arXiv (All)
open access
Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation Details
Sparse deep neural networks have proven to be efficient for predictive model building in large-scale studies. Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection. Sparsity through edge selection mi
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
- Efficiency and generalization of sparse neural networks#476210
- Federated Martingale Posterior Samping#1105052
- Unveiling Hidden Convexity in Deep Learning: A sparse signal processing perspective [Special Issue on the Mathematics of Deep Learning].#489685
- A Dominant Diffuse Phase in the Sparse Autoencoder Phase Diagram#997193
- Adaptive back-propagation in on-line learning of multilayer networks#978333
Record · ID 1061555
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