Georgios Arvanitidis | DeepAI

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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 ...

0 Alison Pouplin, et al. ∙

∙ 06/12/2023

Riemannian Laplace approximations for Bayesian neural networks

Bayesian neural networks often approximate the weight-posterior with a G...

0 Federico Bergamin, et al. ∙

∙ 03/15/2022

Reactive Motion Generation on Learned Riemannian Manifolds

In recent decades, advancements in motion learning have enabled robots t...

3 Hadi Beik-Mohammadi, et al. ∙

∙ 10/05/2021

On the Impact of Stable Ranks in Deep Nets

A recent line of work has established intriguing connections between the...

0 Bogdan Georgiev, et al. ∙

∙ 06/09/2021

Pulling back information geometry

Latent space geometry has shown itself to provide a rich and rigorous fr...

0 Georgios Arvanitidis, et al. ∙

∙ 06/08/2021

Learning Riemannian Manifolds for Geodesic Motion Skills

For robots to work alongside humans and perform in unstructured environm...

12 Hadi Beik-Mohammadi, et al. ∙

∙ 03/09/2021

A prior-based approximate latent Riemannian metric

Stochastic generative models enable us to capture the geometric structur...

16 Georgios Arvanitidis, et al. ∙

∙ 02/12/2021

Bayesian Quadrature on Riemannian Data Manifolds

Riemannian manifolds provide a principled way to model nonlinear geometr...

14 Christian Fröhlich, et al. ∙

∙ 08/02/2020

Geometrically Enriched Latent Spaces

A common assumption in generative models is that the generator immerses ...

20 Georgios Arvanitidis, et al. ∙

∙ 02/12/2020

Variational Autoencoders with Riemannian Brownian Motion Priors

Variational Autoencoders (VAEs) represent the given data in a low-dimens...

0 Dimitris Kalatzis, et al. ∙

∙ 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...

0 Georgios Arvanitidis, et al. ∙

∙ 09/13/2018

Geodesic Clustering in Deep Generative Models

Deep generative models are tremendously successful in learning low-dimen...

6 Tao Yang, et al. ∙

∙ 10/31/2017

Latent Space Oddity: on the Curvature of Deep Generative Models

Deep generative models provide a systematic way to learn nonlinear data ...

0 Georgios Arvanitidis, et al. ∙

∙ 06/08/2016

A Locally Adaptive Normal Distribution

The multivariate normal density is a monotonic function of the distance ...

0 Georgios Arvanitidis, et al. ∙

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