Implementation of Power Law Graph Transformer for Machine Translation and Representation Learning.
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Updated
Jul 19, 2021 - Python
Implementation of Power Law Graph Transformer for Machine Translation and Representation Learning.
Codebase of paper "Balancing structure and position information in Graph Transformer network with a learnable node embedding"
Graph molecular learning to predict blood-brain-barrier penetration and CNS drug delivery.
Code for VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections, ICLR 2024
Assignments and presentation developed in the scope of the Deep Learning discipline, lectured by Professor Dário Oliveira (FGV EMAp). Co-authored with @anacarolerthal.
[AAAI'23] MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis
[MICCAI 2024] Official implementation of "CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs"
Code for our paper "Attending to Graph Transformers"
Pretraining Techniques for Graph Transformers
Test graph isomorphism with 1-WL for different graph classes and labelings
Repository for "Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification""
Protein Structure Transformer (PST): Endowing pretrained protein language models with structural knowledge
Codebase for paper: "Improving GCN with Transformer layer in social-based items recommendation"
The Graph Representation Learning Framework developed by NS Lab @ CUK.
2021 AAAI Modular Graph Transformer Networks for Multi-Label Image Classification; Official GitHub: https://github.com/ReML-AI/MGTN
[MICCAI'23] HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis
Scalable and privacy-enhanced graph generative models for benchmark graph neural networks
Welcome to the Graph Neural Networks (06838-01) class repository for the Department of Artificial Intelligence at the Catholic University of Korea. This platform is dedicated to sharing and archiving lecture materials such as practices, assignments, and sample codes for the class.
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
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