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Found 9 papers, 1 papers with code
BPDec: Unveiling the Potential of Masked Language Modeling Decoder in BERT pretraining
DeBERTa introduced an enhanced decoder adapted for BERT's encoder model for pretraining, proving to be highly effective.
A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing
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.
AlphaZero Gomoku
In the past few years, AlphaZero's exceptional capability in mastering intricate board games has garnered considerable interest.
Cross-Attribute Matrix Factorization Model with Shared User Embedding
A direct and intuitive approach to address this issue is by leveraging the features and attributes of the items and users themselves.
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method
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.
ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder
The utilization of biometric authentication with pattern images is increasingly popular in compact Internet of Things (IoT) devices.
Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid CNN-RNN
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.
A Multi-Variate Triple-Regression Forecasting Algorithm for Long-Term Customized Allergy Season Prediction
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.
Mental Task Classification Using Electroencephalogram Signal
As for the mixed LSTM model with CNN decoder, validation accuracy of 75% and testing accuracy of 70% are obtained.
