Miguel Raul Dias Rodrigues

Artificial Intelligence · 2006–2019

A researcher in the Science corpus: 5 papers; trusts 112 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #19,855 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #9,025 of 21,575 by trust here, #7,750 by citations
• Model Architectures — 22 citations into it
• Electrical and Electronic Engineering — 20 citations into it; #2,782 of 16,539 by trust here, #6,143 by citations
• Optimization & Training — 14 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Computer Networks and Communications — 11 citations into it; #2,401 of 9,886 by trust here, #2,136 by citations
• Statistical Learning Theory — 10 citations into it

Whom Miguel Raul Dias Rodrigues trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Kaiming He
6. Vladimir Naumovich Vapnik
7. Ueli M. Maurer
8. Diederik P. Kingma
9. Ilya Sutskever
10. Peter L. Bartlett

Who trusts Miguel Raul Dias Rodrigues overall

1. F. J. López-Martínez
2. Ashish J. Khisti
3. A. Mukherjee
4. Yiliang Liu
5. Shabnam Shafiee
6. Kenji Kawaguchi

Papers from Miguel Raul Dias Rodrigues's perspective

Ordered by how strongly the researchers Miguel Raul Dias Rodrigues 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. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Robust Large Margin Deep Neural Networks (2016) — 349 citations
• Wireless Information-Theoretic Security (2008) — 1,811 citations
• Secrecy Capacity of Wireless Channels (2006) — 796 citations
• Adversarially Learned Representations for Information Obfuscation and Inference (2019) — 60 citations
• Generalization Error of Invariant Classifiers (2016) — 82 citations