36 citations · 174 across the 17 of their papers we have counts for
10 papers · 1 filter
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
Lirong Wu, Haitao Lin, Yufei Huang +1
To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well-trained…
Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework
Lirong Wu, Haitao Lin, Yufei Huang +2
Recent years have witnessed the great success of Graph Neural Networks (GNNs) in handling graph-related tasks. However, MLPs remain the primary workhorse for practical industrial a…
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy
Yufei Huang, Lirong Wu, Haitao Lin +3
Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design. Having witnessed the success of protein se…
Non-equispaced Fourier Neural Solvers for PDEs
Haitao Lin, Lirong Wu, Yongjie Xu +4
Solving partial differential equations is difficult. Recently proposed neural resolution-invariant models, despite their effectiveness and efficiency, usually require equispaced sp…
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
Lirong Wu, Yufei Huang, Haitao Lin +3
Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning. With hundreds of self-supervised pretext tasks proposed over the past…
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Lirong Wu, Jun Xia, Haitao Lin +4
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) re…
Exploring Generative Neural Temporal Point Process
Haitao Lin, Lirong Wu, Guojiang Zhao +2
Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historica…
LookHops: light multi-order convolution and pooling for graph classification
Zhangyang Gao, Haitao Lin, Stan. Z Li
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive -order() method requires more computatio…
Invertible Manifold Learning for Dimension Reduction
Siyuan Li, Haitao Lin, Zelin Zang +3
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information. In the context of manifold learning,…
Clustering Based on Graph of Density Topology
Zhangyang Gao, Haitao Lin, Stan. Z Li
Data clustering with uneven distribution in high level noise is challenging. Currently, HDBSCAN is considered as the SOTA algorithm for this problem. In this paper, we propose a no…