Results 61 to 70 of about 127,719 (261)
On the Design of LQR Kernels for Efficient Controller Learning
, 2017 Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point
Hennig, Philipp +3 more
core +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization [PDF]
E3S Web of ConferencesDeep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data.
Dewi Irma Amelia +1 more
doaj +1 more source
COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy
Future Internet, 2021 The present work relates to the implementation of core parallel architecture in a deep learning algorithm. At present, deep learning technology forms the main interdisciplinary basis of healthcare, hospital hygiene, biological and medicine.
Manickam Murugappan +4 more
doaj +1 more source
Optuna: Finding the optimal hyperparameters
, 2023 Application of Optuna to find the optimal hyperparameters for transfer learning or fine tuning the pre-trained models This code was used to find best hyperparameters to classify MS and Normal cases using SLO images. However it can be used in any other application.
Aghababaei Ali +2 more
openaire +1 more source
Advanced Functional Materials, EarlyView.By integrating machine learning into flux‐regulated crystallization (FRC), accurate prediction of solvent evaporation rates in real time, improving crystallization control and reducing crystal growth variability by over threefold, is achieved. This enhances the reproducibility and quality of perovskite single crystals, leading to reproducible ...
Tatiane Pretto +8 more
wiley +1 more source
LSTM Hyperparameters optimization with Hparam parameters for Bitcoin Price Prediction
Sakarya University Journal of Computer and Information Sciences, 2023 Machine learning and deep learning algorithms produce very different results with different examples of their hyperparameters. Algorithm parameters require optimization because they aren't specific for all problems.
Fatih Akay, I.sibel Kervancı
doaj +1 more source
EigenGP: Gaussian Process Models with Adaptive Eigenfunctions [PDF]
, 2015 Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost for big data. In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary
Peng, Hao, Qi, Yuan
core
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion
, 2013 © 2014 IEEE.The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting.
Calandra, R +3 more
core +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source