19 papers · ranked by Valyu relevance
Javier Garcia-Barcos, Ruben Martinez-Cantin, Eduardo C. Garrido-Merchán
'Eduardo C. Garrido-Merchán'] Optimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of…
Lucian Chan, Geoffrey R. Hutchison, Garrett M. Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Ekaterina Noskova, Viacheslav Borovitskiy, A Kern
Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model’s parameters that maximize a certain log-likelihood. This log-likelihood is often expensive to evaluate in terms of time and hardware…
Sebastian Blume, Tim Benedens, Dieter Schramm, Soufiene Djahel
Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge is required for the application. At…
Felix Berkenkamp, Andreas Krause, Angela P. Schoellig
Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been used to automate this process. During experiments on real-world systems such as…
Miguel Marcos, Lorenzo Mur-Labadia, Ruben Martinez-Cantin, Tara Rajendran
Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing this random sampling with a personalized query. Bayesian…
Artyom Nikitin, Ilia Fastovets, Dmitrii Shadrin, Mariia Pukalchik + 1 more
Background Efficient seed germination is a crucial task at the beginning of crop cultivation. Although boundaries of environmental parameters that should be maintained are well studied, fine-tuning can significantly improve the efficiency, which is infeasible to be done manually due to the high dimensionality of the…
Ryo Tamura, Koji Hukushima, Nuno Araujo
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be…
Florian Gisperg, Robert Klausser, Mohamed Elshazly, Julian Kopp + 2 more
'Eva Přáda Brichtová' 'Oliver Spadiut'] Title: ABSTRACT Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing…
Gracie M. White, Amanda P. Siegel, Andres Tovar, Jacek Mateusz Bajkowski + 3 more
The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently…
Amala Mary Vincent, P. Jidesh
For any machine learning model, finding the optimal hyperparameter setting has a direct and significant impact on the model’s performance. In this paper, we discuss different types of hyperparameter optimization techniques. We compare the performance of some of the hyperparameter optimization techniques on image…
Rosen Ting-Ying Yu, Cyril Picard, Faez Ahmed
Bayesian Optimization (BO) is a foundational strategy in engineering design optimization for efficiently handling black-box functions with many constraints and expensive evaluations. This paper introduces a novel constraint-handling framework for Bayesian Optimization (BO) using Prior-data Fitted Networks (PFNs), a…
Vicent Girbés-Juan, Joaquín Moll, Antonio Sala, Leopoldo Armesto + 1 more
'Ka-Veng Yuen'] In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We…
Liang Yan, Xiaojun Duan, Bowen Liu, Jin Xu
Bayesian optimization (BO) based on the Gaussian process (GP) surrogate model has attracted extensive attention in the field of optimization and design of experiments (DoE). It usually faces two problems: the unstable GP prediction due to the ill-conditioned Gram matrix of the kernel and the difficulty of determining…
Wojciech M Czarnecki, Sabina Podlewska, Andrzej J Bojarski
Background Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with…
Rubaiyat Mohammad Khondaker, Stephen Gow, Samantha Kanza, Jeremy G Frey + 1 more
'Jeremy G Frey' 'Mahesan Niranjan'] The related problems of chemical reaction optimization and reaction scope search concern the discovery of reaction pathways and conditions that provide the best percentage yield of a target product. The space of possible reaction pathways or conditions is too large to search in full…
Shrooq Alsenan, Isra Al-Turaiki, Mashael Aldayel, Mohamed Tounsi + 1 more
'Yijie Ding'] RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning…
Peter Andras, Cédric Sueur
Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we…
Farkhanda Abbas, Feng Zhang, Muhammad Ismail, Garee Khan + 6 more
'Javed Iqbal' 'Abdulwahed Fahad Alrefaei' 'Mohammed Fahad Albeshr' 'Waheb Abdullah' 'AbdulRahman Alsewari' 'Mario De Oliveira'] Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to…