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

, , , and . (2021)cite arxiv:2110.01515Comment: Accepted as a survey article in IEEE TPAMI.

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Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator., , and . CoRR, (2020)Learning with and for discrete optimization.. ETH Zurich, Zürich, Switzerland, (2023)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/629004).Gradient Estimation with Stochastic Softmax Tricks., , , , and . NeurIPS, (2020)Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs., , , , , and . NeurIPS, (2022)Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 17584-17600. PMLR, (2022)Augment with Care: Contrastive Learning for Combinatorial Problems., , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 5627-5642. PMLR, (2022)Learning to Predict Security Constraints for Large-Scale Unit Commitment Problems., , , and . ISGT EUROPE, page 1-5. IEEE, (2023)Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem., , , , and . CoRR, (2022)Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs., , , , , and . CoRR, (2022)A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning., , , and . CoRR, (2021)