Search Results for author: Xavier Bresson

Found 40 papers, 28 papers with code

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

1 code implementation12 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.

Common Sense Reasoning Graph Classification +4

Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

1 code implementation7 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.

Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification

no code implementations18 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.

Data Augmentation Graph Classification

Graph Transformers for Large Graphs

1 code implementation18 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.

Graph Learning Graph Property Prediction +3

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

3 code implementations31 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)

Decision Making General Knowledge +4

Feature Collapse

1 code implementation25 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.

A Generalization of ViT/MLP-Mixer to Graphs

3 code implementations27 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.

Graph Classification Graph Regression +1

Semantic Role Aware Correlation Transformer for Text to Video Retrieval

1 code implementation26 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.

Retrieval Text to Video Retrieval +1

Learning to Untangle Genome Assembly with Graph Convolutional Networks

1 code implementation1 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.

Long-Tailed Learning Requires Feature Learning

no code implementations29 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.

Transfer Learning

Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem

1 code implementation2 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.

Combinatorial Optimization reinforcement-learning +2

Genome Sequence Reconstruction Using Gated Graph Convolutional Network

no code implementations29 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.

The Transformer Network for the Traveling Salesman Problem

1 code implementation4 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.

Traveling Salesman Problem

An Experimental Study of the Transferability of Spectral Graph Networks

1 code implementation18 Dec 2020 Axel Nilsson, Xavier Bresson

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

Benchmarking General Classification +3

A Generalization of Transformer Networks to Graphs

3 code implementations17 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.

Graph Regression Inductive Bias +3

Benchmarking Graph Neural Networks

16 code implementations2 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.

Benchmarking Graph Classification +3

Multi-Graph Transformer for Free-Hand Sketch Recognition

1 code implementation24 Dec 2019 Peng Xu, Chaitanya K. Joshi, Xavier Bresson

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

Sketch Recognition

On Learning Paradigms for the Travelling Salesman Problem

2 code implementations16 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.

reinforcement-learning Reinforcement Learning (RL)

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

3 code implementations4 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.

GraphTSNE: A Visualization Technique for Graph-Structured Data

1 code implementation15 Apr 2019 Yao Yang Leow, Thomas Laurent, Xavier Bresson

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

Dimensionality Reduction


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.

Community Detection General Classification +2

Residual Gated Graph ConvNets

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.

Clustering General Classification +5

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

2 code implementations22 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.

Community Detection General Classification +3

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

5 code implementations22 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.

Language Modelling

FMA: A Dataset For Music Analysis

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.

The Product Cut

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.

graph partitioning

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

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.

Node Classification Skeleton Based Action Recognition

Source Localization on Graphs via l1 Recovery and Spectral Graph Theory

1 code implementation24 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.

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

1 code implementation8 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.

Collaborative Filtering Matrix Completion +1

Enhanced Lasso Recovery on Graph

no code implementations19 Jun 2015 Xavier Bresson, Thomas Laurent, James Von Brecht

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

Functional correspondence by matrix completion

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.

Matrix Completion

Matrix Completion on Graphs

2 code implementations7 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)

Collaborative Filtering Matrix Completion +1

An Incremental Reseeding Strategy for Clustering

no code implementations15 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.

Clustering graph partitioning

Multiclass Total Variation Clustering

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.


Convergence and Energy Landscape for Cheeger Cut Clustering

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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