paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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00a69dbb-22af-47c5-8bad-3c00c1e5deca | ernie-enhanced-representation-through | 1904.09223 | null | http://arxiv.org/abs/1904.09223v1 | http://arxiv.org/pdf/1904.09223v1.pdf | ERNIE: Enhanced Representation through Knowledge Integration | We present a novel language representation model enhanced by knowledge called
ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the
masking strategy of BERT, ERNIE is designed to learn language representation
enhanced by knowledge masking strategies, which includes entity-level masking
and phra... | ['Hao Tian', 'Yu Sun', 'Xuyi Chen', 'Hua Wu', 'Yukun Li', 'Xin Tian', 'Shikun Feng', 'Han Zhang', 'Danxiang Zhu', 'Shuohuan Wang'] | 2019-04-19 | null | null | null | null | ['cloze-test', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.70777757e-02 -6.70100003e-02 -1.19636856e-01 -1.15255013e-01
-6.61733687e-01 -5.40144145e-01 3.50351274e-01 5.18812597e-01
-8.74744534e-01 8.72977734e-01 6.83984876e-01 -4.06137675e-01
-3.13218087e-02 -1.08662999e+00 -4.77299333e-01 -1.27688631e-01
-5.36455996e-02 1.13760695e-01 1.65121615e-01 -6.35429978... | [9.837624549865723, 9.442853927612305] |
4e5025a2-3fb3-4aeb-a7ff-bfd88b218d4a | known-plaintext-attack-and-ciphertext-only | 1905.13594 | null | https://arxiv.org/abs/1905.13594v1 | https://arxiv.org/pdf/1905.13594v1.pdf | Known-plaintext attack and ciphertext-only attack for encrypted single-pixel imaging | In many previous works, a single-pixel imaging (SPI) system is constructed as an optical image encryption system. Unauthorized users are not able to reconstruct the plaintext image from the ciphertext intensity sequence without knowing the illumination pattern key. However, little cryptanalysis about encrypted SPI has ... | ['Xiaocong Yuan', 'Zhenwei Xie', 'Yang Gao', 'Shuming Jiao', 'Ting Lei'] | 2019-05-31 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 1.10278141e+00 -3.07707071e-01 3.67518127e-01 -1.85158879e-01
-2.98989862e-01 -7.46401608e-01 3.81402701e-01 -4.45433885e-01
-6.87392175e-01 4.38948900e-01 -4.53550190e-01 -4.97016281e-01
-3.62254456e-02 -1.00628936e+00 -6.39230430e-01 -1.32793319e+00
2.21866980e-01 -2.92051792e-01 1.26086518e-01 1.62324697... | [4.41495418548584, 7.989650249481201] |
35d3b1fc-99c6-4299-be40-8863e55386bd | robust-uncertainty-estimation-for | 2307.01325 | null | https://arxiv.org/abs/2307.01325v1 | https://arxiv.org/pdf/2307.01325v1.pdf | Robust Uncertainty Estimation for Classification of Maritime Objects | We explore the use of uncertainty estimation in the maritime domain, showing the efficacy on toy datasets (CIFAR10) and proving it on an in-house dataset, SHIPS. We present a method joining the intra-class uncertainty achieved using Monte Carlo Dropout, with recent discoveries in the field of outlier detection, to gain... | ['Lazaros Nalpantidis', 'Evangelos Boukas', 'Frederik Scholler', 'Jonathan Becktor'] | 2023-07-03 | null | null | null | null | ['classification-1', 'outlier-detection'] | ['methodology', 'methodology'] | [-3.51315588e-01 1.36416659e-01 2.82367945e-01 -5.53986669e-01
-1.25904596e+00 -5.13209879e-01 7.08505273e-01 6.58311099e-02
-9.43210363e-01 1.11276078e+00 1.88949093e-01 -1.00931570e-01
-2.71459579e-01 -5.84921658e-01 -1.11561465e+00 -4.71927971e-01
-3.66883546e-01 7.33808458e-01 4.39985275e-01 5.74447177... | [7.536930561065674, 3.692758798599243] |
b4ac9cd5-3fa6-4e40-b6c0-9c56317ce2bf | for-women-life-freedom-a-participatory-ai | 2307.03764 | null | https://arxiv.org/abs/2307.03764v1 | https://arxiv.org/pdf/2307.03764v1.pdf | For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles | In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. We present an ensemble active learning pipeline to train a stance classifier. Our novelty lies in the inv... | ['Ashiqur R. KhudaBukhsh', 'Sujan Dutta', 'Adel Khorramrouz'] | 2023-07-07 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 6.16862578e-03 1.04493523e+00 -6.78084314e-01 -5.09854019e-01
-9.95838404e-01 -6.49150848e-01 1.38452268e+00 9.56924796e-01
-7.74487853e-01 1.08655870e+00 1.03611386e+00 -3.34301800e-01
1.48540899e-01 -7.66657948e-01 -2.79061764e-01 -7.59857476e-01
1.03743032e-01 1.27697611e+00 -1.53471276e-01 -7.92301059... | [8.966416358947754, 10.068723678588867] |
2a38b44b-acbb-4ece-9fd2-435e33a6b215 | practical-transformer-based-multilingual-text | null | null | https://aclanthology.org/2021.naacl-industry.16 | https://aclanthology.org/2021.naacl-industry.16.pdf | Practical Transformer-based Multilingual Text Classification | Transformer-based methods are appealing for multilingual text classification, but common research benchmarks like XNLI (Conneau et al., 2018) do not reflect the data availability and task variety of industry applications. We present an empirical comparison of transformer-based text classification models in a variety of... | ['Michele Banko', 'Cindy Wang'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['multilingual-text-classification'] | ['miscellaneous'] | [-5.55405058e-02 -3.41470510e-01 -5.82139969e-01 -6.18672013e-01
-9.73494351e-01 -9.15687323e-01 9.48373497e-01 3.19258869e-01
-8.16145778e-01 9.08750117e-01 2.49544710e-01 -9.51203644e-01
6.15519173e-02 -3.39002252e-01 -4.22898024e-01 -1.42860964e-01
3.89185846e-01 7.26307929e-01 -1.40249148e-01 -5.82089841... | [10.936624526977539, 9.980938911437988] |
6b4b0bbb-0cae-4057-9f6a-13a38aa9169c | reasoning-on-knowledge-graphs-with-debate | 2001.00461 | null | https://arxiv.org/abs/2001.00461v1 | https://arxiv.org/pdf/2001.00461v1.pdf | Reasoning on Knowledge Graphs with Debate Dynamics | We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (... | ['Yunpu Ma', 'Jorge Andres Quintero Serna', 'Mitchell Joblin', 'Martin Ringsquandl', 'Marcel Hildebrandt', 'Volker Tresp'] | 2020-01-02 | null | null | null | null | ['triple-classification'] | ['graphs'] | [ 8.91029835e-02 1.04195619e+00 -7.02648997e-01 -2.00300142e-01
-6.24274850e-01 -8.21035266e-01 8.11505198e-01 1.96929932e-01
1.12542957e-02 1.18117154e+00 3.28357279e-01 -9.19727027e-01
-3.17411453e-01 -1.26431477e+00 -9.65953887e-01 -4.37939644e-01
6.61404729e-02 7.91857421e-01 5.88693842e-02 -4.62593615... | [9.636714935302734, 7.974894046783447] |
dee52f23-e176-4c4b-9b59-f026f7afd127 | global-context-enhanced-graph-convolutional | null | null | https://aclanthology.org/2020.coling-main.461 | https://aclanthology.org/2020.coling-main.461.pdf | Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction | Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply takes word nodes and... | ['Haibin Jiang', 'Chengkun Lang', 'Zhe Liu', 'Weihong Yao', 'Yibin Xu', 'Huiwei Zhou'] | 2020-12-01 | null | null | null | coling-2020-8 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.62848115e-01 1.82109177e-01 -1.80580899e-01 -2.95003682e-01
-3.41045380e-01 -5.90108514e-01 6.53233826e-01 3.97404581e-01
-1.30931452e-01 4.89679098e-01 4.30878937e-01 -4.80873853e-01
-2.19859987e-01 -1.21702504e+00 -5.96281648e-01 -3.22026759e-01
-1.40136167e-01 3.80520254e-01 9.22747403e-02 -3.19440871... | [9.030948638916016, 8.229461669921875] |
dca0db50-6616-4203-a209-2f3fee11cee2 | introducing-anisotropic-minkowski-functionals | 2004.01029 | null | https://arxiv.org/abs/2004.01029v1 | https://arxiv.org/pdf/2004.01029v1.pdf | Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens | Bone fragility and fracture caused by osteoporosis or injury are prevalent in adults over the age of 50 and can reduce their quality of life. Hence, predicting the biomechanical bone strength, specifically of the proximal femur, through non-invasive imaging-based methods is an important goal for the diagnosis of Osteop... | ['Titas De'] | 2020-04-02 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-1.38690561e-01 -2.23771095e-01 -2.53307223e-01 -2.62012661e-01
-7.87341952e-01 3.98539037e-01 4.49153669e-02 4.36657786e-01
-5.60354054e-01 8.28900397e-01 7.47895241e-02 9.02175754e-02
-5.01454294e-01 -1.30724049e+00 -3.72757196e-01 -6.86372161e-01
-2.94391155e-01 1.18168747e+00 6.06156230e-01 -2.89662123... | [14.266946792602539, -1.999971628189087] |
20ed48f7-a7f4-4d2e-9fef-630207d96f6e | contrastive-trajectory-similarity-learning | 2210.05155 | null | https://arxiv.org/abs/2210.05155v3 | https://arxiv.org/pdf/2210.05155v3.pdf | Contrastive Trajectory Similarity Learning with Dual-Feature Attention | Trajectory similarity measures act as query predicates in trajectory databases, making them the key player in determining the query results. They also have a heavy impact on the query efficiency. An ideal measure should have the capability to accurately evaluate the similarity between any two trajectories in a very sho... | ['Egemen Tanin', 'Yuxuan Liang', 'Jianzhong Qi', 'Yanchuan Chang'] | 2022-10-11 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-3.65689009e-01 -5.04520297e-01 -6.83980465e-01 -3.73984337e-01
-1.15805364e+00 -6.00945890e-01 7.90401518e-01 7.62808502e-01
-6.71432137e-01 4.94844913e-01 3.53917956e-01 -3.01146656e-01
-2.80039489e-01 -1.17155623e+00 -7.43015110e-01 -3.92795801e-01
-2.18206108e-01 6.34969890e-01 5.39108515e-01 -2.16684371... | [6.5940656661987305, 1.9927822351455688] |
490bf6fa-61a8-426e-8578-c2173f11669b | a-field-test-of-bandit-algorithms-for | 2304.09088 | null | https://arxiv.org/abs/2304.09088v1 | https://arxiv.org/pdf/2304.09088v1.pdf | A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits | Personalized recommender systems suffuse modern life, shaping what media we read and what products we consume. Algorithms powering such systems tend to consist of supervised learning-based heuristics, such as latent factor models with a variety of heuristically chosen prediction targets. Meanwhile, theoretical treatmen... | ['Alan L. Montgomery', 'Zachary C. Lipton', 'Fatma Kılınç-Karzan', 'Giulio Zhou', 'Liu Leqi'] | 2023-04-16 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-2.69944161e-01 -1.11144967e-01 -8.19720685e-01 -1.65564761e-01
-2.89381117e-01 -7.72618115e-01 5.19891381e-01 -2.09359944e-01
-3.85582864e-01 6.09767437e-01 4.28385615e-01 -6.67641401e-01
-5.58410645e-01 -5.80403030e-01 -6.21006668e-01 -6.18570745e-01
1.33592263e-01 7.27330327e-01 -1.12501867e-01 -1.36658251... | [9.692510604858398, 5.591029644012451] |
07f11079-905b-4546-8b68-679ec3d3dab1 | byte-level-grammatical-error-correction-using | 2305.17906 | null | https://arxiv.org/abs/2305.17906v1 | https://arxiv.org/pdf/2305.17906v1.pdf | Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora | Grammatical error correction (GEC) is the task of correcting typos, spelling, punctuation and grammatical issues in text. Approaching the problem as a sequence-to-sequence task, we compare the use of a common subword unit vocabulary and byte-level encoding. Initial synthetic training data is created using an error-gene... | ['Vésteinn Snæbjarnarson', 'Vilhjálmur Þorsteinsson', 'Haukur Barri Símonarson', 'Haukur Páll Jónsson', 'Pétur Orri Ragnarsson', 'Svanhvít Lilja Ingólfsdóttir'] | 2023-05-29 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 4.24619943e-01 -8.99379477e-02 4.59365398e-01 -2.95466840e-01
-8.70363832e-01 -5.48387051e-01 3.43647301e-01 9.45074141e-01
-8.66841376e-01 8.91401470e-01 3.41758639e-01 -6.04555845e-01
2.23500147e-01 -6.63377106e-01 -7.89912462e-01 1.58721767e-02
2.71163613e-01 6.03415430e-01 3.95496666e-01 -6.13973856... | [11.044946670532227, 10.667569160461426] |
33d1af8c-edb5-4061-9961-0283b0455b74 | transformer-based-unet-with-multi-headed | 2306.02815 | null | https://arxiv.org/abs/2306.02815v1 | https://arxiv.org/pdf/2306.02815v1.pdf | Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents | The extraction of text in high quality is essential for text-based document analysis tasks like Document Classification or Named Entity Recognition. Unfortunately, this is not always ensured, as poor scan quality and the resulting artifacts lead to errors in the Optical Character Recognition (OCR) process. Current appr... | ['Michael Munz', 'David Kreuzer'] | 2023-06-05 | null | null | null | null | ['optical-character-recognition', 'document-classification'] | ['computer-vision', 'natural-language-processing'] | [ 8.45805585e-01 -5.01393564e-02 3.72933149e-01 -2.66374528e-01
-5.13501823e-01 -2.14818016e-01 6.78470731e-01 2.57130086e-01
-7.41832197e-01 6.36739969e-01 7.01230243e-02 9.94418748e-03
-1.45538986e-01 -7.85195351e-01 -8.36584032e-01 -7.53625393e-01
2.37701952e-01 1.54327795e-01 1.69342548e-01 -1.30420670... | [11.79328727722168, 2.5644261837005615] |
3da9af02-e726-4ec7-ab21-2bef6ae1f4e6 | improving-event-causality-identification-via | 2106.01654 | null | https://arxiv.org/abs/2106.01654v1 | https://arxiv.org/pdf/2106.01654v1.pdf | Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement | Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current annotated datasets is relatively limited, which cannot provide sufficient support for models to capture useful indicators from causal statemen... | ['Yuguang Chen', 'Weihua Peng', 'Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Pengfei Cao', 'Xinyu Zuo'] | 2021-06-03 | null | https://aclanthology.org/2021.findings-acl.190 | https://aclanthology.org/2021.findings-acl.190.pdf | findings-acl-2021-8 | ['event-causality-identification'] | ['natural-language-processing'] | [ 1.65007368e-01 3.04754764e-01 -8.18603337e-01 -5.61957359e-01
