paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0c898c8c-e511-48ef-a89a-f53a27821204 | hyperbolic-active-learning-for-semantic | 2306.11180 | null | https://arxiv.org/abs/2306.11180v2 | https://arxiv.org/pdf/2306.11180v2.pdf | Hyperbolic Active Learning for Semantic Segmentation under Domain Shift | For the task of semantic segmentation (SS) under domain shift, active learning (AL) acquisition strategies based on image regions and pseudo labels are state-of-the-art (SoA). The presence of diverse pseudo-labels within a region identifies pixels between different classes, which is a labeling efficient active learning... | ['Fabio Galasso', 'Yu-Teng Li', 'Devin Guillory', 'Konstantinos Kallidromitis', 'Paolo Mandica', 'Luca Franco'] | 2023-06-19 | null | null | null | null | ['active-learning', 'pseudo-label', 'active-learning'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 2.55642951e-01 7.92143464e-01 -4.03291702e-01 -2.01796323e-01
-7.85476506e-01 -6.60045385e-01 4.16883737e-01 4.34542596e-01
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-5.11655472e-02 5.05597353e-01 8.57231319e-01 -1.49655730... | [9.616921424865723, 1.1064866781234741] |
5c74a4f4-a470-407c-92b8-c7879368b908 | rethinking-network-design-and-local-geometry-1 | 2202.07123 | null | https://arxiv.org/abs/2202.07123v2 | https://arxiv.org/pdf/2202.07123v2.pdf | Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework | Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors using convolution, graph, or attention mechanisms. These methods, however, incur unfavorable latency during inference, and the... | ['Yun Fu', 'Haoxi Ran', 'Haoxuan You', 'Can Qin', 'Xu Ma'] | 2022-02-15 | rethinking-network-design-and-local-geometry | https://openreview.net/forum?id=3Pbra-_u76D | https://openreview.net/pdf?id=3Pbra-_u76D | iclr-2022-4 | ['point-cloud-segmentation'] | ['computer-vision'] | [-2.61135012e-01 -8.08476880e-02 -5.01735434e-02 -1.97480366e-01
-8.50898504e-01 -4.47522372e-01 5.52646816e-01 2.01577112e-01
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-1.13389276e-01 6.76256835e-01 2.60341376e-01 -2.07176700... | [7.976972579956055, -3.5105786323547363] |
23f61eee-c943-4b0a-ba85-3caa5c400b10 | exploiting-language-model-for-efficient | 2107.12168 | null | https://arxiv.org/abs/2107.12168v3 | https://arxiv.org/pdf/2107.12168v3.pdf | Exploiting Language Model for Efficient Linguistic Steganalysis | Recent advances in linguistic steganalysis have successively applied CNN, RNN, GNN and other efficient deep models for detecting secret information in generative texts. These methods tend to seek stronger feature extractors to achieve higher steganalysis effects. However, we have found through experiments that there ac... | ['Xinpeng Zhang', 'Guorui Feng', 'Hanzhou Wu', 'Biao Yi'] | 2021-07-26 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 4.85662997e-01 1.87488824e-01 1.57904595e-01 7.06993937e-02
-4.18508202e-01 -3.09430003e-01 8.31466615e-01 -3.47237051e-01
-2.33093098e-01 5.54264128e-01 4.01609719e-01 -5.46025813e-01
6.75220013e-01 -1.08264470e+00 -6.35458708e-01 -8.51200938e-01
-2.09285435e-03 1.39123529e-01 9.35861766e-02 -5.97763538... | [4.295944690704346, 8.056109428405762] |
86e8e7c7-b5b3-4688-add1-dbfebb1be465 | a-deep-variational-approach-to-clustering | 2106.05763 | null | https://arxiv.org/abs/2106.05763v3 | https://arxiv.org/pdf/2106.05763v3.pdf | A Deep Variational Approach to Clustering Survival Data | In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed met... | ['Bram Stieltjes', 'Timothy Müller', 'Verena Gotta', 'Alexander Sauter', 'Thomas Weikert', 'Julia E. Vogt', 'Marc Pfister', 'Marian C. Neidert', 'Flavio Vasella', 'Michela C. Massi', 'Ričards Marcinkevičs', 'Laura Manduchi'] | 2021-06-10 | a-deep-variational-approach-to-clustering-1 | https://openreview.net/forum?id=RQ428ZptQfU | https://openreview.net/pdf?id=RQ428ZptQfU | iclr-2022-4 | ['time-to-event-prediction'] | ['time-series'] | [-7.08111376e-02 -1.15695328e-01 -2.31062993e-01 -5.15151620e-01
-1.19953275e+00 -2.11313605e-01 5.46485603e-01 1.72380149e-01
-1.77330121e-01 8.46408367e-01 5.34210205e-01 -3.08480084e-01
-3.94326746e-01 -4.86805737e-01 -2.29153201e-01 -1.27321601e+00
-2.08767563e-01 8.48024428e-01 -2.21292838e-01 2.68411219... | [7.721401214599609, 5.548935413360596] |
617d2ed9-1a0d-4547-b9bc-7396aff71028 | beamsearchqa-large-language-models-are-strong | 2305.14766 | null | https://arxiv.org/abs/2305.14766v2 | https://arxiv.org/pdf/2305.14766v2.pdf | BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver | Open-domain question answering is a crucial task that often requires accessing external information. Existing methods typically adopt a single-turn retrieve-then-read approach, where relevant documents are first retrieved, and questions are then answered based on the retrieved information. However, there are cases wher... | ['Rangan Majumder', 'Linjun Yang', 'Daxin Jiang', 'Jingwen Lu', 'Anlei Dong', 'Nan Duan', 'Yan Zhang', 'Yeyun Gong', 'Xiao Liu', 'Hao Sun'] | 2023-05-24 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 1.55107915e-01 4.04323965e-01 -5.56270331e-02 -2.28011042e-01
-1.64447606e+00 -9.89725649e-01 6.76299751e-01 2.19334900e-01
-4.23056692e-01 9.11224544e-01 6.28370404e-01 -4.98491108e-01
-1.58487990e-01 -9.72108841e-01 -6.63254559e-01 5.76875806e-02
6.85732186e-01 8.20819557e-01 6.61163032e-01 -6.97865367... | [11.311891555786133, 7.949220657348633] |
8b9696f5-4171-4d85-b33d-510796a47639 | part-aware-fine-grained-object-categorization | 1806.06198 | null | https://arxiv.org/abs/1806.06198v2 | https://arxiv.org/pdf/1806.06198v2.pdf | Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network | Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories ... | ['Yabin Zhang', 'Zhixin Wang', 'Kui Jia'] | 2018-06-16 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.9953224e-01 1.6156308e-01 -3.0509683e-01 -5.4577696e-01
-6.6965121e-01 -8.7871516e-01 6.2590384e-01 2.6776642e-01
-1.1682148e-02 5.0557780e-01 -1.5777929e-02 1.3689211e-01
-2.4361885e-01 -9.5292211e-01 -8.4645474e-01 -6.0793442e-01
4.4780962e-02 5.6058264e-01 7.6116538e-01 6.7185163e-02
1.0692925e-01... | [9.579010963439941, 1.9908488988876343] |
fa940aff-d1e2-4af0-94d2-c0cef6ced2fe | time-series-anomaly-detection-via-contextual | 2304.07898 | null | https://arxiv.org/abs/2304.07898v1 | https://arxiv.org/pdf/2304.07898v1.pdf | Time-series Anomaly Detection via Contextual Discriminative Contrastive Learning | Detecting anomalies in temporal data is challenging due to anomalies being dependent on temporal dynamics. One-class classification methods are commonly used for anomaly detection tasks, but they have limitations when applied to temporal data. In particular, mapping all normal instances into a single hypersphere to cap... | ['Tony S. Wirjanto', 'Mingbin Feng', 'Katrina Chen'] | 2023-04-16 | null | null | null | null | ['one-class-classification', 'time-series-anomaly-detection'] | ['miscellaneous', 'time-series'] | [ 3.16544682e-01 -1.50157958e-01 1.68314084e-01 -4.15940434e-01
-3.29540282e-01 -3.19296420e-01 6.64925575e-01 5.53340375e-01
-1.24884672e-01 4.54576343e-01 -3.37787330e-01 -1.50162518e-01
-1.28826156e-01 -7.98063815e-01 -7.16732681e-01 -1.00007963e+00
-4.27790016e-01 2.38099962e-01 4.03827488e-01 -1.97563142... | [7.582258701324463, 2.3880434036254883] |
e88edafc-bc69-4e72-8b0d-c8e0f99ceb7b | intrinseqnet-learning-to-estimate-the | 1906.05893 | null | https://arxiv.org/abs/1906.05893v1 | https://arxiv.org/pdf/1906.05893v1.pdf | IntrinSeqNet: Learning to Estimate the Reflectance from Varying Illumination | Intrinsic image decomposition describes an image based on its reflectance and shading components. In this paper we tackle the problem of estimating the diffuse reflectance from a sequence of images captured from a fixed viewpoint under various illuminations. To this end we propose a deep learning approach to avoid heur... | ['Grégoire Nieto', 'Mohammad Rouhani', 'Philippe Robert'] | 2019-06-13 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.96881688e-01 -1.56116873e-01 4.40279782e-01 -3.61349344e-01
-6.38479173e-01 -2.75487810e-01 2.68827528e-01 -5.95774531e-01
-3.07109028e-01 3.72717768e-01 1.93457529e-01 -1.53642222e-01
3.66359621e-01 -7.65026748e-01 -9.80063736e-01 -8.87165904e-01
2.72248864e-01 1.13879748e-01 -1.68213621e-01 -1.23716220... | [9.757035255432129, -2.8825080394744873] |
2fd256e3-7c49-40e2-b197-42273305b2d1 | pace-unified-multi-modal-dialogue-pre | 2305.14839 | null | https://arxiv.org/abs/2305.14839v2 | https://arxiv.org/pdf/2305.14839v2.pdf | PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts | Perceiving multi-modal information and fulfilling dialogues with humans is a long-term goal of artificial intelligence. Pre-training is commonly regarded as an effective approach for multi-modal dialogue. However, due to the limited availability of multi-modal dialogue data, there is still scarce research on multi-moda... | ['Yongbin Li', 'Fei Huang', 'Min Yang', 'Zhichao Yin', 'Binyuan Hui', 'Yunshui Li'] | 2023-05-24 | null | null | null | null | ['visual-dialogue', 'multimodal-intent-recognition', 'response-generation', 'dialogue-state-tracking', 'visual-dialogue'] | ['computer-vision', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-8.14010948e-02 2.02947676e-01 5.45392260e-02 -5.21500826e-01
-5.82977057e-01 -8.80683482e-01 1.01903105e+00 1.53169539e-02
-4.13668364e-01 9.06797111e-01 5.79007149e-01 -1.96709201e-01
1.02067113e-01 -5.74130714e-01 -3.63636799e-02 -3.46842855e-01
2.94991285e-01 1.08188355e+00 2.36698821e-01 -9.98251081... | [12.827966690063477, 8.009050369262695] |
6950b4e7-e231-48e6-86ae-442457c7f80c | a-multi-task-convolutional-neural-network-for-2 | 2106.09303 | null | https://arxiv.org/abs/2106.09303v3 | https://arxiv.org/pdf/2106.09303v3.pdf | A Multi-task convolutional neural network for blind stereoscopic image quality assessment using naturalness analysis | This paper addresses the problem of blind stereoscopic image quality assessment (NR-SIQA) using a new multi-task deep learning based-method. In the field of stereoscopic vision, the information is fairly distributed between the left and right views as well as the binocular phenomenon. In this work, we propose to integr... | ['Mohammed El Hassouni', 'Aladine Chetouani', 'Ayoub Karine', 'Salima Bourbia'] | 2021-06-17 | null | null | null | null | ['stereoscopic-image-quality-assessment'] | ['computer-vision'] | [-6.96645156e-02 -5.77580810e-01 3.85430336e-01 -2.12902918e-01
-1.05460513e+00 -1.18168160e-01 3.95285726e-01 -2.30997160e-01
-4.74478573e-01 7.16688275e-01 5.13553202e-01 1.11348346e-01
-3.63509893e-01 -6.60199583e-01 -6.34539783e-01 -8.99280787e-01
5.40589020e-02 -6.90307394e-02 2.66758978e-01 -4.45599586... | [11.807125091552734, -1.9383623600006104] |
f1418883-ecbf-49e3-8723-39eb9670c757 | co-variance-tackling-noisy-labels-with-sample | null | null | https://openreview.net/forum?id=4ApXq4y81kY | https://openreview.net/pdf?id=4ApXq4y81kY | Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples | The sample selection approach is popular in learning with noisy labels, which tends to select potentially clean data out of noisy data for robust training. The state-of-the-art methods train two deep networks simultaneously for sample selection, which aims to employ their different learning abilities. To prevent two ne... | ['Tongliang Liu', 'Chen Gong', 'Mingming Gong', 'Jun Yu', 'Yibing Zhan', 'Bo Han', 'Xiaobo Xia'] | 2021-09-29 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-4.47866693e-02 5.11681028e-02 -2.33517230e-01 -6.75489843e-01
-8.56438100e-01 -2.68522382e-01 2.31275260e-01 7.22898394e-02
-4.73297954e-01 9.36337054e-01 -2.87284076e-01 -7.02597527e-03
-3.37683886e-01 -6.77356184e-01 -6.50826395e-01 -1.16101897e+00
2.39188448e-02 3.17082733e-01 1.32881954e-01 2.61032060... | [9.331063270568848, 3.8813915252685547] |
84cfa1fa-9168-456b-91e8-161283b37987 | language-clustering-for-multilingual-named | null | null | https://aclanthology.org/2021.findings-emnlp.4 | https://aclanthology.org/2021.findings-emnlp.4.pdf | Language Clustering for Multilingual Named Entity Recognition | Recent work in multilingual natural language processing has shown progress in various tasks such as natural language inference and joint multilingual translation. Despite success in learning across many languages, challenges arise where multilingual training regimes often boost performance on some languages at the expe... | ['Kyle Shaffer'] | null | null | null | null | findings-emnlp-2021-11 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.31429124e-01 -1.67168558e-01 -4.24106866e-01 -5.68777323e-01
-1.21843207e+00 -9.30150807e-01 8.57709408e-01 3.74352247e-01
-1.09727299e+00 7.66592503e-01 6.70868635e-01 -7.73441494e-01
3.75728846e-01 -4.00488913e-01 -7.20475435e-01 -2.83650219e-01
-1.73603624e-01 8.85485828e-01 -2.42397711e-01 -1.34919733... | [10.70944595336914, 9.941396713256836] |
