1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Elephant Neural Networks: Born to Be a Continual Learner
Qingfeng Lan, A. Rupam Mahmood
Catastrophic forgetting remains a significant challenge to continual learning for decades. While recent works have proposed effective methods to mitigate this problem, they mainly…
cs.LG2023
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization
Homayoon Farrahi, A. Rupam Mahmood
Continuous-time reinforcement learning tasks commonly use discrete steps of fixed cycle times for actions. As practitioners need to choose the action-cycle time for a given task, a…
cs.LG2023★ 1 cited
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning
Mohamed Elsayed, A. Rupam Mahmood
Modern representation learning methods often struggle to adapt quickly under non-stationarity because they suffer from catastrophic forgetting and decaying plasticity. Such problem…