Truong Son Hy

Materials Chemistry · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 33 citations into it; #1,670 of 11,513 by trust here, #6,301 by citations
• Computational Chemistry & Drug Discovery — 29 citations into it; #91 of 425 by trust here, #212 by citations
• Computational Theory and Mathematics — 22 citations into it; #2,795 of 12,182 by trust here, #1,890 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Atomic and Molecular Physics, and Optics — 19 citations into it
• AI for Physical Sciences — 15 citations into it
• Model Architectures — 14 citations into it
• Artificial Intelligence — 12 citations into it

Whom Truong Son Hy 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. Yann LeCun
6. Geoffrey E. Hinton
7. J. Behler
8. Kristof T. Schütt
9. Max Welling
10. Stefan Chmiela

Who trusts Truong Son Hy overall

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

Papers from Truong Son Hy's perspective

Ordered by how strongly the researchers Truong Son Hy 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Cormorant: Covariant Molecular Neural Networks (2019) — 498 citations
• Predicting molecular properties with covariant compositional networks. (2018) — 73 citations