Results 161 to 170 of about 5,065 (216)

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

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

Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges. [PDF]

Front Bioinform
Tchatchoua Ngassam B, Niu H, Pang S, Shydlouskaya V, Andrews TS.   +4 more
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

End-to-End DAE-LDPC-OFDM Transceiver with Learned Belief Propagation Decoder for Robust and Power-Efficient Wireless Communication. [PDF]

Sensors (Basel)
Mohammed M, Çevik M.
europepmc   +1 more source

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning. [PDF]

Sci Rep
Geng PX, Hou J, Guo J, Jiang X, Zhu H.
europepmc   +1 more source

Phase structure and machine learning identification in one dimensional systems with power law correlated disorder and long range hopping. [PDF]

Sci Rep
Pouranvari M.
europepmc   +1 more source

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

Journal of Mathematical Imaging and Vision, 2022
International audienceAutoencoders 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.
Chi-Hieu Pham, Saïd Ladjal, Alasdair Newson   +2 more
openaire   +2 more sources

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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