Rodrigo C. Barros
Artificial Intelligence · 2012–2019
A researcher in the Science corpus: 5 papers; trusts 75 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 87 citations into it; #20,410 of 35,345 by trust here, #13,544 by citations
- Computer Vision and Pattern Recognition — 46 citations into it
- Signal Processing — 17 citations into it
- Representation & Self-Supervised Learning — 16 citations into it
- Optimization & Training — 15 citations into it
- Information Systems — 12 citations into it
- Computational Theory and Mathematics — 9 citations into it
- Computer Networks and Communications — 9 citations into it
Whom Rodrigo C. Barros trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Ross Quinlan
- John R. Koza
- Niko Speybroeck
- Sreerama K. Murthy
- Diederik P. Kingma
- Geoffrey E. Hinton
- Sergey Ioffe
- Steven L. Salzberg
- Lior Rokach
- Ulf Johansson
Who trusts Rodrigo C. Barros overall
- Gilles Vandewiele
- Agoston E. Eiben
- Emilio Carrizosa
- Olivier Janssens
- Michael Hahsler
- Jim E. Smith
- Max Tabord‐Meehan
- Cristina Molero-Río
- Femke Ongenae
- Garrett Wilson
Papers from Rodrigo C. Barros's perspective
Ordered by how strongly the researchers Rodrigo C. Barros trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification (2019) — 163 citations
- Unsupervised domain adaptation for medical imaging segmentation with self-ensembling (2018) — 249 citations
- Hierarchical Multi-Label Classification Networks (2018) — 329 citations
- Data stream clustering: A survey (2013) — 571 citations
- A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012) — 354 citations