clrs
tuninglib
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| clrs | tuninglib | |
|---|---|---|
| 4 | 3 | |
| 376 | 3 | |
| 10.4% | - | |
| 4.7 | 6.8 | |
| about 2 months ago | 10 months ago | |
| Python | C++ | |
| Apache License 2.0 | MIT License |
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.
clrs
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[P] GITModel: Dynamically generate high-quality hierarchical topic tree representations of GitHub repositories using customizable GNN message passing layers, chatgpt, and topic modeling.
Example: https://github.com/deepmind/clrs
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[D] Is there any research into using neural networks to discover classical algorithms?
Bonus, there is this CLRS benchmark that might be useful for evaluation for your task.
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[R] A Generalist Neural Algorithmic Learner
The baseline code for the CLRS benchmark (which we use in the paper) has been open-sourced for a while now: https://github.com/deepmind/clrs
- GitHub - deepmind/clrs
tuninglib
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Spirick TuningLib on GitHub
Spirick Tuning is a C++ class and template library for performance critical applications. Platforms: MS Windows/Linux Version 1.48 of the TuningLib is published as an open source project on GitHub. github.com/spirick/tuninglib
- Spirick Tuning Lib on GitHub
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