R. Todd Hurst

Computer Vision and Pattern Recognition · 2008–2016

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 23 citations into it
• Model Architectures — 6 citations into it

Whom R. Todd Hurst trusts overall

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

1. Kunihiko Fukushima

Papers from R. Todd Hurst's perspective

Ordered by how strongly the researchers R. Todd Hurst trusts endorse each paper. Same corpus, a different order for every researcher.

1. Going deeper with convolutions
2. Understanding the difficulty of training deep feedforward neural networks
3. ImageNet classification with deep convolutional neural networks
4. CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
7. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
8. Gradient-based learning applied to document recognition
9. Deep Learning
10. ImageNet: A large-scale hierarchical image database

Their papers

• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? (2016) — 2,955 citations
• Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force. Endorsed by the Society for Vascular Medicine. (2008) — 2,453 citations