HSG-AIML/SANE
Code Repository for the ICML 2024 paper: "Towards Scalable and Versatile Weight Space Learning".
This project helps machine learning researchers understand and work with large neural networks more effectively. It takes existing neural network models (like ResNet-18 trained on CIFAR-100) as input and provides a compact representation, or 'embedding,' for them. This allows you to predict model properties like accuracy or generalization gap, and even generate new, high-performing neural networks with less effort. This is ideal for machine learning engineers and researchers studying model behavior or exploring new architectures.
No commits in the last 6 months.
Use this if you need to analyze the internal properties of numerous large neural networks or efficiently generate new, well-performing model architectures.
Not ideal if you are looking for a tool to train neural networks from scratch or optimize hyper-parameters for a single specific model.
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30
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6
Language
Python
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Last pushed
Sep 09, 2024
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