Results 151 to 160 of about 1,338,246 (278)
Certain Systems Arising In Stochastic Gradient Descent [PDF]
, 2019 Stochastic approximations is a rich branch of probability theory and has a wide range of application. Here we study stochastic approximations from the perspective of gradient descent.
Karatapanis, Konstantinos
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Advanced Materials Technologies, EarlyView.A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn +2 more
wiley +1 more source
Non-Iterative Phase-Only Hologram Generation via Stochastic Gradient Descent Optimization
PhotonicsIn this work, we explored, for the first time, to the best of our knowledge, the potential of stochastic gradient descent (SGD) to optimize random phase functions for application in non-iterative phase-only hologram generation.
Alejandro Velez-Zea +1 more
doaj +1 more source
Low‐Pressure Plasma‐Based Wrinkling of PDMS and Machine Learning‐Driven Property Engineering
Advanced Materials Technologies, EarlyView.Wrinkled surfaces are well‐suited for controlled surface deformations in the µm range. The key challenge is the relation between the resulting wrinkle features and the necessary process conditions. Machine learning techniques have solved the prediction and inverse design problems for various preparation conditions, opening a precisely controlled ...
Fabian Kopsch +7 more
wiley +1 more source
An Improved Reacceleration Optimization Algorithm Based on the Momentum Method for Image Recognition
MathematicsThe optimization algorithm plays a crucial role in image recognition by neural networks. However, it is challenging to accelerate the model’s convergence and maintain high precision.
Haijing Sun +6 more
doaj +1 more source
Sentiment classification for employees reviews using regression vector- stochastic gradient descent classifier (RV-SGDC). [PDF]
PeerJ Comput Sci, 2021 Gaye B, Zhang D, Wulamu A.
europepmc +1 more source
Parallelized stochastic gradient descent
, 2010 With the increase in available data parallel machine learning has become an increasingly pressing problem. In this paper we present the first parallel stochastic gradient descent algorithm including a detailed analysis and experimental evidence.
Martin A Zinkevich +3 more
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Wavelength‐Multiplexed 2D Beam Steering via a Passive Diffractive Network
Advanced Optical Materials, EarlyView.Illustration of a wavelength‐multiplexed diffractive beam steering system, which is composed of K cascaded diffractive layers, each containing phase‐modulating elements that are jointly optimized using deep learning–based optimization. When illuminated with a set of wavelengths {λ1,λ2,…,λNw}$\{ {{{\lambda }_1},{{\lambda }_2},\ldots ,{{\lambda }_{{{N}_w}
Che‐Yung Shen +5 more
wiley +1 more source
Information Transmission Strategies for Self‐Organized Robotic Aggregation
Advanced Robotics Research, EarlyView.In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Semi-Stochastic Gradient Descent Methods [PDF]
, 2013 In this paper we study the problem of minimizing the average of a large number ($n$) of smooth convex loss functions. We propose a new method, S2GD (Semi-Stochastic Gradient Descent), which runs for one or several epochs in each of which a single full ...
Richtárik, Peter, Konečný, Jakub
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