AIPI-590-XAI / Duke-AI-XAILinks
This is a repository for Duke's AI MEng course, AIPI 590, Emerging Trends in XAI
☆17Updated 4 months ago
Alternatives and similar repositories for Duke-AI-XAI
Users that are interested in Duke-AI-XAI are comparing it to the libraries listed below
Sorting:
- It is a comprehensive resource hub compiling all graph papers accepted at the International Conference on Learning Representations (ICLR)…☆99Updated 11 months ago
 - Official implementation of NeurIPS'21 paper"Motif-based Graph Self-Supervised Learning for Molecular Property Prediction"☆126Updated 2 years ago
 - Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation☆34Updated 2 years ago
 - PyTorch implementation of MolGAN: MolGAN: An implicit generative model for small molecular graphs.☆62Updated 4 years ago
 - All graph/GNN papers accepted at NeurIPS 2024.☆82Updated 11 months ago
 - Source code for how powerful are K-hop message passing graph neural networks (Neurips 2022)☆63Updated last year
 - Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022)…☆61Updated 2 years ago
 - This is an official implementation for "GRIT: Graph Inductive Biases in Transformers without Message Passing".☆128Updated 2 months ago
 - [KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"☆47Updated 7 months ago
 - GNNExplainer implementation using DGL☆31Updated 4 years ago
 - Edge-Augmented Graph Transformer☆80Updated last year
 - Regression Transformer (2023; Nature Machine Intelligence)☆159Updated last month
 - Solutions for CS224W Winter 2021 Colab☆170Updated last year
 - Microsoft Graphormer (https://arxiv.org/abs/2106.05234) rewritten in Pytorch-Geometric☆157Updated last week
 - ☆99Updated 3 years ago
 - ☆55Updated 3 years ago
 - GraphXAI: Resource to support the development and evaluation of GNN explainers☆198Updated last year
 - NUS CS5284 Graph Machine Learning course, Xavier Bresson, 2024☆79Updated 11 months ago
 - ☆37Updated last year
 - GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]☆202Updated 8 months ago
 - Repository associated to the paper: "Explaining the Explainers in Graph Neural Networks: a Comparative Study"☆37Updated 2 years ago
 - Implementation of MolCLR: "Molecular Contrastive Learning of Representations via Graph Neural Networks" in PyG.☆299Updated 2 years ago
 - This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data☆371Updated 4 months ago
 - The official source code for "Conditional Graph Information Bottleneck for Molecular Relational Learning".☆44Updated 2 years ago
 - [ICLR 2023] "Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules"☆126Updated 2 years ago
 - a Large-Scale Multi-Modal Dataset Containing 20 Million Descriptions☆47Updated last month
 - [KDD'23] Source codes of "Semi-Supervised Graph Imbalanced Regression"☆21Updated 4 months ago
 - Codes for "Property-Aware Relation Networks for Few-shot Molecular Property Prediction (NeurIPS 2021)".☆50Updated 3 years ago
 - graph neural network layer for Deep Graph Library with pytorch backend☆29Updated 4 years ago
 - All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.☆224Updated 11 months ago