no code implementations • 29 Jan 2024 • Wen Liang, Youzhi Liang
DeBERTa introduced an enhanced decoder adapted for BERT's encoder model for pretraining, proving to be highly effective.
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 • 4 Sep 2023 • Wen Liang, Chao Yu, Brian Whiteaker, Inyoung Huh, Hua Shao, Youzhi Liang
In the past few years, AlphaZero's exceptional capability in mastering intricate board games has garnered considerable interest.
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 • 23 Jul 2023 • Youzhi Liang, Wen Liang
The utilization of biometric authentication with pattern images is increasingly popular in compact Internet of Things (IoT) devices.
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.
1 code implementation • 2 Oct 2019 • Zeyu Bai, Ruizhi Yang, Youzhi Liang
As for the mixed LSTM model with CNN decoder, validation accuracy of 75% and testing accuracy of 70% are obtained.