11442_deep_learning_methods_for_prox

Lemma 3. Let X be a random variable taking values in X and let F be a family of measurable functions with f 2F: X Let G be a family of measurable functions with g 2G: X! [ M,M], Let ˆR Corollary 4. The inequalities in Lemma 3 can be strengthened to the following: X Using these sharper bounds in the expressions (obtained from McDiarmid's inequality) in Lemma 3 (and using 2 in place of) yields the first pair of equations. Lemma 5. Let h 2 H: A W X! [ M,M] such that if h 2 H, h 2 H, Y [ M,M], k: (A Z X) We analyze each of these four terms separately. A, X, Z! 0, so i Strictly Positive Definite, implies that E [h We tuned the architectures of the Naive Net and NMMR models on both the Demand and dSprite experiments. Within each experiment, the Naive Net and NMMR models used similar architectures. In the Demand experiment, both models consisted of 2-5 ("Network depth" in Table S1) fully connected layers with a variable number ("Network width") of hidden units.