Roland Kwitt

Computer Vision and Pattern Recognition · 2011–2022

A researcher in the Science corpus: 8 papers; trusts 418 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 109 citations into it; #10,481 of 21,575 by trust here, #10,154 by citations
• Artificial Intelligence — 85 citations into it; #29,991 of 35,345 by trust here, #26,682 by citations
• Model Architectures — 24 citations into it
• Optimization & Training — 24 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it; #1,213 of 3,265 by trust here, #1,699 by citations
• Computational Theory and Mathematics — 16 citations into it; #10,346 of 12,182 by trust here, #6,463 by citations
• Statistical Learning Theory — 16 citations into it

Whom Roland Kwitt trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Shaoqing Ren
10. X. Zhang

Who trusts Roland Kwitt overall

1. Arvid Lundervold
2. Guha Balakrishnan
3. Charles Ruizhongtai Qi
4. Sergey I. Nikolenko
5. Xi Yin
6. Amy Zhao
7. Yingzhen Li
8. Jiayi Ma
9. Xingyu Jiang
10. Grant Haskins

Papers from Roland Kwitt's perspective

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

Their papers

• AGA: Attribute-Guided Augmentation (2016) — 105 citations
• Quicksilver: Fast Predictive Image Registration – a Deep Learning Approach (2017) — 643 citations
• A stable multi-scale kernel for topological machine learning (2014) — 392 citations
• Deep Learning with Topological Signatures (2017) — 315 citations
• On Measuring Excess Capacity in Neural Networks (2022) — 13 citations
• Dissecting Supervised Constrastive Learning (2021) — 74 citations
• Fast Predictive Image Registration (2016) — 128 citations
• BlenSor: Blender Sensor Simulation Toolbox (2011) — 301 citations