junxia97
Ph.D. student at Westlake University & Zhejiang University
Westlake University & Zhejiang UniversityHangzhou, Zhejiang, China.
Pinned Repositories
awesome-molecular-graph-representation-learning
A curated list of resources for molecular graph representation learning.
awesome-pretrain-on-molecules
[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models
awesome-self-supervised-gnn
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
Co-training-based_noisy-label-learning
[ACM MM 2021 Oral Presentation] A unified framework for co-training-based noisy label learning methods.
DiscoGNNs
Official implementation of ICDE 2024 paper "DiscoGNN: A Sample-Efficient Framework for Self-Supervised Graph Representation Learning"
IFM
[NeurIPS 2023] "Understanding the Limitations of Deep Models for Molecular Property Prediction: Insights and Solutions"
Mole-BERT
[ICLR 2023] "Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules"
OT-Cleaner
[ICASSP 2022]OT-Cleaner: Label Correction as Optimal Transport
ProGCL
[ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"
SimGRACE
[WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
junxia97's Repositories
junxia97/awesome-pretrain-on-molecules
[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models
junxia97/Mole-BERT
[ICLR 2023] "Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules"
junxia97/SimGRACE
[WWW 2022] "SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation"
junxia97/ProGCL
[ICML 2022] "ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning"
junxia97/awesome-molecular-graph-representation-learning
A curated list of resources for molecular graph representation learning.
junxia97/DiscoGNNs
Official implementation of ICDE 2024 paper "DiscoGNN: A Sample-Efficient Framework for Self-Supervised Graph Representation Learning"
junxia97/IFM
[NeurIPS 2023] "Understanding the Limitations of Deep Models for Molecular Property Prediction: Insights and Solutions"
junxia97/OT-Cleaner
[ICASSP 2022]OT-Cleaner: Label Correction as Optimal Transport
junxia97/awesome-self-supervised-gnn
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
junxia97/benchmarking-gnns
Repository for benchmarking graph neural networks
junxia97/Co-training-based_noisy-label-learning
[ACM MM 2021 Oral Presentation] A unified framework for co-training-based noisy label learning methods.
junxia97/Deep-Lasso
Deep lasso for feature selection
junxia97/Graph-Reinforcement-Learning-Papers
A curated list of graph reinforcement learning papers.
junxia97/grover
This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data
junxia97/junxia97.github.io
homepage
junxia97/molfeat
molfeat - the hub for all your molecular featurizers
junxia97/papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
junxia97/pretrain-gnns
Strategies for Pre-training Graph Neural Networks
junxia97/pytorch-deep-learning
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.