2 citations · 5 across the 8 of their papers we have counts for
8 papers
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
Zhen Zhang, Jielei Chu, Jiangtao Hu +4
Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features lea…
Variational Bayesian Personalized Ranking
Bin Liu, Xiaohong Liu, Qin Luo +7
Pairwise learning underpins implicit collaborative filtering, yet its effectiveness is often hindered by sparse supervision, noisy interactions, and popularity-driven exposure bias…
Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanisms
Shuai Guo, Jielei Chu, Lin Ma +2
Generative Flow Networks (GFlowNets or GFNs) are probabilistic models predicated on Markov flows, and they employ specific amortization algorithms to learn stochastic policies that…
Learning Spiking Neural Network from Easy to Hard task
Lingling Tang, Jiangtao Hu, Hua Yu +2
Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mi…
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
Jielei Chu, Jing Liu, Hongjun Wang +3
The instability is shown in the existing methods of representation learning based on Euclidean distance under a broad set of conditions. Furthermore, the scarcity and high cost of…
Multi-local Collaborative AutoEncoder
Jielei Chu, Hongjun Wang, Jing Liu +2
The excellent performance of representation learning of autoencoders have attracted considerable interest in various applications. However, the structure and multi-local collaborat…
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
Jielei Chu, Hongjun Wang, Jing Liu +2
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering i…
Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints
Jielei Chu, Hongjun Wang, Hua Meng +2
Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach…