A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning-Reference-Cited by-同舟云学术

A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning

Published:2023-02-01 Issue:2 Volume:45 Page:1353-1371
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Huijben Iris A. M.¹^ORCID,Kool Wouter²^ORCID,Paulus Max B.³,van Sloun Ruud J. G.¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

2. Amsterdam Machine Learning Lab (AMLab), University of Amsterdam, Amsterdam, The Netherlands

3. Department of Computer Science, ETH, Zürich, Switzerland

Funder

Onera Health and the Project

European Regional Development Fund

ORTEC

Max Planck ETH Center for Learning Systems

Sustainable Chemical Processes through Catalysis

National Center of Competence in Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10008914/09729603.pdf?arnumber=9729603

Reference171 articles.

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4. Relgan: Relational generative adversarial networks for text generation;nie;Proc Int Conf Learn Representations,2019

5. GANS for sequences of discrete elements with the gumbel-softmax distribution;kusner,2016

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