P. V. D. Putten

Computer Vision and Pattern Recognition · 2016

A researcher in the Science corpus: 2 papers; trusts 270 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #17,691 of 21,575 by trust here, #19,310 by citations
• Artificial Intelligence — 35 citations into it; #28,627 of 35,345 by trust here, #30,347 by citations
• Model Architectures — 18 citations into it
• Optimization & Training — 15 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Signal Processing — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom P. V. D. Putten trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Matthew D. Zeiler
5. Yann LeCun
6. David G. Lowe
7. Diederik P. Kingma
8. Ilya Sutskever
9. Alex Graves
10. Kaiming He

Who trusts P. V. D. Putten overall

1. Marc-André Zöller

Papers from P. V. D. Putten's perspective

Ordered by how strongly the researchers P. V. D. Putten 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML (2016) — 29 citations
• On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks (2016) — 133 citations