# Justin Gilmer

According to our database

Collaborative distances:

^{1}, Justin Gilmer authored at least 13 papers between 2013 and 2018.Collaborative distances:

## Timeline

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## Bibliography

2018

Sanity Checks for Saliency Maps.

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV).

Proceedings of the 35th International Conference on Machine Learning, 2018

Adversarial Spheres.

Proceedings of the 6th International Conference on Learning Representations, 2018

Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values.

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

A Communication Game Related to the Sensitivity Conjecture.

Theory of Computing, 2017

SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Neural Message Passing for Quantum Chemistry.

Proceedings of the 34th International Conference on Machine Learning, 2017

Input Switched Affine Networks: An RNN Architecture Designed for Interpretability.

Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Information Propagation.

Proceedings of the 5th International Conference on Learning Representations, 2017

Explaining the Learning Dynamics of Direct Feedback Alignment.

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

A local central limit theorem for triangles in a random graph.

Random Struct. Algorithms, 2016

2015

A New Approach to the Sensitivity Conjecture.

Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015

2013

Composition Limits and Separating Examples for Some Boolean Function Complexity Measures.

Proceedings of the 28th Conference on Computational Complexity, 2013