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Found 7 papers, 0 papers with code
A Fast Parallel Tensor Decomposition with Optimal Stochastic Gradient Descent: an Application in Structural Damage Identification
Our approach is based on stochastic gradient descent (SGD) algorithm which allows us to parallelize the learning process and it is very useful in online setting since it updates $\mathcal{X}^{t+1}$ in one single step.
Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM and Dynamic Behavioral Models
Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the Covid-19 infection is substantial.
Control Design of Autonomous Drone Using Deep Learning Based Image Understanding Techniques
This paper presents a new framework to use images as the inputs for the controller to have autonomous flight, considering the noisy indoor environment and uncertainties.
Multi-User Remote lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm
The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm and Non-dominated Sorting Genetic Algorithm (NSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access.
NeCPD: An Online Tensor Decomposition with Optimal Stochastic Gradient Descent
Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $.
Multi-Objective Variational Autoencoder: an Application for Smart Infrastructure Maintenance
Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order data sets where standard two-way analysis techniques often fail to discover the hidden correlations between variables in multi-way data.
Online Tensor-Based Learning for Multi-Way Data
The online analysis of multi-way data stored in a tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $ has become an essential tool for capturing the underlying structures and extracting the sensitive features which can be used to learn a predictive model.
