Charles Siegel

Computational Theory and Mathematics · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 193 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 90 citations into it; #5,874 of 12,182 by trust here, #2,565 by citations
• Artificial Intelligence — 70 citations into it
• Materials Chemistry — 69 citations into it; #3,933 of 11,513 by trust here, #7,964 by citations
• Computer Vision and Pattern Recognition — 66 citations into it
• Molecular Biology — 66 citations into it; #12,686 of 17,773 by trust here, #16,150 by citations
• Computational Chemistry & Drug Discovery — 48 citations into it; #342 of 425 by trust here, #380 by citations
• Model Architectures — 22 citations into it
• Optimization & Training — 22 citations into it

Whom Charles Siegel trusts overall

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

1. Garrett B. Goh
2. Kaiming He
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Alex Krizhevsky
6. Steven M. Kearnes
7. Yoshua Bengio
8. Yann LeCun
9. X. Zhang
10. George E. Dahl

Who trusts Charles Siegel overall

1. Garrett B. Goh
2. Nathan Oken Hodas
3. Abhinav Vishnu
4. Martin Simonovsky
5. Shion Honda
6. Sabrina Jaeger
7. Yuanqi Du

Papers from Charles Siegel's perspective

Ordered by how strongly the researchers Charles Siegel 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. Going deeper with convolutions
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models (2017) — 182 citations
• GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent (2018) — 107 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
• Swarm Learning for decentralized and confidential clinical machine learning (2021) — 792 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