Meire Fortunato

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 6 papers; trusts 146 researchers and is trusted by 719 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #561 of 35,345 by trust here, #1,737 by citations
• Reinforcement Learning — 48 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Model Architectures — 25 citations into it; #132 of 2,278 by trust here, #266 by citations
• Natural Language Processing — 20 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Optimization & Training — 19 citations into it
• Management Science and Operations Research — 17 citations into it

Whom Meire Fortunato trusts overall

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

1. Diederik P. Kingma
2. Alex Graves
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Alex Krizhevsky
7. Oriol Vinyals
8. Max Welling
9. Radford M. Neal
10. Richard S. Sutton

Who trusts Meire Fortunato overall

1. Yoshua Bengio
2. Caiming Xiong
3. Minghao Hu
4. Kumar Shridhar
5. Xinyun Chen
6. Sergey Levine
7. Quoc V. Le
8. Richard Socher
9. Kyunghyun Cho
10. Mohit Bansal

Papers from Meire Fortunato's perspective

Ordered by how strongly the researchers Meire Fortunato 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Pointer Networks (2015) — 3,513 citations
• Noisy Networks for Exploration (2017) — 1,022 citations
• Bayesian Recurrent Neural Networks (2017) — 209 citations
• Learning Mesh-Based Simulation with Graph Networks (2020) — 1,318 citations
• GraphCast: Learning skillful medium-range global weather forecasting (2022) — 419 citations
• Generalization of Reinforcement Learners with Working and Episodic Memory (2019) — 74 citations