Greilfang's Stars
hpcaitech/ColossalAI
Making large AI models cheaper, faster and more accessible
microsoft/qlib
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
PRML/PRMLT
Matlab code of machine learning algorithms in book PRML
crytic/slither
Static Analyzer for Solidity and Vyper
onqtam/awesome-cmake
A curated list of awesome CMake resources, scripts, modules and examples.
jettify/pytorch-optimizer
torch-optimizer -- collection of optimizers for Pytorch
axboe/liburing
Library providing helpers for the Linux kernel io_uring support
AberHu/Knowledge-Distillation-Zoo
Pytorch implementation of various Knowledge Distillation (KD) methods.
bytedance/fedlearner
A multi-party collaborative machine learning framework
fra31/auto-attack
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
Iceber/iouring-go
Provides easy-to-use async IO interface with io_uring
ebagdasa/backdoors101
Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.
huweibo/Awesome-Federated-Learning-on-Graph-and-GNN-papers
Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.
NeuralNetworkVerification/Marabou
zhuangdizhu/FedGen
Code and data accompanying the FedGen paper
HFAiLab/ffrecord
FireFlyer Record file format, writer and reader for DL training samples.
DistributedML/FoolsGold
A sybil-resilient distributed learning protocol.
tech-srl/lstar_extraction
implementation of ICML 2018 paper, Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
shaneson0/attacking_federate_learning
基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型
jgshu/Attacks-and-Defenses-in-Federated-Learning
jeremy313/FL-WBC
Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective".
aspwebchh/xiang-qin-ji-lu
一个程序员5年来经历的30次相亲
jwyjohn/TJCovidDiary2022
同济2022抗击疫情实录。
fushuhao6/Attack-Resistant-Federated-Learning
superrrpotato/Defending-Neural-Backdoors-via-Generative-Distribution-Modeling
The code is for our NeurIPS 2019 paper: https://arxiv.org/abs/1910.04749
TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate
The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
kochelmonster/larch-pickle
larch.pickle - A faster python pickle replacement
moranant/attacking_distributed_learning
An implementation for the paper "A Little Is Enough: Circumventing Defenses For Distributed Learning" (NeurIPS 2019)
xiaoningdu/deepstellar
dungdao1995/Federated-Learning-with-MPI
This is the Parallel Programming Project using MPI4PY in the Federated Learning Algorithm concept.