Članki z zahtevami za javni dostop - Sen WuVeč o tem
Na voljo nekje: 15
Snorkel: Rapid training data creation with weak supervision
A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré
Proceedings of the VLDB Endowment 11 (3), 269-282, 2017
Zahteve: US Department of Energy, US Department of Defense, US National Institutes of …
Data programming: Creating large training sets, quickly
AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré
Advances in neural information processing systems 29, 3567-3575, 2016
Zahteve: US National Science Foundation, US Department of Energy, US National …
Snorkel: Rapid training data creation with weak supervision
A Ratner, SH Bach, H Ehrenberg, J Fries, S Wu, C Ré
The VLDB Journal 29 (2-3), 709-730, 2020
Zahteve: US Department of Energy, US Department of Defense, US National Institutes of …
Incremental knowledge base construction using deepdive
J Shin, S Wu, F Wang, C De Sa, C Zhang, C Ré
Proceedings of the VLDB Endowment International Conference on Very Large …, 2015
Zahteve: US National Institutes of Health
DeepDive: Declarative Knowledge Base Construction
C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
ACM SIGMOD Record 45 (1), 60-67, 2016
Zahteve: US National Science Foundation, US National Institutes of Health
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
Zahteve: US National Science Foundation, US Department of Energy, US Department of …
DeepDive: declarative knowledge base construction
C Zhang, C Ré, M Cafarella, C De Sa, A Ratner, J Shin, F Wang, S Wu
Communications of the ACM 60 (5), 93-102, 2017
Zahteve: US National Science Foundation, US Department of Defense, US National …
On the generalization effects of linear transformations in data augmentation
S Wu, H Zhang, G Valiant, C Ré
International Conference on Machine Learning, 10410-10420, 2020
Zahteve: US National Science Foundation, US Department of Energy, US Department of …
Transfer Learning to Infer Social Ties across Heterogeneous Networks
J Tang, T Lou, J Kleinberg, S Wu
ACM Transactions on Information Systems (TOIS) 34 (2), 7, 2016
Zahteve: US National Science Foundation, National Natural Science Foundation of China
Slice-based learning: A programming model for residual learning in critical data slices
V Chen, S Wu, AJ Ratner, J Weng, C Ré
Advances in Neural Information Processing Systems, 9392-9402, 2019
Zahteve: US National Science Foundation, US Department of Defense, US National …
Ivy: Instrumental variable synthesis for causal inference
Z Kuang, F Sala, N Sohoni, S Wu, A Córdova-Palomera, J Dunnmon, ...
International Conference on Artificial Intelligence and Statistics, 398-410, 2020
Zahteve: US National Science Foundation, US Department of Defense, US National …
Understanding the downstream instability of word embeddings
M Leszczynski, A May, J Zhang, S Wu, C Aberger, C Ré
Proceedings of Machine Learning and Systems 2, 262-290, 2020
Zahteve: US National Science Foundation, US Department of Defense, US National …
Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task Learning
L Hsiao, S Wu, N Chiang, C Ré, P Levis
ACM Transactions on Embedded Computing Systems (TECS) 19 (6), 1-26, 2020
Zahteve: US National Science Foundation, US Department of Defense, US National …
Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging
SM Hooper, S Wu, RH Davies, A Bhuva, EB Schelbert, JC Moon, ...
Journal of Medical Imaging 10 (2), 024007-024007, 2023
Zahteve: US National Science Foundation, US Department of Defense, US National …
Automating the Generation of Hardware Component Knowledge Bases
L Hsiao, S Wu, N Chiang, C Ré, P Levis
Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on …, 2019
Zahteve: US National Science Foundation, US Department of Defense, US National …
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