Tom Brosch

Computer Vision and Pattern Recognition · 2013–2017

A researcher in the Science corpus: 4 papers; trusts 80 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 34 citations into it; #5,437 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 18 citations into it; #7,343 of 35,345 by trust here, #10,319 by citations
• Model Architectures — 13 citations into it
• Optimization & Training — 11 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Tom Brosch trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Vinod Nair
4. Yann LeCun
5. Ilya Sutskever
6. Honglak Lee
7. Yoshua Bengio
8. Matthew D. Zeiler
9. Dominik Scherer
10. Simon Osindero

Who trusts Tom Brosch overall

1. Heung-Il Suk
2. Dinggang Shen
3. Sarah Parisot
4. Yu-An Chung
5. Thomas Schlegl
6. Mufti Mahmud
7. Ayush Jaiswal
8. Seong-Whan Lee
9. Sofia Ira Ktena
10. Jeremy Kawahara

Papers from Tom Brosch's perspective

Ordered by how strongly the researchers Tom Brosch 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation (2016) — 457 citations
• Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls (2017) — 88 citations
• Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images (2015) — 57 citations
• Manifold Learning of Brain MRIs by Deep Learning (2013) — 239 citations