Dylan J. Foster

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 23 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #3,982 of 35,345 by trust here, #5,059 by citations
• Computational Mechanics — 36 citations into it
• Optimization & Training — 26 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Statistical Learning Theory — 12 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #3,838 of 21,575 by trust here, #4,105 by citations
• Scaling & Learning Theory — 5 citations into it; #65 of 428 by trust here, #107 by citations

Whom Dylan J. Foster trusts overall

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

1. Tong Zhang
2. Zeyuan Allen-Zhu
3. Peter L. Bartlett
4. Blake E. Woodworth
5. Alex Krizhevsky
6. Yurii E. Nesterov
7. Nati Srebro
8. Geoffrey E. Hinton
9. Saeed Ghadimi
10. Shai Shalev-Shwartz

Who trusts Dylan J. Foster overall

1. Behnam Neyshabur
2. Samet Oymak
3. Colin Wei
4. Alberto Bietti
5. Yiding Jiang
6. Srinadh Bhojanapalli
7. Philip M. Long
8. Tengyu Ma
9. Hanie Sedghi
10. Charles H. Martin

Papers from Dylan J. Foster's perspective

Ordered by how strongly the researchers Dylan J. Foster 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

• Spectrally-normalized margin bounds for neural networks (2017) — 1,473 citations
• The Complexity of Making the Gradient Small in Stochastic Convex Optimization (2019) — 60 citations
• Lower bounds for non-convex stochastic optimization (2019) — 476 citations