Erik J. Bekkers

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 583 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #7,323 of 21,575 by trust here, #12,992 by citations
• Artificial Intelligence — 96 citations into it; #9,505 of 35,345 by trust here, #15,004 by citations
• Model Architectures — 87 citations into it
• Radiology, Nuclear Medicine and Imaging — 33 citations into it; #2,180 of 3,265 by trust here, #3,131 by citations
• Ophthalmology — 24 citations into it; #165 of 222 by trust here, #200 by citations
• Computational Theory and Mathematics — 23 citations into it
• Materials Chemistry — 21 citations into it; #9,473 of 11,513 by trust here, #9,794 by citations
• Computational Mechanics — 19 citations into it

Whom Erik J. Bekkers trusts overall

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

1. Taco Cohen
2. Max Welling
3. Maurice Weiler
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Kaiming He
8. Yann LeCun
9. Yoshua Bengio
10. Daniel E. Worrall

Who trusts Erik J. Bekkers overall

1. Johannes Brandstetter
2. David W. Romero
3. Marc Finzi
4. Maurice Weiler
5. Nichita Diaconu
6. Daniel E. Worrall
7. Rob D. Hesselink
8. Samuel Stanton
9. Max Welling
10. Elise van der Pol

Papers from Erik J. Bekkers's perspective

Ordered by how strongly the researchers Erik J. Bekkers 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. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• B-Spline CNNs on Lie Groups (2019) — 157 citations
• Geometric and Physical Quantities improve E(3) Equivariant Message Passing (2021) — 323 citations
• Attentive Group Equivariant Convolutional Networks (2020) — 102 citations
• Roto-Translation Covariant Convolutional Networks for Medical Image Analysis (2018) — 199 citations
• Wavelet Networks: Scale Equivariant Learning From Raw Waveforms (2020) — 18 citations
• Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores (2016) — 407 citations