First survey of Weight Space Learning unifies understanding, representation and generation
burny_tech · x · 2026-11-10
Sharing the survey A Survey of Weight Space Learning, which treats neural network weights themselves as a meaningful domain for analysis and modeling and offers the first unified taxonomy across three dimensions:
- Weight Space Understanding (WSU): geometry and symmetries of weights;
- Weight Space Representation (WSR): learning embeddings over model weights;
- Weight Space Generation (WSG): synthesizing new weights via hypernetworks or generative models.
The survey also covers applications enabled by these advances, including model retrieval, continual and federated learning, neural architecture search, and data-free reconstruction. Authors include Haggai Maron and Ferrante Neri, with an accompanying resource released.
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