arxiv.org/abs/2207.02420v1 | https://arxiv.org/pdf/2207.02420v1.pdf | Composite FORCE learning of chaotic echo state networks for time-series prediction | Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights. First-order reduced and controlled error (FORCE) learning is an online supervised training appro... | ['Yongping Pan', 'Kohei Nakajima', 'Kai Hu', 'Yansong Li'] | 2022-07-06 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.51740298e-01 -1.30995408e-01 -2.18949784e-02 2.77864575e-01
2.69564331e-01 -1.14836104e-01 6.03880048e-01 -5.57943225e-01
-4.25594956e-01 1.13868546e+00 -2.17227817e-01 -1.05014615e-01
-2.81967580e-01 -5.07845223e-01 -4.74354804e-01 -1.19600892e+00
-3.89860153e-01 2.55968928e-01 3.87436777e-01 -4.89549071... | [6.620084762573242, 3.364825963973999] |
d5acf4fb-db0c-42b9-8718-b168246b2e5f | m2l-at-semeval-2016-task-8-amr-parsing-with | null | null | https://aclanthology.org/S16-1178 | https://aclanthology.org/S16-1178.pdf | M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks | null | ['Yevgeniy Puzikov', 'Sadao Kurohashi', 'Daisuke Kawahara'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.187536716461182, 3.8045973777770996] |
4eccee47-b10f-4a5d-9345-6fecc60ccff3 | deep-outdoor-illumination-estimation | 1611.06403 | null | http://arxiv.org/abs/1611.06403v3 | http://arxiv.org/pdf/1611.06403v3.pdf | Deep Outdoor Illumination Estimation | We present a CNN-based technique to estimate high-dynamic range outdoor
illumination from a single low dynamic range image. To train the CNN, we
leverage a large dataset of outdoor panoramas. We fit a low-dimensional
physically-based outdoor illumination model to the skies in these panoramas
giving us a compact set of ... | ['Jean-François Lalonde', 'Kalyan Sunkavalli', 'Yannick Hold-Geoffroy', 'Sunil Hadap', 'Emiliano Gambaretto'] | 2016-11-19 | deep-outdoor-illumination-estimation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Hold-Geoffroy_Deep_Outdoor_Illumination_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Hold-Geoffroy_Deep_Outdoor_Illumination_CVPR_2017_paper.pdf | cvpr-2017-7 | ['light-source-estimation'] | ['computer-vision'] | [ 6.37120962e-01 -2.84668058e-01 2.95323879e-01 -5.98550916e-01
-6.34942651e-01 -1.08414578e+00 4.25688088e-01 -6.37908757e-01
-1.18507870e-01 4.71239507e-01 2.90982611e-02 -2.36378908e-01
1.13352261e-01 -9.52203631e-01 -1.20207322e+00 -6.32080674e-01
3.15504104e-01 1.67037115e-01 2.00726360e-01 -2.48474538... | [9.706751823425293, -2.988553285598755] |
7877fc0f-46fb-4b57-90fc-f623cafa87d9 | boosting-synthetic-data-generation-with | 2301.07427 | null | https://arxiv.org/abs/2301.07427v1 | https://arxiv.org/pdf/2301.07427v1.pdf | Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery | Synthetic data generation has been widely adopted in software testing, data privacy, imbalanced learning, and artificial intelligence explanation. In all such contexts, it is crucial to generate plausible data samples. A common assumption of approaches widely used for data generation is the independence of the features... | ['Riccardo Guidotti', 'Fosca Giannotti', 'Martina Cinquini'] | 2023-01-18 | null | null | null | null | ['causal-discovery', 'synthetic-data-generation', 'synthetic-data-generation'] | ['knowledge-base', 'medical', 'miscellaneous'] | [ 2.22571135e-01 3.25656593e-01 -3.58000308e-01 -6.05438471e-01
-8.47984478e-02 -3.26896966e-01 4.93851036e-01 3.69546890e-01
1.75947696e-01 1.34230220e+00 5.78645617e-02 -2.16511279e-01
-5.34368694e-01 -1.23381782e+00 -1.00972319e+00 -4.44233745e-01
-1.96618468e-01 5.37795246e-01 -1.24704793e-01 3.73119935... | [8.680005073547363, 5.742864608764648] |
c74f8793-12ba-4f28-b81d-b5789f51b02b | probabilistic-appearance-invariant-topometric | 2107.07707 | null | https://arxiv.org/abs/2107.07707v1 | https://arxiv.org/pdf/2107.07707v1.pdf | Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness | Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data. Recently, probabilistic localization systems utilizing appearance-invariant visual place recognition (VPR) methods as ... | ['Michael Milford', 'Niko Sünderhauf', 'Tobias Fischer', 'Ming Xu'] | 2021-07-16 | null | null | null | null | ['loop-closure-detection', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [-7.68980384e-02 -3.25061440e-01 -3.69705379e-01 -4.59051341e-01
-1.17615414e+00 -9.01226103e-01 7.46975482e-01 2.92829812e-01
-6.87239408e-01 6.37888908e-01 -1.67218506e-01 -1.71106592e-01
-7.81350508e-02 -3.98036391e-01 -1.05450809e+00 -3.77831459e-01
-2.36318067e-01 4.88095045e-01 6.13804817e-01 -3.45148742... | [7.42788028717041, -2.0664968490600586] |
be63e692-d627-44fc-8ec0-cd0637d20176 | composite-biomarker-image-for-advanced | 2304.12423 | null | https://arxiv.org/abs/2304.12423v1 | https://arxiv.org/pdf/2304.12423v1.pdf | Composite Biomarker Image for Advanced Visualization in Histopathology | Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer diagnosis and subtyping. It requires cross-staining comparison among Whole Slide Images (WSIs) of IHCs and hematoxylin and eosin (H&E) slides. Currently, pathologists examine the visually co-localized areas across IHC and H&E glass slides for... | ['H. R. Tizhoosh', 'Soma Sikdar', 'Adrian Batten', 'Ricardo Gonzalez', 'Morteza Babaie', 'Abubakr Shafique'] | 2023-04-24 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.48621896e-01 -9.10656974e-02 -6.89600334e-02 1.13073692e-01
-6.12796247e-01 -9.32138324e-01 3.58133428e-02 8.58602226e-01
-4.02442336e-01 5.51054776e-01 -3.81080687e-01 -3.20328534e-01
-9.10804123e-02 -6.60555720e-01 -1.12477936e-01 -1.23099327e+00
1.23611838e-02 3.89226496e-01 4.59003687e-01 4.27319482... | [15.072463035583496, -3.071242332458496] |
0566ecd2-d682-4075-96df-135f1eff7500 | rrpn-guidance-towards-more-accurate-scene | 2009.13118 | null | https://arxiv.org/abs/2009.13118v1 | https://arxiv.org/pdf/2009.13118v1.pdf | RRPN++: Guidance Towards More Accurate Scene Text Detection | RRPN is among the outstanding scene text detection approaches, but the manually-designed anchor and coarse proposal refinement make the performance still far from perfection. In this paper, we propose RRPN++ to exploit the potential of RRPN-based model by several improvements. Based on RRPN, we propose the Anchor-free ... | ['Jianqi Ma'] | 2020-09-28 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.59987795e-01 4.05908711e-02 -2.79478610e-01 -2.17235938e-01
-8.11019480e-01 -3.34348902e-02 5.46978891e-01 -9.35185403e-02
-3.66775364e-01 3.72340649e-01 3.62142563e-01 -9.77603048e-02
2.13645503e-01 -7.75881112e-01 -4.18122590e-01 -7.78409719e-01
5.11697769e-01 4.76956278e-01 8.85816693e-01 2.33600382... | [12.062909126281738, 2.264540910720825] |
5bdffefb-a07a-4002-ae81-166c221c19bf | low-resource-neural-machine-translation-for | 2104.00366 | null | https://arxiv.org/abs/2104.00366v2 | https://arxiv.org/pdf/2104.00366v2.pdf | Low-Resource Neural Machine Translation for Southern African Languages | Low-resource African languages have not fully benefited from the progress in neural machine translation because of a lack of data. Motivated by this challenge we compare zero-shot learning, transfer learning and multilingual learning on three Bantu languages (Shona, isiXhosa and isiZulu) and English. Our main target is... | ['Bruce A. Bassett', 'Evander Nyoni'] | 2021-04-01 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 1.38103394e-02 -2.94757597e-02 -4.60711002e-01 -1.51079193e-01
-1.67049384e+00 -6.94614589e-01 7.79096305e-01 3.90775315e-03
-6.04589105e-01 1.41525126e+00 3.69276613e-01 -7.24013209e-01
1.92895383e-01 -6.34747207e-01 -8.58331084e-01 -4.03981745e-01
2.09377617e-01 8.94954860e-01 1.93140358e-02 -7.08662689... | [11.438811302185059, 10.241499900817871] |
e83d9bd1-95e4-4f83-8f66-e1e12bc40d52 | underwater-image-restoration-via-contrastive | 2106.10718 | null | https://arxiv.org/abs/2106.10718v1 | https://arxiv.org/pdf/2106.10718v1.pdf | Underwater Image Restoration via Contrastive Learning and a Real-world Dataset | Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in the past decades. However, due to fundamental difficulties associated with imaging/sensing, lighting, and refractive geometric distortions, in capturing clear underwater... | ['Lars Petersson', 'Hongdong Li', 'Mohammad Ali Armin', 'Ran Wei', 'Saeed Anwar', 'Janet Anstee', 'Elizabeth Botha', 'Tim Malthus', 'Mehrdad Shoeiby', 'Junlin Han'] | 2021-06-20 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 6.47450447e-01 -3.13055515e-02 8.47575665e-01 -3.91673863e-01
-9.30357456e-01 -2.93131977e-01 3.74429524e-01 -3.87994438e-01
-6.35488331e-01 7.29604721e-01 4.68121767e-01 -1.01281010e-01
-9.92548466e-02 -8.97761047e-01 -8.87492478e-01 -9.87642407e-01
-2.06902891e-01 -2.78016806e-01 -2.46858206e-02 -3.63625526... | [10.690375328063965, -3.5187036991119385] |
286a52c7-9dc7-4d00-b375-2a6ec548a555 | integrated-planning-of-multi-energy-grids | 2301.08454 | null | https://arxiv.org/abs/2301.08454v1 | https://arxiv.org/pdf/2301.08454v1.pdf | Integrated Planning of Multi-energy Grids: Concepts and Challenges | In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the increased use of renewable energy sources and employing hydrogen as a clean energy carr... | ['Detlef Schulz', 'Christian Becker', 'Arne Speerforck', 'Christian Töbermann', 'Davood Babazadeh', 'Natalia Sanina', 'Alex Povel', 'Johannes Heise', 'Daniela Vorwerk', 'Marwan Mostafa'] | 2023-01-20 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-1.47865802e-01 2.91769207e-01 -6.30585849e-02 1.41476300e-02
-1.87213123e-01 -5.81002355e-01 8.94106627e-01 2.65684456e-01
-2.49544173e-01 1.39862633e+00 5.54083101e-02 -6.42378986e-01
-1.12936527e-01 -1.26992249e+00 2.02473193e-01 -1.14792001e+00
3.92190158e-01 6.21937156e-01 -2.71668464e-01 -3.18342894... | [5.7118659019470215, 2.5361053943634033] |
94f26c87-5579-4e8c-a66e-bf33f071122a | intraday-trading-strategy-based-on-time | 2103.13507 | null | https://arxiv.org/abs/2103.13507v1 | https://arxiv.org/pdf/2103.13507v1.pdf | Intraday trading strategy based on time series and machine learning for Chinese stock market | This article comes up with an intraday trading strategy under T+1 using Markowitz optimization and Multilayer Perceptron (MLP) with published stock data obtained from the Shenzhen Stock Exchange and Shanghai Stock Exchange. The empirical results reveal the profitability of Markowitz portfolio optimization and validate ... | ['J. Shen', 'Y. Zhou', 'Q. Wang'] | 2021-03-24 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-1.03834260e+00 -2.82915473e-01 -6.89776540e-01 -3.91418785e-01
-1.43536627e-01 -4.19725388e-01 4.18244779e-01 -9.07524526e-02
-4.36358094e-01 1.15927899e+00 3.53943080e-01 -6.47415102e-01
