siddharthdivi's Stars
huggingface/transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
PAIR-code/saliency
Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
equialgo/fairness-in-ml
This repository contains the full code for the "Towards fairness in machine learning with adversarial networks" blog post.
Kitware/VTK
Mirror of Visualization Toolkit repository
chaoyanghe/Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
rasbt/stat479-deep-learning-ss19
Course material for STAT 479: Deep Learning (SS 2019) at University Wisconsin-Madison
jeremy313/non-iid-dataset-for-personalized-federated-learning
Official implementation of "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective".
CharlieDinh/pFedMe
Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)
litian96/fair_flearn
Fair Resource Allocation in Federated Learning (ICLR '20)
AshwinRJ/Federated-Learning-PyTorch
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
OpenMined/PySyft
Perform data science on data that remains in someone else's server
coq/coq
Coq is a formal proof management system. It provides a formal language to write mathematical definitions, executable algorithms and theorems together with an environment for semi-interactive development of machine-checked proofs.
TalwalkarLab/leaf
Leaf: A Benchmark for Federated Settings
ebagdasa/federated_adaptation
Salvaging Federated Learning by Local Adaptation
microsoft/hummingbird
Hummingbird compiles trained ML models into tensor computation for faster inference.
chenglou/react-motion
A spring that solves your animation problems.
lxuniverse/defense-vae
Implementation for "Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks"
tangjianpku/generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
sudharsan13296/Hands-On-Meta-Learning-With-Python
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
ashishpatel26/Meta-Learning-
Meta learning is a subfield of machine learning where automatic learning algorithms are applied on metadata about machine learning experiments.
dhuynh95/fastai_bayesian
Quick modules to turn regular Neural Networks to Bayesian Neural Networks with Dropout.
skorch-dev/skorch
A scikit-learn compatible neural network library that wraps PyTorch
sheryl-ai/MetaPred
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (KDD 2019)
tristandeleu/pytorch-meta
A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch
dragen1860/MAML-Pytorch
Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)
openai/supervised-reptile
Code for the paper "On First-Order Meta-Learning Algorithms"
vgsatorras/few-shot-gnn
jakesnell/prototypical-networks
Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
lmzintgraf/cavia
Code for "Fast Context Adaptation via Meta-Learning"
AntreasAntoniou/HowToTrainYourMAMLPytorch
The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.