Learning to detect
In this paper, we consider multiple-input-multiple-output detection using deep neural
networks. We introduce two different deep architectures: a standard fully connected multi …
networks. We introduce two different deep architectures: a standard fully connected multi …
Flow-based characterization of digital rock images using deep learning
X-ray imaging of porous media has revolutionized the interpretation of various microscale
phenomena in subsurface systems. The volumetric images acquired from this technology …
phenomena in subsurface systems. The volumetric images acquired from this technology …
Deep learning-based soft iterative-detection of channel-coded compressed sensing-aided multi-dimensional index modulation
X Feng, ELH Mohammed, C Xu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The concept of Index Modulation (IM) has been actively researched as a benefit of its flexible
trade-off between performance, achievable rate, energy efficiency, hardware cost and …
trade-off between performance, achievable rate, energy efficiency, hardware cost and …
Fast, accurate, and robust T2 mapping of articular cartilage by neural networks
For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by
non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and …
non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and …
Learning to estimate without bias
The Gauss–Markov theorem states that the weighted least squares estimator is a linear
minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a …
minimum variance unbiased estimation (MVUE) in linear models. In this paper, we take a …
AI-aided thermal imaging with multispectral camera for Direct Energy Deposition
Measuring the temperature of red-heat or molten metals is relevant for Additive
Manufacturing (AM) process monitoring and control. Sensors commonly integrated in AM …
Manufacturing (AM) process monitoring and control. Sensors commonly integrated in AM …
Robust loss function for deep learning regression with outliers
L Sadouk, T Gadi, EH Essoufi - … : Proceedings of ESAI 2019, Fez, Morocco, 2020 - Springer
In regression analysis, the presence of outliers in the data set can strongly distort the
classical least squares (known as “L2”) estimator and lead to unreliable results (due to the …
classical least squares (known as “L2”) estimator and lead to unreliable results (due to the …
Strength Lab AI: a mixture-of-experts deep learning approach for limit state analysis and design of monolithic and laminate structures made of glass
The demand for transparent building envelopes, particularly glass facades, is rising in
modern architecture. These facades are expected to meet multiple objectives, including …
modern architecture. These facades are expected to meet multiple objectives, including …
A least distance estimator for a multivariate regression model using deep neural networks
We propose a deep neural network (DNN) based least distance (LD) estimator (DNN-LD) for
a multivariate regression problem, addressing the limitations of the conventional methods …
a multivariate regression problem, addressing the limitations of the conventional methods …
Robust Losses in Deep Regression
A Rubio, JR Dorronsoro - International Conference on Hybrid Artificial …, 2023 - Springer
What is the noise distribution of a given regression problem is not known in advance and,
given that the assumption on which noise is present is reflected on the loss to be used, a …
given that the assumption on which noise is present is reflected on the loss to be used, a …