Michael C. Kampffmeyer

Computer Vision and Pattern Recognition · with Jenssen · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 506 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it; #6,977 of 21,575 by trust here, #10,905 by citations
• Artificial Intelligence — 57 citations into it
• Model Architectures — 25 citations into it
• Optimization & Training — 19 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Biophysics — 6 citations into it
• Oncology — 6 citations into it; #1,066 of 1,698 by trust here, #1,501 by citations
• Safety, Robustness & Interpretability — 6 citations into it

Whom Michael C. Kampffmeyer trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Diederik P. Kingma
5. Ross B. Girshick
6. Karen Simonyan
7. Yann LeCun
8. Yoshua Bengio
9. Christian Szegedy
10. Sergey Ioffe

Who trusts Michael C. Kampffmeyer overall

1. Yarin Gal
2. Di Feng
3. Kristoffer Wickstrøm
4. Xiaohui Yuan
5. Nicolas Audebert
6. Shunyi Zheng
7. Van Nhan Nguyen
8. Huan Wang
9. Robert Jenssen
10. Ruixin Yang

Papers from Michael C. Kampffmeyer's perspective

Ordered by how strongly the researchers Michael C. Kampffmeyer 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks (2016) — 572 citations
• Deep generative models for reject inference in credit scoring (2019) — 57 citations
• UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION (2018) — 77 citations