HAL (France)
open access
On Orthogonal Recurrent Neural Networks Quantization
In recent years, Orthogonal Recurrent Neural Networks (ORNNs) have gained popularity due to their ability to manage tasks involving long-term dependencies, such as the Copy-task, and their linear complexity. However, existing ORNNs utilize full precision weights and activations, which prevents their
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
- When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference#927776
- OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems#287143
- Pretraining Recurrent Networks without Recurrence#259457
- Representation Alignment from a Few Paired Data via Distilled Optimal Transport#1296788
- Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization#1048531
Record · ID 1434327
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