Tallulah S. Andrews

Molecular Biology · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 30 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 136 citations into it; #3,958 of 17,773 by trust here, #5,705 by citations
• Cancer Research — 34 citations into it; #811 of 1,780 by trust here, #1,725 by citations
• Biophysics — 31 citations into it
• Genetics — 17 citations into it
• Immunology — 13 citations into it; #817 of 2,160 by trust here, #1,857 by citations
• Computer Vision and Pattern Recognition — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Tallulah S. Andrews trusts overall

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

1. L. V. D. van der Maaten
2. Simon Anders
3. Emma Pierson
4. Dominic Grün
5. Peter V. Kharchenko
6. Geoffrey E. Hinton
7. Gordon K. Smyth
8. Yang Liao
9. Santo Fortunato
10. Michael I. Love

Who trusts Tallulah S. Andrews overall

1. Saket Kumar Choudhary
2. Angelo Duò
3. Rahul Satija
4. Valentine Svensson
5. Aleksandr Ianevski
6. F. W. Townes
7. Mark D. Robinson
8. Pavlin Gregor Policar
9. Charlotte Soneson
10. Dmitry Kobak

Papers from Tallulah S. Andrews's perspective

Ordered by how strongly the researchers Tallulah S. Andrews 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. Visualizing Data using t-SNE
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• SC3 - consensus clustering of single-cell RNA-Seq data (2016) — 1,337 citations
• Modelling dropouts for feature selection in scRNASeq experiments (2016) — 38 citations
• EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data (2019) — 871 citations
• M3Drop: dropout-based feature selection for scRNASeq (2018) — 236 citations
• Challenges in unsupervised clustering of single-cell RNA-seq data (2019) — 1,049 citations