Christopher Olah

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 120 researchers and is trusted by 1,014 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #1,379 of 21,575 by trust here, #1,427 by citations
• Generative Models — 54 citations into it; #236 of 1,956 by trust here, #228 by citations
• Artificial Intelligence — 51 citations into it; #31,174 of 35,345 by trust here, #17,110 by citations
• Representation & Self-Supervised Learning — 15 citations into it; #2,826 of 3,145 by trust here, #2,805 by citations
• Model Architectures — 11 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Natural Language Processing — 6 citations into it; #5,046 of 5,688 by trust here, #4,879 by citations
• Signal Processing — 5 citations into it

Whom Christopher Olah trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Alex Graves
8. Tomas Mikolov
9. Quoc V. Le
10. Danilo Jimenez Rezende

Who trusts Christopher Olah overall

1. Garrett Wilson
2. Ting-Li Chen
3. Han Zhang
4. Yang Song
5. Mihaela Rosca
6. Sergey I. Nikolenko
7. Zheng Xu
8. Simon Niklaus
9. Augustus Odena
10. Yujun Shen

Papers from Christopher Olah's perspective

Ordered by how strongly the researchers Christopher Olah 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Conditional Image Synthesis with Auxiliary Classifier GANs (2016) — 3,545 citations
• Deconvolution and Checkerboard Artifacts (2016) — 1,756 citations
• Is Generator Conditioning Causally Related to GAN Performance? (2018) — 124 citations
• Document Embedding with Paragraph Vectors (2015) — 382 citations
• Changing Model Behavior at Test-Time Using Reinforcement Learning (2017) — 57 citations