Results 31 to 40 of about 113,519 (298)
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
Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results
IEEE AccessThis paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed structures include four stages: image preprocessing, feature extraction using autoencoder derivatives,
Azin Masoudi, Majid Ahmadi
doaj +1 more source
Error correction algorithm of array time-varying amplitude and phase based on autoencoder
Xibei Gongye Daxue Xuebao, 2023 As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology.
ZHANG Zixuan +3 more
doaj +1 more source
Generating 3D faces using Convolutional Mesh Autoencoders
, 2018 Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation.
Black, Michael J. +3 more
core +1 more source
The application of unsupervised deep learning in predictive models using electronic health records
BMC Medical Research Methodology, 2020 Background The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features
Lei Wang +4 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.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source
, 2016 Many success stories involving deep neural networks are instances of supervised learning, where available labels power gradient-based learning methods. Creating such labels, however, can be expensive and thus there is increasing interest in weak labels ...
Ewert, Sebastian, Sandler, Mark B.
core +1 more source