103 citations · 167 across the 19 of their papers we have counts for
10 papers · 1 filter
Macformer: Transformer with Random Maclaurin Feature Attention
Yuhan Guo, Lizhong Ding, Ye Yuan +1
Random feature attention (RFA) adopts random fourier feature (RFF) methods to approximate the softmax function, resulting in a linear time and space attention mechanism that enable…
DREAM: Domain-free Reverse Engineering Attributes of Black-box Model
Rongqing Li, Jiaqi Yu, Changsheng Li +3
Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (, the number of convolutional layers) of…
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Kaituo Feng, Yikun Miao, Changsheng Li +2
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper te…
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Hanjie Li, Changsheng Li, Kaituo Feng +3
Graph structured data often possess dynamic characters in nature. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph da…
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Kaituo Feng, Changsheng Li, Ye Yuan +1
Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teache…
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Shiye Wang, Changsheng Li, Yanming Li +2
In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck…
Deep Unsupervised Active Learning on Learnable Graphs
Handong Ma, Changsheng Li, Xinchu Shi +2
Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while…
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
Yanming Li, Changsheng Li, Shiye Wang +2
Deep subspace clustering has attracted increasing attention in recent years. Almost all the existing works are required to load the whole training data into one batch for learning…
Causal Effect Estimation using Variational Information Bottleneck
Zhenyu Lu, Yurong Cheng, Mingjun Zhong +3
Causal inference is to estimate the causal effect in a causal relationship when intervention is applied. Precisely, in a causal model with binary interventions, i.e., control and t…
On Deep Unsupervised Active Learning
Changsheng Li, Handong Ma, Zhao Kang +3
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.…