Mordechai Kornbluth

Materials Chemistry · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 57 citations into it; #11,364 of 11,513 by trust here, #7,558 by citations
• Computational Chemistry & Drug Discovery — 25 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Molecular Biology — 21 citations into it; #17,638 of 17,773 by trust here, #9,476 by citations
• Atomic and Molecular Physics, and Optics — 20 citations into it
• Artificial Intelligence — 19 citations into it; #35,218 of 35,345 by trust here, #15,004 by citations
• Model Architectures — 17 citations into it
• AI for Physical Sciences — 15 citations into it

Whom Mordechai Kornbluth trusts overall

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

1. Kristof T. Schütt
2. Ralf Drautz
3. Risi Kondor
4. Kaiming He
5. Johannes Klicpera
6. Stefan Chmiela
7. Diederik P. Kingma
8. A. P. Bart'ok

Who trusts Mordechai Kornbluth overall

1. Tess E. Smidt

Papers from Mordechai Kornbluth's perspective

Ordered by how strongly the researchers Mordechai Kornbluth 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. Attention is All you Need (Neural Information Processing Systems)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials (2021) — 2,192 citations
• Learning local equivariant representations for large-scale atomistic dynamics (2022) — 820 citations