-3.36401194e-01 -9.99835193e-01 -4.91197109e-01 -4.06185687e-01
-5.78886867e-01 2.02246860e-01 -2.74553269e-01 -4.91387360... | [4.489369869232178, 4.219675540924072] |
c4f0e196-4d64-428d-a1e3-235b3533be4a | on-the-stability-plasticity-dilemma-of-class | 2304.01663 | null | https://arxiv.org/abs/2304.01663v1 | https://arxiv.org/pdf/2304.01663v1.pdf | On the Stability-Plasticity Dilemma of Class-Incremental Learning | A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from previously seen classes, and plastic enough to learn concepts from new classes. While previous works demonstrate strong performance on class-in... | ['Bohyung Han', 'Dongwan Kim'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_On_the_Stability-Plasticity_Dilemma_of_Class-Incremental_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_On_the_Stability-Plasticity_Dilemma_of_Class-Incremental_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['class-incremental-learning'] | ['computer-vision'] | [ 2.72292495e-01 1.27684981e-01 -1.99229524e-01 -2.41873562e-01
-4.79375482e-01 -6.79271638e-01 8.57195258e-01 4.14337367e-01
-2.18345508e-01 5.03640473e-01 1.66928768e-01 -5.79042472e-02
-3.95077646e-01 -8.22012603e-01 -8.16694975e-01 -8.58044028e-01
-7.62398988e-02 3.88194054e-01 5.76955736e-01 -2.20135361... | [9.006114959716797, 3.132052183151245] |
c86dfdc2-1d0a-41a7-bac1-d56fa15e2232 | sequence-tagging-with-contextual-and-non | 1906.01569 | null | https://arxiv.org/abs/1906.01569v1 | https://arxiv.org/pdf/1906.01569v1.pdf | Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation | Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them. In this work,... | ['Michael Strube', 'Benjamin Heinzerling'] | 2019-06-04 | sequence-tagging-with-contextual-and-non-1 | https://aclanthology.org/P19-1027 | https://aclanthology.org/P19-1027.pdf | acl-2019-7 | ['multilingual-named-entity-recognition', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [-5.28539360e-01 -4.46176052e-01 -5.65503955e-01 -1.90125957e-01
-1.13244510e+00 -1.08130431e+00 7.87994921e-01 4.32042181e-01
-1.08007622e+00 8.24195206e-01 8.38229358e-01 -7.00223863e-01
9.28010866e-02 -2.86463648e-01 -2.65462458e-01 -3.72085810e-01
-1.41498987e-02 4.16347980e-01 -6.00065887e-02 -1.61618590... | [10.635122299194336, 9.737406730651855] |
35de5623-ec1d-48f4-83a1-52da0f6b931e | detecting-heads-using-feature-refine-net-and | 1803.09256 | null | http://arxiv.org/abs/1803.09256v4 | http://arxiv.org/pdf/1803.09256v4.pdf | Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture | This paper presents a method that can accurately detect heads especially
small heads under the indoor scene. To achieve this, we propose a novel method,
Feature Refine Net (FRN), and a cascaded multi-scale architecture. FRN exploits
the multi-scale hierarchical features created by deep convolutional neural
networks. Th... | ['Zirong Chen', 'Zikai Sun', 'Lele Xie', 'Lianwen Jin', 'Zirui Cai', 'Dezhi Peng'] | 2018-03-25 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-3.38710546e-01 6.25483468e-02 1.84044585e-01 -5.41173339e-01
-4.82576042e-01 1.55394375e-01 1.97259158e-01 9.34903473e-02
-5.91628909e-01 5.56494057e-01 4.14645791e-01 3.96763355e-01
2.07591549e-01 -9.94259000e-01 -6.04321659e-01 -6.07007802e-01
-2.58169800e-01 2.44212911e-01 8.74363005e-01 3.79800685... | [8.195125579833984, -0.5072635412216187] |
b2f6c148-8b2d-45f6-86dc-8044e06ab87d | covidia-covid-19-interdisciplinary-academic | 2304.07242 | null | https://arxiv.org/abs/2304.07242v1 | https://arxiv.org/pdf/2304.07242v1.pdf | Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph | The pandemic of COVID-19 has inspired extensive works across different research fields. Existing literature and knowledge platforms on COVID-19 only focus on collecting papers on biology and medicine, neglecting the interdisciplinary efforts, which hurdles knowledge sharing and research collaborations between fields to... | ['Chenghu Zhou', 'Xinbing Wang', 'Weinan Zhang', 'Luoyi Fu', 'Jiaxin Ding', 'Cheng Deng'] | 2023-04-14 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [-8.04053128e-01 -1.69393122e-01 -6.59793794e-01 4.18827981e-01
1.62569314e-01 -7.74717808e-01 2.79759973e-01 7.24196076e-01
-5.57651408e-02 1.05461144e+00 8.79551321e-02 -5.98226726e-01
-9.92667913e-01 -1.42979646e+00 -4.64442492e-01 -1.13149114e-01
2.04501182e-01 5.22133827e-01 1.54174015e-01 4.25761417... | [9.326519012451172, 8.20254135131836] |
73119800-393e-4226-8677-5273ba855fff | duplex-diffusion-models-improve-speech-to | 2305.12628 | null | https://arxiv.org/abs/2305.12628v1 | https://arxiv.org/pdf/2305.12628v1.pdf | Duplex Diffusion Models Improve Speech-to-Speech Translation | Speech-to-speech translation is a typical sequence-to-sequence learning task that naturally has two directions. How to effectively leverage bidirectional supervision signals to produce high-fidelity audio for both directions? Existing approaches either train two separate models or a multitask-learned model with low eff... | ['Xianchao Wu'] | 2023-05-22 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 7.31025517e-01 2.93003976e-01 -3.94274831e-01 -1.82460204e-01
-1.72821498e+00 -7.72341907e-01 8.88523579e-01 -7.69492865e-01
-5.24321459e-02 8.08654130e-01 7.74720252e-01 -8.96536946e-01
6.06672585e-01 -3.94172460e-01 -1.03080821e+00 -7.85003185e-01
