openLG: A Tunable and Efficient Open-source LSTM on GPUs-Reference-Cited by-同舟云学术

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openLG: A Tunable and Efficient Open-source LSTM on GPUs

Published:2024-06-30 Issue: Volume:135 Page:1-8
ISSN:
Container-title:2024 International Joint Conference on Neural Networks (IJCNN)
language:
Short-container-title:

Author:

Shan Zhaowen¹,Huang Xuanteng¹,Zhou Zheng¹,Zhang Xianwei¹

Affiliation:

1. Sun Yat-Sen University,School of Computer Science,Guangzhou,China

Funder

National Natural Science Foundation of China

Research and Development

Fundamental Research Funds for the Central Universities

Sun Yat-sen University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10649807/10649898/10650733.pdf?arnumber=10650733

Reference29 articles.

1. Dorado;Plc,2023

2. Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling;Pags-Gallego,2022

3. Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems;Basak,2023

4. Gaussian Process Lstm Recurrent Neural Network Language Models for Speech Recognition;Lam

5. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition;Park,2019

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