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∫ ∞ 0 dv 1 vvexk−v k−1∏ i=1 vexi−ui−xk−vexi −ui −xk (15) = exp ( xk + k−1∑ i=1 (xi−ui) ) ( exk + k−1∑ i=1 ( exi−ui ) )−k Γ(k) (16) = Γ(k) exp ( k∑ i=1 (xi−ui) ) ( k∑ i=1 ( exi−ui ) )−k (17) = Γ(k) ( k∏ i=1 exp (xi−ui) ) ( k∑ i=1 exp (xi−ui) · 2017

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Categorical Reparameterization with Gumbel-Softmax

stat.ML · 2016-11-03 · unverdicted · novelty 8.0

Gumbel-Softmax provides a continuous relaxation of categorical sampling that anneals to discrete samples for gradient-based optimization.

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Categorical Reparameterization with Gumbel-Softmax stat.ML · 2016-11-03 · unverdicted · none · ref 14
Gumbel-Softmax provides a continuous relaxation of categorical sampling that anneals to discrete samples for gradient-based optimization.

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