3.58593792e-01 6.95304513e-01 2.93697953e-01 -3.30747336... | [14.527359008789062, 7.1347270011901855] |
5f6e0dde-d66f-4e63-a4e7-7a64be9e9d2a | a-comparative-analysis-of-the-face | 2211.02952 | null | https://arxiv.org/abs/2211.02952v1 | https://arxiv.org/pdf/2211.02952v1.pdf | A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios | Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. In video surveillance based face recognition, face images are typically captured over multiple frames in uncontrolled conditions; where head pose, illumination, shadowing, motion blur and focus change over the ... | ['Bal Murat', 'Eker Onur'] | 2022-11-05 | null | null | null | null | ['face-detection', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [ 4.67428625e-01 -2.77063191e-01 1.06375717e-01 -7.78035760e-01
3.26964468e-01 -4.67380494e-01 5.40910482e-01 -1.07021654e+00
-1.28833652e-01 4.01994377e-01 -1.32660761e-01 -2.88232546e-02
-1.15849353e-01 -2.46327981e-01 -4.70020801e-01 -1.00532150e+00
-4.51637059e-01 3.54476459e-02 -2.75097247e-02 -4.09504175... | [13.282374382019043, 0.8011878132820129] |
43eb7dfd-fa06-4a3a-a18d-cbdced162299 | multi-modal-temporal-convolutional-network | 2107.09504 | null | https://arxiv.org/abs/2107.09504v1 | https://arxiv.org/pdf/2107.09504v1.pdf | Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos | Anticipating human actions is an important task that needs to be addressed for the development of reliable intelligent agents, such as self-driving cars or robot assistants. While the ability to make future predictions with high accuracy is crucial for designing the anticipation approaches, the speed at which the infer... | ['Juergen Gall', 'Yazan Abu Farha', 'Olga Zatsarynna'] | 2021-07-18 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [-6.71323538e-02 -3.13202925e-02 1.24617510e-01 -6.75459027e-01
-2.99557209e-01 -2.33191460e-01 6.11635923e-01 1.59547683e-02
-5.82308710e-01 3.95694613e-01 2.83315009e-03 -1.80771664e-01
1.02164216e-01 -8.66917610e-01 -7.73900628e-01 -5.90284944e-01
-1.35608539e-01 2.35789016e-01 7.97757983e-01 -4.26409602... | [7.191586494445801, 0.4076496660709381] |
9a179d34-20af-4fe5-9b2f-4b5a608acd0d | type-information-utilized-event-detection-via | 2211.08168 | null | https://arxiv.org/abs/2211.08168v1 | https://arxiv.org/pdf/2211.08168v1.pdf | Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems | Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-spe... | ['Pengtao Xie', 'Shan Xue', 'Qingyun Sun', 'Jiawei Sheng', 'Yiming Hei', 'Cheng Ji', 'Lihong Wang', 'JianXin Li', 'Qian Li'] | 2022-11-15 | null | null | null | null | ['type'] | ['speech'] | [-4.49569263e-02 -4.21137828e-03 -4.91945773e-01 -2.61135370e-01
-3.41414899e-01 -5.85036814e-01 3.01478148e-01 5.13947785e-01
-3.54795381e-02 5.34279108e-01 4.83815789e-01 -5.33031762e-01
-4.75535542e-01 -1.38733518e+00 -3.44661385e-01 -5.72936177e-01
-3.17054063e-01 2.58972019e-01 -4.26777489e-02 -4.53292280... | [9.067193984985352, 9.151049613952637] |
6e3bb292-d615-43b7-8b3d-f55c001b33bb | are-facial-attributes-adversarially-robust | 1605.05411 | null | http://arxiv.org/abs/1605.05411v3 | http://arxiv.org/pdf/1605.05411v3.pdf | Are Facial Attributes Adversarially Robust? | Facial attributes are emerging soft biometrics that have the potential to
reject non-matches, for example, based on mismatching gender. To be usable in
stand-alone systems, facial attributes must be extracted from images
automatically and reliably. In this paper, we propose a simple yet effective
solution for automatic... | ['Manuel Günther', 'Ethan M. Rudd', 'Andras Rozsa', 'Terrance E. Boult'] | 2016-05-18 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 7.94326782e-01 3.40639949e-01 3.22909474e-01 -7.61682451e-01
-5.97976565e-01 -9.22440767e-01 6.07028961e-01 -1.15733877e-01
-3.73788685e-01 8.34767580e-01 -4.29963380e-01 -1.35974810e-01
1.05836622e-01 -8.92318189e-01 -1.04743659e+00 -9.74755168e-01
-2.32568458e-01 3.97838920e-01 -2.13698879e-01 -1.81952730... | [12.960293769836426, 1.0183435678482056] |
78849529-2a6c-4867-aaa1-5327088d869b | parametric-representation-for-singing-voice | 2006.04142 | null | https://arxiv.org/abs/2006.04142v1 | https://arxiv.org/pdf/2006.04142v1.pdf | Parametric Representation for Singing Voice Synthesis: a Comparative Evaluation | Various parametric representations have been proposed to model the speech signal. While the performance of such vocoders is well-known in the context of speech processing, their extrapolation to singing voice synthesis might not be straightforward. The goal of this paper is twofold. First, a comparative subjective eval... | ['Daniel Erro', 'Thomas Drugman', 'Onur Babacan', 'Tuomo Raitio', 'Thierry Dutoit'] | 2020-06-07 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-4.55040112e-02 -2.02358410e-01 1.50368989e-01 6.48471899e-03
-6.71121061e-01 -6.67667449e-01 6.79589212e-01 -2.59407312e-01
-6.35704175e-02 9.31699693e-01 5.39063394e-01 -1.93462700e-01
-4.06457782e-01 -1.52115256e-01 -2.04861034e-02 -8.06556523e-01
2.08336748e-02 1.58897534e-01 2.56228328e-01 -1.97014287... | [15.016693115234375, 6.0463995933532715] |
