Tanguy Urvoy

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 6 papers; trusts 39 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #20,093 of 35,345 by trust here, #21,316 by citations
• Management Science and Operations Research — 37 citations into it; #6,145 of 11,946 by trust here, #6,563 by citations
• Reinforcement Learning — 35 citations into it; #802 of 2,242 by trust here, #1,455 by citations
• Natural Language Processing — 23 citations into it
• Computer Networks and Communications — 13 citations into it; #5,067 of 9,886 by trust here, #7,492 by citations
• Information Systems — 11 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Statistical Learning Theory — 8 citations into it

Whom Tanguy Urvoy trusts overall

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

1. Yisong Yue
2. Eyal Even-Dar
3. Peter Auer
4. Andreas Stolcke
5. Jean-Yves Audibert
6. T. L. Lai Andherbertrobbins
7. R´emi Munos
8. Thorsten Joachims
9. Sébastien Bubeck
10. Nicolò Cesa-Bianchi

Who trusts Tanguy Urvoy overall

1. Masrour Zoghi
2. Aadirupa Saha
3. Róbert Istvan Busa-Fekete
4. Raphaël Féraud
5. Shimon Whiteson
6. Akash Kumar Mohankumar
7. Huasen Wu
8. Maarten de Rijke
9. Eyke Hüllermeier
10. Moein Falahatgar

Papers from Tanguy Urvoy's perspective

Ordered by how strongly the researchers Tanguy Urvoy 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Random Forest for the Contextual Bandit Problem (2015) — 50 citations
• Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers (2020) — 15 citations
• A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits (2015) — 54 citations
• Scaling up budgeted reinforcement learning (2019) — 4 citations
• Generic Exploration and K-armed Voting Bandits (2013) — 111 citations
• Detecting Fake Content with Relative Entropy Scoring (2008) — 111 citations