1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift
Haiyang Zhao
Visual model-based reinforcement learning (MBRL) agents can perform well on the training distribution, but often break down once the test environment shifts. In visual MBRL, recogn…
cs.CV2023
One-Shot Pruning for Fast-adapting Pre-trained Models on Devices
Haiyan Zhao, Guodong Long
Large-scale pre-trained models have been remarkably successful in resolving downstream tasks. Nonetheless, deploying these models on low-capability devices still requires an effect…
cs.LG2023★ 1 cited
Does Continual Learning Equally Forget All Parameters?
Haiyan Zhao, Tianyi Zhou, Guodong Long +2
Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedl…