99 citations · 314 across the 26 of their papers we have counts for
4 papers · 1 filter
Why Neural Networks Work
Sayandev Mukherjee, Bernardo A. Huberman
We argue that many properties of fully-connected feedforward neural networks (FCNNs), also called multi-layer perceptrons (MLPs), are explainable from the analysis of a single pair…
Concentration inequalities and optimal number of layers for stochastic deep neural networks
Michele Caprio, Sayan Mukherjee
We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow…
Tight query complexity bounds for learning graph partitions
Xizhi Liu, Sayan Mukherjee
Given a partition of a graph into connected components, the membership oracle asserts whether any two vertices of the graph lie in the same component or not. We prove that for $n\g…
Bounds on sample size for policy evaluation in Markov environments
Leonid Peshkin, Sayan Mukherjee
Reinforcement learning means finding the optimal course of action in Markovian environments without knowledge of the environment's dynamics. Stochastic optimization algorithms used…