autonormalize
python library for automated dataset normalization (by alteryx)
GraphNorm
[ICML 2021] GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training (official implementation) (by lsj2408)
autonormalize | GraphNorm | |
---|---|---|
1 | 1 | |
107 | 98 | |
0.0% | - | |
0.0 | 0.0 | |
10 months ago | over 1 year ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" 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.
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.
autonormalize
Posts with mentions or reviews of autonormalize.
We have used some of these posts to build our list of alternatives
and similar projects.
GraphNorm
Posts with mentions or reviews of GraphNorm.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[D] batch normalization with DGL message-passing GNNs
Look into graph norm: https://github.com/lsj2408/GraphNorm
What are some alternatives?
When comparing autonormalize and GraphNorm you can also consider the following projects:
RecBole - A unified, comprehensive and efficient recommendation library
pytorch_geometric - Graph Neural Network Library for PyTorch
recollapse - REcollapse is a helper tool for black-box regex fuzzing to bypass validations and discover normalizations in web applications