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[HTML][HTML] Recurrent neural networks: A comprehensive review of architectures, variants, and applications
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …
(ML) by enabling the effective processing of sequential data. This paper provides a …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
R-drop: Regularized dropout for neural networks
Dropout is a powerful and widely used technique to regularize the training of deep neural
networks. Though effective and performing well, the randomness introduced by dropout …
networks. Though effective and performing well, the randomness introduced by dropout …
[HTML][HTML] A review on dropout regularization approaches for deep neural networks within the scholarly domain
Dropout is one of the most popular regularization methods in the scholarly domain for
preventing a neural network model from overfitting in the training phase. Develo** an …
preventing a neural network model from overfitting in the training phase. Develo** an …
[HTML][HTML] Temporal fusion transformers for interpretable multi-horizon time series forecasting
Multi-horizon forecasting often contains a complex mix of inputs–including static (ie time-
invariant) covariates, known future inputs, and other exogenous time series that are only …
invariant) covariates, known future inputs, and other exogenous time series that are only …
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
[فهرست منابع][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
A tutorial review of neural network modeling approaches for model predictive control
An overview of the recent developments of time-series neural network modeling is
presented along with its use in model predictive control (MPC). A tutorial on the construction …
presented along with its use in model predictive control (MPC). A tutorial on the construction …
Deep equilibrium models
We present a new approach to modeling sequential data: the deep equilibrium model
(DEQ). Motivated by an observation that the hidden layers of many existing deep sequence …
(DEQ). Motivated by an observation that the hidden layers of many existing deep sequence …
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …
networks (RNN), has received much attention in recent years. However, the potential of RNN …