Timo Lüddecke

Computer Vision and Pattern Recognition · 2021

A researcher in the Science corpus: 1 paper; trusts 484 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #9,795 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 33 citations into it; #15,300 of 35,345 by trust here, #23,077 by citations
• Meta & Continual Learning — 9 citations into it
• Multimodal Learning — 8 citations into it; #544 of 1,211 by trust here, #1,004 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Model Architectures — 6 citations into it
• Natural Language Processing — 6 citations into it

Whom Timo Lüddecke trusts overall

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

1. Kaiming He
2. Karen Simonyan
3. Geoffrey E. Hinton
4. Tomas Mikolov
5. Alex Krizhevsky
6. Ross B. Girshick
7. Ilya Sutskever
8. Yoshua Bengio
9. Yann LeCun
10. Andrew Zisserman

Who trusts Timo Lüddecke overall

1. Muhammad Uzair Khattak
2. Dongxu Li
3. Jingyi Zhang
4. Justin Kerr
5. Xiangtai Li
6. Krishna Murthy Jatavallabhula
7. Junnan Li
8. Jiaxing Huang
9. Hanoona Abdul Rasheed
10. Chung Min Kim

Papers from Timo Lüddecke's perspective

Ordered by how strongly the researchers Timo Lüddecke 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
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

• Image Segmentation Using Text and Image Prompts (2021) — 794 citations