Brandon M. Anderson

Materials Chemistry · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 50 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 75 citations into it; #1,671 of 11,513 by trust here, #6,301 by citations
• Molecular Biology — 61 citations into it; #2,998 of 17,773 by trust here, #8,381 by citations
• Computational Chemistry & Drug Discovery — 55 citations into it; #92 of 425 by trust here, #212 by citations
• Computational Theory and Mathematics — 54 citations into it; #2,796 of 12,182 by trust here, #1,851 by citations
• Computer Vision and Pattern Recognition — 50 citations into it
• Graph Learning — 38 citations into it; #554 of 693 by trust here, #326 by citations
• Artificial Intelligence — 36 citations into it; #28,398 of 35,345 by trust here, #15,866 by citations
• Atomic and Molecular Physics, and Optics — 23 citations into it

Whom Brandon M. Anderson trusts overall

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

1. Risi Kondor
2. Taco Cohen
3. Carlos Esteves
4. A. P. Bart'ok
5. Max Welling
6. Yann LeCun
7. Kristof T. Schütt
8. Geoffrey E. Hinton
9. Stefan Chmiela
10. Justin Gilmer

Who trusts Brandon M. Anderson overall

1. Victor Garcia Satorras
2. Johannes Klicpera
3. Emiel Hoogeboom
4. Maurice Weiler
5. Yi Liao
6. Oliver T. Unke
7. Raphael J. L. Townshend
8. Bowen Jing
9. Stephan Eismann
10. Marc Finzi

Papers from Brandon M. Anderson's perspective

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

Their papers

• Covariant Compositional Networks For Learning Graphs (2018) — 168 citations
• Cormorant: Covariant Molecular Neural Networks (2019) — 498 citations
• Predicting molecular properties with covariant compositional networks. (2018) — 73 citations
• Learning physics confers pose-sensitivity in structure-based virtual screening (2021) — 10 citations
• ATOM3D: Tasks On Molecules in Three Dimensions (2020) — 173 citations