102 citations · 132 across the 16 of their papers we have counts for
4 papers · 1 filter
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
Zhenshan Bing, Yuan Meng, Yuqi Yun +4
Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge o…
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning
Mingyang Wang, Zhenshan Bing, Xiangtong Yao +5
Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing…
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
Rui Song, Dai Liu, Dave Zhenyu Chen +4
In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often lea…
Meta-Reinforcement Learning in Broad and Non-Parametric Environments
Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin +2
Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of int…