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07/10/2023
On the curvature of the loss landscape
One of the main challenges in modern deep learning is to understand why ...
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06/12/2023
Riemannian Laplace approximations for Bayesian neural networks
Bayesian neural networks often approximate the weight-posterior with a G...
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03/15/2022
Reactive Motion Generation on Learned Riemannian Manifolds
In recent decades, advancements in motion learning have enabled robots t...
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10/05/2021
On the Impact of Stable Ranks in Deep Nets
A recent line of work has established intriguing connections between the...
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06/09/2021
Pulling back information geometry
Latent space geometry has shown itself to provide a rich and rigorous fr...
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06/08/2021
Learning Riemannian Manifolds for Geodesic Motion Skills
For robots to work alongside humans and perform in unstructured environm...
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03/09/2021
A prior-based approximate latent Riemannian metric
Stochastic generative models enable us to capture the geometric structur...
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02/12/2021
Bayesian Quadrature on Riemannian Data Manifolds
Riemannian manifolds provide a principled way to model nonlinear geometr...
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08/02/2020
Geometrically Enriched Latent Spaces
A common assumption in generative models is that the generator immerses ...
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02/12/2020
Variational Autoencoders with Riemannian Brownian Motion Priors
Variational Autoencoders (VAEs) represent the given data in a low-dimens...
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01/22/2019
Fast and Robust Shortest Paths on Manifolds Learned from Data
We propose a fast, simple and robust algorithm for computing shortest pa...
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09/13/2018
Geodesic Clustering in Deep Generative Models
Deep generative models are tremendously successful in learning low-dimen...
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10/31/2017
Latent Space Oddity: on the Curvature of Deep Generative Models
Deep generative models provide a systematic way to learn nonlinear data ...
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06/08/2016
