HSG-AIML/NeurIPS_2022-Generative_Hyper_Representations
Code Repository for the NeurIPS 2022 paper: "Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights".
This project helps machine learning researchers explore and generate new neural network models. It takes existing collections of pre-trained models (model zoos) as input and can generate novel model weights that are diverse, performant, and can be used for various downstream tasks. Machine learning researchers and practitioners interested in advanced model creation and analysis would find this useful.
No commits in the last 6 months.
Use this if you are a machine learning researcher looking to generate new neural network architectures or weights for tasks like neural architecture search, model initialization, or ensemble creation.
Not ideal if you are looking for a straightforward tool to train a single model for a specific task without exploring advanced generative model techniques.
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Language
Python
License
MIT
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Last pushed
Jul 10, 2024
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