John Peurifoy

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Model Architectures — 17 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Optimization & Training — 7 citations into it
• Signal Processing — 5 citations into it

Whom John Peurifoy trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. J. Urgen Schmidhuber
5. Oriol Vinyals
6. Yann LeCun
7. Ilya Sutskever
8. Alex Krizhevsky
9. Gregory R. Koch
10. Jimmy Ba

Who trusts John Peurifoy overall

1. Kan Yao
2. Wei Ma

Papers from John Peurifoy's perspective

Ordered by how strongly the researchers John Peurifoy 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. ImageNet classification with deep convolutional neural networks
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Nanophotonic particle simulation and inverse design using artificial neural networks (2017) — 892 citations
• Meta-learning autoencoders for few-shot prediction (2018) — 24 citations
• Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs (2016) — 191 citations