Dieterich Lawson | DeepAI

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∙ 08/28/2023

NAS-X: Neural Adaptive Smoothing via Twisting

We present Neural Adaptive Smoothing via Twisting (NAS-X), a method for ...

0 Dieterich Lawson, et al. ∙

∙ 10/09/2021

Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?

Posterior predictive distributions quantify uncertainties ignored by poi...

0 Ian Osband, et al. ∙

∙ 10/31/2019

Energy-Inspired Models: Learning with Sampler-Induced Distributions

Energy-based models (EBMs) are powerful probabilistic models, but suffer...

18 Dieterich Lawson, et al. ∙

∙ 10/09/2018

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Deep latent variable models have become a popular model choice due to th...

18 George Tucker, et al. ∙

∙ 06/16/2017

An online sequence-to-sequence model for noisy speech recognition

Generative models have long been the dominant approach for speech recogn...

0 Chung-Cheng Chiu, et al. ∙

∙ 05/25/2017

Filtering Variational Objectives

When used as a surrogate objective for maximum likelihood estimation in ...

0 Chris J. Maddison, et al. ∙

∙ 05/16/2017

Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models fo...

0 Dieterich Lawson, et al. ∙

∙ 03/21/2017

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

Learning in models with discrete latent variables is challenging due to ...

0 George Tucker, et al. ∙

∙ 02/24/2017

Changing Model Behavior at Test-Time Using Reinforcement Learning

Machine learning models are often used at test-time subject to constrain...

0 Augustus Odena, et al. ∙

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