Michael Dorkenwald

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 3 papers; trusts 621 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 154 citations into it; #19,393 of 21,575 by trust here, #15,162 by citations
• Generative Models — 92 citations into it; #1,755 of 1,956 by trust here, #1,577 by citations
• Artificial Intelligence — 69 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it
• Optimization & Training — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Model Architectures — 12 citations into it
• Computer Graphics and Computer-Aided Design — 11 citations into it

Whom Michael Dorkenwald trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Karen Simonyan
5. Alex Krizhevsky
6. Yoshua Bengio
7. Yann LeCun
8. Andrew Zisserman
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Michael Dorkenwald overall

1. Andreas Blattmann
2. Jinbo Xing
3. Yu-Quan Wang
4. Timo Milbich
5. Menghan Xia

Papers from Michael Dorkenwald's perspective

Ordered by how strongly the researchers Michael Dorkenwald 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Stochastic Image-to-Video Synthesis using cINNs (2021) — 67 citations
• iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis (2021) — 52 citations
• Behavior-Driven Synthesis of Human Dynamics (2021) — 16 citations