Results 51 to 60 of about 1,925,652 (255)
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
Amortized Bayesian inference of Gaussian process hyperparameters [PDF]
, 2022 The application of Gaussian processes (GPs) is limited by the rather slow process of optimizing the hyperparameters of a GP kernel which causes problems especially in applications -- such as Bayesian optimization -- that involve repeated optimization of ...
Rehn, Aki
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Hyperparameters Optimization for Federated Learning System : Speech Emotion Recognition Case Study
, 2023 Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device.
Mohammadi, Mohammadreza, +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
IEEE Access, 2019 Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments.
Ashir Javeed +5 more
doaj +1 more source
Goal-oriented sensitivity analysis of hyperparameters in deep learning
, 2023 International audienceTackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly impact their performances.
Congedo, Pietro Marco +7 more
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
A Hybrid Sparrow Search Algorithm of the Hyperparameter Optimization in Deep Learning
Mathematics, 2022 Deep learning has been widely used in different fields such as computer vision and speech processing. The performance of deep learning algorithms is greatly affected by their hyperparameters.
Yanyan Fan +5 more
doaj +1 more source
Optimization of Annealed Importance Sampling Hyperparameters
, 2023 AbstractAnnealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between ...
Shirin Goshtasbpour +1 more
openaire +4 more sources
Artificial Neural Network Hyperparameters Optimization: A Survey [PDF]
, 2022 Machine-learning (ML) methods often utilized in applications like computer vision, recommendation systems, natural language processing (NLP), as well as user behavior analytics.
Kadhim, Zahraa Saddi +2 more
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