Harry A. Pierson

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 222 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it; #18,192 of 21,575 by trust here, #19,310 by citations
• Model Architectures — 17 citations into it
• Reinforcement Learning — 17 citations into it
• Control and Systems Engineering — 16 citations into it; #6,898 of 8,257 by trust here, #6,417 by citations
• Optimization & Training — 11 citations into it
• Biomedical Engineering — 9 citations into it
• Human-Computer Interaction — 6 citations into it

Whom Harry A. Pierson trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Sergey Levine
7. Ilya Sutskever
8. Kaiming He
9. J. Urgen Schmidhuber
10. Sepp Hochreiter

Who trusts Harry A. Pierson overall

1. Oludare Isaac Abiodun
2. Justin A. Sirignano
3. Konstantinos Spiliopoulos
4. Aman Bin Jantan

Papers from Harry A. Pierson's perspective

Ordered by how strongly the researchers Harry A. Pierson 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. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning in robotics: a review of recent research (2017) — 299 citations