Dougal Maclaurin

Materials Chemistry · 2011–2016

A researcher in the Science corpus: 9 papers; trusts 184 researchers and is trusted by 774 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 60 citations into it; #1,169 of 11,513 by trust here, #1,110 by citations
• Artificial Intelligence — 55 citations into it; #2,113 of 35,345 by trust here, #5,120 by citations
• Electrical and Electronic Engineering — 44 citations into it
• Probabilistic & Bayesian Methods — 42 citations into it; #356 of 1,525 by trust here, #832 by citations
• Optimization & Training — 41 citations into it; #341 of 2,601 by trust here, #582 by citations
• Computational Theory and Mathematics — 37 citations into it; #2,137 of 12,182 by trust here, #110 by citations
• Computational Chemistry & Drug Discovery — 30 citations into it
• Molecular Biology — 23 citations into it; #2,201 of 17,773 by trust here, #1,166 by citations

Whom Dougal Maclaurin trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Radford M. Neal
4. Alex Krizhevsky
5. Alex Graves
6. Yann LeCun
7. David John Cameron MacKay
8. Max Welling
9. Geoffrey E. Hinton
10. Sepp Hochreiter

Who trusts Dougal Maclaurin overall

1. Luke Metz
2. Ke Li
3. Luca Franceschi
4. David Belanger
5. Jitendra Malik
6. Qiang Liu
7. Trevor Campbell
8. Jeff Clune
9. Yoon Kim
10. Sergey Levine

Papers from Dougal Maclaurin's perspective

Ordered by how strongly the researchers Dougal Maclaurin 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Convolutional Networks on Graphs for Learning Molecular Fingerprints (2015) — 3,666 citations
• Gradient-based Hyperparameter Optimization through Reversible Learning (2015) — 1,048 citations
• Early Stopping is Nonparametric Variational Inference (2015) — 100 citations
• Firefly Monte Carlo: Exact MCMC with Subsets of Data (2014) — 186 citations
• Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. (2016) — 926 citations
• Quantum measurement and orientation tracking of fluorescent nanodiamonds inside living cells. (2011) — 619 citations
• Design of e cient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach (2016)
• Modeling, Inference and Optimization With Composable Differentiable Procedures (2016) — 54 citations
• All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins (2014) — 753 citations