Results 131 to 140 of about 60,311 (308)
Comparison of Variation Autoencoder with Autoencoder Ala Principe [PDF]
, 2022 In the past few years Generative models have become an interesting topic in the field of Machine Learning (ML). Variational Autoencoder (VAE) is one of the popular frameworks of generative models based on the work of D.P Kingma and M.
Naredla, Santhosh
core
Advanced Science, EarlyView.Bulk and genotype‐imputed transcriptomes obscure the cellular drivers of complex disease. CausalCellInfer resolves them by integrating invariant marker selection, deep learning deconvolution, and regularized matrix completion. Applied to ∼500 000 UK Biobank participants across 29 traits, it uncovers hallmark cell‐type proportion shifts, including ...
Liangying Yin +6 more
wiley +1 more source
Structure of semi-supervised additive noise autoencoder.
, 2019 Structure of semi-supervised additive noise autoencoder.
Hongliang Fu (582225) +4 more
core +1 more source
Advanced Science, EarlyView.This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui +4 more
wiley +1 more source
Gaussian processes autoencoder for dimensionality reduction [PDF]
, 2020 . Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing.
Hong, X, J, Cai, Gao X Jiang
core
ReLink: Reliable Deep Learning for Microscopic Motion Tracking
Advanced Science, EarlyView.ReLink enables reliable trajectory linking in single‐particle tracking by integrating physics‐informed motion statistics with confidence‐based adaptive filtering. Across diverse simulated and experimental conditions, ReLink robustly reconstructs trajectories and preserves the fidelity of downstream analyses, enabling accurate characterization of ...
Yuan Jiang +7 more
wiley +1 more source
Scheduled denoising autoencoders
, 2014 We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the ...
Geras, Krzysztof, Sutton, Charles
openaire +4 more sources
Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization
Advanced Electronic Materials, EarlyView.Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali +3 more
wiley +1 more source
Advanced Energy Materials, EarlyView.The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho +5 more
wiley +1 more source
Applied Economic Perspectives and Policy, EarlyView.ABSTRACT Disruptions in global fertilizer markets and climate targets have increased interest in fossil‐free mineral nitrogen fertilizers. This study examines farmers' preferences for conventional (fossil‐based) and fossil‐free fertilizers using a labeled discrete choice experiment with 211 Swedish farmers.
Nasir Adam +5 more
wiley +1 more source