Results 31 to 40 of about 93,522 (265)
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
, 2019 We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Larsson, Erik G., Sadeghi, Meysam
core +1 more source
Detection of cyber-attacks on the power smart grids using semi-supervised deep learning models
Discrete and Continuous Models and Applied Computational Science, 2022 Modern smart energy grids combine advanced information and communication technologies into traditional energy systems for a more efficient and sustainable supply of electricity, which creates vulnerabilities in their security systems that can be used by ...
Eugeny Yu. Shchetinin +1 more
doaj +1 more source
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
, 2017 Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data.
Noé, Frank, Wehmeyer, Christoph
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Multiresolution convolutional autoencoders
Journal of Computational Physics, 2023 20 pages, 11 ...
Yuying Liu +3 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Cuttlebone‐inspired metamaterials exploit a septum‐wall architecture to achieve excellent mechanical and functional properties. This review classifies existing designs into direct biomimetic, honeycomb‐type, and strut‐type architectures, summarizes governing design principles, and presents a decoupled design framework for interpreting multiphysical ...
Xinwei Li, Zhendong Li
wiley +1 more source
An autoencoder-based deep learning method for genotype imputation
Frontiers in Artificial Intelligence, 2022 Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine ...
Meng Song +15 more
doaj +1 more source
Grammar Variational Autoencoder [PDF]
, 2017 Deep generative models have been wildly successful at learning coherent latent representations for continuous data such as video and audio. However, generative modeling of discrete data such as arithmetic expressions and molecular structures still poses ...
Hernández-Lobato, José Miguel +2 more
core +1 more source
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
, 2019 Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however,
Bergmann, Paul +4 more
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Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling
Advanced Healthcare Materials, EarlyView.Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee +7 more
wiley +1 more source
A recurrent neural network for classification of unevenly sampled variable stars
, 2017 Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time ("light curves"). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to ...
Bloom, Joshua S. +3 more
core +1 more source