Hyperparameters - Open Access .click

Results 61 to 70 of about 110,596 (265)

Summary of hyperparameters tuning.

, 2020
Summary of hyperparameters tuning.
Ahmed A. Khalil (2966979) +6 more
core +1 more source

Hyperparameters configurations of proposed architecture.

, 2022
Hyperparameters configurations of proposed architecture.
Jianqiang Li (390212) +5 more
core +1 more source

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

Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving

Applied Sciences, 2021
The runtime behavior of Simulated Annealing (SA), similar to other metaheuristics, is controlled by hyperparameters. For SA, hyperparameters affect how “temperature” varies over time, and “temperature” in turn affects SA’s decisions on whether or not to ...
Vincent A. Cicirello
doaj +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

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

An Enhanced Machine Learning Framework for Network Anomaly Detection

AI
Given the increasing volume and sophistication of cyber-attacks, there has always been a need for improved and adaptive real-time intrusion detection systems.
Oumaima Chentoufi, Mouad Choukhairi, Khalid Chougdali +2 more
doaj +1 more source

Optimal hyperparameters of the models.

, 2022
Optimal hyperparameters of the models.
Uyen Le (2228929) +2 more
core +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source

Sorghum crops classification and segmentation using shifted window transformer neural network and localization based on (YOLO)v9-path aggregation network

Frontiers in Plant Science
IntroductionThe world’s population has been increasing continuously, and this requires prompt action to ensure food security. One of the top five cereals produced worldwide, sorghum, is a staple of the diets of many developing nations.
Javaria Amin +5 more
doaj +1 more source

Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products

Applied Sciences, 2021
Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine.
Yasunari Matsuzaka +4 more
doaj +1 more source

deep learning
classification
optimization

machine learning
convolutional neural networks
convolutional neural network