Garrett B. Goh

Computational Theory and Mathematics · 2017

A researcher in the Science corpus: 6 papers; trusts 135 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 116 citations into it; #2,840 of 12,182 by trust here, #1,291 by citations
• Molecular Biology — 109 citations into it; #4,053 of 17,773 by trust here, #8,182 by citations
• Materials Chemistry — 105 citations into it; #1,710 of 11,513 by trust here, #5,426 by citations
• Computational Chemistry & Drug Discovery — 64 citations into it; #347 of 425 by trust here, #380 by citations
• Computer Vision and Pattern Recognition — 54 citations into it
• Artificial Intelligence — 52 citations into it
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Garrett B. Goh trusts overall

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

1. Kaiming He
2. Steven M. Kearnes
3. Christian Szegedy
4. Geoffrey E. Hinton
5. George E. Dahl
6. David Weininger
7. Diederik P. Kingma
8. X. Zhang
9. Alex Krizhevsky
10. Yoshua Bengio

Who trusts Garrett B. Goh overall

1. Charles Siegel
2. Nathan Oken Hodas
3. Abhinav Vishnu
4. Tânia F Cova
5. Martin Simonovsky
6. Wei Ma
7. Chaopeng Shen
8. Shion Honda
9. D. Finol
10. Sabrina Jaeger

Papers from Garrett B. Goh's perspective

Ordered by how strongly the researchers Garrett B. Goh 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep learning for computational chemistry (2017) — 739 citations
• Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models (2017) — 182 citations
• SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties (2017) — 166 citations
• How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions? (2017) — 48 citations
• ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction (2017) — 21 citations
• Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction (2017) — 100 citations