Paul Patras

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 359 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 141 citations into it
• Computer Vision and Pattern Recognition — 100 citations into it
• Computer Networks and Communications — 78 citations into it; #5,132 of 9,886 by trust here, #2,927 by citations
• Electrical and Electronic Engineering — 67 citations into it; #6,972 of 16,539 by trust here, #7,308 by citations
• Signal Processing — 43 citations into it
• Model Architectures — 25 citations into it
• Optimization & Training — 18 citations into it
• Reinforcement Learning — 17 citations into it

Whom Paul Patras trusts overall

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

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

Who trusts Paul Patras overall

1. Meysam Sadeghi
2. Ke He
3. Yuris Mulya Saputra
4. Mojtaba Vaezi

Papers from Paul Patras's perspective

Ordered by how strongly the researchers Paul Patras 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Learning in Mobile and Wireless Networking: A Survey (2018) — 1,535 citations