Pierre Mahé

Molecular Biology · 2004–2015

A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 19 citations into it; #2,151 of 17,773 by trust here, #10,193 by citations
• Statistical Learning Theory — 9 citations into it
• Artificial Intelligence — 7 citations into it; #22,712 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Pierre Mahé trusts overall

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

1. Christopher K. I. Williams
2. Alex Smola
3. John Shawe-Taylor
4. Thomas Gärtner
5. Bernhard Schölkopf
6. Christina S. Leslie
7. Tamás Horváth
8. L. Eon Bottou
9. Michael Gribskov
10. S. Joshua Swamidass

Who trusts Pierre Mahé overall

1. Nils M. Kriege
2. Nino Shervashidze
3. Karsten M. Borgwardt
4. Giovanni Da San Martino
5. Nicoló Navarin
6. Christopher Morris
7. Alessandro Sperduti
8. Giannis Nikolentzos
9. Petra Mutzel
10. Davide Bacciu

Papers from Pierre Mahé's perspective

Ordered by how strongly the researchers Pierre Mahé 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Extensions of marginalized graph kernels (2004) — 224 citations
• Graph kernels based on tree patterns for molecules (2006) — 279 citations
• Large-scale machine learning for metagenomics sequence classification (2015) — 82 citations