Concrete Dropout
نویسندگان
چکیده
• Gal and Gharamani (2015) reinterpreted dropout regularisation as approximate inference in BNNs •Dropout probabilities pl are variational parameters of the approximate posterior qθ(ω) = ∏ k qMk,pk(Wk), where Wk = Mk · diag (zk) and zkl iid ∼Bernoulli(1− pk) • Concrete distribution (Maddison et al., Jang et al.) relaxes Categorical distribution to obtain gradients wrt the probability vector – Example: zlk iid ∼Bernoulli(1 − pk) is replaced by z̃kl = sigmoid ((log pk 1−pk + log ukl 1−ukl)/t) where ukl iid ∼Uniform(0, 1) Learning dropout probabilities
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