Brett K. Beaulieu-Jones

Molecular Biology · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 48 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 268 citations into it; #4,447 of 17,773 by trust here, #3,866 by citations
• Artificial Intelligence — 244 citations into it; #13,469 of 35,345 by trust here, #4,682 by citations
• Computer Vision and Pattern Recognition — 98 citations into it
• Computational Theory and Mathematics — 71 citations into it
• Safety, Robustness & Interpretability — 57 citations into it
• Genetics — 41 citations into it; #1,445 of 6,854 by trust here, #4,743 by citations
• Biophysics — 40 citations into it
• Computational Biology & Bioinformatics — 28 citations into it

Whom Brett K. Beaulieu-Jones trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Pascal Vincent
6. Christian Szegedy
7. Cynthia Dwork
8. Gordon K. Smyth
9. Reza Shokri
10. Yann LeCun

Who trusts Brett K. Beaulieu-Jones overall

1. Sameer Quazi
2. Daniel S. Himmelstein
3. Ying-Cheng Lu
4. Travers Ching
5. Wei Xie
6. Zhengping Che
7. Casey S. Greene
8. Adnan Qayyum
9. Zhiwei Steven Wu
10. Alexandr A Kalinin

Papers from Brett K. Beaulieu-Jones's perspective

Ordered by how strongly the researchers Brett K. Beaulieu-Jones 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. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
• Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification (2016) — 171 citations
• Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist (2020) — 485 citations
• Privacy-Preserving Distributed Deep Learning for Clinical Data (2018) — 52 citations
• Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing (2017) — 468 citations
• Reproducibility of computational workflows is automated using continuous analysis (2017) — 157 citations