Results 31 to 40 of about 173,823 (173)
Hyperparameter Tuning Approaches
, 2023 AbstractThis chapter provides a broad overview over the different hyperparameter tunings. It details the process of HPT, and discusses popular HPT approaches and difficulties. It focuses on surrogate optimization, because this is the most powerful approach.
Thomas Bartz-Beielstein +1 more
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Learning Individualized Hyperparameter Settings
Algorithms, 2023 The performance of optimization algorithms, and consequently of AI/machine learning solutions, is strongly influenced by the setting of their hyperparameters. Over the last decades, a rich literature has developed proposing methods to automatically determine the parameter setting for a problem of interest, aiming at either robust or instance-specific ...
Vittorio Maniezzo, Tingting Zhou
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SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
, 2018 Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications.
Kinnison, Jeff +3 more
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Accelerating Hyperparameter Optimisation with PyCOMPSs [PDF]
Workshop Proceedings of the 48th International Conference on Parallel Processing, 2019 Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up training. However, besides training, hyperparameters optimisation(HPO) is one of the most time consuming
Njoroge Kahira, Albert +3 more
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Posterior propriety and admissibility of hyperpriors in normal hierarchical models
, 2005 Hierarchical modeling is wonderful and here to stay, but hyperparameter priors are often chosen in a casual fashion. Unfortunately, as the number of hyperparameters grows, the effects of casual choices can multiply, leading to considerably inferior ...
Berger, James O. +2 more
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Lipschitz Adaptivity with Multiple Learning Rates in Online Learning [PDF]
, 2019 We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design
Koolen, Wouter M. +2 more
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Metalearning for Hyperparameter Optimization [PDF]
, 2022 SummaryThis chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses
Brazdil, Pavel +3 more
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Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms [PDF]
, 2012 Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall.
Hoos, Holger H. +3 more
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Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification
DiagnosticsBackground/Objectives: The human brain is responsible for controlling various physiological functions, and hence, the presence of tumors in the brain is a major concern in the medical field.
Syed Muddusir Hussain +4 more
doaj +1 more source
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
, 2019 To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models.
Jin, Zhihua +7 more
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