1 code implementation • 12 Feb 2024 • Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann Lecun, Xavier Bresson, Bryan Hooi

Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.

1 code implementation • 7 Feb 2024 • Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You

Specifically, we employ a curriculum learning strategy to train expert trajectories with more diverse supervision signals from the original graph, and then effectively transfer the information into the condensed graph with expanding window matching.

no code implementations • 18 Jan 2024 • Yutong Xia, Runpeng Yu, Yuxuan Liang, Xavier Bresson, Xinchao Wang, Roger Zimmermann

Graph Neural Networks (GNNs) have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques.

1 code implementation • 18 Dec 2023 • Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, Tong Zhao

As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations.

3 code implementations • 31 May 2023 • Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann Lecun, Bryan Hooi

With the advent of powerful large language models (LLMs) such as GPT or Llama2, which demonstrate an ability to reason and to utilize general knowledge, there is a growing need for techniques which combine the textual modelling abilities of LLMs with the structural learning capabilities of GNNs.

Ranked #2 on Node Property Prediction on ogbn-arxiv (using extra training data)

1 code implementation • 25 May 2023 • Thomas Laurent, James H. von Brecht, Xavier Bresson

We formalize and study a phenomenon called feature collapse that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations.

3 code implementations • 27 Dec 2022 • Xiaoxin He, Bryan Hooi, Thomas Laurent, Adam Perold, Yann Lecun, Xavier Bresson

First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.

Ranked #1 on Graph Regression on Peptides-struct

1 code implementation • 26 Jun 2022 • Burak Satar, Hongyuan Zhu, Xavier Bresson, Joo Hwee Lim

With the emergence of social media, voluminous video clips are uploaded every day, and retrieving the most relevant visual content with a language query becomes critical.

Ranked #13 on Video Retrieval on YouCook2

1 code implementation • 1 Jun 2022 • Lovro Vrček, Xavier Bresson, Thomas Laurent, Martin Schmitz, Mile Šikić

In this work, we explore a different approach to the central part of the genome assembly task that consists of untangling a large assembly graph from which a genomic sequence needs to be reconstructed.

no code implementations • 29 May 2022 • Thomas Laurent, James H. von Brecht, Xavier Bresson

Our data model follows a long-tailed distribution in the sense that some rare subcategories have few representatives in the training set.

1 code implementation • 2 Mar 2022 • Yong Liang Goh, Wee Sun Lee, Xavier Bresson, Thomas Laurent, Nicholas Lim

This paper exemplifies the integration of entropic regularized optimal transport techniques as a layer in a deep reinforcement learning network.

1 code implementation • ICLR 2022 • Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.

Ranked #13 on Graph Regression on ZINC-500k

no code implementations • 29 Sep 2021 • Lovro Vrček, Robert Vaser, Thomas Laurent, Mile Sikic, Xavier Bresson

A quest to determine the human DNA sequence from telomere to telomere started three decades ago and was finally finished in 2021.

1 code implementation • 4 Mar 2021 • Xavier Bresson, Thomas Laurent

The Traveling Salesman Problem (TSP) is the most popular and most studied combinatorial problem, starting with von Neumann in 1951.

1 code implementation • 18 Dec 2020 • Axel Nilsson, Xavier Bresson

Spectral graph convolutional networks are generalizations of standard convolutional networks for graph-structured data using the Laplacian operator.

Ranked #19 on Graph Regression on ZINC

3 code implementations • 17 Dec 2020 • Vijay Prakash Dwivedi, Xavier Bresson

This work closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs.

16 code implementations • 2 Mar 2020 • Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

Ranked #1 on Link Prediction on COLLAB

1 code implementation • 24 Dec 2019 • Peng Xu, Chaitanya K. Joshi, Xavier Bresson

In this work, we propose a new representation of sketches as multiple sparsely connected graphs.

2 code implementations • 16 Oct 2019 • Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem.

no code implementations • 8 Jun 2019 • Xavier Bresson, Thomas Laurent

In this work, we introduce a simple two-step decoding process.

3 code implementations • 4 Jun 2019 • Chaitanya K. Joshi, Thomas Laurent, Xavier Bresson

This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs.

1 code implementation • 15 Apr 2019 • Yao Yang Leow, Thomas Laurent, Xavier Bresson

Our proposed method GraphTSNE produces visualizations which account for both graph structure and node features.

no code implementations • ICLR 2018 • Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

1 code implementation • ICLR 2018 • Xavier Bresson, Thomas Laurent

In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.

Ranked #7 on Node Classification on PATTERN 100k

2 code implementations • 22 May 2017 • Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

2 code implementations • NeurIPS 2017 • Federico Monti, Michael M. Bronstein, Xavier Bresson

Matrix completion models are among the most common formulations of recommender systems.

Ranked #5 on Recommendation Systems on YahooMusic Monti (using extra training data)

5 code implementations • 22 Dec 2016 • Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data.

17 code implementations • ISMIR 2017 • Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson

We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections.

1 code implementation • NeurIPS 2016 • Thomas Laurent, James Von Brecht, Xavier Bresson, Arthur Szlam

We introduce a theoretical and algorithmic framework for multi-way graph partitioning that relies on a multiplicative cut-based objective.

4 code implementations • NeurIPS 2016 • Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.

Ranked #4 on Skeleton Based Action Recognition on SBU

1 code implementation • 24 Mar 2016 • Rodrigo Pena, Xavier Bresson, Pierre Vandergheynst

We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion kernel learning.

1 code implementation • 8 Jan 2016 • Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst

This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs.

no code implementations • 19 Jun 2015 • Xavier Bresson, Thomas Laurent, James Von Brecht

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

no code implementations • ICCV 2015 • Nauman Shahid, Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering.

no code implementations • CVPR 2015 • Artiom Kovnatsky, Michael M. Bronstein, Xavier Bresson, Pierre Vandergheynst

In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework.

no code implementations • 24 Nov 2014 • Nicolas Garcia Trillos, Dejan Slepcev, James Von Brecht, Thomas Laurent, Xavier Bresson

We consider point clouds obtained as samples of a ground-truth measure.

2 code implementations • 7 Aug 2014 • Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst

Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.

Ranked #15 on Recommendation Systems on MovieLens 100K (using extra training data)

no code implementations • 15 Jun 2014 • Xavier Bresson, Huiyi Hu, Thomas Laurent, Arthur Szlam, James Von Brecht

In this work we propose a simple and easily parallelizable algorithm for multiway graph partitioning.

no code implementations • NeurIPS 2013 • Xavier Bresson, Thomas Laurent, David Uminsky, James H. von Brecht

Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation.

no code implementations • NeurIPS 2012 • Xavier Bresson, Thomas Laurent, David Uminsky, James V. Brecht

Unsupervised clustering of scattered, noisy and high-dimensional data points is an important and difficult problem.

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