Empirical Inference

Code and Data

 OpenPhd Guiding

OpenPhd Guiding

2017-08-01

Algorithm using a learned motion correction based on a Gaussian Process to produce better images from a telescope.

https://openphdguiding.org/


c++ boost cmake


Distributed brain-computer interface

Distributed brain-computer interface

2017-02-20

A brain-computer interface (BCI) to assist and interpret thoughts from patients suffering diseases such as amyotrophic lateral sclerosis. This monitoring tool is especially suited for research and for reaching patients living in remote locations.

https://is.mpg.de/person/mhohmann


c++ qt mqtt


 Half-Sibling Regression for High-Contrast Imaging

Half-Sibling Regression for High-Contrast Imaging

Methods for applying a post-processing scheme based on Half-Sibling Regression (HSR) to High-Contrast Imaging (HCI) data

https://github.com/timothygebhard/hsr4hci


Dingo (Deep Inference for Gravitational-wave Observations)

Dingo (Deep Inference for Gravitational-wave Observations)

Dingo (Deep Inference for Gravitational-wave Observations) is a Python program for analyzing gravitational wave data using neural posterior estimation. It dramatically speeds up inference of astrophysical source parameters from data measured at gravitational-wave observatories. Dingo aims to enable the routine use of the most advanced theoretical models in analysing data, to make rapid predictions for multi-messenger counterparts, and to do so in the context of sensitive detectors with high event rates.

https://github.com/dingo-gw/dingo


normflows: A PyTorch Package for Normalizing Flows

normflows: A PyTorch Package for Normalizing Flows

normflows is a PyTorch implementation of discrete normalizing flows. Many popular flow architectures are implemented. The package can be easily installed via pip. The basic usage is described here, and a full documentation is available as well. A more detailed description of this package is given in out accompanying paper.

https://github.com/VincentStimper/normalizing-flows


The o80 C++ templated toolbox for robotics

The o80 C++ templated toolbox for robotics

o80 (pronounced "oh-eighty") is software for synchronizing and organizing message exchange between (realtime) processes via simple customized Python APIs. Its target domain is robotics and machine learning. Our motivation for developing o80 is to ease the setup of robotics experiments (i.e., integration of various hardware and software) by machine learning scientists. Such setup typically requires time and technical effort, especially when realtime processes are involved. Ideally, scientists should have access to a simple Python API that hides the lower level communication details and simply allows the sending of actions and receiving of observations. o80 is a tool box for creating such API.

https://joss.theoj.org/papers/10.21105/joss.02752
https://github.com/intelligent-soft-robots/o80


Omni-Fig: Unleashing Project Configuration and Organization in Python

Omni-Fig: Unleashing Project Configuration and Organization in Python

omni-fig is a lightweight package to help you organize your python projects to make everything clear and easy to understand to collaborators and prospective users, while also offering unparalleled features to accelerate development. The proposed general-purpose project structure is well suited for both small and large projects, and is designed to be easily extensible to fit your needs. Most importantly, with the powerful configuration system, you never have to worry about any boilerplate code to parse command line arguments, read config files, or even import the top-level project components ever again!

https://github.com/felixludos/omni-fig


AutoML Two-Sample Test

AutoML Two-Sample Test

autotst is a Python package for easy-to-use two-sample testing and distribution shift detection.

https://arxiv.org/abs/2206.08843
https://github.com/jmkuebler/auto-tst