Mohammad Pezeshki's repositories
DomainBed
DomainBed is a suite to test domain generalization algorithms
syn-rep-learn
Learning from synthetic data - code and models
pytorch_forward_forward
Implementation of Hinton's forward-forward (FF) algorithm - an alternative to back-propagation
SubpopBench
[ICML 2023] Change is Hard: A Closer Look at Subpopulation Shift
Epoch_wise_Double_Descent
Official implementation of "Multi-scale Feature Learning Dynamics: Insights for Double Descent".
generalization
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST
deepmind-research
This repository contains implementations and illustrative code to accompany DeepMind publications
Gradient_Starvation
Gradient Starvation: A Learning Proclivity in Neural Networks
overparam_spur_corr_forked
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
OptimizationVariance
This repository is the official implementation of "Optimization Variance: Delve into the Epoch-Wise Double Descent of DNNs"
group_DRO
Distributionally robust neural networks for group shifts
pytorch-gan-collections
PyTorch implementation of DCGAN, WGAN-GP and SNGAN.
foolbox
A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
manim
Animation engine for explanatory math videos
A-_Guide_-to_Data_Sciecne_from_mathematics
It is a blueprint to data science from the mathematics to algorithms. It is not completed.
Megalodon
Various ML/DL Resources organised at a single place.
ncsn
Noise Conditional Score Networks
HowToGraduateFromAUT
How to graduate from Computer Eng. and IT Department of Amirkabir University of Technology?
MAT6115_Dynamical_Systems
Support material for MAT6115, Université de Montréal, Fall 2018
improved_wgan_training
Code for reproducing experiments in "Improved Training of Wasserstein GANs"
pytorch-wgan
Pytorch implementation of DCGAN, WGAN-CP, WGAN-GP
wgan-gp
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
Practical_RL
My solutions to Yandex Practical Reinforcement Learning course in PyTorch and Tensorflow