Aníbal R. Figueiras-Vidal

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 5 papers; trusts 87 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #9,259 of 35,345 by trust here, #11,750 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #10,151 of 21,575 by trust here, #13,269 by citations
• Probabilistic & Bayesian Methods — 14 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 11 citations into it
• Statistical Learning Theory — 8 citations into it
• Signal Processing — 6 citations into it; #2,655 of 7,572 by trust here, #6,083 by citations
• Control and Systems Engineering — 5 citations into it; #2,325 of 8,257 by trust here, #2,295 by citations

Whom Aníbal R. Figueiras-Vidal trusts overall

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

1. Yoshua Bengio
2. Yann LeCun
3. Leo Breiman
4. Geoffrey E. Hinton
5. Paul Smolensky
6. Trevor J. Hastie
7. Yoav Freund
8. Christopher K. I. Williams
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Aníbal R. Figueiras-Vidal overall

1. Ali H. Sayed
2. Federico S. Cattivelli
3. Danijar Hafner
4. Cássio Guimarães Lopes
5. Jinsung Yoon
6. S. van Buuren
7. Noriyuki Takahashi
8. Dustin Tran
9. David Kristjanson Duvenaud
10. Dana Hughes

Papers from Aníbal R. Figueiras-Vidal's perspective

Ordered by how strongly the researchers Aníbal R. Figueiras-Vidal 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Sparse Spectrum Gaussian Process Regression (2010) — 546 citations
• On building ensembles of stacked denoising auto-encoding classifiers and their further improvement (2018) — 47 citations
• Pattern classification with missing data: a review (2010) — 799 citations
• Marginalized Neural Network Mixtures for Large-Scale Regression (2010) — 46 citations
• Mean-square performance of a convex combination of two adaptive filters (2006) — 437 citations