Santiago Paternain

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #28,669 of 35,345 by trust here, #28,481 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Optimization & Training — 16 citations into it
• Statistical Learning Theory — 12 citations into it
• Reinforcement Learning — 12 citations into it
• Management Science and Operations Research — 10 citations into it
• Scaling & Learning Theory — 7 citations into it
• Statistics and Probability — 7 citations into it

Whom Santiago Paternain trusts overall

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

1. Dimitri P. Bertsekas
2. Richard S. Sutton
3. Diederik P. Kingma
4. Eric R. Ziegel
5. Geoffrey E. Hinton
6. Vijay R. Konda
7. Yoshua Bengio
8. Alex Krizhevsky
9. John N. Tsitsiklis
10. Shie Mannor

Who trusts Santiago Paternain overall

1. Liangzu Peng
2. Shangding Gu
3. Adam Stooke

Papers from Santiago Paternain's perspective

Ordered by how strongly the researchers Santiago Paternain 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Constrained Reinforcement Learning Has Zero Duality Gap (2019) — 258 citations
• Decentralized Online Learning With Kernels (2017) — 55 citations
• Constrained Learning With Non-Convex Losses (2021) — 65 citations