| Discovering faster matrix multiplication algorithms with reinforcement learning A Fawzi, M Balog, A Huang, T Hubert, B Romera-Paredes, M Barekatain, ... Nature 610 (7930), 47-53, 2022 | 486 | 2022 |
| Sobolev training for neural networks WM Czarnecki, S Osindero, M Jaderberg, G Swirszcz, R Pascanu Advances in neural information processing systems 30, 2017 | 256 | 2017 |
| Distilling policy distillation WM Czarnecki, R Pascanu, S Osindero, S Jayakumar, G Swirszcz, ... The 22nd international conference on artificial intelligence and statistics …, 2019 | 134 | 2019 |
| Grouped orthogonal matching pursuit for variable selection and prediction G Swirszcz, N Abe, AC Lozano Advances in Neural Information Processing Systems 22, 2009 | 129 | 2009 |
| Maximum weight independent sets and matchings in sparse random graphs. Exact results using the local weak convergence method D Gamarnik, T Nowicki, G Swirszcz Random Structures & Algorithms 28 (1), 76-106, 2006 | 100 | 2006 |
| Winning the KDD cup orange challenge with ensemble selection A Niculescu-Mizil, C Perlich, G Swirszcz, V Sindhwani, Y Liu, P Melville, ... KDD-Cup 2009 competition, 23-34, 2009 | 87 | 2009 |
| ℓ1 Regularization in Infinite Dimensional Feature Spaces S Rosset, G Swirszcz, N Srebro, J Zhu Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San …, 2007 | 82 | 2007 |
| Understanding synthetic gradients and decoupled neural interfaces WM Czarnecki, G Świrszcz, M Jaderberg, S Osindero, O Vinyals, ... International Conference on Machine Learning, 904-912, 2017 | 80 | 2017 |
| Local minima in training of neural networks G Swirszcz, WM Czarnecki, R Pascanu arXiv preprint arXiv:1611.06310, 2016 | 77 | 2016 |
| Multi-level lasso for sparse multi-task regression AC Lozano, G Swirszcz Proceedings of the 29th International Coference on International Conference …, 2012 | 69 | 2012 |
| Group orthogonal matching pursuit for logistic regression A Lozano, G Swirszcz, N Abe Proceedings of the fourteenth international conference on artificial …, 2011 | 69 | 2011 |
| On the limit cycles of polynomial vector fields J Llibre, G Swirszcz Dyn. Contin. Discrete Impuls. Syst. Ser. A Math. Anal 18 (2), 203-214, 2011 | 57 | 2011 |
| Methods and systems for variable group selection and temporal causal modeling N Abe, Y Liu, AC Lozano, S Rosset, G Swirszcz US Patent 8,255,346, 2012 | 53 | 2012 |
| Rapid training of deep neural networks without skip connections or normalization layers using deep kernel shaping J Martens, A Ballard, G Desjardins, G Swirszcz, V Dalibard, ... arXiv preprint arXiv:2110.01765, 2021 | 48 | 2021 |
| Verification of non-linear specifications for neural networks C Qin, B O'Donoghue, R Bunel, R Stanforth, S Gowal, J Uesato, ... arXiv preprint arXiv:1902.09592, 2019 | 46 | 2019 |
| Medical data mining: insights from winning two competitions S Rosset, C Perlich, G Świrszcz, P Melville, Y Liu Data Mining and Knowledge Discovery 20, 439-468, 2010 | 46 | 2010 |
| Method and system for scheduling delivery of at least one of goods and services F Barahona, SJ Buckley, PR Chowdhary, JJ Forrest, TJ Kimbrel, ... US Patent App. 11/443,068, 2007 | 41 | 2007 |
| Local minima in training of deep networks G Swirszcz, WM Czarnecki, R Pascanu | 38 | 2016 |
| Multi-level lasso for sparse multi-task regression G Swirszcz, AC Lozano Proceedings of the 29th International Conference on Machine Learning (ICML …, 2012 | 38 | 2012 |
| Invariant algebraic curves of large degree for quadratic system C Christopher, J Llibre, G Świrszcz Journal of mathematical analysis and applications 303 (2), 450-461, 2005 | 33 | 2005 |
