Robert Sabourin

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 11 papers; trusts 155 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #13,501 of 35,345 by trust here, #10,942 by citations
• Computer Vision and Pattern Recognition — 49 citations into it; #13,100 of 21,575 by trust here, #12,689 by citations
• Optimization & Training — 34 citations into it
• Model Architectures — 17 citations into it
• Safety, Robustness & Interpretability — 16 citations into it
• Computational Theory and Mathematics — 15 citations into it; #9,571 of 12,182 by trust here, #7,851 by citations
• Evaluation & Benchmarks — 15 citations into it
• Statistics and Probability — 11 citations into it

Whom Robert Sabourin trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Christian Szegedy
6. Ludmila I. Kuncheva
7. Karen Simonyan
8. Ilya Sutskever
9. James Bergstra
10. Sergey Ioffe

Who trusts Robert Sabourin overall

1. Lucas B. V. de Amorim
2. Gregory Cohen
3. Rafael M. O. Cruz
4. George D. C. Cavalcanti
5. Saeed Afshar

Papers from Robert Sabourin's perspective

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

Their papers

• Estimating accurate multi-class probabilities with support vector machines (2005) — 51 citations
• Supervised learning of fuzzy ARTMAP neural networks through particle swarm optimization (2007) — 32 citations
• Bayesian Hyperparameter Optimization for Ensemble Learning (2016) — 63 citations
• Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses (2018) — 327 citations
• Dynamic selection of classifiers - A comprehensive review (2014) — 353 citations
• Dynamic classifier selection: Recent advances and perspectives (2018) — 430 citations
• Learning features for offline handwritten signature verification using deep convolutional neural networks (2017) — 344 citations
• Bayesian optimization for conditional hyperparameter spaces (2017) — 44 citations
• From dynamic classifier selection to dynamic ensemble selection (2008) — 462 citations
• Using the RRT algorithm to optimize classification systems for handwritten digits and letters (2008) — 9 citations
• An implicit segmentation-based method for recognition of handwritten strings of characters (2006) — 41 citations