4e2330a0-09a4-4707-b9bf-ba62cca7713e | people-and-places-of-historical-europe | 2305.16718 | null | https://arxiv.org/abs/2305.16718v2 | https://arxiv.org/pdf/2305.16718v2.pdf | People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts | Although pre-trained named entity recognition (NER) models are highly accurate on modern corpora, they underperform on historical texts due to differences in language OCR errors. In this work, we develop a new NER corpus of 3.6M sentences from late medieval charters written mainly in Czech, Latin, and German. We show t... | ['Aleš Horák', 'Tereza Vrabcová', 'Michal Štefánik', 'Kristýna Luger', 'Vít Novotný'] | 2023-05-26 | null | null | null | null | ['optical-character-recognition', 'named-entity-recognition-ner', 'information-retrieval'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-1.63617060e-01 -1.64476588e-01 -2.32707351e-01 -3.93421710e-01
-1.28865516e+00 -1.03930187e+00 7.53593147e-01 4.03752476e-01
-1.13458943e+00 1.13480151e+00 2.65723974e-01 -3.63023847e-01
2.82186300e-01 -6.80705488e-01 -5.14382660e-01 -2.28180066e-01
2.62182783e-02 4.67752099e-01 6.47472590e-02 -9.42369998... | [9.853838920593262, 9.786627769470215] |
229e9a65-7012-41c5-855c-da0b5013a4e8 | energy-efficient-blockchain-enabled-user | 2302.10515 | null | https://arxiv.org/abs/2302.10515v1 | https://arxiv.org/pdf/2302.10515v1.pdf | Energy-Efficient Blockchain-enabled User-Centric Mobile Edge Computing | In the traditional mobile edge computing (MEC) system, the availability of MEC services is greatly limited for the edge users of the cell due to serious signal attenuation and inter-cell interference. User-centric MEC (UC-MEC) can be seen as a promising solution to address this issue. In UC-MEC, each user is served by ... | ['Feng Wu', 'Dan Zhao', 'Zhuojia Gu', 'Yuang Chen', 'Hancheng Lu', 'Langtian Qin'] | 2023-02-21 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-3.15063030e-01 -3.25718015e-01 -3.66625935e-01 2.56052315e-01
-2.11258158e-01 -7.73569107e-01 4.08173591e-01 -1.02341138e-01
-2.34457940e-01 9.82553601e-01 -7.41126984e-02 -4.13382351e-01
-4.01903763e-02 -6.82572067e-01 -4.00910944e-01 -1.21632075e+00
-1.27271205e-01 1.64328098e-01 -8.31134841e-02 1.49911493... | [5.903961658477783, 1.7446110248565674] |
10209627-89ca-451d-8004-b052acf7ce29 | facial-emotion-recognition-using-deep-1 | 2111.02717 | null | https://arxiv.org/abs/2111.02717v1 | https://arxiv.org/pdf/2111.02717v1.pdf | Facial Emotion Recognition using Deep Residual Networks in Real-World Environments | Automatic affect recognition using visual cues is an important task towards a complete interaction between humans and machines. Applications can be found in tutoring systems and human computer interaction. A critical step towards that direction is facial feature extraction. In this paper, we propose a facial feature ex... | ['Björn W. Schuller', 'Elnar Hajiyev', 'Dénes Boros', 'Panagiotis Tzirakis'] | 2021-11-04 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 5.62348552e-02 8.44000503e-02 2.60185540e-01 -6.30374014e-01
-1.15644813e-01 -2.39668190e-01 8.80109727e-01 -1.89541057e-01
-6.82495177e-01 7.06646919e-01 8.64643380e-02 4.41156745e-01
6.25725314e-02 -2.74468541e-01 -3.26775521e-01 -6.96368098e-01
-3.56869072e-01 1.60563901e-01 -1.53424283e-02 -2.76070356... | [13.543023109436035, 2.0682997703552246] |
3760ced2-e6cc-48f5-9c5c-72b4fb81fb32 | towards-context-aware-code-comment-generation | null | null | https://aclanthology.org/2020.findings-emnlp.350 | https://aclanthology.org/2020.findings-emnlp.350.pdf | Towards Context-Aware Code Comment Generation | Code comments are vital for software maintenance and comprehension, but many software projects suffer from the lack of meaningful and up-to-date comments in practice. This paper presents a novel approach to automatically generate code comments at a function level by targeting object-oriented programming languages. Unli... | ['Dongyan Zhao', 'Yansong Feng', 'Zheng Wang', 'Quzhe Huang', 'Xiaohan Yu'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['code-comment-generation', 'comment-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.27442473e-01 4.11579549e-01 -2.07138419e-01 -4.00702953e-01
-8.53926182e-01 -4.96485025e-01 3.87228370e-01 4.06799287e-01
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2.84469604e-01 -9.33811724e-01 -7.98312426e-01 -2.17591785e-02
3.71126086e-02 -2.44594976e-01 3.56925189e-01 -2.49472082... | [7.607490539550781, 7.934596538543701] |
002c4fb5-f8d5-4f05-9c79-6970a9e167bf | greedy-subspace-clustering | 1410.8864 | null | http://arxiv.org/abs/1410.8864v1 | http://arxiv.org/pdf/1410.8864v1.pdf | Greedy Subspace Clustering | We consider the problem of subspace clustering: given points that lie on or
near the union of many low-dimensional linear subspaces, recover the subspaces.
To this end, one first identifies sets of points close to the same subspace and
uses the sets to estimate the subspaces. As the geometric structure of the
clusters ... | ['Constantine Caramanis', 'Sujay Sanghavi', 'Dohyung Park'] | 2014-10-31 | greedy-subspace-clustering-1 | http://papers.nips.cc/paper/5308-greedy-subspace-clustering | http://papers.nips.cc/paper/5308-greedy-subspace-clustering.pdf | neurips-2014-12 | ['face-clustering'] | ['computer-vision'] | [-1.68860823e-01 -2.10594475e-01 -3.70216131e-01 -2.34935552e-01
-7.48954952e-01 -8.93918514e-01 3.53619188e-01 -1.32761016e-01
-3.31391662e-01 3.12729299e-01 -3.22229378e-02 -1.06031969e-01
-3.42491388e-01 -2.12145716e-01 -5.54589450e-01 -1.06669581e+00
-2.82011807e-01 1.02634013e+00 3.46891671e-01 2.31312394... | [7.697699069976807, 4.471610069274902] |
f14280c4-f31c-4064-80ad-7cdcd087e84a | exploiting-explainability-to-design | 2305.18585 | null | https://arxiv.org/abs/2305.18585v1 | https://arxiv.org/pdf/2305.18585v1.pdf | Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models | The advent of social media has given rise to numerous ethical challenges, with hate speech among the most significant concerns. Researchers are attempting to tackle this problem by leveraging hate-speech detection and employing language models to automatically moderate content and promote civil discourse. Unfortunately... | ['Bonnie J. Dorr', 'Ian Perera', 'Suhas Harish', 'Prashanth Thamminedi', 'Sohaib Uddin Syed', 'Pranath Reddy Kumbam'] | 2023-05-29 | null | null | null | null | ['adversarial-robustness', 'hate-speech-detection'] | ['adversarial', 'natural-language-processing'] | [ 1.48203105e-01 3.69934738e-01 4.02720198e-02 1.58117250e-01
-2.71037519e-01 -1.08433330e+00 9.42358911e-01 2.73780644e-01
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-2.21968994e-01 -4.99544829e-01 -3.58082145e-01 -2.35747799e-01
-1.21543989e-01 -1.48721144e-01 -5.28608710e-02 -4.51220661... | [8.64702320098877, 10.442399024963379] |
6f154990-10d0-4555-a185-8d3eb3c597d3 | ultrasound-confidence-maps-of-intensity-and | 2011.11956 | null | https://arxiv.org/abs/2011.11956v4 | https://arxiv.org/pdf/2011.11956v4.pdf | Ultrasound Confidence Maps of Intensity and Structure Based on Directed Acyclic Graphs and Artifact Models | Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values.... | ['John Galeotti', 'Wanwen Chen', 'Alex Ling Yu Hung'] | 2020-11-24 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 9.11682367e-01 1.19420700e-01 5.69575429e-01 -5.13885736e-01
-9.59696889e-01 -4.28121001e-01 1.75134346e-01 4.81742650e-01
-2.44887367e-01 7.11745918e-01 1.36643827e-01 -4.68351334e-01
-2.64687061e-01 -4.98175234e-01 -4.75140572e-01 -8.62318635e-01
-6.93752766e-01 2.84637898e-01 8.42786610e-01 5.77414572... | [13.35286808013916, -2.5289251804351807] |
6ae35cab-fa85-4b16-aee0-e8fd6728e862 | table-structure-recognition-using-top-down-1 | 2010.04565 | null | https://arxiv.org/abs/2010.04565v1 | https://arxiv.org/pdf/2010.04565v1.pdf | Table Structure Recognition using Top-Down and Bottom-Up Cues | Tables are information-rich structured objects in document images. While significant work has been done in localizing tables as graphic objects in document images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from th... | ['C. V. Jawahar', 'Ajoy Mondal', 'Sachin Raja'] | 2020-10-09 | table-structure-recognition-using-top-down | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6007_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730069.pdf | eccv-2020-8 | ['table-recognition', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [ 4.01901513e-01 -1.90650180e-01 -1.81667671e-01 -1.75925508e-01
-8.50779176e-01 -1.20676005e+00 6.94952965e-01 7.65776932e-01
4.16352041e-02 5.45934498e-01 4.92851228e-01 -4.02032375e-01
-1.54672980e-01 -7.40827382e-01 -7.45514989e-01 -3.06499749e-01
3.36907012e-03 7.06568539e-01 2.04181626e-01 -1.58665627... | [11.691003799438477, 2.9907186031341553] |
2f3a5c22-b4cf-49c7-b4e6-55f40bd7a3c9 | multilevel-transformer-for-multimodal-emotion | 2211.07711 | null | https://arxiv.org/abs/2211.07711v2 | https://arxiv.org/pdf/2211.07711v2.pdf | Multilevel Transformer For Multimodal Emotion Recognition | Multimodal emotion recognition has attracted much attention recently. Fusing multiple modalities effectively with limited labeled data is a challenging task. Considering the success of pre-trained model and fine-grained nature of emotion expression, it is reasonable to take these two aspects into consideration. Unlike ... | ['Feng Ye', 'Xiaobo Zhu', 'Meng Li', 'Meimei Wu', 'Junyi He'] | 2022-10-26 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.17534512e-01 -4.08080041e-01 -7.32425079e-02 -7.15490103e-01
-1.29265440e+00 -4.97115165e-01 4.68559563e-01 -9.47190821e-02
-3.96609604e-01 5.42038321e-01 7.98426032e-01 1.07742704e-01
1.71164066e-01 -4.15125787e-01 -3.27033281e-01 -6.19798601e-01
2.56452829e-01 -2.80326214e-02 -3.08611304e-01 -2.00817883... | [13.235062599182129, 5.250998020172119] |
33d9cbb2-b6a6-453e-9c62-e93a7424dfac | remote-sensing-image-classification-using | 2206.13392 | null | https://arxiv.org/abs/2206.13392v1 | https://arxiv.org/pdf/2206.13392v1.pdf | Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network | The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of applications such as urban planning, natural hazards detection, environment monitoring,vegetation map... | ['Alexander Schindler', 'Jasmin Lampert', 'Dat Ngo', 'Khoa Tran', 'Lam Pham'] | 2022-06-20 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.80826062e-01 -5.55596590e-01 -1.70086980e-01 -4.05843943e-01
-5.92283249e-01 -1.17100574e-01 7.53459394e-01 6.98796883e-02
-3.93948853e-01 4.76761818e-01 2.13160723e-01 -5.84410846e-01
-2.97334462e-01 -1.22567272e+00 -3.73479009e-01 -7.06210554e-01
-3.00067693e-01 -7.87538104e-03 7.46745840e-02 -3.65861893... | [9.719043731689453, -1.3916746377944946] |
abd987c9-30a0-4762-935f-6590854a7a8d | optimizing-the-reliability-of-a-bank-with | 2004.11122 | null | https://arxiv.org/abs/2004.11122v1 | https://arxiv.org/pdf/2004.11122v1.pdf | Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization | It is well-known that disciplines such as mechanical engineering, electrical engineering, civil engineering, aerospace engineering, chemical engineering and software engineering witnessed successful applications of reliability engineering concepts. However, the concept of reliability in its strict sense is missing in f... | ['Vadlamani Madhav', 'Vadlamani Ravi'] | 2020-03-31 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-1.16889171e-01 1.38988391e-01 -1.75885595e-02 2.47938693e-01
-8.87398422e-02 -3.29670846e-01 1.33318156e-01 4.23817039e-01
-2.47055277e-01 9.93791223e-01 -2.84563988e-01 -7.10336566e-01
-5.64118385e-01 -9.96021748e-01 -2.66372561e-01 -9.98686492e-01
2.56441087e-01 4.01570499e-01 -1.06268570e-01 -2.96184253... | [5.8731689453125, 3.405808210372925] |
bfc519fc-4435-4a73-8685-abee734812b6 | dataset-of-propaganda-techniques-of-the-state | 2106.07544 | null | https://arxiv.org/abs/2106.07544v1 | https://arxiv.org/pdf/2106.07544v1.pdf | Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China | The digital media, identified as computational propaganda provides a pathway for propaganda to expand its reach without limit. State-backed propaganda aims to shape the audiences' cognition toward entities in favor of a certain political party or authority. Furthermore, it has become part of modern information warfare ... | ['Chu-Hsing Lin', 'Kai-Lai Chang', 'Chun-Ming Lai', 'Rong-Ching Chang'] | 2021-06-14 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [-9.23709869e-02 -1.45240426e-01 -8.23092580e-01 1.40901566e-01
-5.95363736e-01 -8.30317736e-01 1.45414364e+00 7.37519741e-01
-5.73939741e-01 4.53150630e-01 1.03862917e+00 -7.78730750e-01
1.25130313e-02 -9.14563179e-01 -2.01732054e-01 -5.36835194e-01
1.58101454e-01 2.62941748e-01 8.02493840e-02 -7.64574587... | [8.55502700805664, 10.42491340637207] |
a805ba6a-0c11-4d67-904f-dadb8a5b70be | generalizing-brain-decoding-across-subjects | 2205.14102 | null | https://arxiv.org/abs/2205.14102v2 | https://arxiv.org/pdf/2205.14102v2.pdf | Group-level Brain Decoding with Deep Learning | Decoding brain imaging data is gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only pr... | ['Mark Woolrich', 'Oiwi Parker Jones', 'Mats Van Es', 'Richard Csaky'] | 2022-05-27 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.19503218e-01 2.21688077e-02 2.59402454e-01 -5.66004217e-01
-6.77822113e-01 -4.35152322e-01 5.35965741e-01 1.58642054e-01
-5.80849707e-01 4.04060245e-01 4.74006534e-01 -3.44261140e-01
-3.67115796e-01 -2.18775243e-01 -5.63881576e-01 -6.41300917e-01
-5.77016413e-01 -6.70078793e-04 -7.22329915e-02 -9.20483749... | [12.877628326416016, 3.4558260440826416] |
e18b3c32-53e1-41c3-99cd-41c4280c4deb | mel-spectrogram-features-for-acoustic-vehicle | 2204.04013 | null | https://arxiv.org/abs/2204.04013v1 | https://arxiv.org/pdf/2204.04013v1.pdf | Mel-spectrogram features for acoustic vehicle detection and speed estimation | The paper addresses acoustic vehicle detection and speed estimation from single sensor measurements. We predict the vehicle's pass-by instant by minimizing clipped vehicle-to-microphone distance, which is predicted from the mel-spectrogram of input audio, in a supervised learning approach. In addition, mel-spectrogram-... | ['Slobodan Djukanovic', 'Nikola Bulatovic'] | 2022-04-08 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [ 1.21327199e-01 -2.35423848e-01 -8.33936036e-02 -5.07175207e-01
-1.11886322e+00 -3.36795002e-01 3.97277921e-01 4.10509050e-01
-7.13919401e-01 5.15471339e-01 -5.03170967e-01 -3.96816969e-01
-1.19363494e-01 -7.20313668e-01 -6.94346726e-01 -8.58993053e-01
-3.37263793e-01 -8.05825926e-03 4.06850159e-01 2.88882822... | [7.920884132385254, -1.02773916721344] |
337a37bd-2561-40e4-904e-bd9d086a3b52 | probabilistic-label-trees-for-extreme-multi | 2009.11218 | null | https://arxiv.org/abs/2009.11218v1 | https://arxiv.org/pdf/2009.11218v1.pdf | Probabilistic Label Trees for Extreme Multi-label Classification | Extreme multi-label classification (XMLC) is a learning task of tagging instances with a small subset of relevant labels chosen from an extremely large pool of possible labels. Problems of this scale can be efficiently handled by organizing labels as a tree, like in hierarchical softmax used for multi-class problems. I... | ['Robert Busa-Fekete', 'Kalina Jasinska-Kobus', 'Marek Wydmuch', 'Mikhail Kuznetsov', 'Krzysztof Dembczynski'] | 2020-09-23 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.12856996e-01 4.70801413e-01 -3.76460999e-01 -8.44823360e-01
-1.26175129e+00 -8.21582913e-01 3.03538293e-01 3.20314527e-01
-5.85578799e-01 8.28970134e-01 -3.19710135e-01 -2.58188814e-01
-2.93905973e-01 -5.71592927e-01 -6.95917904e-01 -9.42680180e-01
-6.39136881e-02 8.34124088e-01 -3.19826528e-02 3.47875386... | [9.387393951416016, 4.306585788726807] |
238d10c7-b921-413e-9272-55d503c95402 | iterative-next-boundary-detection-for | 2212.03022 | null | https://arxiv.org/abs/2212.03022v2 | https://arxiv.org/pdf/2212.03022v2.pdf | Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections | We address the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several unique challenges such as the concentric circular ring shape of the objects and high precision requirements that result in inadequate perform... | ['Uwe Freiherr von Lukas', 'Martin Wilmking', 'Alba Anadon-Rosell', 'Giulia Resente', 'Alexander Gillert'] | 2022-12-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gillert_Iterative_Next_Boundary_Detection_for_Instance_Segmentation_of_Tree_Rings_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gillert_Iterative_Next_Boundary_Detection_for_Instance_Segmentation_of_Tree_Rings_CVPR_2023_paper.pdf | cvpr-2023-1 | ['boundary-detection'] | ['computer-vision'] | [ 4.20328498e-01 2.17092093e-02 6.38547242e-02 7.81562179e-02
-7.30370358e-02 -9.38668191e-01 4.02708769e-01 3.97012949e-01
-1.84315041e-01 5.98183215e-01 -5.65904737e-01 -4.78715599e-01
-1.09266408e-01 -7.98486054e-01 -5.23439586e-01 -6.60928547e-01
-2.78802186e-01 6.83938324e-01 6.39147639e-01 2.70728301... | [14.435789108276367, -3.1395206451416016] |
6d667163-5cf7-42d6-b57d-1840733cdf8a | a-novel-ensemble-deep-learning-model-for | 2007.12620 | null | https://arxiv.org/abs/2007.12620v1 | https://arxiv.org/pdf/2007.12620v1.pdf | A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News | In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep l... | ['Yang Li', 'Yi Pan'] | 2020-07-23 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-4.53747749e-01 -3.68782729e-01 -1.77934483e-01 -3.78017634e-01
-1.78570837e-01 -2.61998266e-01 3.91874045e-01 -1.65880576e-01
-2.17780739e-01 6.75730228e-01 5.11431158e-01 -8.10310721e-01
3.00920725e-01 -1.25442588e+00 -3.54927808e-01 -5.35205662e-01
-1.68268546e-01 -1.04493268e-01 -4.76341061e-02 -7.36629903... | [4.435201644897461, 4.2537360191345215] |
4df92dca-197c-4056-bdcf-4994656c5c11 | road-rutting-detection-using-deep-learning-on | 2209.14225 | null | https://arxiv.org/abs/2209.14225v1 | https://arxiv.org/pdf/2209.14225v1.pdf | Road Rutting Detection using Deep Learning on Images | Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are being actively conducted in the past few years. However, these researches are mostly focused on detectio... | ['Yoshihide Sekimoto', 'Hiroya Maeda', 'Ashutosh Kumar', 'Deeksha Arya', 'Poonam Kumari Saha'] | 2022-09-28 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [ 3.49945426e-01 5.47998734e-02 -3.90273891e-02 -2.84933209e-01
-6.38468564e-01 1.42763346e-01 1.05710119e-01 -1.76651984e-01
-7.23733529e-02 5.85913360e-01 -2.42664009e-01 -4.74611700e-01
1.42498631e-02 -1.49913454e+00 -6.56163216e-01 -4.08979595e-01
9.64060053e-02 5.89045174e-02 8.73922050e-01 -1.73239037... | [7.424946308135986, 1.1541322469711304] |
3d38d645-a9c5-414c-9e3e-a79b0f733f60 | learning-monocular-dense-depth-from-events | 2010.08350 | null | https://arxiv.org/abs/2010.08350v2 | https://arxiv.org/pdf/2010.08350v2.pdf | Learning Monocular Dense Depth from Events | Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames. Compared to conventional image sensors, they offer significant advantages: high temporal resolution, high dynamic range, no motion blur, and much lower bandwidth. Recently, learning... | ['Davide Scaramuzza', 'Daniel Gehrig', 'Javier Hidalgo-Carrió'] | 2020-10-16 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 4.01529104e-01 -6.30531684e-02 1.07105095e-02 -5.95394909e-01
-5.15836418e-01 -2.51407355e-01 8.94708216e-01 3.76667641e-02
-5.38336039e-01 5.86440384e-01 2.97202528e-01 -1.17156498e-01
2.09264711e-01 -7.18202412e-01 -9.74961460e-01 -5.55337489e-01
-1.68638572e-01 6.56401664e-02 6.89482152e-01 3.75069641... | [8.555281639099121, -1.109528660774231] |
d37057f7-1c11-466d-b849-2418413ab63e | lapar-linearly-assembled-pixel-adaptive-1 | 2105.10422 | null | https://arxiv.org/abs/2105.10422v1 | https://arxiv.org/pdf/2105.10422v1.pdf | LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond | Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version. Last few years have witnessed impressive progress propelled by deep learning methods. However, one critical challenge faced by existing methods is to strike a sweet spot o... | ['Jiaya Jia', 'Jiangbo Lu', 'Nianjuan Jiang', 'Lu Qi', 'Kun Zhou', 'Wenbo Li'] | 2021-05-21 | lapar-linearly-assembled-pixel-adaptive | http://proceedings.neurips.cc/paper/2020/hash/eaae339c4d89fc102edd9dbdb6a28915-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/eaae339c4d89fc102edd9dbdb6a28915-Paper.pdf | neurips-2020-12 | ['image-deblocking'] | ['computer-vision'] | [ 4.11721975e-01 -2.37951621e-01 -1.81346402e-01 -1.65517539e-01
-1.30837810e+00 -1.18827865e-01 3.80710006e-01 -4.69340861e-01
-1.42353818e-01 6.86598241e-01 4.19912755e-01 -1.05921574e-01
-1.61988065e-02 -7.15158463e-01 -7.44167984e-01 -9.60377395e-01
1.38153449e-01 2.57502310e-02 -1.55351341e-01 -3.65916461... | [11.099034309387207, -2.172783374786377] |
f55a42f3-931c-49cf-a706-21af4bf5d6cc | towards-geometry-guided-neural-relighting | 2008.05157 | null | https://arxiv.org/abs/2008.05157v1 | https://arxiv.org/pdf/2008.05157v1.pdf | Towards Geometry Guided Neural Relighting with Flash Photography | Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effort and may be unrealistic in certain practical use scenarios. While such approaches rely entirely on cleverly sampling the color images unde... | ['Jin Zeng', 'Chengxi Yang', 'Wenxiu Sun', 'Zhanghan Ke', 'Di Qiu'] | 2020-08-12 | null | null | null | null | ['intrinsic-image-decomposition', 'image-relighting'] | ['computer-vision', 'computer-vision'] | [ 7.25676656e-01 -2.19516933e-01 3.71349901e-01 -3.00717175e-01
-5.23560643e-01 -4.29029733e-01 5.65986931e-01 -4.04246092e-01
2.11806335e-02 6.34608448e-01 1.20047055e-01 -2.48573616e-01
2.37996846e-01 -8.72269332e-01 -9.40878570e-01 -8.42500329e-01
4.07155961e-01 2.48930484e-01 1.15177222e-01 -2.41279989... | [9.856095314025879, -2.8990211486816406] |
12288d1b-1212-4f62-a556-410498282125 | on-the-evolution-of-syntactic-information | 2101.11492 | null | https://arxiv.org/abs/2101.11492v2 | https://arxiv.org/pdf/2101.11492v2.pdf | On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations | The adaptation of pretrained language models to solve supervised tasks has become a baseline in NLP, and many recent works have focused on studying how linguistic information is encoded in the pretrained sentence representations. Among other information, it has been shown that entire syntax trees are implicitly embedde... | ['Laura Pérez-Mayos', 'Leo Wanner', 'Miguel Ballesteros', 'Roberto Carlini'] | 2021-01-27 | null | https://aclanthology.org/2021.eacl-main.191 | https://aclanthology.org/2021.eacl-main.191.pdf | eacl-2021-2 | ['constituency-parsing'] | ['natural-language-processing'] | [ 9.26764086e-02 5.08601725e-01 -2.22422704e-01 -6.35861337e-01
-3.52634162e-01 -8.74994159e-01 4.73221630e-01 6.28307581e-01
-5.28136909e-01 6.85649276e-01 7.92127907e-01 -3.21474642e-01
-2.53776044e-01 -8.22772205e-01 -7.36481905e-01 -6.44137800e-01
-2.04737768e-01 4.84607100e-01 2.98483580e-01 -4.80188757... | [10.551362037658691, 9.232505798339844] |
0ee55e90-1328-49f0-87f8-f388a81c7d99 | automatic-identification-of-epileptic | 1910.11183 | null | http://arxiv.org/abs/1910.11183v2 | http://arxiv.org/pdf/1910.11183v2.pdf | Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification | Identifying seizure activities in non-stationary electroencephalography (EEG)
is a challenging task, since it is time-consuming, burdensome, and dependent on
expensive human resources and subject to error and bias. A computerized seizure
identification scheme can eradicate the above problems, assist clinicians and
bene... | [] | 2020-04-28 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [ 3.86860400e-01 -4.83168453e-01 5.41452527e-01 -1.35190943e-02
-4.58477587e-01 -4.18973684e-01 1.55888470e-02 1.46341041e-01
-4.24331009e-01 9.16489542e-01 -1.29794613e-01 -1.33024469e-01
-4.66379315e-01 -2.60421693e-01 8.65030736e-02 -8.57397020e-01
-2.78877914e-01 1.45709664e-01 1.73754115e-02 -7.77400061... | [13.190361022949219, 3.447903633117676] |
6d3e26a0-3cd6-4d02-ab94-cdbce56c47a6 | deep-association-learning-for-unsupervised | 1808.07301 | null | http://arxiv.org/abs/1808.07301v1 | http://arxiv.org/pdf/1808.07301v1.pdf | Deep Association Learning for Unsupervised Video Person Re-identification | Deep learning methods have started to dominate the research progress of
video-based person re-identification (re-id). However, existing methods mostly
consider supervised learning, which requires exhaustive manual efforts for
labelling cross-view pairwise data. Therefore, they severely lack scalability
and practicality... | ['Yanbei Chen', 'Shaogang Gong', 'Xiatian Zhu'] | 2018-08-22 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.66225185e-02 -2.80117154e-01 -2.92459249e-01 -5.64949512e-01
-4.81046200e-01 -4.02308255e-01 8.25473487e-01 -9.81200710e-02
-8.72540593e-01 5.77353895e-01 2.80287713e-01 1.75599962e-01
4.77199033e-02 -4.09651250e-01 -6.96581125e-01 -5.68688035e-01
3.63689661e-02 6.98845804e-01 -6.15385808e-02 1.23153597... | [14.681193351745605, 0.9653854966163635] |
b65f251c-796e-4025-8950-865d552db205 | repsum-unsupervised-dialogue-summarization | null | null | https://aclanthology.org/2021.acl-long.471 | https://aclanthology.org/2021.acl-long.471.pdf | RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy | In the field of dialogue summarization, due to the lack of training data, it is often difficult for supervised summary generation methods to learn vital information from dialogue context with limited data. Several attempts on unsupervised summarization for text by leveraging semantic information solely or auto-encoder ... | ['Zhenglu Yang', 'Changlong Sun', 'Xiaozhong Liu', 'Tianyi Wang', 'Yating Zhang', 'Xiyan Fu'] | 2021-08-01 | null | null | null | acl-2021-5 | ['sentence-compression'] | ['natural-language-processing'] | [ 7.79242575e-01 7.07933784e-01 -5.15084639e-02 -3.76406372e-01
-6.56690300e-01 -2.20879763e-01 8.09359789e-01 4.24551547e-01
-1.58797890e-01 1.43497789e+00 9.81823266e-01 2.58954447e-02
4.62557450e-02 -5.73206365e-01 -2.45454952e-01 -4.58096057e-01
3.72575939e-01 4.41638678e-01 2.94155572e-02 -5.50260961... | [12.494640350341797, 9.190692901611328] |
e180bf3c-dd5e-4b49-b9eb-0405cfd764fb | multi-agent-path-finding-with-awareness-for | 2009.09355 | null | https://arxiv.org/abs/2009.09355v1 | https://arxiv.org/pdf/2009.09355v1.pdf | Multi Agent Path Finding with Awareness for Spatially Extended Agents | Path finding problems involve identification of a plan for conflict free movement of agents over a common road network. Most approaches to this problem handle the agents as point objects, wherein the size of the agent is significantly smaller than the road on which it travels. In this paper, we consider spatially exten... | ['Dipti Deodhare', 'Shyni Thomas', 'M. N. Murty'] | 2020-09-20 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.40082315e-01 1.80143252e-01 -3.24041806e-02 1.24467276e-01
-2.35547140e-01 -6.88577831e-01 7.35384226e-01 4.63238686e-01
-6.39109015e-01 1.23007882e+00 -1.46060407e-01 -4.74342555e-01
-7.92885244e-01 -1.10543752e+00 -3.83399934e-01 -6.42655909e-01
-6.19640052e-01 1.05730176e+00 9.25758958e-01 -4.59191710... | [4.9178571701049805, 1.8346904516220093] |
f1d259fc-ae4f-4389-b21d-441cd2261930 | tracking-by-animation-unsupervised-learning | 1809.03137 | null | http://arxiv.org/abs/1809.03137v3 | http://arxiv.org/pdf/1809.03137v3.pdf | Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers | Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune a... | ['Hangen He', 'David Barber', 'Jian Li', 'Zhen He', 'Daxue Liu'] | 2018-09-10 | tracking-by-animation-unsupervised-learning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/He_Tracking_by_Animation_Unsupervised_Learning_of_Multi-Object_Attentive_Trackers_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/He_Tracking_by_Animation_Unsupervised_Learning_of_Multi-Object_Attentive_Trackers_CVPR_2019_paper.pdf | cvpr-2019-6 | ['online-multi-object-tracking'] | ['computer-vision'] | [ 1.32978996e-02 -4.72643793e-01 -2.18256593e-01 -1.42247841e-01
-5.58862448e-01 -3.05706143e-01 3.33340287e-01 -1.29308328e-01
-5.48470378e-01 5.52878797e-01 -2.10248724e-01 -1.07618295e-01
1.04177430e-01 -4.58957553e-01 -9.84199822e-01 -8.32623482e-01
2.09765032e-01 3.20745379e-01 7.35297322e-01 2.39794597... | [6.37096643447876, -2.101515531539917] |
91ebd57f-ad39-44dd-be6c-656b628e1f90 | active-exploration-of-multimodal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wanyan_Active_Exploration_of_Multimodal_Complementarity_for_Few-Shot_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wanyan_Active_Exploration_of_Multimodal_Complementarity_for_Few-Shot_Action_Recognition_CVPR_2023_paper.pdf | Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition | Recently, few-shot action recognition receives increasing attention and achieves remarkable progress. However, previous methods mainly rely on limited unimodal data (e.g., RGB frames) while the multimodal information remains relatively underexplored. In this paper, we propose a novel Active Multimodal Few-shot Acti... | ['Changsheng Xu', 'Chaofan Chen', 'Xiaoshan Yang', 'Yuyang Wanyan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['few-shot-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 3.42398852e-01 -2.60090232e-01 -6.21468544e-01 -4.26759034e-01
-1.37285733e+00 -3.98412198e-02 6.39567137e-01 -1.72918469e-01
-4.72432107e-01 6.77603960e-01 7.38979101e-01 4.02895778e-01
7.32737705e-02 -4.88512695e-01 -5.97267628e-01 -1.06986678e+00
4.87543643e-01 4.17845905e-01 3.91709417e-01 5.55198938... | [8.608909606933594, 0.775636613368988] |
4fd0665c-79ee-4fb1-a23b-ab9ce69f2ef9 | auto-encoder-based-model-for-high-dimensional | 2108.02083 | null | https://arxiv.org/abs/2108.02083v2 | https://arxiv.org/pdf/2108.02083v2.pdf | Auto-encoder based Model for High-dimensional Imbalanced Industrial Data | With the proliferation of IoT devices, the distributed control systems are now capturing and processing more sensors at higher frequency than ever before. These new data, due to their volume and novelty, cannot be effectively consumed without the help of data-driven techniques. Deep learning is emerging as a promising ... | ['Chao Zhang', 'Sthitie Bom'] | 2021-08-04 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [ 4.18783516e-01 -4.28528059e-03 -3.66767466e-01 -6.02380037e-01
-2.22762868e-01 -3.70276392e-01 4.39971358e-01 2.90790945e-01
-1.23250425e-01 7.36076236e-01 1.71658486e-01 1.74625069e-01
-6.19615197e-01 -9.50921595e-01 -6.35505557e-01 -6.76837683e-01
1.54252484e-01 6.20445788e-01 -3.16901922e-01 -4.55901235... | [7.256441116333008, 2.3219432830810547] |
257ad5d4-ed8e-4d62-97f9-e39ce9e7e75c | implicit-feedback-for-dense-passage-retrieval | 2204.00718 | null | https://arxiv.org/abs/2204.00718v2 | https://arxiv.org/pdf/2204.00718v2.pdf | Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach | In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is available as implicit feedback. We then exploit such historic implicit interactions to improve the effectiveness of a DR. A key challenge that we ... | ['Guido Zuccon', 'Hang Li', 'Shengyao Zhuang'] | 2022-04-01 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [ 8.98282379e-02 -1.58830583e-02 -4.21175122e-01 -3.07771325e-01
-7.41831660e-01 -6.76986992e-01 9.83265221e-01 -7.66513571e-02
-7.10345089e-01 9.39540923e-01 4.77089614e-01 -2.84494191e-01
-3.59823257e-01 -7.65859246e-01 -1.02059793e+00 -4.46338862e-01
-2.40992814e-01 2.83475041e-01 1.15025774e-01 -2.17508435... | [9.732451438903809, 5.5290093421936035] |
dcbd04a0-2132-4398-b6ca-36abc4a49637 | non-parallel-text-style-transfer-with-self-1 | 2204.08123 | null | https://arxiv.org/abs/2204.08123v1 | https://arxiv.org/pdf/2204.08123v1.pdf | Non-Parallel Text Style Transfer with Self-Parallel Supervision | The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; the style transfer models thus only receive weak supervision of the target sentenc... | ['Soroush Vosoughi', 'Guangxuan Xu', 'Chenyan Jia', 'Chongyang Gao', 'Ruibo Liu'] | 2022-04-18 | non-parallel-text-style-transfer-with-self | https://openreview.net/forum?id=-TSe5o7STVR | https://openreview.net/pdf?id=-TSe5o7STVR | iclr-2022-4 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.60404289e-01 2.59349406e-01 -2.26656541e-01 -7.17004836e-01
-6.00271761e-01 -7.98451722e-01 7.59313226e-01 -2.79700100e-01
-3.41684252e-01 9.05586064e-01 5.37392914e-01 -1.72058389e-01
5.32980323e-01 -7.60027766e-01 -6.79419816e-01 -2.73350567e-01
5.34183502e-01 8.01852047e-01 -1.61768362e-01 -6.64374053... | [11.612604141235352, 9.545339584350586] |
bbadc28f-0909-4f21-a715-514277165f8f | challenges-in-monocular-visual-odometry | 1705.04300 | null | http://arxiv.org/abs/1705.04300v4 | http://arxiv.org/pdf/1705.04300v4.pdf | Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect | Monocular visual odometry (VO) and simultaneous localization and mapping
(SLAM) have seen tremendous improvements in accuracy, robustness and
efficiency, and have gained increasing popularity over recent years.
Nevertheless, not so many discussions have been carried out to reveal the
influences of three very influentia... | ['Xiang Gao', 'Rui Wang', 'Nan Yang', 'Daniel Cremers'] | 2017-05-11 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [ 4.72282246e-02 -3.23267162e-01 -2.10114047e-01 -5.63200772e-01
-3.62968415e-01 -5.47755122e-01 9.30680811e-01 -2.04658955e-01
-4.38037038e-01 1.11611438e+00 1.00156926e-01 -3.47410768e-01
-2.04705149e-01 -5.37990212e-01 -4.95507717e-01 -5.41628599e-01
-2.87469745e-01 5.83324432e-01 4.04380232e-01 -3.52948755... | [7.365614891052246, -2.105556011199951] |
9fda07dd-aa60-46d7-a68b-565a423c93f6 | learning-to-learn-group-alignment-a-self | 2304.07337 | null | https://arxiv.org/abs/2304.07337v1 | https://arxiv.org/pdf/2304.07337v1.pdf | Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams | Mixed incentives among a population with multiagent teams has been shown to have advantages over a fully cooperative system; however, discovering the best mixture of incentives or team structure is a difficult and dynamic problem. We propose a framework where individual learning agents self-regulate their configuration... | ['Kyle Tilbury', 'David Radke'] | 2023-04-14 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.63154745e-01 3.59808028e-01 -1.96955934e-01 -1.57739744e-01
-2.72842616e-01 -5.60364842e-01 6.04236901e-01 3.42454582e-01
-5.33010781e-01 1.21929693e+00 1.29783601e-01 2.10875496e-01
-3.54466140e-01 -7.08594382e-01 -3.76614153e-01 -9.36533868e-01
-2.31136143e-01 9.80591297e-01 2.29801998e-01 -5.99187315... | [3.7545526027679443, 2.052415370941162] |
5f75befa-62d9-46a6-99ff-42c2f2ef8b58 | pg-vton-a-novel-image-based-virtual-try-on | 2304.08956 | null | https://arxiv.org/abs/2304.08956v1 | https://arxiv.org/pdf/2304.08956v1.pdf | PG-VTON: A Novel Image-Based Virtual Try-On Method via Progressive Inference Paradigm | Virtual try-on is a promising computer vision topic with a high commercial value wherein a new garment is visually worn on a person with a photo-realistic effect. Previous studies conduct their shape and content inference at one stage, employing a single-scale warping mechanism and a relatively unsophisticated content ... | ['Kerui Hu', 'Zili Wang', 'Shuyou Zhang', 'Lemiao Qiu', 'Naiyu Fang'] | 2023-04-18 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 6.15132391e-01 1.61285773e-01 -2.27706641e-01 -3.19915086e-01
-8.49424183e-01 -6.71337724e-01 7.14005411e-01 -6.89928472e-01
-7.75138438e-02 3.14123958e-01 1.42656893e-01 -2.95695275e-01
1.96355626e-01 -7.41624594e-01 -7.08676159e-01 -4.99568224e-01
4.45263475e-01 7.28806034e-02 9.02281478e-02 -1.09253630... | [11.923931121826172, -0.866944432258606] |
bdd30313-6896-4c20-ac48-ff6c12fe50c3 | thu_ngn-at-semeval-2019-task-12-toponym | null | null | https://aclanthology.org/S19-2229 | https://aclanthology.org/S19-2229.pdf | THU\_NGN at SemEval-2019 Task 12: Toponym Detection and Disambiguation on Scientific Papers | First name: Tao Last name: Qi Email: taoqi.qt@gmail.com Affiliation: Department of Electronic Engineering, Tsinghua University First name: Suyu Last name: Ge Email: gesy17@mails.tsinghua.edu.cn Affiliation: Department of Electronic Engineering, Tsinghua University First name: Chuhan Last name: Wu Email: wuch15@mails.ts... | ['Suyu Ge', 'Yongfeng Huang', 'Yubo Chen', 'Tao Qi', 'Chuhan Wu'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['toponym-resolution'] | ['natural-language-processing'] | [-3.71221334e-01 -6.78583026e-01 -8.07985812e-02 -1.30329370e-01
-6.40970886e-01 -3.41671497e-01 4.71802503e-01 4.01751995e-01
-7.21921027e-01 7.72212863e-01 -2.73949001e-02 -4.00402784e-01
-2.71427304e-01 -1.25136673e+00 -1.95591599e-01 -4.42543387e-01
9.81931090e-02 6.46575272e-01 -1.29405648e-01 -4.72094268... | [9.979114532470703, 9.170188903808594] |
da3b7973-16f1-479f-8573-516fde083f7d | hybrid-clustering-based-on-content-and | 1703.09646 | null | http://arxiv.org/abs/1703.09646v1 | http://arxiv.org/pdf/1703.09646v1.pdf | Hybrid Clustering based on Content and Connection Structure using Joint Nonnegative Matrix Factorization | We present a hybrid method for latent information discovery on the data sets
containing both text content and connection structure based on constrained low
rank approximation. The new method jointly optimizes the Nonnegative Matrix
Factorization (NMF) objective function for text clustering and the Symmetric
NMF (SymNMF... | ['Rundong Du', 'Haesun Park', 'Barry Drake'] | 2017-03-28 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [ 9.45995525e-02 -1.24436304e-01 -3.15717131e-01 -1.30598679e-01
-1.89707294e-01 -5.98549426e-01 6.94574535e-01 2.38556489e-01
-2.28958294e-01 3.99351150e-01 3.96194607e-01 -3.61197025e-01
-9.08439279e-01 -6.09784424e-01 -2.93613464e-01 -9.10107911e-01
-1.31547838e-01 7.95777857e-01 -1.04143701e-01 -8.64134803... | [7.351987361907959, 4.971467971801758] |
e43534fb-6f44-4d02-b6bf-d91756480747 | rodeo-robust-de-aliasing-autoencoder-for-real | 1912.07519 | null | https://arxiv.org/abs/1912.07519v1 | https://arxiv.org/pdf/1912.07519v1.pdf | RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction | In this work we address the problem of real-time dynamic medical MRI and X Ray CT image reconstruction from parsimonious samples Fourier frequency space for MRI and sinogram tomographic projections for CT. Today the de facto standard for such reconstruction is compressed sensing. CS produces high quality images (with m... | ['Janki Mehta', 'Angshul Majumdar'] | 2019-12-11 | null | null | null | null | ['de-aliasing'] | ['computer-vision'] | [ 4.34431255e-01 2.09369153e-01 2.78494567e-01 -1.53305799e-01
-4.51864511e-01 -2.33289655e-02 2.52527148e-01 -1.34143770e-01
-7.71261632e-01 6.77643180e-01 -1.45303249e-01 -2.21340686e-01
-3.39105040e-01 -7.61736274e-01 -7.07662582e-01 -9.21077549e-01
-2.19667822e-01 6.04680896e-01 1.20942257e-01 -9.98989642... | [13.437948226928711, -2.466742515563965] |
b3045f39-e9e2-4580-89ea-ba2ddbd83125 | is-rewiring-actually-helpful-in-graph-neural | 2305.19717 | null | https://arxiv.org/abs/2305.19717v1 | https://arxiv.org/pdf/2305.19717v1.pdf | Is Rewiring Actually Helpful in Graph Neural Networks? | Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can leverage longer-range interactions between nodes is hindered by the issues of over-smoothing and over-squashing. In particular, the latter is a... | ['Alessio Micheli', 'Domenico Tortorella'] | 2023-05-31 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 3.26725356e-02 4.12607580e-01 -3.75386924e-02 -3.09224665e-01
