Results 21 to 30 of about 38,367 (259)
Hyperparameter Optimization with Differentiable Metafeatures
CoRR, 2021 Metafeatures, or dataset characteristics, have been shown to improve the performance of hyperparameter optimization (HPO). Conventionally, metafeatures are precomputed and used to measure the similarity between datasets, leading to a better initialization of HPO models.
Hadi S. Jomaa +2 more
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Is one hyperparameter optimizer enough? [PDF]
Proceedings of the 4th ACM SIGSOFT International Workshop on Software Analytics, 2018 Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics.
Huy Tu, Vivek Nair
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Scaling Laws for Hyperparameter Optimization
Advances in Neural Information Processing Systems 36, 2023 Accepted at NeurIPS ...
Arlind Kadra +3 more
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Federated learning with hyper-parameter optimization
Journal of King Saud University: Computer and Information Sciences, 2023 Federated Learning is a new approach for distributed training of a deep learning model on data scattered across a large number of clients while ensuring data privacy.
Majid Kundroo, Taehong Kim
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Optimization of hyperparameters for SMS reconstruction [PDF]
Magnetic Resonance Imaging, 2020 Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices simultaneously. Overlapping slices are then separated using a mathematical model. Several parameters used in SMS reconstruction impact the quality of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure
Muftuler, L. Tugan +7 more
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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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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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Hyperparameter Tuning on Classification Algorithm with Grid Search
Sistemasi: Jurnal Sistem Informasi, 2022 Currently, machine learning algorithms continue to be developed to perform optimization with various methods to produce the best-performing model. In Supervised learning or classification, most of the algorithms have hyperparameters.
Wahyu Nugraha, Agung Sasongko
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Improving stroke diagnosis accuracy using hyperparameter optimized deep learning
IJAIN (International Journal of Advances in Intelligent Informatics), 2019 Stroke may cause death for anyone, including youngsters. One of the early stroke detection techniques is a Computerized Tomography (CT) scan. This research aimed to optimize hyperparameter in Deep Learning, Random Search and Bayesian Optimization for ...
Tessy Badriyah +3 more
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Squirrel: A Switching Hyperparameter Optimizer
CoRR, 2020 In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach switches between different optimizers. Since the team names on the competition's leaderboard were randomly generated "alliteration nicknames", consisting of an adjective and an ...
Noor H. Awad +11 more
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