Learning, Optimization and Data Translation with Deep Neural Networks

Neural networks have been intensively studied as machine learning models and widely applied in various areas. This thesis investigates three problems related to the theory and application of neural networks. First, we analyze a learning scheme for neural networks that uses random weights in the backpropagation training algorithm, which is considered to be more biologically plausible than the standard training procedure. We establish theory that shows the convergence of the loss and the alignment between the forward weights of the network and the random weights used in the backward pass. Second, we study a family of optimization problems where the objective involves a trained generative network, with the goal of inverting the network. We introduce a novel algorithm that takes advantage of a sequential optimization technique to deal with the problem of non-convexity. The third part of this thesis is an application of modern neural network models to certain problems in neuroscience. We analyze data that contains two concurrent imaging modalities of the brain activity in mice, and build translation models to predict one modality the other. Our study is one of the first examples of advanced machine learning models applied to concurrent multi-model brain imaging data and demonstrates the potential of deep neural networks in the emerging area of neuroscience

Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms

AbstractThe paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives