2021
DOI: 10.48550/arxiv.2110.01515
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

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

Abstract: 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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
6
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(6 citation statements)
references
References 70 publications
0
6
0
Order By: Relevance
“…This allows us to obtain the REINFORCE estimator (Williams 1992), an unbiased estimator of the expectation but does not change the complexity of the method which stays linear in the catalog size. Indeed, sampling needs the computation of Z θ (x i ) or can be done with the gumbel trick (Huijben et al 2021) which both scale in O(P ). To lower the time complexity, we need to avoid sampling directly from π θ and use Monte Carlo techniques instead such as importance sampling (Chopin and Papaspiliopoulos 2020) with carefully chosen proposals to achieve fast sampling and accurate gradient approximation.…”
Section: Optimizing the Objectivementioning
confidence: 99%
“…This allows us to obtain the REINFORCE estimator (Williams 1992), an unbiased estimator of the expectation but does not change the complexity of the method which stays linear in the catalog size. Indeed, sampling needs the computation of Z θ (x i ) or can be done with the gumbel trick (Huijben et al 2021) which both scale in O(P ). To lower the time complexity, we need to avoid sampling directly from π θ and use Monte Carlo techniques instead such as importance sampling (Chopin and Papaspiliopoulos 2020) with carefully chosen proposals to achieve fast sampling and accurate gradient approximation.…”
Section: Optimizing the Objectivementioning
confidence: 99%
“…assumption, i.e., is constructed by drawing samples independently from the categorical distribution VOLUME 11, 2023 Pr(y i |µ ). The algorithm for drawing samples from the categorical distribution is presented in Algorithm 1 where the core is Gumbel-Max trick [15].…”
Section: Sampling From a Categorical Distributionmentioning
confidence: 99%
“…The datasets used for testing are publicly available datasets GL3D [59], SUN3D [60], and YFCC [61] (details of the testing dataset are illustrated in Table 2). These datasets only contain image sequences; thus, we use the structure from motion (SfM) algorithm to estimate poses and the intrinsic matrices of cameras, and subsequently resort to Equation (15) to recover the essential matrices of image pairs. The tentative matches and their corresponding labels are obtained analogously to the methods for building labels of MR3D dataset.…”
Section: ) Testing Datasetmentioning
confidence: 99%
See 1 more Smart Citation
“…The last layer has one output neuron per action and each neuron value encodes the probability of choosing its corresponding action. In order to sample an action, we use the Gumbel-max trick [57] which does not introduce any additional latencies.…”
Section: Implementation Of the Low-latency Networkmentioning
confidence: 99%