Ulisses M. Braga-Neto

Artificial Intelligence · 2004–2020

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #5,783 of 35,345 by trust here, #15,866 by citations
• Statistical and Nonlinear Physics — 11 citations into it; #4,432 of 10,127 by trust here, #7,024 by citations
• Optimization & Training — 8 citations into it
• Statistics and Probability — 7 citations into it

Whom Ulisses M. Braga-Neto trusts overall

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

1. Bradley Efron
2. Sebastian Ruder
3. Maizar Raissi
4. Vladimir Naumovich Vapnik
5. Dong C. Liu
6. Sifan Wang
7. Arthur Jacot
8. Paris Perdikaris
9. Diederik P. Kingma
10. Jiequn Han

Who trusts Ulisses M. Braga-Neto overall

1. Chakkrit Kla Tantithamthavorn
2. Ji-Hyun Kim
3. Richard Jensen
4. Edwin Raczko
5. Yvan Saeys
6. Shane McIntosh
7. Zexuan Zhu
8. Annette M. Molinaro
9. Juan Diego Rodríguez
10. Donald Geman

Papers from Ulisses M. Braga-Neto's perspective

Ordered by how strongly the researchers Ulisses M. Braga-Neto trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Is cross-validation valid for small-sample microarray classification? (2004) — 638 citations
• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism (2020) — 764 citations