arXiv (All)
open access
When Does Depth Matter For In-Context Learning? Adaptive Inference in Deep Transformers
Transformers perform computations through many successive attention and feedforward blocks, allowing them to learn complex correlations between a large collection of coupled variables. When does stacking successive attention-feedforward computations over many layers provide a computational advantage
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
- Theory on Attention Dynamics for Out-of-Distribution In-Context Learning#1131290
- Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers#1106157
- Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models#1141451
- Official Review · NEURIPS 2024#787167
- Pretraining Latent Information Feedback Transformers with Teacher Supervision#1142444
Record · ID 1118607
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