-1.74257234e-01 -4.26853269e-01 9.02061284e-01 8.89209330e-01
-3.00734401e-01 5.48540056e-01 1.42966462e-02 -4.35004085e-01
-1.60096914e-01 -1.10507870e+00 -6.70490682e-01 -6.04281068e-01
-8.19733381e-01 1.90427706e-01 4.58156377e-01 -3.40791672... | [6.9446492195129395, 6.17426872253418] |
7c869e97-55e1-4d97-aa67-24847a95af84 | a-driver-fatigue-recognition-algorithm-based | 2003.08134 | null | https://arxiv.org/abs/2003.08134v1 | https://arxiv.org/pdf/2003.08134v1.pdf | A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence | Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great developm... | ['Xiaobo Lu', 'Chen Zhang', 'Zhiliang Huang'] | 2020-03-18 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-5.07178754e-02 -6.66091800e-01 4.07839380e-02 -4.25060809e-01
7.52959624e-02 2.77037710e-01 1.27958864e-01 -9.80339050e-01
-5.79206467e-01 4.74291205e-01 7.69510567e-02 1.77423488e-02
-2.93224633e-01 -3.87563497e-01 -4.70188558e-02 -8.75350893e-01
2.24020258e-01 7.30780736e-02 3.36090326e-01 -1.73673719... | [8.103251457214355, -0.5561326742172241] |
d360cb0f-7f13-4db3-ab3f-1c842708ad35 | ontotype-ontology-guided-zero-shot-fine | 2305.12307 | null | https://arxiv.org/abs/2305.12307v1 | https://arxiv.org/pdf/2305.12307v1.pdf | OntoType: Ontology-Guided Zero-Shot Fine-Grained Entity Typing with Weak Supervision from Pre-Trained Language Models | Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, will play an important role in natural language understanding. A supervised FET method, which typically relies on human-annotated corpora for training, is costly and difficult to scale. Recent studies l... | ['Jiawei Han', 'Xuan Wang', 'Minhao Jiang', 'Tanay Komarlu'] | 2023-05-21 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 9.78421718e-02 2.67319620e-01 -5.85027874e-01 -5.71263254e-01
-4.70477730e-01 -5.39212644e-01 5.40015221e-01 4.46561038e-01
-5.18326283e-01 9.80418146e-01 3.17766488e-01 -1.18886329e-01
-2.43377298e-01 -1.22916460e+00 -7.14262784e-01 -1.68376446e-01
1.87579691e-01 9.16052163e-01 4.64409709e-01 -5.04088581... | [9.634610176086426, 8.759969711303711] |
ef49ed16-60a9-49a6-bd58-3fd47fd46457 | image-based-localization-using-lstms-for | 1611.07890 | null | http://arxiv.org/abs/1611.07890v4 | http://arxiv.org/pdf/1611.07890v4.pdf | Image-based localization using LSTMs for structured feature correlation | In this work we propose a new CNN+LSTM architecture for camera pose
regression for indoor and outdoor scenes. CNNs allow us to learn suitable
feature representations for localization that are robust against motion blur
and illumination changes. We make use of LSTM units on the CNN output, which
play the role of a struc... | ['Sebastian Hilsenbeck', 'Laura Leal-Taixé', 'Torsten Sattler', 'Daniel Cremers', 'Caner Hazirbas', 'Florian Walch'] | 2016-11-23 | image-based-localization-using-lstms-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Walch_Image-Based_Localization_Using_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Walch_Image-Based_Localization_Using_ICCV_2017_paper.pdf | iccv-2017-10 | ['image-based-localization'] | ['computer-vision'] | [-1.23762801e-01 -6.42083228e-01 -1.49334863e-01 -4.21167791e-01
-7.22783625e-01 -5.35222232e-01 4.57978606e-01 -3.09795529e-01
-5.69462836e-01 4.47800219e-01 2.33994603e-01 2.65208632e-02
-1.02588572e-01 -6.87719345e-01 -1.06538045e+00 -5.82970023e-01
-3.40310708e-02 -4.13031653e-02 -9.03098658e-02 -3.40776257... | [7.67680025100708, -2.1088693141937256] |
6bce8df7-97bd-4d2a-89fa-860bacfae146 | mmdenselstm-an-efficient-combination-of | 1805.02410 | null | https://arxiv.org/abs/1805.02410v2 | https://arxiv.org/pdf/1805.02410v2.pdf | MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation | Deep neural networks have become an indispensable technique for audio source separation (ASS). It was recently reported that a variant of CNN architecture called MMDenseNet was successfully employed to solve the ASS problem of estimating source amplitudes, and state-of-the-art results were obtained for DSD100 dataset. ... | ['Naoya Takahashi', 'Yuki Mitsufuji', 'Nabarun Goswami'] | 2018-05-07 | null | null | null | null | ['music-source-separation'] | ['music'] | [-3.52651030e-02 -4.92682308e-01 1.96772188e-01 -4.64724377e-02
-9.25064027e-01 -1.62412047e-01 1.73747659e-01 -3.08985084e-01
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-8.86861756e-02 -3.91723156e-01 -3.79384905e-01 -7.15123653e-01
-1.58295110e-01 -2.02353626e-01 2.46329099e-01 -1.16270073... | [15.331668853759766, 5.6016011238098145] |
b2339332-8bd7-4c17-a7e9-59742feb080d | calip-zero-shot-enhancement-of-clip-with | 2209.14169 | null | https://arxiv.org/abs/2209.14169v2 | https://arxiv.org/pdf/2209.14169v2.pdf | CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention | Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classification. To further improve its downstream performance, existing works propose additional learnable modules upon CLIP and fine-tune them by few... | ['Bin Cui', 'Xuming He', 'Xupeng Miao', 'Xianzheng Ma', 'Longtian Qiu', 'Renrui Zhang', 'Ziyu Guo'] | 2022-09-28 | null | null | null | null | ['training-free-3d-point-cloud-classification'] | ['computer-vision'] | [ 1.73668209e-02 -1.35983035e-01 -3.80660534e-01 -3.55134785e-01
-9.62126970e-01 -2.74534762e-01 7.98835516e-01 -2.26791918e-01
-3.78902137e-01 2.91599005e-01 3.60822558e-01 8.27999115e-02
-7.34148622e-02 -6.55217707e-01 -8.65309536e-01 -7.28335738e-01
2.03585550e-01 1.44970194e-01 2.01158047e-01 -3.27280939... | [10.192434310913086, 1.7809630632400513] |
56532c8b-1ec3-44e6-ae14-8dd40a328e56 | semantic-table-detection-with-layoutlmv3 | 2211.15504 | null | https://arxiv.org/abs/2211.15504v1 | https://arxiv.org/pdf/2211.15504v1.pdf | Semantic Table Detection with LayoutLMv3 | This paper presents an application of the LayoutLMv3 model for semantic table detection on financial documents from the IIIT-AR-13K dataset. The motivation behind this paper's experiment was that LayoutLMv3's official paper had no results for table detection using semantic information. We concluded that our approach di... | ['Phillip Mortimer', 'Niels Victor', 'Ivan Silajev'] | 2022-11-25 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-1.24807067e-01 3.19462270e-01 6.11241646e-02 -3.57068181e-01
-8.87766957e-01 -7.86257446e-01 9.09674108e-01 7.06027329e-01
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2.01272383e-01 7.37975776e-01 6.78640783e-01 -3.74298811... | [9.64558219909668, 7.88485860824585] |
29908b22-2702-4c5a-8723-0a08cb91e062 | a-unified-framework-with-meta-dropout-for-few | 2210.06409 | null | https://arxiv.org/abs/2210.06409v1 | https://arxiv.org/pdf/2210.06409v1.pdf | A Unified Framework with Meta-dropout for Few-shot Learning | Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations. In this paper, we utilize the idea of meta-learning to explain two very different streams of few-shot learning, i.e., the episodic meta-learning-based and pre-train finetune-based few-shot learn... | ['Rui Zhao', 'Xingyu Zeng', 'Shaobo Lin'] | 2022-10-12 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [-3.02696805e-02 -1.21033959e-01 -1.44253209e-01 -5.36478639e-01
-4.13540810e-01 1.79020345e-01 4.31708217e-01 -1.79434925e-01
-5.19395351e-01 7.33928502e-01 -1.35062024e-01 1.72445670e-01
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4.26875830e-01 -5.74223101e-02 7.07796156e-01 -2.07175940... | [9.954940795898438, 3.022618293762207] |
6d7329b6-380a-4c75-9148-d313df417a19 | ftso-effective-nas-via-first-topology-second-1 | 2303.12948 | null | https://arxiv.org/abs/2303.12948v1 | https://arxiv.org/pdf/2303.12948v1.pdf | FTSO: Effective NAS via First Topology Second Operator | Existing one-shot neural architecture search (NAS) methods have to conduct a search over a giant super-net, which leads to the huge computational cost. To reduce such cost, in this paper, we propose a method, called FTSO, to divide the whole architecture search into two sub-steps. Specifically, in the first step, we on... | ['Lei Chen', 'Likang Wang'] | 2023-02-28 | ftso-effective-nas-via-first-topology-second | https://openreview.net/forum?id=7Z29QbHxIL | https://openreview.net/pdf?id=7Z29QbHxIL | null | ['architecture-search'] | ['methodology'] | [-2.61410236e-01 -2.14479566e-01 5.20010479e-02 -9.03126299e-02
-6.72511816e-01 -3.77538592e-01 -4.49957736e-02 -3.09754163e-01
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9.31515917e-02 5.59718251e-01 6.25397921e-01 2.43642703... | [8.555354118347168, 3.141591787338257] |
9197d93b-99cd-49c5-ab1e-e9149061d1bd | pattern-revising-enhanced-simple-question | null | null | https://aclanthology.org/C18-1277 | https://aclanthology.org/C18-1277.pdf | Pattern-revising Enhanced Simple Question Answering over Knowledge Bases | Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks. Simple questions constitute a large part of questions queried on the web, still being a challenge... | ['Hao liu', 'Jun Zhao', 'Yanchao Hao', 'Shizhu He', 'Kang Liu'] | 2018-08-01 | pattern-revising-enhanced-simple-question-1 | https://aclanthology.org/C18-1277 | https://aclanthology.org/C18-1277.pdf | coling-2018-8 | ['fact-selection'] | ['natural-language-processing'] | [ 7.30939656e-02 4.91236985e-01 -3.00875694e-01 -2.06500009e-01
-1.18596959e+00 -7.23404527e-01 6.92261100e-01 6.21726632e-01
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-6.40363276e-01 -1.14278591e+00 -7.49636531e-01 -1.35354489e-01
6.88954443e-02 7.62841105e-01 8.67303789e-01 -5.04730284... | [10.559683799743652, 7.94567346572876] |
c8841a3c-bde5-47a0-8482-512560f9cca1 | first-steps-toward-camera-model | 1603.01068 | null | http://arxiv.org/abs/1603.01068v2 | http://arxiv.org/pdf/1603.01068v2.pdf | First Steps Toward Camera Model Identification with Convolutional Neural Networks | Detecting the camera model used to shoot a picture enables to solve a wide
series of forensic problems, from copyright infringement to ownership
attribution. For this reason, the forensic community has developed a set of
camera model identification algorithms that exploit characteristic traces left
on acquired images b... | ['Paolo Bestagini', 'David Güera', 'Luca Bondi', 'Edward J. Delp', 'Stefano Tubaro', 'Luca Baroffio'] | 2016-03-03 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.26559114e-01 -6.49670064e-01 -3.93724330e-02 -2.63124973e-01
-5.89569092e-01 -7.92071700e-01 6.93295598e-01 8.35443139e-02
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-1.36670873e-01 -5.32228947e-01 -8.25579166e-01 -4.78901058e-01
1.98073927e-02 1.13292605e-01 4.74031791e-02 3.80164176... | [12.385390281677246, 1.0024831295013428] |
3f3e5688-d597-40af-8621-10aafce4a354 | slam-a-unified-encoder-for-speech-and | 2110.10329 | null | https://arxiv.org/abs/2110.10329v1 | https://arxiv.org/pdf/2110.10329v1.pdf | SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training | Unsupervised pre-training is now the predominant approach for both text and speech understanding. Self-attention models pre-trained on large amounts of unannotated data have been hugely successful when fine-tuned on downstream tasks from a variety of domains and languages. This paper takes the universality of unsupervi... | ['Yu Zhang', 'Alexis Conneau', 'Jason Riesa', 'Melvin Johnson', 'Jonathan H. Clark', 'Ye Jia', 'Anmol Gulati', 'Nan Wu', 'Yu-An Chung', 'Ankur Bapna'] | 2021-10-20 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 6.81953073e-01 4.47113246e-01 -4.50458229e-01 -7.15197563e-01
-1.36223233e+00 -4.71825927e-01 9.48888183e-01 2.56636608e-02
-6.58614218e-01 4.11040574e-01 9.12655413e-01 -8.50493968e-01
3.02046686e-01 -8.84897411e-02 -9.98981893e-01 -2.06323847e-01
5.68080604e-01 7.30128884e-01 -2.69929677e-01 -3.58186811... | [14.377996444702148, 7.128015995025635] |
fb77dad9-172c-4a35-8246-5fd128d6eac2 | ji-yu-zhu-ti-ti-shi-xue-xi-de-ling-yang-ben | null | null | https://aclanthology.org/2022.ccl-1.48 | https://aclanthology.org/2022.ccl-1.48.pdf | 基于主题提示学习的零样本立场检测方法(A Topic-based Prompt Learning Method for Zero-Shot Stance Detection) | “零样本立场检测目的是针对未知目标数据进行立场极性预测。一般而言,文本的立场表达是与所讨论的目标主题是紧密联系的。针对未知目标的立场检测,本文将立场表达划分为两种类型:一类在说话者面向不同的主题和讨论目标时表达相同的立场态度,称之为目标无关的表达;另一类在说话者面向特定主题和讨论目标时才表达相应的立场态度,本文称之为目标依赖的表达。对这两种表达进行区分,有效学习到目标无关的表达方式并忽略目标依赖的表达方式,有望强化模型的可迁移能力,使其更加适应零样本立场检测任务。据此,本文提出了一种基于主题提示学习的零样本立场检测方法。具体而言,受自监督学习的启发,本文为了零样本立场检测设置了一个代理任务框架。其中,代理任务通过掩盖上下文中的目标主... | ['Ruifeng Xu', 'Bin Liang', 'Zixiao Chen'] | null | null | null | null | ccl-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [-7.06793725e-01 -1.08910787e+00 8.04058909e-01 2.17016280e-01
5.46010315e-01 -1.40819919e+00 3.30559686e-02 6.02229416e-01
1.88348874e-01 1.51442230e+00 1.48498222e-01 -2.03226045e-01
-3.66683751e-01 -1.19703507e+00 7.98188671e-02 -1.28747332e+00
-3.17240596e-01 1.60136974e+00 5.50615191e-01 -2.19347119... | [-3.316145896911621, 6.907724857330322] |
