Casey Murphy has fanned his passion for finance through years of writing about active trading, technical analysis, market commentary, exchange-traded funds (ETFs), commodities, futures, options, and ...

Linear algebra is the foundation of science and engineering. Knowledge of linear algebra is a prerequisite for studying statistics, machine learning, computer graphics, signal processing, chemistry, ...

Abstract: We analyze why the orthogonality penalty improves quantization in deep neural networks. Using results from perturbation theory as well as through extensive experiments with Resnet50, ...

Monday 9:45-11:00 AM, Wednesday 9:45-11:00 AM, Thursday 3:00-4:30 PM, by arrangement, and the 15 minutes that immediately follows class John Schmitt is Professor of Mathematics and has been at ...

Abstract: Recent research in deep Convolutional Neural Networks(CNN) faces the challenges of vanishing/exploding gradient issues, training instability, and feature redundancy. Orthogonality ...

The course provides an introduction to the use of mathematics as a tool for understanding physical processes. The main emphasis is on linear algebra, in particular as a tool for understanding discrete ...

Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learned representations, there is significant interest ...

The traditional television industry is undergoing significant changes due to the increasing popularity of new content consumption habits among audiences. This shift is disrupting the entire linear TV ...

This short course is a quick review of linear algebra, intended for students who have already taken a previous course in linear algebra or have some experience with vectors and matrices. The goal of ...

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