Nino Vieillard

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 80 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it
• Reinforcement Learning — 74 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Natural Language Processing — 15 citations into it
• Management Science and Operations Research — 14 citations into it
• Computational Theory and Mathematics — 12 citations into it
• Optimization & Training — 9 citations into it
• Electrical and Electronic Engineering — 8 citations into it

Whom Nino Vieillard trusts overall

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

1. Richard S. Sutton
2. Marc G. Bellemare
3. Leemon C. Baird III
4. Geoffrey E. Hinton
5. Tuomas Haarnoja
6. Diederik P. Kingma
7. Sergey Levine
8. R´emi Munos
9. David Silver
10. Yoshua Bengio

Who trusts Nino Vieillard overall

1. Rishabh Agarwal
2. Robert Dadashi
3. John D. Co-Reyes
4. Max Schwarzer
5. L'eonard Hussenot

Papers from Nino Vieillard's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes (2023) — 576 citations
• Offline Reinforcement Learning with Pseudometric Learning (2021) — 44 citations
• Munchausen Reinforcement Learning (2020) — 116 citations