Henrik Kretzschmar

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 157 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #15,532 of 21,575 by trust here, #3,827 by citations
• Artificial Intelligence — 42 citations into it
• Aerospace Engineering — 24 citations into it
• Optimization & Training — 24 citations into it
• Control and Systems Engineering — 12 citations into it
• Reinforcement Learning — 8 citations into it
• Ocean Engineering — 6 citations into it; #1,929 of 3,193 by trust here, #1,086 by citations
• Representation & Self-Supervised Learning — 6 citations into it

Whom Henrik Kretzschmar trusts overall

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

1. Andrew Y. Ng
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Pieter Abbeel
5. Brian D. Ziebart
6. Kaiming He
7. Diederik P. Kingma
8. Martin A. Riedmiller
9. Sebastian Thrun
10. Yoshua Bengio

Who trusts Henrik Kretzschmar overall

1. Lei Tai
2. Saurabh Arora
3. Yu Fan Chen
4. Mark Pfeiffer
5. Changan Chen
6. Christoforos Mavrogiannis
7. Prashant Doshi
8. Edouard Leurent
9. Julian Jara-Ettinger
10. Jingwei Zhang

Papers from Henrik Kretzschmar's perspective

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

Their papers

• Scalability in Perception for Autonomous Driving: Waymo Open Dataset (2019) — 4,247 citations
• Block-NeRF: Scalable Large Scene Neural View Synthesis (2022) — 1,104 citations
• Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout (2020) — 300 citations
• Socially compliant mobile robot navigation via inverse reinforcement learning (2016) — 511 citations