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REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models

Nov 06, 2017

George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

Nov 06, 2017

George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

* NIPS 2017

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The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.

* V2: - reworded the last paragraph of Section 2 to clarify that the argmax is a sample from the normalized measure. - fixed notation in Algorithm 1. - fixed a typo in paragraph 2 of Section 5

* V2: - reworded the last paragraph of Section 2 to clarify that the argmax is a sample from the normalized measure. - fixed notation in Algorithm 1. - fixed a typo in paragraph 2 of Section 5

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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Mar 05, 2017

Chris J. Maddison, Andriy Mnih, Yee Whye Teh

Mar 05, 2017

Chris J. Maddison, Andriy Mnih, Yee Whye Teh

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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Oct 09, 2018

George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

Oct 09, 2018

George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

**Click to Read Paper**

Move Evaluation in Go Using Deep Convolutional Neural Networks

Apr 10, 2015

Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

Apr 10, 2015

Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

* Minor edits and included captures in Figure 2

**Click to Read Paper**

Hamiltonian Descent Methods

Sep 13, 2018

Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet

Sep 13, 2018

Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet

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Hierarchical Representations with Poincaré Variational Auto-Encoders

Jan 17, 2019

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

Jan 17, 2019

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

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Particle Value Functions

Mar 16, 2017

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

Mar 16, 2017

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

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Tighter Variational Bounds are Not Necessarily Better

Jun 25, 2018

Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

Jun 25, 2018

Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

* To appear at ICML 2018

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Filtering Variational Objectives

Nov 12, 2017

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh

Nov 12, 2017

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh

**Click to Read Paper**

Conditional Neural Processes

Jul 04, 2018

Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami

Jul 04, 2018

Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami

**Click to Read Paper**