Tobias Ritschel

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 6 papers; trusts 573 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #20,170 of 21,575 by trust here, #7,421 by citations
• Computational Mechanics — 59 citations into it
• Computer Graphics and Computer-Aided Design — 41 citations into it
• 3D Vision & Pose — 36 citations into it
• Artificial Intelligence — 35 citations into it
• Geology — 21 citations into it
• Molecular Biology — 21 citations into it
• Model Architectures — 19 citations into it

Whom Tobias Ritschel trusts overall

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

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

Who trusts Tobias Ritschel overall

1. Zhiyuan Zhang
2. Yifan Wang
3. Wenxuan Wu
4. Benjamin Ummenhofer
5. Yongheng Zhao

Papers from Tobias Ritschel's perspective

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

Their papers

• Monte Carlo convolution for learning on non-uniformly sampled point clouds (2018) — 282 citations
• Unsupervised Learning of 3D Object Categories from Videos in the Wild (2021) — 81 citations
• What Is Holding Back Convnets for Detection? (2015) — 66 citations
• Deep Shading: Convolutional Neural Networks for Screen Space Shading (2016) — 175 citations
• Learning a Neural 3D Texture Space From 2D Exemplars (2019) — 95 citations
• Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures (2021) — 124 citations