Ryan Burmeister

Optimization & Training · 2016

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 11 citations into it
• Computational Mechanics — 7 citations into it

Whom Ryan Burmeister trusts overall

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

1. Tom Goldstein
2. Martin Fodslette Møller
3. James Martens
4. Ilya Sutskever
5. Geoffrey E. Hinton
6. Yann LeCun
7. Yoshua Bengio
8. M. A. Carreira-Perpiñán
9. Quoc V. Le
10. Pierre Baldi

Who trusts Ryan Burmeister overall

1. Zhouyuan Huo
2. Tal Ben-Nun
3. Dinghuai Zhang
4. Eugene Belilovsky

Papers from Ryan Burmeister's perspective

Ordered by how strongly the researchers Ryan Burmeister trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
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

• Training Neural Networks Without Gradients: A Scalable ADMM Approach (2016) — 297 citations