Jordan Burgess

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 104 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 21 citations into it
• Artificial Intelligence — 20 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 7 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Jordan Burgess trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ruslan Salakhutdinov
4. Ilya Sutskever
5. Yoshua Bengio
6. Sergey Ioffe
7. Li Fei-Fei
8. Yann LeCun
9. Oriol Vinyals
10. Diederik P. Kingma

Who trusts Jordan Burgess overall

1. Matthias Bauer
2. Mateo Rojas-Carulla

Papers from Jordan Burgess's perspective

Ordered by how strongly the researchers Jordan Burgess 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• One-Shot Learning in Discriminative Neural Networks (2017) — 11 citations