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Hyperparameter Optimization [PDF]
, 2019 Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and deep neural networks, has resulted in a resurgence of research on hyperparameter optimization (HPO). In this chapter, we give an overview of the most prominent approaches for HPO.
Feurer, Matthias, Hutter, Frank
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AutoRL Hyperparameter Landscapes
International Conference on AutoML, 2023 Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics of the hyperparameter landscapes ...
Mohan, Aditya +4 more
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Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan
Applied Sciences, 2022 Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications.
Wen-Hui Lin +5 more
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Applied Sciences, 2022 Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims.
Souha Kamhi +7 more
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Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting
Цифровая трансформация, 2019 Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed.
S. V. Sholtanyuk
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Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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IEEE Access, 2023 Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work.
Mahdi Aghaabbasi +4 more
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No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
, 2022 The performance of reinforcement learning (RL) agents is sensitive to the choice of hyperparameters. In real-world settings like robotics or industrial control systems, however, testing different hyperparameter configurations directly on the environment can be financially prohibitive, dangerous, or time consuming.
Wang, Han +9 more
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Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines
Micromachines, 2021 Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots.
Mingming Shen +4 more
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Mathematics, 2022 Over the past few decades, convolutional neural networks (CNNs) have achieved outstanding results in addressing a broad scope of computer vision problems.
Rogeany Kanza +4 more
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