Kyle Cranmer

Artificial Intelligence · with Battaglia · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #34,526 of 35,345 by trust here, #17,776 by citations
• ML Systems & Hardware — 6 citations into it

Whom Kyle Cranmer trusts overall

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

1. Peter W. Battaglia
2. Alvaro Sanchez-Gonzalez

Who trusts Kyle Cranmer overall

1. Miles Cranmer

Papers from Kyle Cranmer's perspective

Ordered by how strongly the researchers Kyle Cranmer 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. The Graph Neural Network Model
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Interaction Networks for Learning about Objects, Relations and Physics
8. Gradient-based learning applied to document recognition
9. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
10. PyTorch: An Imperative Style, High-Performance Deep Learning Library

Their papers

• Hamiltonian Graph Networks with ODE Integrators (2019) — 195 citations
• Discovering Symbolic Models from Deep Learning with Inductive Biases (2020) — 676 citations