George Tucker

According to our database1, George Tucker authored at least 21 papers between 2013 and 2019.

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Bibliography

2019
On Variational Bounds of Mutual Information.
Proceedings of the 36th International Conference on Machine Learning, 2019

Guided evolutionary strategies: augmenting random search with surrogate gradients.
Proceedings of the 36th International Conference on Machine Learning, 2019

The Laplacian in RL: Learning Representations with Efficient Approximations.
Proceedings of the 7th International Conference on Learning Representations, 2019

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives.
Proceedings of the 7th International Conference on Learning Representations, 2019

Understanding Posterior Collapse in Generative Latent Variable Models.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Revisiting Auxiliary Latent Variables in Generative Models.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

2018
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Smoothed Action Value Functions for Learning Gaussian Policies.
Proceedings of the 35th International Conference on Machine Learning, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling.
Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Hard Alignments with Variational Inference.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.
Proceedings of the 5th International Conference on Learning Representations, 2017

Regularizing Neural Networks by Penalizing Confident Output Distributions.
Proceedings of the 5th International Conference on Learning Representations, 2017

Particle Value Functions.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Max-pooling loss training of long short-term memory networks for small-footprint keyword spotting.
Proceedings of the 2016 IEEE Spoken Language Technology Workshop, 2016

Model Compression Applied to Small-Footprint Keyword Spotting.
Proceedings of the Interspeech 2016, 2016

2014
Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach.
BMC Systems Biology, 2014

2013
A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis.
BMC Bioinformatics, 2013


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