arXiv (All)
open access
ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling
Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep layers are often underutilized. This paper revisits the default mechanism for deepening neural networks, namely residual connections, from an optimization persp
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
- Template-Based Deployment for Real-Time Deep Learning on Point Clouds / Marc Neu ; Jürgen Becker, Torben Ferber#669207
- Weight Decay and Neuron Condensation: A Three-Stage Analysis of Two-Layer ReLU Networks#1200184
- Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance#924259
- Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance#920028
- RepNN: Tackling spectral bias in deep neural networks for regression and PDE problems via parameter reparameterization#1129304
Record · ID 1186417
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