Document Details

Clip: A Constructive Approach for One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings W. Brent Daniel, Enoch Yeung Abstract— In this paper we presented a novel constructive approach for training deep neural networks using geometric approaches. We show that a topological covering can be used to dene a class of distributed linear matrix inequalities, which in turn directly specify the shape and depth of a neural network architecture. The key insight is a fundamental relationship between linear matrix inequalities and their ability to bound the shape of data, and the rectied linear unit (ReLU) activation function employed in modern neural networks. We show that unit cover geometry and cover porosity are two design variables in cover-constructive learning that play a critical role in dening the complexity of the model and generalizability of the resulting

Filename: 1901.02878v1.pdf

Notes: