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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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Impact of Hyperparameter Optimization on Cross-Version Defect Prediction: An Empirical Study [PDF]
Jisuanji kexue yu tansuo, 2023 In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project ...
HAN Hui, YU Qiao, ZHU Yi
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Hyperparameter Optimization: A Spectral Approach
CoRR, 2017 We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters.
Elad Hazan +2 more
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Methods for Hyperparameters Optimization in Learning Approaches: an overview
, 2020 Automatic learning research focuses on the development of methods capable of extracting useful information from a given dataset. A large variety of learning methods exists, ranging from biologically inspired neural networks to statistical methods.
F. Esposito, N. Del Buono, L. Selicato
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A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values.
Prativa Pokhrel, Alina Lazar
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, 2023 Benchmarks are crucial for driving progress in scientific disciplines. To be effective, benchmarks should closely mimic real-world tasks while being computationally efficient, allowing for accessibility and repeatability. Developing surrogate models that
Jeet N., Parikh +2 more
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Bayesian optimization of hyperparameters from noisy marginal likelihood estimates
, 2023 Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new ...
Mattias Villani +6 more
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Optimizing Deep Learning Hyperparameters Using Interpolation-Based Optimization [PDF]
Control and Optimization in Applied MathematicsHyperparameter optimization (HPO) is essential for maximizing the performance of deep learning models. Traditional approaches, such as grid search and Bayesian Optimization (BO), are widely used but can be computationally expensive.
Michael Oluwaseun Ayansiji +1 more
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Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
Applied Sciences, 2022 Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks.
Quanqiang Zhou +2 more
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Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization [PDF]
E3S Web of Conferences, 2023 There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations.
Muliawan Nicholas Hans +2 more
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