Pim Moeskops

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 30 citations into it; #20,251 of 21,575 by trust here, #6,146 by citations
• Radiology, Nuclear Medicine and Imaging — 12 citations into it

Whom Pim Moeskops trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Karen Simonyan
5. Stephen M. Smith
6. Sergey Ioffe
7. Ilya Sutskever
8. Yann LeCun
9. Vinod Nair
10. Daniel S. Marcus

Who trusts Pim Moeskops overall

1. Adnan Qayyum
2. Kenji Suzuki
3. Qi-Yan Dou
4. M. Bakator
5. Hao Chen
6. Dinggang Shen
7. Seyed Sadegh Mohseni Salehi
8. Maram Mahmoud A. Monshi
9. Syed Muhammad Anwar
10. Justin Ker

Papers from Pim Moeskops's perspective

Ordered by how strongly the researchers Pim Moeskops trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Automatic Segmentation of MR Brain Images With a Convolutional Neural Network (2016) — 831 citations
• Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI (2017) — 79 citations
• Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities (2016) — 339 citations