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Published in Biological Cybernetics

In Bourlard and Kamp (Biol Cybern 59(4):291–294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called “auto-associative multilayer perceptrons”) were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least squares so...

Unsupervised remaining useful life prediction through long range health index estimation based on encoders-decoders

The prediction of the Remaining Useful Life (RUL) is a critical step in Prognostics and Health Management (PHM) of systems under degradation. For efficient RUL predictions, most of the Artificial Intelligence (AI-based) methods perform direct mapping between raw sensor data input and RUL data as output targets for supervised learning. However, in t...

PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks

Autoencoders and generative models produce some of the most spectacular deep learning results to date. However, understanding and controlling the latent space of these models presents a considerable challenge. Drawing inspiration from principal component analysis and autoencoders, we propose the Principal Component Analysis Autoencoder (PCA-AE). Th...

Fast and Robust Exudate Detection in Retinal Fundus Images Using Extreme Learning Machine Autoencoders and Modified KAZE...

Published in Journal of digital imaging

Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes a...

Emulation and prediction of cosmic web simulations through deep learning

The standard cosmological model provides a description of the Universe as a whole: its content, its evolution and its dynamics. A standard way of determining the evolution of matter in the Universe rests on the use of numerical simulations that are very expensive in terms of running time, storage and computing power. We explore the use of deep neur...

A twin-decoder structure for incompressible laminar flow reconstruction with uncertainty estimation around 2D obstacles

Over the past few years, deep learning methods have proved to be of great interest for the computational fluid dynamics community, especially when used as surrogate models, either for flow reconstruction, turbulence modeling, or for the prediction of aerodynamic coefficients. Overall exceptional levels of accuracy have been obtained but the robustn...

DEEP-LEARNING-ENHANCED MULTIPHYSICS FLOW COMPUTATIONS FOR PROPULSION APPLICATIONS

Numerical simulation is a critical part of research into and development of engineering systems. Engineers often use simulation to explore design settings both analytically and numerically before prototypes are built and tested. Even with the most advanced high performance computing facility, however, high-fidelity numerical simulations are extreme...

Generative Deep Learning for Targeted Compound Design.

Published in Journal of chemical information and modeling

In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechno...

Structured Learning with Manifold Representations of Natural Data Variations

According to the manifold hypothesis, natural variations in high-dimensional data lie on or near a low-dimensional, nonlinear manifold. Additionally, many identity-preserving transformations are shared among classes of data which can allow for an efficient representation of data variations: a limited set of transformations can describe a majority o...