Results 231 to 240 of about 113,519 (298)

Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification. [PDF]

Sci Rep
Munquad S, Dash BK, Sengupta S, Singh V, Ushakiran Y, Das AB.   +5 more
europepmc   +1 more source

Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation

, 2017
Varghese Alex, Kiran Vaidhya, Subramaniam Thirunavukkarasu, Chandrasekharan Kesavadas, Ganapathy Krishnamurthi   +4 more
openalex   +2 more sources

Reduced‐Order Modeling of Energetic Materials Using Physics‐Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)

Propellants, Explosives, Pyrotechnics, EarlyView.
Physics‐Aware Recurrent Convolutional Neural Networks (PARC) can reliably learn the thermomechanics of energetic materials as a function of morphology. This work introduces LatentPARC, which accelerates PARC by modeling the dynamics in a low‐dimensional latent space.
Zoë J. Gray, Joseph B. Choi, Youngsoo Choi, H. Keo Springer, H. S. Udaykumar, Stephen S. Baek   +5 more
wiley   +1 more source

A new hybrid model for improving outlier detection using combined autoencoder and variational autoencoder. [PDF]

Sci Rep
Daoud AM, Elkomy OM, Khedr WI, Hosny KM.
europepmc   +1 more source

Ensemble Kalman filter in latent space using a variational autoencoder pair

Quarterly Journal of the Royal Meteorological Society, EarlyView.
The use of the ensemble Kalman filter (EnKF) in strongly nonlinear or constrained atmospheric, oceanographic, or sea‐ice models can be challenging. Applying the EnKF in the latent space of a variational autoencoder (VAE) ensures that the ensemble members satisfy the balances and constraints present in the model.
Ivo Pasmans, Yumeng Chen, Tobias Sebastian Finn, Marc Bocquet, Alberto Carrassi   +4 more
wiley   +1 more source

Error-aware probabilistic training for memristive neural networks. [PDF]

Nat Commun
Liu J, Lu J, Tang S, Zhou R, Ma H, Lyu B, Tian Y, Shi T, Liu Q.   +8 more
europepmc   +1 more source

Mitigating the Impact of Temperature Variations on Ultrasonic Guided Wave-Based Structural Health Monitoring through Variational Autoencoders [PDF]

Rafael Junges, Luca Lomazzi, Lorenzo Miele, Marco Giglio, Francesco Cadini   +4 more
openalex   +1 more source

A Mini Review on Evolution of High‐Entropy Alloy Design: From Experimental Approaches to Machine Learning Integration

Rare Metals, EarlyView.
ABSTRACT High‐entropy alloys (HEAs) have emerged as a transformative class of materials distinguished by their complex chemical compositions, unique microstructures, and remarkable mechanical and functional properties. Traditionally, the discovery and optimization of HEAs have relied on conventional methods, including trial‐and‐error experimentation ...
Chrispin Ouko Zamzu, Zhe Jia, Baolong Shen   +2 more
wiley   +1 more source

Comprehensive framework of machine learning and deep learning architectures with metaheuristic optimization for high-fidelity prediction of nanofluid specific heat capacity. [PDF]

Sci Rep
Mathur P, Kumar D, Sheth F, Shaikh H, Gupta AK.   +4 more
europepmc   +1 more source

Enabling dynamic 3D coherent diffraction imaging via adaptive latent space tuning of generative autoencoders [PDF]

Alexander Scheinker, Reeju Pokharel
openalex   +1 more source