Sam Greydanus

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 91 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #17,619 of 35,345 by trust here, #11,750 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Reinforcement Learning — 20 citations into it; #1,699 of 2,242 by trust here, #1,443 by citations
• Model Architectures — 14 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Materials Chemistry — 11 citations into it; #7,737 of 11,513 by trust here, #9,513 by citations
• Computational Theory and Mathematics — 10 citations into it
• AI for Physical Sciences — 8 citations into it

Whom Sam Greydanus trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Karen Simonyan
7. Jimmy Ba
8. Volodymyr Mnih
9. Yann LeCun
10. Kaiming He

Who trusts Sam Greydanus overall

1. Ramitha Sundar
2. Miles Cranmer
3. Raghuram Mandyam Annasamy
4. Yaofeng Desmond Zhong
5. Akanksha Atrey
6. Christian Rupprecht
7. Wojciech Samek
8. Dmitry Nikulin
9. Daniel S. Brown
10. Zhao Yang

Papers from Sam Greydanus's perspective

Ordered by how strongly the researchers Sam Greydanus 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visualizing and Understanding Atari Agents (2017) — 406 citations
• Learning Finite State Representations of Recurrent Policy Networks (2018) — 99 citations
• Hamiltonian Neural Networks (2019) — 1,264 citations
• Lagrangian Neural Networks (2020) — 615 citations