Robert M. Patton

Electrical and Electronic Engineering · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 80 citations into it
• Artificial Intelligence — 42 citations into it
• Cognitive Neuroscience — 30 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Optimization & Training — 10 citations into it

Whom Robert M. Patton trusts overall

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

1. Alex Krizhevsky
2. James Bergstra
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Eugene M. Izhikevich
6. Yann LeCun
7. David E. Rumelhart
8. Hugo Larochelle

Papers from Robert M. Patton's perspective

Ordered by how strongly the researchers Robert M. Patton trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Learning Multiple Layers of Features from Tiny Images
3. ImageNet classification with deep convolutional neural networks
4. Going deeper with convolutions
5. ImageNet Large Scale Visual Recognition Challenge
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Et al
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Optimizing deep learning hyper-parameters through an evolutionary algorithm (2015) — 435 citations
• A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017) — 805 citations
• Evolving Deep Networks Using HPC (2017) — 45 citations