Pedro M. Esperança

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 3 papers; trusts 169 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #17,791 of 35,345 by trust here, #24,417 by citations
• Computer Vision and Pattern Recognition — 31 citations into it
• Optimization & Training — 14 citations into it
• Model Architectures — 11 citations into it
• Neural Architecture Search & AutoML — 9 citations into it; #121 of 283 by trust here, #191 by citations
• Privacy & Security — 8 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Evaluation & Benchmarks — 5 citations into it; #1,414 of 3,147 by trust here, #2,369 by citations

Whom Pedro M. Esperança trusts overall

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

1. David J. Wu
2. Alex Krizhevsky
3. Robert Tibshirani
4. Craig Gentry
5. Junfeng Fan
6. Yurii E. Nesterov
7. Kaiming He
8. Thore Graepel
9. Geoffrey E. Hinton
10. Eric R. Ziegel

Who trusts Pedro M. Esperança overall

1. Colin White
2. Richard Nock
3. Ehsan Hesamifard
4. Marius Thomas Lindauer
5. Sam Nolen
6. Yash Savani
7. Jian Liu
8. Hassan Takabi
9. Stephen Hardy
10. Han Shi

Papers from Pedro M. Esperança's perspective

Ordered by how strongly the researchers Pedro M. Esperança 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. ImageNet: A large-scale hierarchical image database
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• NAS evaluation is frustratingly hard (2019) — 181 citations
• Encrypted Accelerated Least Squares Regression (2017) — 17 citations
• Encrypted statistical machine learning: new privacy preserving methods (2015) — 68 citations