Ricardo Bastos Cavalcante Prudêncio

Artificial Intelligence · 2004–2012

A researcher in the Science corpus: 5 papers; trusts 27 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #7,291 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 14 citations into it; #9,918 of 21,575 by trust here, #18,266 by citations
• Optimization & Training — 9 citations into it
• Statistical Learning Theory — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom Ricardo Bastos Cavalcante Prudêncio trusts overall

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

1. Spyros Makridakis
2. David E. Rumelhart
3. Vladimir Naumovich Vapnik
4. Pavel Brazdil
5. Chih-Jen Lin
6. Geoffrey E. Hinton
7. S. Sathiya Keerthi
8. Shawkat Ali
9. K. Smith‐Miles
10. M. Hibon

Who trusts Ricardo Bastos Cavalcante Prudêncio overall

1. Christiane Lemke
2. Marc-André Zöller
3. Frank Hutter
4. Bogdan Gabrys
5. Matthias Feurer
6. Katharina Eggensperger
7. Marco F. Huber
8. Martin Wistuba
9. Marcin Budka
10. Tong Yu

Papers from Ricardo Bastos Cavalcante Prudêncio's perspective

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

Their papers

• Predicting the Performance of Learning Algorithms Using Support Vector Machines as Meta-regressors (2008) — 33 citations
• Combining meta-learning and search techniques to select parameters for support vector machines (2012) — 167 citations
• Selection of time series forecasting models based on performance information (2004) — 20 citations
• Using Machine Learning Techniques to Combine Forecasting Methods (2004) — 37 citations
• An Experimental Study of the Combination of Meta-Learning with Particle Swarm Algorithms for SVM Parameter Selection (2012) — 14 citations