Romain Tavenard

Artificial Intelligence · 2011–2021

A researcher in the Science corpus: 5 papers; trusts 55 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it; #8,911 of 21,575 by trust here, #12,689 by citations
• Signal Processing — 21 citations into it
• Graph Learning — 14 citations into it
• Computational Mechanics — 8 citations into it
• ML Systems & Hardware — 8 citations into it
• Optimization & Training — 8 citations into it
• Aerospace Engineering — 6 citations into it

Whom Romain Tavenard trusts overall

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

1. Marco Cuturi
2. David G. Lowe
3. Yoshua Bengio
4. Herv'e J'egou
5. Alex Krizhevsky
6. Yann LeCun
7. Geoffrey E. Hinton
8. Ilya Sutskever
9. Anthony J. Bagnall
10. Ronan Collobert

Who trusts Romain Tavenard overall

1. Artem Babenko
2. Jingdong Wang
3. Victor S. Lempitsky
4. Matthijs Douze
5. Laurent Amsaleg
6. Kaiming He
7. Chao Du
8. Mohammad Norouzi
9. Jeff Johnson
10. Fabien André

Papers from Romain Tavenard's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Searching in one billion vectors: Re-rank with source coding (2011) — 384 citations
• POT: Python Optimal Transport (2021) — 1,118 citations
• Tslearn, A Machine Learning Toolkit for Time Series Data (2020) — 534 citations
• Optimal Transport for structured data with application on graphs (2018) — 361 citations
• Data Augmentation for Time Series Classification using Convolutional Neural Networks (2016) — 451 citations