113 citations · 454 across the 27 of their papers we have counts for
4 papers · 1 filter
Class Incremental Learning via Likelihood Ratio Based Task Prediction
Haowei Lin, Yijia Shao, Weinan Qian +3
Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key…
Dealing with Cross-Task Class Discrimination in Online Continual Learning
Yiduo Guo, Bing Liu, Dongyan Zhao
Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the only challenge. This paper argues for another challenge in class-incremental learning (…
On Context Distribution Shift in Task Representation Learning for Offline Meta RL
Chenyang Zhao, Zihao Zhou, Bin Liu
Offline Meta Reinforcement Learning (OMRL) aims to learn transferable knowledge from offline datasets to enhance the learning process for new target tasks. Context-based Reinforcem…
Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?
Bing Liu, Wei Luo, Gang Li +2
As deep learning gains popularity in modelling dynamical systems, we expose an underappreciated misunderstanding relevant to modelling dynamics on networks. Strongly influenced by…