Search Results for author: Guojiang Zhao

Found 9 papers, 4 papers with code

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

1 code implementation16 Jun 2024 Haitao Lin, Guojiang Zhao, Odin Zhang, Yufei Huang, Lirong Wu, Zicheng Liu, Siyuan Li, Cheng Tan, Zhifeng Gao, Stan Z. Li

To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks.

Fairness

A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation

1 code implementation6 Mar 2024 Lirong Wu, Haitao Lin, Zhangyang Gao, Guojiang Zhao, Stan Z. Li

As a result, TGS enjoys the benefits of graph topology awareness in training but is free from data dependency in inference.

Knowledge Distillation

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction

no code implementations24 Apr 2023 Zhifeng Gao, Xiaohong Ji, Guojiang Zhao, Hongshuai Wang, Hang Zheng, Guolin Ke, Linfeng Zhang

Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery.

Drug Discovery Model Selection +4

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings

1 code implementation ACL 2022 Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Z. Li

Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.

Word Embeddings

Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification

no code implementations5 Oct 2022 Lirong Wu, Jun Xia, Haitao Lin, Zhangyang Gao, Zicheng Liu, Guojiang Zhao, Stan Z. Li

Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.

Classification Node Classification

Exploring Generative Neural Temporal Point Process

1 code implementation3 Aug 2022 Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu, Stan Z. Li

While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations.

Denoising

STONet: A Neural-Operator-Driven Spatio-temporal Network

no code implementations18 Apr 2022 Haitao Lin, Guojiang Zhao, Lirong Wu, Stan Z. Li

Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks.

Time Series Time Series Analysis

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