-8.08068931e-01 -5.07146299e-01 8.79623234e-01 2.13998958e-01
-1.40224949e-01 1.09829235e+00 6.31717086e-01 -3.58151406e-01
-1.73586294e-01 -7.20340133e-01 -8.04643512e-01 -2.67046988e-01
-2.60552585e-01 2.10520968e-01 3.60285699e-01 1.96934074... | [9.104774475097656, 9.104517936706543] |
b3eb3d85-de02-4a35-81be-d0fc51ce7a9f | influence-of-initialization-on-the | 2003.03789 | null | https://arxiv.org/abs/2003.03789v1 | https://arxiv.org/pdf/2003.03789v1.pdf | Influence of Initialization on the Performance of Metaheuristic Optimizers | All metaheuristic optimization algorithms require some initialization, and the initialization for such optimizers is usually carried out randomly. However, initialization can have some significant influence on the performance of such algorithms. This paper presents a systematic comparison of 22 different initialization... | ['Xin-She Yang', 'San-Yang Liu', 'Qian Li'] | 2020-03-08 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-2.72666723e-01 -5.84650457e-01 3.73683199e-02 1.98920071e-01
3.08243811e-01 -5.27260661e-01 2.44695529e-01 1.88480228e-01
-6.99782968e-01 1.16504717e+00 -2.86892444e-01 -1.90731943e-01
-5.46200931e-01 -1.16703629e+00 -2.43173867e-01 -1.24947536e+00
-5.41289672e-02 2.97867239e-01 2.80828446e-01 -4.71495152... | [5.683096885681152, 3.5018553733825684] |
952b1fe0-5978-43c2-af8f-57a7675b90e6 | unsupervised-ehr-based-phenotyping-via-matrix | 2209.00322 | null | https://arxiv.org/abs/2209.00322v1 | https://arxiv.org/pdf/2209.00322v1.pdf | Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions | Computational phenotyping allows for unsupervised discovery of subgroups of patients as well as corresponding co-occurring medical conditions from electronic health records (EHR). Typically, EHR data contains demographic information, diagnoses and laboratory results. Discovering (novel) phenotypes has the potential to ... | ['Evrim Acar', 'Age K. Smilde', 'Florian Becker'] | 2022-09-01 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [ 1.82483788e-03 -3.36421579e-01 -2.93087602e-01 -3.90622139e-01
-4.39817697e-01 -5.88913798e-01 -2.03980491e-01 4.53486443e-01
1.68503806e-01 6.96530819e-01 4.76552337e-01 -2.12951243e-01
-8.00077915e-01 -3.99599433e-01 -1.05246007e-01 -7.90406585e-01
-5.06942987e-01 6.67293847e-01 -7.89385438e-01 2.03508094... | [6.472021102905273, 5.8940229415893555] |
d42c02d9-2307-43fa-8420-ed638f77398e | prompting-electra-few-shot-learning-with | 2205.15223 | null | https://arxiv.org/abs/2205.15223v3 | https://arxiv.org/pdf/2205.15223v3.pdf | Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models | Pre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. However, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA do not fit into the paradigm. In this work, we adapt prompt-based few-shot learning to ELECT... | ['Ves Stoyanov', 'Danqi Chen', 'Jingfei Du', 'Mikel Artetxe', 'Mengzhou Xia'] | 2022-05-30 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 2.54309654e-01 1.32742912e-01 -2.71316320e-01 -3.33388478e-01
-1.08627105e+00 -3.63449007e-01 9.50589240e-01 3.07676882e-01
-7.02287436e-01 5.26038527e-01 5.84945023e-01 -5.10877192e-01
2.35281020e-01 -7.92863071e-01 -4.96815056e-01 -4.83363479e-01
1.13167368e-01 5.06970644e-01 6.02745354e-01 -3.06971610... | [10.892176628112793, 8.182221412658691] |
d34ac7e3-3b8b-4bc7-8cc6-14a076aeaa27 | fast-and-correct-gradient-based-optimisation | 2301.03415 | null | https://arxiv.org/abs/2301.03415v1 | https://arxiv.org/pdf/2301.03415v1.pdf | Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing | We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient descent. In particular, a variant using the so-called reparameterisation gradient estimator exhibits fa... | ['Dominik Wagner', 'C. -H. Luke Ong', 'Basim Khajwal'] | 2023-01-09 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 1.01410225e-01 2.52201051e-01 5.22899255e-02 -4.19464767e-01
-1.07873046e+00 -6.61484122e-01 7.45478094e-01 6.26192689e-02
-6.64184690e-01 8.40996027e-01 -1.20149009e-01 -4.89826232e-01
-3.26436937e-01 -7.51531780e-01 -8.88496101e-01 -1.09415233e+00
1.30870042e-03 5.18067896e-01 2.46597737e-01 -7.91703016... | [6.961679935455322, 4.0986433029174805] |
d8c7e29f-c276-4f8f-996f-767bf23f2261 | point-cloud-instance-segmentation-using | 1912.00145 | null | https://arxiv.org/abs/1912.00145v2 | https://arxiv.org/pdf/1912.00145v2.pdf | Point Cloud Instance Segmentation using Probabilistic Embeddings | In this paper we propose a new framework for point cloud instance segmentation. Our framework has two steps: an embedding step and a clustering step. In the embedding step, our main contribution is to propose a probabilistic embedding space for point cloud embedding. Specifically, each point is represented as a tri-var... | ['Biao Zhang', 'Peter Wonka'] | 2019-11-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Point_Cloud_Instance_Segmentation_Using_Probabilistic_Embeddings_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [-2.62412708e-03 2.19144419e-01 1.87126026e-02 -3.63981545e-01
-8.53131652e-01 -3.91092449e-01 3.39010596e-01 2.51253456e-01
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5.05967885e-02 -9.97998536e-01 -8.23354661e-01 -7.15697408e-01
1.06029369e-01 6.53564453e-01 7.13029563e-01 3.24061096... | [7.983331203460693, -3.2629058361053467] |
c108bc57-33d6-45b4-80d2-2708edca0fd7 | measuring-intersectional-biases-in-historical | 2305.12376 | null | https://arxiv.org/abs/2305.12376v1 | https://arxiv.org/pdf/2305.12376v1.pdf | Measuring Intersectional Biases in Historical Documents | Data-driven analyses of biases in historical texts can help illuminate the origin and development of biases prevailing in modern society. However, digitised historical documents pose a challenge for NLP practitioners as these corpora suffer from errors introduced by optical character recognition (OCR) and are written i... | ['Isabelle Augenstein', 'Natacha Klein Käfer', 'Natália da Silva Perez', 'Thea Rolskov', 'Karolina Stańczak', 'Nadav Borenstein'] | 2023-05-21 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 5.02354838e-02 -2.42632985e-01 -3.86755526e-01 -4.42845613e-01
-3.39469343e-01 -9.08115923e-01 1.36284912e+00 7.19222009e-01
-1.00308311e+00 4.20795351e-01 1.24085093e+00 -4.70087141e-01
-1.33325443e-01 -7.86248386e-01 -4.07137305e-01 -4.69090521e-01
1.86969087e-01 2.29080230e-01 -2.93655276e-01 -5.88693380... | [9.33457088470459, 10.146484375] |
207ceb1e-af9f-4976-a9a3-4d2138edfde3 | detecting-word-level-adversarial-text-attacks | null | null | https://aclanthology.org/2022.repl4nlp-1.16 | https://aclanthology.org/2022.repl4nlp-1.16.pdf | Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations | State-of-the-art machine learning models are prone to adversarial attacks”:" Maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence. While defense strategies have been extensively explored in the computer vision domain, research in natural language processing still lacks... | ['Georg Groh', 'Marc Alexander Kühn', 'Lukas Huber', 'Edoardo Mosca'] | null | null | null | null | repl4nlp-acl-2022-5 | ['adversarial-text'] | ['adversarial'] | [ 4.87077683e-01 4.18213278e-01 -9.33464840e-02 -2.22271487e-01
-8.16858470e-01 -1.12466872e+00 8.76589894e-01 2.89336801e-01
-2.71889418e-01 4.75338668e-01 -1.06572755e-01 -8.02890718e-01
4.21948969e-01 -7.63891995e-01 -1.16497183e+00 -4.70815331e-01
3.05789918e-01 4.37747061e-01 2.70125687e-01 -2.05306143... | [5.877494812011719, 7.952759265899658] |
ecdab4cc-d6d4-43fa-bc9b-d2ea64ce1bbb | does-entity-abstraction-help-generative-1 | 2201.01787 | null | https://arxiv.org/abs/2201.01787v2 | https://arxiv.org/pdf/2201.01787v2.pdf | Does Entity Abstraction Help Generative Transformers Reason? | We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning: (1) compositional language understanding with text-based relational reasoning (CLUTRR), (2) abductive reasoning (ProofWriter), (3) multi-h... | ['Christopher Pal', 'Siva Reddy', 'Nicolas Gontier'] | 2022-01-05 | does-entity-abstraction-help-generative | https://openreview.net/forum?id=rSI-tyrv-ni | https://openreview.net/pdf?id=rSI-tyrv-ni | null | ['multi-hop-question-answering', 'relational-reasoning'] | ['knowledge-base', 'natural-language-processing'] | [-1.36187330e-01 8.90514731e-01 -8.85593519e-02 -2.71355122e-01
-9.91454482e-01 -7.42701888e-01 8.37408245e-01 4.04998839e-01
-2.61069566e-01 8.30504954e-01 6.12082124e-01 -1.01219261e+00
-2.28892371e-01 -1.10731423e+00 -8.26175630e-01 8.15971196e-02
1.14045598e-01 8.69074464e-01 2.41183430e-01 -5.10721684... | [9.740581512451172, 7.530217170715332] |
a2645a02-87b6-440e-94bb-9c344778f053 | improved-probabilistic-image-text | 2305.18171 | null | https://arxiv.org/abs/2305.18171v1 | https://arxiv.org/pdf/2305.18171v1.pdf | Improved Probabilistic Image-Text Representations | Image-Text Matching (ITM) task, a fundamental vision-language (VL) task, suffers from the inherent ambiguity arising from multiplicity and imperfect annotations. Deterministic functions are not sufficiently powerful to capture ambiguity, prompting the exploration of probabilistic embeddings to tackle the challenge. How... | ['Sanghyuk Chun'] | 2023-05-29 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 3.71774524e-01 -2.50029981e-01 -6.82856366e-02 -2.96797693e-01
-1.26313138e+00 -1.15092769e-01 8.04696739e-01 -7.52973929e-02
-5.39148331e-01 4.35423464e-01 3.62198830e-01 -1.84341557e-02
-4.85172495e-02 -3.46681327e-01 -6.83211327e-01 -6.46085024e-01
3.41048777e-01 5.31244159e-01 3.22264284e-01 6.42098859... | [10.508296012878418, 1.184250831604004] |
c6a54472-5e0d-4cb1-866d-0e1f2aba5404 | omnivore-a-single-model-for-many-visual | 2201.08377 | null | https://arxiv.org/abs/2201.08377v2 | https://arxiv.org/pdf/2201.08377v2.pdf | Omnivore: A Single Model for Many Visual Modalities | Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our 'Omnivor... | ['Ishan Misra', 'Armand Joulin', 'Laurens van der Maaten', 'Nikhila Ravi', 'Mannat Singh', 'Rohit Girdhar'] | 2022-01-20 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Girdhar_Omnivore_A_Single_Model_for_Many_Visual_Modalities_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Girdhar_Omnivore_A_Single_Model_for_Many_Visual_Modalities_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-recognition'] | ['computer-vision'] | [-1.23339161e-01 -5.36437213e-01 -5.50649405e-01 -4.03974146e-01
-6.50548995e-01 -8.66186261e-01 7.68028438e-01 -4.34345514e-01
-4.35728967e-01 1.57855749e-01 4.25221711e-01 -3.78256470e-01
3.08850169e-01 -3.92749578e-01 -8.47410381e-01 -2.66472727e-01
1.54106542e-01 1.32719576e-01 9.46376473e-02 -2.50073671... | [10.064738273620605, 1.3994168043136597] |
4a17e8fd-d3de-4324-adbd-4d53dfc5a97b | optimal-power-flow-for-integrated-primary | 2306.13287 | null | https://arxiv.org/abs/2306.13287v1 | https://arxiv.org/pdf/2306.13287v1.pdf | Optimal Power Flow for Integrated Primary-Secondary Distribution Networks with Service Transformers | Secondary distribution networks (SDNets) play an increasingly important role in smart grids due to a high proliferation of distributed energy resources (DERs) in SDNets. However, most existing optimal power flow (OPF) problems do not take into account SDNets with service transformers. Handling the nonlinear and nonconv... | ['Zixiao Ma', 'Zhaoyu Wang', 'Naihao Shi', 'Rui Cheng'] | 2023-06-23 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-4.96242285e-01 -2.81179120e-04 -3.43258440e-01 -7.17332587e-02
-2.53661811e-01 -8.95753086e-01 4.14780527e-02 -1.12186097e-01
3.91337305e-01 1.02469313e+00 1.57890528e-01 -4.17749196e-01
-7.15737998e-01 -7.79544055e-01 7.35140666e-02 -1.03004634e+00
-7.71294301e-03 4.82178271e-01 -3.89069736e-01 -4.60080385... | [5.669195175170898, 2.561311721801758] |
7ba92acc-a73f-4057-8b91-9c87fa3cf327 | is-chatgpt-the-ultimate-programming-assistant | 2304.11938 | null | https://arxiv.org/abs/2304.11938v1 | https://arxiv.org/pdf/2304.11938v1.pdf | Is ChatGPT the Ultimate Programming Assistant -- How far is it? | The recent progress in generative AI techniques has significantly influenced software engineering, as AI-driven methods tackle common developer challenges such as code synthesis from descriptions, program repair, and natural language summaries for existing programs. Large-scale language models (LLMs), like OpenAI's Cod... | ['Tegawendé F. Bissyandé', 'Jacques Klein', 'Shing-Chi Cheung', 'Xunzhu Tang', 'Tsz On Li', 'Weiqi Lu', 'Haoye Tian'] | 2023-04-24 | null | null | null | null | ['program-repair', 'prompt-engineering', 'program-repair'] | ['computer-code', 'natural-language-processing', 'reasoning'] | [ 1.93356141e-01 6.26938760e-01 -2.56221682e-01 -1.51054189e-01
-8.39529634e-01 -6.58493757e-01 3.02719593e-01 1.57600209e-01
4.31478381e-01 1.52742922e-01 1.71768755e-01 -6.96722209e-01
1.77159123e-02 -6.46430433e-01 -5.86387873e-01 -8.44735727e-02
1.11305609e-01 2.14606419e-01 1.15940839e-01 -3.87676746... | [7.91172456741333, 7.698004722595215] |
539f5b98-6531-41f9-8774-9fc4492fe52e | a-data-driven-approach-for-motion-planning-of | 1904.08784 | null | https://arxiv.org/abs/1904.08784v4 | https://arxiv.org/pdf/1904.08784v4.pdf | Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization | Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the gener... | ['Chenyang Xi', 'Yuankai Wu', 'Tianyu Shi', 'Lijun Sun'] | 2019-04-18 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 8.93010050e-02 5.04941028e-03 -6.92460477e-01 -3.15718055e-01
-6.48486972e-01 -9.37851667e-02 7.79930472e-01 -8.76200497e-02
-3.87193292e-01 1.01714325e+00 -2.44728057e-03 -5.51585495e-01
-4.51300204e-01 -8.22819948e-01 -2.79239506e-01 -8.28023970e-01
-1.67746156e-01 4.19151723e-01 4.50969696e-01 -2.55013734... | [5.364114761352539, 1.4910842180252075] |
7a2b6460-a647-4af1-b102-ba1ab07a71be | which-neural-network-to-choose-for-post-fault | 2104.03115 | null | https://arxiv.org/abs/2104.03115v1 | https://arxiv.org/pdf/2104.03115v1.pdf | Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems? | We consider a power transmission system monitored with Phasor Measurement Units (PMUs) placed at significant, but not all, nodes of the system. Assuming that a sufficient number of distinct single-line faults, specifically pre-fault state and (not cleared) post-fault state, are recorded by the PMUs and are available fo... | ['Michael Chertkov', 'Andrei Afonin'] | 2021-04-07 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-3.64600956e-01 -1.30197965e-02 -2.01749988e-02 1.70382023e-01
-8.63645822e-02 -7.02775657e-01 3.17805886e-01 3.45120639e-01
7.07308114e-01 8.01423430e-01 -2.26002514e-01 -7.21588790e-01
-6.49682343e-01 -7.37539291e-01 -5.93225956e-01 -6.43603802e-01
-1.01362920e+00 5.82964420e-01 -5.73637523e-02 -2.09064350... | [6.118586540222168, 2.618344783782959] |
22630cc7-2969-4b19-8839-de2d98c2e672 | improving-sequential-determinantal-point | 1807.10957 | null | http://arxiv.org/abs/1807.10957v2 | http://arxiv.org/pdf/1807.10957v2.pdf | Improving Sequential Determinantal Point Processes for Supervised Video Summarization | It is now much easier than ever before to produce videos. While the
ubiquitous video data is a great source for information discovery and
extraction, the computational challenges are unparalleled. Automatically
summarizing the videos has become a substantial need for browsing, searching,
and indexing visual content. Th... | ['Tianbao Yang', 'Ali Borji', 'Aidean Sharghi', 'Boqing Gong', 'Chengtao Li'] | 2018-07-28 | improving-sequential-determinantal-point-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Aidean_Sharghi_Improving_Sequential_Determinantal_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Aidean_Sharghi_Improving_Sequential_Determinantal_ECCV_2018_paper.pdf | eccv-2018-9 | ['supervised-video-summarization'] | ['computer-vision'] | [ 3.62189144e-01 5.46660833e-02 -4.13018823e-01 -2.51147985e-01
-1.03474593e+00 -6.86704397e-01 6.10330343e-01 2.30007902e-01
-3.69101673e-01 6.29776239e-01 9.96069908e-01 -4.79968414e-02
1.19284175e-01 -4.18287754e-01 -9.47846055e-01 -6.57304585e-01
4.38171625e-02 2.27349564e-01 3.74001116e-01 1.71847284... | [10.439117431640625, 0.48687297105789185] |
943db0f3-501d-4c2a-974b-a5f4c629a6ef | learning-inner-group-relations-on-point | 2108.12468 | null | https://arxiv.org/abs/2108.12468v1 | https://arxiv.org/pdf/2108.12468v1.pdf | Learning Inner-Group Relations on Point Clouds | The prevalence of relation networks in computer vision is in stark contrast to underexplored point-based methods. In this paper, we explore the possibilities of local relation operators and survey their feasibility. We propose a scalable and efficient module, called group relation aggregator. The module computes a feat... | ['Li Lu', 'Jun Liu', 'Wei Zhuo', 'Haoxi Ran'] | 2021-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ran_Learning_Inner-Group_Relations_on_Point_Clouds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ran_Learning_Inner-Group_Relations_on_Point_Clouds_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-classification'] | ['computer-vision'] | [-1.30579034e-02 4.64039475e-01 -1.43522277e-01 -3.21453691e-01
-3.75474304e-01 -4.78096426e-01 6.72850192e-01 1.18940160e-01
-2.18170539e-01 1.58185542e-01 3.12101822e-02 -2.21523866e-01
-3.91121209e-01 -9.82752085e-01 -5.58813989e-01 -5.62320769e-01
-1.12322204e-01 5.86488307e-01 7.67512262e-01 -1.46905512... | [7.94253396987915, -3.346328020095825] |
1a889270-cbe7-443a-8322-a31bd472431c | revisiting-few-shot-relation-classification | 2104.08481 | null | https://arxiv.org/abs/2104.08481v1 | https://arxiv.org/pdf/2104.08481v1.pdf | Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes | We explore Few-Shot Learning (FSL) for Relation Classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, aka NOTA), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distri... | ['Ido Dagan', 'Yoav Goldberg', 'Yanai Elazar', 'Ofer Sabo'] | 2021-04-17 | null | null | null | null | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 4.13925052e-01 5.04489005e-01 -3.30528408e-01 -2.62157351e-01
-5.31928241e-01 -5.00728667e-01 9.23876345e-01 3.47294331e-01
-2.21906602e-01 9.37950850e-01 1.98130295e-01 -3.48351210e-01
-8.41571212e-01 -9.30541217e-01 -3.35814744e-01 -8.10372412e-01
-6.14908598e-02 5.66168070e-01 4.30489272e-01 -3.15247297... | [9.82834243774414, 3.0138676166534424] |
8c665b90-c72e-47f5-8407-4937afd6474c | exploiting-unlabelled-photos-for-stronger | 2303.13779 | null | https://arxiv.org/abs/2303.13779v1 | https://arxiv.org/pdf/2303.13779v1.pdf | Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR | This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not e... | ['Yi-Zhe Song', 'Tao Xiang', 'Soumitri Chattopadhyay', 'Pinaki Nath Chowdhury', 'Subhadeep Koley', 'Ayan Kumar Bhunia', 'Aneeshan Sain'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.90842795e-01 4.64970618e-02 -1.08603299e-01 -1.68228388e-01
-1.25353837e+00 -8.29867423e-01 8.93277407e-01 -1.02371596e-01
-2.61588573e-01 4.93463784e-01 1.76303312e-01 -1.17807016e-01
-3.78480613e-01 -5.72699666e-01 -8.50057781e-01 -8.02726984e-01
1.46381646e-01 4.78395998e-01 2.27278858e-01 -1.05832063... | [11.605019569396973, 0.5884585976600647] |
0e04ebea-6990-40da-a190-507655811a3d | learning-to-blindly-assess-image-quality-in | 1907.00516 | null | https://arxiv.org/abs/1907.00516v3 | https://arxiv.org/pdf/1907.00516v3.pdf | Learning to Blindly Assess Image Quality in the Laboratory and Wild | Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images. Similarly, BIQA models optimized for images captured in the wild cannot adequately handle synthetically distorted images. To fa... | ['Xiaokang Yang', 'Kede Ma', 'Weixia Zhang', 'Guangtao Zhai'] | 2019-07-01 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 1.53470039e-01 -4.76544976e-01 5.43213367e-01 -4.69068378e-01
-1.19883478e+00 -7.26007342e-01 3.68138641e-01 -1.10964112e-01
-4.42829728e-01 4.94093686e-01 2.15067908e-01 -2.92219102e-01
-2.31227711e-01 -6.09313250e-01 -8.35540891e-01 -4.68389004e-01
8.87626708e-02 1.93346068e-01 -1.39962867e-01 -9.59137753... | [11.899995803833008, -1.7972522974014282] |
8d1e2f5d-f8c3-48d4-872e-36f226840ab2 | soft-prompt-decoding-for-multilingual-dense | 2305.09025 | null | https://arxiv.org/abs/2305.09025v1 | https://arxiv.org/pdf/2305.09025v1.pdf | Soft Prompt Decoding for Multilingual Dense Retrieval | In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages. We demonstrate that applying state-of-the-art approaches developed for cross-lingual information retrieval to MLIR tasks leads to sub-optimal performance. This is due to the heteroge... | ['James Allan', 'Hamed Zamani', 'Hansi Zeng', 'Zhiqi Huang'] | 2023-05-15 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.55108505e-01 -2.57878274e-01 -7.26833999e-01 -1.55649453e-01
-1.69617689e+00 -6.87160313e-01 7.30223656e-01 1.51981071e-01
-8.19654822e-01 7.36496925e-01 5.43417931e-01 -3.29422265e-01
-1.08686283e-01 -4.65383053e-01 -7.79665530e-01 -3.38931710e-01
2.85519511e-01 6.92776740e-01 -9.42523852e-02 -5.62042594... | [11.369599342346191, 9.803342819213867] |
8c129263-b0df-46dd-8eb2-2a4e1fe57d9f | generalized-difference-in-differences-models | 2211.06710 | null | https://arxiv.org/abs/2211.06710v4 | https://arxiv.org/pdf/2211.06710v4.pdf | Generalized Difference-in-differences Models: Robust Bounds | The difference-in-differences (DID) method identifies the average treatment effects on the treated (ATT) under mainly the so-called parallel trends (PT) assumption. The most common and widely used approach to justify the PT assumption is the pre-treatment period examination. If a null hypothesis of the same trend in th... | ['Désiré Kédagni', 'Kyunghoon Ban'] | 2022-11-12 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 1.71215832e-01 2.10721418e-01 -1.02354741e+00 -2.32311368e-01
-5.71417689e-01 -5.96197844e-01 6.39407814e-01 3.44544470e-01
-4.14596349e-01 7.71847248e-01 6.27373278e-01 -6.59871578e-01
-5.77168584e-01 -7.70690322e-01 -6.65817440e-01 -7.81776607e-01
2.21793026e-01 2.65430748e-01 -1.73234805e-01 1.43002570... | [7.980116844177246, 5.189881801605225] |
2f3f48d9-c7cb-46e6-a187-cd45b8e0d879 | bapgan-gan-based-bone-age-progression-of | 2110.08509 | null | https://arxiv.org/abs/2110.08509v1 | https://arxiv.org/pdf/2110.08509v1.pdf | BAPGAN: GAN-based Bone Age Progression of Femur and Phalange X-ray Images | Convolutional Neural Networks play a key role in bone age assessment for investigating endocrinology, genetic, and growth disorders under various modalities and body regions. However, no researcher has tackled bone age progression/regression despite its valuable potential applications: bone-related disease diagnosis, c... | ['Toshifumi Ozaki', 'Ryuichi Nakahara', 'Joe Hasei', 'Changhee Han', 'Shinji Nakazawa'] | 2021-10-16 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.06459828e-02 4.18718606e-01 -9.74084660e-02 -3.12093068e-02
-6.96127534e-01 6.14907816e-02 2.19375789e-01 -8.72428194e-02
-3.82234931e-01 8.98854792e-01 2.89432049e-01 -3.81993800e-01
-2.13504463e-01 -8.83513391e-01 -6.25413060e-01 -6.20035529e-01
-3.59385848e-01 6.82277203e-01 -1.87356144e-01 -1.54138550... | [14.132866859436035, -1.9663728475570679] |
7ae060eb-aeed-48f1-a7fd-560dd098324a | yolo-facev2-a-scale-and-occlusion-aware-face | 2208.02019 | null | https://arxiv.org/abs/2208.02019v2 | https://arxiv.org/pdf/2208.02019v2.pdf | YOLO-FaceV2: A Scale and Occlusion Aware Face Detector | In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been w... | ['Xiuying Wang', 'Yahui Liu', 'YongXin Su', 'Weijun Chen', 'Hongbo Huang', 'Ziping Yu'] | 2022-08-03 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [-4.68128234e-01 -2.68445641e-01 -9.64339450e-02 -2.71031380e-01
-8.18943530e-02 5.05490554e-03 7.42549002e-02 -6.31949246e-01
-4.08380538e-01 1.35906070e-01 -6.31410480e-02 1.85102895e-01
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3.31843227e-01 6.53951541e-02 3.83492023e-01 -1.47015825... | [13.3101167678833, 0.6427334547042847] |
6cda27b9-717e-4a41-898f-5978594b5cc1 | the-design-of-stratega-a-general-strategy | 2009.05643 | null | https://arxiv.org/abs/2009.05643v1 | https://arxiv.org/pdf/2009.05643v1.pdf | The Design Of "Stratega": A General Strategy Games Framework | Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games. In contrast to other strategy game frameworks, Stratega allows to create a wide variety of turn-based and real-time strategy games using a common API for agent development. While the curr... | ['Alexander Dockhorn', 'Diego Perez-Liebana', 'Jorge Hurtado Grueso', 'Dominik Jeurissen'] | 2020-09-11 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.59816498e-01 1.60595104e-01 1.95729300e-01 9.15283710e-02
-2.37614661e-01 -9.03138220e-01 9.65727031e-01 -2.45511711e-01
-4.14447635e-01 7.67712414e-01 1.92470595e-01 -8.31033409e-01
-4.36576068e-01 -1.28380930e+00 -1.12110330e-02 -4.10162419e-01
-1.78837869e-02 9.64563608e-01 7.10196733e-01 -8.19019437... | [3.4513096809387207, 1.4707000255584717] |
35a708db-4a12-42be-867a-b2fcff5a893b | spatiotemporal-networks-for-video-emotion | 1704.00570 | null | http://arxiv.org/abs/1704.00570v3 | http://arxiv.org/pdf/1704.00570v3.pdf | Spatiotemporal Networks for Video Emotion Recognition | Our experiment adapts several popular deep learning methods as well as some
traditional methods on the problem of video emotion recognition. In our
experiment, we use the CNN-LSTM architecture for visual information extraction
and classification and utilize traditional methods such as for audio feature
classification. ... | ['Lijie Fan', 'Yunjie Ke'] | 2017-04-03 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [-3.86558115e-01 -7.27050960e-01 -1.53479442e-01 -3.35037857e-01
-5.04309595e-01 -2.31067747e-01 1.37003839e-01 -9.86031070e-02
-6.26962364e-01 6.21288657e-01 2.44312853e-01 -5.70845418e-02
3.13319325e-01 -4.86469328e-01 -4.41607147e-01 -6.15477622e-01
-2.66693264e-01 -3.86752099e-01 -2.67304450e-01 -3.48532230... | [13.328503608703613, 5.156844139099121] |
19ce0b0f-6f99-432b-8db6-6a3100fd1ded | unify-a-unified-policy-designing-framework | 2210.14030 | null | https://arxiv.org/abs/2210.14030v1 | https://arxiv.org/pdf/2210.14030v1.pdf | UNIFY: a Unified Policy Designing Framework for Solving Constrained Optimization Problems with Machine Learning | The interplay between Machine Learning (ML) and Constrained Optimization (CO) has recently been the subject of increasing interest, leading to a new and prolific research area covering (e.g.) Decision Focused Learning and Constrained Reinforcement Learning. Such approaches strive to tackle complex decision problems und... | ['Michela Milano', 'Michele Lombardi', 'Allegra De Filippo', 'Mattia Silvestri'] | 2022-10-25 | null | null | null | null | ['energy-management'] | ['time-series'] | [ 6.06580615e-01 2.62758046e-01 -9.67548430e-01 -2.22623408e-01
-9.17354286e-01 -4.02631968e-01 4.29186016e-01 3.85821849e-01
-3.10995638e-01 1.22178543e+00 -1.00345939e-01 -3.52227479e-01
-4.79406387e-01 -8.80689561e-01 -6.51305556e-01 -8.35053205e-01
-7.00324997e-02 4.55041021e-01 -1.70128569e-02 -3.76834646... | [4.607207298278809, 2.568974494934082] |
483790d5-195b-4d21-a69d-5f49b76a6a52 | spatial-moment-pooling-improves-neural-image | 2209.14583 | null | https://arxiv.org/abs/2209.14583v1 | https://arxiv.org/pdf/2209.14583v1.pdf | Spatial Moment Pooling Improves Neural Image Assessment | In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA). A large number of works start by extracting deep features from CNN. Then, those features are processed through spatial average pooling (SAP) and fully connected layers to predict ... | ['Hongwei Qin', 'Yan Wang', 'Yifan Shao', 'Tongda Xu'] | 2022-09-29 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-1.24788262e-01 -5.74761808e-01 -3.73597853e-02 -3.74000400e-01
-7.54693985e-01 -2.79569387e-01 5.03696740e-01 -4.28001443e-03
-5.61262310e-01 5.48849046e-01 4.31718320e-01 -3.23756039e-01
-2.90631205e-01 -9.08510029e-01 -4.27187890e-01 -5.83466649e-01
-1.55228540e-01 -6.02121055e-01 -4.43873135e-03 -2.97360271... | [11.870079040527344, -1.8123410940170288] |
0f262caa-fa31-4d42-84a9-6a49acdc6cbc | learning-with-neighbor-consistency-for-noisy-1 | 2202.02200 | null | https://arxiv.org/abs/2202.02200v2 | https://arxiv.org/pdf/2202.02200v2.pdf | Learning with Neighbor Consistency for Noisy Labels | Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in featur... | ['Cordelia Schmid', 'Anurag Arnab', 'Jack Valmadre', 'Ahmet Iscen'] | 2022-02-04 | learning-with-neighbor-consistency-for-noisy | http://openaccess.thecvf.com//content/CVPR2022/html/Iscen_Learning_With_Neighbor_Consistency_for_Noisy_Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Iscen_Learning_With_Neighbor_Consistency_for_Noisy_Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.88801491e-01 4.03441153e-02 -1.97347393e-03 -8.58886480e-01
-1.22883010e+00 -5.93527019e-01 7.58773446e-01 1.91552863e-01
-7.67052233e-01 8.11412871e-01 9.48711578e-03 -1.81876160e-02
4.75730039e-02 -6.87898040e-01 -9.17029083e-01 -6.86388075e-01
8.96551460e-02 6.03271008e-01 2.56832600e-01 5.39078303... | [9.409334182739258, 3.801140785217285] |
0f9d419b-9e00-4e6b-a9d8-4ee13feb9810 | asymptotically-unbiased-off-policy-policy | 2302.11725 | null | https://arxiv.org/abs/2302.11725v1 | https://arxiv.org/pdf/2302.11725v1.pdf | Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments | In this work, we consider the off-policy policy evaluation problem for contextual bandits and finite horizon reinforcement learning in the nonstationary setting. Reusing old data is critical for policy evaluation, but existing estimators that reuse old data introduce large bias such that we can not obtain a valid confi... | ['Martha White', 'Philip Thomas', 'Yash Chandak', 'Vincent Liu'] | 2023-02-23 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 8.45679939e-02 -1.88923642e-01 -1.14749193e+00 -2.46115372e-01
-1.11895096e+00 -7.21527815e-01 3.20950180e-01 3.39521058e-02
-4.36542511e-01 1.65790975e+00 2.34328076e-01 -8.96664321e-01
-3.68508220e-01 -7.64432907e-01 -1.06025636e+00 -6.76404715e-01
-1.02445096e-01 3.70986551e-01 1.00543343e-01 1.85548827... | [4.464090347290039, 3.1358742713928223] |
b9acc6b9-ea6a-4c66-8257-5310b5d4d9a0 | divergence-based-quadrangle-and-applications | 2306.16525 | null | https://arxiv.org/abs/2306.16525v1 | https://arxiv.org/pdf/2306.16525v1.pdf | Divergence Based Quadrangle and Applications | This paper introduces a novel framework for assessing risk and decision-making in the presence of uncertainty, the \emph{$\varphi$-Divergence Quadrangle}. This approach expands upon the traditional Risk Quadrangle, a model that quantifies uncertainty through four key components: \emph{risk, deviation, regret}, and \emp... | ['Stan Uryasev', 'Cheng Peng', 'Siddhartha Gupte', 'Anton Malandii'] | 2023-06-28 | null | null | null | null | ['management', 'decision-making'] | ['miscellaneous', 'reasoning'] | [-1.76167339e-01 4.44144040e-01 -3.65189128e-02 -4.63986695e-01
-1.09955287e+00 -6.40455067e-01 1.44643426e-01 5.02669036e-01
-4.60626423e-01 7.17247903e-01 1.57408878e-01 -6.95180416e-01
-1.04988384e+00 -9.74116623e-01 -2.95271307e-01 -6.76539481e-01
-4.25281733e-01 7.13132992e-02 -5.08564055e-01 -1.54414937... | [5.043295383453369, 3.940107583999634] |
f32894db-3031-45a5-9184-5b81101db213 | in-search-of-deep-learning-architectures-for | 2302.13046 | null | https://arxiv.org/abs/2302.13046v1 | https://arxiv.org/pdf/2302.13046v1.pdf | In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance | In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple ty... | ['John Psarras', 'Nuno Amaro', 'Georgios Kormpakis', 'Spiros Mouzakitis', 'Vasileios Schoinas', 'Francisco Silva', 'Evangelos Karakolis', 'Sotiris Pelekis'] | 2023-02-25 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-2.75457740e-01 -4.19719815e-01 -5.28720655e-02 -2.85404734e-02
-2.39714131e-01 -6.85434461e-01 7.66329169e-01 2.00147688e-01
1.70609161e-01 7.40326822e-01 3.48371208e-01 -8.75338495e-01
-4.21778619e-01 -9.89176869e-01 -2.88189709e-01 -9.97638643e-01
-6.72295809e-01 4.70295221e-01 -5.77562690e-01 -2.86879182... | [6.124459266662598, 2.7681636810302734] |
9919ddde-5565-46e4-85c1-7a929979bdad | maskcon-masked-contrastive-learning-for | 2303.12756 | null | https://arxiv.org/abs/2303.12756v1 | https://arxiv.org/pdf/2303.12756v1.pdf | MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset | Deep learning has achieved great success in recent years with the aid of advanced neural network structures and large-scale human-annotated datasets. However, it is often costly and difficult to accurately and efficiently annotate large-scale datasets, especially for some specialized domains where fine-grained labels a... | ['Ioannis Patras', 'Chen Feng'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Feng_MaskCon_Masked_Contrastive_Learning_for_Coarse-Labelled_Dataset_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Feng_MaskCon_Masked_Contrastive_Learning_for_Coarse-Labelled_Dataset_CVPR_2023_paper.pdf | cvpr-2023-1 | ['learning-with-coarse-labels'] | ['computer-vision'] | [ 2.09727958e-01 3.11760008e-01 -3.10082257e-01 -7.94952631e-01
-9.22956765e-01 -6.20688856e-01 3.79010826e-01 1.94275662e-01
-5.92489541e-01 8.30541193e-01 -1.96741313e-01 -1.25045717e-01
-1.63579077e-01 -7.16694832e-01 -8.39192808e-01 -6.58923328e-01
1.77481212e-02 8.24092805e-01 2.71674007e-01 -2.55774781... | [9.508539199829102, 3.188457727432251] |
f3bc6365-141b-4087-8fa2-79db15b175e8 | adversarial-attack-by-limited-point-cloud | 2110.03745 | null | https://arxiv.org/abs/2110.03745v1 | https://arxiv.org/pdf/2110.03745v1.pdf | Adversarial Attack by Limited Point Cloud Surface Modifications | Recent research has revealed that the security of deep neural networks that directly process 3D point clouds to classify objects can be threatened by adversarial samples. Although existing adversarial attack methods achieve high success rates, they do not restrict the point modifications enough to preserve the point cl... | ['Shohreh Kasaei', 'Hanieh Naderi', 'Atrin Arya'] | 2021-10-07 | adversarial-attack-by-limited-point-cloud-1 | https://openreview.net/forum?id=MACKPM_haAu | https://openreview.net/pdf?id=MACKPM_haAu | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.21250831e-01 -2.21709251e-01 9.14146751e-02 1.26245897e-02
-4.19827312e-01 -6.56256437e-01 6.17116332e-01 1.14873871e-01
-5.12834251e-01 4.23514128e-01 -7.88199902e-01 -4.35132086e-01
-8.01556781e-02 -1.15100527e+00 -9.24761534e-01 -9.41689134e-01
-1.31852478e-01 3.34893823e-01 4.49053824e-01 -3.38746637... | [7.700366973876953, -4.470816612243652] |
13b165ba-7444-46f2-a7ec-18f5e3a6af61 | theory-of-minds-understanding-behavior-in | 1901.06085 | null | http://arxiv.org/abs/1901.06085v1 | http://arxiv.org/pdf/1901.06085v1.pdf | Theory of Minds: Understanding Behavior in Groups Through Inverse Planning | Human social behavior is structured by relationships. We form teams, groups,
tribes, and alliances at all scales of human life. These structures guide
multi-agent cooperation and competition, but when we observe others these
underlying relationships are typically unobservable and hence must be inferred.
Humans make the... | ['Max Kleiman-Weiner', 'Michael Shum', 'Michael L. Littman', 'Joshua B. Tenenbaum'] | 2019-01-18 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [-2.73358762e-01 3.77176970e-01 2.79269278e-01 -3.89096588e-01
1.47240786e-02 -5.00515223e-01 8.83276701e-01 2.22872362e-01
-2.35012919e-01 9.53263879e-01 4.06328619e-01 -1.86174046e-02
-7.38411844e-01 -1.05838168e+00 -2.99540132e-01 -5.93733132e-01
-4.46009755e-01 1.31343699e+00 2.25504115e-01 -5.78804672... | [3.917890787124634, 1.5873730182647705] |
974d25a5-3b77-4f20-9718-634f172cce03 | camera-pose-voting-for-large-scale-image | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zeisl_Camera_Pose_Voting_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zeisl_Camera_Pose_Voting_ICCV_2015_paper.pdf | Camera Pose Voting for Large-Scale Image-Based Localization | Image-based localization approaches aim to determine the camera pose from which an image was taken. Finding correct 2D-3D correspondences between query image features and 3D points in the scene model becomes harder as the size of the model increases. Current state-of-the-art methods therefore combine elaborate matching... | ['Bernhard Zeisl', 'Marc Pollefeys', 'Torsten Sattler'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['image-based-localization'] | ['computer-vision'] | [ 4.74015549e-02 -3.49496901e-01 1.72123894e-01 -1.83699504e-01
-1.06336904e+00 -6.43502414e-01 5.50284207e-01 5.00185311e-01
-7.92494833e-01 2.44568944e-01 -4.06594127e-01 -1.29005954e-01
4.41458225e-02 -5.04587531e-01 -8.45312476e-01 -2.61100262e-01
8.19209665e-02 7.00630069e-01 8.11945558e-01 -6.32369965... | [7.763842582702637, -2.2073795795440674] |
72069244-5b7b-46b4-8d62-7d507dae34a5 | show-control-and-tell-a-framework-for | 1811.10652 | null | https://arxiv.org/abs/1811.10652v3 | https://arxiv.org/pdf/1811.10652v3.pdf | Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions | Current captioning approaches can describe images using black-box architectures whose behavior is hardly controllable and explainable from the exterior. As an image can be described in infinite ways depending on the goal and the context at hand, a higher degree of controllability is needed to apply captioning algorithm... | ['Lorenzo Baraldi', 'Rita Cucchiara', 'Marcella Cornia'] | 2018-11-26 | show-control-and-tell-a-framework-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Cornia_Show_Control_and_Tell_A_Framework_for_Generating_Controllable_and_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cornia_Show_Control_and_Tell_A_Framework_for_Generating_Controllable_and_CVPR_2019_paper.pdf | cvpr-2019-6 | ['controllable-image-captioning'] | ['computer-vision'] | [ 3.63228858e-01 6.27206028e-01 -3.43905210e-01 -3.48995328e-01
-6.83073401e-01 -9.13889289e-01 8.53259027e-01 -8.25842619e-02
2.20277742e-01 7.31764019e-01 6.30988598e-01 3.45101207e-02
4.04365808e-01 -6.28286541e-01 -1.29457676e+00 -4.17482764e-01
2.15729475e-01 6.43422484e-01 -1.73142955e-01 -3.78376901... | [10.998027801513672, 0.9797388315200806] |
d814b3d6-c5d1-46ad-9c79-8881e71f4465 | scene-text-telescope-text-focused-scene-image | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Scene_Text_Telescope_Text-Focused_Scene_Image_Super-Resolution_CVPR_2021_paper.pdf | Scene Text Telescope: Text-Focused Scene Image Super-Resolution | Image super-resolution, which is often regarded as a preprocessing procedure of scene text recognition, aims to recover the realistic features from a low-resolution text image. It has always been challenging due to large variations in text shapes, fonts, backgrounds, etc. However, most existing methods employ gener... | ['xiangyang xue', 'Bin Li', 'Jingye Chen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['scene-text-recognition'] | ['computer-vision'] | [ 8.78324807e-01 -6.43514872e-01 1.82051152e-01 -3.36556405e-01
-7.51187921e-01 -4.14846897e-01 8.54607880e-01 -3.72497946e-01
