Results 81 to 90 of about 1,858,266 (298)
Frugal Optimization for Cost-related Hyperparameters
, 2021 The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Wang, Chi, Huang, Silu, Wu, Qingyun
core +1 more source
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
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
Design of adaptive soft sensor based on Bayesian optimization
Case Studies in Chemical and Environmental Engineering, 2022 When adaptive soft sensors are introduced to industrial plants, an appropriate combination of the type of adaptation mechanism, hyperparameters of the mechanism, regression model, and hyperparameters of the model must be selected for predictive soft ...
Shuto Yamakage, Hiromasa Kaneko
doaj +1 more source
Optimizing Hyperparameters with Conformal Quantile Regression
CoRR, 2023 Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be ...
David Salinas +4 more
openaire +4 more sources
An Effective Hyperparameters Optimization Algorithm for Metro Passenger Flow Prediction
, 2021 An accurate neural network model must rely on suitable model architecture and an appropriate model training process. The hyperparameters optimization problem aims to search for the best neural network architecture in a large solution space and maximize ...
Fang, Zhi-Yan
core
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
Computer Assisted Methods in Engineering and ScienceThis paper describes the application of particle swarm optimization (PSO) for the hyperparameter optimization problem of multi-layered perceptron (MLP) model.
Kenta Shiomi, Tetsuya Sato, Eisuke Kita
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Optimization-seeking experiment with hyperparameters K.
Optimization-seeking experiment with hyperparameters K.
Xi Zhang (83736) +5 more
core +1 more source
Multimode Oxide‐Based Optoelectronic Memtransistor for In‐Sensor Vision Processing
Advanced Functional Materials, EarlyView.A multimode optoelectronic memtransistor (OEMT) is demonstrated for vision explainable artificial intelligence (VXAI) hardware. By integrating optical sensing, electrical masking, and non‐volatile memory, the device enables key operations required for generating saliency information.
Min Gu Lee +10 more
wiley +1 more source