Stebėti
Christopher De Sa
Christopher De Sa
Assistant Professor of Computer Science, Cornell University
Patvirtintas el. paštas cs.cornell.edu - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
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, 2016
8212016
Representation tradeoffs for hyperbolic embeddings
F Sala, C De Sa, A Gu, C Ré
International conference on machine learning, 4460-4469, 2018
4362018
Improving neural network quantization without retraining using outlier channel splitting
R Zhao, Y Hu, J Dotzel, C De Sa, Z Zhang
International conference on machine learning, 7543-7552, 2019
3312019
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
3032015
Taming the wild: A unified analysis of hogwild-style algorithms
CM De Sa, C Zhang, K Olukotun, C Ré
Advances in neural information processing systems 28, 2015
2062015
A kernel theory of modern data augmentation
T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré
International conference on machine learning, 1528-1537, 2019
2052019
Channel gating neural networks
W Hua, Y Zhou, CM De Sa, Z Zhang, GE Suh
Advances in Neural Information Processing Systems, 1884-1894, 2019
2002019
Global convergence of stochastic gradient descent for some non-convex matrix problems
C De Sa, C Re, K Olukotun
International conference on machine learning, 2332-2341, 2015
1992015
Understanding and optimizing asynchronous low-precision stochastic gradient descent
C De Sa, M Feldman, C Ré, K Olukotun
Proceedings of the 44th annual international symposium on computer …, 2017
1832017
High-accuracy low-precision training
C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ...
arXiv preprint arXiv:1803.03383, 2018
1262018
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
1192016
Pipemare: Asynchronous pipeline parallel dnn training
B Yang, J Zhang, J Li, C Ré, C Aberger, C De Sa
Proceedings of Machine Learning and Systems 3, 269-296, 2021
1182021
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
1152016
SWALP: Stochastic weight averaging in low precision training
G Yang, T Zhang, P Kirichenko, J Bai, AG Wilson, C De Sa
International Conference on Machine Learning, 7015-7024, 2019
1032019
Generating configurable hardware from parallel patterns
R Prabhakar, D Koeplinger, KJ Brown, HJ Lee, C De Sa, C Kozyrakis, ...
Acm Sigplan Notices 51 (4), 651-665, 2016
1012016
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
902017
Accelerated stochastic power iteration
P Xu, B He, C De Sa, I Mitliagkas, C Re
International Conference on Artificial Intelligence and Statistics, 58-67, 2018
892018
Differentiating through the fréchet mean
A Lou, I Katsman, Q Jiang, S Belongie, SN Lim, C De Sa
International conference on machine learning, 6393-6403, 2020
782020
Optimal complexity in decentralized training
Y Lu, C De Sa
International conference on machine learning, 7111-7123, 2021
742021
Neural manifold ordinary differential equations
A Lou, D Lim, I Katsman, L Huang, Q Jiang, SN Lim, CM De Sa
Advances in Neural Information Processing Systems 33, 17548-17558, 2020
722020
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Straipsniai 1–20