Particle Gibbs-based optimal control with performance guarantees for unknown systems with latent states

markdown editor with live preview

State Space Models library in JAX

The Operator Splitting QP Solver

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

A massively parallel, high-level programming language

Compositional Linear Algebra

BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

A vector field explorer

Gaussian Processes for Experimental Sciences

[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax for Partial Differential Equations

Machine learning algorithms for many-body quantum systems

Gaussian processes in JAX.

Geometric kernels on manifolds, meshes and graphs

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Koopman Kernels for Learning Dynamical Systems from Trajectory Data

Structured state space sequence models

Positive-definite "approximations" to matrices

Deep universal probabilistic programming with Python and PyTorch

Data Science, Time Complexity and Inferential Uncertainty (TCIU)

Large-scale, multi-GPU capable, kernel solver

[NeurIPS'23 Spotlight] Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance (LPS), in PyTorch

[ICLR'23 Oral] Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching

Learning in infinite dimension with neural operators.

A Library for Advanced Deep Time Series Models.

Code for "Deep Signature Transforms" (NeurIPS 2019)

A Python package to learn the Koopman operator.

A convenient way to convert files from DjVu to PDF format while preserving the text layer