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
A Ratner, C De Sa, S Wu, D Selsam, C Ré
Advances in neural information processing systems 29, 3567, 2016
3272016
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
2122015
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
1582015
Taming the wild: A unified analysis of hogwild!-style algorithms
C De Sa, C Zhang, K Olukotun, C Ré
Advances in neural information processing systems 28, 2656, 2015
1392015
Representation tradeoffs for hyperbolic embeddings
F Sala, C De Sa, A Gu, C Ré
International conference on machine learning, 4460-4469, 2018
1252018
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
1112017
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
682019
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
682016
High-accuracy low-precision training
C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ...
arXiv preprint arXiv:1803.03383, 2018
652018
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
582016
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
562016
Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns
KJ Brown, HJ Lee, T Romp, AK Sujeeth, C De Sa, C Aberger, K Olukotun
2016 IEEE/ACM International Symposium on Code Generation and Optimization …, 2016
552016
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
482019
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
412017
Ensuring rapid mixing and low bias for asynchronous Gibbs sampling
C De Sa, C Re, K Olukotun
International Conference on Machine Learning, 1567-1576, 2016
412016
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
402018
Channel gating neural networks
W Hua, Y Zhou, CM De Sa, Z Zhang, GE Suh
Advances in Neural Information Processing Systems, 1884-1894, 2019
322019
A formal framework for probabilistic unclean databases
C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas
arXiv preprint arXiv:1801.06750, 2018
262018
Gaussian quadrature for kernel features
T Dao, C De Sa, C Ré
Advances in neural information processing systems 30, 6109, 2017
222017
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
212019
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