Jonathan Lorraine

Artificial Intelligence · 2019

A researcher in the Science corpus: 3 papers; trusts 206 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #12,023 of 35,345 by trust here, #19,018 by citations
• Optimization & Training — 48 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it; #8,016 of 21,575 by trust here, #12,689 by citations
• Model Architectures — 18 citations into it
• Management Science and Operations Research — 12 citations into it
• Generative Models — 10 citations into it
• Computational Mechanics — 8 citations into it
• Mathematical Optimization — 8 citations into it

Whom Jonathan Lorraine trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Yann LeCun
6. Ilya Sutskever
7. Sepp Hochreiter
8. J. Urgen Schmidhuber
9. Kaiming He
10. Radford M. Neal

Who trusts Jonathan Lorraine overall

1. Xuanyi Dong
2. Miao Zhang
3. Timothy Nguyen
4. Zalán Borsos
5. Ondrej Bohdal
6. Mingxing Tan
7. Anamika Dhillon
8. Samy Wu Fung
9. Mathieu Blondel
10. Quanming Yao

Papers from Jonathan Lorraine's perspective

Ordered by how strongly the researchers Jonathan Lorraine 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Optimizing Millions of Hyperparameters by Implicit Differentiation (2019) — 488 citations
• Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions (2019) — 194 citations
• Understanding Neural Architecture Search Techniques (2019) — 46 citations