ad1dbe7e-1c12-4875-a72e-227cdd8d3793 | erasing-appearance-preservation-in | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4677_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510052.pdf | Erasing Appearance Preservation in Optimization-based Smoothing | Optimization-based image smoothing is routinely formulated as the game between a smoothing energy and an appearance preservation energy. Achieving adequate smoothing is a fundamental goal of these image smoothing algorithms. We show that partially ""erasing"" the appearance preservation facilitate adequate image smooth... | ['Tien-Tsin Wong', 'Yi Ji', 'Lvmin Zhang', 'Chunping Liu', 'Chengze Li'] | null | null | null | null | eccv-2020-8 | ['image-smoothing'] | ['computer-vision'] | [ 7.12764382e-01 -1.26579928e-03 4.19416398e-01 -8.53325054e-02
-1.18941769e-01 -3.31788838e-01 5.04711211e-01 -1.77006394e-01
-1.62867233e-01 5.22694588e-01 8.51750094e-03 -3.72971088e-01
-5.00720218e-02 -5.27023494e-01 -4.75352407e-01 -1.01969397e+00
8.57744962e-02 -2.29187891e-01 3.63774657e-01 -3.97288442... | [11.077857971191406, -2.3587615489959717] |
68f80bcf-0789-4d78-9862-d54bf48b3795 | superpixelgraph-semi-automatic-generation-of | 2304.05661 | null | https://arxiv.org/abs/2304.05661v2 | https://arxiv.org/pdf/2304.05661v2.pdf | SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks | Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons c... | ['Qing Zhu', 'Zhendong Wang', 'Qisen Shang', 'Bo Xu', 'Han Hu', 'Haojia Yu'] | 2023-04-12 | null | null | null | null | ['superpixels'] | ['computer-vision'] | [ 6.85504377e-01 1.17994145e-01 1.47412494e-01 -2.65174210e-01
-5.95236838e-01 -5.81419468e-01 5.59384048e-01 5.24108708e-01
-3.58860761e-01 6.21186554e-01 -1.45977838e-02 -1.86112761e-01
4.81022112e-02 -1.31056321e+00 -6.15213215e-01 -5.18040657e-01
-9.83636975e-02 1.44018725e-01 3.65796924e-01 -2.25882262... | [9.393410682678223, -0.14888212084770203] |
95da890d-d22d-4f2e-b872-dd291d7c21d7 | implementation-of-a-practical-markov-chain | 2109.14445 | null | https://arxiv.org/abs/2109.14445v1 | https://arxiv.org/pdf/2109.14445v1.pdf | Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit | Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), ... | ['Richard G. Posner', 'William S. Hlavacek', 'Ye Chen', 'Abell T. Duprat1', 'Joshua Colvin', 'Ely F. Miller', 'Abhishek Mallela', 'Yen Ting Lin', 'Jacob Neumann'] | 2021-09-29 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 3.74722704e-02 -5.52154183e-01 1.88927248e-01 -2.42800504e-01
-5.57447076e-01 -6.46801353e-01 6.53132558e-01 -8.82821307e-02
-4.36504126e-01 1.05887938e+00 -4.63564068e-01 -9.32995796e-01
1.82679892e-02 -6.04628980e-01 -5.63418806e-01 -1.13177800e+00
4.72522303e-02 1.27939582e+00 2.83075005e-01 5.42673528... | [6.62697172164917, 4.053398132324219] |
6244fd97-2c16-4b39-bc78-8069b5211f97 | gvcnn-group-view-convolutional-neural | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Feng_GVCNN_Group-View_Convolutional_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Feng_GVCNN_Group-View_Convolutional_CVPR_2018_paper.pdf | GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition | 3D shape recognition has attracted much attention recently. Its recent advances advocate the usage of deep features and achieve the state-of-the-art performance. However, existing deep features for 3D shape recognition are restricted to a view-to-shape setting, which learns the shape descriptor from the view-level feat... | ['Yue Gao', 'Zizhao Zhang', 'Yifan Feng', 'Xibin Zhao', 'Rongrong Ji'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-shape-retrieval', '3d-shape-recognition', '3d-shape-representation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.63887972e-01 -5.46615005e-01 -1.56871751e-01 -6.20662570e-01
-6.68678701e-01 -6.21393383e-01 8.21284711e-01 5.69808744e-02
2.40730956e-01 -1.47365347e-01 3.42475146e-01 2.06989288e-01
-2.26991802e-01 -9.86149311e-01 -2.80561715e-01 -8.85844409e-01
3.75914514e-01 4.10278708e-01 8.19867179e-02 7.01509416... | [8.15119457244873, -3.865414619445801] |
8f6f0c08-9af3-4d23-8a74-f92c7287a6ad | hengam-an-adversarially-trained-transformer | null | null | https://aclanthology.org/2022.aacl-main.74/ | https://aclanthology.org/2022.aacl-main.74.pdf | Hengam: An Adversarially Trained Transformer for Persian Temporal Tagging | Many NLP main tasks benefit from an accurate understanding of temporal expressions, e.g., text summarization, question answering, and information retrieval. This paper introduces Hengam, an adversarially trained transformer for Persian temporal tagging outperforming state-of-the-art approaches on a diverse and manually... | ['Ehsaneddin Asgari', 'Hinrich Schütze', 'Amir Hossein Kargaran', 'Sajad Mirzababaei'] | 2022-11-20 | null | null | null | proceedings-of-the-2nd-conference-of-the-asia | ['text-anonymization', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.94757320e-02 4.70137239e-01 -3.06537330e-01 -1.50803000e-01
-1.37809575e+00 -1.21113276e+00 8.71369064e-01 -8.03879648e-03
-3.27598929e-01 9.27055836e-01 4.13834870e-01 -3.53227377e-01
2.34650031e-01 -4.97364789e-01 -5.81803679e-01 -4.89449352e-01
-1.46067977e-01 8.39456916e-01 5.22963822e-01 -4.62206334... | [10.224520683288574, 9.297150611877441] |
f715982f-2ed4-4bad-9656-2ebcbc8d6e95 | use-your-head-improving-long-tail-video | 2304.01143 | null | https://arxiv.org/abs/2304.01143v1 | https://arxiv.org/pdf/2304.01143v1.pdf | Use Your Head: Improving Long-Tail Video Recognition | This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their tails. In response, ... | ['Dima Damen', 'Majid Mirmehdi', 'Tilo Burghardt', 'Saptarshi Sinha', 'Toby Perrett'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Perrett_Use_Your_Head_Improving_Long-Tail_Video_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Perrett_Use_Your_Head_Improving_Long-Tail_Video_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-recognition'] | ['computer-vision'] | [ 3.22860032e-01 -4.51641262e-01 -8.38748753e-01 -6.11979961e-01
-1.27224636e+00 -6.05329514e-01 6.85352266e-01 -6.31013393e-01
-9.44445506e-02 6.51307702e-01 2.32402846e-01 -2.32627451e-01
6.44752523e-03 -1.13287129e-01 -1.08578658e+00 -7.70244122e-01
5.47683276e-02 3.85967702e-01 5.27940512e-01 6.40168607... | [9.031224250793457, 0.9206150770187378] |
6fbc0767-b1eb-4aa0-8ddc-ef7ff9aea093 | joint-multiview-segmentation-and-localization | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Joint_Multiview_Segmentation_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Joint_Multiview_Segmentation_CVPR_2016_paper.pdf | Joint Multiview Segmentation and Localization of RGB-D Images Using Depth-Induced Silhouette Consistency | In this paper, we propose an RGB-D camera localization approach which takes an effective geometry constraint, i.e. silhouette consistency, into consideration. Unlike existing approaches which usually assume the silhouettes are provided, we consider more practical scenarios and generate the silhouettes for multiple view... | ['Zhiwei Li', 'Yong Rui', 'Rui Cai', 'Hongyang Chao', 'Chi Zhang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['camera-localization'] | ['computer-vision'] | [ 3.21904182e-01 -1.12754531e-01 -8.05190280e-02 -5.83498538e-01
-1.03941596e+00 -8.25571060e-01 1.21436819e-01 5.05632162e-03
-4.39611375e-01 3.22705835e-01 -4.09185886e-01 2.70651244e-02
6.86893836e-02 -4.87871230e-01 -8.40520859e-01 -5.82736313e-01
5.46823442e-01 4.58259493e-01 3.80555093e-01 5.87673709... | [7.798305511474609, -2.3468387126922607] |
11520337-b846-43dc-8dc7-f4690361bdab | neural-nonnegative-matrix-factorization-for | 2303.00058 | null | https://arxiv.org/abs/2303.00058v1 | https://arxiv.org/pdf/2303.00058v1.pdf | Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling | We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. Neural NMF recursively applies NMF in layer... | ['Deanna Needell', 'Denali Molitor', 'Jamie Haddock', 'Joshua Vendrow', 'Mengdi Gao', 'Eli Sadovnik', 'Runyu Zhang', 'Tyler Will'] | 2023-02-28 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 1.75837755e-01 3.21911097e-01 -4.92539614e-01 -4.34516966e-01
-4.90958899e-01 -2.53133953e-01 2.49281392e-01 1.63259670e-01
-8.51115808e-02 6.49025142e-01 8.82448792e-01 -3.78820479e-01
-6.17611170e-01 -4.96751785e-01 -5.38171589e-01 -6.12142324e-01
-6.07894599e-01 5.66087008e-01 -4.78470206e-01 2.16136411... | [9.540022850036621, 4.397922515869141] |
20f7ad7b-4063-4f8f-a9b6-71e6369e682f | 3d-object-recognition-with-deep-belief-nets | null | null | http://papers.nips.cc/paper/3872-3d-object-recognition-with-deep-belief-nets | http://papers.nips.cc/paper/3872-3d-object-recognition-with-deep-belief-nets.pdf | 3D Object Recognition with Deep Belief Nets | We introduce a new type of Deep Belief Net and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB database(normalized-uniform version), whic... | ['Vinod Nair', 'Geoffrey E. Hinton'] | 2009-12-01 | null | null | null | neurips-2009-12 | ['3d-object-recognition'] | ['computer-vision'] | [ 1.66226879e-01 8.09604153e-02 -1.86771125e-01 -6.79693639e-01
-7.68577456e-01 -5.63898921e-01 9.61028636e-01 -4.07081068e-01
-8.40563238e-01 6.98634326e-01 3.69638801e-02 -2.90019333e-01
2.54578292e-01 -5.90422213e-01 -1.02442813e+00 -1.00099075e+00
-4.02514376e-02 1.01894879e+00 3.80991608e-01 -3.39840241... | [9.457414627075195, 2.3341968059539795] |
cfbe8349-c061-4479-b3c2-0ceea552989a | robust-filtering-and-smoothing-with-gaussian | 1203.4345 | null | https://arxiv.org/abs/1203.4345v1 | https://arxiv.org/pdf/1203.4345v1.pdf | Robust Filtering and Smoothing with Gaussian Processes | We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning,... | ['Carl Edward Rasmussen', 'Uwe D. Hanebeck', 'Marco F. Huber', 'Ryan Turner', 'Marc Peter Deisenroth'] | 2012-03-20 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [-1.47617266e-01 1.05587365e-04 3.65214229e-01 1.89734235e-01
-4.15887177e-01 -3.33460748e-01 6.89498484e-01 3.47710326e-02
-1.99827150e-01 8.37747514e-01 -1.43539712e-01 -2.30344236e-01
-4.31471795e-01 -4.47768420e-01 -6.80719137e-01 -1.06945980e+00
-2.74203479e-01 4.14610088e-01 3.66854012e-01 -4.46499214... | [6.439033508300781, 3.4814810752868652] |
823b8937-c09e-4574-8c47-4ddd63a63b4b | deep-neural-network-approximation-theory | 1901.02220 | null | https://arxiv.org/abs/1901.02220v4 | https://arxiv.org/pdf/1901.02220v4.pdf | Deep Neural Network Approximation Theory | This paper develops fundamental limits of deep neural network learning by characterizing what is possible if no constraints are imposed on the learning algorithm and on the amount of training data. Concretely, we consider Kolmogorov-optimal approximation through deep neural networks with the guiding theme being a relat... | ['Helmut Bölcskei', 'Dmytro Perekrestenko', 'Dennis Elbrächter', 'Philipp Grohs'] | 2019-01-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.18175872e-01 4.82360780e-01 2.01992288e-01 6.10957555e-02
-1.30931646e-01 -4.79838252e-01 3.78594548e-01 7.29458481e-02
-5.04347980e-01 9.93024290e-01 -9.35370624e-02 -2.73666710e-01
-2.86724329e-01 -1.17723501e+00 -9.29607153e-01 -8.76990259e-01
-5.22904098e-01 4.76991385e-01 7.32174963e-02 -3.84789079... | [7.699287414550781, 3.617872714996338] |
4d752fd1-fdaf-4f2b-882a-98e91fc56ba6 | what-is-it-like-to-be-a-bot-simulated | 2103.12638 | null | https://arxiv.org/abs/2103.12638v1 | https://arxiv.org/pdf/2103.12638v1.pdf | What is it Like to Be a Bot: Simulated, Situated, Structurally Coherent Qualia (S3Q) Theory of Consciousness | A novel representationalist theory of consciousness is presented that is grounded in neuroscience and provides a path to artificially conscious computing. Central to the theory are representational affordances of the conscious experience based on the generation of qualia, the fundamental unit of the conscious represent... | ['S. Rogers', 'M. Molineaux', 'O. Larue', 'H. S. Clouse', 'C. Cox', 'J. Culbertson', 'K. Schmidt'] | 2021-03-13 | null | null | null | null | ['2d-human-pose-estimation', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.20375383e-01 2.22693592e-01 3.52497905e-01 -2.10250765e-01
4.48533833e-01 -5.41544676e-01 1.13827264e+00 1.78703308e-01
-2.94010937e-01 4.29455042e-01 7.67602623e-01 -3.70787054e-01
-2.62871653e-01 -7.57367849e-01 -3.63718390e-01 -4.74693507e-01
-3.03865492e-01 -1.73119307e-01 -2.99811691e-01 -5.19005477... | [8.817514419555664, 6.283960819244385] |
ec282c0d-bbd9-4b25-9ad5-d491333dd4bc | enhancing-covid-19-diagnosis-through-vision | 2306.06914 | null | https://arxiv.org/abs/2306.06914v2 | https://arxiv.org/pdf/2306.06914v2.pdf | Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Images | The advent of 2019 Coronavirus (COVID-19) has engendered a momentous global health crisis, necessitating the identification of the ailment in individuals through diverse diagnostic modalities. Radiological imaging, particularly the deployment of X-ray imaging, has been recognized as a pivotal instrument in the detectio... | ['Sultan Zavrak'] | 2023-06-12 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 4.95740831e-01 -3.66452426e-01 1.22321337e-01 -1.43927401e-02
-5.34628630e-01 -5.50137162e-01 8.04319143e-01 3.67162824e-01
-4.23027575e-01 6.45245492e-01 -2.61828452e-01 -5.37758887e-01
-6.04653716e-01 -4.62656379e-01 -1.53851151e-01 -9.61462259e-01
-4.90188152e-02 9.25556898e-01 -3.56824964e-01 2.32118323... | [15.554816246032715, -1.6875970363616943] |
4210f546-1ffb-4221-973c-ca4bc9a076ac | general-e2-equivariant-steerable-cnns | null | null | http://papers.nips.cc/paper/9580-general-e2-equivariant-steerable-cnns | http://papers.nips.cc/paper/9580-general-e2-equivariant-steerable-cnns.pdf | General E(2)-Equivariant Steerable CNNs | The big empirical success of group equivariant networks has led in recent years to the sprouting of a great variety of equivariant network architectures. A particular focus has thereby been on rotation and reflection equivariant CNNs for planar images. Here we give a general description of E(2)-equivariant convolutions... | ['Maurice Weiler', 'Gabriele Cesa'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['rotated-mnist'] | ['computer-vision'] | [-1.93912778e-02 3.67489606e-01 3.95051479e-01 -7.22708821e-01
1.21882871e-01 -6.95701063e-01 9.97491241e-01 -1.05287862e+00
-5.90524793e-01 3.41141433e-01 5.87773263e-01 -3.87814760e-01
-4.61601973e-01 -6.40283942e-01 -7.04990089e-01 -7.51723230e-01
-2.06336498e-01 1.19556502e-01 1.86417371e-01 -8.06727469... | [8.886938095092773, 2.4075276851654053] |
84f6a80a-6fc4-4132-9744-9b16f76f9f87 | i-spy-a-metaphor-large-language-models-and | 2305.14724 | null | https://arxiv.org/abs/2305.14724v1 | https://arxiv.org/pdf/2305.14724v1.pdf | I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors | Visual metaphors are powerful rhetorical devices used to persuade or communicate creative ideas through images. Similar to linguistic metaphors, they convey meaning implicitly through symbolism and juxtaposition of the symbols. We propose a new task of generating visual metaphors from linguistic metaphors. This is a ch... | ['Smaranda Muresan', 'Marianna Apidianaki', 'Yue Yang', 'Artemis Panagopoulou', 'Olivia Winn', 'Arkadiy Saakyan', 'Tuhin Chakrabarty'] | 2023-05-24 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 8.07779208e-02 2.10927248e-01 1.08526357e-01 -8.90273675e-02
1.91887151e-02 -9.13758397e-01 1.51555550e+00 1.08241692e-01
-2.35315338e-01 1.95739701e-01 5.80181420e-01 -9.90972102e-01
1.09460227e-01 -8.52123141e-01 -4.27173823e-01 -1.73399031e-01
1.18230879e-01 6.05875075e-01 -2.67492592e-01 -5.03089786... | [10.989360809326172, 1.4034862518310547] |
cc888e99-6214-4031-8d0a-3621a5f3a212 | anoshift-a-distribution-shift-benchmark-for | 2206.15476 | null | https://arxiv.org/abs/2206.15476v4 | https://arxiv.org/pdf/2206.15476v4.pdf | AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection | Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models. The existing benchmarks are focused on supervised learning, and to the best ... | ['Marius Dragoi', 'Florin Brad', 'Andrei Manolache', 'Emanuela Haller', 'Elena Burceanu'] | 2022-06-30 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-1.75702035e-01 -4.25142020e-01 -3.03347617e-01 -4.21811372e-01
-4.59827363e-01 -5.87365925e-01 5.81805110e-01 4.90073740e-01
-4.49849308e-01 8.04215014e-01 -3.76622021e-01 -6.66848898e-01
-5.35648525e-01 -8.66408110e-01 -8.87650967e-01 -8.47930610e-01
-6.53050303e-01 7.77793348e-01 2.67540932e-01 -2.27024689... | [7.490002155303955, 2.552647352218628] |
ebf7fca1-32d8-4638-807d-f3f56d776edc | prompt-learning-for-domain-adaptation-in-task | 2211.05596 | null | https://arxiv.org/abs/2211.05596v1 | https://arxiv.org/pdf/2211.05596v1.pdf | Prompt Learning for Domain Adaptation in Task-Oriented Dialogue | Conversation designers continue to face significant obstacles when creating production quality task-oriented dialogue systems. The complexity and cost involved in schema development and data collection is often a major barrier for such designers, limiting their ability to create natural, user-friendly experiences. We f... | ['Christopher Parisien', 'Makesh Narsimhan Sreedhar'] | 2022-11-10 | null | null | null | null | ['intent-classification', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.38099530e-01 4.89842296e-01 -1.77919984e-01 -6.89578295e-01
-8.96332622e-01 -9.01444435e-01 8.17504346e-01 1.48532718e-01
-4.41417485e-01 7.66945481e-01 6.73664033e-01 -2.24338949e-01
6.77177235e-02 -5.26251674e-01 1.17952801e-01 8.11681896e-02
1.61849722e-01 8.61750841e-01 1.53284818e-01 -7.93852031... | [12.763280868530273, 7.956464767456055] |
5d31a879-02ef-450c-b30a-7763b1fdef54 | intersectional-bias-in-hate-speech-and | 2005.05921 | null | https://arxiv.org/abs/2005.05921v3 | https://arxiv.org/pdf/2005.05921v3.pdf | Intersectional Bias in Hate Speech and Abusive Language Datasets | Algorithms are widely applied to detect hate speech and abusive language in social media. We investigated whether the human-annotated data used to train these algorithms are biased. We utilized a publicly available annotated Twitter dataset (Founta et al. 2018) and classified the racial, gender, and party identificatio... | ['Sarah Nam', 'Jae Yeon Kim', 'Carlos Ortiz', 'Sarah Santiago', 'Vivek Datta'] | 2020-05-12 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-3.19737583e-01 1.88413247e-01 -4.39810932e-01 -2.34316170e-01
-2.72962630e-01 -9.77160037e-01 1.11365080e+00 6.16968989e-01
-5.80918550e-01 6.88605070e-01 6.73399270e-01 -1.87346324e-01
5.04085541e-01 -7.13385820e-01 -2.52061576e-01 -4.57686305e-01
-8.07596445e-02 8.03753436e-02 -2.97114044e-01 -1.62539974... | [8.715365409851074, 10.469146728515625] |
9ff64218-1d4e-4d1d-806c-8740c819e0dd | a-survey-of-automated-data-augmentation | 2206.06544 | null | https://arxiv.org/abs/2206.06544v2 | https://arxiv.org/pdf/2206.06544v2.pdf | A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks | In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Aug... | ['Qiuhong Ke', 'James Bailey', 'Richard O. Sinnott', 'Zihan Yang'] | 2022-06-14 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.58421332e-01 3.94724756e-02 -3.62240881e-01 -2.77919859e-01
-1.49976656e-01 -4.44962561e-01 6.50865257e-01 -4.63615842e-02
-4.59184140e-01 4.86582100e-01 -7.03804865e-02 -2.78271079e-01
7.16512874e-02 -8.71364474e-01 -4.48717713e-01 -9.90963995e-01
2.98047692e-01 5.47506452e-01 -4.26008999e-02 -1.91888511... | [9.348225593566895, 2.241117238998413] |
3d4263f1-34f4-48e7-98a8-5898e7d40f35 | retrieval-augmented-generative-question | 2211.07067 | null | https://arxiv.org/abs/2211.07067v1 | https://arxiv.org/pdf/2211.07067v1.pdf | Retrieval-Augmented Generative Question Answering for Event Argument Extraction | Event argument extraction has long been studied as a sequential prediction problem with extractive-based methods, tackling each argument in isolation. Although recent work proposes generation-based methods to capture cross-argument dependency, they require generating and post-processing a complicated target sequence (t... | ['Heng Ji', 'Xinya Du'] | 2022-11-14 | null | null | null | null | ['generative-question-answering'] | ['natural-language-processing'] | [ 4.52537626e-01 1.99060827e-01 -3.10719460e-01 -4.37781543e-01
-1.79673100e+00 -8.26091707e-01 1.00902700e+00 1.04519188e-01
-5.34758270e-01 8.27592909e-01 6.03148639e-01 -4.95239556e-01
3.96718383e-02 -7.55875468e-01 -9.59203959e-01 -5.73589206e-01
3.09326112e-01 7.82880247e-01 2.31706753e-01 -9.74176601... | [10.757536888122559, 8.479764938354492] |
5f408e4a-4832-4040-bc9b-b8f3145c5117 | andrejjan-at-semeval-2019-task-7-a-fusion | null | null | https://aclanthology.org/S19-2190 | https://aclanthology.org/S19-2190.pdf | AndrejJan at SemEval-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour Analysis | The viral spread of false, unverified and misleading information on the Internet has attracted a heightened attention of an interdisciplinary research community on the phenomenon. This paper contributes to the research efforts of automatically determining the veracity of rumourous tweets and classifying their replies a... | ['Andrej Janchevski', 'Sonja Gievska'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['rumour-detection'] | ['natural-language-processing'] | [-3.08509474e-03 2.84788400e-01 -4.36493129e-01 5.85145764e-02
-8.89511853e-02 -6.31942868e-01 1.11406446e+00 7.03715682e-01
-9.15025175e-02 5.88464618e-01 9.69920218e-01 -6.57042623e-01
1.53437257e-01 -5.50344169e-01 -3.31660546e-02 -2.75403440e-01
2.48493224e-01 9.91934240e-02 -1.09682493e-02 -5.66914141... | [8.339365005493164, 10.085081100463867] |
975499d2-cd03-4232-b9f6-534587be7013 | bottrinet-a-unified-and-efficient-embedding | 2304.03144 | null | https://arxiv.org/abs/2304.03144v4 | https://arxiv.org/pdf/2304.03144v4.pdf | BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning | The rapid and accurate identification of bot accounts in online social networks is an ongoing challenge. In this paper, we propose BOTTRINET, a unified embedding framework that leverages the textual content posted by accounts to detect bots. Our approach is based on the premise that account personalities and habits can... | ['Yanyuet Man', 'Xuesong Ye', 'Jun Wu'] | 2023-04-06 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-8.70656371e-02 -2.93718278e-01 -4.11650777e-01 2.13598564e-01
-1.88894838e-01 -6.53128624e-01 7.89696991e-01 1.54557943e-01
-6.32477283e-01 3.97186339e-01 8.99967402e-02 -1.92185655e-01
2.31938779e-01 -9.52932775e-01 1.37332097e-01 -2.66363204e-01
5.61911575e-02 5.03521800e-01 7.94370472e-01 -3.93004239... | [8.126452445983887, 10.131301879882812] |
a7a23235-624c-4adb-ad7f-82c2d937fe2c | source-free-video-domain-adaptation-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Source-Free_Video_Domain_Adaptation_With_Spatial-Temporal-Historical_Consistency_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Source-Free_Video_Domain_Adaptation_With_Spatial-Temporal-Historical_Consistency_Learning_CVPR_2023_paper.pdf | Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning | Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data. It is derived from unsupervised domain adaptation but has the advantage of not requiring labeled source data to learn adaptive models. This makes it particularly useful... | ['Martin Renqiang Min', 'Erik Kruus', 'Deep Patel', 'Kai Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 1.66102946e-01 -5.38941801e-01 -6.26330853e-01 -3.25480819e-01
-4.06245679e-01 -5.28532267e-01 6.52907968e-01 -3.35855216e-01
-4.07916665e-01 7.39443958e-01 4.43436205e-01 8.46207291e-02
1.30421249e-02 -4.69903499e-01 -7.54776239e-01 -7.80407071e-01
1.39766664e-03 1.68208241e-01 4.14209843e-01 -1.22439608... | [8.870343208312988, 0.983022153377533] |
d6a0af82-7ae4-4e2f-8b72-72435363c45d | self-supervised-contrastive-representation-1 | 2208.06616 | null | https://arxiv.org/abs/2208.06616v2 | https://arxiv.org/pdf/2208.06616v2.pdf | Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification | Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this wor... | ['Cuntai Guan', 'XiaoLi Li', 'Chee-Keong Kwoh', 'Min Wu', 'Zhenghua Chen', 'Mohamed Ragab', 'Emadeldeen Eldele'] | 2022-08-13 | null | null | null | null | ['semi-supervised-time-series-classification'] | ['time-series'] | [ 2.71059811e-01 -2.74638146e-01 -4.02538627e-01 -6.73319757e-01
-1.09184587e+00 -6.23539031e-01 7.99869716e-01 -7.69890994e-02
-1.95066109e-01 5.95768511e-01 1.62854403e-01 -5.46969958e-02
-2.46008545e-01 -5.14022768e-01 -6.12878740e-01 -8.11144650e-01
-2.55639791e-01 5.68935014e-02 -1.35663792e-01 -1.13079615... | [7.41714334487915, 2.939249038696289] |
4cd305f9-5986-422b-80de-fe9378d97cf8 | learning-differentiable-logic-programs-for | 2307.00928 | null | https://arxiv.org/abs/2307.00928v1 | https://arxiv.org/pdf/2307.00928v1.pdf | Learning Differentiable Logic Programs for Abstract Visual Reasoning | Visual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. However, due to the memory intensity, most existing approaches... | ['Kristian Kersting', 'Devendra Singh Dhami', 'Viktor Pfanschilling', 'Hikaru Shindo'] | 2023-07-03 | null | null | null | null | ['program-induction', 'visual-reasoning', 'visual-reasoning'] | ['computer-code', 'computer-vision', 'reasoning'] | [ 1.22519895e-01 3.02936256e-01 2.52630692e-02 -3.45124841e-01
-5.53682968e-02 -6.61849499e-01 9.02216315e-01 2.90550172e-01
-3.13100576e-01 2.66640931e-01 4.61340025e-02 -9.56540704e-01
-2.21443340e-01 -1.19203258e+00 -9.53713894e-01 -2.73633122e-01
-1.55926615e-01 7.11125672e-01 2.16495574e-01 -3.41362208... | [10.608108520507812, 2.2625999450683594] |
7418f808-3e64-40e3-b968-8463310e0de8 | cue-an-uncertainty-interpretation-framework | 2306.03598 | null | https://arxiv.org/abs/2306.03598v1 | https://arxiv.org/pdf/2306.03598v1.pdf | CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models | Text classifiers built on Pre-trained Language Models (PLMs) have achieved remarkable progress in various tasks including sentiment analysis, natural language inference, and question-answering. However, the occurrence of uncertain predictions by these classifiers poses a challenge to their reliability when deployed in ... | ['Yulan He', 'Lin Gui', 'Bin Liang', 'Zhaoyue Sun', 'Jiazheng Li'] | 2023-06-06 | null | null | null | null | ['emotion-classification', 'natural-language-inference', 'sentiment-analysis', 'linguistic-acceptability', 'emotion-classification'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.82879531e-01 4.35287923e-01 -3.31418455e-01 -8.30074310e-01
-1.01053929e+00 -3.96904677e-01 8.93486977e-01 2.79003859e-01
5.10850847e-02 8.28465044e-01 4.54950780e-01 -1.90883383e-01
-2.28541400e-02 -6.78148925e-01 -7.75038302e-01 -6.16452694e-01
3.54213923e-01 3.10780734e-01 -3.06021899e-01 1.49183542... | [11.535274505615234, 8.721552848815918] |
1622e6c5-4ebd-4def-966f-f8a6c2089739 | target-active-speaker-detection-with-audio | 2305.12831 | null | https://arxiv.org/abs/2305.12831v3 | https://arxiv.org/pdf/2305.12831v3.pdf | Target Active Speaker Detection with Audio-visual Cues | In active speaker detection (ASD), we would like to detect whether an on-screen person is speaking based on audio-visual cues. Previous studies have primarily focused on modeling audio-visual synchronization cue, which depends on the video quality of the lip region of a speaker. In real-world applications, it is possib... | ['Haizhou Li', 'Zexu Pan', 'Ruijie Tao', 'Yidi Jiang'] | 2023-05-22 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 7.06183491e-03 1.20116130e-01 -2.41094425e-01 -3.19761425e-01
-1.32672477e+00 -6.35313570e-01 3.72976571e-01 -2.80556120e-02
-4.16882895e-02 1.44728750e-01 4.05288398e-01 -8.01830888e-02
5.04076362e-01 -1.34371653e-01 -3.81446302e-01 -6.17801011e-01
1.11166671e-01 -2.44799033e-01 2.55632490e-01 2.60888517... | [14.438026428222656, 5.192652225494385] |
750f3080-091e-43d8-a3d4-2a8a4fd3d126 | towards-understanding-and-improving-knowledge | 2305.08096 | null | https://arxiv.org/abs/2305.08096v1 | https://arxiv.org/pdf/2305.08096v1.pdf | Towards Understanding and Improving Knowledge Distillation for Neural Machine Translation | Knowledge distillation (KD) is a promising technique for model compression in neural machine translation. However, where the knowledge hides in KD is still not clear, which may hinder the development of KD. In this work, we first unravel this mystery from an empirical perspective and show that the knowledge comes from ... | ['Yufeng Chen', 'Jinan Xu', 'Jian Liu', 'Wenjuan Han', 'Shuaibo Wang', 'Yunlong Liang', 'Songming Zhang'] | 2023-05-14 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.35913059e-01 3.17347705e-01 -6.15593195e-01 -1.89766139e-01
-1.07520950e+00 -6.42817616e-01 1.82751134e-01 1.90033615e-02
-7.13428855e-01 1.04761291e+00 8.23800266e-02 -8.33183825e-01
-2.45716944e-01 -7.01066375e-01 -1.20098364e+00 -5.82930684e-01
4.84388292e-01 6.43246472e-01 1.17307544e-01 -4.57560807... | [10.972457885742188, 8.342584609985352] |
b637e8d4-a080-4c0b-8bc2-943c888e8883 | an-improved-approach-of-intention-discovery | 2009.09354 | null | https://arxiv.org/abs/2009.09354v1 | https://arxiv.org/pdf/2009.09354v1.pdf | An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management | An Embodied Conversational Agent (ECA) is an intelligent agent that works as the front end of software applications to interact with users through verbal/nonverbal expressions and to provide online assistance without the limits of time, location, and language. To help to improve the experience of human-computer interac... | ['Ruturaj Raval'] | 2020-09-20 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-1.11010328e-01 5.09231031e-01 -1.08388849e-01 -4.57165152e-01
1.07384905e-01 -5.21447599e-01 6.80260122e-01 2.48054206e-01
-1.96238682e-01 4.47378188e-01 4.46829915e-01 -2.15059727e-01
3.74084748e-02 -6.45495117e-01 4.63076055e-01 -3.40956181e-01
2.15300828e-01 4.34475064e-01 -3.14270675e-01 -9.47894335... | [13.121038436889648, 7.6531596183776855] |
0cbc4f22-30c0-4da3-8dfe-893ae666a7dc | learning-dynamic-point-cloud-compression-via | 2305.05356 | null | https://arxiv.org/abs/2305.05356v2 | https://arxiv.org/pdf/2305.05356v2.pdf | Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block Matching | 3D dynamic point cloud (DPC) compression relies on mining its temporal context, which faces significant challenges due to DPC's sparsity and non-uniform structure. Existing methods are limited in capturing sufficient temporal dependencies. Therefore, this paper proposes a learning-based DPC compression framework via hi... | ['Zhu Li', 'Jenq-Neng Hwang', 'Yiling Xu', 'Tingyu Fan', 'Shuting Xia'] | 2023-05-09 | null | null | null | null | ['motion-compensation', 'motion-estimation'] | ['computer-vision', 'computer-vision'] | [-1.22559734e-01 -5.99519789e-01 -4.29688603e-01 -2.03607053e-01
-2.76611865e-01 -4.20285622e-03 2.07428455e-01 -2.46767685e-01
-1.94275841e-01 1.96046948e-01 5.79610825e-01 2.11632866e-02
-2.93681800e-01 -6.13905847e-01 -5.68927109e-01 -7.09688663e-01
-4.24296856e-01 1.21220909e-01 5.30400872e-01 -3.80189791... | [10.964595794677734, -1.6671537160873413] |
b87ef0d0-eab8-4814-bf6e-b410a49c52ae | alternating-between-spectral-and-spatial | 1911.07953 | null | https://arxiv.org/abs/1911.07953v3 | https://arxiv.org/pdf/1911.07953v3.pdf | Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement | This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks for separation use an advanced convolutional architecture trained with a novel stabilized signal-to-noise ratio loss function. For beamformi... | ['Zhuo Chen', 'Shinji Watanabe', 'Desh Raj', 'Zhong-Qiu Wang', 'Scott Wisdom', 'John R. Hershey', 'Kevin Wilson', 'Hakan Erdogan'] | 2019-11-18 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 5.38656890e-01 -6.40095472e-01 5.68392336e-01 -3.55674833e-01
-1.48902464e+00 -7.67719507e-01 4.18136269e-01 -2.35542595e-01
-6.58596814e-01 4.07591522e-01 7.98250079e-01 -4.67633933e-01
-3.11704844e-01 -1.18919358e-01 -4.44744080e-01 -9.43159401e-01
-2.74734616e-01 -5.30278683e-01 8.15518275e-02 -1.82617530... | [15.040948867797852, 5.876130104064941] |
e1e2a6db-ba01-479b-b3a3-664676671c2b | a-zero-shot-adaptive-quadcopter-controller | 2209.09232 | null | https://arxiv.org/abs/2209.09232v2 | https://arxiv.org/pdf/2209.09232v2.pdf | Learning a Single Near-hover Position Controller for Vastly Different Quadcopters | This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime. The core algorithmic idea is to learn a single policy that can adapt online at te... | ['Mark W. Mueller', 'Jitendra Malik', 'Ashish Kumar', 'Xiangyu Wu', 'Antonio Loquercio', 'Dingqi Zhang'] | 2022-09-19 | null | null | null | null | ['drone-controller'] | ['robots'] | [-4.45844382e-01 1.22462243e-01 1.18509941e-01 3.43507916e-01
6.48604333e-02 -8.59081089e-01 1.27490819e-01 -1.05963156e-01
-4.46529061e-01 7.58434236e-01 -6.76537573e-01 -3.12246680e-01
-2.42902860e-01 -5.52199841e-01 -1.08032119e+00 -8.02093923e-01
-6.40955091e-01 6.84556305e-01 3.60096961e-01 -6.21078730... | [4.855281829833984, 1.8723255395889282] |
9563d841-c202-4837-83d1-976d259fc1ba | semi-supervised-offline-reinforcement-1 | 2210.06518 | null | https://arxiv.org/abs/2210.06518v3 | https://arxiv.org/pdf/2210.06518v3.pdf | Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories | Natural agents can effectively learn from multiple data sources that differ in size, quality, and types of measurements. We study this heterogeneity in the context of offline reinforcement learning (RL) by introducing a new, practically motivated semi-supervised setting. Here, an agent has access to two sets of traject... | ['Aditya Grover', 'Brandon Amos', 'Mikael Henaff', 'Qinqing Zheng'] | 2022-10-12 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.41074210e-01 1.89280268e-02 -5.69570482e-01 -1.44706160e-01
-8.35509658e-01 -1.04651165e+00 1.03755903e+00 2.57071167e-01
-8.00576031e-01 8.14902306e-01 2.25242615e-01 -3.79164398e-01
-2.39674821e-01 -6.46088660e-01 -7.57507622e-01 -7.24657536e-01
-3.85348827e-01 8.65615368e-01 3.73230204e-02 -2.36065745... | [4.095372200012207, 1.6736195087432861] |
2d79a0ab-418f-4823-a99a-fe5347f2d2e6 | a-sequence-matching-network-for-polyphonic | 2002.05865 | null | http://arxiv.org/abs/2002.05865v1 | http://arxiv.org/pdf/2002.05865v1.pdf | A Sequence Matching Network for Polyphonic Sound Event Localization and Detection | Polyphonic sound event detection and direction-of-arrival estimation require
different input features from audio signals. While sound event detection mainly
relies on time-frequency patterns, direction-of-arrival estimation relies on
magnitude or phase differences between microphones. Previous approaches use the
same i... | [] | 2020-02-14 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio'] | [-7.84701705e-02 -4.84866619e-01 5.13401747e-01 -2.04715073e-01
-1.15088689e+00 -5.98496735e-01 2.25945637e-01 4.84402359e-01
-4.03670251e-01 1.00333408e-01 3.91338132e-02 -1.37562737e-01
1.23959705e-02 -6.61913514e-01 -4.74349916e-01 -6.46265566e-01
-2.26239339e-01 -1.12713777e-01 8.10768366e-01 2.58479148... | [15.154099464416504, 5.302259922027588] |
53dd4c65-c0c2-45e5-8dbf-03bfd6c42f52 | seeg-semantic-energized-co-speech-gesture | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf | SEEG: Semantic Energized Co-Speech Gesture Generation | Talking gesture generation is a practical yet challenging task which aims to synthesize gestures in line with speech. Gestures with meaningful signs can better convey useful information and arouse sympathy in the audience. Current works focus on aligning gestures with the speech rhythms, which are hard to mine the ... | ['Yi Yang', 'Pan Pan', 'Li Hu', 'Linchao Zhu', 'Qianyu Feng', 'Yuanzhi Liang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['gesture-generation'] | ['robots'] | [ 2.98099697e-01 4.19744343e-01 -3.45352829e-01 -8.01440358e-01
-6.12662673e-01 -3.98699462e-01 9.10791099e-01 -7.42791593e-01
-2.82304622e-02 4.04949129e-01 1.11377478e+00 3.70135128e-01
9.19084065e-03 -4.28755343e-01 -3.64263237e-01 -7.44873166e-01
3.04403305e-01 4.91258711e-01 -2.36931667e-01 -4.09398258... | [5.642239093780518, -0.133840411901474] |
51539e48-1265-4029-8c70-a8c0c62b1028 | learning-better-intent-representations-for | 2210.14304 | null | https://arxiv.org/abs/2210.14304v1 | https://arxiv.org/pdf/2210.14304v1.pdf | Learning Better Intent Representations for Financial Open Intent Classification | With the recent surge of NLP technologies in the financial domain, banks and other financial entities have adopted virtual agents (VA) to assist customers. A challenging problem for VAs in this domain is determining a user's reason or intent for contacting the VA, especially when the intent was unseen or open during th... | ['Stephen W. Thomas', 'Xiaodan Zhu', 'Will Aitken', 'Xianzhi Li'] | 2022-10-25 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [-1.59004331e-01 5.45563638e-01 -4.24847424e-01 -6.51021540e-01
-8.00716221e-01 -1.02769458e+00 8.02794397e-01 3.19228232e-01
-4.90399420e-01 4.85704929e-01 5.08400381e-01 -8.46484303e-01
1.26653209e-01 -8.31624031e-01 -3.41744632e-01 -2.46168319e-02
1.14680327e-01 1.02620149e+00 -1.54393807e-01 -5.48629165... | [11.280313491821289, 7.491511344909668] |
d80b6f38-83bf-4a59-845f-0f9026b889a6 | text-infilling | 1901.00158 | null | http://arxiv.org/abs/1901.00158v2 | http://arxiv.org/pdf/1901.00158v2.pdf | Text Infilling | Recent years have seen remarkable progress of text generation in different
contexts, such as the most common setting of generating text from scratch, and
the emerging paradigm of retrieval-and-rewriting. Text infilling, which fills
missing text portions of a sentence or paragraph, is also of numerous use in
real life, ... | ['Zhiting Hu', 'Wanrong Zhu', 'Eric Xing'] | 2019-01-01 | null | https://openreview.net/forum?id=r1zmVhCqKm | https://openreview.net/pdf?id=r1zmVhCqKm | null | ['text-infilling'] | ['natural-language-processing'] | [ 8.14760029e-01 2.17891321e-01 -2.42336616e-01 -2.06186146e-01
-9.36171293e-01 -6.95729733e-01 8.72880995e-01 1.04137562e-01
-2.83048540e-01 1.12834918e+00 7.32459009e-01 -7.88038194e-01
4.40290928e-01 -6.62022412e-01 -9.52655613e-01 -4.01602507e-01
4.98499215e-01 4.97842848e-01 1.18738003e-01 -4.89219874... | [11.765270233154297, 9.088308334350586] |
31a63b66-9aa1-4aac-ace6-fa1054271e40 | a-simple-temporal-information-matching | 2209.09677 | null | https://arxiv.org/abs/2209.09677v1 | https://arxiv.org/pdf/2209.09677v1.pdf | A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs | Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanism to inco... | ['Man Lan', 'Jianchao Zhu', 'Hao Yuan', 'Meirong Ma', 'Xin Mao', 'Li Cai'] | 2022-09-20 | null | https://aclanthology.org/2022.coling-1.181 | https://aclanthology.org/2022.coling-1.181.pdf | coling-2022-10 | ['entity-alignment', 'entity-embeddings', 'entity-alignment'] | ['knowledge-base', 'methodology', 'natural-language-processing'] | [-3.23297948e-01 1.73036326e-02 -6.42263710e-01 -2.97543705e-01
-7.94373974e-02 -3.84404510e-01 6.28517151e-01 5.66184342e-01
-4.14036423e-01 4.09081995e-01 2.30047688e-01 -2.05686659e-01
-3.41526538e-01 -1.16259170e+00 -7.62625933e-01 -4.23906475e-01
-2.88510650e-01 5.16151547e-01 5.46483457e-01 -2.31816322... | [8.647992134094238, 7.85780143737793] |
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