Results 21 to 30 of about 36,835 (256)
Rethinking the Hyperparameters for Fine-tuning
CoRR, 2020 Published as a conference paper at ICLR ...
Hao Li +6 more
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Hyperparameters and tuning strategies for random forest [PDF]
WIREs Data Mining and Knowledge Discovery, 2019 The random forest (RF) algorithm has several hyperparameters that have to be set by the user, for example, the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain, and ...
Philipp Probst +2 more
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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
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On Noisy Evaluation in Federated Hyperparameter Tuning
CoRR, 2022 v1: 19 pages, 15 figures, submitted to MLSys2023; v2: Fixed citation formatting; v3: Fixed typo, update acks v4: MLSys2023 camera ...
Kevin Kuo +6 more
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EMPLOYING GENETIC ALGORITHM INSPIRED HYPERPARAMETER OPTIMIZATION IN MOBILE NET V2 ARCHITECTURE [PDF]
Proceedings on Engineering SciencesThis paper presents a novel approach for hyperparameter optimization for the MobileNetV2 architecture using a genetic algorithm. The proposed approach aims to automate the hyperparameter tuning leading to performance enhancement.
Baljinder Kaur +3 more
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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
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Hyperparameter tuning in echo state networks
Proceedings of the Genetic and Evolutionary Computation Conference, 2022 Echo State Networks represent a type of recurrent neural network with a large randomly generated reservoir and a small number of readout connections trained via linear regression. The most common topology of the reservoir is a fully connected network of up to thousands of neurons.
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On the Performance of Differential Evolution for Hyperparameter Tuning [PDF]
2019 International Joint Conference on Neural Networks (IJCNN), 2019 2019 International Joint Conference on Neural Networks (IJCNN)
Mischa Schmidt +5 more
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Hyperparameter Tuning with Renyi Differential Privacy
CoRR, 2021 For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fine tune the value of the training ...
Nicolas Papernot, Thomas Steinke 0002
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Mango: A Python Library for Parallel Hyperparameter Tuning [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 5 pages, 3 figures, ICASSP ...
Sandeep Singh Sandha +3 more
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