Thomas Laurent

Artificial Intelligence · arXiv.org · 2007–2024

A researcher in the Science corpus: 14 papers; trusts 395 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #7,216 of 35,345 by trust here, #8,365 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #6,577 of 21,575 by trust here, #8,021 by citations
• Graph Learning — 70 citations into it
• Model Architectures — 38 citations into it
• Computational Theory and Mathematics — 37 citations into it
• Applied Mathematics — 35 citations into it; #959 of 7,620 by trust here, #859 by citations
• Molecular Biology — 32 citations into it; #2,220 of 17,773 by trust here, #13,563 by citations
• Materials Chemistry — 27 citations into it; #1,935 of 11,513 by trust here, #8,433 by citations

Whom Thomas Laurent trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Andrea L. Bertozzi
3. Diederik P. Kingma
4. Yann LeCun
5. Geoffrey E. Hinton
6. Kaiming He
7. Sergey Ioffe
8. Alex Krizhevsky
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Thomas Laurent overall

1. Andrea L. Bertozzi
2. José Antonio Carrillo
3. Dong Li
4. Vijay Prakash Dwivedi
5. José L. Rodrigo
6. Dejan Slepcev
7. Jeremy Brandman
8. Razvan C. Fetecau
9. Martin Burger
10. Chaitanya K. Joshi

Papers from Thomas Laurent's perspective

Ordered by how strongly the researchers Thomas Laurent trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Benchmarking Graph Neural Networks (2023) — 1,223 citations
• Residual Gated Graph ConvNets (2017) — 626 citations
• An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem (2019) — 481 citations
• Graph Neural Networks with Learnable Structural and Positional Representations (2021) — 497 citations
• Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global (2017) — 149 citations
• Local and Global Existence for an Aggregation Equation (2007) — 118 citations
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering (2024) — 316 citations
• An Experimental Study of Neural Networks for Variable Graphs (2018) — 44 citations
• Enhanced lasso recovery on graph (2015)
• Dimensionality of Local Minimizers of the Interaction Energy (2012) — 165 citations
• Global-in-time weak measure solutions and finite-time aggregation for nonlocal interaction equations (2011) — 309 citations
• Lp theory for the multidimensional aggregation equation (2011) — 173 citations
• Blow-up in multidimensional aggregation equations with mildly singular interaction kernels (2009) — 238 citations
• Finite-Time Blow-up of Solutions of an Aggregation Equation in Rn (2007) — 115 citations