86 citations · 133 across the 6 of their papers we have counts for
6 papers
Automatic Differentiation: Theory and Practice
Mario Lezcano-Casado
We present the classical coordinate-free formalism for forward and backward mode ad in the real and complex setting. We show how to formally derive the forward and backward formula…
Geometric Optimisation on Manifolds with Applications to Deep Learning
Mario Lezcano-Casado
We design and implement a Python library to help the non-expert using all these powerful tools in a way that is efficient, extensible, and simple to incorporate into the workflow o…
Adaptive and Momentum Methods on Manifolds Through Trivializations
Mario Lezcano-Casado
Adaptive methods do not have a direct generalization to manifolds as the adaptive term is not invariant. Momentum methods on manifolds suffer from efficiency problems stemming from…
Trivializations for Gradient-Based Optimization on Manifolds
Mario Lezcano-Casado
We introduce a framework to study the transformation of problems with manifold constraints into unconstrained problems through parametrizations in terms of a Euclidean space. We ca…
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
Mario Lezcano-Casado, David Martínez-Rubio
We introduce a novel approach to perform first-order optimization with orthogonal and unitary constraints. This approach is based on a parametrization stemming from Lie group theor…
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from po…