Jan Eric Lenssen

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 317 researchers and is trusted by 160 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 84 citations into it; #2,899 of 21,575 by trust here, #3,191 by citations
• Graph Learning — 73 citations into it; #29 of 693 by trust here, #87 by citations
• Artificial Intelligence — 65 citations into it; #1,976 of 35,345 by trust here, #4,602 by citations
• Computational Mechanics — 47 citations into it
• 3D Vision & Pose — 30 citations into it
• Computer Graphics and Computer-Aided Design — 27 citations into it
• Statistical and Nonlinear Physics — 26 citations into it
• Model Architectures — 24 citations into it; #1,668 of 2,278 by trust here, #1,014 by citations

Whom Jan Eric Lenssen trusts overall

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

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

Who trusts Jan Eric Lenssen overall

1. Matthias Fey
2. Guohao Li
3. David W. Romero
4. Weihua Hu
5. Erik J. Bekkers
6. Uri Alon
7. Christopher Morris
8. Guo-Jun Qi
9. Cristian Bodnar
10. Ziniu Hu

Papers from Jan Eric Lenssen's perspective

Ordered by how strongly the researchers Jan Eric Lenssen 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Fast Graph Representation Learning with PyTorch Geometric (2019) — 5,499 citations
• Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction (2020) — 522 citations
• SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels (2017) — 511 citations
• Group Equivariant Capsule Networks (2018) — 119 citations
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018) — 2,039 citations