John E. Herr

Materials Chemistry · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 84 citations into it
• Atomic and Molecular Physics, and Optics — 48 citations into it
• Computational Theory and Mathematics — 40 citations into it
• Molecular Biology — 26 citations into it
• AI for Physical Sciences — 24 citations into it

Whom John E. Herr trusts overall

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

1. J. Behler
2. Matthias Rupp 
3. Sergei Manzhos
4. Johannes Hachmann

Who trusts John E. Herr overall

1. Jared D. Willard
2. Linfeng Zhang

Papers from John E. Herr's perspective

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

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

Their papers

• The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics (2017) — 327 citations
• Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. (2017) — 120 citations
• The many-body expansion combined with neural networks. (2016) — 104 citations