Robert M. Freund

Optimization & Training · 2004–2016

A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 18 citations into it; #2,555 of 2,601 by trust here, #2,313 by citations
• Computational Mechanics — 11 citations into it; #9,685 of 10,001 by trust here, #7,884 by citations
• Artificial Intelligence — 10 citations into it; #11,222 of 35,345 by trust here, #20,377 by citations
• Numerical Analysis — 10 citations into it; #761 of 3,586 by trust here, #765 by citations
• Mathematical Optimization — 7 citations into it; #222 of 422 by trust here, #292 by citations
• Computational Theory and Mathematics — 5 citations into it; #3,150 of 12,182 by trust here, #4,850 by citations

Whom Robert M. Freund trusts overall

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

1. Yurii E. Nesterov
2. Martin Jaggi
3. Guanghui Lan
4. Kenneth L. