Roberto B. Togneri

Artificial Intelligence · 2010–2015

A researcher in the Science corpus: 4 papers; trusts 87 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #25,358 of 35,345 by trust here, #13,544 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #8,720 of 21,575 by trust here, #7,750 by citations
• Signal Processing — 13 citations into it; #2,196 of 7,572 by trust here, #4,560 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Media Technology — 7 citations into it
• Model Architectures — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Roberto B. Togneri trusts overall

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

1. Douglas A. Reynolds
2. C. Burges
3. Tomi H. Kinnunen
4. William M. Campbell
5. Martin P. Cooke
6. David Ruppert
7. Ming-Hsuan Yang
8. Peter N. Belhumeur
9. Geoffrey E. Hinton
10. Richard C. Rose

Who trusts Roberto B. Togneri overall

1. Zhongxin Bai
2. Ran He
3. Xiao-Lei Zhang
4. Arnout Devos
5. Munawar Hayat
6. Jian Yang
7. Julien Mairal
8. Weishi Zheng
9. Mohamed Loey
10. Zheng Zhang

Papers from Roberto B. Togneri's perspective

Ordered by how strongly the researchers Roberto B. Togneri 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data (2015) — 977 citations
• Linear Regression for Face Recognition (2010) — 1,012 citations
• Automatic Feature Learning for Robust Shadow Detection (2014) — 153 citations
• An Overview of Speaker Identification: Accuracy and Robustness Issues (2011) — 307 citations