Constrained optimization of neural network architecture

By introducing a well-motivated information theoretic metric and new convex optimization algorithms, the architecture of a neural network is designed to enhance its supervised learning capability. We formulate two optimization frameworks that allow efficient algorithms for a large number of variables and accommodate a variety of practical constraints on structural randomness of neural networks. Convex optimization is also used for independent component analysis (ICA) and multi-antenna fading channel capacity