Search Results for author: Victor Garcia Satorras

Found 9 papers, 7 papers with code

Equivariant Diffusion for Molecule Generation in 3D

1 code implementation31 Mar 2022 Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling

This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations.

Multivariate Time Series Forecasting with Latent Graph Inference

no code implementations7 Mar 2022 Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski

This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series.

Multivariate Time Series Forecasting

A Study of Joint Graph Inference and Forecasting

no code implementations10 Sep 2021 Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus

We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.

Graph Learning Time Series

E(n) Equivariant Normalizing Flows

1 code implementation NeurIPS 2021 Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, Max Welling

This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs).

E(n) Equivariant Graph Neural Networks

2 code implementations19 Feb 2021 Victor Garcia Satorras, Emiel Hoogeboom, Max Welling

This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs).

Representation Learning

The Convolution Exponential and Generalized Sylvester Flows

1 code implementation NeurIPS 2020 Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak, Max Welling

Empirically, we show that the convolution exponential outperforms other linear transformations in generative flows on CIFAR10 and the graph convolution exponential improves the performance of graph normalizing flows.

Neural Enhanced Belief Propagation on Factor Graphs

1 code implementation4 Mar 2020 Victor Garcia Satorras, Max Welling

In this work we first extend graph neural networks to factor graphs (FG-GNN).

Few-Shot Learning with Graph Neural Networks

2 code implementations ICLR 2018 Victor Garcia Satorras, Joan Bruna Estrach

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.

Active Learning Few-Shot Learning

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