Pattern Recognition and Machine Learning

A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239

Generative AI

Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 2,267

Emerging Artificial Intelligence Methods in Structural Engineering

Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068

Machine Learning Approaches for Clinical Psychology and Psychiatry

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922

Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915

The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation

Whittington, J. C. R., Muller, T. H., Mark, S., Chen, G., Barry, C., Burgess, N., & Behrens, T. E. J. (2020) · Cell · doi:10.1016/j.cell.2020.10.024 · Cited by 660

Calibrating Probability with Undersampling for Unbalanced Classification

Pozzolo, A. D., Caelen, O., Johnson, R. A., & Bontempi, G. (2015) · 2015 IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci.2015.33 · Cited by 667

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022) · Structural Health Monitoring · doi:10.1177/14759217211036880 · Cited by 607

Meta-analysis of cellular toxicity for cadmium-containing quantum dots

Oh, E., Liu, R., Nel, A., Gemill, K. B., Bilal, M., Cohen, Y., & Medintz, I. L. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2015.338 · Cited by 502

Automatic speech emotion recognition using modulation spectral features

Wu, S., Falk, T. H., & Chan, W.-Y. (2011) · Speech Communication · doi:10.1016/j.specom.2010.08.013 · Cited by 389

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368

Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex

Murugan, M., Jang, H. J., Park, M., Miller, E. M., Cox, J., Taliaferro, J. P., Parker, N. F., Bhave, V., Hur, H., Liang, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.11.002 · Cited by 311

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Ashmore, R., Calinescu, R., & Paterson, C. (2022) · ACM Computing Surveys · doi:10.1145/3453444 · Cited by 256

A Survey on Semi-, Self- and Unsupervised Learning for Image Classification

Schmarje, L., Santarossa, M., Schroder, S.-M., & Koch, R. (2021) · IEEE Access · doi:10.1109/access.2021.3084358 · Cited by 246

Perspectives on the integration between first-principles and data-driven modeling

Bradley, W., Kim, J., Kilwein, Z., Blakely, L., Eydenberg, M., Jalvin, J., Laird, C., & Boukouvala, F. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107898 · Cited by 231

Foresee: A user-centric home energy management system for energy efficiency and demand response

Jin, X., Baker, K., Christensen, D., & Isley, S. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.08.166 · Cited by 204

Event Prediction in the Big Data Era: A Systematic Survey

Zhao, L. (2022) · ACM Computing Surveys · doi:10.1145/3450287 · Cited by 135

Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review

Ansart, M., Epelbaum, S., Bassignana, G., Bône, A., Bottani, S., Cattai, T., Couronné, R., Faouzi, J., Koval, I., Louis, M., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101848 · Cited by 116

Decentralized Stochastic Optimization and Machine Learning: A Unified Variance-Reduction Framework for Robust Performance and Fast Convergence

Xin, R., Kar, S., & Khan, U. A. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2974267 · Cited by 114

Active inference as a theory of sentient behavior

Pezzulo, G., Parr, T., & Friston, K. (2024) · Biological Psychology · doi:10.1016/j.biopsycho.2023.108741 · Cited by 109

Molecular Insights from Conformational Ensembles via Machine Learning

Fleetwood, O., Kasimova, M. A., Westerlund, A. M., & Delemotte, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2019.12.016 · Cited by 93

Artificial Neural Networks for Space and Safety-Critical Applications: Reliability Issues and Potential Solutions

Rech, P. (2024) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2024.3349956 · Cited by 75

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction

Ahmad, F. S., Luo, Y., Wehbe, R. M., Thomas, J. D., & Shah, S. J. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.12.002 · Cited by 34

AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests

Zhang, H., Bosch, J., Olsson, H. H., & Koppisetty, A. C. (2021) · 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa53835.2021.00047 · Cited by 10

Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study

Lee, S.-Y., Hayes, L. W., Ozaydin, B., Howard, S., Garretson, A. M., Bradley, H. M., Land, A. M., DeLaney, E. W., Pritchett, A. O., Furr, A. L., et al. (2024) · JMIR Research Protocols · doi:10.2196/56049 · Cited by 5