Tim O'Shea

Artificial Intelligence · 2007–2017

A researcher in the Science corpus: 6 papers; trusts 244 researchers and is trusted by 77 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 48 citations into it; #2,985 of 35,345 by trust here, #6,848 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #7,708 of 21,575 by trust here, #12,419 by citations
• Model Architectures — 34 citations into it
• Optimization & Training — 19 citations into it
• Signal Processing — 16 citations into it; #382 of 7,572 by trust here, #785 by citations
• Electrical and Electronic Engineering — 10 citations into it
• Computer Networks and Communications — 8 citations into it
• ML Systems & Hardware — 7 citations into it

Whom Tim O'Shea trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. Ilya Sutskever
8. Jimmy Ba
9. Christian Szegedy
10. Vinod Nair

Who trusts Tim O'Shea overall

1. Meysam Sadeghi
2. Mingzhe Chen
3. Nathan E. West
4. Paul de Kerret
5. Hao Ye
6. Deniz Gündüz
7. Erik G. Larsson
8. Minhoe Kim
9. Woongsup Lee
10. David Gesbert

Papers from Tim O'Shea's perspective

Ordered by how strongly the researchers Tim O'Shea 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Convolutional Radio Modulation Recognition Networks (2016) — 1,385 citations
• Over-the-Air Deep Learning Based Radio Signal Classification (2017) — 1,429 citations
• An Introduction to Deep Learning for the Physical Layer (2017) — 2,620 citations
• Deep architectures for modulation recognition (2017) — 516 citations
• Radio Machine Learning Dataset Generation with GNU Radio (2016) — 688 citations
• Applications of Machine Learning to Cognitive Radio Networks (2007) — 339 citations