Nathan Brown

Computational Theory and Mathematics · 2018

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

Subfields

• Computational Theory and Mathematics — 35 citations into it; #10,656 of 12,182 by trust here, #3,681 by citations
• Materials Chemistry — 20 citations into it; #8,829 of 11,513 by trust here, #8,144 by citations
• Artificial Intelligence — 11 citations into it

Whom Nathan Brown trusts overall

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

1. J. Urgen Schmidhuber
2. Jan H. Jensen
3. Sepp Hochreiter
4. Marwin H. S. Segler
5. Peter Ertl
6. Gabriel Lima Guimaraes
7. Qi Liu
8. Jaechang Lim
9. Hongming Chen
10. Geoffrey E. Hinton

Who trusts Nathan Brown overall

1. Simon Axelrod
2. Tianfan Fu
3. Kevin Maik Jablonka
4. José Jiménez-Luna
5. Wenhao Gao
6. Mengying Sun
7. Clément Vignac
8. Josep Arús‐Pous
9. Kexin Huang
10. Francesca Grisoni

Papers from Nathan Brown's perspective

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

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

Their papers

• GuacaMol: Benchmarking Models for De Novo Molecular Design (2018) — 942 citations
• Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design (2018) — 79 citations