Results 71 to 80 of about 6,929,542 (320)
, 2021 Due to the rising demand for individualized product specifications and short innovation cycles, industrial robots gain increasing attention for machining operations as milling and forming.
Julian Blumberg +4 more
core +1 more source
Technologies, 2023 This paper explores the application of various machine learning techniques for predicting customer churn in the telecommunications sector. We utilized a publicly accessible dataset and implemented several models, including Artificial Neural Networks ...
Mehdi Imani, H. Arabnia
semanticscholar +1 more source
On Hyperparameter Optimization for Deep Learning [PDF]
, 2020 Deep learning has recently achieved many breakthroughs. Neural networks - the models behind deep learning - have a large number of hyperparameters whose correct settings are crucial to obtain optimal performance.
Hertel, Lars Heinrich
core +1 more source
Replication Data for: eSLP optimization algorithm
, 2023 m-files implementing the eSLP optimization algorithm for the three simulations (3.1. - 3.3.) described in the associated paper. File README.txt contains analytical directions regarding requirements, how to verify the results and videos generated by the ...
Optimization, eSLP
core +1 more source
Predictive Value of Composite Inflammatory Markers for Stroke Prognosis: A Prospective Cohort Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Novel composite inflammatory markers' role in stroke prognosis is understudied, and the best predictor is unclear, requiring further exploration. Objectives This study aimed to systematically evaluate the associations of 6 novel composite inflammatory markers on stroke prognosis.
Bing Wu +7 more
wiley +1 more source
Journal of Electrical Engineering and ComputerThis research aims to optimize the classification of diseases on corn leaves using Convolutional Neural Network (CNN) architecture, ResNet50, combined with hyperparameter optimization techniques using Bayesian Optimization.
Yahya Auliya Abdillah, Kusrini Kusrini
doaj +1 more source
HDS-LEE Course on Hyperparameter Optimization [PDF]
, 2020 Part I: Theory - Basics of Hyperparameter Optimization - Exhausive Searches - Surrogate-based Optimization, Sequential Model-based Optimization and Bayesian Inference - Evolutionary Strategies Part II: Hands-on programming session "Hyperparameter
Rüttgers, Alexander, Debus, Charlotte
core
kravitsjacob/multiobjective-hyperparameter: Update function names
, 2022 Updated function names PEP8 compliant What's Changed Update function names by @kravitsjacob in https://github.com/kravitsjacob/multiobjective-hyperparameter/pull/1 New Contributors @kravitsjacob made their first contribution in https://github.com ...
Jacob Kravits
core +1 more source
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification
IEEE AccessThis paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification applications often suffer from data inadequacy.
Amala Mary Vincent +2 more
doaj +1 more source