icnaam2022

Fit interpretable models. Explain blackbox machine learning.

Learning in infinite dimension with neural operators.

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

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

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/