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🔗 Source: meta-learn.github.io
📊 Size: 321 KB
📄 Pages: 11 pages
⬇️ Downloads: 69

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Researchers propose using hypernetworks for meta-learning, demonstrating their effectiveness in adapting features and leveraging inner loop optimization for better adaptation. They introduce a soft weight-sharing hypernetwork architecture, achieving state-of-the-art results comparable to model-agnostic methods, even in the compressed regime. Hypernetworks learn features supporting fast adaptation without hand-designed add-ons or optimization variants, and can model the shared structure underlying a family of tasks, specifically learning a preconditioning matrix equal to the inverse of the tasks' shared curvature matrix.

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Researchers propose using hypernetworks for meta-learning, demonstrating their effectiveness in adapting features and leveraging inner loop optimization for...

Technical Information

  • File Format: PDF
  • File Size: 321 KB
  • Pages: 11
  • Language: EN
  • Total Downloads: 69
  • Last Updated: 3 hours ago

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