31 citations · 125 across the 25 of their papers we have counts for
7 papers · 1 filter
When and Why Momentum Accelerates SGD:An Empirical Study
Jingwen Fu, Bohan Wang, Huishuai Zhang +3
Momentum has become a crucial component in deep learning optimizers, necessitating a comprehensive understanding of when and why it accelerates stochastic gradient descent (SGD). T…
Learning Trajectories are Generalization Indicators
Jingwen Fu, Zhizheng Zhang, Dacheng Yin +2
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using (stochastic) gradient des…
Versatile Neural Processes for Learning Implicit Neural Representations
Zongyu Guo, Cuiling Lan, Zhizheng Zhang +2
Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Ne…
Mask-based Latent Reconstruction for Reinforcement Learning
Tao Yu, Zhizheng Zhang, Cuiling Lan +2
For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance. However, in practice, limited experience and h…
Confounder Identification-free Causal Visual Feature Learning
Xin Li, Zhizheng Zhang, Guoqiang Wei +4
Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free o…
Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments
Zhizheng Zhang, Jiale Chen, Zhibo Chen +1
Deep Deterministic Policy Gradient (DDPG) has been proved to be a successful reinforcement learning (RL) algorithm for continuous control tasks. However, DDPG still suffers from da…