Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Yifan Yang
M.S. Final
Dec 22, 2021, 10:00 am - 11:30 am
Teams: email student or advisor for link
A Recursive Least Squares Training Approach for Convolutional Neural Networks
Abstract: This thesis comes up with a fast method to train convolutional neural networks (CNNs) using recursive least squares (RLS) algorithm in conjunction with the back-propagation learning. The recursive updating equations for CNNs are derived via the back-propagation method and normal equations. This method does not need the choice of a learning rate nor suffer from speed-accuracy trade-off. Additionally, it is much faster than the conventional gradient-based methods in a sense that it needs less epochs to converge. The performance of the proposed method together with those of the standard gradient-based methods are compared on the MNIST handwritten digits and Fashion-MNIST clothes databases. The simulation results show that the proposed RLS-based training method requires only one epoch to meet the error goal during the training phase while offering comparable accuracy on the testing data sets.
Adviser: Mahmood Azimi-Sadjadi
Co-Adviser: N/A
Non-ECE Member: Iuliana Oprea, Mathematics
Member 3: Ali Pezeshki, ECE
Addional Members: N/A
Program of Study: