36 citations · 174 across the 17 of their papers we have counts for
17 papers
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…
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
Haitao Lin, Yufei Huang, Odin Zhang +4
In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atom-level-based method…
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…
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Haitao Lin, Yufei Huang, Odin Zhang +7
Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where e…
MogaNet: Multi-order Gated Aggregation Network
Siyuan Li, Zedong Wang, Zicheng Liu +6
By contextualizing the kernel as global as possible, Modern ConvNets have shown great potential in computer vision tasks. However, recent progress on multi-order game-theoretic int…
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…
Pulsar Candidate Sifting Using Multi-input Convolution Neural Networks
Haitao Lin, Xiangru Li, Qingguo Zeng
Pulsar candidate sifting is an essential process for discovering new pulsars. It aims to search for the most promising pulsar candidates from an all-sky survey, such as High Time R…
Concat Convolutional Neural Network for Pulsar Candidate Selection
Qingguo Zeng, Xiangru Li, Haitao Lin
Pulsar searching is essential for the scientific research in the field of physics and astrophysics. As the development of the radio telescope, the exploding volume and it growth sp…
Neural Pose Transfer by Spatially Adaptive Instance Normalization
Jiashun Wang, Chao Wen, Yanwei Fu +4
Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose o…
Pulsars Detection by Machine Learning with Very Few Features
Haitao Lin, Xiangru Li, Ziying Luo
It is an active topic to investigate the schemes based on machine learning (ML) methods for detecting pulsars as the data volume growing exponentially in modern surveys. To improve…