-1.26363244e-02 4.65722352e-01 4.82417554e-01 7.93635622e-02
-1.17564902e-01 -7.97680736e-01 -7.51772523e-01 -9.05262232e-01
6.88197494e-01 2.76325166e-01 4.49318856e-01 -4.37447727... | [11.424978256225586, -1.8380905389785767] |
f0c80d2c-bb93-410a-a03d-3580bf3da920 | unsupervised-extractive-summarization-of | 2306.01444 | null | https://arxiv.org/abs/2306.01444v1 | https://arxiv.org/pdf/2306.01444v1.pdf | Unsupervised Extractive Summarization of Emotion Triggers | Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoing disasters. Recent approaches trained supervised models to both detect emotions and explain emotion triggers (events and appraisals) via ab... | ['Cornelia Caragea', 'Junyi Jessy Li', 'Hongli Zhan', 'Tiberiu Sosea'] | 2023-06-02 | null | null | null | null | ['unsupervised-extractive-summarization', 'abstractive-text-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.55812287e-01 3.15120310e-01 -3.10850382e-01 -4.93664324e-01
-1.26038313e+00 -7.20390439e-01 6.88184261e-01 1.03485727e+00
-5.10981321e-01 9.12042558e-01 1.41335261e+00 7.45971575e-02
1.82248697e-01 -5.60977936e-01 -2.59757489e-01 -2.22988784e-01
-2.56617695e-01 5.10064423e-01 -4.86442000e-01 -2.76642591... | [12.760396003723145, 6.376925945281982] |
800b9cb7-c793-479c-aebc-c5ccf3215fd9 | a-semi-paired-approach-for-label-to-image | 2306.13585 | null | https://arxiv.org/abs/2306.13585v2 | https://arxiv.org/pdf/2306.13585v2.pdf | A Semi-Paired Approach For Label-to-Image Translation | Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition tasks alleviating the need for large amounts of labeled data, yet it remains understudied in image-to-image translation (I2I) tasks. In this ... | ['Bin Yang', 'Diandian Guo', 'Mark Youssef', 'Mohamed Abdelsamad', 'Shuai Zhang', 'George Eskandar'] | 2023-06-23 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 9.47684467e-01 3.75740647e-01 -2.07013264e-01 -6.01808965e-01
-1.13341951e+00 -7.82877505e-01 1.00226724e+00 -5.04685283e-01
-2.83118367e-01 8.13180447e-01 -1.30328834e-02 -1.87487096e-01
2.75859207e-01 -5.76740444e-01 -1.03574252e+00 -9.10237908e-01
6.10185683e-01 8.21150362e-01 -6.99763820e-02 7.11128935... | [11.472912788391113, -0.03469494357705116] |
e88fd109-7676-4e58-9d34-abd811c1fd0e | mesoscopic-structure-of-the-stock-market-and | 2112.06544 | null | https://arxiv.org/abs/2112.06544v1 | https://arxiv.org/pdf/2112.06544v1.pdf | Mesoscopic Structure of the Stock Market and Portfolio Optimization | The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic `equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) t... | ['Diego Garlaschelli', 'Tiziano Squartini', 'Giorgio Fagiolo', 'Sebastiano Michele Zema'] | 2021-12-13 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.38485768e-01 -2.08538794e-03 1.18660867e-01 4.80835050e-01
-1.44014180e-01 -9.13945317e-01 8.84691238e-01 1.20651171e-01
2.34517939e-02 7.93691635e-01 3.65862936e-01 -2.41653442e-01
-1.12926686e+00 -1.05842400e+00 -1.70652479e-01 -9.12614644e-01
-5.38362801e-01 4.45157290e-01 2.70364106e-01 -1.28060609... | [5.012136459350586, 4.052708625793457] |
288f3e3d-88b4-429b-8a3a-8184e55b3070 | causal-fault-localisation-in-dataflow-systems | 2304.11987 | null | https://arxiv.org/abs/2304.11987v1 | https://arxiv.org/pdf/2304.11987v1.pdf | Causal fault localisation in dataflow systems | Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic features of dataflow computing is the natural access to the dataflow graph of the e... | ['Neil D. Lawrence', 'Andrei Paleyes'] | 2023-04-24 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 8.51177499e-02 1.17646813e-01 -4.27921236e-01 -3.20382625e-01
2.28967547e-01 -3.94806027e-01 6.97290897e-01 2.62733936e-01
5.38788795e-01 3.57023001e-01 1.18885845e-01 -8.98468018e-01
-5.66539109e-01 -8.28650296e-01 -5.65064192e-01 -1.31686509e-01
-7.23054349e-01 4.19200882e-02 3.49569023e-01 -2.16380864... | [7.8822832107543945, 5.577849388122559] |
d3273ad7-316a-4430-8428-22e5d1b3d2d3 | neuralangelo-high-fidelity-neural-surface-1 | 2306.03092 | null | https://arxiv.org/abs/2306.03092v2 | https://arxiv.org/pdf/2306.03092v2.pdf | Neuralangelo: High-Fidelity Neural Surface Reconstruction | Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution... | ['Chen-Hsuan Lin', 'Ming-Yu Liu', 'Mathias Unberath', 'Russell H. Taylor', 'Alex Evans', 'Thomas Müller', 'Zhaoshuo Li'] | 2023-06-05 | neuralangelo-high-fidelity-neural-surface | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Neuralangelo_High-Fidelity_Neural_Surface_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Neuralangelo_High-Fidelity_Neural_Surface_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 3.72147232e-01 1.90801814e-01 5.34709394e-01 -1.34864137e-01
-1.15206504e+00 -3.06431115e-01 4.23378915e-01 -1.94256678e-02
-1.02124199e-01 5.85721433e-01 2.08466396e-01 -8.86754543e-02
1.98848829e-01 -1.25789058e+00 -9.62314725e-01 -4.66850132e-01
-1.47211671e-01 6.66784346e-01 3.43505561e-01 -2.74192572... | [9.135470390319824, -3.1236929893493652] |
4b9421cb-c8ed-45c3-8cae-73f119776751 | echovest-real-time-sound-classification-and | 2307.04604 | null | https://arxiv.org/abs/2307.04604v1 | https://arxiv.org/pdf/2307.04604v1.pdf | EchoVest: Real-Time Sound Classification and Depth Perception Expressed through Transcutaneous Electrical Nerve Stimulation | Over 1.5 billion people worldwide live with hearing impairment. Despite various technologies that have been created for individuals with such disabilities, most of these technologies are either extremely expensive or inaccessible for everyday use in low-medium income countries. In order to combat this issue, we have de... | ['Ryan Park', 'Siddhant Sood', 'Jesse Choe'] | 2023-07-10 | null | null | null | null | ['environmental-sound-classification', 'sound-classification', 'classification-1'] | ['audio', 'audio', 'methodology'] | [-8.39887932e-02 -3.83478582e-01 5.79603255e-01 1.26620233e-01
-1.11998475e+00 -4.46694374e-01 7.49596134e-02 -1.04964741e-01
-6.52393281e-01 4.79688406e-01 6.05760992e-01 -1.74909592e-01
-5.14099700e-03 -7.24901497e-01 -2.40961447e-01 -6.36028171e-01
-1.11065611e-01 1.30851820e-01 2.88668454e-01 -2.79178947... | [15.038809776306152, 5.759720325469971] |
939b9661-6aa3-45a7-a15f-658a815f9959 | temporal-relation-classification-in-persian | null | null | https://aclanthology.org/R13-1034 | https://aclanthology.org/R13-1034.pdf | Temporal Relation Classification in Persian and English contexts | null | ['Yadollah Yaghoobzadeh', 'Gholamreza Ghassem-Sani', 'Negin Karimi Hosseini', 'Mirrosh', 'Seyed Abolghasem el', 'Mahbaneh Eshaghzadeh Torbati'] | 2013-09-01 | temporal-relation-classification-in-persian-1 | https://aclanthology.org/R13-1034 | https://aclanthology.org/R13-1034.pdf | ranlp-2013-9 | ['temporal-relation-classification'] | ['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... | [-7.381738662719727, 3.5931644439697266] |
b3f2d3fb-66f7-4e7d-8bd8-e7a3f3035aa5 | medical-diagnosis-with-large-scale-multimodal | 2212.09162 | null | https://arxiv.org/abs/2212.09162v2 | https://arxiv.org/pdf/2212.09162v2.pdf | Medical Diagnosis with Large Scale Multimodal Transformers: Leveraging Diverse Data for More Accurate Diagnosis | Multimodal deep learning has been used to predict clinical endpoints and diagnoses from clinical routine data. However, these models suffer from scaling issues: they have to learn pairwise interactions between each piece of information in each data type, thereby escalating model complexity beyond manageable scales. Thi... | ['Daniel Truhn', 'Jakob Nikolas Kather', 'Sven Nebelung', 'Christiane Kuhl', 'Keno Bressem', 'Johannes Stegmaier', 'Christoph Haarburger', 'Soroosh Tayebi Arasteh', 'Tianyu Han', 'Tianci Wang', 'Gustav Mueller-Franzes', 'Firas Khader'] | 2022-12-18 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 3.89707267e-01 -1.44959893e-02 -4.95507509e-01 -4.12213534e-01
-1.17599952e+00 -4.84826535e-01 4.01925892e-01 6.95355117e-01
-4.27152187e-01 8.17544937e-01 4.86413419e-01 -3.37616742e-01
-7.43824720e-01 -4.37642872e-01 -4.98776495e-01 -7.52432048e-01
-4.48617101e-01 8.52846384e-01 -1.07400179e-01 -1.83642268... | [15.072677612304688, -2.631326675415039] |
bee3c20e-ed0f-463b-b624-de4102da1183 | multi-view-azimuth-stereo-via-tangent-space | 2303.16447 | null | https://arxiv.org/abs/2303.16447v1 | https://arxiv.org/pdf/2303.16447v1.pdf | Multi-View Azimuth Stereo via Tangent Space Consistency | We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods. We introduce the concept of tangent space consistency: Multi-view azi... | ['Yasuyuki Matsushita', 'Fumio Okura', 'Hiroaki Santo', 'Xu Cao'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cao_Multi-View_Azimuth_Stereo_via_Tangent_Space_Consistency_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cao_Multi-View_Azimuth_Stereo_via_Tangent_Space_Consistency_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 4.39956158e-01 -1.24691248e-01 2.24258319e-01 -6.19601786e-01
-9.72898483e-01 -1.03054893e+00 8.27558398e-01 -7.59638309e-01
1.47556722e-01 5.70441484e-01 3.37544113e-01 -2.58938462e-01
-1.94093749e-01 -6.09798431e-01 -6.09834552e-01 -8.63333821e-01
5.64124465e-01 6.64832711e-01 -9.81232896e-02 -3.86408240... | [9.57697868347168, -2.9189815521240234] |
765b3839-d263-4eb0-aad0-a881a3b5e38f | a-3d-probabilistic-deep-learning-system-for | 1902.03233 | null | https://arxiv.org/abs/1902.03233v3 | https://arxiv.org/pdf/1902.03233v3.pdf | A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans | We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy cla... | ['Rebecca L. Russell', 'Onur Ozdemir', 'Andrew A. Berlin'] | 2019-02-08 | null | null | null | null | ['probabilistic-deep-learning', 'lung-nodule-detection'] | ['computer-vision', 'medical'] | [ 5.33435643e-02 6.37670338e-01 -7.18153298e-01 -4.86246407e-01
-1.40897763e+00 -4.24088389e-01 3.85039926e-01 2.24927932e-01
-2.27692381e-01 1.50059626e-01 3.14380974e-01 -9.73671496e-01
-3.81384194e-01 -7.87913203e-01 -7.86437094e-01 -5.73190451e-01
-1.58523083e-01 1.22902107e+00 6.33822739e-01 4.57840025... | [15.296072006225586, -2.1978023052215576] |
529202f3-855a-4d1b-bdca-77ef702b2330 | online-low-rank-matrix-completion | 2209.03997 | null | https://arxiv.org/abs/2209.03997v2 | https://arxiv.org/pdf/2209.03997v2.pdf | Online Low Rank Matrix Completion | We study the problem of {\em online} low-rank matrix completion with $\mathsf{M}$ users, $\mathsf{N}$ items and $\mathsf{T}$ rounds. In each round, the algorithm recommends one item per user, for which it gets a (noisy) reward sampled from a low-rank user-item preference matrix. The goal is to design a method with sub-... | ['Soumyabrata Pal', 'Prateek Jain'] | 2022-09-08 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 1.07069448e-01 5.59947751e-02 -1.58080518e-01 -2.85104394e-01
-1.34011614e+00 -1.12818670e+00 -2.93304324e-01 8.49292427e-02
-8.09524536e-01 7.37866640e-01 -1.23443276e-01 -7.71670759e-01
-1.08841348e+00 -6.94613159e-01 -1.04780960e+00 -6.67302430e-01
-5.05140722e-01 7.31174588e-01 -3.02767217e-01 -9.29943696... | [4.920475006103516, 3.6521778106689453] |
b2a8a9fe-f3a6-4dee-9386-d637c76ce968 | improving-transformer-based-image-matching-by | 2303.02885 | null | https://arxiv.org/abs/2303.02885v1 | https://arxiv.org/pdf/2303.02885v1.pdf | Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints | Learning robust local image feature matching is a fundamental low-level vision task, which has been widely explored in the past few years. Recently, detector-free local feature matchers based on transformers have shown promising results, which largely outperform pure Convolutional Neural Network (CNN) based ones. But c... | ['Yanwei Fu', 'Chenjie Cao'] | 2023-03-06 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-8.99802148e-02 -5.70577800e-01 -3.90027538e-02 -3.10374439e-01
-9.75957811e-01 -2.32828960e-01 5.07325351e-01 -1.01736739e-01
-5.00922918e-01 3.17725420e-01 1.98177561e-01 2.26232275e-01
-3.74849230e-01 -8.17042291e-01 -9.11364079e-01 -5.97031832e-01
2.61372268e-01 1.20734654e-01 5.97903073e-01 -3.15449864... | [7.870059013366699, -2.151845932006836] |
0f8a4324-cd9b-4b79-8811-fadc924283ff | photorealistic-monocular-3d-reconstruction-of | 2204.08906 | null | https://arxiv.org/abs/2204.08906v1 | https://arxiv.org/pdf/2204.08906v1.pdf | Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing | We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel-aligned method estimates detailed 3D geometry and, for the first time, the unshaded surface color together with the scene illumination. Observing that... | ['Cristian Sminchisescu', 'Mihai Zanfir', 'Thiemo Alldieck'] | 2022-04-19 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Alldieck_Photorealistic_Monocular_3D_Reconstruction_of_Humans_Wearing_Clothing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Alldieck_Photorealistic_Monocular_3D_Reconstruction_of_Humans_Wearing_Clothing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [ 1.29678145e-01 1.01698525e-01 5.21927774e-01 -2.85070270e-01
-4.83099788e-01 -5.47010958e-01 4.28333461e-01 -3.25928420e-01
-2.25274742e-01 4.63620871e-01 2.11131111e-01 2.23814454e-02
2.84403712e-01 -5.02865732e-01 -9.02979612e-01 -4.09071356e-01
1.95618331e-01 3.69325250e-01 -1.91360891e-01 -4.99529280... | [9.492656707763672, -3.0228922367095947] |
7699a2cd-72b4-4d0f-b0f8-bbb2713a8e6a | single-image-lens-flare-removal | 2011.12485 | null | https://arxiv.org/abs/2011.12485v4 | https://arxiv.org/pdf/2011.12485v4.pdf | How to Train Neural Networks for Flare Removal | When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color bleeding, haze, etc.) and this diversity in appearance makes flare removal challenging. Existing analytical solutions make strong assumptions a... | ['Jonathan T. Barron', 'Ashok Veeraraghavan', 'Jiawen Chen', 'Rahul Garg', 'Tianfan Xue', 'Qiurui He', 'Yicheng Wu'] | 2020-11-25 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wu_How_To_Train_Neural_Networks_for_Flare_Removal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wu_How_To_Train_Neural_Networks_for_Flare_Removal_ICCV_2021_paper.pdf | iccv-2021-1 | ['flare-removal'] | ['computer-vision'] | [ 6.15045071e-01 -6.12589300e-01 5.76402903e-01 -1.57019421e-01
-3.28171998e-01 -1.10099363e+00 5.03237963e-01 -4.43498462e-01
3.15195054e-01 7.33002782e-01 2.49415889e-01 2.30969843e-02
-5.13040200e-02 -5.23815334e-01 -6.66900277e-01 -6.19082093e-01
2.13477686e-01 -7.13632256e-02 4.14694160e-01 -9.16687474... | [10.665538787841797, -2.977074384689331] |
04175314-55a2-4061-8e52-db1543731489 | dynamic-structural-brain-network-construction | 2305.10077 | null | https://arxiv.org/abs/2305.10077v1 | https://arxiv.org/pdf/2305.10077v1.pdf | Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN using T1-MRI | Constructing structural brain networks using T1-weighted magnetic resonance imaging (T1-MRI) presents a significant challenge due to the lack of direct regional connectivity information. Current methods with T1-MRI rely on predefined regions or isolated pretrained location modules to obtain atrophic regions, which negl... | ['Jian Zheng', 'Zheng Yanyan', 'Chen Bai', 'Wenju Cui', 'Yilin Leng'] | 2023-05-17 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 5.66074206e-03 7.20008016e-02 -6.34590164e-02 -4.41320866e-01
-2.16051087e-01 -3.98620993e-01 4.44689006e-01 -3.17265451e-01
-2.94806004e-01 5.22741437e-01 4.43967879e-01 4.99103330e-02
-6.54457569e-01 -8.32441092e-01 -5.16371667e-01 -6.23046219e-01
-2.99170107e-01 7.00307012e-01 3.44067812e-01 3.28588076... | [12.467683792114258, 3.3576953411102295] |
25998e78-c4d8-4661-b733-f4dd050c9d0c | facial-expression-video-generation-based-on-1 | 2210.11182 | null | https://arxiv.org/abs/2210.11182v1 | https://arxiv.org/pdf/2210.11182v1.pdf | Facial Expression Video Generation Based-On Spatio-temporal Convolutional GAN: FEV-GAN | Facial expression generation has always been an intriguing task for scientists and researchers all over the globe. In this context, we present our novel approach for generating videos of the six basic facial expressions. Starting from a single neutral facial image and a label indicating the desired facial expression, w... | ['Lahoucine Ballihi', 'Hamza Bouzid'] | 2022-10-20 | facial-expression-video-generation-based-on | https://www.sciencedirect.com/science/article/pii/S266730532200076X | https://doi.org/10.1016/j.iswa.2022.200139 | intelligent-systems-with-applications-2022-11 | ['video-generation', 'facial-expression-generation'] | ['computer-vision', 'computer-vision'] | [ 3.38007540e-01 1.00286298e-01 1.03914939e-01 -3.37970674e-01
-4.47887868e-01 -3.59490305e-01 6.65729403e-01 -7.95310974e-01
-8.84310976e-02 9.28322077e-01 1.64283112e-01 2.59986728e-01
2.99006581e-01 -6.69867337e-01 -7.10515618e-01 -1.14856279e+00
4.57205027e-02 -2.70621255e-02 -3.15820336e-01 -2.62738347... | [12.840998649597168, -0.07432319223880768] |
4610ab3d-3d6b-45f2-89a0-0eec97816d53 | extracting-temporal-event-relation-with | 2104.09570 | null | https://arxiv.org/abs/2104.09570v2 | https://arxiv.org/pdf/2104.09570v2.pdf | Extracting Temporal Event Relation with Syntax-guided Graph Transformer | Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that when the events of interest are far away in text, the context in-between often becomes complicated, making it challenging to resolve the temp... | ['Qiang Ning', 'Lifu Huang', 'Shuaicheng Zhang'] | 2021-04-19 | extracting-temporal-event-relation-with-1 | https://aclanthology.org/2022.findings-naacl.29 | https://aclanthology.org/2022.findings-naacl.29.pdf | findings-naacl-2022-7 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.95916035e-03 1.33964811e-02 -2.85978585e-01 -4.17093366e-01
-7.82770157e-01 -8.28873098e-01 7.36427724e-01 6.83578610e-01
-3.64272356e-01 4.68701184e-01 3.87823194e-01 -4.50112224e-01
-2.63320804e-01 -7.42452025e-01 -5.08313298e-01 -4.59177166e-01
-5.18059850e-01 5.55513322e-01 4.92846906e-01 -2.21996948... | [9.072601318359375, 9.124466896057129] |
e675773f-fa0b-480c-8e19-ca4179de5568 | idisc-internal-discretization-for-monocular | 2304.06334 | null | https://arxiv.org/abs/2304.06334v1 | https://arxiv.org/pdf/2304.06334v1.pdf | iDisc: Internal Discretization for Monocular Depth Estimation | Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of full geometric constraints. Although a scene can consist of millions of pixels, there are fewer high-level patterns. We pro... | ['Fisher Yu', 'Christos Sakaridis', 'Luigi Piccinelli'] | 2023-04-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Piccinelli_iDisc_Internal_Discretization_for_Monocular_Depth_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Piccinelli_iDisc_Internal_Discretization_for_Monocular_Depth_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['surface-normals-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.69985646e-01 2.75485724e-01 -1.32231697e-01 -3.78092170e-01
-6.29203022e-01 -3.16620499e-01 5.58645248e-01 -2.57023335e-01
-3.78921568e-01 4.81306314e-01 -7.85187483e-02 -2.09712535e-01
3.29966210e-02 -9.98741627e-01 -9.92338896e-01 -7.25943148e-01
1.60339683e-01 5.53123176e-01 4.14781421e-01 -1.75893173... | [8.575189590454102, -2.5651438236236572] |
90f4e4e7-45f7-4182-b4d9-197177a391ab | proof-of-swarm-based-ensemble-learning-for | 2212.14050 | null | https://arxiv.org/abs/2212.14050v2 | https://arxiv.org/pdf/2212.14050v2.pdf | Proof of Swarm Based Ensemble Learning for Federated Learning Applications | Ensemble learning combines results from multiple machine learning models in order to provide a better and optimised predictive model with reduced bias, variance and improved predictions. However, in federated learning it is not feasible to apply centralised ensemble learning directly due to privacy concerns. Hence, a m... | ['Shujun Li', 'Ludovic Koehl', 'Kim Phuc Tran', 'Ali Raza'] | 2022-12-28 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 9.38334912e-02 8.34130123e-02 1.95242181e-01 -3.53743911e-01
-3.92745733e-01 -3.87528062e-01 2.81422943e-01 7.05390275e-01
-3.51255685e-01 1.27842617e+00 -4.21090543e-01 -6.34466186e-02
-8.53510201e-01 -8.28106701e-01 -5.59971631e-01 -1.12127888e+00
-2.34385520e-01 8.15810740e-01 -3.94922346e-02 2.37791408... | [6.322873592376709, 6.287884712219238] |
e823675b-70fc-460f-b4e5-e0aee6295347 | cross-modal-information-fusion-for-voice | null | null | https://www.sciencedirect.com/science/article/pii/S0167639323000109 | https://www.sciencedirect.com/science/article/pii/S0167639323000109 | Cross-modal information fusion for voice spoofing detection | In recent years, speaker verification systems have been used in many production scenarios. Unfortunately, they are still very vulnerable to different kinds of spoofing attacks, such as speech synthesis attacks, replay attacks, etc. Researchers have proposed many methods to defend against these attacks, but in the exist... | ['Lei Shi', 'Bin Wu', 'Huawei Song', 'Hao Zhou', 'Junxiao Xue'] | 2023-02-01 | null | null | null | journal-2023-2 | ['fake-voice-detection', 'voice-anti-spoofing', 'speaker-verification', 'speech-synthesis'] | ['audio', 'audio', 'speech', 'speech'] | [ 3.45499218e-02 -3.09593767e-01 -4.06359211e-02 -2.99463212e-01
-3.33058655e-01 -2.27107838e-01 4.28102046e-01 6.86366707e-02
-3.13182741e-01 2.94193953e-01 2.77750671e-01 -3.36631298e-01
5.01971580e-02 -8.58232498e-01 -1.13097161e-01 -7.50758648e-01
1.42331734e-01 -2.06624418e-01 2.95732349e-01 -3.50330561... | [13.974494934082031, 5.748691082000732] |
718bb9dd-6a39-4075-9ec5-5413ac7b7922 | learning-to-learn-with-compound-hd-models | null | null | http://papers.nips.cc/paper/4474-learning-to-learn-with-compound-hd-models | http://papers.nips.cc/paper/4474-learning-to-learn-with-compound-hd-models.pdf | Learning to Learn with Compound HD Models | We introduce HD (or ``Hierarchical-Deep'') models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian models. Specifically we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a Deep Boltz... | ['Antonio Torralba', 'Ruslan R. Salakhutdinov', 'Joshua B. Tenenbaum'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['novel-concepts'] | ['reasoning'] | [-2.21644446e-01 2.23173738e-01 -1.54791594e-01 -7.99110115e-01
-5.26462853e-01 -1.66561473e-02 1.09612012e+00 2.13632241e-01
-6.68418467e-01 5.20654500e-01 4.39584523e-01 2.95275390e-01
-2.39765465e-01 -1.02659011e+00 -8.20070446e-01 -9.49097037e-01
-6.14852965e-01 9.48468089e-01 6.40459657e-01 3.17865849... | [9.263550758361816, 2.9338130950927734] |
9fddd881-a9fb-428c-9deb-9643a0ae435f | instance-variant-loss-with-gaussian-rbf | 2305.04239 | null | https://arxiv.org/abs/2305.04239v1 | https://arxiv.org/pdf/2305.04239v1.pdf | Instance-Variant Loss with Gaussian RBF Kernel for 3D Cross-modal Retriveal | 3D cross-modal retrieval is gaining attention in the multimedia community. Central to this topic is learning a joint embedding space to represent data from different modalities, such as images, 3D point clouds, and polygon meshes, to extract modality-invariant and discriminative features. Hence, the performance of cros... | ['Heng Tao Shen', 'Ning Xie', 'Zhenjiang Du', 'Guan Wang', 'Jiwei Wei', 'Zengyu Liu', 'Zhitao Liu'] | 2023-05-07 | null | null | null | null | ['cross-modal-retrieval'] | ['miscellaneous'] | [-6.16576895e-02 -5.67202628e-01 -2.67610908e-01 -2.72976816e-01
-1.10893607e+00 -7.86469460e-01 5.44750631e-01 3.87418181e-01
-2.91709036e-01 1.76501602e-01 9.41297039e-02 1.81464225e-01
-5.86869895e-01 -7.36002386e-01 -3.45972478e-01 -8.94655108e-01
3.39998752e-02 1.63762555e-01 1.07938908e-01 2.82895211... | [11.232954025268555, 1.0055433511734009] |
c2209e5a-d6a3-4b4f-aaeb-cfb652f506b1 | an-efficient-membership-inference-attack-for | 2305.18355 | null | https://arxiv.org/abs/2305.18355v1 | https://arxiv.org/pdf/2305.18355v1.pdf | An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization | Recently, diffusion models have achieved remarkable success in generating tasks, including image and audio generation. However, like other generative models, diffusion models are prone to privacy issues. In this paper, we propose an efficient query-based membership inference attack (MIA), namely Proximal Initialization... | ['Kaidi Xu', 'Xiaoshuang Shi', 'Xiaofeng Zhu', 'HengTao Shen', 'RuiPeng Ma', 'Jinhao Duan', 'Fei Kong'] | 2023-05-26 | null | null | null | null | ['inference-attack', 'audio-generation', 'membership-inference-attack'] | ['adversarial', 'audio', 'computer-vision'] | [ 4.46626283e-02 -2.34434884e-02 3.50417078e-01 -1.29494563e-01
-1.37535548e+00 -7.32330859e-01 7.41550267e-01 4.65553673e-03
-3.95592034e-01 5.53064764e-01 -1.12652242e-01 -5.37238002e-01
1.20025016e-02 -8.86253536e-01 -7.63660073e-01 -7.40398824e-01
-2.18237221e-01 1.57888770e-01 1.64171472e-01 9.80523378... | [5.767412185668945, 7.744274616241455] |
05823270-d435-40d9-8dd9-beb3e547e8a9 | sentitel-tabsa-for-twitter-reviews-on-uganda | null | null | https://aclanthology.org/2020.winlp-1.14 | https://aclanthology.org/2020.winlp-1.14.pdf | SentiTel: TABSA for Twitter reviews on Uganda Telecoms | In this paper, we present a fine-grained opinion mining dataset called SentiTel. SentiTel is human annotated for targeted aspect-based sentiment analysis (TABSA). SentiTel contains Twitter reviews about three major Ugandan telecoms posted in the period between February 2019 and September 2019. The dataset contains revi... | ['Joyce Nakatumba Nabende', 'David Kabiito'] | 2020-07-01 | null | null | null | ws-2020-7 | ['aspect-category-detection'] | ['natural-language-processing'] | [-6.45390823e-02 3.83715093e-01 -2.65523225e-01 -5.41893780e-01
-8.03656280e-01 -6.91763401e-01 9.62547958e-01 3.64355475e-01
-4.65328068e-01 6.52409673e-01 3.63949716e-01 -4.08350289e-01
2.17215374e-01 -6.95226610e-01 -1.98989376e-01 -5.76957285e-01
2.53073722e-01 5.53034604e-01 -2.32566092e-02 -6.96057320... | [11.204784393310547, 6.875819683074951] |
2424c359-0699-4056-a442-0f894ee8a569 | improving-the-quality-control-of-seismic-data | 2201.06616 | null | https://arxiv.org/abs/2201.06616v2 | https://arxiv.org/pdf/2201.06616v2.pdf | Improving the quality control of seismic data through active learning | In image denoising problems, the increasing density of available images makes an exhaustive visual inspection impossible and therefore automated methods based on machine-learning must be deployed for this purpose. This is particulary the case in seismic signal processing. Engineers/geophysicists have to deal with milli... | ['Stephan Clémençon', 'Emilie Chautru', 'Raphaël Butez', 'Mathieu Chambefort'] | 2022-01-17 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 0.4167132 0.12970562 0.31097716 -0.32914177 -1.3010716 -0.36437005
0.3799286 0.68533367 -0.96763456 0.7691945 -0.22107905 -0.2624063
-0.46270123 -0.83638024 -0.4399854 -1.2657846 -0.3610929 0.5408014
0.34571868 -0.04727588 0.5007102 0.6865145 -1.3646693 -0.07114991
1.0852562 1.0404596 0.37... | [7.965648651123047, 2.200573444366455] |
d7b1c497-6f52-46ae-b5a9-1ef644447860 | enhancing-next-active-object-based-egocentric | 2305.12953 | null | https://arxiv.org/abs/2305.12953v2 | https://arxiv.org/pdf/2305.12953v2.pdf | Enhancing Next Active Object-based Egocentric Action Anticipation with Guided Attention | Short-term action anticipation (STA) in first-person videos is a challenging task that involves understanding the next active object interactions and predicting future actions. Existing action anticipation methods have primarily focused on utilizing features extracted from video clips, but often overlooked the importan... | ['Alessio Del Bue', 'Vittorio Murino', 'Pietro Morerio', 'Cigdem Beyan', 'Sanket Thakur'] | 2023-05-22 | null | null | null | null | ['short-term-object-interaction-anticipation', 'action-anticipation'] | ['computer-vision', 'computer-vision'] | [ 2.86838174e-01 2.55783591e-02 -2.73231030e-01 -4.37492311e-01
-6.50387764e-01 -1.48264527e-01 6.37481451e-01 -2.49999419e-01
-3.52949679e-01 4.43028957e-01 8.79812837e-01 5.95899940e-01
-1.95147812e-01 -2.76731610e-01 -7.17606664e-01 -8.26427877e-01
-3.55032444e-01 4.34798658e-01 2.93810606e-01 1.56705469... | [8.291463851928711, 0.52166348695755] |
4e9ec6fd-4ed4-4791-a95a-426c274bd2d7 | gaussian-processes-for-music-audio-modelling | 1606.01039 | null | http://arxiv.org/abs/1606.01039v2 | http://arxiv.org/pdf/1606.01039v2.pdf | Gaussian Processes for Music Audio Modelling and Content Analysis | Real music signals are highly variable, yet they have strong statistical
structure. Prior information about the underlying physical mechanisms by which
sounds are generated and rules by which complex sound structure is constructed
(notes, chords, a complete musical score), can be naturally unified using
Bayesian modell... | ['Dan Stowell', 'Pablo A. Alvarado'] | 2016-06-03 | null | null | null | null | ['music-transcription'] | ['music'] | [ 4.22590554e-01 -2.70194739e-01 3.46588612e-01 2.10744977e-01
-9.46137607e-01 -8.72257054e-01 5.06116450e-01 -4.83024679e-02
-9.34278592e-02 4.85365212e-01 3.50002974e-01 1.91013440e-01
-6.66351795e-01 -2.43539289e-01 -3.85773718e-01 -9.17775989e-01
-2.52052337e-01 2.30025887e-01 2.14644179e-01 1.37931988... | [15.729720115661621, 5.486980438232422] |
b39d7f4b-05c1-4003-b1a2-5bea689d6b50 | lanns-a-web-scale-approximate-nearest | 2010.09426 | null | https://arxiv.org/abs/2010.09426v1 | https://arxiv.org/pdf/2010.09426v1.pdf | LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System | Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks(HNSW), is unable to scale to large datasets of 100M records... | ['Niranjan Balasubramanian', 'Rushi Bhatt', 'Rajeev Kumar', 'Ashish Bhutani', 'Dhritiman Das', 'Ishita Doshi'] | 2020-10-19 | null | null | null | null | ['2048'] | ['playing-games'] | [-5.54038346e-01 -5.70548475e-01 -3.68109465e-01 -5.89790106e-01
-9.67625558e-01 -6.66051090e-01 5.02015173e-01 5.45741618e-01
-6.31552517e-01 7.10173786e-01 2.98898786e-01 -4.65085596e-01
-9.54245329e-01 -1.29466283e+00 -4.84621406e-01 -6.88484758e-02
-5.93506336e-01 1.22714138e+00 9.34024632e-01 -1.08144999... | [8.615116119384766, 3.5770280361175537] |
640c4356-e733-43d4-be3c-83b109701810 | viena2-a-driving-anticipation-dataset | 1810.09044 | null | http://arxiv.org/abs/1810.09044v2 | http://arxiv.org/pdf/1810.09044v2.pdf | VIENA2: A Driving Anticipation Dataset | Action anticipation is critical in scenarios where one needs to react before
the action is finalized. This is, for instance, the case in automated driving,
where a car needs to, e.g., avoid hitting pedestrians and respect traffic
lights. While solutions have been proposed to tackle subsets of the driving
anticipation t... | ['Mohammad Sadegh Aliakbarian', 'Mathieu Salzmann', 'Lars Petersson', 'Lars Andersson', 'Fatemeh Sadat Saleh', 'Basura Fernando'] | 2018-10-22 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.79431331e-01 -1.15364596e-01 -5.53532243e-02 -6.58804059e-01
-6.19633377e-01 -3.39264601e-01 7.32783973e-01 -2.47775372e-02
-6.45740747e-01 6.79850757e-01 2.33807340e-01 -1.53003186e-01
-2.65717924e-01 -5.48803985e-01 -6.47707641e-01 -9.10223961e-01
-1.29654855e-01 3.43340248e-01 3.99275869e-01 -2.91219026... | [7.242404460906982, 0.29085931181907654] |
08842831-27bc-4e15-a2d0-507d758ac671 | deep-neural-networks-for-hdr-imaging | 1611.00591 | null | http://arxiv.org/abs/1611.00591v1 | http://arxiv.org/pdf/1611.00591v1.pdf | Deep Neural Networks for HDR imaging | We propose novel methods of solving two tasks using Convolutional Neural
Networks, firstly the task of generating HDR map of a static scene using
differently exposed LDR images of the scene captured using conventional cameras
and secondly the task of finding an optimal tone mapping operator that would
give a better sco... | ['Kshiteej Sheth'] | 2016-09-04 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.78739798e-01 9.61100608e-02 3.90526146e-01 -3.04582387e-01
-6.39603615e-01 -3.34262669e-01 4.87734824e-01 -7.89960623e-01
-4.11660314e-01 8.65132391e-01 1.47511393e-01 -2.25907013e-01
1.02694407e-01 -7.46106863e-01 -7.80403137e-01 -5.33226073e-01
-3.08488924e-02 3.65893811e-01 2.16825679e-01 -4.95489597... | [10.970787048339844, -2.287334680557251] |
72e06319-3338-4f42-8e92-5fa4eceba345 | ucphrase-unsupervised-context-aware-quality | 2105.14078 | null | https://arxiv.org/abs/2105.14078v1 | https://arxiv.org/pdf/2105.14078v1.pdf | UCPhrase: Unsupervised Context-aware Quality Phrase Tagging | Identifying and understanding quality phrases from context is a fundamental task in text mining. The most challenging part of this task arguably lies in uncommon, emerging, and domain-specific phrases. The infrequent nature of these phrases significantly hurts the performance of phrase mining methods that rely on suffi... | ['Jingbo Shang', 'Jiawei Han', 'Liyuan Liu', 'Yu Meng', 'Zhenyu Bi', 'Zihan Wang', 'Xiaotao Gu'] | 2021-05-28 | null | null | null | null | ['phrase-tagging', 'phrase-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.92062694e-01 -9.36311930e-02 -5.10315478e-01 -3.31784874e-01
-1.20392871e+00 -8.99838567e-01 3.03410888e-01 4.55704898e-01
-5.26479244e-01 7.92647779e-01 4.45050180e-01 -5.10306418e-01
-1.50308579e-01 -8.25505614e-01 -7.43266106e-01 -5.78463972e-01
1.20838419e-01 4.62607175e-01 9.11648497e-02 -3.10363531... | [10.8886137008667, 7.553292274475098] |
d9b6e13e-9cde-4a4e-9b9c-9151de6eea44 | geometry-aware-multi-task-learning-for | 2111.10882 | null | https://arxiv.org/abs/2111.10882v1 | https://arxiv.org/pdf/2111.10882v1.pdf | Geometry-Aware Multi-Task Learning for Binaural Audio Generation from Video | Binaural audio provides human listeners with an immersive spatial sound experience, but most existing videos lack binaural audio recordings. We propose an audio spatialization method that draws on visual information in videos to convert their monaural (single-channel) audio to binaural audio. Whereas existing approache... | ['Kristen Grauman', 'Ruohan Gao', 'Rishabh Garg'] | 2021-11-21 | null | null | null | null | ['audio-generation', 'room-impulse-response'] | ['audio', 'audio'] | [-5.23522981e-02 -6.36755645e-01 4.80689913e-01 1.28093185e-02
-1.31015456e+00 -7.24555671e-01 3.56473029e-01 -4.15839814e-02
1.25343859e-01 3.48067999e-01 8.46507430e-01 9.04876664e-02
-4.10924479e-02 -4.94992048e-01 -1.07825530e+00 -5.87716699e-01
-3.58228534e-01 -2.39131227e-01 3.08205068e-01 5.89924045... | [14.970193862915039, 5.095459461212158] |
1713c657-9b84-4b4d-a789-920bc093c466 | rt-track-robust-tricks-for-multi-pedestrian | 2303.09668 | null | https://arxiv.org/abs/2303.09668v1 | https://arxiv.org/pdf/2303.09668v1.pdf | Rt-Track: Robust Tricks for Multi-Pedestrian Tracking | Object tracking is divided into single-object tracking (SOT) and multi-object tracking (MOT). MOT aims to maintain the identities of multiple objects across a series of continuous video sequences. In recent years, MOT has made rapid progress. However, modeling the motion and appearance models of objects in complex scen... | ['Shan Zhao', 'Yang Yang', 'Limin Zhao', 'Mengzhen Li', 'Housheng Xie', 'Yunhua Jia', 'Yukuan Zhang'] | 2023-03-16 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-4.11397159e-01 -8.26031148e-01 2.90353242e-02 -1.20493710e-01
-3.16526294e-01 -3.78208518e-01 4.31215584e-01 -9.77461562e-02
-3.01169485e-01 6.03871107e-01 -1.17337205e-01 1.66212663e-01
2.67182128e-03 -3.78897160e-01 -6.95512414e-01 -8.64835203e-01
-1.29272621e-02 2.49791458e-01 7.73067355e-01 1.12737916... | [6.449484348297119, -2.022310495376587] |
41fc4f2d-70d7-4a7b-af45-333b5757422a | atrial-fibrillation-detection-and-ecg | 2011.06187 | null | https://arxiv.org/abs/2011.06187v1 | https://arxiv.org/pdf/2011.06187v1.pdf | Atrial Fibrillation Detection and ECG Classification based on CNN-BiLSTM | It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis. In this paper, we proposed, implemented, and compared an automated system using two different... | ['Weiheng Li', 'Jiacheng Wang'] | 2020-11-12 | null | null | null | null | ['ecg-classification', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 8.58052894e-02 -4.84904289e-01 3.90491188e-01 -1.24055028e-01
-8.78810287e-01 -2.99242079e-01 -2.45468497e-01 -6.40880018e-02
-2.48011023e-01 8.30797315e-01 -1.32832274e-01 -6.09073937e-01
-1.53736696e-01 -3.28753501e-01 -1.24043301e-01 -6.91147327e-01
-6.35425568e-01 -8.39796811e-02 -4.39884961e-01 3.19190979... | [14.311387062072754, 3.2938904762268066] |
a8b9297c-c97e-46d2-9031-3089ac4986a2 | hydraplus-net-attentive-deep-features-for | 1709.09930 | null | http://arxiv.org/abs/1709.09930v1 | http://arxiv.org/pdf/1709.09930v1.pdf | HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis | Pedestrian analysis plays a vital role in intelligent video surveillance and
is a key component for security-centric computer vision systems. Despite that
the convolutional neural networks are remarkable in learning discriminative
features from images, the learning of comprehensive features of pedestrians for
fine-grai... | ['Xiaogang Wang', 'Haiyu Zhao', 'Lu Sheng', 'Xihui Liu', 'Jing Shao', 'Shuai Yi', 'Maoqing Tian', 'Junjie Yan'] | 2017-09-28 | hydraplus-net-attentive-deep-features-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_HydraPlus-Net_Attentive_Deep_ICCV_2017_paper.pdf | iccv-2017-10 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-2.86089003e-01 -5.06071985e-01 1.08689994e-01 -5.75924456e-01
-2.55659282e-01 -9.67396982e-03 7.36939549e-01 3.43439984e-03
-6.64440572e-01 5.44245124e-01 4.57068264e-01 -2.98400316e-02
1.85725372e-02 -8.89763236e-01 -6.57548308e-01 -8.64896536e-01
-1.11514494e-01 -8.79366845e-02 4.97510165e-01 -3.18355173... | [14.523003578186035, 0.9606629610061646] |
240769e2-71ef-4b05-a243-c508c4a1a899 | reciprocal-feature-learning-via-explicit-and | 2105.06229 | null | https://arxiv.org/abs/2105.06229v2 | https://arxiv.org/pdf/2105.06229v2.pdf | Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition | Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. The implicit task plays as an auxiliary branch for complementing the sequential recognition. We design a two-... | ['Wenming Tan', 'Fei Wu', 'Wenqi Ren', 'Yi Niu', 'ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Hui Jiang'] | 2021-05-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.76671100e-01 -3.57410789e-01 -3.44489068e-01 -5.30725837e-01
-4.18791234e-01 -4.45645809e-01 7.63501048e-01 -6.58077821e-02
-6.81972563e-01 8.24331820e-01 9.94786061e-03 -3.66928309e-01
1.00712273e-02 -5.64949989e-01 -2.30374604e-01 -8.65316570e-01
3.15460414e-01 2.60532290e-01 2.62017697e-01 1.35438532... | [11.903766632080078, 2.1768527030944824] |
2cad59ab-f23b-463d-8ab6-1c5ccf2972a1 | rethinking-clustering-based-pseudo-labeling | 2209.13635 | null | https://arxiv.org/abs/2209.13635v1 | https://arxiv.org/pdf/2209.13635v1.pdf | Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning | The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. However, it often suffers from label inconsistency or limited diversity, which leads to poor per... | ['Ling Shao', 'Jianbing Shen', 'Xingping Dong'] | 2022-09-27 | null | null | null | null | ['unsupervised-few-shot-image-classification'] | ['computer-vision'] | [-3.29695567e-02 -1.48017257e-01 -6.25641465e-01 -4.44318920e-01
-8.55990350e-01 -6.18075371e-01 5.82726836e-01 2.47528866e-01
-4.83990252e-01 4.87202764e-01 3.93082276e-02 -6.50658309e-02
-3.21125209e-01 -5.35457850e-01 -3.64192456e-01 -1.03017163e+00
1.35145351e-01 4.66649204e-01 1.11673594e-01 3.57913114... | [9.382707595825195, 3.151479959487915] |
c3567c66-b2d2-4131-a151-954ff18ed73f | video-description-a-survey-of-methods | 1806.00186 | null | https://arxiv.org/abs/1806.00186v4 | https://arxiv.org/pdf/1806.00186v4.pdf | Video Description: A Survey of Methods, Datasets and Evaluation Metrics | Video description is the automatic generation of natural language sentences that describe the contents of a given video. It has applications in human-robot interaction, helping the visually impaired and video subtitling. The past few years have seen a surge of research in this area due to the unprecedented success of d... | ['Ajmal Mian', 'Nayyer Aafaq', 'Wei Liu', 'Syed Zulqarnain Gilani', 'Mubarak Shah'] | 2018-06-01 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 1.30308628e-01 -2.72608370e-01 -3.16692561e-01 -2.78040677e-01
-7.23197639e-01 -5.61031222e-01 1.06549180e+00 1.19217537e-01
-4.40117657e-01 8.31133306e-01 5.94251454e-01 2.56001294e-01
-1.57082662e-01 -2.73987859e-01 -2.92771429e-01 -5.84342241e-01
-1.40692383e-01 4.51164514e-01 2.44346216e-01 -2.49354675... | [10.577402114868164, 0.703292191028595] |
cd090cd2-8128-41cf-8275-70e561420d89 | mask-cnn-localizing-parts-and-selecting | 1605.06878 | null | http://arxiv.org/abs/1605.06878v1 | http://arxiv.org/pdf/1605.06878v1.pdf | Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition | Fine-grained image recognition is a challenging computer vision problem, due
to the small inter-class variations caused by highly similar subordinate
categories, and the large intra-class variations in poses, scales and
rotations. In this paper, we propose a novel end-to-end Mask-CNN model without
the fully connected l... | ['Chen-Wei Xie', 'Xiu-Shen Wei', 'Jianxin Wu'] | 2016-05-23 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 0.02622079 -0.23480462 -0.14804405 -0.5628798 -0.6910321 -0.4506332
0.52689284 0.01915467 -0.21490067 0.43192837 0.27019483 0.58224154
-0.35978687 -0.61532265 -0.5261732 -0.6524363 0.20408857 0.3785735
0.30043623 0.25268146 0.22725707 1.0663501 -1.920466 0.3742102
0.58236545 1.7369967 0.060... | [9.60496711730957, 2.0020620822906494] |
3b1c72cd-114c-4a4c-9c84-1538a9c2742f | benchmarks-for-corruption-invariant-person-re | 2111.00880 | null | https://arxiv.org/abs/2111.00880v2 | https://arxiv.org/pdf/2111.00880v2.pdf | Benchmarks for Corruption Invariant Person Re-identification | When deploying person re-identification (ReID) model in safety-critical applications, it is pivotal to understanding the robustness of the model against a diverse array of image corruptions. However, current evaluations of person ReID only consider the performance on clean datasets and ignore images in various corrupte... | ['Feng Zheng', 'Zhiqiang Wang', 'Minghui Chen'] | 2021-11-01 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [-1.24761432e-01 -5.19219160e-01 2.33712085e-02 -2.50914663e-01
-7.26304591e-01 -6.34564400e-01 7.00531006e-01 -2.06707090e-01
-5.53940415e-01 6.94864094e-01 5.53524852e-01 -3.13836522e-02
2.17768196e-02 -5.23602366e-01 -8.81354153e-01 -5.79008698e-01
-7.96441808e-02 4.98463325e-02 -3.67807895e-01 -2.57431835... | [14.642303466796875, 0.9346874952316284] |
d74231fe-86c4-466c-bc9a-ad962d65ccfd | occ3d-a-large-scale-3d-occupancy-prediction | 2304.14365 | null | https://arxiv.org/abs/2304.14365v2 | https://arxiv.org/pdf/2304.14365v2.pdf | Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving | Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semanti... | ['Huitong Yang', 'Yucheng Mao', 'Hang Zhao', 'Yilun Wang', 'Yue Wang', 'Longfei Yun', 'Tao Jiang', 'Xiaoyu Tian'] | 2023-04-27 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-1.51359662e-01 -5.56463525e-02 -1.76888525e-01 -3.89622360e-01
-7.00150251e-01 -3.45513225e-01 8.56440008e-01 2.21533682e-02
-3.67447615e-01 6.41576350e-01 3.05007458e-01 -1.56032071e-01
8.08403641e-02 -8.94077599e-01 -8.48869264e-01 -5.22024751e-01
-1.92006767e-01 8.75193179e-01 6.02539480e-01 -3.37184779... | [8.177175521850586, -2.4868249893188477] |
5b3b9696-b7e8-4944-b89c-a3e5af52e2c1 | one-class-learning-towards-generalized-voice-1 | 2010.13995 | null | https://arxiv.org/abs/2010.13995v1 | https://arxiv.org/pdf/2010.13995v1.pdf | One-class learning towards generalized voice spoofing detection | Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of... | ['Zhiyao Duan', 'Fei Jiang', 'You Zhang'] | 2020-10-27 | one-class-learning-towards-generalized-voice | https://arxiv.org/abs/2010.13995 | https://arxiv.org/pdf/2010.13995.pdf | null | ['voice-anti-spoofing'] | ['audio'] | [ 5.84549792e-02 -2.32783973e-01 -1.70640603e-01 -6.43471256e-02
-8.39406669e-01 -1.00930464e+00 6.05982244e-01 -1.21139780e-01
-9.18438062e-02 3.54698658e-01 3.52101654e-01 -8.47871602e-01
4.32844400e-01 -3.06659609e-01 -4.35371995e-01 -6.95240259e-01
5.82023337e-02 -1.63598247e-02 2.63502687e-01 -1.74353689... | [14.07840633392334, 5.87157678604126] |
ae40648d-bbc0-4486-96b0-0703cc0e5cd5 | multitrack-music-transformer-learning-long | 2207.06983 | null | https://arxiv.org/abs/2207.06983v4 | https://arxiv.org/pdf/2207.06983v4.pdf | Multitrack Music Transformer | Existing approaches for generating multitrack music with transformer models have been limited in terms of the number of instruments, the length of the music segments and slow inference. This is partly due to the memory requirements of the lengthy input sequences necessitated by existing representations. In this work, w... | ['Taylor Berg-Kirkpatrick', 'Julian McAuley', 'Shlomo Dubnov', 'Ke Chen', 'Hao-Wen Dong'] | 2022-07-14 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.43657541e-01 -3.21896374e-01 -4.26323153e-02 1.68351293e-01
-9.07508612e-01 -8.57466877e-01 3.24373543e-01 -8.78246799e-02
-1.91315427e-01 5.56424201e-01 4.39675331e-01 -4.88105156e-02
-4.19053406e-01 -6.99946165e-01 -4.83752429e-01 -5.52399457e-01
9.35697258e-02 6.58710778e-01 1.92109674e-01 -4.10487115... | [15.899904251098633, 5.4330573081970215] |
fc67732a-5dbd-4da4-828d-f0a8ffaa2ff1 | simplified-boardgames | 1606.02645 | null | http://arxiv.org/abs/1606.02645v2 | http://arxiv.org/pdf/1606.02645v2.pdf | Simplified Boardgames | We formalize Simplified Boardgames language, which describes a subclass of
arbitrary board games. The language structure is based on the regular
expressions, which makes the rules easily machine-processable while keeping the
rules concise and fairly human-readable. | ['Marek Szykuła', 'Jakub Kowalski', 'Jakub Sutowicz'] | 2016-06-08 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.06683505e-01 6.63519144e-01 -2.99840808e-01 -5.73593900e-02
8.92224562e-05 -9.68062878e-01 5.86644590e-01 -4.32955753e-03
-2.19156638e-01 9.11720753e-01 8.43838677e-02 -9.27032292e-01
-1.54523939e-01 -1.45029759e+00 -3.96362752e-01 -1.05631448e-01
-4.67353433e-01 4.79568928e-01 9.75018919e-01 -9.67823505... | [3.4427685737609863, 1.4646984338760376] |
15833bf7-33a8-4833-a9f0-d210a7e83137 | collecting-fluency-corrections-for-spoken | null | null | https://aclanthology.org/W17-5010 | https://aclanthology.org/W17-5010.pdf | Collecting fluency corrections for spoken learner English | We present crowdsourced collection of error annotations for transcriptions of spoken learner English. Our emphasis in data collection is on fluency corrections, a more complete correction than has traditionally been aimed for in grammatical error correction research (GEC). Fluency corrections require improvements to th... | ['Paula Buttery', 'Emma Flint', 'Andrew Caines'] | 2017-09-01 | null | null | null | ws-2017-9 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 1.33407339e-01 6.97757125e-01 4.64152575e-01 -6.52461588e-01
-9.86646533e-01 -3.81131887e-01 5.71393847e-01 6.85829520e-01
-1.04505825e+00 9.80444849e-01 1.17691648e+00 -2.87755877e-01
1.20457254e-01 -1.18609868e-01 -7.86087215e-01 1.76826894e-01
5.73236942e-01 5.37579656e-01 2.34903619e-01 -7.14131832... | [11.063887596130371, 10.72111701965332] |
caf44986-6540-45c8-801a-94d2dfd46c51 | solar-irradiance-forecasting-with-transformer | null | null | https://www.mdpi.com/2076-3417/12/17/8852 | https://www.mdpi.com/2076-3417/12/17/8852/pdf?version=1662438246 | Solar Irradiance Forecasting with Transformer Model | Solar energy is one of the most popular sources of renewable energy today. It is therefore essential to be able to predict solar power generation and adapt energy needs to these predictions. This paper uses the Transformer deep neural network model, in which the attention mechanism is typically applied in NLP or vision... | ['Iveta Dirgová Luptáková', 'Martin Kubovčík', 'Jiří Pospíchal'] | 2022-09-02 | null | null | null | mdpi-applied-sciences-2022-9 | ['solar-irradiance-forecasting'] | ['time-series'] | [-2.79807989e-02 -6.84406757e-02 5.23344129e-02 -2.19799966e-01
-1.64423943e-01 -5.31191051e-01 9.81613040e-01 -8.15415010e-03
-2.05810189e-01 1.20292783e+00 2.80385196e-01 -2.69475102e-01
-3.11919421e-01 -1.13813841e+00 -7.36778140e-01 -9.99996483e-01
1.13442928e-01 4.35947925e-02 -1.52995393e-01 1.13582827... | [6.253729820251465, 2.8142998218536377] |
4ccc1666-1de8-486f-8c18-269ddef9d3d6 | slot-order-matters-for-compositional-scene | 2206.01370 | null | https://arxiv.org/abs/2206.01370v2 | https://arxiv.org/pdf/2206.01370v2.pdf | Towards Improving the Generation Quality of Autoregressive Slot VAEs | Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations ("slots") from images, unconditional generation of scenes from slots has received less attention. This is primarily because ... | ['Anand Rangarajan', 'Sanjay Ranka', 'Pan He', 'Patrick Emami'] | 2022-06-03 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 7.74629354e-01 3.50191772e-01 -8.86565149e-02 -7.46763825e-01
-9.13522065e-01 -4.00121570e-01 1.06380939e+00 -2.14818746e-01
5.47339581e-02 5.98883331e-01 4.26655680e-01 -1.53399110e-01
-2.38621652e-01 -7.64434934e-01 -1.00943160e+00 -5.52708745e-01
1.39461428e-01 8.19509566e-01 2.11229995e-01 5.89094497... | [10.31635570526123, 0.1875665932893753] |
56370129-1198-4ad5-87f2-ecad7944ac1b | remote-atrial-fibrillation-burden-estimation | 2008.02228 | null | https://arxiv.org/abs/2008.02228v1 | https://arxiv.org/pdf/2008.02228v1.pdf | Remote atrial fibrillation burden estimation using deep recurrent neural network | The atrial fibrillation burden (AFB) is defined as the percentage of time spend in atrial fibrillation (AF) over a long enough monitoring period. Recent research has demonstrated the added prognosis value that becomes available by using the AFB as compared with the binary diagnosis. We evaluate, for the first time, the... | ['Joachim Behar', 'Yehoshua Y. Zeevi', 'Meyer Elbaz', 'Mandel Franck', 'Shany Biton', 'Julien Oster', 'Armand Chocron'] | 2020-08-05 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.54096082e-01 -2.33833909e-01 -2.31063843e-01 -4.91607100e-01
-9.03639615e-01 -7.19663322e-01 4.25832532e-02 2.04952389e-01
-3.65258098e-01 1.24601579e+00 1.94921196e-01 -7.99826443e-01
-6.70345783e-01 -6.54487729e-01 -2.48901442e-01 -6.57933295e-01
-8.45954835e-01 3.52524310e-01 -8.21729720e-01 3.23051304... | [14.3283109664917, 3.2525110244750977] |
65631162-5de7-42c9-a26a-ee2a80678089 | online-clustering-of-contextual-cascading | 1711.08594 | null | http://arxiv.org/abs/1711.08594v2 | http://arxiv.org/pdf/1711.08594v2.pdf | Online Clustering of Contextual Cascading Bandits | We consider a new setting of online clustering of contextual cascading
bandits, an online learning problem where the underlying cluster structure over
users is unknown and needs to be learned from a random prefix feedback. More
precisely, a learning agent recommends an ordered list of items to a user, who
checks the li... | ['Shuai Li'] | 2017-11-23 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-2.10873902e-01 1.40713885e-01 -6.44624829e-01 -2.27983803e-01
-8.77948284e-01 -9.60661709e-01 -9.70270336e-02 2.45398343e-01
-3.90124738e-01 8.82502913e-01 -9.42539051e-03 -5.67818642e-01
-5.30506551e-01 -5.80294549e-01 -1.07928479e+00 -8.90237987e-01
-6.42077267e-01 7.49498844e-01 5.00595905e-02 2.10191488... | [4.595046043395996, 3.383399724960327] |
b43387e3-9dcd-4fb7-877c-85c3755b4f5d | domain-adaptation-in-multilingual-and-multi-1 | 2205.07283 | null | https://arxiv.org/abs/2205.07283v1 | https://arxiv.org/pdf/2205.07283v1.pdf | Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification | Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a ... | ['Mihai Dascalu', 'Dumitru-Clementin Cercel', 'Răzvan-Alexandru Smădu', 'George-Eduard Zaharia'] | 2022-05-15 | domain-adaptation-in-multilingual-and-multi | https://aclanthology.org/2022.acl-long.6 | https://aclanthology.org/2022.acl-long.6.pdf | acl-2022-5 | ['lexical-complexity-prediction', 'complex-word-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.17153329e-01 -7.56046399e-02 -3.09241749e-02 -3.77158597e-02
-8.09597492e-01 -5.90989888e-01 9.51997817e-01 6.26548052e-01
-9.67460930e-01 6.60320520e-01 1.63245350e-01 -2.86679536e-01
-1.68555111e-01 -6.06307089e-01 -5.28866291e-01 -4.11016285e-01
4.69404399e-01 5.34711957e-01 3.32743861e-02 -3.94246429... | [10.356927871704102, 9.658768653869629] |
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