Andreas Loukas

research

∙ 07/18/2023

Batched Predictors Generalize within Distribution

We study the generalization properties of batched predictors, i.e., mode...

0 Andreas Loukas, et al. ∙

research

∙ 06/05/2023

Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning

The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most ...

0 Mattia Atzeni, et al. ∙

research

∙ 05/11/2023

Towards Understanding and Improving GFlowNet Training

Generative flow networks (GFlowNets) are a family of algorithms that lea...

2 Max W. Shen, et al. ∙

research

∙ 10/21/2022

Conditional Diffusion with Less Explicit Guidance via Model Predictive Control

How much explicit guidance is necessary for conditional diffusion? We co...

0 Max W. Shen, et al. ∙

research

∙ 10/19/2022

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences

Deep generative models have emerged as a popular machine learning-based ...

8 Natasa Tagasovska, et al. ∙

research

∙ 08/16/2022

On the generalization of learning algorithms that do not converge

Generalization analyses of deep learning typically assume that the train...

0 Nisha Chandramoorthy, et al. ∙

research

∙ 08/08/2022

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Integrating functions on discrete domains into neural networks is key to...

0 Nikolaos Karalias, et al. ∙

research

∙ 04/04/2022

SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators

We approach the graph generation problem from a spectral perspective by ...

0 Karolis Martinkus, et al. ∙

research

∙ 10/27/2021

SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning

State-of-the-art approaches to reasoning and question answering over kno...

0 Mattia Atzeni, et al. ∙

research

∙ 07/05/2021

Partition and Code: learning how to compress graphs

Can we use machine learning to compress graph data? The absence of order...

67 Giorgos Bouritsas, et al. ∙

research

∙ 06/08/2021

What training reveals about neural network complexity

This work explores the hypothesis that the complexity of the function a ...

0 Andreas Loukas, et al. ∙

research

∙ 03/05/2021

Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

Attention-based architectures have become ubiquitous in machine learning...

0 Yihe Dong, et al. ∙

research

∙ 06/29/2020

Multi-Head Attention: Collaborate Instead of Concatenate

Attention layers are widely used in natural language processing (NLP) an...

0 Jean-Baptiste Cordonnier, et al. ∙

research

∙ 06/26/2020

Building powerful and equivariant graph neural networks with message-passing

Message-passing has proved to be an effective way to design graph neural...

1 Clément Vignac, et al. ∙

research

∙ 06/18/2020

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Combinatorial optimization problems are notoriously challenging for neur...

0 Nikolaos Karalias, et al. ∙

research

∙ 05/13/2020

How hard is graph isomorphism for graph neural networks?

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

0 Andreas Loukas, et al. ∙

research

∙ 11/08/2019

On the Relationship between Self-Attention and Convolutional Layers

Recent trends of incorporating attention mechanisms in vision have led r...

0 Jean-Baptiste Cordonnier, et al. ∙

research

∙ 07/06/2019

What graph neural networks cannot learn: depth vs width

This paper studies the capacity limits of graph neural networks (GNN). R...

0 Andreas Loukas, et al. ∙

research

∙ 05/31/2019

Discriminative structural graph classification

This paper focuses on the discrimination capacity of aggregation functio...

0 Younjoo Seo, et al. ∙

research

∙ 05/23/2019

Some limitations of norm based generalization bounds in deep neural networks

Deep convolutional neural networks have been shown to be able to fit a l...

5 Konstantinos Pitas, et al. ∙

research

∙ 03/18/2019

Extrapolating paths with graph neural networks

We consider the problem of path inference: given a path prefix, i.e., a ...

0 Jean-Baptiste Cordonnier, et al. ∙

research

∙ 01/29/2019

Approximating Spectral Clustering via Sampling: a Review

Spectral clustering refers to a family of unsupervised learning algorith...

0 Nicolas Tremblay, et al. ∙

research

∙ 08/31/2018

Graph reduction by local variation

How can we reduce the size of a graph without significantly altering its...

0 Andreas Loukas, et al. ∙

research

∙ 02/21/2018

Spectrally approximating large graphs with smaller graphs

How does coarsening affect the spectrum of a general graph? We provide c...

0 Andreas Loukas, et al. ∙

research

∙ 06/12/2017

Fast Approximate Spectral Clustering for Dynamic Networks

Spectral clustering is a widely studied problem, yet its complexity is p...

0 Lionel Martin, et al. ∙

research

∙ 02/17/2017

How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?

How many samples are sufficient to guarantee that the eigenvectors and e...

0 Andreas Loukas, et al. ∙

research

∙ 11/01/2016

Stationary time-vertex signal processing

The goal of this paper is to improve learning for multivariate processes...

0 Andreas Loukas, et al. ∙

research

∙ 07/12/2016

Predicting the evolution of stationary graph signals

An emerging way of tackling the dimensionality issues arising in the mod...

0 Andreas Loukas, et al. ∙

research

∙ 06/22/2016

Towards stationary time-vertex signal processing

Graph-based methods for signal processing have shown promise for the ana...

0 Nathanaël Perraudin, et al. ∙

research

∙ 02/14/2016

Autoregressive Moving Average Graph Filtering

One of the cornerstones of the field of signal processing on graphs are ...

0 Elvin Isufi, et al. ∙

research

∙ 02/14/2016

Frequency Analysis of Temporal Graph Signals

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

0 Andreas Loukas, et al. ∙

Andreas Loukas

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