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, 3567-3575, 2016
2632016
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
2002015
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
1432015
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, 2674-2682, 2015
1212015
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
942017
Representation tradeoffs for hyperbolic embeddings
C De Sa, A Gu, C Ré, F Sala
Proceedings of machine learning research 80, 4460, 2018
912018
High-accuracy low-precision training
C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ...
arXiv preprint arXiv:1803.03383, 2018
532018
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
522016
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
492016
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
462016
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
452016
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
362017
Ensuring rapid mixing and low bias for asynchronous Gibbs sampling
C De Sa, K Olukotun, C Ré
JMLR workshop and conference proceedings 48, 1567, 2016
352016
Accelerated stochastic power iteration
C De Sa, B He, I Mitliagkas, C Ré, P Xu
Proceedings of machine learning research 84, 58, 2018
332018
Improving neural network quantization without retraining using outlier channel splitting
R Zhao, Y Hu, J Dotzel, C De Sa, Z Zhang
arXiv preprint arXiv:1901.09504, 2019
302019
A kernel theory of modern data augmentation
T Dao, A Gu, AJ Ratner, V Smith, C De Sa, C Ré
Proceedings of machine learning research 97, 1528, 2019
232019
Swalp: Stochastic weight averaging in low-precision training
G Yang, T Zhang, P Kirichenko, J Bai, AG Wilson, C De Sa
arXiv preprint arXiv:1904.11943, 2019
202019
The convergence of stochastic gradient descent in asynchronous shared memory
D Alistarh, C De Sa, N Konstantinov
Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing …, 2018
192018
Learnable structured clustering framework for deep metric learning
HO Song, S Jegelka, V Rathod, K Murphy
arXiv preprint arXiv:1612.01213 1 (2), 8, 2016
172016
A formal framework for probabilistic unclean databases
C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas
arXiv preprint arXiv:1801.06750, 2018
162018
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