Parabolic Behavior of a Hyperbolic Delay Equation. - Thomas Laurent 001; Brian Rider; Michael Reed (SIAM J. Math. Anal., 2006)

Convergence and Energy Landscape for Cheeger Cut Clustering. - Xavier Bresson; Thomas Laurent 001; David Uminsky 001; James H. von Brecht (NIPS, 2012)

Characterization of Radially Symmetric Finite Time Blowup in Multidimensional Aggregation Equations. - Andrea L. Bertozzi; John B. Garnett; Thomas Laurent 001 (SIAM J. Math. Anal., 2012)

Multiclass Total Variation Clustering. - Xavier Bresson; Thomas Laurent 001; David Uminsky 001; James H. von Brecht (NIPS, 2013)

A Method Based on Total Variation for Network Modularity Optimization Using the MBO Scheme. - Huiyi Hu; Thomas Laurent 001; Mason A. Porter; Andrea L. Bertozzi (SIAM J. Appl. Math., 2013)

An Incremental Reseeding Strategy for Clustering. - Xavier Bresson; Huiyi Hu; Thomas Laurent 001; Arthur Szlam; James H. von Brecht (CoRR, 2014)

Enhanced lasso recovery on graph. - Xavier Bresson; Thomas Laurent 001; James H. von Brecht (EUSIPCO, 2015)

The Product Cut. - Thomas Laurent 001; James H. von Brecht; Xavier Bresson; Arthur Szlam (NIPS, 2016)

Consistency of Cheeger and Ratio Graph Cuts. - Nicolás García Trillos; Dejan Slepcev; James H. von Brecht; Thomas Laurent 001; Xavier Bresson (J. Mach. Learn. Res., 2016)

The Regularity of the Boundary of a Multidimensional Aggregation Patch. - Andrea L. Bertozzi; John B. Garnett; Thomas Laurent 001; Joan Verdera (SIAM J. Math. Anal., 2016)

A recurrent neural network without chaos. - Thomas Laurent 001; James von Brecht (ICLR (Poster), 2017)

Residual Gated Graph ConvNets. - Xavier Bresson; Thomas Laurent 001 (CoRR, 2017)

Deep linear neural networks with arbitrary loss: All local minima are global. - Thomas Laurent 001; James von Brecht (CoRR, 2017)

An Experimental Study of Neural Networks for Variable Graphs. - Xavier Bresson; Thomas Laurent 001 (ICLR (Workshop), 2018)

Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global. - Thomas Laurent 001; James von Brecht (ICML, 2018)

The Multilinear Structure of ReLU Networks. - Thomas Laurent 001; James von Brecht (ICML, 2018)

GraphTSNE: A Visualization Technique for Graph-Structured Data. - Yao Yang Leow; Thomas Laurent 001; Xavier Bresson (CoRR, 2019)

An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem. - Chaitanya K. Joshi; Thomas Laurent 001; Xavier Bresson (CoRR, 2019)

A Two-Step Graph Convolutional Decoder for Molecule Generation. - Xavier Bresson; Thomas Laurent 001 (CoRR, 2019)

On Learning Paradigms for the Travelling Salesman Problem. - Chaitanya K. Joshi; Thomas Laurent 001; Xavier Bresson (CoRR, 2019)

Benchmarking Graph Neural Networks. - Vijay Prakash Dwivedi; Chaitanya K. Joshi; Thomas Laurent 001; Yoshua Bengio; Xavier Bresson (CoRR, 2020)

Learning TSP Requires Rethinking Generalization. - Chaitanya K. Joshi; Quentin Cappart; Louis-Martin Rousseau; Thomas Laurent 001; Xavier Bresson (CoRR, 2020)

Learning TSP Requires Rethinking Generalization. - Chaitanya K. Joshi; Quentin Cappart; Louis-Martin Rousseau; Thomas Laurent 001 (CP, 2021)

The Transformer Network for the Traveling Salesman Problem. - Xavier Bresson; Thomas Laurent 001 (CoRR, 2021)

Graph Neural Networks with Learnable Structural and Positional Representations. - Vijay Prakash Dwivedi; Anh Tuan Luu; Thomas Laurent 001; Yoshua Bengio; Xavier Bresson (ICLR, 2022)

Learning the travelling salesperson problem requires rethinking generalization. - Chaitanya K. Joshi; Quentin Cappart; Louis-Martin Rousseau; Thomas Laurent 001 (Constraints An Int. J., 2022)

Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman Problem. - Yong Liang Goh; Wee Sun Lee; Xavier Bresson; Thomas Laurent 001; Nicholas Lim (CoRR, 2022)

A Model of One-Shot Generalization. - Thomas Laurent 001; James H. von Brecht; Xavier Bresson (CoRR, 2022)

Learning to Untangle Genome Assembly with Graph Convolutional Networks. - Lovro Vrcek; Xavier Bresson; Thomas Laurent 001; Martin Schmitz 002; Mile Sikic (CoRR, 2022)

Long-Tailed Learning Requires Feature Learning. - Thomas Laurent 001; James von Brecht; Xavier Bresson (ICLR, 2023)

A Generalization of ViT/MLP-Mixer to Graphs. - Xiaoxin He; Bryan Hooi; Thomas Laurent 001; Adam Perold; Yann LeCun; Xavier Bresson (ICML, 2023)

Reconstruction of short genomic sequences with graph convolutional networks. - Lovro Vrcek; Xavier Bresson; Thomas Laurent 001; Martin Schmitz 002; Mile Sikic (MIPRO, 2023)

Explanations as Features: LLM-Based Features for Text-Attributed Graphs. - Xiaoxin He; Xavier Bresson; Thomas Laurent 001; Bryan Hooi (CoRR, 2023)

Benchmarking Graph Neural Networks. - Vijay Prakash Dwivedi; Chaitanya K. Joshi; Anh Tuan Luu; Thomas Laurent 001; Yoshua Bengio; Xavier Bresson (J. Mach. Learn. Res., 2023)

Feature Collapse. - Thomas Laurent 001; James von Brecht; Xavier Bresson (ICLR, 2024)

Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning. - Xiaoxin He; Xavier Bresson; Thomas Laurent 001; Adam Perold; Yann LeCun; Bryan Hooi (ICLR, 2024)

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. - Xiaoxin He; Yijun Tian 001; Yifei Sun 002; Nitesh V. Chawla; Thomas Laurent 001; Yann LeCun; Xavier Bresson; Bryan Hooi (NeurIPS, 2024)

Advancing Graph Convolutional Networks via General Spectral Wavelets. - Nian Liu; Xiaoxin He; Thomas Laurent 001; Francesco Di Giovanni; Michael M. Bronstein; Xavier Bresson (CoRR, 2024)

Generative diffusion models from a PDE perspective. - Fei Cao; Kimball Johnston; Thomas Laurent 001; Justin Le; Sébastien Motsch (CoRR, 2025)