| Predicting drug side-effect profiles: a chemical fragment-based approach E Pauwels, V Stoven, Y Yamanishi BMC bioinformatics 12, 1-13, 2011 | 266 | 2011 |
| Relating drug–protein interaction network with drug side effects S Mizutani, E Pauwels, V Stoven, S Goto, Y Yamanishi Bioinformatics 28 (18), i522-i528, 2012 | 233 | 2012 |
| Drug side-effect prediction based on the integration of chemical and biological spaces Y Yamanishi, E Pauwels, M Kotera Journal of chemical information and modeling 52 (12), 3284-3292, 2012 | 158 | 2012 |
| Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning J Bolte, E Pauwels Mathematical Programming 188, 19-51, 2021 | 143 | 2021 |
| Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers Y Tabei, E Pauwels, V Stoven, K Takemoto, Y Yamanishi Bioinformatics 28 (18), i487-i494, 2012 | 105 | 2012 |
| Extracting sets of chemical substructures and protein domains governing drug-target interactions Y Yamanishi, E Pauwels, H Saigo, V Stoven Journal of chemical information and modeling 51 (5), 1183-1194, 2011 | 93 | 2011 |
| Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs J Bolte, E Pauwels Mathematics of Operations Research 41 (2), 442-465, 2016 | 88 | 2016 |
| A mathematical model for automatic differentiation in machine learning J Bolte, E Pauwels Advances in Neural Information Processing Systems 33, 10809-10819, 2020 | 85 | 2020 |
| An inertial newton algorithm for deep learning C Castera, J Bolte, C Févotte, E Pauwels Journal of Machine Learning Research 22 (134), 1-31, 2021 | 68 | 2021 |
| Nonsmooth implicit differentiation for machine-learning and optimization J Bolte, T Le, E Pauwels, T Silveti-Falls Advances in neural information processing systems 34, 13537-13549, 2021 | 59 | 2021 |
| On Fienup methods for sparse phase retrieval EJR Pauwels, A Beck, YC Eldar, S Sabach IEEE Transactions on Signal Processing 66 (4), 982-991, 2017 | 57 | 2017 |
| Semialgebraic optimization for lipschitz constants of relu networks T Chen, JB Lasserre, V Magron, E Pauwels Advances in Neural Information Processing Systems 33, 19189-19200, 2020 | 56 | 2020 |
| Inverse optimal control with polynomial optimization E Pauwels, D Henrion, JB Lasserre 53rd IEEE Conference on Decision and Control, 5581-5586, 2014 | 54 | 2014 |
| Sorting out typicality with the inverse moment matrix SOS polynomial E Pauwels, JB Lasserre Advances in Neural Information Processing Systems 29, 2016 | 53 | 2016 |
| Semi-algebraic approximation using Christoffel–Darboux kernel S Marx, E Pauwels, T Weisser, D Henrion, JB Lasserre Constructive Approximation, 1-39, 2021 | 47 | 2021 |
| The empirical Christoffel function with applications in data analysis JB Lasserre, E Pauwels Advances in Computational Mathematics 45, 1439-1468, 2019 | 47 | 2019 |
| The cyclic block conditional gradient method for convex optimization problems A Beck, E Pauwels, S Sabach SIAM Journal on Optimization 25 (4), 2024-2049, 2015 | 45 | 2015 |
| Data analysis from empirical moments and the Christoffel function E Pauwels, M Putinar, JB Lasserre Foundations of Computational Mathematics 21, 243-273, 2021 | 39 | 2021 |
| Numerical influence of ReLU’(0) on backpropagation D Bertoin, J Bolte, S Gerchinovitz, E Pauwels Advances in Neural Information Processing Systems 34, 468-479, 2021 | 38 | 2021 |
| The Christoffel–Darboux Kernel for Data Analysis JB Lasserre, E Pauwels, M Putinar Cambridge University Press, 2022 | 35 | 2022 |
