Predicting The Generalization Gap In Deep Networks With Margin Distributions

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In this paper (Jiang et al., 2018), they discuss a method that can predict the generalization gap from trained deep neural networks. The authors used marginal distribution information from input training set as a feature vector used by an...

Introduction to Mixed Integer Programming

Mixed-Integer programming are used to solve optimization problems with discrete decision variables. Hence, its feasible region is a set of disconnected integer points and gradient based algorithms cannot be directly applied.

Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming

In this research, we attempt to understand the neural model architecture by computing an importance score to neurons. The computed importance score can be used to prune the model or to understand which features are more meaningful to the trained ANN (artificial neural...