Implementation of Large Scale Deep Learning Non-Destructive Methods for Characterizing 4H-SiC Materials

Deep Learning Methods for Efficient Large Scale Video Labeling

We present a solution to “Google Cloud and YouTube8M Video Understanding Challenge” that ranked 5th place. The proposed model is an ensemble of three model families, two frame level and one video level. The training was performed on augmented dataset, with cross validation.

Overview of Non-Destructive Testing (NDT) Methods of Materials Evaluation

The opening paper of this symposium was dedicated to an overview of the existing state of the practice and the advantages and limitations of non-destructive testing methods for the evaluation of historic materials of construction. The methods discussed in this paper include the state of the practice in visual, rebound, penetration, and ultrasound methods, as well as the physical measurement of ...

Optimization Methods for Large-Scale Machine Learning

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machi...

Large-Scale Methods for Nonlinear Manifold Learning

Non-Contact Characterization of Recombination Processes in 4H-SiC∗

Carrier decay transients in 4H-SiC n-type and p-type epilayers have been characterized using a non-destructive, non-contact microwave photoconductivity technique. Decay transients show a two-stage exponential decay with first decay constants as high as 400 ns in 10 μm p-type epilayers. The second decay constant increases with temperature and is dominated by interface recombination.