Results 61 to 70 of about 255,317 (213)
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
Kullback-Leibler divergence for the Fr\'echet extreme-value distribution [PDF]
, 2023 We derive a closed-form solution for the Kullback-Leibler divergence between two Fr\'echet extreme-value distributions.
Pain, Jean-Christophe
core +1 more source
Uniqueness and Optimality of Dynamical Extensions of Divergences
PRX Quantum, 2021 We introduce an axiomatic approach for channel divergences and channel relative entropies that is based on three information-theoretic axioms of monotonicity under superchannels, i.e., generalized data processing inequality, additivity under tensor ...
Gilad Gour
doaj +1 more source
Kullback-Leibler divergence as a function of the contrast.
, 2013 Average Kullback-Leibler divergence ±95% confidence interval at the different contrast (N = 12). For each recorded cell the Kullback-Leibler divergence was estimated at each instance of time and was subsequently averaged across time giving a single ...
Anne-Kathrin Warzecha (57382) +3 more
core +1 more source
Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs
Advanced Intelligent Discovery, EarlyView.Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal +3 more
wiley +1 more source
Balancing Reconstruction Error and Kullback-Leibler Divergence in Variational Autoencoders
IEEE Access, 2020 Likelihood-based generative frameworks are receiving increasing attention in the deep learning community, mostly on account of their strong probabilistic foundation.
Andrea Asperti, Matteo Trentin
doaj +1 more source
Current Standards of Monitoring Models in Healthcare Settings
Advanced Intelligent Discovery, EarlyView.AI/ML‐enabled medical devices are entering clinical practice faster than monitoring standards mature. This review highlights gaps in postmarket surveillance, limited use of predetermined change‐control plans, and the need for ongoing performance tracking, drift detection, explainability, and workflow‐aware governance to support safer, more reliable ...
Alan Kay +5 more
wiley +1 more source
Physical Review ResearchWe demonstrate a model-free data analysis framework that leverages escort-weighted Shannon entropy and several divergence matrices to detect phase transitions in scattering and imaging datasets.
Jared Coles +11 more
doaj +1 more source
A Kullback–Leibler divergence method for input–system–state identification [PDF]
The capability of a novel Kullback–Leibler divergence method is examined herein within the Kalman filter framework to select the input–parameter–state estimation execution with the most plausible results.
Impraimakis, M. +2 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.We present CatTransVAE, a catalyst‐specialized chemical language model (CLM) built on a transformer variational autoencoder (VAE), developed through pretraining on general compounds followed by fine‐tuning on diverse catalyst databases. A template‐guided generation framework is introduced to enable controlled catalyst design under structural ...
Apakorn Kengkanna, Masahito Ohue
wiley +1 more source