Joseph Barfett

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 60 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it
• Model Architectures — 39 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it
• Natural Language Processing — 35 citations into it
• Signal Processing — 23 citations into it
• Optimization & Training — 14 citations into it
• Generative Models — 9 citations into it

Whom Joseph Barfett trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Tomas Mikolov
6. Diederik P. Kingma
7. J. Urgen Schmidhuber
8. Yann LeCun
9. James Martens
10. Radford M. Neal

Papers from Joseph Barfett's perspective

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

Their papers

• Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018) — 429 citations
• Recent Advances in Recurrent Neural Networks (2017) — 744 citations