Martin Engelcke

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 490 researchers and is trusted by 291 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 127 citations into it; #2,314 of 21,575 by trust here, #3,582 by citations
• Artificial Intelligence — 67 citations into it
• Model Architectures — 40 citations into it
• Generative Models — 38 citations into it
• Computational Mechanics — 28 citations into it; #781 of 10,001 by trust here, #817 by citations
• Aerospace Engineering — 17 citations into it; #406 of 6,421 by trust here, #1,001 by citations
• 3D Vision & Pose — 14 citations into it
• Geology — 14 citations into it

Whom Martin Engelcke trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Christian Szegedy
8. J. Urgen Schmidhuber
9. Danilo Jimenez Rezende
10. Karen Simonyan

Who trusts Martin Engelcke overall

1. Zetong Yang
2. Shaoshuai Shi
3. Jindong Jiang
4. Tianwei Yin
5. Charles Ruizhongtai Qi
6. Klaus Greff
7. Yan Yan
8. Yin Zhou
9. Binh Yang
10. Li Jiang

Papers from Martin Engelcke's perspective

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

Their papers

• 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks (2017) — 1,854 citations
• GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations (2019) — 343 citations
• Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks (2016) — 541 citations
• Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017) — 70 citations
• On the Limitations of Representing Functions on Sets (2019) — 186 citations
• RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces (2020) — 60 citations