5 papers
Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation
Zeyi Liu, Guangyao Liu, Yinuo Qu +6
Human-in-the-loop reinforcement learning (HIL-RL) improves sample efficiency in real-robot manipulation through online human intervention. However, successful trajectories may incl…
Efficient state transition algorithm with guaranteed optimality
Xiaojun Zhou, Chunhua Yang, Weihua Gui +1
The state transition algorithm (STA), as an intelligent optimization method grounded in constructivist learning, has been demonstrated to be highly effective in solving complex opt…
Consistency-Driven Calibration and Matching for Few-Shot Class-Incremental Learning
Qinzhe Wang, Zixuan Chen, Keke Huang +3
Few-Shot Class Incremental Learning (FSCIL) is crucial for adapting to the complex open-world environments. Contemporary prospective learning-based space construction methods strug…
AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models
Lei Ren, Haiteng Wang, Jinwang Li +2
With the remarkable success of generative models like ChatGPT, Artificial Intelligence Generated Content (AIGC) is undergoing explosive development. Not limited to text and images,…
Canonical Correlation Guided Deep Neural Network
Zhiwen Chen, Siwen Mo, Haobin Ke +4
Learning representations of two views of data such that the resulting representations are highly linearly correlated is appealing in machine learning. In this paper, we present a c…