Christopher M. Reardon

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 3 papers; trusts 266 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it
• Artificial Intelligence — 23 citations into it
• Biomedical Engineering — 17 citations into it
• Human-Computer Interaction — 17 citations into it
• 3D Vision & Pose — 10 citations into it

Whom Christopher M. Reardon trusts overall

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

1. Ivan Laptev
2. David G. Lowe
3. Marcin Marszalek
4. Cordelia Schmid
5. Piotr Doll'ar
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Kaiming He
9. Yoshua Bengio
10. Ross B. Girshick

Papers from Christopher M. Reardon's perspective

Ordered by how strongly the researchers Christopher M. Reardon 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Histograms of oriented gradients for human detection

Their papers

• Simplex-Based 3D Spatio-temporal Feature Description for Action Recognition (2014) — 42 citations
• Simultaneous Feature and Body-Part Learning for real-time robot awareness of human behaviors (2017) — 11 citations
• Minimum uncertainty latent variable models for robot recognition of sequential human activities (2017)