Chloé-Agathe Azencott

Molecular Biology · 2007–2018

A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 37 citations into it
• Genetics — 28 citations into it
• Artificial Intelligence — 24 citations into it; #33,086 of 35,345 by trust here, #21,878 by citations
• Privacy & Security — 9 citations into it
• Computational Biology & Bioinformatics — 7 citations into it
• Sociology and Political Science — 5 citations into it

Whom Chloé-Agathe Azencott trusts overall

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

1. Latanya Sweeney
2. Grigorios Loukides
3. Charu C. Aggarwal
4. Nicholas J. Schork
5. Aris Gkoulalas-Divanis
6. Yaniv Erlich
7. Kristin E. Lauter
8. Aaron Johnson
9. Fei Yu
10. Fida Kamal Dankar

Who trusts Chloé-Agathe Azencott overall

1. Gisbert Schneider
2. Kyle Swanson
3. Amos Beimel
4. Wengong Jin
5. Abhishek Niroula

Papers from Chloé-Agathe Azencott's perspective

Ordered by how strongly the researchers Chloé-Agathe Azencott 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning to Predict Chemical Reactions (2011) — 175 citations
• One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties (2007) — 63 citations
• The Evaluation of Tools Used to Predict the Impact of Missense Variants Is Hindered by Two Types of Circularity (2015) — 342 citations
• Machine learning and genomics: precision medicine versus patient privacy (2018) — 68 citations
• The Influence Relevance Voter: An Accurate And Interpretable Virtual High Throughput Screening Method (2009) — 56 citations