受强制性开放获取政策约束的文章 - Theodoros Rekatsinas了解详情
无法在其他位置公开访问的文章：1 篇
Forecasting rare disease outbreaks from open source indicators
T Rekatsinas, S Ghosh, SR Mekaru, EO Nsoesie, JS Brownstein, L Getoor, ...
Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (2 …, 2017
强制性开放获取政策： US Office of the Director of National Intelligence
可在其他位置公开访问的文章：15 篇
Deep learning for entity matching: A design space exploration
S Mudgal, H Li, T Rekatsinas, AH Doan, Y Park, G Krishnan, R Deep, ...
Proceedings of the 2018 international conference on management of data, 19-34, 2018
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Holodetect: Few-shot learning for error detection
A Heidari, J McGrath, IF Ilyas, T Rekatsinas
Proceedings of the 2019 International Conference on Management of Data, 829-846, 2019
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Fonduer: Knowledge base construction from richly formatted data
S Wu, L Hsiao, X Cheng, B Hancock, T Rekatsinas, P Levis, C Ré
Proceedings of the 2018 international conference on management of data, 1301 …, 2018
强制性开放获取政策： US National Science Foundation, US Department of Energy, US Department of …
Slimfast: Guaranteed results for data fusion and source reliability
T Rekatsinas, M Joglekar, H Garcia-Molina, A Parameswaran, C Ré
Proceedings of the 2017 ACM International Conference on Management of Data …, 2017
强制性开放获取政策： US Department of Defense, Gordon and Betty Moore Foundation
Marius: Learning massive graph embeddings on a single machine
J Mohoney, R Waleffe, H Xu, T Rekatsinas, S Venkataraman
15th {USENIX} Symposium on Operating Systems Design and Implementation …, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
Marius++: Large-scale training of graph neural networks on a single machine
R Waleffe, J Mohoney, T Rekatsinas, S Venkataraman
arXiv preprint arXiv:2202.02365 8, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense
A statistical perspective on discovering functional dependencies in noisy data
Y Zhang, Z Guo, T Rekatsinas
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
强制性开放获取政策： US National Science Foundation, US Department of Defense
Picket: guarding against corrupted data in tabular data during learning and inference
Z Liu, Z Zhou, T Rekatsinas
The VLDB Journal 31 (5), 927-955, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense
On robust mean estimation under coordinate-level corruption
Z Liu, JH Park, T Rekatsinas, C Tzamos
International Conference on Machine Learning, 6914-6924, 2021
强制性开放获取政策： US National Science Foundation
Approximate inference in structured instances with noisy categorical observations
A Heidari, IF Ilyas, T Rekatsinas
Uncertainty in Artificial Intelligence, 412-421, 2020
强制性开放获取政策： US National Science Foundation, Natural Sciences and Engineering Research …
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis
Y Zhang, Y Chronis, JM Patel, T Rekatsinas
Proceedings of the VLDB Endowment 16 (11), 2962-2975, 2023
强制性开放获取政策： US National Science Foundation, US Department of Defense
Demo of marius: a system for large-scale graph embeddings
A Xie, A Carlsson, J Mohoney, R Waleffe, S Peters, T Rekatsinas, ...
Proceedings of the VLDB Endowment 14 (12), 2759-2762, 2021
强制性开放获取政策： US National Science Foundation, US Department of Defense
CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction
T Rekatsinas, A Deshpande, A Parameswaran
Proceedings of the 28th ACM International Conference on Information and …, 2019
强制性开放获取政策： US National Science Foundation
Tractable probabilistic reasoning through effective grounding
E Augustine, T Rekatsinas, L Getoor
Third ICML workshop on Tractable Probabilistic Modeling, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Unsupervised Functional Dependency Discovery for Data Preparation
Z Guo, T Rekatsinas
强制性开放获取政策： US National Science Foundation
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