pytea VS uncertainty-toolbox

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

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pytea uncertainty-toolbox
3 1
310 1,711
0.3% 3.1%
1.8 10.0
about 2 years ago over 1 year ago
TypeScript Python
GNU General Public License v3.0 or later 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.

pytea

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

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 pytea and uncertainty-toolbox you can also consider the following projects:

examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

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

TorchDrift - Drift Detection for your PyTorch Models

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

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.

vivit - [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps

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

Transformer-MM-Explainability - [ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

uq-vae - Solving Bayesian Inverse Problems via Variational Autoencoders

delve - PyTorch model training and layer saturation monitor

ipme - An interactive visualization tool that transforms probabilistic programming models into an "Interactive Probabilistic Models Explorer".