Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group M Lezcano-Casado, D Martínez-Rubio 36th International Conference on Machine Learning 97 (Proceedings of Machine …, 2019 | 220 | 2019 |
Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation J Ansel, E Yang, H He, N Gimelshein, A Jain, M Voznesensky, B Bao, ... Proceedings of the 29th ACM International Conference on Architectural …, 2024 | 157 | 2024 |
Trivializations for gradient-based optimization on manifolds M Lezcano-Casado Advances in Neural Information Processing Systems (NeurIPS), 9154-9164, 2019 | 129 | 2019 |
Improving normalizing flows via better orthogonal parameterizations A Golinski, M Lezcano-Casado, T Rainforth ICML Workshop on Invertible Neural Networks and Normalizing Flows, 2019 | 16 | 2019 |
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators M Lezcano-Casado, AG Baydin, DM Rubio, TA Le, F Wood, L Heinrich, ... Deep Learning for Physical Sciences workshop (NeurIPS, 2017 | 10* | 2017 |
Curvature-dependant global convergence rates for optimization on manifolds of bounded geometry M Lezcano-Casado arXiv preprint arXiv:2008.02517, 2020 | 9 | 2020 |
Adaptive and momentum methods on manifolds through trivializations M Lezcano-Casado arXiv preprint arXiv:2010.04617, 2020 | 7 | 2020 |
Geometric optimisation on manifolds with applications to deep learning M Lezcano-Casado arXiv preprint arXiv:2203.04794, 2022 | 4 | 2022 |
Compiled inference with probabilistic programming for large-scale scientific simulations M Lezcano-Casado University of Oxford, 2017 | 3 | 2017 |
Automatic Differentiation: Theory and Practice M Lezcano-Casado arXiv preprint arXiv:2207.06114, 2022 | 1 | 2022 |
Python Array API Standard: Toward Array Interoperability in the Scientific Python Ecosystem A Meurer, A Reines, R Gommers, YLL Fang, J Kirkham, M Barber, ... | | 2023 |