5 papers
Scalable Strategies for Continual Learning with Replay
Truman Hickok
Future deep learning models will be distinguished by systems that perpetually learn through interaction, imagination, and cooperation, blurring the line between training and infere…
Marrying Compressed Sensing and Deep Signal Separation
Truman Hickok, Sriram Nagaraj
Blind signal separation (BSS) is an important and challenging signal processing task. Given an observed signal which is a superposition of a collection of unknown (hidden/latent) s…
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines
Sriram Nagaraj, Truman Hickok
This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft en…
BrowNNe: Brownian Nonlocal Neurons & Activation Functions
Sriram Nagaraj, Truman Hickok
It is generally thought that the use of stochastic activation functions in deep learning architectures yield models with superior generalization abilities. However, a sufficiently…
Watch Your Step: Optimal Retrieval for Continual Learning at Scale
Truman Hickok, Dhireesha Kudithipudi
In continual learning, a model learns incrementally over time while minimizing interference between old and new tasks. One of the most widely used approaches in continual learning…