28 papers · ranked by Valyu relevance
Philipp Probst, Anne‐Laure Boulesteix, Bernd Bischl
Modern supervised machine learning algorithms involve hyperparameters that have to be set before running them. Options for setting hyperparameters are default values from the software package, manual configuration by the user or configuring them for optimal predictive performance by a tuning procedure. The goal of this…
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…
Rafael Gomes Mantovani, Tomáš Horváth, Ricardo Cerri, Sylvio Barbon + 2 more
'Joaquin Vanschoren' 'André C. P. L. F. de Carvalho'] Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations, and their complex interactions, it…
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…
Ahmed M. Elshewey, Mahmoud Y. Shams, Sayed M. Tawfeek, Amal H. Alharbi + 8 more
'Amal H. Alharbi' 'Abdelhameed Ibrahim' 'Abdelaziz A. Abdelhamid' 'Marwa M. Eid' 'Nima Khodadadi' 'Laith Abualigah' 'Doaa Sami Khafaga' 'Zahraa Tarek' 'Kathiravan Srinivasan'] The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence…
Tinu Theckel Joy, Santu Rana, Sunil Gupta, Svetha Venkatesh
In this paper, we develop a Bayesian optimization based hyperparameter tuning framework inspired by statistical learning theory for classifiers. We utilize two key facts from PAC learning theory; the generalization bound will be higher for a small subset of data compared to the whole, and the highest accuracy for a…
Rafael Gomes Mantovani, André Luis Debiaso Rossi, Edesio Alcobaça, Jadson Castro Gertrudes + 2 more
'Jadson Castro Gertrudes' 'Sylvio Barbon' 'André C. P. L. F. de Carvalho'] Machine Learning (ML) algorithms have been increasingly applied to problems from several different areas. Despite their growing popularity, their predictive performance is usually affected by the values assigned to their hyperparameters (HPs).…
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…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Ishtiaque Ahammad, Anika Bushra Lamisa, Arittra Bhattacharjee, Tabassum Binte Jamal + 6 more
Neurodegenerative diseases, such as Alzheimer’s disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer’s Identification Tool using RNA-Seq (AITeQ), a machine learning model based on an optimized random forest algorithm for…
Mikolaj Wojciuk, Zaneta Swiderska-Chadaj, Krzysztof Siwek, Arkadiusz Gertych
The immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task, often requiring repetitive numerical experiments. As a result…
Moshe Sipper
Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. But just how useful is said tuning? While smaller-scale experiments have been previously conducted, herein we carry out a large-scale…
Subhasis Dasgupta, Jaydip Sen
The world is changing fast towards automation and artificial intelligence (AI). At the heart of AI rests different machine learning models. It is a known fact that each machine learning model has its bias-variance trade-off (Briscoe & Feldman, 2011; Doroudi, 2020). This trade-off is important to make sure that the…
Dawei Zou, Chunhua Ma, Peng Wang, Yanqiu Geng + 1 more
Hyperparameter optimization (HPO), which is also called hyperparameter tuning, is a vital component of developing machine learning models. These parameters, which regulate the behavior of the machine learning algorithm and cannot be directly learned from the given training data, can significantly affect the performance…
Authors not listed
The integration of artificial intelligence technologies into pharmaceutical research is crucial for gaining an early understanding of molecular properties, thereby facilitating successful drug design. Constructing a machine learning (ML) model however, requires knowledge spanning from data preprocessing and feature…
Hassan Ashraf, Asim Waris, Syed Omer Gilani, Uzma Shafiq + 6 more
Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on…
Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons
MLtuner automatically tunes settings for training tunables such as the learning rate, the momentum, the mini-batch size, and the data staleness bound—that have a significant impact on large-scale machine learning (ML) performance. Traditionally, these tunables are set manually, which is unsurprisingly error prone and…
Md Ochiuddin Miah, Umme Habiba, Md Faisal Kabir
Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students’ cognitive states. Real-time classification of students’ confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since…
Junjie Han, Cedric Gondro, Kenneth Reid, Juan P. Steibel
There is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the performance of DL is affected by hyperparameters that are typically manually set by users. These hyperparameters do not simply specify the architecture of the model, they…
Henry Munroe, Bright Osatohanmwen, Reza Sharifi
Machine learning (ML) models with stochastic and non-deterministic characteristics are increasingly used for genomic prediction in plant breeding, but evaluation often neglects important aspects like prediction stability and ranking performance. This study addresses this gap by evaluating how two hyperparameters of a…
Jenke Scheen, Wilson Wu, Antonia S. J. S. Mey, Paolo Tosco + 2 more
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the…
Hua-Dong Xiong, Li Ji-An, Marcelo G. Mattar, Robert C. Wilson
Cognitive modeling provides a formal method to articulate and test hypotheses about cognitive processes. However, accurately and reliably estimating model parameters remains challenging due to common issues in behavioral science, such as limited data, measurement noise, experimental constraints, and model complexity.…
Etinosa Osaro, Yamil Colón
The application of machine learning (ML) techniques in materials science has revolutionized the pace and scope of materials research and design. In the case of metal-organic frameworks (MOFs), a promising class of materials due to their tunable properties and versatile applications in gas adsorption and separation, ML…
Authors not listed
Background: Janus Kinase 2 (JAK2) is a key kinase in cellular signal transduction. Its abnormal activation is closely related to various myeloproliferative neoplasms and inflammatory diseases. Developing selective JAK2 inhibitors is an important direction in drug discovery. Accurate prediction of compound inhibitory…
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
Trang T. Le, Weixuan Fu, Jason H. Moore
Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programming to…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Florian Meier, Raphaël Dang-Nhu, Angelika Steger
Natural brains perform miraculously well in learning new tasks from a small number of samples, whereas sample efficient learning is still a major open problem in the field of machine learning. Here, we raise the question, how the neural coding scheme affects sample efficiency, and make first progress on this question…