paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
efb58071-ce4c-45d2-866e-5c172ef938c6 | sit-at-mixmt-2022-fluent-translation-built-on | 2210.11670 | null | https://arxiv.org/abs/2210.11670v2 | https://arxiv.org/pdf/2210.11670v2.pdf | SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models | This paper describes the Stevens Institute of Technology's submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask $1$ Hindi/English to Hinglish and subtask $2$ Hinglish to English translation. Our findings lie in the improvements made through the use... | ['Jia Xu', 'Preet Jhanglani', 'Girish Amar Budhrani', 'Hrishikesh Kanade', 'Abdul Rafae Khan'] | 2022-10-21 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.94738016e-01 1.42955169e-01 -1.16767153e-01 -3.62586826e-01
-1.77844846e+00 -6.76775336e-01 7.66997576e-01 -2.11605668e-01
-6.32830679e-01 1.23191381e+00 1.02265269e-01 -7.39989519e-01
5.98662384e-02 -2.82840401e-01 -8.68465126e-01 -6.33499324e-02
1.03422187e-01 6.55131757e-01 -2.06026569e-01 -5.78683138... | [11.532742500305176, 10.363393783569336] |
a6bedbf8-c87f-4268-9c0a-bd10a9f086cb | automatic-product-copywriting-for-e-commerce | 2112.11915 | null | https://arxiv.org/abs/2112.11915v1 | https://arxiv.org/pdf/2112.11915v1.pdf | Automatic Product Copywriting for E-Commerce | Product copywriting is a critical component of e-commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (A... | ['Lingfei Wu', 'Han Yu', 'Bo Long', 'Yun Xiao', 'Xueqi He', 'Zhen He', 'Zhuoye Ding', 'Jiajia Chen', 'Shiliang Diao', 'Jing Zhou', 'Hainan Zhang', 'Yanyan Zou', 'Xueying Zhang'] | 2021-12-15 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 0.17907602 -0.03413224 -0.30544865 -0.28754577 -0.8618309 -0.8810105
0.62109065 0.20097083 -0.14135066 0.39667 0.24566415 -0.33367515
0.2680692 -0.92018527 -0.74785674 -0.2371259 0.1924357 0.63738483
0.09522975 -0.7427198 0.44455522 0.18702216 -1.2371986 0.5566819
0.9173652 1.3249756 0.32... | [10.049405097961426, 5.879283905029297] |
047e4f4a-46ac-41ac-8d30-77e3113d06df | classification-of-tumor-histology-via | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Chang_Classification_of_Tumor_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Chang_Classification_of_Tumor_2013_CVPR_paper.pdf | Classification of Tumor Histology via Morphometric Context | Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models o... | ['Bahram Parvin', 'Paul Spellman', 'Alexander Borowsky', 'Hang Chang'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['nuclear-segmentation'] | ['medical'] | [ 4.95425522e-01 -2.99748629e-01 -2.25728095e-01 -2.67229497e-01
-1.05799651e+00 -5.22920549e-01 5.98240197e-01 7.41359949e-01
-3.24413329e-01 4.06189471e-01 2.84420520e-01 -2.28235498e-01
-4.54012275e-01 -7.53074467e-01 -2.05447108e-01 -1.19283056e+00
-1.98365927e-01 4.13291276e-01 3.86876255e-01 1.27309319... | [15.050707817077637, -2.9369728565216064] |
23718a62-6923-490d-9d2d-1a2a8fa037ed | perceptual-optimization-of-a-biologically | 2206.09146 | null | https://arxiv.org/abs/2206.09146v2 | https://arxiv.org/pdf/2206.09146v2.pdf | A Perceptually Optimized and Self-Calibrated Tone Mapping Operator | With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by t... | ['Kede Ma', 'Yuming Fang', 'Chenyang Le', 'Peibei Cao'] | 2022-06-18 | null | null | null | null | ['multi-exposure-image-fusion', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.36950374e-01 -2.21486732e-01 -3.91037315e-02 -3.05852830e-01
-5.59643328e-01 -3.19177747e-01 1.76806986e-01 -3.78716171e-01
-5.19599378e-01 5.72904825e-01 7.93852285e-02 -1.94044515e-01
9.10148025e-02 -8.08131695e-01 -9.69449401e-01 -5.97054064e-01
1.79682076e-01 -2.14640439e-01 1.70473933e-01 -3.14069062... | [10.98402214050293, -2.1849260330200195] |
9234b803-07c9-4767-8d3e-2ba5ee525867 | time-domain-speech-super-resolution-with-gan | 2209.01702 | null | https://arxiv.org/abs/2209.01702v1 | https://arxiv.org/pdf/2209.01702v1.pdf | Time-domain speech super-resolution with GAN based modeling for telephony speaker verification | Automatic Speaker Verification (ASV) technology has become commonplace in virtual assistants. However, its performance suffers when there is a mismatch between the train and test domains. Mixed bandwidth training, i.e., pooling training data from both domains, is a preferred choice for developing a universal model that... | ['Najim Dehak', 'Piotr Żelasko', 'Laureano Moro-Velázquez', 'Jesús Villalba', 'Saurabh Kataria'] | 2022-09-04 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 3.36143941e-01 -5.46403900e-02 1.12255275e-01 -4.85097587e-01
-1.43903506e+00 -7.86712646e-01 5.11707187e-01 -3.41395795e-01
-2.52243996e-01 6.06784761e-01 3.39107245e-01 -7.35117435e-01
3.87765616e-02 -4.41174507e-01 -5.66667855e-01 -7.49296546e-01
1.29166394e-01 2.12895095e-01 -1.90365175e-03 -2.13634968... | [14.76653003692627, 6.252399921417236] |
6818c40b-4d06-4326-9668-8977fae37b2f | craft-shared-tasks-2019-overview-integrated | null | null | https://aclanthology.org/D19-5725 | https://aclanthology.org/D19-5725.pdf | CRAFT Shared Tasks 2019 Overview --- Integrated Structure, Semantics, and Coreference | As part of the BioNLP Open Shared Tasks 2019, the CRAFT Shared Tasks 2019 provides a platform to gauge the state of the art for three fundamental language processing tasks {---} dependency parse construction, coreference resolution, and ontology concept identification {---} over full-text biomedical articles. The struc... | ['Harrison Pielke-Lombardo', 'Negacy Hailu', 'Manuel R. Ciosici', 'Michael Regan', 'Michael Bada', 'William Baumgartner', 'Sampo Pyysalo', 'Lawrence Hunter'] | 2019-11-01 | null | null | null | ws-2019-11 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 5.29787958e-01 8.08984041e-01 -2.74099737e-01 -7.38934696e-01
-1.16443539e+00 -6.06767237e-01 5.27166486e-01 1.03383577e+00
-9.33509111e-01 1.06043231e+00 6.35760009e-01 -9.81212705e-02
-5.31525731e-01 -3.49834532e-01 -4.33637679e-01 -4.56234157e-01
-1.59113705e-01 1.19922316e+00 9.04621035e-02 -2.27971837... | [9.150406837463379, 9.325599670410156] |
b0824db0-2c07-4931-89a2-b383180b8438 | empirical-interpretation-of-the-relationship | 2306.17500 | null | https://arxiv.org/abs/2306.17500v1 | https://arxiv.org/pdf/2306.17500v1.pdf | Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition | Speech emotion recognition (SER) is vital for obtaining emotional intelligence and understanding the contextual meaning of speech. Variations of consonant-vowel (CV) phonemic boundaries can enrich acoustic context with linguistic cues, which impacts SER. In practice, speech emotions are treated as single labels over an... | ['Thomas Hain', 'Rosanna Milner', 'Md Asif Jalal', 'Anna Ollerenshaw'] | 2023-06-30 | null | null | null | null | ['emotion-recognition', 'emotional-intelligence', 'speech-emotion-recognition'] | ['computer-vision', 'natural-language-processing', 'speech'] | [ 1.55981183e-01 -7.24131092e-02 1.92827135e-01 -5.50983965e-01
-4.67710823e-01 -3.82782519e-01 2.32679084e-01 3.20832253e-01
-4.86445665e-01 3.19758147e-01 5.83699644e-01 -1.14598662e-01
-1.41223073e-02 -4.38314974e-01 -4.93399084e-01 -5.46655715e-01
-1.46149442e-01 -3.65094066e-01 -1.91612035e-01 -2.30270013... | [13.90052318572998, 5.857675075531006] |
bdd133c1-4c24-45cc-99b2-ef230d50b42a | price-does-matter-modeling-price-and-interest | 2205.04181 | null | https://arxiv.org/abs/2205.04181v1 | https://arxiv.org/pdf/2205.04181v1.pdf | Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation | Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many m... | ['Hongfei Lin', 'Haifeng Liu', 'Fenglong Ma', 'Chenliang Li', 'Liang Yang', 'Bo Xu', 'Xiaokun Zhang'] | 2022-05-09 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-1.71402127e-01 -1.34410381e-01 -8.71224046e-01 -5.81039667e-01
-3.48086774e-01 -3.39681298e-01 7.78635666e-02 5.90423606e-02
-1.28961101e-01 5.21507621e-01 6.67667687e-01 -8.58235061e-02
-4.43339288e-01 -1.15775681e+00 -5.69264710e-01 -5.85376441e-01
-3.01084220e-01 3.56198877e-01 -5.00668027e-02 -4.50075597... | [10.098955154418945, 5.614314556121826] |
b7eeb427-333f-4ff3-a3ad-152c39488e1c | causal-coupled-mechanisms-a-control-method | 2209.07368 | null | https://arxiv.org/abs/2209.07368v1 | https://arxiv.org/pdf/2209.07368v1.pdf | Causal Coupled Mechanisms: A Control Method with Cooperation and Competition for Complex System | Complex systems are ubiquitous in the real world and tend to have complicated and poorly understood dynamics. For their control issues, the challenge is to guarantee accuracy, robustness, and generalization in such bloated and troubled environments. Fortunately, a complex system can be divided into multiple modular str... | ['Xue Li', 'Yi Guan', 'Xinmiao Yu', 'Jingchi Jiang', 'Xuehui Yu'] | 2022-09-15 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-1.36940509e-01 1.20211877e-01 6.25043660e-02 4.24783140e-01
3.31791222e-01 -6.11235678e-01 6.60383165e-01 1.34518445e-01
-5.34978919e-02 1.04001510e+00 -1.74468234e-01 -6.54278249e-02
-3.66203904e-01 -8.42875361e-01 -7.17211306e-01 -1.08316731e+00
-2.08592296e-01 2.98062116e-01 5.80957353e-01 -8.67843032... | [4.057784557342529, 1.8588298559188843] |
be94d7f7-79a2-467d-a6e9-d5cbf4489f81 | lad-language-models-as-data-for-zero-shot | 2207.14393 | null | https://arxiv.org/abs/2207.14393v1 | https://arxiv.org/pdf/2207.14393v1.pdf | LAD: Language Models as Data for Zero-Shot Dialog | To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD). LAD is a paradigm for creating diverse and accurate synthetic data which conveys the necessary structural constraints and can be used to train a downstream neural dialog model. LAD leverages GPT-3 to induce... | ['Maxine Eskenazi', 'Yasemin Altun', 'Shikib Mehri'] | 2022-07-28 | null | https://aclanthology.org/2022.sigdial-1.55 | https://aclanthology.org/2022.sigdial-1.55.pdf | sigdial-acl-2022-9 | ['slot-filling'] | ['natural-language-processing'] | [-2.48497739e-01 6.33986831e-01 -4.88221437e-01 -6.55834258e-01
-8.35795522e-01 -5.49808085e-01 7.93903589e-01 -1.95993781e-01
-4.41132307e-01 1.00654447e+00 7.39824533e-01 -4.42487776e-01
4.71569091e-01 -3.95487010e-01 -1.72009468e-01 1.07160583e-02
2.89540496e-02 1.03556561e+00 2.31563061e-01 -9.32223499... | [12.810175895690918, 8.04819393157959] |
95e48b71-f233-4b6f-873a-3360a8b924f4 | a-continuous-occlusion-model-for-road-scene | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Dhiman_A_Continuous_Occlusion_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Dhiman_A_Continuous_Occlusion_CVPR_2016_paper.pdf | A Continuous Occlusion Model for Road Scene Understanding | We present a physically interpretable, continuous 3D model for handling occlusions with applications to road scene understanding. We probabilistically assign each point in space to an object with a theoretical modeling of the reflection and transmission probabilities for the corresponding camera ray. Our modeling is un... | ['Jason J. Corso', 'Quoc-Huy Tran', 'Manmohan Chandraker', 'Vikas Dhiman'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['road-scene-understanding'] | ['computer-vision'] | [ 6.16112016e-02 1.05624259e-01 -4.41401720e-01 -2.24053264e-01
-8.44134212e-01 -6.76308453e-01 7.39102244e-01 5.52912429e-02
-2.19890535e-01 3.30079496e-01 -2.88769267e-02 -5.09509921e-01
-1.30139604e-01 -7.60749519e-01 -9.47401941e-01 -5.97713411e-01
-3.99049893e-02 1.26150799e+00 1.04665792e+00 2.51968712... | [7.81953763961792, -2.363063335418701] |
513d1f00-df62-4559-9e90-0e3725c6d8f2 | m-vaal-multimodal-variational-adversarial | 2306.12376 | null | https://arxiv.org/abs/2306.12376v1 | https://arxiv.org/pdf/2306.12376v1.pdf | M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks | Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation. Active learning attempts to minimize the need for large annotated samples by actively sampling the most informative examples for annotation. These examples contribute significan... | ['Cristian A. Linte', 'Danail Stoyanov', 'Bishesh Khanal', 'Binod Bhattarai', 'Bidur Khanal'] | 2023-06-21 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation', 'active-learning', 'active-learning'] | ['computer-vision', 'medical', 'methodology', 'natural-language-processing'] | [ 6.32721782e-01 4.91757393e-01 -6.36985362e-01 -4.55557495e-01
-1.56225598e+00 -3.01517576e-01 4.20257509e-01 5.64888895e-01
-7.81724393e-01 8.23636651e-01 9.25097615e-02 -5.49206771e-02
-7.75533989e-02 -6.51555777e-01 -4.57826614e-01 -1.19732928e+00
3.06989312e-01 8.51150155e-01 2.16925144e-01 1.66257799... | [14.76504135131836, -2.1804041862487793] |
0de10265-1214-4b7d-8634-870e520b205c | invisible-to-visible-privacy-aware-human | 2204.07280 | null | https://arxiv.org/abs/2204.07280v1 | https://arxiv.org/pdf/2204.07280v1.pdf | Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder | In action understanding in indoor, we have to recognize human pose and action considering privacy. Although camera images can be used for highly accurate human action recognition, camera images do not preserve privacy. Therefore, we propose a new task for human instance segmentation from invisible information, especial... | ['Takayoshi Yamashita', 'Kazuki Kozuka', 'Yasunori Ishii', 'Risako Tanigawa'] | 2022-04-15 | null | null | null | null | ['action-understanding', 'human-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.11778718e-01 9.98794660e-02 1.47518963e-01 -4.07843560e-01
-6.77299738e-01 -5.21303654e-01 2.27262482e-01 -4.42154050e-01
-5.94474554e-01 6.18142128e-01 -7.91473389e-02 -8.11061449e-03
2.45734692e-01 -1.03558660e+00 -8.42148662e-01 -8.69172812e-01
7.20146656e-01 4.15000498e-01 1.74418762e-01 4.95452911... | [7.930518627166748, 0.311688095331192] |
9f55315c-00b9-4928-a8c1-4a0d9427bb5d | optimized-hybrid-focal-margin-loss-for-crack | 2302.04395 | null | https://arxiv.org/abs/2302.04395v1 | https://arxiv.org/pdf/2302.04395v1.pdf | Optimized Hybrid Focal Margin Loss for Crack Segmentation | Many loss functions have been derived from cross-entropy loss functions such as large-margin softmax loss and focal loss. The large-margin softmax loss makes the classification more rigorous and prevents overfitting. The focal loss alleviates class imbalance in object detection by down-weighting the loss of well-classi... | ['Jiajie Chen'] | 2023-02-09 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 9.49512646e-02 1.04687095e-01 -4.85975295e-01 -4.58581984e-01
-1.02549767e+00 -2.08438128e-01 -2.52944291e-01 2.70559460e-01
-5.39627016e-01 6.84653401e-01 -3.61449718e-01 -6.13297075e-02
-4.56305109e-02 -8.75954092e-01 -5.54206371e-01 -8.04042220e-01
3.47521573e-01 8.29131342e-03 6.87607348e-01 1.22059353... | [14.279997825622559, -2.041977882385254] |
d51ad70f-3b37-4262-83eb-8a615b15c655 | t-ukapo-at-semeval-2020-task-6-def-n-tly-not | null | null | https://aclanthology.org/2020.semeval-1.95 | https://aclanthology.org/2020.semeval-1.95.pdf | T\"uKaPo at SemEval-2020 Task 6: Def(n)tly Not BERT: Definition Extraction Using pre-BERT Methods in a post-BERT World | We describe our system (T{\"u}KaPo) submitted for Task 6: DeftEval, at SemEval 2020. We developed a hybrid approach that combined existing CNN and RNN methods and investigated the impact of purely-syntactic and semantic features on the task of definition extraction. Our final model achieved a F1-score of 0.6851 in subt... | ['Haemanth Santhi Ponnusamy', 'Madeeswaran Kannan'] | 2020-12-01 | null | null | null | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 1.41435757e-01 4.52725112e-01 -1.33227870e-01 -5.76222777e-01
-6.52790844e-01 -7.93428063e-01 6.69228792e-01 8.25426206e-02
-9.83209431e-01 1.06759644e+00 3.52836251e-01 -6.71622396e-01
1.18328825e-01 -7.34603107e-01 -6.53633952e-01 1.73834637e-01
3.07069957e-01 2.32586294e-01 1.20391697e-01 -4.95751977... | [9.86816120147705, 9.039162635803223] |
ecb8ef62-5a0c-45ea-a48a-7d73fcd6172d | iterative-constrained-clustering-for | null | null | https://aclanthology.org/E14-1029 | https://aclanthology.org/E14-1029.pdf | Iterative Constrained Clustering for Subjectivity Word Sense Disambiguation | null | ['Rada Mihalcea', 'Janyce Wiebe', 'Cem Akkaya'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.365653038024902, 3.787386894226074] |
a2458fee-8b2d-4835-8d32-457587ceed9a | automating-horizon-scanning-in-future-studies | null | null | https://aclanthology.org/2022.lrec-1.34 | https://aclanthology.org/2022.lrec-1.34.pdf | Automating Horizon Scanning in Future Studies | We introduce document retrieval and comment generation tasks for automating horizon scanning. This is an important task in the field of futurology that collects sufficient information for predicting drastic societal changes in the mid- or long-term future. The steps used are: 1) retrieving news articles that imply dras... | ['Akihiko Murai', 'Yuichi Washida', 'Yukari Nagai', 'Hiroki Igarashi', 'Sohei Washino', 'Suzuko Nishino', 'Tatsuya Ishigaki'] | null | null | null | null | lrec-2022-6 | ['comment-generation'] | ['natural-language-processing'] | [ 9.68635920e-03 2.75672257e-01 -5.10581613e-01 -2.49643877e-01
-1.06707144e+00 -7.57221580e-01 9.66754794e-01 7.61951268e-01
-2.97539502e-01 9.04872119e-01 1.08825731e+00 -4.07090604e-01
7.55831972e-03 -7.81631708e-01 -5.11934340e-01 -3.18681329e-01
1.10471867e-01 5.09823442e-01 2.72125721e-01 -4.71448511... | [12.390738487243652, 9.462531089782715] |
a8fcf1cf-102d-40d3-be25-aa609e6b17aa | structural-information-preserving-for-graph-1 | 2102.06749 | null | https://arxiv.org/abs/2102.06749v1 | https://arxiv.org/pdf/2102.06749v1.pdf | Structural Information Preserving for Graph-to-Text Generation | The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer tr... | ['Dong Yu', 'Yubin Ge', 'Kun Xu', 'Yue Zhang', 'Jinsong Su', 'Ante Wang', 'Linfeng Song'] | 2021-02-12 | structural-information-preserving-for-graph | https://aclanthology.org/2020.acl-main.712 | https://aclanthology.org/2020.acl-main.712.pdf | acl-2020-6 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.30860007e-01 6.70074344e-01 -7.17135742e-02 -2.80637830e-01
-9.38457906e-01 -7.22616136e-01 9.11545455e-01 1.37768283e-01
2.76680142e-01 8.99533093e-01 6.63381457e-01 -3.03352445e-01
3.34998101e-01 -1.12737858e+00 -1.08938253e+00 -5.36598265e-01
4.14903224e-01 5.20434201e-01 -6.94452878e-03 -5.52880287... | [10.281126022338867, 8.288256645202637] |
ba8e4ddd-cf90-455d-948e-434eed69cb23 | pgd-a-large-scale-professional-go-dataset-for | 2205.00254 | null | https://arxiv.org/abs/2205.00254v1 | https://arxiv.org/pdf/2205.00254v1.pdf | PGD: A Large-scale Professional Go Dataset for Data-driven Analytics | Lee Sedol is on a winning streak--does this legend rise again after the competition with AlphaGo? Ke Jie is invincible in the world championship--can he still win the title this time? Go is one of the most popular board games in East Asia, with a stable professional sports system that has lasted for decades in China, J... | ['Yifan Gao'] | 2022-04-30 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.75131369e-01 -3.70636225e-01 -3.63441199e-01 -6.43102601e-02
-8.32013845e-01 -4.07905132e-01 -2.20027268e-02 1.89025924e-01
-6.08891308e-01 5.72141230e-01 2.95648545e-01 -4.87536639e-02
-5.38548291e-01 -1.14554465e+00 -4.79778945e-01 -3.62728894e-01
-2.11638242e-01 5.37165046e-01 3.25644314e-01 -1.16472661... | [6.633011341094971, 0.3437465727329254] |
dc5b7501-a625-4a51-8581-0a0ec867bcaa | knowledge-diffusion-process-common-islamic | 2002.04067 | null | https://arxiv.org/abs/2002.04067v1 | https://arxiv.org/pdf/2002.04067v1.pdf | Knowledge Diffusion Process & Common Islamic Banking Governance Principles: Integrative Perspective (s) of Managers and Shariah Scholars | Islamic banks being commercial entities strive to earn profit within shariah ambit. Therefore, they seem to be basing themselves upon two knowledge streams namely i) Islamic jurisprudence principles, and ii) banking principles. Islamic jurisprudence principles primarily aim at bringing shariah compliance while banking ... | ['Shakir Ullah', 'Karim Ullah', 'Adnan Malik'] | 2020-01-23 | null | null | null | null | ['jurisprudence'] | ['miscellaneous'] | [-6.55275822e-01 1.95429370e-01 -4.64056969e-01 -7.53401145e-02
2.37228468e-01 -3.64006072e-01 5.32121360e-01 1.73457165e-03
1.84867401e-02 6.48441732e-01 6.94236994e-01 -8.24076653e-01
-3.20115209e-01 -1.11580861e+00 1.17080703e-01 -8.14351201e-01
4.10825789e-01 3.60686481e-01 -2.41148323e-01 -9.56079364... | [9.122615814208984, 6.193658828735352] |
8dd70e53-149a-43f4-91f1-b7ca0795c275 | blind-face-restoration-via-integrating-face | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.pdf | Blind Face Restoration via Integrating Face Shape and Generative Priors | Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-quality images, they often fail to restore natural face shapes and high-fidelity facial details from... | ['Ying Tai', 'Chengjie Wang', 'Xiaozhong Ji', 'Xinyi Zhang', 'Wenqing Chu', 'Junwei Zhu', 'Feida Zhu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['blind-face-restoration'] | ['computer-vision'] | [ 3.43054175e-01 6.08786717e-02 4.98787642e-01 -5.70070863e-01
-9.03801501e-01 -8.82606879e-02 6.26771033e-01 -1.02306426e+00
1.79794773e-01 7.35014021e-01 4.72694308e-01 1.35974377e-01
-2.36507580e-02 -8.70516241e-01 -8.82395446e-01 -8.88329446e-01
4.49212432e-01 2.73688614e-01 -2.10781813e-01 -2.04469115... | [12.78560733795166, -0.11237670481204987] |
c13e4781-e223-4c51-81e6-744d096d4a4d | survcaus-representation-balancing-for | 2203.15672 | null | https://arxiv.org/abs/2203.15672v1 | https://arxiv.org/pdf/2203.15672v1.pdf | SurvCaus : Representation Balancing for Survival Causal Inference | Individual Treatment Effects (ITE) estimation methods have risen in popularity in the last years. Most of the time, individual effects are better presented as Conditional Average Treatment Effects (CATE). Recently, representation balancing techniques have gained considerable momentum in causal inference from observatio... | ['Blaise Hanczar', 'Agathe Guilloux', 'Ayoub Abraich'] | 2022-03-29 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.71884757e-01 2.28748828e-01 -9.91564214e-01 -4.48319137e-01
-7.30257511e-01 -1.67678460e-01 6.41695142e-01 4.71672982e-01
-3.32156301e-01 1.46165514e+00 7.27071285e-01 -5.59826612e-01
-6.02717578e-01 -8.40444148e-01 -8.12957942e-01 -8.04843426e-01
-6.74811661e-01 3.99459094e-01 -6.11917436e-01 2.59312928... | [8.040963172912598, 5.373728275299072] |
5965322a-dad9-4cb2-8368-2cdba117ab86 | self-supervised-video-representation-using | 2010.15464 | null | https://arxiv.org/abs/2010.15464v2 | https://arxiv.org/pdf/2010.15464v2.pdf | Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning | Recently, pretext-task based methods are proposed one after another in self-supervised video feature learning. Meanwhile, contrastive learning methods also yield good performance. Usually, new methods can beat previous ones as claimed that they could capture "better" temporal information. However, there exist setting d... | ['Toshihiko Yamasaki', 'Xueting Wang', 'Li Tao'] | 2020-10-29 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 8.43693614e-02 -3.25631678e-01 -5.76571584e-01 -2.84769267e-01
-4.88387346e-01 -2.49166250e-01 9.78620172e-01 -2.68422157e-01
-6.64807320e-01 6.79804564e-01 2.18936995e-01 6.02008700e-02
-2.27108687e-01 -4.12391454e-01 -6.90299749e-01 -9.17717457e-01
-3.17390561e-01 6.14849590e-02 4.88773882e-01 -4.15973932... | [8.944378852844238, 1.1823514699935913] |
f91b3aa2-3356-45d1-858e-31922a51ec64 | improving-machine-translation-with-phrase | 2301.08008 | null | https://arxiv.org/abs/2301.08008v1 | https://arxiv.org/pdf/2301.08008v1.pdf | Improving Machine Translation with Phrase Pair Injection and Corpus Filtering | In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed appro... | ['Pushpak Bhattacharyya', 'Akshay Batheja'] | 2023-01-19 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.49932253e-01 3.24778371e-02 -3.70218992e-01 -2.21626386e-01
-1.52308857e+00 -7.94745386e-01 8.60759735e-01 -2.45236501e-01
-7.55995154e-01 1.11985731e+00 1.83260664e-01 -7.48018086e-01
4.19980437e-01 -3.13452154e-01 -1.07085419e+00 -4.62855697e-01
2.21926346e-01 1.10726798e+00 -2.19657436e-01 -7.95172274... | [11.583060264587402, 10.398076057434082] |
8946e361-e017-4be3-876a-71d98b01ac59 | building-3d-object-models-during-manipulation | 1905.03907 | null | https://arxiv.org/abs/1905.03907v1 | https://arxiv.org/pdf/1905.03907v1.pdf | Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization | Object shape provides important information for robotic manipulation; for instance, selecting an effective grasp depends on both the global and local shape of the object of interest, while reaching into clutter requires accurate surface geometry to avoid unintended contact with the environment. Model-based 3D object ma... | ['Kanrun Huang', 'Tucker Hermans'] | 2019-05-10 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.12466358e-01 2.22220886e-02 -5.90986982e-02 -2.53125757e-01
