Frank E. Curtis

Optimization & Training · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 182 researchers and is trusted by 91 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 102 citations into it; #353 of 2,601 by trust here, #401 by citations
• Artificial Intelligence — 88 citations into it; #2,943 of 35,345 by trust here, #4,063 by citations
• Computational Mechanics — 53 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Mathematical Optimization — 14 citations into it
• Numerical Analysis — 14 citations into it
• Signal Processing — 13 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Frank E. Curtis trusts overall

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

1. James Martens
2. Jorge Nocedal
3. L. Eon Bottou
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Yoshua Bengio
7. Yurii E. Nesterov
8. Yann LeCun
9. Alex Krizhevsky
10. Barak A. Pearlmutter

Who trusts Frank E. Curtis overall

1. Jianyu Wang
2. Lam M. Nguyen
3. Sebastian U. Stich
4. Zhouyuan Huo
5. Yujing Chen
6. Lumin Liu
7. Nicolas Loizou
8. Mohammad Emtiyaz Khan
9. Farzin Haddadpour
10. Gauri Joshi

Papers from Frank E. Curtis's perspective

Ordered by how strongly the researchers Frank E. Curtis 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. 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

• Optimization Methods for Large-Scale Machine Learning (2016) — 3,928 citations
• Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning (2017) — 47 citations