Aurelie C. Lozano

Molecular Biology · 2010–2022

A researcher in the Science corpus: 5 papers; trusts 78 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 51 citations into it
• Artificial Intelligence — 40 citations into it; #21,931 of 35,345 by trust here, #30,347 by citations
• Computational Biology & Bioinformatics — 29 citations into it
• Statistics and Probability — 28 citations into it; #8,050 of 12,932 by trust here, #9,714 by citations
• Computational Mechanics — 21 citations into it; #5,999 of 10,001 by trust here, #8,258 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Graph Learning — 15 citations into it

Whom Aurelie C. Lozano trusts overall

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

1. Robert Tibshirani
2. Joel A. Tropp
3. Guillaume Obozinski
4. Ming Yuan
5. Charles A. Micchelli
6. Massimiliano Pontil
7. Jerome H. Friedman
8. Michael I. Jordan
9. Patrick Breheny
10. B. Taskar

Who trusts Aurelie C. Lozano overall

1. Yu Zhang
2. Keerthiram Murugesan
3. Lei Han
4. Xin Wang
5. Yu Zhang
6. Jaime G. Carbonell
7. Sebastian Ruder
8. Yu Zhang
9. Qiang Yang
10. Jinbo Bi

Papers from Aurelie C. Lozano's perspective

Ordered by how strongly the researchers Aurelie C. Lozano 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality (2012) — 63 citations
• Protein Representation Learning by Geometric Structure Pretraining (2022) — 328 citations
• Sparse Quantile Huber Regression for Efficient and Robust Estimation (2014) — 19 citations
• Multi-level Lasso for Sparse Multi-task Regression (2012) — 103 citations
• Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference (2010) — 19 citations