Kyle Swanson

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 97 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it
• Molecular Biology — 57 citations into it
• Computational Theory and Mathematics — 55 citations into it
• Materials Chemistry — 42 citations into it
• Computational Chemistry & Drug Discovery — 34 citations into it
• Graph Learning — 20 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Natural Language Processing — 12 citations into it

Whom Kyle Swanson trusts overall

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

1. Jerome H. Friedman
2. David Weininger
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. John J. Irwin
6. Thomas Kipf
7. Max Welling
8. Yann LeCun
9. Yoshua Bengio
10. Bharath Ramsundar

Papers from Kyle Swanson's perspective

Ordered by how strongly the researchers Kyle Swanson 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A Deep Learning Approach to Antibiotic Discovery. (2020) — 1,250 citations
• Improving Molecular Design by Stochastic Iterative Target Augmentation (2020) — 31 citations
• From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. (2023) — 507 citations
• Uncertainty Quantification Using Neural Networks for Molecular Property Prediction (2020) — 259 citations
• Message passing neural networks for molecular property prediction (2019) — 23 citations