1 code implementation • 8 Jan 2024 • Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola
The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy.
1 code implementation • 8 Oct 2023 • Jason Yim, Andrew Campbell, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Regina Barzilay, Tommi Jaakkola, Frank Noé
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
no code implementations • 1 Feb 2023 • Marloes Arts, Victor Garcia Satorras, Chin-wei Huang, Daniel Zuegner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, Rianne van den Berg
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
1 code implementation • 11 Oct 2022 • Ilia Igashov, Hannes Stärk, Clément Vignac, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, Bruno Correia
Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments.
3 code implementations • 31 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.
no code implementations • 7 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.
Computational Efficiency Multivariate Time Series Forecasting +1
no code implementations • 10 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.
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).
5 code implementations • 19 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).
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.
1 code implementation • 4 Mar 2020 • Victor Garcia Satorras, Max Welling
In this work we first extend graph neural networks to factor graphs (FG-GNN).
1 code implementation • NeurIPS 2019 • Victor Garcia Satorras, Zeynep Akata, Max Welling
A graphical model is a structured representation of the data generating process.
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.