695f5f18-e319-4345-8855-69671cd4e3cf | clr-gam-contrastive-point-cloud-learning-with | 2302.14306 | null | https://arxiv.org/abs/2302.14306v1 | https://arxiv.org/pdf/2302.14306v1.pdf | CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping | Point cloud data plays an essential role in robotics and self-driving applications. Yet, annotating point cloud data is time-consuming and nontrivial while they enable learning discriminative 3D representations that empower downstream tasks, such as classification and segmentation. Recently, contrastive learning-based ... | ['Yi-Ting Chen', 'Srikanth Malla'] | 2023-02-28 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 1.33878469e-01 -9.90853980e-02 -4.42131132e-01 -3.56689602e-01
-8.05841982e-01 -4.49520439e-01 6.07761502e-01 3.24126214e-01
-1.25996381e-01 1.44013301e-01 -4.47414905e-01 -2.93260783e-01
-3.24501425e-01 -7.00075388e-01 -8.30738187e-01 -5.89518964e-01
-3.04004908e-01 9.50522542e-01 3.19272786e-01 -1.03603154... | [8.005725860595703, -3.247001886367798] |
a7954252-f700-4027-90a5-7a17e62bcfd4 | youtube-asl-a-large-scale-open-domain | 2306.15162 | null | https://arxiv.org/abs/2306.15162v1 | https://arxiv.org/pdf/2306.15162v1.pdf | YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus | Machine learning for sign languages is bottlenecked by data. In this paper, we present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language (ASL) videos and accompanying English captions drawn from YouTube. With ~1000 hours of videos and >2500 unique signers, YouTube-ASL is ~3x as large and has ~10x... | ['Manfred Georg', 'Garrett Tanzer', 'David Uthus'] | 2023-06-27 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-1.48547903e-01 -2.19716296e-01 -6.75560534e-01 -3.91400456e-01
-1.21509659e+00 -9.79005814e-01 5.63205183e-01 -9.59524333e-01
-8.54786992e-01 6.76065803e-01 8.29897344e-01 -1.97762311e-01
4.67033088e-01 6.22362196e-02 -8.35631728e-01 -1.32110015e-01
6.77514970e-02 3.45826417e-01 3.34791243e-01 -5.92905357... | [9.192279815673828, -6.518263339996338] |
36160953-4de5-4078-826e-10913ab2e450 | probabilistic-failure-analysis-in-model | 1611.05083 | null | http://arxiv.org/abs/1611.05083v2 | http://arxiv.org/pdf/1611.05083v2.pdf | Probabilistic Failure Analysis in Model Validation & Verification | Automated fault localization is an important issue in model validation and
verification. It helps the end users in analyzing the origin of failure. In
this work, we show the early experiments with probabilistic analysis approaches
in fault localization. Inspired by the Kullback-Leibler Divergence from
Bayesian probabil... | ['Xavier Crégut', 'Marc Pantel', 'Ning Ge'] | 2016-11-15 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-1.15960985e-01 1.08306704e-03 2.28841249e-02 -2.87825823e-01
-6.79074764e-01 -2.54587024e-01 2.93645740e-01 3.04724693e-01
4.12784278e-01 4.27057803e-01 -1.83668166e-01 -7.75327563e-01
-5.80154240e-01 -6.67409718e-01 -6.39353156e-01 -4.10070300e-01
-4.13808227e-01 5.46696365e-01 7.94031799e-01 1.72446474... | [5.476309299468994, 2.6866700649261475] |
48a86aa5-d71b-4aa9-8e43-fb767231817f | uwsod-toward-fully-supervised-level-capacity | null | null | http://proceedings.neurips.cc/paper/2020/hash/4e0928de075538c593fbdabb0c5ef2c3-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/4e0928de075538c593fbdabb0c5ef2c3-Paper.pdf | UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection | Weakly supervised object detection (WSOD) has attracted extensive research attention due to its great flexibility of exploiting large-scale dataset with only image-level annotations for detector training. Despite its great advance in recent years, WSOD still suffers limited performance, which is far below that of fully... | ['Feiyue Huang', 'Yongjian Wu', 'Zhiwei Chen', 'Rongrong Ji', 'Yunhang Shen'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['object-proposal-generation'] | ['computer-vision'] | [ 1.05254008e-02 4.20091301e-02 -4.08351272e-01 -3.31017762e-01
-1.05979371e+00 -5.36177576e-01 4.13473219e-01 -2.12261379e-02
-5.98625779e-01 3.29341441e-01 -1.34033144e-01 8.05438459e-02
2.53011346e-01 -6.82057261e-01 -8.68648648e-01 -6.08331144e-01
1.24871954e-01 2.72398382e-01 1.16378665e+00 -2.30823651... | [9.179481506347656, 0.8976779580116272] |
3e6b24e3-787a-423f-bd9b-d4dede21fd64 | graph-signal-processing-over-multilayer | 2108.13639 | null | https://arxiv.org/abs/2108.13639v3 | https://arxiv.org/pdf/2108.13639v3.pdf | Image Processing via Multilayer Graph Spectra | Graph signal processing (GSP) has become an important tool in image processing because of its ability to reveal underlying data structures. Many real-life multimedia datasets, however, exhibit heterogeneous structures across frames. Multilayer graphs (MLG), instead of traditional single-layer graphs, provide better rep... | ['Zhi Ding', 'Qinwen Deng', 'Songyang Zhang'] | 2021-08-31 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 6.27325833e-01 -3.70885283e-01 1.65112749e-01 -2.78531071e-02
7.70799145e-02 -2.48459816e-01 6.57674745e-02 2.58718342e-01
2.44278178e-01 1.62409961e-01 -4.41661850e-02 -3.29286367e-01
-4.90140259e-01 -1.01507771e+00 -2.41808042e-01 -7.66876578e-01
-6.23476207e-01 -4.33333069e-01 4.04926419e-01 -2.00742051... | [10.12912654876709, -1.946967363357544] |
a077d184-2e11-479c-960f-5d43823c3494 | mask-guided-portrait-editing-with-conditional-1 | 1905.10346 | null | https://arxiv.org/abs/1905.10346v1 | https://arxiv.org/pdf/1905.10346v1.pdf | Mask-Guided Portrait Editing with Conditional GANs | Portrait editing is a popular subject in photo manipulation. The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing. In this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. To... | ['Jianmin Bao', 'Dong Chen', 'Hao Yang', 'Shuyang Gu', 'Lu Yuan', 'Fang Wen'] | 2019-05-24 | mask-guided-portrait-editing-with-conditional | http://openaccess.thecvf.com/content_CVPR_2019/html/Gu_Mask-Guided_Portrait_Editing_With_Conditional_GANs_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gu_Mask-Guided_Portrait_Editing_With_Conditional_GANs_CVPR_2019_paper.pdf | cvpr-2019-6 | ['face-parsing'] | ['computer-vision'] | [ 5.65803170e-01 5.95930517e-01 -1.33525178e-01 -5.48932433e-01
-4.72130507e-01 -7.33057559e-01 5.61048567e-01 -9.78124917e-01
3.53045285e-01 7.87060022e-01 3.33367556e-01 5.26446924e-02
3.73652965e-01 -9.46431756e-01 -1.11192334e+00 -7.01387167e-01
4.17123139e-01 2.58090109e-01 -6.46069705e-01 -3.33893269... | [12.477517127990723, -0.22139418125152588] |
f69edb0c-b3ae-4b81-b60b-ef193b56da4a | can-language-models-solve-graph-problems-in | 2305.10037 | null | https://arxiv.org/abs/2305.10037v1 | https://arxiv.org/pdf/2305.10037v1.pdf | Can Language Models Solve Graph Problems in Natural Language? | Large language models (LLMs) are increasingly adopted for a variety of tasks with implicit graphical structures, such as planning in robotics, multi-hop question answering or knowledge probing, structured commonsense reasoning, and more. While LLMs have advanced the state-of-the-art on these tasks with structure implic... | ['Yulia Tsvetkov', 'Xiaochuang Han', 'Zhaoxuan Tan', 'Tianxing He', 'Shangbin Feng', 'Heng Wang'] | 2023-05-17 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.55401641e-01 7.58383572e-01 -2.22339809e-01 -1.42096192e-01
-4.63699222e-01 -8.69076848e-01 5.75455606e-01 6.63567305e-01
-1.83582664e-01 5.06093085e-01 2.56368488e-01 -9.84498620e-01
-4.91213113e-01 -9.73540723e-01 -8.71155798e-01 -1.62755791e-02
-5.80245316e-01 8.54531944e-01 1.93605170e-01 -3.92211348... | [8.921626091003418, 7.457149505615234] |
bd60b107-1e8a-432b-971a-af0f14d39a93 | securing-visually-aware-recommender-systems | 2306.07992 | null | https://arxiv.org/abs/2306.07992v1 | https://arxiv.org/pdf/2306.07992v1.pdf | Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework | With rich visual data, such as images, becoming readily associated with items, visually-aware recommendation systems (VARS) have been widely used in different applications. Recent studies have shown that VARS are vulnerable to item-image adversarial attacks, which add human-imperceptible perturbations to the clean imag... | ['Xin Li', 'Neil Zhenqiang Gong', 'Bin Liu', 'Minglei Yin'] | 2023-06-11 | null | null | null | null | ['contrastive-learning', 'image-reconstruction', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 7.41005689e-02 -6.77161813e-01 1.90204427e-01 2.17318282e-01
-5.26770592e-01 -1.16994369e+00 6.06043398e-01 -2.98727065e-01
-7.70256966e-02 2.07888767e-01 -1.85492523e-02 -3.92480582e-01
3.11599195e-01 -9.82064188e-01 -7.55695343e-01 -7.16156900e-01
-9.02226344e-02 -2.34084934e-01 3.96142691e-01 -4.02849495... | [5.534847259521484, 7.919505596160889] |
26158862-1fb1-46b7-86f3-c2c5bfcabe59 | learning-the-predictability-of-the-future | 2101.01600 | null | https://arxiv.org/abs/2101.01600v1 | https://arxiv.org/pdf/2101.01600v1.pdf | Learning the Predictability of the Future | We introduce a framework for learning from unlabeled video what is predictable in the future. Instead of committing up front to features to predict, our approach learns from data which features are predictable. Based on the observation that hyperbolic geometry naturally and compactly encodes hierarchical structure, we ... | ['Carl Vondrick', 'Ruoshi Liu', 'Dídac Surís'] | 2021-06-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Suris_Learning_the_Predictability_of_the_Future_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Suris_Learning_the_Predictability_of_the_Future_CVPR_2021_paper.pdf | cvpr-2021-1 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 1.08600609e-01 7.06787467e-01 -4.16530222e-01 -4.81875420e-01
-3.65767717e-01 -4.34916019e-01 3.57686579e-01 2.41782948e-01
1.66493639e-01 6.10990405e-01 9.27977264e-01 -7.11677894e-02
-1.32463336e-01 -7.49232829e-01 -7.53130734e-01 -4.26903576e-01
-6.44809067e-01 3.04404795e-01 3.61876577e-01 -2.30613332... | [8.4578275680542, 0.6941186189651489] |
5ce6549a-f954-41ed-8c21-3dee78ae21a9 | pseudo-lidar-from-visual-depth-estimation | 1812.07179 | null | https://arxiv.org/abs/1812.07179v6 | https://arxiv.org/pdf/1812.07179v6.pdf | Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving | 3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower ac... | ['Bharath Hariharan', 'Wei-Lun Chao', 'Divyansh Garg', 'Kilian Q. Weinberger', 'Yan Wang', 'Mark Campbell'] | 2018-12-18 | pseudo-lidar-from-visual-depth-estimation-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Pseudo-LiDAR_From_Visual_Depth_Estimation_Bridging_the_Gap_in_3D_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Pseudo-LiDAR_From_Visual_Depth_Estimation_Bridging_the_Gap_in_3D_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [ 2.90731877e-01 -2.52523333e-01 -1.51885986e-01 -4.20887262e-01
-7.85868824e-01 -4.75269079e-01 5.39277673e-01 7.80642629e-02
-6.92030966e-01 3.41979533e-01 -3.01516503e-01 -5.22845507e-01
7.46723637e-02 -8.90808940e-01 -8.85927200e-01 -3.73235404e-01
1.37694120e-01 7.40618527e-01 6.32665634e-01 -4.07019019... | [7.778453350067139, -2.585094928741455] |
a6ccf720-681f-497f-b847-eb0f0449223c | language-models-are-greedy-reasoners-a | 2210.01240 | null | https://arxiv.org/abs/2210.01240v4 | https://arxiv.org/pdf/2210.01240v4.pdf | Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought | Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks measure reasoning ability indirectly, by evaluating accuracy on downstream tasks such as mathematical reasoning. However, it is unclear how these mod... | ['He He', 'Abulhair Saparov'] | 2022-10-03 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.31827429e-01 1.04602385e+00 -1.08746327e-02 -1.87717155e-01
-1.01116514e+00 -1.05161011e+00 9.15197849e-01 1.75594270e-01
2.08189547e-01 7.84113586e-01 1.58183053e-01 -1.43095803e+00
-3.66565198e-01 -1.29071295e+00 -9.63169396e-01 2.07254902e-01
-1.06023267e-01 8.04113209e-01 3.44827503e-01 -5.51167905... | [9.267304420471191, 7.220120429992676] |
911c25e3-09da-435d-bf87-1f1e150e9cfb | metric-learning-based-timing-synchronization | 2307.00217 | null | https://arxiv.org/abs/2307.00217v1 | https://arxiv.org/pdf/2307.00217v1.pdf | Metric Learning-Based Timing Synchronization by Using Lightweight Neural Network | Timing synchronization (TS) is one of the key tasks in orthogonal frequency division multiplexing (OFDM) systems. However, multi-path uncertainty corrupts the TS correctness, making OFDM systems suffer from a severe inter-symbol-interference (ISI). To tackle this issue, we propose a timing-metric learning-based TS meth... | ['Hui Lin', 'Jiafan Wang', 'Chuangui Rao', 'Shuhai Tang', 'Na Yang', 'Chaojin Qing'] | 2023-07-01 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-8.86802524e-02 -1.30936280e-01 -5.79017162e-01 -5.23542538e-02
-6.14548147e-01 -3.14168096e-01 2.11577907e-01 -2.97593057e-01
3.72723080e-02 9.11296964e-01 -1.64313093e-01 -8.11787486e-01
-6.20728910e-01 -3.46303850e-01 -5.04509211e-01 -9.92154896e-01
-8.16518784e-01 -2.43319139e-01 -4.31445539e-01 -2.75715917... | [6.327794551849365, 1.4934821128845215] |
309b0b68-88b2-48dc-a0c0-d1c9c9f0ca63 | mhsnet-multi-head-and-spatial-attention | 2201.13392 | null | https://arxiv.org/abs/2201.13392v6 | https://arxiv.org/pdf/2201.13392v6.pdf | MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection | The mortality of lung cancer has ranked high among cancers for many years. Early detection of lung cancer is critical for disease prevention, cure, and mortality rate reduction. However, existing detection methods on pulmonary nodules introduce an excessive number of false positive proposals in order to achieve high se... | ['Jinsheng Tao', 'Hua Ji', 'Bo wang', 'Xiangcheng Qiu', 'Yang Yang', 'Airu Yin', 'Jian You', 'Jianyu Xiao', 'Yulong Chen', 'Xinliang Fu', 'Zhaoqi Diao', 'Yanbo Shao', 'Jiayin Zheng', 'Minghao Wang', 'Juanyun Mai'] | 2022-01-31 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-1.76796631e-03 2.36735627e-01 -1.67810649e-01 -3.69112915e-03
-6.84698164e-01 -2.01538399e-01 2.97995031e-01 -1.33373961e-01
-6.08923495e-01 3.80802065e-01 1.98835842e-02 -3.70471448e-01
-1.25306502e-01 -9.19658542e-01 -3.63685668e-01 -8.32604825e-01
1.28070757e-01 4.20754611e-01 8.51386011e-01 2.18772173... | [15.395317077636719, -2.1502885818481445] |
82c3c1de-0445-4fea-9850-a9ca1b7a7842 | gun-source-and-muzzle-head-detection | 2001.11120 | null | https://arxiv.org/abs/2001.11120v1 | https://arxiv.org/pdf/2001.11120v1.pdf | Gun Source and Muzzle Head Detection | There is a surging need across the world for protection against gun violence. There are three main areas that we have identified as challenging in research that tries to curb gun violence: temporal location of gunshots, gun type prediction and gun source (shooter) detection. Our task is gun source detection and muzzle ... | ['Florian Metze', 'Isak Czeresnia Etinger', 'Zhong Zhou', 'Alexander Hauptmann', 'Alexander Waibel'] | 2020-01-29 | null | null | null | null | ['type-prediction', 'head-detection'] | ['computer-code', 'computer-vision'] | [ 2.85104692e-01 -3.08279723e-01 -1.23011604e-01 4.43933159e-01
-5.28868794e-01 -7.78806746e-01 5.52432597e-01 2.71269888e-01
-2.19715983e-01 2.95048356e-01 2.83083409e-01 -3.66836846e-01
9.96009558e-02 -7.44711936e-01 -4.78958458e-01 -5.98400891e-01
3.04818377e-02 1.78951561e-01 6.23915374e-01 -9.58214924... | [8.346089363098145, 0.18464429676532745] |
23848ae5-0be9-4540-92af-eb4bc6354ea0 | piveted-granite-computational-phenotypes | 1808.02602 | null | http://arxiv.org/abs/1808.02602v1 | http://arxiv.org/pdf/1808.02602v1.pdf | PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization | It has been recently shown that sparse, nonnegative tensor factorization of
multi-modal electronic health record data is a promising approach to
high-throughput computational phenotyping. However, such approaches typically
do not leverage available domain knowledge while extracting the phenotypes;
hence, some of the su... | ['Joydeep Ghosh', 'Bradley A. Malin', 'Jette Henderson', 'Joyce C. Ho'] | 2018-08-08 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [ 1.14571996e-01 -2.24576950e-01 -2.81099170e-01 -4.02055174e-01
-5.62106967e-01 -6.27128303e-01 -2.54299194e-01 6.98783517e-01
