Paweł Liskowski

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 1 paper; trusts 40 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 19 citations into it; #15,061 of 21,575 by trust here, #13,815 by citations
• Radiology, Nuclear Medicine and Imaging — 12 citations into it; #1,703 of 3,265 by trust here, #2,016 by citations
• Ophthalmology — 11 citations into it; #99 of 222 by trust here, #76 by citations
• Artificial Intelligence — 10 citations into it
• Model Architectures — 7 citations into it

Whom Paweł Liskowski trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Xavier Glorot
5. Yann LeCun
6. Nitish Srivastava
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. Elisa Ricci
10. Ruslan Salakhutdinov

Who trusts Paweł Liskowski overall

1. Sara Moccia
2. Leyuan Fang
3. Qiangguo Jin
4. Pedro Costa
5. Hyunkwang Lee
6. Zaiwang Gu
7. Elena De Momi
8. Zhao-Peng Meng
9. David Cunefare
10. Adrian Galdran

Papers from Paweł Liskowski's perspective

Ordered by how strongly the researchers Paweł Liskowski 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Segmenting Retinal Blood Vessels With Deep Neural Networks (2016) — 840 citations