10 citations · 42 across the 16 of their papers we have counts for
9 papers · 1 filter
Optimizing Class Distribution in Memory for Multi-Label Online Continual Learning
Yan-Shuo Liang, Wu-Jun Li
Online continual learning, especially when task identities and task boundaries are unavailable, is a challenging continual learning setting. One representative kind of methods for…
State-based Episodic Memory for Multi-Agent Reinforcement Learning
Xiao Ma, Wu-Jun Li
Multi-agent reinforcement learning (MARL) algorithms have made promising progress in recent years by leveraging the centralized training and decentralized execution (CTDE) paradigm…
TOMA: Topological Map Abstraction for Reinforcement Learning
Zhao-Heng Yin, Wu-Jun Li
Animals are able to discover the topological map (graph) of surrounding environment, which will be used for navigation. Inspired by this biological phenomenon, researchers have rec…
Weight Normalization based Quantization for Deep Neural Network Compression
Wen-Pu Cai, Wu-Jun Li
With the development of deep neural networks, the size of network models becomes larger and larger. Model compression has become an urgent need for deploying these network models t…
Clustered Reinforcement Learning
Xiao Ma, Shen-Yi Zhao, Wu-Jun Li
Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards. Durin…
Quantized Epoch-SGD for Communication-Efficient Distributed Learning
Shen-Yi Zhao, Hao Gao, Wu-Jun Li
Due to its efficiency and ease to implement, stochastic gradient descent (SGD) has been widely used in machine learning. In particular, SGD is one of the most popular optimization…