Inference on a mean-field model of spiking neurons

Open standard for machine learning interoperability

A curated collection of resources and research on vision neuroscience 👁️ 🧠

Simulation-based inference toolkit

Matplotlib style sheets to nicely format figures for scientific papers, thesis and presentations while keeping them fully editable in Adobe Illustrator.

Python tools for spatial trajectory and time-series data analysis

Exercises and examples for the latent dynamics workshop

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

A library for efficient similarity search and clustering of dense vectors.

Repository for Deterministic Particle Flow Control framework

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

An Aspiring Drop-In Replacement for NumPy at Scale

Code accompanying the NeurIPS 2021 Paper: A Probabilistic State Space Model for Joint Inference from Differential Equations and Data (Schmidt, Krämer, Hennig)

Web Scraping for Healthcare

Sequential Monte Carlo in python

Gaussian processes in TensorFlow

Productive, portable, and performant GPU programming in Python.

Python notebooks for Numerical Analysis

Bayesian learning and inference for state space models

Lecture material for part (4/12) of the lecture **Network Dynamics & Complex Systems - Theoretical and Computational Tools** given during winter semester 2016/17 at Georg-August-Universität Göttingen

Probabilistic programming, Bayesian Thinking, Bayesian Data Analysis and Applications

Neuromechanics_Course_2020

Content for the workshop "Pyro Demystified : Bayesian Deep Learning" (PyCon 2019 - Chennai)

This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.

Anatomy of Probabilistic Programming Languages

PyTorch model training and layer saturation monitor

Unit Testing for pytorch, based on mltest

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.