| Python Machine Learning, 2nd Ed. S Raschka, V Mirjalili Packt Publishing, 2017 | 3225* | 2017 |
| ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data T Eslami, V Mirjalili, A Fong, AR Laird, F Saeed Frontiers in neuroinformatics 13, 70, 2019 | 270 | 2019 |
| Rank consistent ordinal regression for neural networks with application to age estimation W Cao, V Mirjalili, S Raschka Pattern Recognition Letters 140, 325-331, 2020 | 233 | 2020 |
| Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images V Mirjalili, S Raschka, A Namboodiri, A Ross 11th IAPR International Conference on Biometrics (ICB 2018), 2018 | 122 | 2018 |
| Physics‐based protein structure refinement through multiple molecular dynamics trajectories and structure averaging V Mirjalili, K Noyes, M Feig Proteins: Structure, Function, and Bioinformatics 82, 196-207, 2014 | 118 | 2014 |
| Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python S Raschka, YH Liu, V Mirjalili, D Dzhulgakov Packt Publishing Ltd, 2022 | 112 | 2022 |
| Protein structure refinement through structure selection and averaging from molecular dynamics ensembles V Mirjalili, M Feig Journal of chemical theory and computation 9 (2), 1294-1303, 2013 | 103 | 2013 |
| Soft biometric privacy: Retaining biometric utility of face images while perturbing gender V Mirjalili, A Ross International Joint Conference on Biometrics (IJCB 2017), 2017 | 94 | 2017 |
| PrivacyNet: Semi-adversarial networks for multi-attribute face privacy V Mirjalili, S Raschka, A Ross IEEE Transactions on Image Processing 29, 9400-9412, 2020 | 86 | 2020 |
| Direct simulation Monte Carlo solution of subsonic flow through micro/nanoscale channels E Roohi, M Darbandi, V Mirjalili | 85 | 2009 |
| Protein structure refinement via molecular‐dynamics simulations: what works and what does not? M Feig, V Mirjalili Proteins: Structure, Function, and Bioinformatics 84, 282-292, 2016 | 74 | 2016 |
| Some research problems in biometrics: The future beckons A Ross, S Banerjee, C Chen, A Chowdhury, V Mirjalili, R Sharma, ... 2019 international conference on biometrics (ICB), 1-8, 2019 | 71 | 2019 |
| Python 機械学習プログラミング S Raschka, Y Liu, V Mirjalili (No Title), 2018 | 64* | 2018 |
| Gender privacy: An ensemble of semi adversarial networks for confounding arbitrary gender classifiers V Mirjalili, S Raschka, A Ross 2018 IEEE 9th International Conference on Biometrics Theory, Applications …, 2018 | 51 | 2018 |
| Flowsan: Privacy-enhancing semi-adversarial networks to confound arbitrary face-based gender classifiers V Mirjalili, S Raschka, A Ross IEEE Access 7, 99735-99745, 2019 | 50 | 2019 |
| Python machine learning V Mirjalili, S Raschka Marcombo, 2020 | 44 | 2020 |
| Density-biased sampling: a robust computational method for studying pore formation in membranes V Mirjalili, M Feig Journal of chemical theory and computation 11 (1), 343-350, 2015 | 25 | 2015 |
| DSMC solution of supersonic scale to choked subsonic flow in micro to nano channels E Roohi, M Darbandi, V Mirjalili ASME ICNMM 62282, 23-25, 2008 | 24 | 2008 |
| Interactions of amino acid side-chain analogs within membrane environments V Mirjalili, M Feig The Journal of Physical Chemistry B 119 (7), 2877-2885, 2015 | 18 | 2015 |
| Python machine learning: aprendizaje automático y aprendizaje profundo con Python, scikit-learn y TensorFlow S Raschka, V Mirjalili Marcombo, 2019 | 17 | 2019 |
Sebastian RaschkaLead AI Educator at Lightning AI, Asst. Professor of Statistics, University of Wisconsin-MadisonVerified email at wisc.edu