-8.30070257e-01 -6.20440185e-01 1.26048759e-01 7.13496804e-02
-1.75514013e-01 3.80364925e-01 -3.25542539e-01 4.07954827e-02
-3.99941951e-01 -7.10688412e-01 -1.01621222e+00 -8.90762150e-01
-3.59655768e-02 1.22720182e+00 3.09085011e-01 1.93406969... | [5.810682773590088, -0.8540130853652954] |
1cbc4059-d335-416e-924d-1ed11037b8f2 | visual-fault-detection-of-multi-scale-key | 2211.14522 | null | https://arxiv.org/abs/2211.14522v1 | https://arxiv.org/pdf/2211.14522v1.pdf | Visual Fault Detection of Multi-scale Key Components in Freight Trains | Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are highly reliant on hardware resources and are complex to implement. In addition, no train fault dete... | ['Guodong Sun', 'Bo Wu', 'Huilin Pan', 'Yang Zhou', 'Yang Zhang'] | 2022-11-26 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.29980558e-01 -3.50566983e-01 -4.96136285e-02 -2.34617010e-01
-9.17059064e-01 -1.72518134e-01 1.51131311e-02 -8.60504434e-02
-1.91362545e-01 3.62460852e-01 -4.11944389e-01 -4.15032268e-01
-1.07363038e-01 -8.14378500e-01 -8.04608107e-01 -4.64252174e-01
8.83254260e-02 2.89635807e-01 1.02889144e+00 -1.67694837... | [8.753435134887695, -0.4571641683578491] |
a2d07b7a-a7dd-47b4-b172-67e03dc948d2 | misa-modality-invariant-and-specific | 2005.03545 | null | https://arxiv.org/abs/2005.03545v3 | https://arxiv.org/pdf/2005.03545v3.pdf | MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis | Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional... | ['Soujanya Poria', 'Roger Zimmermann', 'Devamanyu Hazarika'] | 2020-05-07 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [ 1.36711359e-01 -3.37144673e-01 -2.53344983e-01 -2.22263768e-01
-9.42603111e-01 -6.02098107e-01 7.26140141e-01 6.91158324e-02
-9.45277512e-02 3.24079245e-01 1.03602159e+00 2.42687166e-01
2.92047918e-01 -2.41979107e-01 -4.61953759e-01 -8.04802418e-01
4.73208994e-01 -1.97018147e-01 -4.81445700e-01 -6.47862911... | [13.154638290405273, 5.0840744972229] |
95dcf7dd-7cdc-42d5-8027-172726fd3ff3 | resatom-system-protein-and-ligand-affinity | 2105.05125 | null | https://arxiv.org/abs/2105.05125v1 | https://arxiv.org/pdf/2105.05125v1.pdf | ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learning | Motivation: Protein-ligand affinity prediction is an important part of structure-based drug design. It includes molecular docking and affinity prediction. Although molecular dynamics can predict affinity with high accuracy at present, it is not suitable for large-scale virtual screening. The existing affinity predictio... | ['Yong Huang', 'Yanwen Duan', 'Shuo Wu', 'Yeji Wang'] | 2021-04-17 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-2.12681651e-01 -4.72341985e-01 -4.09421861e-01 -3.48776042e-01
-7.66107202e-01 -6.11803412e-01 2.42092699e-01 5.83584130e-01
-6.41836703e-01 1.53020406e+00 -4.43652347e-02 -5.98413289e-01
-6.88410178e-02 -6.42762423e-01 -8.87983024e-01 -9.44031179e-01
-5.53284027e-02 8.46672297e-01 3.11890751e-01 -3.91618192... | [4.935766220092773, 5.641643047332764] |
e7fc2672-30f6-4d64-a5b7-e02c1f40db1a | single-image-haze-removal-using-a-generative | 1810.09479 | null | https://arxiv.org/abs/1810.09479v2 | https://arxiv.org/pdf/1810.09479v2.pdf | Single Image Haze Removal using a Generative Adversarial Network | Traditional methods to remove haze from images rely on estimating a transmission map. When dealing with single images, this becomes an ill-posed problem due to the lack of depth information. In this paper, we propose an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network ... | ['Bharath Raj N.', 'Venkateswaran N'] | 2018-10-22 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 4.36008036e-01 2.21684705e-02 7.32795835e-01 -1.25500709e-01
-6.43467367e-01 -2.27556750e-01 5.08720398e-01 -2.94798523e-01
-3.64558309e-01 9.16465878e-01 -5.03525548e-02 -1.63469493e-01
1.73830874e-02 -1.02878153e+00 -6.95230663e-01 -1.15629947e+00
2.88257778e-01 -1.27107471e-01 5.36924779e-01 -2.66129106... | [10.900676727294922, -3.1486098766326904] |
f39ede64-8036-4310-a1b5-fc374ab481f4 | deep-hybrid-real-and-synthetic-training-for | 1807.11226 | null | http://arxiv.org/abs/1807.11226v1 | http://arxiv.org/pdf/1807.11226v1.pdf | Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition | Intrinsic image decomposition is the process of separating the reflectance
and shading layers of an image, which is a challenging and underdetermined
problem. In this paper, we propose to systematically address this problem using
a deep convolutional neural network (CNN). Although deep learning (DL) has been
recently u... | ['Ravi Ramamoorthi', 'Sai Bi', 'Nima Khademi Kalantari'] | 2018-07-30 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.48174506e-01 -1.09233586e-02 6.72178745e-01 -3.24884355e-01
-4.15695250e-01 -3.47066015e-01 3.94155085e-01 -5.67048967e-01
-3.21756661e-01 7.61708498e-01 -3.63587022e-01 -1.79547027e-01
3.02325398e-01 -7.68555105e-01 -8.38350892e-01 -8.92455280e-01
5.11898041e-01 2.08816752e-01 2.56987989e-01 -5.59279881... | [10.075071334838867, -2.742591381072998] |
86a6f954-2d9c-424d-a65e-cbfe866dc49b | multiscale-multimodal-transformer-for | null | null | https://openreview.net/pdf?id=aqP3WFwMPbe | https://openreview.net/pdf?id=aqP3WFwMPbe | Multiscale Multimodal Transformer for Multimodal Action Recognition | While action recognition has been an active research area for several years, most existing approaches merely leverage the video modality as opposed to humans that efficiently process video and audio cues simultaneously. This limits the usage of recent models to applications where the actions are visually well-defined. ... | ['Mohamed Omar', 'Linda Liu', 'Xiang Hao', 'Xiaohang Sun', 'Jingru Yi', 'Wentao Zhu'] | 2022-09-22 | null | null | null | submitted-to-iclr-2022-9 | ['audio-classification', 'multi-modal-classification'] | ['audio', 'miscellaneous'] | [ 4.91477072e-01 -5.93165398e-01 -9.58618894e-02 -4.89901304e-02
-1.27347183e+00 -4.63254094e-01 6.18169904e-01 3.04078400e-01
-2.44519562e-01 3.23270202e-01 4.36978757e-01 1.37012959e-01
-9.49746522e-04 -4.90284592e-01 -6.55001760e-01 -6.43560469e-01
-5.15861996e-02 -1.99783787e-01 3.08575124e-01 -1.85299013... | [13.580430030822754, 4.747525215148926] |
98a425d9-3850-4869-847e-608271ab6664 | on-the-sensitivity-of-reward-inference-to | 2212.04717 | null | https://arxiv.org/abs/2212.04717v1 | https://arxiv.org/pdf/2212.04717v1.pdf | On the Sensitivity of Reward Inference to Misspecified Human Models | Inferring reward functions from human behavior is at the center of value alignment - aligning AI objectives with what we, humans, actually want. But doing so relies on models of how humans behave given their objectives. After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining ac... | ['Anca Dragan', 'Kush Bhatia', 'Joey Hong'] | 2022-12-09 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 1.77875727e-01 5.42907894e-01 -2.17505485e-01 -2.41267264e-01
-3.40953052e-01 -6.17274761e-01 1.90395936e-01 2.09729135e-01
-7.71278739e-01 1.04320657e+00 -1.43616557e-01 -4.00513500e-01
-1.61320522e-01 -7.15326130e-01 -8.67306530e-01 -5.44895113e-01
-2.07863063e-01 4.06231046e-01 -9.08491611e-02 -2.69285262... | [4.146288871765137, 2.272106409072876] |
bfe02e06-1a46-4392-8e21-529129a87c1e | generative-pretraining-from-pixels | null | null | https://openai.com/blog/image-gpt/ | https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf | Generative Pretraining from Pixels | Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels, without incorporating knowledge of the 2D input structure. Despite training on low-resolu... | ['Mark Chen', 'Jeff Wu', 'Rewon Child', 'Ilya Sutskever', 'David Luan', 'Alec Radford', 'Heewoo Jun', 'Prafulla Dhariwal'] | 2020-07-17 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/6022-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/6022-Paper.pdf | icml-2020-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.62087631e-01 3.15384269e-01 -4.33302850e-01 -5.11262357e-01
-1.04421139e+00 -5.12498736e-01 8.34825754e-01 -2.02108324e-01
-5.03245533e-01 5.56411386e-01 3.91023725e-01 -1.33824319e-01
1.11766025e-01 -8.55513752e-01 -9.20572281e-01 -5.62728524e-01
-1.91821352e-01 3.78319770e-01 1.27737179e-01 -1.44994453... | [9.476527214050293, 2.535041332244873] |
60f0ddfb-00f9-48b4-bce7-85b967789d38 | online-refinement-of-a-scene-recognition | 2208.06636 | null | https://arxiv.org/abs/2208.06636v1 | https://arxiv.org/pdf/2208.06636v1.pdf | Online Refinement of a Scene Recognition Model for Mobile Robots by Observing Human's Interaction with Environments | This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversable plants growing out to the paths, misclassification may lead the robo... | ['Jun Miura', 'Hiroaki Masuzawa', 'Shigemichi Matsuzaki'] | 2022-08-13 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 1.05469406e+00 5.71526110e-01 1.35709763e-01 -3.53129119e-01
-7.21931383e-02 -5.52089453e-01 1.28929734e-01 1.57020673e-01
-2.16235057e-01 5.08812116e-03 -6.96871698e-01 -3.83996457e-01
-4.76129591e-01 -8.13144445e-01 -7.17921734e-01 -4.14389044e-01
1.38101056e-02 9.01227117e-01 6.89657092e-01 -2.62241215... | [8.861143112182617, -0.7277693152427673] |
6dc7d511-1f8c-477a-ab3e-ed1588d0b181 | internal-external-boundary-attention-fusion | 2307.00212 | null | https://arxiv.org/abs/2307.00212v1 | https://arxiv.org/pdf/2307.00212v1.pdf | Internal-External Boundary Attention Fusion for Glass Surface Segmentation | Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of other reflected or transmitted surfaces as well. Detecting glass regions from a single-color image is a challenging task. Recent deep-learn... | ['Seungkyu Lee', 'Dongshen Han'] | 2023-07-01 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 6.96281612e-01 3.64477456e-01 9.98429880e-02 -4.82327431e-01
-6.17674112e-01 -6.19782329e-01 2.34948114e-01 4.93244007e-02
1.30107924e-01 -3.03834099e-02 -2.89647907e-01 1.83997765e-01
5.35348833e-01 -7.58778512e-01 -8.16549182e-01 -9.24134612e-01
3.00415069e-01 5.35005391e-01 6.86305106e-01 1.34429708... | [9.548358917236328, -1.0202367305755615] |
6559034c-920d-4bea-a988-106e5ecfe6fc | codebert-a-pre-trained-model-for-programming | 2002.08155 | null | https://arxiv.org/abs/2002.08155v4 | https://arxiv.org/pdf/2002.08155v4.pdf | CodeBERT: A Pre-Trained Model for Programming and Natural Languages | We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language codesearch, code documentation generation, etc. We develop CodeBERT with Transformer-based neural... | ['Nan Duan', 'Daya Guo', 'Zhangyin Feng', 'Xiaocheng Feng', 'Ting Liu', 'Ming Zhou', 'Ming Gong', 'Duyu Tang', 'Daxin Jiang', 'Bing Qin', 'Linjun Shou'] | 2020-02-19 | null | https://aclanthology.org/2020.findings-emnlp.139 | https://aclanthology.org/2020.findings-emnlp.139.pdf | findings-of-the-association-for-computational | ['type-prediction', 'code-documentation-generation', 'code-search', 'code-search', 'code-documentation-generation'] | ['computer-code', 'computer-code', 'computer-code', 'computer-vision', 'natural-language-processing'] | [-1.97810397e-01 2.92619795e-01 -5.44469714e-01 -6.29283190e-02
-1.36909735e+00 -6.43959284e-01 6.28520072e-01 1.78276002e-02
9.46937278e-02 5.07487416e-01 4.55678165e-01 -7.64521480e-01
3.50167871e-01 -8.07206690e-01 -7.34350801e-01 -3.33265871e-01
7.67957121e-02 6.29833102e-01 1.00803971e-01 -2.16459244... | [7.764390468597412, 7.907708168029785] |
a054a7f7-943e-4abf-a03c-53df8b328d7c | compositional-prompt-tuning-with-motion-cues | 2302.00268 | null | https://arxiv.org/abs/2302.00268v1 | https://arxiv.org/pdf/2302.00268v1.pdf | Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection | Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary predictions trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of... | ['Qianru Sun', 'Jun Xiao', 'Hanwang Zhang', 'Long Chen', 'Kaifeng Gao'] | 2023-02-01 | null | null | null | null | ['video-visual-relation-detection'] | ['computer-vision'] | [-1.08155780e-01 -2.66068220e-01 -4.16416883e-01 -1.42339751e-01
-5.67403913e-01 -7.67490566e-01 7.93699384e-01 -1.60522908e-01
-2.16100365e-01 2.54692137e-01 3.18360150e-01 -3.16944093e-01
1.88433751e-01 -2.70039380e-01 -7.26617992e-01 -5.66078901e-01
1.48951381e-01 3.14889640e-01 5.63521206e-01 -2.75563985... | [10.034673690795898, 1.010627269744873] |
7f2f1f32-b167-4f28-8de4-a5d60d913fc1 | unsupervised-sentiment-analysis-of-plastic | 2307.02640 | null | https://arxiv.org/abs/2307.02640v1 | https://arxiv.org/pdf/2307.02640v1.pdf | Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts | The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases based on the sheer volume and velocity of textual data. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in hu... | ['Alexandrea K. Ramnarine'] | 2023-07-05 | null | null | null | null | ['sentiment-analysis', 'document-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.00293407e-01 5.10212243e-01 -5.88269889e-01 -4.20857221e-01
-6.37426555e-01 -3.08908135e-01 9.01531041e-01 1.03988659e+00
-6.97432339e-01 5.48975170e-01 7.14297712e-01 -3.26158941e-01
-1.37479022e-01 -8.27247262e-01 -1.08640783e-01 -7.15242684e-01
-2.65586466e-01 6.94903314e-01 -4.58166540e-01 -3.80097181... | [10.551353454589844, 8.266257286071777] |
fecdf339-a47f-46af-ab0d-3e9b8ecec09a | automated-vulnerability-detection-in-source | 1807.04320 | null | http://arxiv.org/abs/1807.04320v2 | http://arxiv.org/pdf/1807.04320v2.pdf | Automated Vulnerability Detection in Source Code Using Deep Representation Learning | Increasing numbers of software vulnerabilities are discovered every year
whether they are reported publicly or discovered internally in proprietary
code. These vulnerabilities can pose serious risk of exploit and result in
system compromise, information leaks, or denial of service. We leveraged the
wealth of C and C++ ... | ['Paul M. Ellingwood', 'Rebecca L. Russell', 'Marc W. McConley', 'Louis Kim', 'Tomo Lazovich', 'Onur Ozdemir', 'Jacob A. Harer', 'Lei H. Hamilton'] | 2018-07-11 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-3.50710183e-01 -1.88493863e-01 -4.46120769e-01 -4.49931443e-01
-1.18590558e+00 -1.43986380e+00 3.21856700e-02 4.93168503e-01
2.72245049e-01 1.52030930e-01 2.08848283e-01 -1.22681952e+00
2.56327242e-01 -9.45916772e-01 -8.14122081e-01 2.46068940e-01
-3.58239859e-01 -4.72983658e-01 2.77053952e-01 -2.20607966... | [7.065115928649902, 7.77139139175415] |
fd5bf2b5-baf6-462e-8cac-4e8b2a67acd9 | map-induction-compositional-spatial-submap-1 | 2110.12301 | null | https://arxiv.org/abs/2110.12301v2 | https://arxiv.org/pdf/2110.12301v2.pdf | Map Induction: Compositional spatial submap learning for efficient exploration in novel environments | Humans are expert explorers. Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments efficiently by inferring the structure of unobserved spaces using s... | ['Ila Fiete', 'Josh Tenenbaum', 'Marta Kryven', 'Aidan Curtis', 'Sugandha Sharma'] | 2021-10-23 | map-induction-compositional-spatial-submap | https://openreview.net/forum?id=1NUsBU-7HAL | https://openreview.net/pdf?id=1NUsBU-7HAL | iclr-2022-4 | ['program-induction'] | ['computer-code'] | [-1.73817091e-02 5.20815790e-01 -1.19054966e-01 -5.33727050e-01
-2.46573657e-01 -4.72853035e-01 6.02409840e-01 5.07553756e-01
-6.71952009e-01 8.25187266e-01 4.78349060e-01 -7.44665980e-01
-4.33667600e-01 -1.14599395e+00 -9.08758342e-01 -7.60836303e-02
-6.51641130e-01 1.15199077e+00 3.68077815e-01 2.00572852... | [4.126644134521484, 1.145478367805481] |
29e59af2-5bcb-4c26-9d33-adf1b374fcbd | neural-network-state-estimation-for-full | 1811.06654 | null | http://arxiv.org/abs/1811.06654v2 | http://arxiv.org/pdf/1811.06654v2.pdf | Neural network state estimation for full quantum state tomography | An efficient state estimation model, neural network estimation (NNE),
empowered by machine learning techniques, is presented for full quantum state
tomography (FQST). A parameterized function based on neural network is applied
to map the measurement outcomes to the estimated quantum states. Parameters are
updated with ... | ['Shuqi Xu', 'Qian Xu'] | 2018-11-16 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.34875154e-01 1.19146936e-01 -4.17127639e-01 -3.82495463e-01
-7.43577659e-01 -2.87219018e-01 5.61442435e-01 -2.44823962e-01
-5.62170625e-01 1.19543529e+00 -2.10243136e-01 -7.24933982e-01
-3.81971270e-01 -7.45509684e-01 -5.79810381e-01 -8.28348696e-01
-3.86200510e-02 6.19683504e-01 -1.76748797e-01 -2.24645197... | [5.5738091468811035, 4.924363613128662] |
1906e928-a09a-42da-9769-0e38baae555a | implicit-neural-representations-for | 2112.08539 | null | https://arxiv.org/abs/2112.08539v1 | https://arxiv.org/pdf/2112.08539v1.pdf | Implicit Neural Representations for Deconvolving SAS Images | Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point scatterers in the scene. In theory, deconvolving the reconstructed SAS image with the scene PSF restore... | ['Suren Jayasuriya', 'Daniel C. Brown', 'Thomas Blanford', 'Albert Reed'] | 2021-12-16 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 6.59269929e-01 -5.36537655e-02 9.35863614e-01 -2.30942190e-01
-8.07191014e-01 -7.02867091e-01 4.67731178e-01 -4.59896386e-01
-3.50091696e-01 5.87989688e-01 4.81121600e-01 -1.39666768e-02
-2.75294572e-01 -5.89038968e-01 -9.26773608e-01 -1.08690596e+00
-6.31621107e-02 3.32671106e-01 -3.00014298e-02 -1.67938292... | [11.43325138092041, -2.6633386611938477] |
f7bb8ee3-6504-4ebe-ab40-c5518e18739e | a-unified-framework-for-information-theoretic | 2305.11042 | null | https://arxiv.org/abs/2305.11042v1 | https://arxiv.org/pdf/2305.11042v1.pdf | A unified framework for information-theoretic generalization bounds | This paper presents a general methodology for deriving information-theoretic generalization bounds for learning algorithms. The main technical tool is a probabilistic decorrelation lemma based on a change of measure and a relaxation of Young's inequality in $L_{\psi_p}$ Orlicz spaces. Using the decorrelation lemma in c... | ['Maxim Raginsky', 'Yifeng Chu'] | 2023-05-18 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 3.32831472e-01 7.46246725e-02 5.06467093e-03 -1.88108414e-01
-4.56304789e-01 -6.58779800e-01 2.62876838e-01 3.44808936e-01
-5.63239932e-01 1.04119253e+00 -2.40164921e-01 -3.50213498e-01
-8.48369777e-01 -7.52739429e-01 -4.54283983e-01 -1.21587408e+00
-2.80112505e-01 2.63784200e-01 1.28160626e-01 5.94345853... | [7.359363079071045, 4.28594970703125] |
a4ef17e6-16c0-4a3d-80a9-74034f4d8df3 | yolop-you-only-look-once-for-panoptic-driving | 2108.11250 | null | https://arxiv.org/abs/2108.11250v7 | https://arxiv.org/pdf/2108.11250v7.pdf | YOLOP: You Only Look Once for Panoptic Driving Perception | A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentati... | ['Wenyu Liu', 'Wenqing Cheng', 'Xiang Bai', 'Xinggang Wang', 'Weitian Zhang', 'Manwen Liao', 'Dong Wu'] | 2021-08-25 | null | null | null | null | ['drivable-area-detection'] | ['computer-vision'] | [-1.19816504e-01 -2.92841464e-01 -2.19327405e-01 -6.15691721e-01
-6.49610639e-01 -3.28240275e-01 5.19370496e-01 -2.15970576e-01
-3.20440531e-01 1.76274940e-01 -4.51875001e-01 -8.09621155e-01
3.70662034e-01 -5.96459210e-01 -7.63250411e-01 -5.04238665e-01
2.44667962e-01 1.70048133e-01 8.59497726e-01 -2.97792256... | [8.040983200073242, -1.2396982908248901] |
7fa2192e-5e9f-40a6-905f-037aa1f12c39 | blackvip-black-box-visual-prompting-for | 2303.14773 | null | https://arxiv.org/abs/2303.14773v2 | https://arxiv.org/pdf/2303.14773v2.pdf | BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning | With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimis... | ['Kyungwoo Song', 'Hosik Choi', 'Jiyoung Jung', 'Geunyoung Jung', 'Yongtaek Lim', 'Hee-young Lee', 'Hyeji Hwang', 'Changdae Oh'] | 2023-03-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Oh_BlackVIP_Black-Box_Visual_Prompting_for_Robust_Transfer_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-prompting'] | ['computer-vision'] | [-1.54003024e-01 -2.35605612e-01 -2.75384456e-01 -1.43238485e-01
-1.00666130e+00 -5.90754271e-01 5.51783442e-01 -2.31557578e-01
-4.30805951e-01 6.78896308e-01 -5.50987460e-02 -3.10491562e-01
1.59075722e-01 -3.43490481e-01 -9.83536303e-01 -8.03939760e-01
3.26338202e-01 5.96866846e-01 6.69514894e-01 -1.05940722... | [9.984439849853516, 2.6762685775756836] |
91a4b629-03b4-48c2-aa53-5b513484fc07 | human-language-modeling-1 | 2205.05128 | null | https://arxiv.org/abs/2205.05128v1 | https://arxiv.org/pdf/2205.05128v1.pdf | Human Language Modeling | Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem whereby a human-level exists to connect sequences of documents (e.g. social media ... | ['H. Andrew Schwartz', 'Niranjan Balasubramanian', 'Matthew Matero', 'Nikita Soni'] | 2022-05-10 | null | https://aclanthology.org/2022.findings-acl.52 | https://aclanthology.org/2022.findings-acl.52.pdf | findings-acl-2022-5 | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.20040290e-01 4.76332366e-01 -4.46951210e-01 -4.42440450e-01
-3.76543939e-01 -4.85898376e-01 1.29238319e+00 4.08284277e-01
-5.19256592e-01 5.77568829e-01 6.22242808e-01 -4.58346009e-01
6.40259981e-01 -6.40783250e-01 -3.90506327e-01 2.85972189e-02
-7.73628205e-02 8.26713264e-01 -1.16568953e-01 -4.01649266... | [10.658296585083008, 8.84811782836914] |
a215efc2-7f25-4a09-b512-db1321bc6af7 | flicu-a-federated-learning-workflow-for | 2205.15104 | null | https://arxiv.org/abs/2205.15104v1 | https://arxiv.org/pdf/2205.15104v1.pdf | FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality Prediction | Although Machine Learning (ML) can be seen as a promising tool to improve clinical decision-making for supporting the improvement of medication plans, clinical procedures, diagnoses, or medication prescriptions, it remains limited by access to healthcare data. Healthcare data is sensitive, requiring strict privacy prac... | ['Panagiotis Papapetrou', 'Jaakko Hollmén', 'David Pitts', 'Annaclaudia Montanino', 'Ioanna Miliou', 'Lena Mondrejevski'] | 2022-05-30 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-3.74174751e-02 -4.04273793e-02 -6.29094958e-01 -4.05888170e-01
-6.72927260e-01 -5.55021644e-01 9.67486668e-03 7.67303824e-01
-5.06750524e-01 9.40801620e-01 6.49905205e-02 -6.52452290e-01
-5.48495889e-01 -7.54790187e-01 -4.23074573e-01 -9.22157526e-01
-3.55265796e-01 4.63818550e-01 -4.19893473e-01 4.51729387... | [6.164210319519043, 6.430863857269287] |
c7d35d3f-3a93-4378-8c3a-af4edb0e0d16 | leveraging-class-abstraction-for-commonsense | 2201.12126 | null | https://arxiv.org/abs/2201.12126v2 | https://arxiv.org/pdf/2201.12126v2.pdf | Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods | Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in ... | ['Herke van Hoof', 'Ilaria Tiddi', 'Niklas Höpner'] | 2022-01-28 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.33856654e-01 4.85874653e-01 -1.98115278e-02 -1.14095071e-02
-5.93952835e-01 -9.16483581e-01 5.11743009e-01 1.21119745e-01
-7.71493673e-01 1.34595954e+00 1.82054415e-01 -1.07648894e-01
-6.23049676e-01 -1.02281451e+00 -7.07123816e-01 -4.97506708e-01
-3.97171795e-01 6.72686398e-01 5.43174148e-01 -3.95174950... | [3.9433975219726562, 1.4434932470321655] |
7474daf2-cf43-491f-b8b2-15bc06ba8448 | domain-adaptive-semantic-segmentation-with | 2104.13613 | null | https://arxiv.org/abs/2104.13613v2 | https://arxiv.org/pdf/2104.13613v2.pdf | Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation | Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks~(such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to eac... | ['Luc van Gool', 'Olga Fink', 'Lukas Hoyer', 'Dengxin Dai', 'Qin Wang'] | 2021-04-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Domain_Adaptive_Semantic_Segmentation_With_Self-Supervised_Depth_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.84882098e-01 4.74493802e-01 -3.03020567e-01 -5.93988061e-01
-9.94952679e-01 -3.99854273e-01 2.31821835e-01 -4.70597334e-02
-4.59289759e-01 5.66876531e-01 -4.19314802e-02 -2.71691121e-02
2.96892256e-01 -7.06513882e-01 -8.30692291e-01 -8.03207695e-01
5.13305306e-01 3.79498124e-01 5.57908595e-01 -1.80133820... | [9.632330894470215, 0.923687219619751] |
d367514c-56b4-4c44-992b-2fe67d040341 | rethink-diversity-in-deep-learning-testing | 2305.15698 | null | https://arxiv.org/abs/2305.15698v1 | https://arxiv.org/pdf/2305.15698v1.pdf | Rethink Diversity in Deep Learning Testing | Deep neural networks (DNNs) have demonstrated extraordinary capabilities and are an integral part of modern software systems. However, they also suffer from various vulnerabilities such as adversarial attacks and unfairness. Testing deep learning (DL) systems is therefore an important task, to detect and mitigate those... | ['Somesh Jha', 'Jihye Choi', 'Zi Wang'] | 2023-05-25 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [-1.21918239e-01 6.65616095e-02 -3.49971764e-02 -3.79386574e-01
-3.04613173e-01 -6.90488875e-01 1.72470644e-01 -8.45763385e-02
-1.08748466e-01 7.06274867e-01 -2.29381442e-01 -9.13484752e-01
-2.68193096e-01 -9.56482947e-01 -8.91398311e-01 -3.98845345e-01
-1.37435049e-01 -1.04509987e-01 3.86843920e-01 -4.51215953... | [6.640969753265381, 7.684077739715576] |
48b44b8c-330f-4c73-9869-75cbd3b93b05 | nuts-network-for-unsupervised-telegraphic | null | null | https://openreview.net/forum?id=rkGcYi09Km | https://openreview.net/pdf?id=rkGcYi09Km | NUTS: Network for Unsupervised Telegraphic Summarization | Extractive summarization methods operate by ranking and selecting the sentences which best encapsulate the theme of a given document. They do not fare well in domains like fictional narratives where there is no central theme and core information is not encapsulated by a small set of sentences. For the purpose of reduci... | ['Manish Shrivastava', 'Sajal Maheshwari', 'Tirth Maniar', 'Chanakya Malireddy'] | 2018-09-27 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 5.07299483e-01 5.51090717e-01 -7.10699633e-02 -4.93509322e-01
-1.17985690e+00 -7.09431946e-01 7.52372861e-01 5.12895644e-01
-2.20767826e-01 8.95864725e-01 1.25607467e+00 5.86792873e-03
2.68109068e-02 -6.64032698e-01 -7.50114143e-01 -3.02520037e-01
1.06946819e-01 4.23303634e-01 -1.68381423e-01 -5.32558322... | [12.522787094116211, 9.498762130737305] |
fbe2f902-d929-4c8f-91db-67dcd43be030 | self-appearance-aided-differential-evolution | 2110.04658 | null | https://arxiv.org/abs/2110.04658v1 | https://arxiv.org/pdf/2110.04658v1.pdf | Self-appearance-aided Differential Evolution for Motion Transfer | Image animation transfers the motion of a driving video to a static object in a source image, while keeping the source identity unchanged. Great progress has been made in unsupervised motion transfer recently, where no labelled data or ground truth domain priors are needed. However, current unsupervised approaches stil... | ['Ser-Nam Lim', 'Camille Couprie', 'Maxime Oquab', 'Ashish Shah', 'Yipin Zhou', 'Xuefei Cao', 'Rui Wang', 'Peirong Liu'] | 2021-10-09 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 2.80457616e-01 1.40363485e-01 -1.33920446e-01 -2.17286527e-01
-4.28783625e-01 -6.03919506e-01 7.48131216e-01 -5.74885488e-01
-1.80259332e-01 7.56380320e-01 1.41706258e-01 3.32502872e-01
4.05911982e-01 -5.05352676e-01 -8.94944370e-01 -8.83865416e-01
1.63890451e-01 2.84165114e-01 5.08531690e-01 -3.64017904... | [10.813156127929688, -0.8126379251480103] |
d5a962ca-b9bc-41be-a80c-2eb6b78c0efe | towards-reliable-neural-machine-translation | 2303.10966 | null | https://arxiv.org/abs/2303.10966v1 | https://arxiv.org/pdf/2303.10966v1.pdf | Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning | Neural machine translation (NMT) has achieved remarkable success in producing high-quality translations. However, current NMT systems suffer from a lack of reliability, as their outputs that are often affected by lexical or syntactic changes in inputs, resulting in large variations in quality. This limitation hinders t... | ['Min Zhang', 'Changfeng Zhu', 'Wensen Cheng', 'Qiang Wang', 'Rongxiang Weng'] | 2023-03-20 | null | null | null | null | ['nmt', 'bilevel-optimization'] | ['computer-code', 'methodology'] | [ 4.85272139e-01 -2.90288329e-01 -3.91680241e-01 -5.87192893e-01
-1.32479668e+00 -5.06906569e-01 6.43479824e-01 -2.30976939e-01
-1.41889900e-01 8.29520404e-01 2.86403269e-01 -4.24574167e-01
3.07798177e-01 -5.56736171e-01 -9.92073476e-01 -5.41623890e-01
6.42496526e-01 6.40084803e-01 -4.42818224e-01 -4.02768493... | [11.644682884216309, 10.104180335998535] |
687f2da3-0ea4-40b2-ba21-0fc926bf8f8d | intel-labs-at-ego4d-challenge-2022-a-better | 2210.07764 | null | https://arxiv.org/abs/2210.07764v1 | https://arxiv.org/pdf/2210.07764v1.pdf | Intel Labs at Ego4D Challenge 2022: A Better Baseline for Audio-Visual Diarization | This report describes our approach for the Audio-Visual Diarization (AVD) task of the Ego4D Challenge 2022. Specifically, we present multiple technical improvements over the official baselines. First, we improve the detection performance of the camera wearer's voice activity by modifying the training scheme of its mode... | ['Kyle Min'] | 2022-10-14 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-1.35372162e-01 7.94854090e-02 -2.87871301e-01 -1.08383045e-01
-1.09120834e+00 -6.56986117e-01 7.44577885e-01 -5.27156651e-01
-1.78850561e-01 1.60234049e-01 7.16460586e-01 1.10441104e-01
4.20635045e-01 1.78049766e-02 -3.64441812e-01 -4.15506661e-01
6.44062310e-02 -4.58505787e-02 2.80653656e-01 2.68629611... | [14.458067893981934, 5.412063121795654] |
cd850887-62ae-47b1-9faf-ba35d933beab | deep-feature-compression-for-collaborative | 1802.03931 | null | http://arxiv.org/abs/1802.03931v1 | http://arxiv.org/pdf/1802.03931v1.pdf | Deep feature compression for collaborative object detection | Recent studies have shown that the efficiency of deep neural networks in
mobile applications can be significantly improved by distributing the
computational workload between the mobile device and the cloud. This paradigm,
termed collaborative intelligence, involves communicating feature data between
the mobile and the ... | ['Ivan V. Bajic', 'Hyomin Choi'] | 2018-02-12 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 9.40337107e-02 -2.60379046e-01 1.14759497e-01 -4.12256986e-01
-4.53064799e-01 -1.76474392e-01 3.02542806e-01 1.96803525e-01
-8.36113513e-01 6.53801918e-01 -2.22352877e-01 -2.13872254e-01
-2.98582047e-01 -1.00911188e+00 -7.45318651e-01 -7.28406191e-01
-1.43854797e-01 6.56487048e-02 3.22893530e-01 2.26577312... | [8.413776397705078, 2.897343158721924] |
cc7d4f07-d8d4-43c8-9331-ddf656336282 | deep-learning-for-iris-recognition-a-review | 2303.08514 | null | https://arxiv.org/abs/2303.08514v1 | https://arxiv.org/pdf/2303.08514v1.pdf | Deep Learning for Iris Recognition: A Review | Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional m... | ['Jinghua Zhang', 'Xu Han', 'Hongli Chang', 'Renye Zhang', 'Siliang He', 'Yimin Yin'] | 2023-03-15 | null | null | null | null | ['iris-recognition', 'feature-engineering'] | ['computer-vision', 'methodology'] | [-7.93592166e-03 -5.02777040e-01 -5.09465098e-01 -4.39335376e-01
3.84800173e-02 -3.58530819e-01 1.96960211e-01 -1.46556392e-01
-2.83700824e-01 3.51345986e-01 -2.11497545e-02 -3.47095847e-01
-1.87680840e-01 -6.19468391e-01 -1.11773260e-01 -1.02867627e+00
1.33959725e-01 4.92034182e-02 -7.10245073e-01 2.10208625... | [3.7494564056396484, -3.627730369567871] |
dffda9f8-f174-4ec9-a193-3374d482402b | unsupervised-domain-adaptation-for-sparse | 2211.03988 | null | https://arxiv.org/abs/2211.03988v1 | https://arxiv.org/pdf/2211.03988v1.pdf | Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps | IR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because it shows robustness to out-of-domain datasets. However, SPLADE still struggles with exact matching of low-frequ... | ['Naoaki Okazaki', 'Hiroki Iida'] | 2022-11-08 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 1.34449229e-01 -2.20005661e-01 -8.09376538e-01 -2.86381036e-01
-1.13488817e+00 -6.35431647e-01 6.35733664e-01 1.16167054e-01
-7.80758798e-01 8.89358699e-01 6.23289049e-01 -9.91496742e-02
1.18745573e-01 -7.82556593e-01 -7.07232058e-01 -3.74704003e-01
4.44983900e-01 8.72548223e-01 4.23627138e-01 -6.67926550... | [11.020556449890137, 8.041537284851074] |
457f5898-bb96-4817-9484-cdec7edc85cc | simple-and-controllable-music-generation | 2306.05284 | null | https://arxiv.org/abs/2306.05284v1 | https://arxiv.org/pdf/2306.05284v1.pdf | Simple and Controllable Music Generation | We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patte... | ['Alexandre Défossez', 'Yossi Adi', 'Gabriel Synnaeve', 'David Kant', 'Tal Remez', 'Itai Gat', 'Felix Kreuk', 'Jade Copet'] | 2023-06-08 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [ 3.59364271e-01 -3.51284817e-02 -2.49212563e-01 6.77124038e-02
-1.35595918e+00 -7.48199284e-01 8.78164411e-01 6.72383308e-02
-2.86902636e-01 5.00571012e-01 6.61544561e-01 -9.74898636e-02
2.52332211e-01 -4.25507367e-01 -7.27881372e-01 -5.23539543e-01
-1.86797064e-02 2.27664754e-01 -2.18734428e-01 -2.14166865... | [15.718011856079102, 5.708714008331299] |
f7e03592-bae8-4201-a12c-fb50c2b8f39e | clear-generative-counterfactual-explanations | 2210.08443 | null | https://arxiv.org/abs/2210.08443v2 | https://arxiv.org/pdf/2210.08443v2.pdf | CLEAR: Generative Counterfactual Explanations on Graphs | Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?". The comparison of this instance before and after perturbation can enhance human interpretation. Most existing studies on counterfactua... | ['Jundong Li', 'Aidong Zhang', 'Saumitra Mishra', 'Ruocheng Guo', 'Jing Ma'] | 2022-10-16 | null | null | null | null | ['counterfactual-explanation', 'explanation-generation'] | ['miscellaneous', 'natural-language-processing'] | [ 4.97893244e-01 8.95940423e-01 -5.10510683e-01 -2.26018578e-01
-1.27357528e-01 -2.93374628e-01 7.97124207e-01 6.28906488e-02
4.86937791e-01 9.42412198e-01 6.45316482e-01 -6.03032172e-01
-2.15725601e-01 -9.74418283e-01 -1.09804475e+00 -4.80271816e-01
-2.73480028e-01 3.86113852e-01 -2.32945666e-01 -8.18641782... | [8.524133682250977, 5.86121129989624] |
68b736db-4548-45ed-9fad-3d204ed18e9d | recognizability-embedding-enhancement-for | 2304.10066 | null | https://arxiv.org/abs/2304.10066v1 | https://arxiv.org/pdf/2304.10066v1.pdf | Recognizability Embedding Enhancement for Very Low-Resolution Face Recognition and Quality Estimation | Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space inst... | ['Andrew Beng Jin Teoh', 'Jaewoo Park', 'Cheng-Yaw Low', 'Tiong-Sik Ng', 'Jacky Chen Long Chai'] | 2023-04-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chai_Recognizability_Embedding_Enhancement_for_Very_Low-Resolution_Face_Recognition_and_Quality_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-recognition'] | ['computer-vision'] | [ 3.66508216e-02 -4.74610068e-02 -2.57773958e-02 -5.39411247e-01
-6.49708569e-01 -2.61844039e-01 4.14247066e-01 -3.74897093e-01
-6.73227981e-02 2.38147557e-01 2.73198187e-01 2.83683985e-01
-4.55216229e-01 -7.45201766e-01 -6.73609376e-01 -7.26282179e-01
-5.64998612e-02 1.79255366e-01 -3.46718997e-01 4.18054834... | [13.120716094970703, 0.6000733375549316] |
801e5575-3f86-4f46-9372-b4c3e26d2538 | improved-and-interpretable-defense-to | 2207.13036 | null | https://arxiv.org/abs/2207.13036v4 | https://arxiv.org/pdf/2207.13036v4.pdf | Jacobian Norm with Selective Input Gradient Regularization for Improved and Interpretable Adversarial Defense | Deep neural networks (DNNs) are known to be vulnerable to adversarial examples that are crafted with imperceptible perturbations, i.e., a small change in an input image can induce a mis-classification, and thus threatens the reliability of deep learning based deployment systems. Adversarial training (AT) is often adopt... | ['Xianghua Xie', 'Mohammed Bennamoun', 'Farid Boussaid', 'Haifeng Zhao', 'Lin Wu', 'Deyin Liu'] | 2022-07-09 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 4.80140984e-01 3.80244106e-01 4.10832793e-01 -2.70606190e-01
-3.09454471e-01 -9.38429594e-01 6.45340323e-01 -3.46253932e-01
-1.41941354e-01 6.05156183e-01 -9.13895220e-02 -4.15258348e-01
2.83035915e-02 -6.95467710e-01 -1.20822096e+00 -8.20951283e-01
1.45953402e-01 -2.89595276e-02 2.08934024e-01 -4.87103194... | [5.619331359863281, 8.003409385681152] |
15c9d153-ba6c-4c5f-8ccd-6f8a27d62fc0 | multi-expert-adversarial-attack-detection-in | 2108.09891 | null | https://arxiv.org/abs/2108.09891v2 | https://arxiv.org/pdf/2108.09891v2.pdf | Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency | The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirem... | ['Amit K. Roy-Chowdhury', 'Yaonan Wang', 'Min Liu', 'Shasha Li', 'Xueping Wang'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Multi-Expert_Adversarial_Attack_Detection_in_Person_Re-Identification_Using_Context_Inconsistency_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Multi-Expert_Adversarial_Attack_Detection_in_Person_Re-Identification_Using_Context_Inconsistency_ICCV_2021_paper.pdf | iccv-2021-1 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [-1.46123096e-01 -3.96558762e-01 4.50900137e-01 -1.01910137e-01
-5.40706635e-01 -9.11384881e-01 7.39370883e-01 8.73821080e-02
-4.63898420e-01 6.81723416e-01 -1.64718255e-02 1.77442771e-03
-7.61167854e-02 -8.00273716e-01 -8.30901563e-01 -9.43392277e-01
-1.86256707e-01 3.74253511e-01 2.31834084e-01 -3.24689955... | [14.24246597290039, 1.023639440536499] |
e49f166a-c9ea-40cc-abee-31f013d03b09 | generalized-low-rank-update-model-parameter | 2306.12670 | null | https://arxiv.org/abs/2306.12670v1 | https://arxiv.org/pdf/2306.12670v1.pdf | Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications | In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or removed. This problem holds practical importance in model selection, such as cross-validation (CV) and feature selection. Among the class of M... | ['Ichiro Takeuchi', 'Kouichi Taji', 'Noriaki Hashimoto', 'Hiroyuki Hanada'] | 2023-06-22 | null | null | null | null | ['model-selection'] | ['methodology'] | [ 3.75999570e-01 -2.72146761e-01 -5.53731978e-01 -3.32308412e-01
-8.87090683e-01 -3.56825501e-01 1.99504182e-01 4.02396411e-01
-3.38186085e-01 1.02369940e+00 -4.92518902e-01 -2.75121003e-01
-6.04199111e-01 -7.22322583e-01 -7.53277242e-01 -6.22505844e-01
-2.15165347e-01 2.94765592e-01 -1.57894325e-02 6.27762452... | [8.235625267028809, 4.182450771331787] |
bd9e74f3-ec77-446e-b1c0-4e47558c58bd | zero-resource-cross-lingual-named-entity | 1911.09812 | null | https://arxiv.org/abs/1911.09812v1 | https://arxiv.org/pdf/1911.09812v1.pdf | Zero-Resource Cross-Lingual Named Entity Recognition | Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an ... | ['Prathyusha Jwalapuram', 'Shafiq Joty', 'M Saiful Bari'] | 2019-11-22 | null | null | null | null | ['low-resource-named-entity-recognition', 'cross-lingual-ner'] | ['natural-language-processing', 'natural-language-processing'] | [-1.56339154e-01 -8.10058266e-02 -8.43220502e-02 -4.38227385e-01
-8.88308764e-01 -9.37202215e-01 6.52289271e-01 -1.71432719e-02
-1.00324631e+00 8.61396968e-01 2.18209237e-01 -4.63915169e-01
3.56368840e-01 -7.17989862e-01 -7.67015815e-01 -3.75247806e-01
2.89286733e-01 4.84387279e-01 -1.37021825e-01 -3.70202839... | [10.00112533569336, 9.701324462890625] |
3c240b6c-01a0-49f3-926c-033a6d462a7c | bridging-gap-between-image-pixels-and | 2107.13757 | null | https://arxiv.org/abs/2107.13757v3 | https://arxiv.org/pdf/2107.13757v3.pdf | Bridging Gap between Image Pixels and Semantics via Supervision: A Survey | The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem. The semantic gap problem is reviewed and a survey on recent efforts in bridging the gap is made in this work. Most import... | ['C. -C. Jay Kuo', 'Jiali Duan'] | 2021-07-29 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 6.22190714e-01 -1.11328937e-01 -6.99369371e-01 -7.28328168e-01
-8.95370603e-01 -1.68786064e-01 6.43829346e-01 4.39648122e-01
-3.46283704e-01 3.76146823e-01 8.27888399e-02 -2.25799698e-02
-8.29464793e-01 -5.23851871e-01 -2.45079130e-01 -5.33811629e-01
-7.77764320e-02 9.22216475e-02 1.51839599e-01 -1.52196750... | [9.9149169921875, 2.293501615524292] |
a8271712-2428-4b06-857c-57ac9d4e1652 | adaptive-low-complexity-sequential-inference | 1409.8185 | null | http://arxiv.org/abs/1409.8185v3 | http://arxiv.org/pdf/1409.8185v3.pdf | Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models | We develop a sequential low-complexity inference procedure for Dirichlet
process mixtures of Gaussians for online clustering and parameter estimation
when the number of clusters are unknown a-priori. We present an easily
computable, closed form parametric expression for the conditional likelihood,
in which hyperparamet... | ['Keith W. Forsythe', 'Theodoros Tsiligkaridis'] | 2014-09-29 | adaptive-low-complexity-sequential-inference-1 | http://papers.nips.cc/paper/6035-adaptive-low-complexity-sequential-inference-for-dirichlet-process-mixture-models | http://papers.nips.cc/paper/6035-adaptive-low-complexity-sequential-inference-for-dirichlet-process-mixture-models.pdf | neurips-2015-12 | ['online-clustering'] | ['computer-vision'] | [-2.66049236e-01 -8.01918432e-02 3.73144783e-02 -3.51344764e-01
-9.89587188e-01 -4.43812668e-01 4.90511984e-01 4.43148226e-01
-5.13573945e-01 5.81763625e-01 -1.46391734e-01 -1.45474911e-01
-1.68389156e-01 -6.80281818e-01 -7.34862864e-01 -9.87165987e-01
-4.26393032e-01 1.42551303e+00 4.08760697e-01 5.42979360... | [6.844305515289307, 4.095895290374756] |
2a98a539-c4d7-4290-a867-921b771540a7 | joint-convolutional-analysis-and-synthesis | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Gu_Joint_Convolutional_Analysis_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Gu_Joint_Convolutional_Analysis_ICCV_2017_paper.pdf | Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation | Analysis sparse representation (ASR) and synthesis sparse representation (SSR) are two representative approaches for sparsity-based image modeling. An image is described mainly by the non-zero coefficients in SSR, while it is characterized by the indices of zeros in ASR. To exploit the complementary representation mech... | ['Lei Zhang', 'WangMeng Zuo', 'Shuhang Gu', 'Deyu Meng'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['tone-mapping'] | ['computer-vision'] | [ 3.68378788e-01 -5.23534417e-01 -4.09945399e-02 -1.27842396e-01
-4.95656967e-01 -6.90376759e-02 3.40725482e-01 -2.96485245e-01
2.90018082e-01 3.37919474e-01 4.06597733e-01 2.61687458e-01
1.20447077e-01 -7.87395895e-01 -5.51744461e-01 -1.00081575e+00
1.41954750e-01 -4.30930912e-01 3.39924157e-01 -5.13709426... | [10.959210395812988, -2.1787853240966797] |
c83de78f-e00c-4745-aaed-f9e9950689f4 | transcc-transformer-based-multiple-illuminant | 2211.08772 | null | https://arxiv.org/abs/2211.08772v2 | https://arxiv.org/pdf/2211.08772v2.pdf | MIMT: Multi-Illuminant Color Constancy via Multi-Task Learning | The assumption of a uniform light color distribution, which holds true in single light color scenes, is no longer applicable in scenes that have multiple light colors. The spatial variability in multiple light colors causes the color constancy problem to be more challenging and requires the extraction of local surface/... | ['Robby T. Tan', 'Michael S. Brown', 'Jikai Wang', 'Shuwei Li'] | 2022-11-16 | null | null | null | null | ['color-constancy', 'edge-detection'] | ['computer-vision', 'computer-vision'] | [ 2.96728969e-01 -1.08254075e+00 1.53529286e-01 -3.55207980e-01
-7.50283718e-01 -7.21876025e-01 2.51554161e-01 -3.77043992e-01
-2.77071059e-01 5.36222875e-01 -4.34666514e-01 -8.23494419e-02
2.93604493e-01 -5.93764484e-01 -7.67119586e-01 -1.11868238e+00
4.62259024e-01 -1.48237690e-01 2.69079685e-01 -9.88926515... | [10.387866020202637, -2.6197028160095215] |
baddf940-24a2-44ea-afee-4477b1adef08 | calibrenet-calibration-networks-for | 2011.05723 | null | https://arxiv.org/abs/2011.05723v1 | https://arxiv.org/pdf/2011.05723v1.pdf | CalibreNet: Calibration Networks for Multilingual Sequence Labeling | Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC). One major obstacle is the errors on the boundary of predicted answers. To tackle this problem, we propose CalibreNet, which predicts answers... | ['Daxin Jiang', 'Wanli Zuo', 'Ming Gong', 'Jian Pei', 'Linjun Shou', 'Shining Liang'] | 2020-11-11 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [ 5.56204677e-01 5.85291050e-02 9.49467183e-04 -3.41557890e-01
-1.14204335e+00 -8.49799573e-01 2.29955852e-01 2.82664180e-01
-7.75759757e-01 9.76518452e-01 2.45125756e-01 -6.01453424e-01
2.53159046e-01 -7.13401079e-01 -7.05135942e-01 -1.21106341e-01
7.25117445e-01 5.93011081e-01 5.67536712e-01 -3.64312172... | [9.6983060836792, 9.516432762145996] |
13d4c867-8900-414b-84de-846d6a2fd8ec | fully-point-wise-convolutional-neural-network | 1801.06302 | null | http://arxiv.org/abs/1801.06302v3 | http://arxiv.org/pdf/1801.06302v3.pdf | Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images | Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color ... | ['Chang Wen Chen', 'Yang Wang', 'Jing Zhang', 'Yang Cao', 'Chenglin Wen'] | 2018-01-19 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 1.58565253e-01 -2.47891665e-01 1.37839004e-01 -4.88044381e-01
-8.81765783e-02 -1.94733277e-01 1.83568224e-01 -4.15984280e-02
-5.17381847e-01 6.25125110e-01 -1.20955132e-01 -6.28370419e-02
-8.18187594e-02 -6.76085949e-01 -8.83712411e-01 -1.10620117e+00
-7.91845694e-02 -5.62911928e-01 9.16132852e-02 -8.75533298... | [10.838934898376465, -2.4793174266815186] |
ac516fdf-a6f8-420a-a747-7387453d9661 | out-of-distribution-detection-without-class | 2112.07662 | null | https://arxiv.org/abs/2112.07662v2 | https://arxiv.org/pdf/2112.07662v2.pdf | Out-of-Distribution Detection Without Class Labels | Out-of-distribution detection seeks to identify novelties, samples that deviate from the norm. The task has been found to be quite challenging, particularly in the case where the normal data distribution consists of multiple semantic classes (e.g., multiple object categories). To overcome this challenge, current approa... | ['Yedid Hoshen', 'Ron Abutbul', 'Niv Cohen'] | 2021-12-14 | null | null | null | null | ['physical-video-anomaly-detection', 'image-clustering'] | ['computer-vision', 'computer-vision'] | [ 4.67962921e-01 -2.80788243e-02 -9.17771012e-02 -3.20213884e-01
-8.12355757e-01 -8.28188360e-01 7.79556811e-01 3.45704347e-01
-3.41536462e-01 5.32707930e-01 -2.49773815e-01 4.35984246e-02
-2.23953381e-01 -4.69167769e-01 -5.31895220e-01 -6.92067802e-01
1.60190508e-01 5.88851631e-01 3.01255703e-01 1.36439979... | [9.373537063598633, 2.9280481338500977] |
6224099c-a435-47f5-8cd8-722f6715faf9 | a-gaussian-mixture-model-representation-of | 1710.00075 | null | http://arxiv.org/abs/1710.00075v2 | http://arxiv.org/pdf/1710.00075v2.pdf | A Gaussian mixture model representation of endmember variability in hyperspectral unmixing | Hyperspectral unmixing while considering endmember variability is usually
performed by the normal compositional model (NCM), where the endmembers for
each pixel are assumed to be sampled from unimodal Gaussian distributions.
However, in real applications, the distribution of a material is often not
Gaussian. In this pa... | ['Yuan Zhou', 'Paul D. Gader', 'Anand Rangarajan'] | 2017-09-29 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 3.90178800e-01 -3.26940924e-01 1.40106782e-01 2.27495581e-02
-3.66121352e-01 -4.66196954e-01 6.80164695e-01 -1.70128763e-01
-1.20982140e-01 8.52410436e-01 -5.28892782e-03 -1.27291858e-01
4.71607633e-02 -9.62312520e-01 -7.50290632e-01 -1.50477362e+00
3.22120816e-01 3.54925483e-01 -2.74626404e-01 1.58995822... | [10.088669776916504, -2.1059188842773438] |
e9c1200b-50bf-43e1-ace5-e5a95916b0b3 | armas-active-reconstruction-of-missing-audio | 2111.10891 | null | https://arxiv.org/abs/2111.10891v3 | https://arxiv.org/pdf/2111.10891v3.pdf | ARMAS: Active Reconstruction of Missing Audio Segments | Digital audio signal reconstruction of a lost or corrupt segment using deep learning algorithms has been explored intensively in recent years. Nevertheless, prior traditional methods with linear interpolation, phase coding and tone insertion techniques are still in vogue. However, we found no research work on reconstru... | ['Abbas Cheddad', 'Zohra Cheddad'] | 2021-11-21 | null | null | null | null | ['audio-inpainting'] | ['audio'] | [ 7.53572464e-01 6.96729794e-02 7.58256316e-02 3.22573215e-01
-1.04759169e+00 -4.68185432e-02 3.96242321e-01 -5.14506221e-01
-1.65953338e-01 7.40428567e-01 4.31659371e-01 -3.53041142e-01
-1.38988374e-02 -8.20791006e-01 -8.10201645e-01 -1.07253194e+00
-4.00167972e-01 -1.62261158e-01 -1.12499803e-01 -3.28474343... | [15.341039657592773, 5.904638290405273] |
569a6db6-f64e-406d-9235-0a48a97fb28e | registration-of-multiresolution-remote | null | null | https://ieeexplore.ieee.org/document/9264687 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9264687 | Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model | The registration of multiresolution optical remote sensing images has been widely used in image fusion, change detection, and image stitching. However, traditional registration methods achieve poor accuracy in the registration of multiresolution remote sensing images. In this study, we propose a framework for generatin... | ['Zenglin Hong', 'Jianhua Yang', 'Jinbao Liu', 'Bochuan Hou', 'Rongbo Fan'] | 2020-11-19 | null | null | null | ieee-journal-of-selected-topics-in-applied-4 | ['image-stitching'] | ['computer-vision'] | [ 4.99563664e-01 -7.22584069e-01 2.22993508e-01 -4.80991453e-01
-1.33145225e+00 -3.12482804e-01 5.33889115e-01 -5.09324707e-02
-6.10840321e-01 4.53533500e-01 1.59027934e-01 3.45190793e-01
-3.78028661e-01 -9.00438547e-01 -4.76387322e-01 -1.09821224e+00
-1.90270618e-02 -6.07910119e-02 -2.39867970e-01 -3.36285621... | [10.31439208984375, -1.7872838973999023] |
37f7e102-1237-4614-bfc6-f80b44b364d6 | xgboost-a-scalable-tree-boosting-system | 1603.02754 | null | http://arxiv.org/abs/1603.02754v3 | http://arxiv.org/pdf/1603.02754v3.pdf | XGBoost: A Scalable Tree Boosting System | Tree boosting is a highly effective and widely used machine learning method.
