Tristan Swedish

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 94 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #11,966 of 35,345 by trust here, #11,327 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Privacy & Security — 37 citations into it; #389 of 709 by trust here, #380 by citations
• Federated & Distributed Learning — 19 citations into it; #149 of 319 by trust here, #184 by citations
• Aerospace Engineering — 17 citations into it
• Efficiency & Compression — 11 citations into it
• Radiology, Nuclear Medicine and Imaging — 11 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Tristan Swedish trusts overall

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

1. Iro Armeni
2. Cynthia Dwork
3. Yann LeCun
4. Geoffrey E. Hinton
5. Praneeth Vepakomma
6. Otkrist Gupta
7. Alex Krizhevsky
8. Yoshua Bengio
9. Alex Kendall
10. Kaiming He

Who trusts Tristan Swedish overall

1. Praneeth Vepakomma
2. Vivek Sharma
3. Iker Ceballos
4. Chaoyang He
5. Ramesh Raskar
6. Abhishek Singh
7. Otkrist Gupta

Papers from Tristan Swedish's perspective

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

Their papers

• ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations (2019) — 11 citations
• ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries (2019) — 7 citations
• Split learning for health: Distributed deep learning without sharing raw patient data (2018) — 964 citations
• Deep Visual Teach and Repeat on Path Networks (2018) — 17 citations
• No Peek: A Survey of private distributed deep learning (2018) — 113 citations