57 citations · 144 across the 7 of their papers we have counts for
8 papers
Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization
Ye Shi, Leijie Zhang, Zehong Cao +2
Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot sh…
Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data
Leijie Zhang, Ye Shi, Yu-Cheng Chang +1
Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning dif…
Weak Human Preference Supervision For Deep Reinforcement Learning
Zehong Cao, KaiChiu Wong, Chin-Teng Lin
The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed…
A General Approach for Using Deep Neural Network for Digital Watermarking
Yurui Ming, Weiping Ding, Zehong Cao +1
Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way. However, consideration from the privacy or legislation pers…
Reinforcement Learning from Hierarchical Critics
Zehong Cao, Chin-Teng Lin
In this study, we investigate the use of global information to speed up the learning process and increase the cumulative rewards of reinforcement learning (RL) in competition tasks…
Quantum topology identification with deep neural networks and quantum walks
Yurui Ming, Chin-Teng Lin, Stephen D. Bartlett +1
Topologically ordered materials may serve as a platform for new quantum technologies such as fault-tolerant quantum computers. To fulfil this promise, efficient and general methods…
Semi-supervised Feature Learning For Improving Writer Identification
Shiming Chen, Yisong Wang, Chin-Teng Lin +2
Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overf…
Tensor Decomposition for EEG Signal Retrieval
Zehong Cao, Mukesh Prasad, M. Tanveer +1
Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial sce…