Daniel Forsberg

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 30 citations into it; #19,166 of 21,575 by trust here, #17,757 by citations
• Radiology, Nuclear Medicine and Imaging — 18 citations into it; #1,590 of 3,265 by trust here, #2,195 by citations
• Artificial Intelligence — 10 citations into it
• Biomedical Engineering — 7 citations into it; #7,814 of 10,734 by trust here, #9,262 by citations
• Computational Mechanics — 5 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Daniel Forsberg trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Michael Elad
6. Christian Szegedy
7. Gitta Kutyniok
8. Paul A. Viola
9. Jianbo Shi
10. Yann LeCun

Who trusts Daniel Forsberg overall

1. Arvid Lundervold
2. Shiva Maleki Varnosfaderani

Papers from Daniel Forsberg's perspective

Ordered by how strongly the researchers Daniel Forsberg 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data (2017) — 99 citations
• Medical image processing on the GPU - Past, present and future (2013) — 415 citations