Jordan Dotzel

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 95 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 32 citations into it
• Artificial Intelligence — 23 citations into it
• Model Architectures — 13 citations into it

Whom Jordan Dotzel trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Ilya Sutskever
6. Yoshua Bengio
7. Ross B. Girshick
8. Christian Szegedy
9. Andrew Zisserman
10. Yann LeCun

Who trusts Jordan Dotzel overall

1. Young Geun Kim
2. Itay Hubara

Papers from Jordan Dotzel's perspective

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

Their papers

• Building Efficient Deep Neural Networks With Unitary Group Convolutions (2018) — 29 citations
• Improving Neural Network Quantization without Retraining using Outlier Channel Splitting (2019) — 351 citations