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An automatic hyperparameter optimization DNN model for precipitation prediction

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0 Published in 2021 at "Applied Intelligence"

DOI: 10.1007/s10489-021-02507-y

Abstract: Deep neural networks (DNN) have gained remarkable success on many rainfall predictions tasks in recent years. However, the performance of DNN highly relies upon the hyperparameter setting. In order to design DNNs with the best… read more here.

Keywords: dnn; precipitation; hyperparameter optimization; hyperparameter ... See more keywords

Optimizing the Hyperparameter Tuning of YOLOv5 for Underwater Detection

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1 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3174583

Abstract: This study optimized the latest YOLOv5 framework, including its subset models, with training on different datasets that differed in image contrast and cloudiness to assess model performances based on quantitative metrics and image processing speed.… read more here.

Keywords: optimizing hyperparameter; hyperparameter tuning; learning rate; hyperparameter ... See more keywords

Adaptive Similarity-Aware Hyperparameter Tuners for Classification Tasks

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2 Published in 2023 at "IEEE Access"

DOI: 10.1109/access.2022.3229390

Abstract: With the success of deep learning in recent years, lots of different AI models have been applied to the real world. At the same time, how to train a model with good performance becomes a… read more here.

Keywords: similarity aware; hyperparameter; tuners classification; aware hyperparameter ... See more keywords

Dynamical Hyperparameter Optimization via Deep Reinforcement Learning in Tracking

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0 Published in 2021 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2019.2956703

Abstract: Hyperparameters are numerical pre-sets whose values are assigned prior to the commencement of a learning process. Selecting appropriate hyperparameters is often critical for achieving satisfactory performance in many vision problems, such as deep learning-based visual… read more here.

Keywords: optimization via; hyperparameter optimization; dynamical hyperparameter; hyperparameter ... See more keywords

A New Automatic Hyperparameter Recommendation Approach Under Low-Rank Tensor Completion e Framework

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1 Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"

DOI: 10.1109/tpami.2022.3195658

Abstract: Hyperparameter optimization (HPO), characterized by hyperparameter tuning, is not only a critical step for effective modeling but also is the most time-consuming process in machine learning. Traditional search-based algorithms tend to require extensive configuration evaluations… read more here.

Keywords: completion; hyperparameter; tensor completion; approach ... See more keywords

Hyperparameter-Free Localized Simple Multiple Kernel K-Means With Global Optimum.

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2 Published in 2023 at "IEEE transactions on pattern analysis and machine intelligence"

DOI: 10.1109/tpami.2022.3233635

Abstract: The newly proposed localized simple multiple kernel k-means (SimpleMKKM) provides an elegant clustering framework which sufficiently considers the potential variation among samples. Although achieving superior clustering performance in some applications, we observe that it is… read more here.

Keywords: hyperparameter; free localized; multiple kernel; simple multiple ... See more keywords

Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets

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0 Published in 2023 at "Algorithms"

DOI: 10.3390/a16010046

Abstract: User requests to a customer service, also known as tickets, are essentially short texts in natural language. They should be grouped by topic to be answered efficiently. The effectiveness increases if this semantic categorization becomes… read more here.

Keywords: optimization; classification; hyperparameter; black box ... See more keywords

Bayesian-Based Hyperparameter Optimization of 1D-CNN for Structural Anomaly Detection

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1 Published in 2023 at "Sensors"

DOI: 10.3390/s23115058

Abstract: With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies.… read more here.

Keywords: hyperparameter optimization; bayesian based; hyperparameter; optimization cnn ... See more keywords

High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms

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0 Published in 2022 at "Algorithms"

DOI: 10.48550/arxiv.2207.06028

Abstract: 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?… read more here.

Keywords: hyperparameter; hyperparameter tuning; large scale; machine learning ... See more keywords