3 papers
cs.LG2022
DropNet: Reducing Neural Network Complexity via Iterative Pruning
John Tan Chong Min, Mehul Motani
Modern deep neural networks require a significant amount of computing time and power to train and deploy, which limits their usage on edge devices. Inspired by the iterative weight…
cs.LG2022
Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments
John Tan Chong Min, Mehul Motani
Traditional reinforcement learning (RL) environments typically are the same for both the training and testing phases. Hence, current RL methods are largely not generalizable to a t…
cs.LG2021
S-Cyc: A Learning Rate Schedule for Iterative Pruning of ReLU-based Networks
Shiyu Liu, Chong Min John Tan, Mehul Motani
We explore a new perspective on adapting the learning rate (LR) schedule to improve the performance of the ReLU-based network as it is iteratively pruned. Our work and contribution…