A powerful tool for creating datasets for LLM fine-tuning 、RAG and Eval

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Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Integrate the DeepSeek API into popular software

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A collection of AWESOME things about Graph-Related LLMs.

A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining, ICML'23

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner in WWW'23

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[ICLR 2023] "Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules"

Unified 2D and 3D Pre-Training of Molecular Representations

[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)

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Source Code of IJCAI 2022 paper "Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport"

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[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.

Code for KDD'22 paper, COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning

A curated list of graph data augmentation papers.

links to conference publications in graph-based deep learning

[IJCAI 2023 survey track]A curated list of resources for chemical pre-trained models

Code for Molformer (AAAI 2023)

Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

Graph model implementation

Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.

Implementation for the paper MoCL: Contrastive Learning on Molecular Graph with multi-level Domain Knowledge

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。声明：所有内容来自（仅供学习）：https://github.com/scutan90/DeepLearning-500-questions