Michelle L. Coote

Computational Theory and Mathematics · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 86 citations into it; #8,677 of 12,182 by trust here, #6,463 by citations
• Materials Chemistry — 80 citations into it; #6,501 of 11,513 by trust here, #10,083 by citations
• Molecular Biology — 65 citations into it
• Physical and Theoretical Chemistry — 15 citations into it
• AI for Physical Sciences — 6 citations into it
• Catalysis — 6 citations into it

Whom Michelle L. Coote trusts overall

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

1. J. Behler
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Alex Graves
5. Yoshua Bengio
6. Sepp Hochreiter
7. Garrett B. Goh
8. Kristof T. Schütt

Who trusts Michelle L. Coote overall

1. Tânia F Cova
2. A. Pais

Papers from Michelle L. Coote's perspective

Ordered by how strongly the researchers Michelle L. Coote 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. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Learning in Chemistry (2019) — 466 citations
• Molecular dynamics-driven drug discovery: leaping forward with confidence. (2017) — 312 citations
• Electrostatic catalysis of a Diels–Alder reaction (2016) — 703 citations