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|a 10.1109/TPAMI.2022.3157042
|2 doi
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Huijben, Iris A M
|e verfasserin
|4 aut
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| 245 |
1 |
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|a A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning
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| 264 |
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|c 2023
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 06.04.2023
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|a Date Revised 06.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by its unnormalized (log-)probabilities. Over the past years, the machine learning community has proposed several extensions of this trick to facilitate, e.g., drawing multiple samples, sampling from structured domains, or gradient estimation for error backpropagation in neural network optimization. The goal of this survey article is to present background about the Gumbel-max trick, and to provide a structured overview of its extensions to ease algorithm selection. Moreover, it presents a comprehensive outline of (machine learning) literature in which Gumbel-based algorithms have been leveraged, reviews commonly-made design choices, and sketches a future perspective
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| 650 |
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|a Journal Article
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| 700 |
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|a Kool, Wouter
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Paulus, Max B
|e verfasserin
|4 aut
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| 700 |
1 |
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|a van Sloun, Ruud J G
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 2 vom: 07. Feb., Seite 1353-1371
|w (DE-627)NLM098212257
|x 1939-3539
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| 773 |
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|g volume:45
|g year:2023
|g number:2
|g day:07
|g month:02
|g pages:1353-1371
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|u http://dx.doi.org/10.1109/TPAMI.2022.3157042
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