arXiv (All)
open access
From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy
Although Deep Neural Networks have become foundational in many areas of Machine Learning, high computational demands limit their application in resource-constrained environments. To address this issue, depth compression methods have been proposed to identify and linearize redundant activation functi
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
- Thread-Efficient Decoding for Neural Texture Compression#781170
- Thread-Efficient Decoding for Neural Texture Compression#779795
- XMerge: Cross-Axis Selection and Reconstructive Layer Merging for LLM Depth Compression#822486
- Breaking the Compression Barrier: Cross-Architecture Compression Boundary Learning via Reverse Regrowth#617684
- Breaking the Compression Barrier: Cross-Architecture Compression Boundary Learning via Reverse Regrowth#617184
Record · ID 971542
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