Laurent Jacob

Statistics and Probability · 2008–2018

A researcher in the Science corpus: 7 papers; trusts 51 researchers and is trusted by 110 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 34 citations into it; #1,457 of 12,932 by trust here, #1,338 by citations
• Molecular Biology — 26 citations into it; #844 of 17,773 by trust here, #4,105 by citations
• Artificial Intelligence — 25 citations into it; #3,344 of 35,345 by trust here, #6,848 by citations
• Computational Mechanics — 20 citations into it
• Statistical Learning Theory — 18 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it; #3,651 of 21,575 by trust here, #5,236 by citations
• Computational Theory and Mathematics — 13 citations into it; #7,964 of 12,182 by trust here, #4,059 by citations
• Optimization & Training — 11 citations into it

Whom Laurent Jacob trusts overall

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

1. Francis R. Bach
2. Robert Tibshirani
3. Martin J. Wainwright
4. Bernhard Schölkopf
5. Peng Zhao
6. Volker Roth
7. Ming Yuan
8. Gert R. G. Lanckriet
9. Theodoros Evgeniou
10. Bernhard E. Boser

Who trusts Laurent Jacob overall

1. Yu Zhang
2. Qiang Yang
3. Yu Zhang
4. Francis R. Bach
5. Qian Xu
6. Lei Han
7. Sebastian Ruder
8. Christian Widmer
9. Carlo Ciliberto
10. Pratik Jawanpuria

Papers from Laurent Jacob's perspective

Ordered by how strongly the researchers Laurent Jacob 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. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Clustered Multi-Task Learning: A Convex Formulation (2008) — 521 citations
• Group lasso with overlap and graph lasso (2009) — 1,011 citations
• Group Lasso with Overlaps: the Latent Group Lasso approach (2011) — 191 citations
• A More Powerful Two-Sample Test in High Dimensions using Random Projection (2011) — 146 citations
• Efficient peptide-MHC-I binding prediction for alleles with few known binders (2008) — 147 citations
• Protein-ligand interaction prediction: an improved chemogenomics approach (2008) — 376 citations
• Supplementary Material for Biological Sequence Modeling with Convolutional Kernel Networks (2018)