pursueorigin's repositories
BNS-GCN
[MLSys 2022] "BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling" by Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin
PipeGCN
[ICLR 2022] "PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication" by Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, Yingyan Lin
MD-PGT
Repository for MDPGT
HFW
Code of NeurIPS 2021 paper: Heavy Ball Momentum for Conditional Gradient
awesome-beamer
Creating presentation slides by using Beamer in LaTeX.
FedScale
FedScale: Benchmarking Model and System Performance of Federated Learning
Optimization-Mavericks
This repository provides a unified framework to perform Optimization experiments across Stochastic, Mini-Batch, Decentralized and Federated Setting.
SGMRL
SG-MRL codebase based on ProMP
pyg_autoscale
Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch
GraphLoG
Implementation of Self-supervised Graph-level Representation Learning with Local and Global Structure (ICML 2021).
graph-barlow-twins
The official implementation of the Graph Barlow Twins method with the experimental pipeline
CFLP
Author: Tong Zhao (tzhao2@nd.edu). Counterfactual Graph Learning for Link Prediction
CGS
The official implementation of Convergent Graph Solvers (CGS).
IGNN
Implicit Graph Neural Networks
CPF
The official code of WWW2021 paper: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
stocBiO
Example code for paper "Bilevel Optimization: Nonasymptotic Analysis and Faster Algorithms"
PTDNet
Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsification
dpcmu.github.io
Course webpage
chop
CHOP: An optimization library based on PyTorch, with applications to adversarial examples.
AdvancedOptML
CS 7301: Spring 2021 Course on Advanced Topics in Optimization in Machine Learning
cornell-cs5785-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech
GMT
Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021).
Dec-TD-GT
The code of paper "DECENTRALIZED TD(0) WITH GRADIENT TRACKING"
WEGL
The implementation code for our paper Wasserstein Embedding for Graph Learning (WEGL).
multi-center-fed-learning
fully ready experiments
variance_reduced_neural_networks
Implementation of SVRG and SAGA optimization algorithms for deep learning topics.