Results 41 to 50 of about 6,929,542 (320)
Use of Static Surrogates in Hyperparameter Optimization [PDF]
Operations Research Forum, 2022 http://www.optimization-online.org/DB_HTML/2021/03/8296 ...
Dounia Lakhmiri, Sébastien Le Digabel
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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
doaj +1 more source
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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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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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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Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference [PDF]
AutoML, 2023 Large Language Models (LLMs) have sparked significant interest in their generative capabilities, leading to the development of various commercial applications.
Chi Wang, Susan Liu, A. Awadallah
semanticscholar +1 more source
Scaling Laws for Hyperparameter Optimization
Advances in Neural Information Processing Systems 36, 2023 Accepted at NeurIPS ...
Arlind Kadra +3 more
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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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Online Hyperparameter Optimization for Class-Incremental Learning [PDF]
AAAI Conference on Artificial Intelligence, 2023 Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and
Yaoyao Liu +3 more
semanticscholar +1 more source
Bilevel hyperparameter optimization for nonlinear support vector machines
, 2023 While the problem of tuning the hyperparameters of a support vector machine (SVM) via cross-validation is easily understood as a bilevel optimization problem, so far, the corresponding literature has mainly focused on the linear-kernel case.
Zemkoho, Alain
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