M. J. Willatt

Materials Chemistry · 2018

A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 43 citations into it
• Computational Theory and Mathematics — 22 citations into it
• Atomic and Molecular Physics, and Optics — 21 citations into it
• Computational Chemistry & Drug Discovery — 21 citations into it
• Physical and Theoretical Chemistry — 12 citations into it
• Electrical and Electronic Engineering — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom M. J. Willatt trusts overall

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

1. Radford M. Neal
2. J. Behler
3. A. P. Bart'ok
4. Leo Breiman
5. Bradley Efron
6. Michalis K. Titsias
7. Gábor Csányi
8. John Fox
9. Volker L. Deringer
10. Michele Ceriotti

Who trusts M. J. Willatt overall

1. Ilyes Batatia
2. D. Kov'acs
3. Chi Chen

Papers from M. J. Willatt's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Deep Learning
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Pattern Recognition and Machine Learning
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements. (2018) — 59 citations
• Fast and Accurate Uncertainty Estimation in Chemical Machine Learning. (2018) — 148 citations
• A Data-Driven Construction of the Periodic Table of the Elements (2018) — 50 citations