arXiv (All)
open access
Graph Metanetworks for Transformer Based Language Models
Weight-space learning treats the weights of neural networks as data, processed by other models called metanetworks. A key challenge is that different weights can realize the same function, related by a group of symmetries the metanetwork should respect. While symmetry-aware metanetworks for simple a
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models#1141451
- Amortizing intractable inference in large language models#798540
- Amortizing intractable inference in large language models#797127
- Can Circuit Alignment Predict OOD Generalization?#1115952
- Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model#926129
Record · ID 1343976
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive