Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials …

Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property c…

Deep Learning Visualization Toolkit（『飞桨』深度学习可视化工具 ）

Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, 2022)

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)

A beautiful, simple, clean, and responsive Jekyll theme for academics

High-quality implementations of standard and SOTA methods on a variety of tasks.

Barnes-Hut t-SNE

Implementation of the certificates proposed in the paper "Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More"

Hierarchical Inter-Message Passing for Learning on Molecular Graphs

Large-Scale Machine and Deep Learning in PyTorch.

A curated list of projects related to the reMarkable tablet

Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

Below are some simple methods for exiting vim.

Implementation of "Overlapping Community Detection with Graph Neural Networks"

Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)

Differentiable Optimization-Based Modeling for Machine Learning

A repository of pretty cool datasets that I collected for network science and machine learning research.

Graph Neural Network Library for PyTorch

Web-based dashboard for Sacred

Approximating Wasserstein distances with PyTorch

PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)

Framework for evaluating Graph Neural Network models on semi-supervised node classification task

Gaussian node embeddings. Implementation of "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking".

Robust Spectral Clustering. Implementation of "Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings".

links to conference publications in graph-based deep learning

Must-read papers on network representation learning (NRL) / network embedding (NE)