Andrews Cordolino Sobral
Computer Vision and Pattern Recognition · 2014–2015
A researcher in the Science corpus: 2 papers; trusts 52 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 46 citations into it; #10,469 of 21,575 by trust here, #17,258 by citations
- Computational Mechanics — 38 citations into it
- Artificial Intelligence — 13 citations into it
- Media Technology — 13 citations into it
- Signal Processing — 13 citations into it
- Biomedical Engineering — 9 citations into it
- Optimization & Training — 9 citations into it
- Numerical Analysis — 6 citations into it
Whom Andrews Cordolino Sobral trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thierry Bouwmans
- Zoran Zivkovic
- Martin Hofmann
- Robert Tibshirani
- Lucia Maddalena
- E.J. Candes
- Pakorn KaewTrakulPong
- Joel A. Tropp
- Yurii E. Nesterov
- Zhou Wang
Who trusts Andrews Cordolino Sobral overall
- Yuxiang Sun
- Jhony-Heriberto Giraldo-Zuluaga
- S. M. I. J. Andrew Zhang
- Thierry Bouwmans
- Ming Liu
- Augusto Salazar
- Md. Lushanur Rahman
- Max Qing-Hu Meng
- Alexander Gómez
- N. Benjamin Erichson
Papers from Andrews Cordolino Sobral's perspective
Ordered by how strongly the researchers Andrews Cordolino Sobral 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
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset (2015) — 346 citations
- A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos (2014) — 688 citations