Gaussian processes in JAX.

Machine Learning Methods for Neural Data Analysis

A playbook for systematically maximizing the performance of deep learning models.

COGS118C [Neural Signal Processing] @ UCSanDiego

Structural Time Series in JAX

A deep learning framework for multi-animal pose tracking.

Python code for "Probabilistic Machine learning" book by Kevin Murphy

Poisson Identifiable VAE (pi-VAE)

Probabilistic Numerics in Python.

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

Experimental folder for state space modeling code

WIP: Python port of Kilosort2

The COntextual INference (COIN) model

Optax is a gradient processing and optimization library for JAX.

Code for Krause and Drugowitsch (2022). "A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum". Neuron.

Jax SSM Library

Implementation of https://srush.github.io/annotated-s4

Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend

Functional matrix factorization via Bayesian tensor filtering

Interactive Markov-chain Monte Carlo Javascript demos

Neuroproc dataset descriptions and dictionaries

Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.

YASS: Yet Another Spike Sorter

Neyman-Scott point process model to identify sequential firing patterns in high-dimensional spike trains

Convolutive Matrix Factorization in Julia

Time-varying Autoregression with Low Rank Tensors

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.