Popular GitHub Repositories for Differential Builds

Fit interpretable models. Explain blackbox machine learning.

The first AI agent that builds permissionless integrations through reverse engineering platforms' internal APIs.

Learning in infinite dimension with neural operators.

A unified framework for privacy-preserving data analysis and machine learning

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

Master Federated Learning in 2 Hours—Run It on Your PC!

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

Training PyTorch models with differential privacy

Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

Brian is a free, open source simulator for spiking neural networks.

A curated collection of resources and research related to the geometry of representations in the brain, deep networks, and beyond

PDEBench: An Extensive Benchmark for Scientific Machine Learning

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods