Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study
Sign Up to like & getrecommendations! Published in 2019 at "Human Brain Mapping"
DOI: 10.1002/hbm.24423
Abstract: Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in… read more here.
Keywords: using deep; autoencoders identify; model; deep autoencoders ... See more keywords
On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
Sign Up to like & getrecommendations! Published in 2024 at "Advances in Computational Mathematics"
DOI: 10.1007/s10444-024-10189-6
Abstract: Deep Learning is having a remarkable impact on the design of Reduced Order Models (ROMs) for Partial Differential Equations (PDEs), where it is exploited as a powerful tool for tackling complex problems for which classical… read more here.
Keywords: reduced order; latent dimension; deep autoencoders; dimension deep ... See more keywords
Searching for new physics with deep autoencoders
Sign Up to like & getrecommendations! Published in 2020 at "Physical Review D"
DOI: 10.1103/physrevd.101.075021
Abstract: We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning. The key idea of the autoencoder is that it learns to map "normal" events… read more here.
Keywords: physics deep; physics; new physics; searching new ... See more keywords