Results 11 to 20 of about 93,556 (252)
Collaborative hyperparameter tuning
, 2013 International audienceHyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches.
Bardenet, R. +3 more
core +3 more sources
Refining the ONCE Benchmark With Hyperparameter Tuning
IEEE AccessIn response to the growing demand for 3D object detection in applications such as autonomous driving, robotics, and augmented reality, this work focuses on the evaluation of semi-supervised learning approaches for point cloud data.
Maksim Golyadkin +3 more
doaj +3 more sources
A Study on the Implementation of YOLOv4 Algorithm with Hyperparameter Tuning for Car Detection in Unmanned Aerial Vehicle Images [PDF]
, 2023 Unmanned Aerial Vehicles (UAVs) for surveillance and monitoring have become more prevalent due to their versatility and mobility. These vehicles capture highresolution images that provide a broad field of view in real-time.
Azhar, Yufis +2 more
core +1 more source
High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
Algorithms, 2022 Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. However, just how useful is said tuning?
Moshe Sipper
doaj +1 more source
A hyperparameter‐tuning approach to automated inverse planning [PDF]
Medical Physics, 2022 AbstractBackgroundIn current practice, radiotherapy inverse planning often requires treatment planners to modify multiple parameters in the treatment planning system's objective function to produce clinically acceptable plans. Due to the manual steps in this process, plan quality can vary depending on the planning time available and the planner's ...
Maass, Kelsey +2 more
openaire +3 more sources
Journal of Ocean Engineering and Science, 2022 An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of ...
Tianrui Zhou +3 more
doaj +1 more source
Diagnostics, 2022 Numerous research have demonstrated that Convolutional Neural Network (CNN) models are capable of classifying visual field (VF) defects with great accuracy.
Masyitah Abu +6 more
doaj +1 more source
Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate
Frontiers in Public Health, 2021 This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction
Jayakumar Kaliappan +5 more
doaj +1 more source
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress? [PDF]
, 2020 Network embedding methods map a network's nodes to vectors in an embedding space, in such a way that these representations are useful for estimating some notion of similarity or proximity between pairs of nodes in the network.
De Bie, Tijl +2 more
core +2 more sources
Hyperparameter Tuning Approaches
, 2023 AbstractThis chapter provides a broad overview over the different hyperparameter tunings. It details the process of HPT, and discusses popular HPT approaches and difficulties. It focuses on surrogate optimization, because this is the most powerful approach.
Thomas Bartz-Beielstein +1 more
openaire +1 more source