Fernando Perez-Cruz

Artificial Intelligence · 2008–2017

A researcher in the Science corpus: 4 papers; trusts 106 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #4,953 of 35,345 by trust here, #5,424 by citations
• Computer Vision and Pattern Recognition — 58 citations into it; #7,905 of 21,575 by trust here, #4,525 by citations
• Generative Models — 38 citations into it
• Safety, Robustness & Interpretability — 37 citations into it
• Signal Processing — 33 citations into it; #6,693 of 7,572 by trust here, #6,836 by citations
• Privacy & Security — 31 citations into it; #321 of 709 by trust here, #118 by citations
• Optimization & Training — 18 citations into it
• Statistical and Nonlinear Physics — 15 citations into it

Whom Fernando Perez-Cruz trusts overall

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

1. Liam Paninski
2. XuanLong Nguyen
3. David John Cameron MacKay
4. Yuhai Wu
5. Arthur Gretton
6. Michael I. Jordan
7. Geoffrey E. Hinton
8. Martin J. Wainwright
9. Yann LeCun
10. Inderjit S. Dhillon

Who trusts Fernando Perez-Cruz overall

1. Akshay Krishnamurthy
2. Barnab'as P'oczos
3. Aleksei Triastcyn
4. David Lopez-Paz
5. Kirthevasan Kandasamy
6. Weihao Gao
7. Rajat Sen
8. Dominik Janzing
9. Masashi Sugiyama
10. Boi Faltings

Papers from Fernando Perez-Cruz's perspective

Ordered by how strongly the researchers Fernando Perez-Cruz 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. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning (2017) — 1,613 citations
• Kullback-Leibler divergence estimation of continuous distributions (2008) — 416 citations
• PassGAN: A Deep Learning Approach for Password Guessing (2017) — 311 citations
• Estimation of Information Theoretic Measures for Continuous Random Variables (2008) — 76 citations