Results 131 to 140 of about 3,896 (184)

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data. [PDF]

Health Data Sci
Sun C, Song M, Cai D, Zhang B, Li H, Hong S.   +5 more
europepmc   +1 more source

Decoding brain structure-function dynamics in health and in psychosis via an autoencoder. [PDF]

Sci Rep
Cai Q, Thomas H, Hyde V, Laguna PL, McNabb CB, Singh KD, Jones DK, Messaritaki E.   +7 more
europepmc   +1 more source

AE-RAGX: Combining Autoencoders with Retrieval-Augmented Generation for Explainable Anomaly Detection using LLMs

2025 IEEE Latin-American Conference on Communications (LATINCOM)
Perera, Lasith, Perera, Ryan, Amantha, Yasindu, Sheshan, Nithil, Moremada, Moremada Watte Gedara Charuka Kavinda, Wickrema Seneviratne, Chatura, Liyanage, Madhusanka   +6 more
openaire   +2 more sources

Exploring the spiking neural autoencoder: from hyperexcitability to noise-driven compensation. [PDF]

Front Syst Neurosci
Khodashenas M, Martins DP.
europepmc   +1 more source

An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars. [PDF]

Sci Rep
Buyukcakir B, De Tobel J, Thevissen P, Vandermeulen D, Claes P.   +4 more
europepmc   +1 more source

Anomaly Detection and Objective Security Evaluation Using Autoencoder, Isolation Forest, and Multi-Criteria Decision Methods. [PDF]

Sensors (Basel)
Zhang H, Zhang H.
europepmc   +1 more source

Explainable AI based cervical cancer prediction using FSAE feature engineering and H2O AutoML. [PDF]

Sci Rep
Karthikeyan P, Malaserene I, Deepakraj E.   +2 more
europepmc   +1 more source

Physics-informed differentiable solvers for learning parametric solution manifolds in heterogeneous physical systems. [PDF]

PNAS Nexus
Panahi M, Porta GM, Riva M, Guadagnini A.   +3 more
europepmc   +1 more source

End-to-end deep learning for smart maritime threat detection: an AE-CNN-LSTM-based approach. [PDF]

Sci Rep
Anuja R, Annrose J.
europepmc   +1 more source

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AE-MCCF: An Autoencoder-Based Multi-criteria Recommendation Algorithm

Arabian Journal for Science and Engineering, 2019
Recommender systems enable users to deal with the information overload problem by serving personalized predictions. Traditional recommendation techniques produce referrals for users by considering their overall opinions over items. On the other hand, users may consider several criteria while evaluating an item.
Zeynep Batmaz, Cihan Kaleli
openaire   +1 more source