Tyler J. Bradshaw

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 10 citations into it
• Artificial Intelligence — 7 citations into it

Whom Tyler J. Bradshaw trusts overall

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

1. L. Eon Bottou

Papers from Tyler J. Bradshaw's perspective

Ordered by how strongly the researchers Tyler J. Bradshaw trusts endorse each paper. Same corpus, a different order for every researcher.

1. Fully convolutional networks for semantic segmentation
2. ImageNet classification with deep convolutional neural networks
3. Caffe: Convolutional Architecture for Fast Feature Embedding
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
6. Gradient-based learning applied to document recognition
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. What is the best multi-stage architecture for object recognition?
10. Long Short-Term Memory

Their papers

• Deep learning Mr imaging–based attenuation correction for PeT/Mr imaging 1 (2017)