39 citations · 64 across the 15 of their papers we have counts for
5 papers · 1 filter
Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder
Yadong Zhou, Zhihao Ding, Xiaoming Liu +3
User attributes, such as gender and education, face severe incompleteness in social networks. In order to make this kind of valuable data usable for downstream tasks like user prof…
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
Lin Lan, Pinghui Wang, Xuefeng Du +3
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The n…
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations
Xiaoming Liu, Qirui Li, Chao Shen +3
Graph convolution network (GCN) attracts intensive research interest with broad applications. While existing work mainly focused on designing novel GCN architectures for better per…
Adversarial Example Detection by Classification for Deep Speech Recognition
Saeid Samizade, Zheng-Hua Tan, Chao Shen +1
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regar…
Meta Reinforcement Learning with Task Embedding and Shared Policy
Lin Lan, Zhenguo Li, Xiaohong Guan +1
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization. Recent efforts apply meta-learning to learn a meta-learner…