Christopher De Sa
Christopher De Sa
Assistant Professor of Computer Science, Cornell University
Verified email at cs.cornell.edu - Homepage
TitleCited byYear
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
1962016
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
1822015
Global convergence of stochastic gradient descent for some non-convex matrix problems
C De Sa, K Olukotun, C Ré
arXiv preprint arXiv:1411.1134, 2014
1352014
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
992015
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
712017
Representation tradeoffs for hyperbolic embeddings
C De Sa, A Gu, C Ré, F Sala
Proceedings of machine learning research 80, 4460, 2018
622018
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
442016
High-accuracy low-precision training
C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ...
arXiv preprint arXiv:1803.03383, 2018
402018
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
402016
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
382016
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
312016
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
292017
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
292016
Gaussian quadrature for kernel features
T Dao, CM De Sa, C Ré
Advances in neural information processing systems, 6107-6117, 2017
152017
Incremental knowledge base construction using DeepDive
C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
The VLDB Journal 26 (1), 81-105, 2017
142017
Socratic learning: Augmenting generative models to incorporate latent subsets in training data
P Varma, B He, D Iter, P Xu, R Yu, C De Sa, C Ré
arXiv preprint arXiv:1610.08123, 2016
142016
Scan order in Gibbs sampling: Models in which it matters and bounds on how much
BD He, CM De Sa, I Mitliagkas, C Ré
Advances in neural information processing systems, 1-9, 2016
132016
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
122019
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
122019
A formal framework for probabilistic unclean databases
C De Sa, IF Ilyas, B Kimelfeld, C Ré, T Rekatsinas
arXiv preprint arXiv:1801.06750, 2018
122018
The system can't perform the operation now. Try again later.
Articles 1–20