Alain Rakotomamonjy

Artificial Intelligence · 2003–2018

A researcher in the Science corpus: 12 papers; trusts 231 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it; #6,711 of 35,345 by trust here, #8,157 by citations
• Computer Vision and Pattern Recognition — 90 citations into it; #4,730 of 21,575 by trust here, #4,623 by citations
• Computational Mechanics — 59 citations into it; #2,870 of 10,001 by trust here, #2,942 by citations
• Cognitive Neuroscience — 52 citations into it; #2,895 of 10,852 by trust here, #5,990 by citations
• Representation & Self-Supervised Learning — 50 citations into it; #1,142 of 3,145 by trust here, #1,321 by citations
• Statistics and Probability — 48 citations into it
• Statistical Learning Theory — 45 citations into it
• Optimization & Training — 33 citations into it

Whom Alain Rakotomamonjy trusts overall

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

1. Yoshua Bengio
2. Bernhard Schölkopf
3. Yuhai Wu
4. Francis R. Bach
5. Nello Cristianini
6. Jason Aaron Edward Weston
7. Vladimir Naumovich Vapnik
8. Olivier Chapelle
9. Michael I. Jordan
10. Geoffrey E. Hinton

Who trusts Alain Rakotomamonjy overall

1. Sebastián Maldonado
2. Francis R. Bach
3. Marc-André Zöller
4. Richard Weber
5. Verónica Bolón-Canedo
6. Chang Xu
7. Dmitry Storcheus
8. Jinjie Huang
9. Marius Kloft
10. Mohammad Rostami

Papers from Alain Rakotomamonjy's perspective

Ordered by how strongly the researchers Alain Rakotomamonjy 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Optimal Transport for Domain Adaptation (2014) — 1,364 citations
• More efficiency in multiple kernel learning (2007) — 326 citations
• Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming (2009) — 329 citations
• Pedestrian Detection using Infrared images and Histograms of Oriented Gradients (2006) — 305 citations
• Operator-valued Kernels for Learning from Functional Response Data (2015) — 154 citations
• A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018) — 2,013 citations
• Joint distribution optimal transportation for domain adaptation (2017) — 681 citations
• Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014) — 118 citations
• Multiple Operator-valued Kernel Learning (2012) — 19 citations
• $\ell_{p}-\ell_{q}$ Penalty for Sparse Linear and Sparse Multiple Kernel Multitask Learning (2011) — 94 citations
• BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller (2008) — 535 citations
• Variable Selection Using SVM-based Criteria (2003) — 747 citations