ipme VS uncertainty-toolbox

Compare ipme vs uncertainty-toolbox and see what are their differences.

ipme

An interactive visualization tool that transforms probabilistic programming models into an "Interactive Probabilistic Models Explorer". (by evdoxiataka)
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ipme uncertainty-toolbox
1 1
24 1,711
- 3.1%
0.0 10.0
over 1 year ago over 1 year ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ipme

Posts with mentions or reviews of ipme. We have used some of these posts to build our list of alternatives and similar projects.

uncertainty-toolbox

Posts with mentions or reviews of uncertainty-toolbox. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing ipme and uncertainty-toolbox you can also consider the following projects:

EOmaps - A library to create interactive maps of geographical datasets

cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both

deep-kernel-transfer - Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

TorchDrift - Drift Detection for your PyTorch Models

surface_normal_uncertainty - (ICCV 2021 - oral) Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

pytea - PyTea: PyTorch Tensor shape error analyzer

deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.

uq-vae - Solving Bayesian Inverse Problems via Variational Autoencoders

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks