Suchergebnisse - sparse conventional autoencoder (((same OR sae) OR space))

An Explainable AI Framework Integrating Variational Sparse Autoencoder and Random Forest for EEG-Based Epilepsy Detection

Autoren: Pratiti Mishra Himansu Das

Quelle: IEEE Access, Vol 13, Pp 179568-179583 (2025)

Schlagwörter: EEG, epilepsy, LIME, RF, SAE, SHAP, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Dateibeschreibung: electronic resource

Relation: https://ieeexplore.ieee.org/document/11202459/; https://doaj.org/toc/2169-3536

Zugangs-URL: https://doaj.org/article/5ce5fe264c50481f8759c3ca8ec138c9

Comparing Functional and Effective Connectivity Features in Diagnosis of Autism Spectrum Disorder Using Stacked Autoencoder by Resting-State fMRI Data.

Autoren: Navaei, Pegah Khadem, Ali Zaferani, Effat Jalaeian

Quelle: Frontiers in Biomedical Technologies; Autumn2025, Vol. 12 Issue 4, p830-843, 14p

Schlagwörter: AUTISM spectrum disorders, FUNCTIONAL connectivity, AUTOENCODERS, ARTIFICIAL neural networks, FUNCTIONAL magnetic resonance imaging, GRANGER causality test

A Novel Deep Neural Network-Based Framework for Subsurface Analysis in Non-Stationary Thermal Wave Imaging.

Autoren: Swapna, M. N. Suman, M. Prasanthi, Y. N. et al.

Quelle: Sensing & Imaging; 9/5/2025, Vol. 26 Issue 1, p1-18, 18p

Efficient sparse-view medical image classification for low radiation and rapid COVID-19 diagnosis.

Autoren: Gwak, Seunghyun Yang, Sooyoung Jeong, Heawon et al.

Quelle: Biomedical Engineering Letters; Jul2025, Vol. 15 Issue 4, p785-795, 11p

Visual Exploration of Feature Relationships in Sparse Autoencoders with Curated Concepts

Autoren: Yan, Xinyuan Liu, Shusen Thopalli, Kowshik et al.

Schlagwörter: Computation and Language, Machine Learning

Relation: http://arxiv.org/abs/2511.06048

Verfügbarkeit: http://arxiv.org/abs/2511.06048

Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone

Autoren: Bărbălau, Antonio Păduraru, Cristian Daniel Poncu, Teodor et al.

Schlagwörter: Machine Learning, Artificial Intelligence

Relation: http://arxiv.org/abs/2509.10809

Verfügbarkeit: http://arxiv.org/abs/2509.10809

Interpretable Reward Model via Sparse Autoencoder

Autoren: Zhang, Shuyi Shi, Wei Li, Sihang et al.

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2508.08746

Harnessing Generative Deep Learning for Enhanced Ensemble Data Assimilation.

Autoren: Foroumandi, Ehsan Moradkhani, Hamid

Quelle: Water Resources Research; Jul2025, Vol. 61 Issue 7, p1-25, 25p

Schlagwörter: DATA assimilation, HYDROLOGIC models, GENERATIVE artificial intelligence, AUTOENCODERS

WOT-AE: Weighted Optimal Transport Autoencoder for Patterned Fabric Defect Detection.

Autoren: Yang, Hui Kang, Linyan Yang, Tianjin

Quelle: Symmetry (20738994). Nov2025, Vol. 17 Issue 11, p1829. 18p.

Schlagwörter: *TEXTILES, *AUTOENCODERS, *MATRIX decomposition, *MACHINE learning, *SYMMETRY, *OUTLIER detection

DEEP LEARNING-BASED FACE-IRIS RECOGNITION WITH CAPSULE NEURAL NETWORK (CAPSNET) FOR BIOMETRIC ATTENDANCE SYSTEM.

Autoren: KARUNAKARAN, SANGEETHA AKILA, A.

Quelle: TPM: Testing, Psychometrics, Methodology in Applied Psychology; 2025 Supplement, Vol. 32, p345-358, 14p

ASD-SAENet: A Sparse Autoencoder, and Deep-Neural Network Model for Detecting Autism Spectrum Disorder (ASD) Using fMRI Data.

Autoren: Almuqhim, Fahad Saeed, Fahad

Quelle: Frontiers in Computational Neuroscience; 4/8/2021, Vol. 15, pN.PAG-N.PAG, 10p

Schlagwörter: AUTISM spectrum disorders, FUNCTIONAL magnetic resonance imaging, BRAIN imaging, NEURAL development, FEATURE extraction

A Trustworthy Artificial Intelligence Framework for Predicting Gasoline Octane Loss Using Sparse Autoencoder and Stacking Ensemble Learning in Petrochemical Processes

Autoren: Xiaochun Lin Shitao Yin Xiang Li

Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling.

Autoren: Park, Yeonjin Hong, Jungpyo

Quelle: Applied Sciences (2076-3417); Jan2025, Vol. 15 Issue 1, p92, 21p

Schlagwörter: VECTOR quantization, SIGNAL reconstruction, AUTOENCODERS, SIGNALS & signaling

Deep-Learning-Based Dictionary Construction for MIMO Radar Detection in Complex Scenes

Autoren: Hongyan Wang He Zhou Qinghua Guo et al.

Quelle: IEEE Internet of Things Journal. 11:12419-12434

Schlagwörter: nonlinear correction, Multiple-input multiple-output (MIMO) radar, transceiving space-time resource allocation, sparse representation, complex-valued convolutional autoencoder

Beyond Input Activations: Identifying Influential Latents by Gradient Sparse Autoencoders

Autoren: Shu, Dong Wu, Xuansheng Zhao, Haiyan et al.

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Artificial Intelligence, Computation and Language, Computation and Language (cs.CL), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2505.08080

Resurrecting the Salmon: Rethinking Mechanistic Interpretability with Domain-Specific Sparse Autoencoders

Autoren: O'Neill, Charles Jayasekara, Mudith Kirkby, Max

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2508.09363

Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy

Autoren: Balagansky, Nikita Aksenov, Yaroslav Laptev, Daniil et al.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2505.24473

AI in radiology: a comprehensive survey on content-based medical image analysis for lung diseases.

Autoren: Prakash, Ankur Singh, Vibhav Prakash

Quelle: Network Modeling & Analysis in Health Informatics & Bioinformatics; 12/4/2025, Vol. 14 Issue 1, p1-44, 44p

Digital Twin Applications in the Water Sector: A Review.

Autoren: Ghorbani Bam, Pooria Rezaei, Nader Roubanis, Alexander et al.

Quelle: Water (20734441); Oct2025, Vol. 17 Issue 20, p2957, 49p

Schlagwörter: DIGITAL twin, WATER management, REAL-time computing, PREDICTIVE control systems, RESOURCE allocation, RESOURCE management, ENVIRONMENTAL infrastructure

The Liver Meeting: 2025 Abstracts.

Quelle: Hepatology (02709139); 2025Supplement, Vol. 82, pS1-S2308, 2308p