-3.03227380e-02 9.96968627e-01 4.25488651e-01 -3.72025102e-01
-6.48024082e-01 -4.86648858e-01 -3.58362854e-01 -4.54530358e-01
-1.77547514e-01 9.42471266e-01 -3.64198208e-01 1.96585447... | [6.30036735534668, 5.876377105712891] |
d419837e-a0f6-4ce2-b93a-16ac8bc5b28a | ppg-signals-for-hypertension-diagnosis-a | 2304.06952 | null | https://arxiv.org/abs/2304.06952v1 | https://arxiv.org/pdf/2304.06952v1.pdf | PPG Signals for Hypertension Diagnosis: A Novel Method using Deep Learning Models | Hypertension is a medical condition characterized by high blood pressure, and classifying it into its various stages is crucial to managing the disease. In this project, a novel method is proposed for classifying stages of hypertension using Photoplethysmography (PPG) signals and deep learning models, namely AvgPool_VG... | ['Brintha Therese A', 'Yaswant T', 'Graham Frederick'] | 2023-04-14 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [-1.81365654e-01 -1.53430656e-01 -8.85808393e-02 -6.44365132e-01
-3.94792072e-02 -8.20538774e-02 8.16535670e-03 -1.41072035e-01
-1.67417347e-01 9.22015071e-01 4.18734819e-01 -3.76870215e-01
1.65188447e-01 -9.64648128e-01 9.37034041e-02 -6.71972156e-01
-5.52731037e-01 3.26288909e-01 -2.63973653e-01 2.34045401... | [14.078546524047852, 2.988685131072998] |
73052bb8-a9d4-4bbe-b4b8-35379e662d4f | scimrc-multi-perspective-scientific-machine | 2306.14149 | null | https://arxiv.org/abs/2306.14149v1 | https://arxiv.org/pdf/2306.14149v1.pdf | SciMRC: Multi-perspective Scientific Machine Reading Comprehension | Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one dataset focused on exploring full-text scientific machine reading comprehension. However, the dataset has ignored the fact that different readers ... | ['Xian-Ling Mao', 'Heyan Huang', 'Yuxiang Nie', 'Heqi Zheng', 'Xiao Zhang'] | 2023-06-25 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97981650e-01 2.23887384e-01 1.60036072e-01 -4.20098931e-01
-9.85171080e-01 -8.44804108e-01 6.68264747e-01 5.80833793e-01
-2.10700601e-01 5.64399660e-01 4.87779081e-01 -8.00309360e-01
-3.58940005e-01 -8.19837868e-01 -7.98437357e-01 -2.35752970e-01
6.14768386e-01 4.03473139e-01 1.56102851e-01 -3.44406962... | [11.33234691619873, 8.158875465393066] |
849bf5ca-1fbd-47c4-979b-45e43983b7bf | domain-adaptation-of-thai-word-segmentation | null | null | https://aclanthology.org/2020.emnlp-main.315 | https://aclanthology.org/2020.emnlp-main.315.pdf | Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble | Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent. Researchers have been relying on transfer learning to adapt an existing model to a new domain. However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as {``... | ['Sarana Nutanong', 'Ekapol Chuangsuwanich', 'Raheem Sarwar', 'Wannaphong Phatthiyaphaibun', 'Peerat Limkonchotiwat'] | null | null | null | null | emnlp-2020-11 | ['thai-word-tokenization'] | ['natural-language-processing'] | [ 2.68880218e-01 -2.10858118e-02 -3.61362472e-02 -5.54863155e-01
-7.88883686e-01 -5.58305860e-01 3.25719118e-01 -1.20935798e-01
-8.21070492e-01 9.79668915e-01 -1.14503391e-01 -6.17350817e-01
1.01094030e-01 -8.03994060e-01 -8.31695676e-01 -5.11376679e-01
3.36024940e-01 5.97522318e-01 7.41838455e-01 -1.66600883... | [9.700654983520508, 1.676377296447754] |
d13ec9a3-f58f-42e0-83eb-65ee063cd709 | continual-learning-for-out-of-distribution | 2306.15117 | null | https://arxiv.org/abs/2306.15117v1 | https://arxiv.org/pdf/2306.15117v1.pdf | Continual Learning for Out-of-Distribution Pedestrian Detection | A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection. While recent pedestrian detection models have achieved impressive performance on various datasets, they remain sensitive to shifts in the distribution of the inference data. Our method adopts an... | ['Michael Greenspan', 'Ali Etemad', 'Mahdiyar Molahasani'] | 2023-06-26 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-9.70599428e-02 -2.65799344e-01 -8.12057182e-02 -3.56843024e-01
-2.82675803e-01 -2.37619132e-01 5.50683022e-01 2.73606420e-01
-1.03029704e+00 9.45617437e-01 1.56535745e-01 -2.55670715e-02
2.90687650e-01 -7.41048634e-01 -6.63925588e-01 -7.68544376e-01
-3.64008956e-02 6.48468196e-01 1.20697379e+00 -8.56740307... | [8.214146614074707, -0.2475028932094574] |
1b66a5d6-43e5-4b02-bdb8-0687acd4c722 | unsupervised-domain-adaptation-for-cross-1 | 2109.05664 | null | https://arxiv.org/abs/2109.05664v3 | https://arxiv.org/pdf/2109.05664v3.pdf | Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning | Liver segmentation on images acquired using computed tomography (CT) and magnetic resonance imaging (MRI) plays an important role in clinical management of liver diseases. Compared to MRI, CT images of liver are more abundant and readily available. However, MRI can provide richer quantitative information of the liver c... | ['Simon Chun-Ho Yu', 'Weitian Chen', 'Jin Hong'] | 2021-09-13 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 5.19376516e-01 3.25835258e-01 -2.93088797e-02 -5.41751921e-01
-9.09145713e-01 -6.15107298e-01 3.69024485e-01 -3.32672112e-02
-5.22739768e-01 8.33640456e-01 1.92675784e-01 -2.56154358e-01
5.20080179e-02 -6.73472345e-01 -6.12654567e-01 -1.01847410e+00
-2.30917141e-01 6.83545709e-01 3.53724398e-02 2.67413139... | [14.543741226196289, -2.096034288406372] |
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