Weight Space Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

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Most implemented papers archive 2025-07-28

3 shown of 3 papers with code (6 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• The Impact of Model Zoo Size and Composition on Weight Space Learning
  14 Apr 2025 1 repository listed Syntology ran 4 of 10 samples · 6 unverified
  Weight space learning - using the weights of trained models as data modality - is a promising new field to re-use populations of pre-trained models for future tasks.
• Towards Scalable and Versatile Weight Space Learning
  14 Jun 2024 1 repository listed Syntology ran 11 of 15 samples · 4 unverified · 15 pointer-only (licence)
  Learning representations of well-trained neural network models holds the promise to provide an understanding of the inner workings of those models.
• Hyper-Representations for Pre-Training and Transfer Learning
  22 Jul 2022 1 repository listed
  Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.