A Detailed Accounting of Error in Neural Networks: Applications to Electromagnetics

Multi-modal Deep Learning

Cycles Improve Conditional Generators

Anomaly Detection with Advanced Nonlinear Dimensionality Reduction

Improving Model Performance with Robust PCA

Machine learning approaches to manufacturing and materials: Applications to semi-supervised, unbalanced and heterogeneous data problems

Dimension Reduction and LASSO using Pointwise and Group Norms

Parameter Continuation with Secant Approximation for Deep Neural Networks

Kernel Coherence Encoders