In this paper, we describe a scalable end-to-end tree boosting system called
XGBoost, which is used widely by data scientists to achieve state-of-the-art
results on many machine learning challenges. We propose a novel sparsity-aware
algorithm ... | ['Carlos Guestrin', 'Tianqi Chen'] | 2016-03-09 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-6.81955159e-01 -4.26448703e-01 -9.76009786e-01 -7.69708991e-01
-1.12476563e+00 -8.31991732e-02 4.45122719e-01 7.86247909e-01
-7.73549378e-02 6.50813222e-01 4.86553013e-01 -5.59237719e-01
-3.29791941e-02 -9.10923958e-01 -5.83764315e-01 -2.53237963e-01
-2.62684405e-01 5.82672954e-01 1.52965084e-01 4.57929745... | [8.391790390014648, 4.270198345184326] |
22bb0625-4572-431e-be4e-5c18859fde30 | can-we-predict-new-facts-with-open-knowledge | null | null | https://aclanthology.org/2020.acl-main.209 | https://aclanthology.org/2020.acl-main.209.pdf | Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction | Open Information Extraction systems extract ({``}subject text{''}, {``}relation text{''}, {``}object text{''}) triples from raw text. Some triples are textual versions of facts, i.e., non-canonicalized mentions of entities and relations. In this paper, we investigate whether it is possible to infer new facts directly f... | ['Rainer Gemulla', 'Samuel Broscheit', 'Kiril Gashteovski', 'Yanjie Wang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['open-knowledge-graph-embedding', 'open-information-extraction'] | ['knowledge-base', 'natural-language-processing'] | [ 6.37637638e-03 1.02351952e+00 -2.93112636e-01 -1.82083443e-01
-4.14275229e-01 -9.31226313e-01 5.09853542e-01 6.81747079e-01
-7.18908533e-02 1.30819368e+00 2.54295409e-01 -8.18090141e-01
-4.44605172e-01 -1.32519114e+00 -1.46055806e+00 -1.55279055e-01
-5.94166182e-02 7.80806184e-01 6.85378671e-01 -3.04016858... | [9.354869842529297, 8.432171821594238] |
33cb657c-7b82-432b-8af4-a415841d80ec | an-early-study-on-intelligent-analysis-of | 2005.00096 | null | https://arxiv.org/abs/2005.00096v2 | https://arxiv.org/pdf/2005.00096v2.pdf | An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety | The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this glo... | ['Björn W. Schuller', 'Yoshiharu Yamamoto', 'Shuo Liu', 'Meishu Song', 'Xiao Li', 'Huaiyuan Zheng', 'Zijiang Yang', 'Wei Ji', 'Kun Qian', 'Juan Liu', 'Zixing Zhang', 'Zhao Ren', 'Tomoya Koike', 'Jing Han'] | 2020-04-30 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 2.05354795e-01 6.40118793e-02 3.23643923e-01 -1.87936798e-01
-5.49668431e-01 -2.72582650e-01 3.69470268e-01 7.62181342e-01
-5.04495323e-01 5.21211445e-01 4.96011257e-01 -1.13280408e-01
-4.90403175e-01 -4.54847634e-01 2.98231602e-01 -5.93213499e-01
-3.55536431e-01 6.47742391e-01 -1.03906557e-01 -7.21286684... | [14.395854949951172, 3.872201681137085] |
bf60f78e-5b63-4242-9774-4abee9c08af6 | detie-multilingual-open-information | 2206.12514 | null | https://arxiv.org/abs/2206.12514v1 | https://arxiv.org/pdf/2206.12514v1.pdf | DetIE: Multilingual Open Information Extraction Inspired by Object Detection | State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order not to produce duplicates. In this work, we propose a different approach to the problem that can be equally or more successful. Namely, we pre... | ['Sergey Nikolenko', 'Andrey Chertok', 'Mikhail Stepnov', 'Dmitriy Salikhov', 'Elena Tutubalina', 'Ilya Shenbin', 'Valentin Malykh', 'Anton Alekseev', 'Michael Vasilkovsky'] | 2022-06-24 | null | null | null | null | ['open-information-extraction', 'multilingual-nlp'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.46655291e-02 1.31589949e-01 -5.79490662e-02 -2.97714710e-01
-1.30087042e+00 -8.10910523e-01 6.20738268e-01 1.50304750e-01
-5.47638059e-01 1.09309292e+00 1.21449143e-01 -4.30949003e-01
3.92194353e-02 -8.69763076e-01 -1.28989863e+00 -2.33930409e-01
1.41110003e-01 9.36509788e-01 3.31914350e-02 -4.19395983... | [9.815667152404785, 8.890193939208984] |
2a63257b-c02a-406e-b978-935c4d5a40fc | video-processing-from-electro-optical-sensors | 1611.05842 | null | http://arxiv.org/abs/1611.05842v1 | http://arxiv.org/pdf/1611.05842v1.pdf | Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey | We present a survey on maritime object detection and tracking approaches,
which are essential for the development of a navigational system for autonomous
ships. The electro-optical (EO) sensor considered here is a video camera that
operates in the visible or the infrared spectra, which conventionally
complement radar a... | ['D. K. Prasad', 'L. Rachmawati', 'E. Rajabaly', 'C. Quek', 'D. Rajan'] | 2016-11-17 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 3.66161913e-01 -8.24556589e-01 7.42927074e-01 6.46151826e-02
-3.74028474e-01 -8.18065345e-01 6.40323520e-01 -6.94044456e-02
-9.33468640e-01 4.43186849e-01 -2.30311081e-01 -1.61291972e-01
-3.30666602e-01 -3.78941745e-01 -1.42789558e-01 -1.31209397e+00
-3.07494104e-01 -1.26067251e-01 6.15674734e-01 -5.23717046... | [8.797280311584473, -0.921929657459259] |
f74eb3a0-4802-4e3a-aa59-8d75a1675b5c | musicface-music-driven-expressive-singing | 2303.14044 | null | https://arxiv.org/abs/2303.14044v1 | https://arxiv.org/pdf/2303.14044v1.pdf | MusicFace: Music-driven Expressive Singing Face Synthesis | It is still an interesting and challenging problem to synthesize a vivid and realistic singing face driven by music signal. In this paper, we present a method for this task with natural motions of the lip, facial expression, head pose, and eye states. Due to the coupling of the mixed information of human voice and back... | ['Ming Zeng', 'Xiaohu Guo', 'Yiwei Ding', 'Yinglin Zheng', 'Jintai Wang', 'Hengda Li', 'Wenjin Deng', 'PengFei Liu'] | 2023-03-24 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [-9.71210003e-02 -7.73667991e-02 1.44652486e-01 -9.55934227e-02
-4.71528113e-01 -4.19896513e-01 4.82786059e-01 -8.21701825e-01
2.97439516e-01 3.38029623e-01 4.09202099e-01 3.53994846e-01
1.94952130e-01 -2.50334203e-01 -4.07594442e-01 -8.60022128e-01
2.70532578e-01 -2.47686550e-01 -3.11655831e-02 -3.36414397... | [13.199300765991211, -0.4098752737045288] |
ba5ead7e-c506-4761-bb3a-8b970b5f3ac8 | exploring-conversation-topics-in | null | null | https://doi.org/10.1016/j.chb.2022.107326 | https://doi.org/10.1016/j.chb.2022.107326 | Exploring conversation topics in conversational artificial intelligence–Based social mediated communities of practice | This study utilized ecologically valid social media data to identify motivations and relevant topics regarding the interaction with conversational artificial intelligence (AI) in a natural setting through investigating user conversations on Reddit, a social mediated community of practice. By applying the latent Dirichl... | ['Zhihuai Lin', 'Yu-Leung Ng'] | 2022-05-11 | null | null | null | computers-in-human-behavior-2022-5 | ['topic-models'] | ['natural-language-processing'] | [-3.59214395e-01 9.22625840e-01 -1.51492301e-02 -2.91312128e-01
-3.75650660e-03 -4.57358092e-01 8.93784642e-01 -1.25843763e-01
2.01190308e-01 2.15608791e-01 9.13972259e-01 6.05034307e-02
-2.30827495e-01 -3.21680874e-01 5.97593375e-02 -5.60080111e-01
1.06207885e-01 6.45766556e-01 -3.74369830e-01 -3.72548997... | [12.424490928649902, 7.819214344024658] |
98b9ddf2-d8f4-4290-9818-50a6f34ba460 | searching-inhibitors-for-three-important | 2004.08095 | null | https://arxiv.org/abs/2004.08095v3 | https://arxiv.org/pdf/2004.08095v3.pdf | Searching inhibitors for three important proteins of COVID-19 through molecular docking studies | The lack of recommended drugs or vaccines to deal with the COVID-19 is the main concern of this pandemic. The approved drugs for similar health problems, drugs under clinical trials, and molecules from medicinal plants extracts are investigated randomly to deal with the COVID-19 infection. Molecular docking, one of the... | ['Suban K Sahoo', 'Seshu Vardhan'] | 2020-04-17 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 2.46780217e-02 -2.78617233e-01 -9.90575179e-02 1.72099233e-01
-1.57344136e-02 -7.18026042e-01 2.08243504e-01 3.80685776e-01
-3.05126518e-01 1.43199432e+00 -6.36061803e-02 -3.46383423e-01
-3.92200202e-02 -3.84803921e-01 -1.05112679e-01 -1.07337153e+00
-5.78655839e-01 6.32387221e-01 -1.55662537e-01 -2.88709551... | [4.656396389007568, 5.098800182342529] |
c2e8a2e9-109d-46ab-89df-3f0ba026afff | rst-discourse-parsing-with-second-stage-edu | null | null | https://aclanthology.org/2022.acl-long.294 | https://aclanthology.org/2022.acl-long.294.pdf | RST Discourse Parsing with Second-Stage EDU-Level Pre-training | Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing.Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU).To this end, we... | ['Min Zhang', 'Guohong Fu', 'Meishan Zhang', 'Nan Yu'] | null | null | null | null | acl-2022-5 | ['discourse-parsing', 'discourse-marker-prediction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.50392497e-01 7.26070940e-01 -5.66259086e-01 -3.58651996e-01
-1.18389022e+00 -4.19278026e-01 7.62045920e-01 3.71986896e-01
-4.49113488e-01 7.06077635e-01 6.82698607e-01 -8.44004571e-01
5.30074239e-01 -8.38843167e-01 -9.51563120e-01 -3.01263630e-01
-2.25521927e-03 2.71322727e-01 4.19241130e-01 -3.83882374... | [10.791232109069824, 9.386838912963867] |
7f1a8270-fe33-4c0f-b084-e0b8db9a33f8 | underwater-fish-detection-with-weak-multi | 1905.10708 | null | https://arxiv.org/abs/1905.10708v2 | https://arxiv.org/pdf/1905.10708v2.pdf | Underwater Fish Detection with Weak Multi-Domain Supervision | Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, ... | ['Mangalam Sankupellay', 'Dmitry A. Konovalov', 'Simone Marini', 'Michael Bradley', 'Marcus Sheaves', 'Alzayat Saleh'] | 2019-05-26 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [ 1.12310223e-01 -1.53500006e-01 5.13283372e-01 -3.84208411e-01
-3.58501226e-01 -8.06229472e-01 2.47444227e-01 2.12037936e-01
-1.14263773e+00 5.07155597e-01 -2.81000584e-01 -1.18509248e-01
2.32515797e-01 -1.14252508e+00 -9.53866839e-01 -8.79460871e-01
-5.08329749e-01 1.41284779e-01 4.59541053e-01 -6.19811900... | [8.475390434265137, -1.202252745628357] |
0450c395-c3f2-4f86-afeb-da8730b1d4e6 | 6d-camera-relocalization-in-visually | 2207.06333 | null | https://arxiv.org/abs/2207.06333v1 | https://arxiv.org/pdf/2207.06333v1.pdf | 6D Camera Relocalization in Visually Ambiguous Extreme Environments | We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Data acquired under these challenging conditions are corrupted by textureless surfaces, image degradation, and presence of repetitive and highly... | ['Leonidas J. Guibas', 'Yueqi Duan', 'Yanchao Yang', 'Fei Xia', 'Tolga Birdal', 'Yang Zheng'] | 2022-07-13 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 4.46940333e-01 -1.73610985e-01 4.99093026e-01 -4.08373684e-01
-7.40089178e-01 -7.81670213e-01 5.17939925e-01 -3.60814393e-01
-7.63767540e-01 6.00912452e-01 -1.84312701e-01 -1.20483570e-01
-7.00888038e-02 -5.03312886e-01 -9.79195356e-01 -7.74096787e-01
-4.18288410e-01 2.48404890e-01 5.23600698e-01 -3.97445112... | [7.614058971405029, -1.930516004562378] |
b114d385-1026-4d16-8439-40049e169fb0 | taylor-lagrange-neural-ordinary-differential | 2201.05715 | null | https://arxiv.org/abs/2201.05715v2 | https://arxiv.org/pdf/2201.05715v2.pdf | Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs | Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data. However, every forward evaluation of a NODE requires numerical integration of the neural network ... | ['Ufuk Topcu', 'Sylvie Putot', 'Eric Goubault', 'Cyrus Neary', 'Franck Djeumou'] | 2022-01-14 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-4.26130623e-01 -1.14732012e-01 7.15039903e-03 -1.75756980e-02
-3.62552792e-01 -2.39085779e-01 3.14518511e-01 1.05524950e-01
-6.68098569e-01 8.65820885e-01 -8.47095609e-01 -5.34877539e-01
-4.93782980e-04 -6.60357237e-01 -7.18493998e-01 -1.01775181e+00
-3.07462841e-01 5.97742438e-01 1.15423612e-01 -2.07593262... | [6.630977153778076, 3.444044828414917] |
0f2b604e-9331-42b7-b31d-11a6585bae93 | a-privacy-preserving-energy-management-system | 2207.04359 | null | https://arxiv.org/abs/2207.04359v1 | https://arxiv.org/pdf/2207.04359v1.pdf | A Privacy-Preserving Energy Management System for Cooperative Multi-Microgrid Networks | This paper presents an Energy Management System (EMS) that considers power exchanges between a set of interconnected microgrids (MGs) and the main grid, in the context of Multi-MG (MMG) systems. The model is first formulated as a centralized optimization problem, which is then decomposed into subproblems corresponding ... | ['Claudio A. Cañizares', 'Mehrdad Pirnia', 'Carlos Ceja-Espinosa'] | 2022-07-10 | null | null | null | null | ['energy-management'] | ['time-series'] | [-5.71504056e-01 3.62773448e-01 1.99624822e-01 1.97398379e-01
-3.02023739e-01 -9.70500648e-01 2.76772767e-01 3.11595827e-01
-1.41571328e-01 1.29372907e+00 -1.85296178e-01 1.83335856e-01
-3.88581604e-01 -8.80049467e-01 -2.94991415e-02 -1.47051013e+00
-4.90292698e-01 4.14047390e-01 -4.79440600e-01 8.80892649... | [5.702943801879883, 2.502811908721924] |
770a4f75-8081-4a48-a809-39576a79d621 | cross-modal-coherence-modeling-for-caption | null | null | https://aclanthology.org/2020.acl-main.583 | https://aclanthology.org/2020.acl-main.583.pdf | Cross-modal Coherence Modeling for Caption Generation | We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image{--}caption coherence relations, we annotate 10,000 instances from publicly-available image{--}caption pairs. We int... | ['Matthew Stone', 'Malihe Alikhani', 'Radu Soricut', 'Piyush Sharma', 'Shengjie Li'] | 2020-07-01 | null | null | null | acl-2020-6 | ['controllable-image-captioning'] | ['computer-vision'] | [ 7.01004803e-01 1.02969158e+00 -3.79934698e-01 -7.99087286e-01
-1.06282115e+00 -3.85398597e-01 1.00037658e+00 2.63480365e-01
9.30273458e-02 8.36783469e-01 9.98936296e-01 1.27370164e-01
-1.21258803e-01 -4.04807985e-01 -1.00072491e+00 -2.65768677e-01
-1.96313828e-01 7.63684273e-01 -1.60932571e-01 -9.90194604... | [10.930068016052246, 1.1275066137313843] |
038b6275-2fda-42c2-9f8c-678754f9be32 | deciding-monotone-duality-and-identifying | 1212.1881 | null | http://arxiv.org/abs/1212.1881v3 | http://arxiv.org/pdf/1212.1881v3.pdf | Deciding Monotone Duality and Identifying Frequent Itemsets in Quadratic Logspace | The monotone duality problem is defined as follows: Given two monotone
formulas f and g in iredundant DNF, decide whether f and g are dual. This
problem is the same as duality testing for hypergraphs, that is, checking
whether a hypergraph H consists of precisely all minimal transversals of a
simple hypergraph G. By ex... | ['Georg Gottlob'] | 2012-12-09 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 2.57532120e-01 6.08054996e-01 -5.54841220e-01 -2.06802368e-01
-3.15244853e-01 -8.89541805e-01 -1.20527476e-01 5.96103370e-01
1.17921650e-01 1.13743401e+00 -1.05548792e-01 -6.02077842e-01
-7.12639570e-01 -1.37607050e+00 -1.19817984e+00 -5.90134382e-01
-6.63442850e-01 1.07505846e+00 1.67423666e-01 -2.23752901... | [6.833073139190674, 5.102703094482422] |
99cfb626-3f15-4143-bea9-c92efd5371e1 | neighborhood-preserved-sparse-representation | 1601.07336 | null | http://arxiv.org/abs/1601.07336v1 | http://arxiv.org/pdf/1601.07336v1.pdf | Neighborhood Preserved Sparse Representation for Robust Classification on Symmetric Positive Definite Matrices | Due to its promising classification performance, sparse representation based
classification(SRC) algorithm has attracted great attention in the past few
years. However, the existing SRC type methods apply only to vector data in
Euclidean space. As such, there is still no satisfactory approach to conduct
classification ... | ['Ming Yin', 'Shengli Xie', 'Yun Zhang', 'Yi Guo', 'Junbin Gao'] | 2016-01-27 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [-1.43530011e-01 -3.46069306e-01 -1.34359151e-01 -1.50807589e-01
-5.65548956e-01 -3.24171931e-01 5.27353466e-01 -2.58477002e-01
-8.28580931e-02 3.60446960e-01 9.03062448e-02 -1.65090695e-01
-3.74555498e-01 -5.34415245e-01 -2.07610995e-01 -9.62087750e-01
-4.55600694e-02 -3.05745065e-01 1.32945642e-01 -9.48332772... | [7.912051200866699, 4.061423301696777] |
fc46d7c9-2fc1-4a7f-9d50-8f317218d012 | consistent-video-style-transfer-via | null | null | https://doi.org/10.1109/TIP.2020.3024018 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9204808 | Consistent Video Style Transfer via Relaxation and Regularization | In recent years, neural style transfer has attracted more and more attention, especially for image style transfer. However, temporally consistent style transfer for videos is still a challenging problem. Existing methods, either relying on a significant amount of video data with optical flows or using single-frame regu... | ['Jiaying Liu', 'Jizheng Xu', 'Shuai Yang', 'Wenjing Wang'] | 2020-09-23 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 1.03014521e-01 -4.70950693e-01 -3.02457154e-01 -1.86083019e-01
-2.56468534e-01 -4.36781585e-01 4.04685408e-01 -3.88902068e-01
-2.71778524e-01 8.15865278e-01 1.53124347e-01 5.77820539e-02
-1.89238220e-01 -6.46536052e-01 -6.98670387e-01 -7.33741224e-01
3.58538419e-01 -2.73512155e-01 4.70884681e-01 -2.05389515... | [11.167760848999023, -0.8331283926963806] |
7a8a59a7-7f8a-4aed-9ea1-992d6b9c3d44 | managed-geo-distributed-feature-store | 2305.20077 | null | https://arxiv.org/abs/2305.20077v1 | https://arxiv.org/pdf/2305.20077v1.pdf | Managed Geo-Distributed Feature Store: Architecture and System Design | Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process. They are building feature engineering pipelines as part of MLOps life cycle to transform data from various data sources and materialize the same for future consumption. Without feature ... | ['Vivienne Tang', 'Shail Paragbhai Shah', 'Sethu Raman', 'Runhan Li', 'Qianjun Xu', 'Mickey Zhang', 'Feng Pan', 'Bhala Ranganathan', 'Anya Li'] | 2023-05-31 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-2.83042043e-01 -2.12573987e-02 -2.81976014e-01 -6.94480419e-01
-4.47211772e-01 -8.45581770e-01 6.04572967e-02 4.69835192e-01
1.93713680e-01 2.70803660e-01 -2.55799025e-01 -5.38608670e-01
-1.40891373e-01 -7.01415956e-01 -4.39170361e-01 -1.16664749e-02
5.03148437e-02 2.93938994e-01 7.28940070e-02 -3.00113738... | [8.607388496398926, 7.193079948425293] |
1375ac5c-9fc1-47c8-aeb7-2f6b185c1dda | beyond-disentangled-representations-an | 2004.05085 | null | https://arxiv.org/abs/2004.05085v2 | https://arxiv.org/pdf/2004.05085v2.pdf | LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition | Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architectures for... | ['Ngan Le', 'Thanh-Dat Truong', 'Kha Gia Quach', 'Tien D. Bui', 'Khoa Luu', 'Chi Nhan Duong'] | 2020-04-09 | null | null | null | null | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.41041072e-02 2.92199373e-01 -5.63708544e-02 -5.77466846e-01
-5.03952563e-01 4.60208990e-02 3.15741599e-01 -5.40878773e-01
-4.40707356e-01 9.15445685e-01 -4.82382402e-02 -5.60729317e-02
-5.34649670e-01 -7.01389432e-01 -5.61319292e-01 -8.44699502e-01
-4.05925512e-01 5.09932756e-01 -4.39711243e-01 -2.27263540... | [13.360128402709961, 0.6975436210632324] |
098f4a23-6065-498c-a72a-bdec34f0dbd2 | an-empirical-study-of-uniform-architecture | 2302.04112 | null | https://arxiv.org/abs/2302.04112v1 | https://arxiv.org/pdf/2302.04112v1.pdf | An Empirical Study of Uniform-Architecture Knowledge Distillation in Document Ranking | Although BERT-based ranking models have been commonly used in commercial search engines, they are usually time-consuming for online ranking tasks. Knowledge distillation, which aims at learning a smaller model with comparable performance to a larger model, is a common strategy for reducing the online inference latency.... | ['Yutao Zhu', 'Jie Liu', 'Xiongfeng Zheng', 'Xiyuan Liu', 'Xubo Qin'] | 2023-02-08 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-1.05252437e-01 5.22992648e-02 -2.11507604e-01 -7.58400261e-01
-1.26304924e+00 -6.38970494e-01 6.17315829e-01 2.39789516e-01
-5.94158053e-01 6.91756129e-01 9.86141041e-02 -5.04681170e-01
-1.42499536e-01 -6.36722744e-01 -8.78246367e-01 -5.01249552e-01
-1.93757817e-01 9.63823259e-01 2.40667969e-01 -3.08454841... | [11.41457462310791, 7.610604763031006] |
92ead198-2189-491a-b5d0-3dc43a817d12 | robust-representation-learning-with-feedback | 2101.12463 | null | https://arxiv.org/abs/2101.12463v3 | https://arxiv.org/pdf/2101.12463v3.pdf | Robust Representation Learning with Feedback for Single Image Deraining | A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly... | ['Hao Li', 'Chenghao Chen'] | 2021-01-29 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.pdf | cvpr-2021-1 | ['single-image-deraining'] | ['computer-vision'] | [ 2.39493728e-01 1.93032682e-01 1.81809347e-02 -5.10842204e-01
-8.16015422e-01 -9.18436423e-02 3.37666869e-01 -5.53198338e-01
-1.49832562e-01 8.86730850e-01 9.94225442e-02 -9.79373679e-02
2.18823358e-01 -7.55340397e-01 -1.03007770e+00 -9.37520087e-01
4.82832752e-02 -3.65642160e-01 -1.40041098e-01 7.81818181... | [10.921417236328125, -3.1683132648468018] |
9bbe4623-f73c-4806-8ad1-7dc419d70cd8 | mme-a-comprehensive-evaluation-benchmark-for | 2306.13394 | null | https://arxiv.org/abs/2306.13394v2 | https://arxiv.org/pdf/2306.13394v2.pdf | MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models | Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully reflect the performance of MLLM, lacking a comprehensive evaluation. In t... | ['Rongrong Ji', 'Xing Sun', 'Ke Li', 'Xiawu Zheng', 'Jinrui Yang', 'Wei Lin', 'Zhenyu Qiu', 'Xu Lin', 'Mengdan Zhang', 'Yulei Qin', 'Yunhang Shen', 'Peixian Chen', 'Chaoyou Fu'] | 2023-06-23 | null | null | null | null | ['benchmarking', 'prompt-engineering', 'benchmarking'] | ['miscellaneous', 'natural-language-processing', 'robots'] | [ 6.59872219e-02 8.27266648e-02 1.05147930e-02 -1.98181301e-01
-1.15366936e+00 -6.53250337e-01 4.91585433e-01 4.22983617e-01
-5.33409417e-01 4.86138701e-01 3.17604899e-01 -4.36635554e-01
-4.80267704e-02 -4.23336595e-01 -8.03521752e-01 -3.28027576e-01
1.82467178e-01 2.78790116e-01 8.29065517e-02 -4.48723823... | [11.062779426574707, 8.003032684326172] |
8ed6cd28-868b-4033-8be8-70387b86e2d8 | on-the-effectiveness-of-parameter-efficient | 2211.15583 | null | https://arxiv.org/abs/2211.15583v1 | https://arxiv.org/pdf/2211.15583v1.pdf | On the Effectiveness of Parameter-Efficient Fine-Tuning | Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new model for each task. Currently, many research works propose to only fine-tune a small portion of the parameters whil... | ['Nigel Collier', 'Lidong Bing', 'Wai Lam', 'Anthony Man-Cho So', 'Haoran Yang', 'Zihao Fu'] | 2022-11-28 | null | null | null | null | ['natural-questions'] | ['miscellaneous'] | [ 6.22029556e-03 2.26319749e-02 -4.62898582e-01 -4.92197186e-01
-6.86601758e-01 -5.90713859e-01 5.72109699e-01 -4.08859491e-01
-2.25727633e-01 7.42967367e-01 2.98945069e-01 -2.07247123e-01
-4.42768544e-01 -5.75645447e-01 -7.10690200e-01 -8.73532832e-01
4.46841657e-01 4.94602263e-01 3.39785814e-01 -4.20005947... | [10.094400405883789, 3.2885499000549316] |
0d21c33e-9547-4644-8e71-e8fdf264d13c | self-supervised-activity-representation | 2305.00619 | null | https://arxiv.org/abs/2305.00619v1 | https://arxiv.org/pdf/2305.00619v1.pdf | Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study | In the context of mobile sensing environments, various sensors on mobile devices continually generate a vast amount of data. Analyzing this ever-increasing data presents several challenges, including limited access to annotated data and a constantly changing environment. Recent advancements in self-supervised learning ... | ['Flora D. Salim', 'Van Nguyen', 'Hao Xue', 'Shohreh Deldari', 'Jason Liu'] | 2023-05-01 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 8.65903199e-01 5.48105463e-02 -4.04264063e-01 -6.82402015e-01
-7.13226199e-01 -6.54015720e-01 6.43913329e-01 4.04718667e-01
-4.45025563e-01 7.42878437e-01 3.26535821e-01 -2.94555396e-01
-3.90950963e-02 -7.17853546e-01 -6.52503610e-01 -4.65000898e-01
-1.89000085e-01 1.25845596e-01 2.20625445e-01 -9.05830786... | [7.583338260650635, 1.6236300468444824] |
f21b6871-d1ce-4c47-9ec1-d2c80eaef749 | online-computation-of-terminal-ingredients-in | 2207.09216 | null | https://arxiv.org/abs/2207.09216v1 | https://arxiv.org/pdf/2207.09216v1.pdf | Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking | A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline. Unlike many standard existing schemes, this scheme yields large feasible regions without performing offline centralize... | ['John Lygeros', 'Annika Eichler', 'Goran Banjac', 'Ahmed Aboudonia'] | 2022-07-19 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 1.53640851e-01 4.18701708e-01 -5.53982556e-01 2.19456986e-01
-5.84938884e-01 -1.13409340e+00 4.18120250e-02 2.21092582e-01
-2.64681820e-02 1.15059853e+00 -3.81718308e-01 -5.45956671e-01
-7.43093610e-01 -3.64636660e-01 -5.53949058e-01 -1.00517464e+00
-4.17639405e-01 7.05201864e-01 -1.00068957e-01 -1.59186557... | [5.218170166015625, 2.6255452632904053] |
1cbdbbe2-9606-49d9-91d9-c6e10f1438e2 | aligning-open-ie-relations-and-kb-relations | null | null | https://aclanthology.org/W19-0412 | https://aclanthology.org/W19-0412.pdf | Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding | Open Information Extraction (Open IE) aims at generating entity-relation-entity triples from a large amount of text, aiming at capturing key semantics of the text. Given a triple, the relation expresses the type of semantic relation between the entities. Although relations from an Open IE system are more extensible tha... | ['Sung-Hyon Myaeng', 'Rifki Afina Putri', 'Giwon Hong'] | 2019-05-01 | null | null | null | ws-2019-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 5.22991940e-02 7.80432045e-01 -3.57960582e-01 -3.51179600e-01
-4.00659710e-01 -4.49949533e-01 7.14949548e-01 5.72536469e-01
-4.66764510e-01 8.90227973e-01 3.61792773e-01 -3.66387606e-01
-2.91511923e-01 -1.40481973e+00 -7.36065805e-01 -3.15214634e-01
6.46846741e-02 7.73767889e-01 5.06822824e-01 -6.07452273... | [9.33288288116455, 8.400729179382324] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.