Peter Orbanz

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 23 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Computer Vision and Pattern Recognition — 26 citations into it
• Efficiency & Compression — 17 citations into it
• Scaling & Learning Theory — 16 citations into it
• Statistical Learning Theory — 11 citations into it

Whom Peter Orbanz trusts overall

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

1. David A. McAllester
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Alex Krizhevsky
6. Jorma Rissanen
7. Yoshua Bengio
8. Olivier Catoni

Papers from Peter Orbanz's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach (2018) — 236 citations
• Compressibility and Generalization in Large-Scale Deep Learning (2018) — 18 citations