Optimizing the Learnable RoPE Theta Parameter in Transformers
Rotary Position Embedding (RoPE) enhances Transformer models by encoding relative positions through a frequency parameter <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, but conventional implementations fix <inline-formula>...
Saved in:
Main Authors: | Zhigao Huang, Musheng Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/11084811/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
The Subword‐Character Multi‐Scale Transformer With Learnable Positional Encoding for Machine Translation
by: Wenjing Yao, et al.
Published: (2025-07-01) -
Hardware Trojan vulnerability assessment in digital integrated circuits using learnable classifiers
by: Hadi Jahanirad, et al.
Published: (2024-07-01) -
A domain free of the zeros of the partial theta function
by: V. Kostov
Published: (2023-01-01) -
R-Sparse R-CNN: SAR Ship Detection Based on Background-Aware Sparse Learnable Proposals
by: Kamirul Kamirul, et al.
Published: (2025-01-01) -
Analysis of Gearbox Bearing Fault Diagnosis Method Based on 2D Image Transformation and 2D-RoPE Encoding
by: Xudong Luo, et al.
Published: (2025-06-01)