scikit-optimize/scikit-optimize

On hyperparameter optimization of machine learning algorithms: Theory and practice

Yang, L., & Shami, A. A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.061 · Cited by 3,347 · see citing works

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Zhu, J., Yang, M., & Ren, Z. J. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c00026 · Cited by 690 · see citing works

Nanoparticle synthesis assisted by machine learning

Tao, H., Wu, T., Aldeghi, M., Wu, T., Aspuru‐Guzik, A., & Kumacheva, E. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00337-5 · Cited by 558 · see citing works

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang, L., Moubayed, A., & Shami, A. A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3084796 · Cited by 433 · see citing works

A Race-Track Trapped-Ion Quantum Processor

Moses, S. A., Baldwin, C. H., Allman, M. S., Ancona, R., Ascarrunz, L., Barnes, C., Bartolotta, J., Bjork, B., Blanchard, P., Bohn, M., et al. (2023) · Physical Review X · doi:10.1103/physrevx.13.041052 · Cited by 391 · see citing works

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Jiao, W., Atwal, G., Polak, P. P., Karlić, R., Cuppen, E. P. J. G., Al‐Shahrour, F., Atwal, G., Bailey, P. J., Biankin, A. V., Boutros, P. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13825-8 · Cited by 216 · see citing works

Calorimetry with deep learning: particle simulation and reconstruction for collider physics

Belayneh, D., Carminati, F., Farbin, A., Hooberman, B. H., Khattak, G. R., Liu, M., Liu, J., Olivito, D., Pacela, V. B., Maurizio Pierini, B., et al. (2020) · The European Physical Journal C · doi:10.1140/epjc/s10052-020-8251-9 · Cited by 157 · see citing works

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study

Wunderlich, T. C., Kungl, Á. F., Müller, E., Hartel, A., Stradmann, Y., Aamir, S. A., Grübl, A., Heimbrecht, A., Schreiber, K., Stöckel, D., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00260 · Cited by 132 · see citing works

MethylNet: an automated and modular deep learning approach for DNA methylation analysis

Levy, J., Titus, A., Petersen, C. L., Chen, Y., Salas, L. A., & Christensen, B. C. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3443-8 · Cited by 127 · see citing works

OCTIS: Comparing and Optimizing Topic models is Simple!

Terragni, S., Fersini, E., Galuzzi, B. G., Tropeano, P., & Candelieri, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-demos.31 · Cited by 110 · see citing works

Large model structural uncertainty in global projections of urban heat waves

Zheng, Z., Zhao, L., & Oleson, K. W. (2021) · Nature Communications · doi:10.1038/s41467-021-24113-9 · Cited by 112 · see citing works

A physics-informed operator regression framework for extracting data-driven continuum models

Patel, R. G., Trask, N., Wood, M. A., & Cyr, E. C. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113500 · Cited by 104 · see citing works

Time-dependent reliability analysis of unsaturated slopes under rapid drawdown with intelligent surrogate models

Guardiani, C., Soranzo, E., & Wu, W. (2021) · Acta Geotechnica · doi:10.1007/s11440-021-01364-w · Cited by 94 · see citing works

New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain

Bellido-Jiménez, J. A., Estévez, J., & García-Marín, A. P. (2020) · Agricultural Water Management · doi:10.1016/j.agwat.2020.106558 · Cited by 93 · see citing works

Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm

Xie, C., Qiu, H., Liu, L., You, Y., Li, H., Li, Y., Sun, Z., Lin, J., & An, L. (2025) · SmartMat · doi:10.1002/smm2.1320 · Cited by 94 · see citing works

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

Noman, F. M., Ting, C., Kang, H., Phan, R. C. ‐., & Ombao, H. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3351177 · Cited by 83 · see citing works

The application of pangenomics and machine learning in genomic selection in plants

Bayer, P. E., Petereit, J., Danilevicz, M. F., Anderson, R., Batley, J., & Edwards, D. (2021) · The Plant Genome · doi:10.1002/tpg2.20112 · Cited by 85 · see citing works

Two-stage variational mode decomposition and support vector regression for streamflow forecasting

Zuo, G., Luo, J., Wang, N., Lian, Y., & He, X. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-5491-2020 · Cited by 80 · see citing works

Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy

Tan, C. L., Lindner, K. A. M., Boschert, T., Meng, Z., Rodriguez-Ehrenfried, A., Roia, A. D., Haltenhof, G., Faenza, A., Imperatore, F., Bunse, L., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02161-y · Cited by 80 · see citing works

Magnetostatics and micromagnetics with physics informed neural networks

Kovacs, A., Exl, L., Kornell, A., Fischbacher, J., Hovorka, M., Gusenbauer, M., Breth, L., Oezelt, H., Praetorius, D., Suess, D., et al. (2022) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2021.168951 · Cited by 79 · see citing works

Developing a parametric morphable annual daylight prediction model with improved generalization capability for the early stages of office building design

Han, Y., Shen, L., & Sun, C. T. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107932 · Cited by 76 · see citing works

DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations

Barth, A., Azcarate, A. A., Troupin, C., & Beckers, J.-M. (2022) · Geoscientific model development · doi:10.5194/gmd-15-2183-2022 · Cited by 76 · see citing works

Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes

Lourie, N., Le Bras, R., & Choi, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17589 · Cited by 73 · see citing works

LensKit for Python: Next-Generation Software for Recommender Systems Experiments

Ekstrand, M. D. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412778 · Cited by 72 · see citing works

Maximizing mRNA vaccine production with Bayesian optimization

Rosa, S. S., Nunes, D., Antunes, L., Prazeres, D. M. F., Marques, M. P. C., & Azevedo, A. M. (2022) · Biotechnology and Bioengineering · doi:10.1002/bit.28216 · Cited by 70 · see citing works

Variational preparation of finite-temperature states on a quantum computer

Sagastizabal, R., Premaratne, S., Klaver, B., Rol, M. A., Negîrneac, V., Moreira, M. S., Zou, X., Johri, S., Muthusubramanian, N., Beekman, M., et al. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00468-1 · Cited by 60 · see citing works

Open-Source Machine Learning in Computational Chemistry

Hagg, A., & Kirschner, K. N. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00643 · Cited by 62 · see citing works

Simplicity and informativeness in semantic category systems

Carr, J. W., Smith, K., Culbertson, J., & Kirby, S. (2020) · Cognition · doi:10.1016/j.cognition.2020.104289 · Cited by 56 · see citing works

A population data-driven workflow for COVID-19 modeling and learning

Ozik, J., Wozniak, J. M., Collier, N. T., Macal, C. M., & Binois, M. (2021) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420211035164 · Cited by 58 · see citing works

A kinetic ensemble of the Alzheimer’s Aβ peptide

Löhr, T., Kohlhoff, K., Heller, G. T., Camilloni, C., & Vendruscolo, M. (2021) · Nature Computational Science · doi:10.1038/s43588-020-00003-w · Cited by 57 · see citing works

Machine-learning-based prediction of hydrogen adsorption capacity at varied temperatures and pressures for MOFs adsorbents

Zhang, X., Zheng, Q.-R., & He, H. (2022) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2022.104479 · Cited by 52 · see citing works

Advancing microfluidic design with machine learning: a Bayesian optimization approach

Kundačina, I., Kundačina, O., Mišković, D., & Radonić, V. (2025) · Lab on a Chip · doi:10.1039/d4lc00872c · Cited by 53 · see citing works

ENTMOOT: A framework for optimization over ensemble tree models

Thebelt, A., Kronqvist, J., Mistry, M., Lee, R. M., Sudermann‐Merx, N., & Misener, R. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107343 · Cited by 50 · see citing works

A novel interpretable machine learning model approach for the prediction of TiO2 photocatalytic degradation of air contaminants

Schossler, R. T., Ojo, S., Jiang, Z., Hu, J., & Yu, X. B. (2024) · Scientific Reports · doi:10.1038/s41598-024-62450-z · Cited by 44 · see citing works

Transferability of deep learning models in searches for new physics at colliders

Romão, M., Castro, N. F., Pedro, R., & Vale, T. (2019) · Physical Review D · doi:10.1103/physrevd.101.035042 · Cited by 31 · see citing works

