Michael Cole

Molecular Biology · with Jordan · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 63 citations into it; #13,647 of 17,773 by trust here, #7,747 by citations
• Biophysics — 27 citations into it; #944 of 1,196 by trust here, #440 by citations
• Artificial Intelligence — 21 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Optimization & Training — 9 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Generative Models — 5 citations into it

Whom Michael Cole trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Max Welling
4. Sergey Ioffe
5. Emma Pierson
6. Yoshua Bengio
7. Alex Krizhevsky
8. L. V. D. van der Maaten
9. David M. Blei
10. Pascal Vincent

Who trusts Michael Cole overall

1. Christoph Hafemeister
2. Romain Lopez

Papers from Michael Cole's perspective

Ordered by how strongly the researchers Michael Cole trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018) — 38 citations
• Deep Generative Modeling for Single-cell Transcriptomics (2018) — 2,423 citations
• A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes (2017) — 7 citations