Caleb D. Richter

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 13 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it

Whom Caleb D. Richter trusts

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

1. Yoshua Bengio (in Computer Vision and Pattern Recognition)
2. Geoffrey E. Hinton (in Computer Vision and Pattern Recognition)
3. Alex Krizhevsky (in Computer Vision and Pattern Recognition)

Papers from Caleb D. Richter's perspective

Ordered by how strongly the researchers Caleb D. Richter trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Neural Networks: Tricks of the Trade
3. PathNet: Evolution Channels Gradient Descent in Super Neural Networks
4. Practical Recommendations for Gradient-Based Training of Deep Architectures
5. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
6. A survey on deep learning in medical image analysis
7. Transfer of Learning
8. Digital Mammography
9. Gradient-based learning applied to document recognition
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms (2017) — 203 citations