Results 101 to 110 of about 1,338,397 (280)

A Static Security Region Analysis of New Power Systems Based on Improved Stochastic–Batch Gradient Pile Descent

Applied Sciences
The 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, Yide Zhou, Haiyun Wang, Weiqing Wang   +3 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, Ivica Nikolic, Racchit Jain, Divesh Aggarwal, Prateek Saxena   +4 more
openaire   +1 more source

Semi-Cyclic Stochastic Gradient Descent

CoRR, 2019
We consider convex SGD updates with a block-cyclic structure, i.e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution. This situation arises, e.g., in Federated Learning where the mobile devices available for updates at different times during the day have different ...
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar   +4 more
openaire   +3 more sources

Load Distributing Metamaterials Via Discrete Optimization

Advanced Functional Materials, EarlyView.
Mechanical metamaterials are computationally optimized to homogenize transmitted forces by minimizing the spread of reaction forces. The resulting architectures transform localized loading into broader, more uniform force distributions and experimentally demonstrate robust load spreading under quasi‐static and impact loading.
Andrea Detry, Hannes Holey, Raja Zulkarnain, Roberto Guerra, Ilaria Papa, Michael Zaiser, Stefano Zapperi   +6 more
wiley   +1 more source

Spatially Regulated Silicon Clusters in Trimodal Composite Anodes for High‐Energy Lithium‐Ion Batteries

Advanced Functional Materials, EarlyView.
A trimodal anode architecture spatially regulates silicon clusters within confined interstitial environments formed by graphite and contorted hexabenzocoronene. This confinement suppresses silicon aggregation and localized stress while enabling efficient Li‐ion transport, achieving high‐capacity, stable lithium‐ion batteries.
Jeongmi Joo, Minsung Kang, Seung Hak Oh, Jee Ho Ha, Hyunji Cha, Jimin Han, Ji‐Su Lim, Dae Hyeon Kwon, Won‐Jin Kwak, Sang Kyu Kwak, Seokhoon Ahn, Seok Ju Kang   +11 more
wiley   +1 more source

On the Hyperparameters in Stochastic Gradient Descent with Momentum

CoRR, 2021
34 pages, 4 figures.
openaire   +3 more sources

Architecture‐Driven Functional Coupling in Vertically Aligned Nanocomposites

Advanced Functional Materials, EarlyView.
Vertically aligned nanocomposites define a growth‐engineered architecture in which vertical interfaces, strain fields, defect pathways, and phase connectivity are created simultaneously. This review shows how these architectural features couple ferroic, optical, ionic, electrochemical, and device responses, establishing design rules and open challenges
Md Shatil Islam‐Shanto, Javier Zamudio‐García, Abraham Sánchez‐Caballero, David Marrero‐López, Vincenzo Esposito   +4 more
wiley   +1 more source

Adam Algorithm with Step Adaptation

Algorithms
Adam (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, Elena Tovbis, Lev Kazakovtsev   +2 more
doaj   +1 more source

Accelerated Discovery of Topological Spin Phase Diagrams in CrSBr Magnets Via High‐Throughput Optimization

Advanced Functional Materials, EarlyView.
Here, we develop a high‐throughput algorithm that accelerates the elucidation of phase diagrams of topological spin textures using limited computational resources at high numerical accuracy. Applying this framework to the van der Waals magnet CrSBr, we unveiled a hierarchy of previously unknown topological textures (domain‐wall bimerons, bimeron chains,
Andrew Lyall, Mohammad H. Badarneh, Cristian Bonato, Rebecca Cheung, Elton J. G. Santos   +4 more
wiley   +1 more source

Adaptive Natural Gradient Method for Learning of Stochastic Neural Networks in Mini-Batch Mode

Applied Sciences, 2019
Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of ...
Hyeyoung Park, Kwanyong Lee
doaj   +1 more source