arXiv (All)
open access
When Can Attention Heads Be Statically Defined?
Some attention heads learn similar patterns across inputs. Reusing these patterns could reduce training cost by avoiding repeated query-key score computation and softmax. Through controlled pretraining comparisons, we identify Selective Attention Freezing (SAF), which selects heads with low attentio
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
- From Gaze to Meaning: A Training-Free AI Agent for Unified Grounding and Explanation#973807
- Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG.#170477
- Neuralyzing the Trace: Selective Representation-Level Unlearning with Contrastive Sparse Autoencoders#1105614
- TBI Project Amsterdam#899979
- An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning#793414
Record · ID 1128912
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