Edward O. Pyzer-Knapp

Computational Theory and Mathematics · 2015–2017

A researcher in the Science corpus: 5 papers; trusts 19 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 36 citations into it; #5,319 of 12,182 by trust here, #1,667 by citations
• Materials Chemistry — 36 citations into it; #4,546 of 11,513 by trust here, #6,536 by citations
• Atomic and Molecular Physics, and Optics — 24 citations into it
• Molecular Biology — 24 citations into it
• Artificial Intelligence — 11 citations into it
• Physical and Theoretical Chemistry — 11 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Optimization & Training — 8 citations into it

Whom Edward O. Pyzer-Knapp trusts overall

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

1. Roman M. Balabin
2. J. Perdew
3. José Miguel Hernández-Lobato
4. Shyue Ping Ong
5. Charles Blundell
6. Jasper Snoek
7. Geoffrey E. Hinton
8. Ekaterina I. Lomakina
9. Florian Weigend
10. James Bergstra

Who trusts Edward O. Pyzer-Knapp overall

1. José Miguel Hernández-Lobato
2. Ryan-Rhys Griffiths
3. Hanjun Dai
4. Kevin Maik Jablonka
5. Pascal Friederich
6. Yingzhen Li
7. Florian Hase
8. Bo Dai
9. Alán Aspuru-Guzik
10. Daniele Ongari

Papers from Edward O. Pyzer-Knapp's perspective

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

Their papers

• Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space (2017) — 206 citations
• What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery (2015) — 286 citations
• Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery (2015) — 205 citations
• Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models (2016) — 7 citations
• Machine learning exciton dynamics (2015) — 86 citations