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Christopher De Sa
Christopher De Sa
Assistant Professor of Computer Science, Cornell University
Verified email at cs.cornell.edu - Homepage
Title
Cited by
Cited by
Year
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
7702016
Representation tradeoffs for hyperbolic embeddings
F Sala, C De Sa, A Gu, C Ré
International conference on machine learning, 4460-4469, 2018
3922018
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
3082019
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
3012015
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
2022015
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
1972015
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
1872019
Channel gating neural networks
W Hua, Y Zhou, CM De Sa, Z Zhang, GE Suh
Advances in Neural Information Processing Systems, 1884-1894, 2019
1822019
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
1752017
High-accuracy low-precision training
C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ...
arXiv preprint arXiv:1803.03383, 2018
1242018
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
1132016
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
1072016
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
1062021
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
992016
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
952019
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
882017
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
802018
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
682020
Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns
KJ Brown, HJ Lee, T Rompf, AK Sujeeth, C De Sa, C Aberger, K Olukotun
Proceedings of the 2016 International Symposium on Code Generation and …, 2016
662016
Optimal complexity in decentralized training
Y Lu, C De Sa
International Conference on Machine Learning, 7111-7123, 2021
642021
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