Hyperparameter optimization ≈ Hyperparameter optimization
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Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization Open
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techniques have been develo…
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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Open
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding up random search throug…
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Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges Open
Most machine learning algorithms are configured by a set of hyperparameters whose values must be carefully chosen and which often considerably impact performance. To avoid a time‐consuming and irreproducible manual process of trial‐and‐err…
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Tune: A Research Platform for Distributed Model Selection and Training Open
Modern machine learning algorithms are increasingly computationally demanding, requiring specialized hardware and distributed computation to achieve high performance in a reasonable time frame. Many hyperparameter search algorithms have be…
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Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS Open
In this paper, we compare the three most popular algorithms for hyperparameter optimization (Grid Search, Random Search, and Genetic Algorithm) and attempt to use them for neural architecture search (NAS). We use these algorithms for build…
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SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance Open
Machine Learning algorithms have been widely used to solve various kinds of data classification problems. Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches…
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GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks Open
Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalizatio…
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Stealing Hyperparameters in Machine Learning Open
Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidenti…
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Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis Open
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a…
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An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure Open
About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improv…
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Population Based Training of Neural Networks Open
Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In …
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Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks Open
Compared to the traditional machine learning models, deep neural networks (DNN) are known to be highly sensitive to the choice of hyperparameters. While the required time and effort for manual tuning has been rapidly decreasing for the wel…
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CMA-ES for Hyperparameter Optimization of Deep Neural Networks Open
Hyperparameters of deep neural networks are often optimized by grid search, random search or Bayesian optimization. As an alternative, we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which is known for its s…
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Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity Open
For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search …
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BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search Open
Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for…
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Water quality prediction using machine learning models based on grid search method Open
Water quality is very dominant for humans, animals, plants, industries, and the environment. In the last decades, the quality of water has been impacted by contamination and pollution. In this paper, the challenge is to anticipate Water Qu…
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures Open
Many studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design s…
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Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron Open
Coronavirus (COVID-19) is a highly infectious disease that has captured the attention of the worldwide public. Modeling of such diseases can be extremely important in the prediction of their impact. While classic, statistical, modeling can…
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Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization Open
In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor,…
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Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models Open
The accuracy of some density functional (DF) models widely used in material science depends on empirical or free parameters that are commonly tuned using reference physical properties. Grid-search methods are the standard numerical approxi…
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BOHB: Robust and Efficient Hyperparameter Optimization at Scale Open
Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible. On the other han…
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OptiLog V2: Model, Solve, Tune and Run Open
We present an extension of the OptiLog Python framework. We fully redesign the solvers module to support the dynamic loading of incremental SAT solvers with support for external libraries. We introduce new modules for modelling problems in…
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Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 Open
This paper presents the results and insights from the black-box optimization (BBO) challenge at NeurIPS 2020 which ran from July-October, 2020. The challenge emphasized the importance of evaluating derivative-free optimizers for tuning the…
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Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm Open
This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross validation (K-CV). The SCA includes two learner layers: a pri…
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Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction Open
The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in …
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Advanced hyperparameter optimization of deep learning models for wind power prediction Open
The uncertainty of wind power as the main obstacle of its integration into the power grid can be addressed by an accurate and efficient wind power forecast. Among the various wind power forecasting methods, machine learning (ML) algorithms…
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Hyperparameter Optimization in Convolutional Neural Network using Genetic Algorithms Open
Optimizing hyperparameters in Convolutional Neural Network (CNN) is a tedious problem for many researchers and practitioners. To get hyperparameters with better performance, experts are required to configure a set of hyperparameter choices…
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Deep learning framework for material design space exploration using active transfer learning and data augmentation Open
Neural network-based generative models have been actively investigated as an inverse design method for finding novel materials in a vast design space. However, the applicability of conventional generative models is limited because they can…
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Grid Path Planning with Deep Reinforcement Learning: Preliminary Results Open
Single-shot grid-based path finding is an important problem with the applications in robotics, video games etc. Typically in AI community heuristic search methods (based on A* and its variations) are used to solve it. In this work we prese…
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A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks Open
Convolutional Neural Network (CNN) is a prevalent topic in deep learning (DL) research for their architectural advantages. CNN relies heavily on hyperparameter configurations, and manually tuning these hyperparameters can be time-consuming…