Naren Ramakrishnan

Molecular Biology · 2005–2017

A researcher in the Science corpus: 11 papers; trusts 36 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 37 citations into it; #3,586 of 17,773 by trust here, #13,284 by citations
• Artificial Intelligence — 29 citations into it
• Statistical and Nonlinear Physics — 29 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it
• Sociology and Political Science — 19 citations into it
• Computational Biology & Bioinformatics — 12 citations into it; #246 of 660 by trust here, #546 by citations
• Computational Mechanics — 12 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it; #842 of 1,525 by trust here, #904 by citations

Whom Naren Ramakrishnan trusts overall

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

1. S. W. Lockless
2. Aharon Armon
3. Anthony Fodor
4. Rachel Karchin
5. Richard Durbin
6. Nir Friedman
7. David John Cameron MacKay
8. John Thomas
9. W. P. Russ
10. William S. J. Valdar

Who trusts Naren Ramakrishnan overall

1. Sivaraman Balakrishnan
2. Hetunandan Kamisetty
3. Jianzhu Ma
4. John Thomas
5. Susann Vorberg
6. Sheng Wang
7. C. Bailey‐Kellogg
8. Roshan Rao
9. S. Fatakia
10. Christopher James Langmead

Papers from Naren Ramakrishnan's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Et al
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Information Theory, Inference, and Learning Algorithms
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Protein Design by Sampling an Undirected Graphical Model of Residue Constraints (2009) — 31 citations
• Graphical models of protein–protein interaction specificity from correlated mutations and interaction data (2009) — 34 citations
• Graphical Models of Residue Coupling in Protein Families (2008) — 15 citations
• Epidemiological modeling of news and rumors on Twitter (2013) — 373 citations
• Fine-Grained Photovoltaic Output Prediction Using a Bayesian Ensemble (2012) — 49 citations
• Feature Constrained Multi-Task Learning Models for Spatiotemporal Event Forecasting (2017) — 44 citations
• Flow of Information in Feed-Forward Deep Neural Networks (2016) — 20 citations
• BiSet: Semantic Edge Bundling with Biclusters for Sensemaking (2016) — 53 citations
• ThemeDelta: Dynamic Segmentations over Temporal Topic Models (2015) — 62 citations
• Multi-Task Learning for Spatio-Temporal Event Forecasting (2015) — 156 citations
• Graphical models of residue coupling in protein families (2005) — 76 citations