Michaël Garcia Ortiz

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 47 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #13,043 of 35,345 by trust here, #9,968 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #12,706 of 21,575 by trust here, #7,657 by citations
• Generative Models — 21 citations into it; #1,171 of 1,956 by trust here, #865 by citations
• Reinforcement Learning — 18 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 10 citations into it
• Biophysics — 6 citations into it

Whom Michaël Garcia Ortiz trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Hanul Shin
6. J. Urgen Schmidhuber
7. R. M. French
8. John Schulman
9. Alex Krizhevsky
10. Alex Graves

Who trusts Michaël Garcia Ortiz overall

1. Timothée Lesort
2. Craig Atkinson
3. Kalifou René Traoré
4. Martin Mundt
5. Massimo Caccia
6. Brendan McCane
7. James Smith
8. Hugo Caselles-Dupré
9. Zheda Mai
10. Oleksiy Ostapenko

Papers from Michaël Garcia Ortiz's perspective

Ordered by how strongly the researchers Michaël Garcia Ortiz 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. 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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Generative Models from the perspective of Continual Learning (2018) — 171 citations
• Continual State Representation Learning for Reinforcement Learning using Generative Replay (2018) — 20 citations
• S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay (2019) — 18 citations