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cs.LG2022
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…
stat.ME2022★ 1 cited
Generalized Bayes Approach to Inverse Problems with Model Misspecification
Youngsoo Baek, Wilkins Aquino, Sayan Mukherjee
We propose a general framework for obtaining probabilistic solutions to PDE-based inverse problems. Bayesian methods are attractive for uncertainty quantification but assume knowle…
cs.LG2022
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…