Results 81 to 90 of about 151,967 (341)
Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization
HeliyonThe immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task,
Mikołaj Wojciuk +3 more
semanticscholar +1 more source
Quantum Machine Learning hyperparameter search
, 2023 This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry.
Corretgé, Àlex +6 more
core
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
MathematicsSpeckle reduction in Synthetic Aperture Radar (SAR) images is a crucial challenge for effective image analysis and interpretation in remote sensing applications. This study proposes a novel deep learning-based approach using autoencoder architectures for
Ahmed Alejandro Cardona-Mesa +5 more
doaj +1 more source
Hyperparameter Optimization with Neural Network Pruning [PDF]
, 2022 Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time.
Yim, Junho, Lee, Kangil
core
New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Optimizing lung cancer classification through hyperparameter tuning
Digital HealthArtificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate.
S. Nabeel +9 more
semanticscholar +1 more source
Hyperparameter Optimization Across Problem Tasks [PDF]
, 2019 Hyperparameter Optimization is a task that is generally hard to accomplish as the correct setting of hyperparameters cannot be learned from the data directly.
Schmidt-Thieme, Lars +2 more
core +1 more source
Fairness-Aware Hyperparameter Optimization [PDF]
, 2020 In recent years, increased usage of machine learning algorithms has been accompanied by several reports of machine bias in areas from recidivism assessment, to job-applicant screening tools, and estimating mortgage default risk.
André Miguel Ferreira da Cruz
core +1 more source
Implantable Ionic Memristors Based on Natural Polymer Heterojunctions
Advanced Functional Materials, EarlyView.We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee +6 more
wiley +1 more source