Fang Wu

Orcid: 0000-0001-7240-3915

Affiliations:
  • Stanford University, CA, USA
  • Columbia University, NY, USA (2029-2021)


According to our database1, Fang Wu authored at least 20 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

2021
2022
2023
2024
0
1
2
3
4
5
6
7
8
9
1
3
6
1
4
5

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Discovering the Representation Bottleneck of Graph Neural Networks.
IEEE Trans. Knowl. Data Eng., December, 2024

Instructor-inspired Machine Learning for Robust Molecular Property Prediction.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

SemiReward: A General Reward Model for Semi-supervised Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2023
Improving molecular representation learning with metric learning-enhanced optimal transport.
Patterns, April, 2023

InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems.
CoRR, 2023

Explaining Graph Neural Networks via Non-parametric Subgraph Matching.
CoRR, 2023

A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching.
Proceedings of the International Conference on Machine Learning, 2023

Architecture-Agnostic Masked Image Modeling - From ViT back to CNN.
Proceedings of the International Conference on Machine Learning, 2023

Molformer: Motif-Based Transformer on 3D Heterogeneous Molecular Graphs.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
When Geometric Deep Learning Meets Pretrained Protein Language Models.
CoRR, 2022

Architecture-Agnostic Masked Image Modeling - From ViT back to CNN.
CoRR, 2022

Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions.
CoRR, 2022

A Score-based Geometric Model for Molecular Dynamics Simulations.
CoRR, 2022

Pre-training of Deep Protein Models with Molecular Dynamics Simulations for Drug Binding.
CoRR, 2022

Metric Learning-enhanced Optimal Transport for Biochemical Regression Domain Adaptation.
CoRR, 2022

2021
3D-Transformer: Molecular Representation with Transformer in 3D Space.
CoRR, 2021


  Loading...