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• W2892457101 abstract "In many applications we seek to maximize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribution parameters is a challenging problem. We analyze existing solutions including finite-difference (FD) estimators and continuous relaxation (CR) estimators in terms of bias and variance. We show that the commonly used Gumbel-Softmax estimator is biased and propose a simple method to reduce it. We also derive a simpler piece-wise linear continuous relaxation that also possesses reduced bias. We demonstrate empirically that reduced bias leads to a better performance in variational inference and on binary optimization tasks." @default.
• W2892457101 created "2018-10-05" @default.
• W2892457101 creator A5038984764 @default.
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• W2892457101 date "2018-09-29" @default.
• W2892457101 modified "2023-09-27" @default.
• W2892457101 title "Improved Gradient-Based Optimization Over Discrete Distributions." @default.
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• W2892457101 hasPublicationYear "2018" @default.
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