no code implementations • 9 Dec 2023 • Jianguo Jia, Wen Liang, Youzhi Liang
This review presents a comprehensive exploration of hybrid and ensemble deep learning models within Natural Language Processing (NLP), shedding light on their transformative potential across diverse tasks such as Sentiment Analysis, Named Entity Recognition, Machine Translation, Question Answering, Text Classification, Generation, Speech Recognition, Summarization, and Language Modeling.
no code implementations • 14 Aug 2023 • Wen Liang, Zeng Fan, Youzhi Liang, Jianguo Jia
A direct and intuitive approach to address this issue is by leveraging the features and attributes of the items and users themselves.
no code implementations • 5 Aug 2023 • Wen Liang, Youzhi Liang, Jianguo Jia
Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks.
no code implementations • 11 Mar 2023 • Youzhi Liang, Wen Liang, Jianguo Jia
Vibration signals have been increasingly utilized in various engineering fields for analysis and monitoring purposes, including structural health monitoring, fault diagnosis and damage detection, where vibration signals can provide valuable information about the condition and integrity of structures.
no code implementations • 10 May 2020 • Xiaoyu Wu, Zeyu Bai, Jianguo Jia, Youzhi Liang
In this paper, we propose a novel multi-variate algorithm using a triple-regression methodology to predict the airborne-pollen allergy season that can be customized for each patient in the long term.