Jim Winkens

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #23,562 of 35,345 by trust here, #10,942 by citations
• Computer Vision and Pattern Recognition — 36 citations into it; #14,526 of 21,575 by trust here, #8,831 by citations
• Model Architectures — 8 citations into it

Whom Jim Winkens trusts overall

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

1. Alex Krizhevsky
2. Dan Hendrycks
3. Kaiming He
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Taco Cohen
7. Max Welling
8. Yoshua Bengio
9. Yann LeCun
10. Ilya Sutskever

Who trusts Jim Winkens overall

1. Taco Cohen
2. Hao Liu
3. Erik J. Bekkers
4. Stanislav Fort
5. David W. Romero
6. Vikash Sehwag
7. Maurice Weiler
8. Ben Hutchinson
9. Jie Jessie Ren
10. Mario Geiger

Papers from Jim Winkens's perspective

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

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

Their papers

• Rotation Equivariant CNNs for Digital Pathology (2018) — 735 citations
• Contrastive Training for Improved Out-of-Distribution Detection (2020) — 271 citations
• Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks (2018) — 17 citations