An open-source academic paper management tool.

Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

Writing AI Conference Papers: A Handbook for Beginners

😎 A curated list of awesome practical Metric Learning and its applications

🌟🌟CS224W Fall 2021 | Stanford 的个人学习路线🌟🌟

Python package built to ease deep learning on graph, on top of existing DL frameworks.

A collection of papers on Graph Structural Learning (GSL)

Papers of Medical Image Analysis on CVPR

Meta-learning Gaussian process (GP) priors via PAC-Bayes bounds

Chatgpt openclash分流规则

Sparse Variational Dropout, ICML 2017

General API for Deep Bayesian Variational Inference by Backpropagation. The repository has been designed to work with Transformers like architectures. Compatible with the HuggingFace Transformers m…

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

Benchmark of federated learning. Dedicated to the community. 🤗

Optimization Algorithms in Python

Hey,this is a torch implementation of LEAF(a benchmark of FL)

Personalized Federated Learning via Variational Bayesian Inference [ICML 2022]

Laplace Redux -- Effortless Bayesian Deep Learning

Laplace approximations for Deep Learning.

Code for "Federated Accelerated Stochastic Gradient Descent" (NeurIPS 2020)

Implementation of FedDR algorithm for federated learning.

Collect optimizer related papers, data, repositories

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

Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.

Simple stochastic optimizer comparison over classification tasks.

On Bridging Generic and Personalized Federated Learning for Image Classification