uncertainty-toolbox VS vivit

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

vivit

[TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps (by f-dangel)
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uncertainty-toolbox vivit
1 1
1,711 17
3.1% -
10.0 0.0
over 1 year ago 10 months 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.

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.

vivit

Posts with mentions or reviews of vivit. 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 uncertainty-toolbox and vivit you can also consider the following projects:

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

pytea - PyTea: PyTorch Tensor shape error analyzer

TorchDrift - Drift Detection for your PyTorch Models

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

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.

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

uq-vae - Solving Bayesian Inverse Problems via Variational Autoencoders

delve - PyTorch model training and layer saturation monitor