Bayesian optimization ≈ Bayesian 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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Bayesian Optimization for Adaptive Experimental Design: A Review Open
Bayesian optimisation is a statistical method that efficiently models and optimises expensive “black-box” functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of…
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Auto-sklearn: Efficient and Robust Automated Machine Learning Open
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically …
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Chemprop: A Machine Learning Package for Chemical Property Prediction Open
Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approa…
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A Tutorial on Bayesian Optimization Open
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic no…
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Insights on Transfer Optimization: Because Experience is the Best Teacher Open
Traditional optimization solvers tend to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of solvers do not automatically grow with experience. In contrast, however…
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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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Phoenics: A Bayesian Optimizer for Chemistry Open
We report Phoenics, a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets. Phoenics combines ideas from Bayesian optimization with conc…
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Bayesian Optimization in a Billion Dimensions via Random Embeddings Open
Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach …
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Constrained Bayesian optimization for automatic chemical design using variational autoencoders Open
Automatic Chemical Design is a framework for generating novel molecules with optimized properties.
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LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes Open
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the b…
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Review of numerical optimization techniques for meta-device design [Invited] Open
Optimization techniques have been indispensable for designing high-performance meta-devices targeted to a wide range of applications. In fact, today optimization is no longer an afterthought and is a fundamental tool for many optical and R…
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A Bayesian experimental autonomous researcher for mechanical design Open
Automated testing, Bayesian optimization, and additive manufacturing combine for the autonomous design of structures.
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Safe controller optimization for quadrotors with Gaussian processes Open
One of the most fundamental problems when designing controllers for dynamic\nsystems is the tuning of the controller parameters. Typically, a model of the\nsystem is used to obtain an initial controller, but ultimately the controller\npara…
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Neural Architecture Search with Bayesian Optimisation and Optimal Transport Open
Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function $f$ which is only accessible via point evaluations. It is typically used in settings where $f$ is expensive to evaluate. A common use case for BO…
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Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible Open
Designing future‐proof materials goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial‐and‐error pr…
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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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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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Unsupervised Machine Learning on a Hybrid Quantum Computer Open
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has…
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BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Open
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. We introduce BoTorch, a modern programming framewor…
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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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Max-value Entropy Search for Efficient Bayesian Optimization Open
Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the $\ar…
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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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Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization Open
Within density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+…
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Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization Open
We computationally designed an ultranarrow-band wavelength-selective thermal radiator via a materials informatics method alternating between Bayesian optimization and thermal electromagnetic field calculation. For a given target infrared w…
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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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Estimating Electric Motor Temperatures With Deep Residual Machine Learning Open
Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected…