George Philipp

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 176 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #27,358 of 35,345 by trust here, #26,682 by citations
• Model Architectures — 41 citations into it
• Optimization & Training — 37 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Scaling & Learning Theory — 13 citations into it; #340 of 428 by trust here, #384 by citations
• Materials Chemistry — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom George Philipp trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Xavier Glorot
7. Jimmy Ba
8. X. Zhang
9. Yann LeCun
10. Ilya Sutskever

Who trusts George Philipp overall

1. Greg Yang
2. Wujie Wen
3. Yann Dauphin
4. Sam Schoenholz
5. Jeffrey Pennington
6. Yuxiong He

Papers from George Philipp's perspective

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

Their papers

• The Nonlinearity Coefficient - Predicting Generalization in Deep Neural Networks (2018) — 14 citations
• The Nonlinearity Coefficient - Predicting Overfitting in Deep Neural Networks (2018) — 11 citations
• Nonparametric Neural Networks (2017) — 21 citations