arXiv (All)
open access
Machine learning modularity
Based on a transformer based sequence-to-sequence architecture combined with a dynamic batching algorithm, this work introduces a machine learning framework for automatically simplifying complex expressions involving multiple elliptic Gamma functions, including the $q$-$θ$ function and the elliptic
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
- QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs#1144190
- Align, Integrate, and Fire: Efficient Token-Level Alignment for Zero-Shot SpeechLLMs#1012697
- Training the power: Physical size guides the processing of exponential expressions#276617
- WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms#1142416
- WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms#1122283
Record · ID 1233757
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