Sören Pirk

Computer Vision and Pattern Recognition · with Guibas · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 232 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #18,477 of 21,575 by trust here, #9,305 by citations
• Artificial Intelligence — 43 citations into it
• Control and Systems Engineering — 18 citations into it
• Computational Mechanics — 12 citations into it; #8,536 of 10,001 by trust here, #2,782 by citations
• Model Architectures — 12 citations into it
• Optimization & Training — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom Sören Pirk trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ross B. Girshick
7. Yann LeCun
8. Diederik P. Kingma
9. Karen Simonyan
10. Kyunghyun Cho

Who trusts Sören Pirk overall

1. Charles Ruizhongtai Qi
2. Truc Le
3. Roman Klokov
4. Matan Atzmon
5. Jiaxin Li
6. Rohit Girdhar
7. Mingyang Jiang
8. Korrawe Karunratanakul

Papers from Sören Pirk's perspective

Ordered by how strongly the researchers Sören Pirk trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos (2019) — 24 citations
• FPNN: Field Probing Neural Networks for 3D Data (2016) — 302 citations
• Modeling Long-horizon Tasks as Sequential Interaction Landscapes (2020) — 30 citations