Toward high-performance energy and power battery cells with machine learning-based optimization of electrode manufacturing

Duquesnoy, M., Liu, C., Kumar, V., Ayerbe, E., & Franco, A. A. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233674 · Cited by 40 · see citing works

Predicting intelligence from brain gray matter volume

Hilger, K., Winter, N. R., Leenings, R., Sassenhagen, J., Hahn, T., Basten, U., & Fiebach, C. J. (2020) · Brain Structure and Function · doi:10.1007/s00429-020-02113-7 · Cited by 41 · see citing works

Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland

Hartonen, T., Jermy, B. S., Sõnajalg, H., Vartiainen, P., Krebs, K., Vabalas, A., Metspalu, A., Esko, T., Nelis, M., Hudjashov, G., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01591-z · Cited by 33 · see citing works

Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density

Vargas, S., Hennefarth, M. R., Liu, Z. J., & Alexandrova, A. N. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00623 · Cited by 40 · see citing works

AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

Abbas, A., Boithias, L., Pachepsky, Y. A., Kim, K., Chun, J. A., & Cho, K. H. (2022) · Geoscientific model development · doi:10.5194/gmd-15-3021-2022 · Cited by 39 · see citing works

Distributed Bayesian optimization of deep reinforcement learning algorithms

Young, M. T., Hinkle, J. D., Kannan, R., & Ramanathan, A. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2019.07.008 · Cited by 38 · see citing works

Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

Lüthi, S., Aznar–Siguán, G., Fairless, C., & Bresch, D. N. (2021) · Geoscientific model development · doi:10.5194/gmd-14-7175-2021 · Cited by 36 · see citing works

Predicting residential electricity consumption using aerial and street view images

Rosenfelder, M., Wussow, M., Gust, G., Cremades, R., & Neumann, D. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117407 · Cited by 36 · see citing works

PHOTONAI—A Python API for rapid machine learning model development

Leenings, R., Winter, N. R., Plagwitz, L., Holstein, V., Ernsting, J., Sarink, K., Fisch, L., Steenweg, J., Vennekate, L. K., Gebker, J., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254062 · Cited by 35 · see citing works

Predicting Future Laboratory Fault Friction Through Deep Learning Transformer Models

Wang, K., Johnson, C. W., Bennett, K. C., & Johnson, P. A. (2022) · Geophysical Research Letters · doi:10.1029/2022gl098233 · Cited by 32 · see citing works

Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters

Werther, M., Spyrakos, E., Simis, S. G. H., Odermatt, D., Stelzer, K., Krawczyk, H., Berlage, O., Hunter, P., & Tyler, A. N. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.04.003 · Cited by 35 · see citing works

Identification of the Raman Salivary Fingerprint of Parkinson’s Disease Through the Spectroscopic– Computational Combinatory Approach

Carlomagno, C., Bertazioli, D., Gualerzi, A., Picciolini, S., Andrico, M., Rodà, F., Meloni, M., Banfi, P. I., Verde, F., Ticozzi, N., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.704963 · Cited by 32 · see citing works

Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem

Mandl, L., Mielke, A., Seyedpour, S. M., & Ricken, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-42141-x · Cited by 31 · see citing works

A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning

Zhu, F., Zhu, O., Han, M., Liu, W., Guo, X., Hou, T., Zhao, L., Xu, C., & Pingan, Z. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134082 · Cited by 31 · see citing works

Multidimensional analysis and detection of informative features in human brain white matter

Richie-Halford, A., Yeatman, J. D., Simon, N., & Rokem, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009136 · Cited by 29 · see citing works

Machine learning for complete intersection Calabi-Yau manifolds: A methodological study

Erbin, H., & Finotello, R. (2021) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.103.126014 · Cited by 29 · see citing works

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Cromwell, E. A., Osborne, J. C. P., Unnasch, T. R., Basáñez, M.-G., Gass, K., Barbre, K. A., Hill, E., Johnson, K. B., Donkers, K. M., Shirude, S., et al. (2021) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0008824 · Cited by 29 · see citing works

IDS-ML: An open source code for Intrusion Detection System development using Machine Learning

Yang, L., & Shami, A. A. (2022) · Software Impacts · doi:10.1016/j.simpa.2022.100446 · Cited by 29 · see citing works

Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices

Seifrid, M. T., Lo, S., Choi, D. G., Tom, G., Le, M. L., Li, K., Sankar, R., Vuong, H., Wakidi, H., Yi, A., et al. (2024) · Journal of Materials Chemistry A · doi:10.1039/d4ta01942c · Cited by 28 · see citing works

Multi-objective constrained optimization for energy applications via tree ensembles

Thebelt, A., Tsay, C., Lee, R. M., Sudermann‐Merx, N., Walz, D., Tranter, T. G., & Misener, R. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118061 · Cited by 28 · see citing works

Distance approximation to support customer selection in vehicle routing problems

Akkerman, F., & Mes, M. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04674-8 · Cited by 28 · see citing works

Accelerated chemical space search using a quantum-inspired cluster expansion approach

Choubisa, H., Abed, J., Mendoza, D., Matsumura, H., Sugimura, M., Yao, Z., Wang, Z., Sutherland, B. R., Aspuru‐Guzik, A., & Sargent, E. H. (2022) · Matter · doi:10.1016/j.matt.2022.11.031 · Cited by 27 · see citing works

Transfer learning of deep neural network representations for fMRI decoding

Svanera, M., Savardi, M., Benini, S., Signoroni, A., Raz, G., Hendler, T., Muckli, L. F., Goebel, R., & Valente, G. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108319 · Cited by 27 · see citing works

ML and CFD Simulation of Flow Structure around Tandem Bridge Piers in Pressurized Flow

Azma, A., Kiyanfar, R., Liu, Y., Azma, M., Zhang, D., Cao, Z., & Li, Z. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.036680 · Cited by 26 · see citing works

Deep Neural Network with Hyperparameter Tuning for Detection of Heart Disease

Firdaus, F. F., Nugroho, H. A., & Soesanti, I. (2021) · IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob) · doi:10.1109/apwimob51111.2021.9435250 · Cited by 26 · see citing works

Machine Learning Workflow to Explain Black-Box Models for Early Alzheimer’s Disease Classification Evaluated for Multiple Datasets

Bloch, L., Friedrich, C. M., & Initiative, A. D. N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01371-y · Cited by 33 · see citing works

Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations

Focassio, B., Domina, M., Patil, U., Fazzio, A., & Sanvito, S. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01053-0 · Cited by 28 · see citing works

Unlocking the potential of microRNAs: Machine learning identifies key biomarkers for myocardial infarction diagnosis

Samadishadlou, M., Rahbarghazi, R., Piryaei, Z., Esmaeili, M., Avcı, Ç. B., Bani, F., & Kavousi, K. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01957-7 · Cited by 26 · see citing works

Intelligent colocation of HPC workloads

Vieira, F., Petrucci, V., Nishtala, R., Carpenter, P., & Mossé, D. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2021.02.010 · Cited by 25 · see citing works

Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials’ Discovery

Bai, Y., Khoo, Z. H. J., Made, R. I., Xie, H., Lim, C. Y. J., Handoko, A. D., Chellappan, V., Cheng, J. J. W., Wei, F., Lim, Y., et al. (2023) · Advanced Materials · doi:10.1002/adma.202304269 · Cited by 25 · see citing works

Using a theory of mind to find best responses to memory-one strategies

Glynatsi, N. E., & Knight, V. (2020) · Scientific Reports · doi:10.1038/s41598-020-74181-y · Cited by 25 · see citing works

An analysis-ready and quality controlled resource for pediatric brain white-matter research

Richie-Halford, A., Cieslak, M., Ai, L., Caffarra, S., Covitz, S. C., Franco, A. R., Karipidis, I. I., Kruper, J., Milham, M. P., Avelar‐Pereira, B., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01695-7 · Cited by 31 · see citing works

An explainable machine learning approach for automated medical decision support of heart disease

Mesquita, F., & Marques, G. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102339 · Cited by 24 · see citing works

Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model

Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00889 · Cited by 23 · see citing works

Active learning-guided exploration of parameter space of air plasmas to enhance the energy efficiency of NO x production

Shao, K., Pei, X., Graves, D. B., & Mesbah, A. (2022) · Plasma Sources Science and Technology · doi:10.1088/1361-6595/ac6e04 · Cited by 24 · see citing works

AutoComBat: a generic method for harmonizing MRI-based radiomic features

Carré, A., Battistella, E., Niyoteka, S., Sun, R., Deutsch, É., & Robert, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-16609-1 · Cited by 24 · see citing works

On the implementation of a global optimization method for mixed-variable problems

Nannicini, G. (2021) · Open Journal of Mathematical Optimization · doi:10.5802/ojmo.3 · Cited by 23 · see citing works

Long-term prediction of hourly indoor air temperature using machine learning

Laukkarinen, A., & Vinha, J. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114972 · Cited by 21 · see citing works

A geothermal heat flow model of Africa based on random forest regression

Al‐Aghbary, M., Sobh, M., & Gerhards, C. (2022) · Frontiers in Earth Science · doi:10.3389/feart.2022.981899 · Cited by 21 · see citing works

Challenges in data-based reactor modeling: A critical analysis of purely data-driven and hybrid models for a CSTR case study

Peterson, L., Bremer, J., & Sundmacher, K. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108643 · Cited by 22 · see citing works

HyperSpace: Distributed Bayesian Hyperparameter Optimization

Young, M. T., Hinkle, J. D., Ramanathan, A., & Kannan, R. (2018) · International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) · doi:10.1109/cahpc.2018.8645954 · Cited by 21 · see citing works

Health & Gait: a dataset for gait-based analysis

Zafra-Palma, J., Marín‐Jiménez, N., Castro‐Piñero, J., Cuenca‐García, M., Muñoz‐Salinas, R., & Marín-Jiménez, M. J. (2025) · Scientific Data · doi:10.1038/s41597-024-04327-4 · Cited by 21 · see citing works

Fanpy: A python library for prototyping multideterminant methods in ab initio quantum chemistry

Kim, T. D., Richer, M., Sánchez‐Díaz, G., Miranda‐Quintana, R. A., Verstraelen, T., Heidar‐Zadeh, F., & Ayers, P. W. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.27034 · Cited by 19 · see citing works

On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling

McConkey, R., Yee, E., & Lien, F. (2022) · International journal of computational fluid dynamics · doi:10.1080/10618562.2022.2113520 · Cited by 19 · see citing works

Prediction of entrainment fraction in two-phase gas-liquid co-current annular flow-A machine learning approach

Mondal, A., & Sharma, S. (2024) · International Journal of Heat and Mass Transfer · doi:10.1016/j.ijheatmasstransfer.2024.125499 · Cited by 19 · see citing works

Enabling Efficient Data Integration of Industry 5.0 Nodes Through Highly Accurate Neural CSI Feedback

Hussien, M., Nguyen, K. K., Ranjha, A., Krichen, M., Alshammari, A., & Cheriet, M. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3294832 · Cited by 18 · see citing works

Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

Fite, S., Wahab, A., Paenurk, E., Gross, Z., & Gershoni‐Poranne, R. (2022) · Journal of Physical Organic Chemistry · doi:10.1002/poc.4458 · Cited by 17 · see citing works

Auditing and Achieving Intersectional Fairness in Classification ProblemsPreprint

Morina, G., Oliinyk, V., Waton, J., Marušić, I., & Georgatzis, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.01468 · Cited by 18 · see citing works

Separating the wheat from the chaff: A topic and keyword-based procedure for identifying research-relevant text*✰

Eads, A., Schofield, A., Mahootian, F., Mimno, D., & Wilderom, R. (2020) · Poetics · doi:10.1016/j.poetic.2020.101527 · Cited by 17 · see citing works

Supervised machine learning to estimate instabilities in chaotic systems: Estimation of local Lyapunov exponents

Ayers, D., Lau, J., Amezcua, J., Carrassi, A., & Ojha, V. (2023) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4450 · Cited by 18 · see citing works

Learning the Low Frequency Earthquake Activity on the Central San Andreas Fault

Johnson, C. W., & Johnson, P. A. (2021) · Geophysical Research Letters · doi:10.1029/2021gl092951 · Cited by 17 · see citing works

Timing of the solar wind propagation delay between L1 and Earth based on machine learning

Baumann, C., & McCloskey, A. E. (2021) · Journal of Space Weather and Space Climate · doi:10.1051/swsc/2021026 · Cited by 17 · see citing works

Local Latin hypercube refinement for multi-objective design uncertainty optimization

Bogoclu, C., Roos, D., & Nestorovic, T. N. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107807 · Cited by 17 · see citing works

Characterization of portuguese sown rainfed grasslands using remote sensing and machine learning

Morais, T. G., Jongen, M., Tufik, C., Rodrigues, N. R., Gama, I., Fangueiro, D. P., Serrano, J. M. P. R., Vieira, S. M., Domingos, T., & Teixeira, R. F. M. (2022) · Precision Agriculture · doi:10.1007/s11119-022-09937-9 · Cited by 16 · see citing works

An Autonomous Electrochemical Discovery Robot that Utilises Probabilistic Algorithms: Probing the Redox Behaviour of Inorganic Materials

Laws, K., Ng, M. T., Sharma, A., Jiang, Y., Hammer, A. J. S., & Cronin, L. (2023) · ChemElectroChem · doi:10.1002/celc.202300532 · Cited by 16 · see citing works

Fast adaptation to rule switching using neuronal surprise

Barry, M., & Wulfram Gerstner, B. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011839 · Cited by 18 · see citing works

Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods

García-García, F., Lee, D., Mendoza-Garcés, F. J., & García‐Gutiérrez, S. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108118 · Cited by 16 · see citing works

Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks

Balaji, S. S., & Parhi, K. K. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad5938 · Cited by 16 · see citing works

A new approach of preheating and powder sintering in electron beam powder bed fusion

Böhm, J., Breuning, C., Markl, M., & Körner, C. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13966-1 · Cited by 16 · see citing works

Predicting infrasound transmission loss using deep learning

Brissaud, Q., Näsholm, S. P., Turquet, A., & Pichon, A. L. (2022) · Geophysical Journal International · doi:10.1093/gji/ggac307 · Cited by 16 · see citing works

Predictions of heat of combustion and formation by interpretable machine learning algorithms

Maraschin, M., Lanzanova, T. D. M., & Salau, N. P. G. (2025) · Fuel · doi:10.1016/j.fuel.2025.134699 · Cited by 15 · see citing works

Flexynesis: A deep learning toolkit for bulk multi-omics data integration for precision oncology and beyond

Uyar, B., Savchyn, T., Nilchi, A. N., Sarıgün, A., Wurmus, R., Shaik, M. M., Grüning, B. A., Franke, V., & Akalin, A. (2025) · Nature Communications · doi:10.1038/s41467-025-63688-5 · Cited by 15 · see citing works

Predicting Wearing-Off of Parkinson’s Disease Patients Using a Wrist-Worn Fitness Tracker and a Smartphone: A Case Study

Victorino, J. N., Shibata, Y., Inoue, S., & Shibata, T. (2021) · Applied Sciences · doi:10.3390/app11167354 · Cited by 15 · see citing works

Neuromorphic Bayesian Optimization in Lava

Snyder, S., Risbud, S. R., & Parsa, M. (2023) · International Conference on Neuromorphic Systems (ICONS) · doi:10.1145/3589737.3605998 · Cited by 15 · see citing works

Gemini: Divide-and-Conquer for Practical Learning-Based Internet Congestion Control

Yang, W., Liu, Y., Tian, C., Jiang, J., & Guo, L. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229069 · Cited by 15 · see citing works

holgern/scikit-optimize

Head, T., Kumar, M., Nahrstaedt, H., Louppe, G., & Shcherbatyi, I. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.10804382 · Cited by 2 · see citing works

scikit-package version 0.2.1

contributors, S.-P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20768434

scikit-package version 0.1.0

contributors, S.-P. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20768404

C-OPT: composite optimization in Python

Pedregosa, F., & Seong, L. K. (2017) · Figshare · doi:10.5281/zenodo.597854 · Cited by 4 · see citing works

A Data-Centric Optimization Workflow for the Python Language

Ziogas, A. N. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000586638