179 citations · 236 across the 5 of their papers we have counts for
7 papers
Self-supervised Consensus Representation Learning for Attributed Graph
Changshu Liu, Liangjian Wen, Zhao Kang +2
Attempting to fully exploit the rich information of topological structure and node features for attributed graph, we introduce self-supervised learning mechanism to graph represent…
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
Xu Luo, Yuxuan Chen, Liangjian Wen +2
The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classificati…
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
Qingzhong Ai, Lirong He, Shiyu Liu +1
Recent studies show that advanced priors play a major role in deep generative models. Exemplar VAE, as a variant of VAE with an exemplar-based prior, has achieved impressive result…
AFINet: Attentive Feature Integration Networks for Image Classification
Xinglin Pan, Jing Xu, Yu Pan +4
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Recent advanced models in CNNs, such as ResNets,…
Mutual Information Gradient Estimation for Representation Learning
Liangjian Wen, Yiji Zhou, Lirong He +2
Mutual Information (MI) plays an important role in representation learning. However, MI is unfortunately intractable in continuous and high-dimensional settings. Recent advances es…
Structured Pruning of Recurrent Neural Networks through Neuron Selection
Liangjian Wen, Xuanyang Zhang, Haoli Bai +1
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it d…