1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Deep Random Features for Scalable Interpolation of Spatiotemporal Data
Weibin Chen, Azhir Mahmood, Michel Tsamados +1
The rapid growth of earth observation systems calls for a scalable approach to interpolate remote-sensing observations. These methods in principle, should acquire more information…
cs.LG2024★ 1 cited
Weight Clipping for Deep Continual and Reinforcement Learning
Mohamed Elsayed, Qingfeng Lan, Clare Lyle +1
Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting.…
cs.LG2024★ 1 cited
More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
Haque Ishfaq, Yixin Tan, Yu Yang +5
Thompson sampling (TS) is one of the most popular exploration techniques in reinforcement learning (RL). However, most TS algorithms with theoretical guarantees are difficult to im…