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06/25/2019
Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at ou...
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06/06/2018
Universal Conditional Machine
We propose a single neural probabilistic model based on variational auto...
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05/22/2018
Implicit Reparameterization Gradients
By providing a simple and efficient way of computing low-variance gradie...
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01/05/2018
Tensor Train decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learn...
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12/01/2017
Probabilistic Adaptive Computation Time
We present a probabilistic model with discrete latent variables that con...
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12/07/2016
Spatially Adaptive Computation Time for Residual Networks
This paper proposes a deep learning architecture based on Residual Netwo...
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04/30/2015