Xavier Bresson

research

∙ 05/31/2023

Explanations as Features: LLM-Based Features for Text-Attributed Graphs

Representation learning on text-attributed graphs (TAGs) has become a cr...

7 Xiaoxin He, et al. ∙

research

∙ 05/25/2023

Feature Collapse

We formalize and study a phenomenon called feature collapse that makes p...

1 Thomas Laurent, et al. ∙

research

∙ 12/27/2022

A Generalization of ViT/MLP-Mixer to Graphs

Graph Neural Networks (GNNs) have shown great potential in the field of ...

4 Xiaoxin He, et al. ∙

research

∙ 06/26/2022

Semantic Role Aware Correlation Transformer for Text to Video Retrieval

With the emergence of social media, voluminous video clips are uploaded ...

2 Burak Satar, et al. ∙

research

∙ 06/01/2022

Learning to Untangle Genome Assembly with Graph Convolutional Networks

A quest to determine the complete sequence of a human DNA from telomere ...

20 Lovro Vrček, et al. ∙

research

∙ 05/29/2022

A Model of One-Shot Generalization

We provide a theoretical framework to study a phenomenon that we call on...

11 Thomas Laurent, et al. ∙

research

∙ 03/02/2022

Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem

The traveling salesman problem is a fundamental combinatorial optimizati...

4 Yong Liang Goh, et al. ∙

research

∙ 10/15/2021

Graph Neural Networks with Learnable Structural and Positional Representations

Graph neural networks (GNNs) have become the standard learning architect...

21 Vijay Prakash Dwivedi, et al. ∙

research

∙ 03/04/2021

The Transformer Network for the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is the most popular and most studie...

9 Xavier Bresson, et al. ∙

research

∙ 12/18/2020

An Experimental Study of the Transferability of Spectral Graph Networks

Spectral graph convolutional networks are generalizations of standard co...

7 Axel Nilsson, et al. ∙

research

∙ 12/17/2020

A Generalization of Transformer Networks to Graphs

We propose a generalization of transformer neural network architecture f...

8 Vijay Prakash Dwivedi, et al. ∙

research

∙ 06/12/2020

Learning TSP Requires Rethinking Generalization

End-to-end training of neural network solvers for combinatorial problems...

36 Chaitanya K. Joshi, et al. ∙

research

∙ 03/02/2020

Benchmarking Graph Neural Networks

Graph neural networks (GNNs) have become the standard toolkit for analyz...

31 Vijay Prakash Dwivedi, et al. ∙

research

∙ 12/24/2019

Multi-Graph Transformer for Free-Hand Sketch Recognition

Learning meaningful representations of free-hand sketches remains a chal...

15 Peng Xu, et al. ∙

research

∙ 10/16/2019

On Learning Paradigms for the Travelling Salesman Problem

We explore the impact of learning paradigms on training deep neural netw...

40 Chaitanya K. Joshi, et al. ∙

research

∙ 06/08/2019

A Two-Step Graph Convolutional Decoder for Molecule Generation

We propose a simple auto-encoder framework for molecule generation. The ...

7 Xavier Bresson, et al. ∙

research

∙ 06/04/2019

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

This paper introduces a new learning-based approach for approximately so...

46 Chaitanya K. Joshi, et al. ∙

research

∙ 04/15/2019

GraphTSNE: A Visualization Technique for Graph-Structured Data

We present GraphTSNE, a novel visualization technique for graph-structur...

44 Yao Yang Leow, et al. ∙

research

∙ 11/20/2017

Residual Gated Graph ConvNets

Graph-structured data such as functional brain networks, social networks...

0 Xavier Bresson, et al. ∙

research

∙ 04/22/2017

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

Matrix completion models are among the most common formulations of recom...

0 Federico Monti, et al. ∙

research

∙ 12/22/2016

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

This paper introduces Graph Convolutional Recurrent Network (GCRN), a de...

0 Youngjoo Seo, et al. ∙

research

∙ 06/30/2016

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

In this work, we are interested in generalizing convolutional neural net...

0 Michaël Defferrard, et al. ∙

research

∙ 01/08/2016

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

This work formulates a novel song recommender system as a matrix complet...

0 Kirell Benzi, et al. ∙

research

∙ 06/19/2015

Enhanced Lasso Recovery on Graph

This work aims at recovering signals that are sparse on graphs. Compress...

0 Xavier Bresson, et al. ∙

research

∙ 04/23/2015

Robust Principal Component Analysis on Graphs

Principal Component Analysis (PCA) is the most widely used tool for line...

0 Nauman Shahid, et al. ∙

research

∙ 12/27/2014

Functional correspondence by matrix completion

In this paper, we consider the problem of finding dense intrinsic corres...

0 Artiom Kovnatsky, et al. ∙

research

∙ 11/24/2014

Consistency of Cheeger and Ratio Graph Cuts

This paper establishes the consistency of a family of graph-cut-based al...

0 Nicolas Garcia Trillos, et al. ∙

research

∙ 08/07/2014

Matrix Completion on Graphs

The problem of finding the missing values of a matrix given a few of its...

0 Vassilis Kalofolias, et al. ∙

research

∙ 06/05/2013

Multiclass Total Variation Clustering

Ideas from the image processing literature have recently motivated a new...

0 Xavier Bresson, et al. ∙

research

∙ 10/11/2012

Enhanced Compressed Sensing Recovery with Level Set Normals

We propose a compressive sensing algorithm that exploits geometric prope...

0 Virginia Estellers, et al. ∙

Xavier Bresson

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