284 citations · 406 across the 32 of their papers we have counts for
8 papers · 1 filter
Controlling Style and Semantics in Weakly-Supervised Image Generation
Dario Pavllo, Aurelien Lucchi, Thomas Hofmann
We propose a weakly-supervised approach for conditional image generation of complex scenes where a user has fine control over objects appearing in the scene. We exploit sparse sema…
Shadowing Properties of Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
Ordinary differential equation (ODE) models of gradient-based optimization methods can provide insights into the dynamics of learning and inspire the design of new algorithms. Unfo…
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization
Foivos Alimisis, Antonio Orvieto, Gary Bécigneul +1
We propose a novel second-order ODE as the continuous-time limit of a Riemannian accelerated gradient-based method on a manifold with curvature bounded from below. This ODE can be…
Cosmological N-body simulations: a challenge for scalable generative models
Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi +3
Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, t…
The Role of Memory in Stochastic Optimization
Antonio Orvieto, Jonas Kohler, Aurelien Lucchi
The choice of how to retain information about past gradients dramatically affects the convergence properties of state-of-the-art stochastic optimization methods, such as Heavy-ball…
Cosmological constraints with deep learning from KiDS-450 weak lensing maps
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi +4
Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential…