230 citations · 513 across the 30 of their papers we have counts for
8 papers · 1 filter
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
Miao Zhang, Steven Su, Shirui Pan +3
\textit{Differentiable ARchiTecture Search} (DARTS) has recently become the mainstream of neural architecture search (NAS) due to its efficiency and simplicity. With a gradient-bas…
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Siyi Hu, Fengda Zhu, Xiaojun Chang +1
Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task. The limitation is due to the restricted model…
Self-Weighted Robust LDA for Multiclass Classification with Edge Classes
Caixia Yan, Xiaojun Chang, Minnan Luo +4
Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are p…
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
Pengzhen Ren, Yun Xiao, Xiaojun Chang +4
Deep learning has made breakthroughs and substantial in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is…
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Zonghan Wu, Shirui Pan, Guodong Long +3
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumptio…
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
Fengda Zhu, Xiaojun Chang, Runhao Zeng +1
Deep reinforcement learning has made significant progress in the field of continuous control, such as physical control and autonomous driving. However, it is challenging for a rein…