Christopher De Sa
According to our database^{1},
Christopher De Sa
authored at least 25 papers
between 2015 and 2019.
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at stanford.edu
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Bibliography
2019
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting.
Proceedings of the 36th International Conference on Machine Learning, 2019
SWALP : Stochastic Weight Averaging in Low Precision Training.
Proceedings of the 36th International Conference on Machine Learning, 2019
A Kernel Theory of Modern Data Augmentation.
Proceedings of the 36th International Conference on Machine Learning, 2019
Distributed Learning with Sublinear Communication.
Proceedings of the 36th International Conference on Machine Learning, 2019
A Formal Framework for Probabilistic Unclean Databases.
Proceedings of the 22nd International Conference on Database Theory, 2019
2018
A Twopronged Progress in Structured Dense Matrix Vector Multiplication.
Proceedings of the TwentyNinth Annual ACMSIAM Symposium on Discrete Algorithms, 2018
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.
Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing, 2018
Representation Tradeoffs for Hyperbolic Embeddings.
Proceedings of the 35th International Conference on Machine Learning, 2018
Minibatch Gibbs Sampling on Large Graphical Models.
Proceedings of the 35th International Conference on Machine Learning, 2018
Accelerated Stochastic Power Iteration.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018
2017
Incremental knowledge base construction using DeepDive.
VLDB J., 2017
Flipper: A Systematic Approach to Debugging Training Sets.
Proceedings of the 2nd Workshop on HumanIntheLoop Data Analytics, 2017
Gaussian Quadrature for Kernel Features.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
Understanding and Optimizing Asynchronous LowPrecision Stochastic Gradient Descent.
Proceedings of the 44th Annual International Symposium on Computer Architecture, 2017
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling.
Proceedings of the TwentySixth International Joint Conference on Artificial Intelligence, 2017
2016
DeepDive: Declarative Knowledge Base Construction.
SIGMOD Record, 2016
Data Programming: Creating Large Training Sets, Quickly.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling.
Proceedings of the 33nd International Conference on Machine Learning, 2016
Have abstraction and eat performance, too: optimized heterogeneous computing with parallel patterns.
Proceedings of the 2016 International Symposium on Code Generation and Optimization, 2016
Generating Configurable Hardware from Parallel Patterns.
Proceedings of the TwentyFirst International Conference on Architectural Support for Programming Languages and Operating Systems, 2016
2015
Incremental Knowledge Base Construction Using DeepDive.
PVLDB, 2015
Taming the Wild: A Unified Analysis of HogwildStyle Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015
Global Convergence of Stochastic Gradient Descent for Some Nonconvex Matrix Problems.
Proceedings of the 32nd International Conference on Machine Learning, 2015