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no code implementations • 21 Oct 2022 • Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia, Alex Tseng, Nathaniel Diamant, Tommaso Biancalani, Andreas Loukas

How much explicit guidance is necessary for conditional diffusion?

no code implementations • 19 Oct 2022 • Nataša Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijević

Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.

no code implementations • 16 Aug 2022 • Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry, Stefanie Jegelka

To reduce this discrepancy between theory and practice, this paper focuses on the generalization of neural networks whose training dynamics do not necessarily converge to fixed points.

1 code implementation • 8 Aug 2022 • Nikolaos Karalias, Joshua Robinson, Andreas Loukas, Stefanie Jegelka

Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects.

1 code implementation • 4 Apr 2022 • Karolis Martinkus, Andreas Loukas, Nathanaël Perraudin, Roger Wattenhofer

We approach the graph generation problem from a spectral perspective by first generating the dominant parts of the graph Laplacian spectrum and then building a graph matching these eigenvalues and eigenvectors.

no code implementations • NeurIPS 2021 • Mattia Atzeni, Jasmina Bogojeska, Andreas Loukas

State-of-the-art approaches to reasoning and question answering over knowledge graphs (KGs) usually scale with the number of edges and can only be applied effectively on small instance-dependent subgraphs.

no code implementations • 29 Sep 2021 • Nikolaos Karalias, Joshua David Robinson, Andreas Loukas, Stefanie Jegelka

Our framework includes well-known extensions such as the Lovasz extension of submodular set functions and facilitates the design of novel continuous extensions based on problem-specific considerations, including constraints.

1 code implementation • NeurIPS 2021 • Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein

Can we use machine learning to compress graph data?

1 code implementation • NeurIPS 2021 • Andreas Loukas, Marinos Poiitis, Stefanie Jegelka

This work explores the Benevolent Training Hypothesis (BTH) which argues that the complexity of the function a deep neural network (NN) is learning can be deduced by its training dynamics.

1 code implementation • 5 Mar 2021 • Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas

Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited.

no code implementations • 1 Jan 2021 • Igor Krawczuk, Pedro Abranches, Andreas Loukas, Volkan Cevher

We study the fundamental problem of graph generation.

2 code implementations • 29 Jun 2020 • Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

We also show that it is possible to re-parametrize a pre-trained multi-head attention layer into our collaborative attention layer.

1 code implementation • NeurIPS 2020 • Clement Vignac, Andreas Loukas, Pascal Frossard

We address this problem and propose a powerful and equivariant message-passing framework based on two ideas: first, we propagate a one-hot encoding of the nodes, in addition to the features, in order to learn a local context matrix around each node.

1 code implementation • NeurIPS 2020 • Nikolaos Karalias, Andreas Loukas

Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances.

no code implementations • NeurIPS 2020 • Andreas Loukas

A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their inputs.

1 code implementation • ICLR 2020 • Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice.

Ranked #143 on Image Classification on CIFAR-10

no code implementations • ICLR 2020 • Andreas Loukas

This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp).

no code implementations • 31 May 2019 • Younjoo Seo, Andreas Loukas, Nathanaël Perraudin

This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes.

no code implementations • 23 May 2019 • Konstantinos Pitas, Andreas Loukas, Mike Davies, Pierre Vandergheynst

Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels.

1 code implementation • 18 Mar 2019 • Jean-Baptiste Cordonnier, Andreas Loukas

We consider the problem of path inference: given a path prefix, i. e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix.

no code implementations • 29 Jan 2019 • Nicolas Tremblay, Andreas Loukas

Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph.

no code implementations • 31 Aug 2018 • Andreas Loukas

Can one reduce the size of a graph without significantly altering its basic properties?

no code implementations • ICML 2018 • Andreas Loukas, Pierre Vandergheynst

How does coarsening affect the spectrum of a general graph?

no code implementations • ICML 2017 • Andreas Loukas

How many samples are sufficient to guarantee that the eigenvectors of the sample covariance matrix are close to those of the actual covariance matrix?

no code implementations • ICML 2018 • Lionel Martin, Andreas Loukas, Pierre Vandergheynst

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size.

no code implementations • 5 May 2017 • Francesco Grassi, Andreas Loukas, Nathanaël Perraudin, Benjamin Ricaud

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph.

no code implementations • 17 Feb 2017 • Andreas Loukas

How many samples are sufficient to guarantee that the eigenvectors and eigenvalues of the sample covariance matrix are close to those of the actual covariance matrix?

no code implementations • 1 Nov 2016 • Andreas Loukas, Nathanaël Perraudin

This paper considers regression tasks involving high-dimensional multivariate processes whose structure is dependent on some {known} graph topology.

no code implementations • 12 Jul 2016 • Andreas Loukas, Nathanael Perraudin

An emerging way of tackling the dimensionality issues arising in the modeling of a multivariate process is to assume that the inherent data structure can be captured by a graph.

no code implementations • 22 Jun 2016 • Nathanael Perraudin, Andreas Loukas, Francesco Grassi, Pierre Vandergheynst

Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks.

no code implementations • 14 Feb 2016 • Elvin Isufi, Andreas Loukas, Andrea Simonetto, Geert Leus

We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation.

no code implementations • 14 Feb 2016 • Andreas Loukas, Damien Foucard

This letter extends the concept of graph-frequency to graph signals that evolve with time.

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