LEARNING DISCRETE STRUCTURED VARIATIONAL AUTO-ENCODER USING NATURAL EVOLUTION STRATEGIES

Alon Berliner, Guy Rotman, Yossi Adi, Roi Reichart, Tamir Hazan

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and propagating gradients through the relevant structures often requires enumerating over an exponentially large latent space. Recently, various approaches were devised to propagate approximated gradients without enumerating over the space of possible structures. In this work, we use Natural Evolution Strategies (NES), a class of gradient-free black-box optimization algorithms, to learn discrete structured VAEs. The NES algorithms are computationally appealing as they estimate gradients with forward pass evaluations only, thus they do not require to propagate gradients through their discrete structures. We demonstrate empirically that optimizing discrete structured VAEs using NES is as effective as gradient-based approximations. Lastly, we prove NES converges for non-Lipschitz functions as appear in discrete structured VAEs.

Original languageEnglish
StatePublished - 2022
Externally publishedYes
Event10th International Conference on Learning Representations, ICLR 2022 - Virtual, Online
Duration: 25 Apr 202229 Apr 2022

Conference

Conference10th International Conference on Learning Representations, ICLR 2022
CityVirtual, Online
Period25/04/2229/04/22

Bibliographical note

Publisher Copyright:
© 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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