openmedical/registry
← registry

GPim

imported

software/gpim

Gaussian processes and Bayesian optimization for images and hyperspectral data

Machine-generated from the listed sources and not yet reviewed by a human.

record
Category
Software & Systems
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
bayesian-optimization · colab-notebook · gaussian-processes · hyperspectral-images · image-processing · lattice-models · microscopy
Regulatory
unknown
built by · 3

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • Mellongaussian-processes

    Non-parametric density inference for single-cell analysis.

  • prob-epigaussian-processes

    Course materials of "Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology"

  • flu-sequence-predictorgaussian-processes

    An experimental deep learning & genotype network-based system for predicting new influenza protein sequences.

  • czitoolsimage-processing · microscopy

    This repository provides a collection of tools to simplify reading CZI (Carl Zeiss Image) pixel and metadata in Python.

  • DeconvOptim.jlimage-processing · microscopy

    A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs.

  • DeepTrack2image-processing · microscopy

    DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data pipelines for machine learning and experimental imaging.

sources
  1. api.github.com/repos/ziatdinovmax/GPim
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2023-11-24, 57 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/20.json→ .entries["gpim"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.