Results 81 to 90 of about 22,674 (253)
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
Advanced Materials, EarlyView.This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Applied SciencesThe uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system ...
Jiahui Wu +3 more
doaj +1 more source
Advanced Materials, EarlyView.Path‐decoupled III–V van der Waals memtransistors spatially separate ionic and electronic transport to overcome the conventional trade‐off between accuracy and energy in neuromorphic hardware. Mobile K+ ions in the vdW gaps set a wide conductance window, Gmax/Gmin, while gate‐tunable hole conduction lowers programming energy, enabling reliable ...
Jihong Bae +13 more
wiley +1 more source
On the Hyperparameters in Stochastic Gradient Descent with Momentum
CoRR, 2021 34 pages, 4 figures.
openaire +3 more sources
Resolving Heterogeneity of Targeted Lipid Nanoparticles Through Solution‐Based Biophysical Analyses
Advanced Materials, EarlyView.AF4‐UV‐DLS‐MALS‐SAXS resolves previously inaccessible targeted lipid nanoparticle (tLNP) subpopulations that differ in size, shape, and composition. Correlation of subpopulation‐resolved biophysical properties with in vivo RNA delivery reveals that targeted placental transfection is associated with distinct tLNP subpopulations rather than ensemble ...
Hannah C. Geisler +14 more
wiley +1 more source
Adam Algorithm with Step Adaptation
AlgorithmsAdam (Adaptive Moment Estimation) is a well-known algorithm for the first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.
Vladimir Krutikov +2 more
doaj +1 more source
Unforgeability in Stochastic Gradient Descent
Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023 Teodora Baluta +4 more
openaire +1 more source
On the regularizing property of stochastic gradient descent
Inverse Problems, 2018 Stochastic gradient descent is one of the most successful approaches for solving large-scale problems, especially in machine learning and statistics. At each iteration, it employs an unbiased estimator of the full gradient computed from one single randomly selected data point.
Bangti Jin, Xiliang Lu
openaire +3 more sources
A 3D‐Printed Blister Test Platform for Quantifying Biointerface Adhesion Mechanisms
Advanced Materials Interfaces, EarlyView.A 3D‐printed blister platform enables energy‐resolved characterization of soft hydrogel–rigid interfaces. Integrating precision pressure control with hyperelastic modeling directly quantifies adhesion energy (G) and R‐curve toughening. Results reveal that modulating hydrogel concentration and surface roughness drives a tunable transition from cohesive ...
Yoontae Kim +4 more
wiley +1 more source
A Bootstrap Perspective on Stochastic Gradient Descent
CoRRMachine learning models trained with \emph{stochastic} gradient descent (SGD) can generalize better than those trained with deterministic gradient descent (GD). In this work, we study SGD's impact on generalization through the lens of the statistical bootstrap: SGD uses gradient variability under batch sampling as a proxy for solution variability under
Hongjian Lan +2 more
openaire +2 more sources