This paper presents a unified MILP training framework for dense and convolutional networks, but the equations force pre-activations to be nonnegative, so the exact ReLU encoding and the global optimality claims are not supported as written.
FLAP 6(4), 611–632 (2019)
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Mathematical Programming Models for Exact and Interpretable Formulation of Neural Networks
This paper presents a unified MILP training framework for dense and convolutional networks, but the equations force pre-activations to be nonnegative, so the exact ReLU encoding and the global optimality claims are not supported as written.