Roger C. Tam

Computer Vision and Pattern Recognition · 2013–2018

A researcher in the Science corpus: 5 papers; trusts 80 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 38 citations into it; #5,438 of 21,575 by trust here, #7,249 by citations
• Artificial Intelligence — 33 citations into it; #7,344 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 Roger C. Tam trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Vinod Nair
4. Ilya Sutskever
5. Yann LeCun
6. Honglak Lee
7. Yoshua Bengio
8. Ruslan Salakhutdinov
9. Jia Deng
10. Radford M. Neal

Who trusts Roger C. Tam overall

1. Heung-Il Suk
2. Dinggang Shen
3. Sarah Parisot
4. Yu-An Chung
5. Thomas Schlegl
6. Tom Brosch
7. Mufti Mahmud
8. Ayush Jaiswal
9. Sofia Ira Ktena
10. Saptarshi Sengupta

Papers from Roger C. Tam's perspective

Ordered by how strongly the researchers Roger C. Tam 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. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation (2016) — 457 citations
• Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology (2018) — 429 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