clrs
callofcode
clrs | callofcode | |
---|---|---|
4 | 2 | |
461 | 14 | |
1.1% | - | |
7.5 | 0.0 | |
about 1 month ago | about 2 years ago | |
Jupyter Notebook | C++ | |
Apache License 2.0 | MIT License |
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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
callofcode
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New Issue: Checking credentials
Link to the issue Checking credentials
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Github contributions for beginners
github.com/callofcode
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