Christopher De Sa

According to our database1, Christopher De Sa authored at least 25 papers between 2015 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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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 Two-pronged Progress in Structured Dense Matrix Vector Multiplication.
Proceedings of the Twenty-Ninth Annual ACM-SIAM 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 Human-In-the-Loop 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 Low-Precision 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 Twenty-Sixth 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 Twenty-First 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 Hogwild-Style 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 Non-convex Matrix Problems.
Proceedings of the 32nd International Conference on Machine Learning, 2015


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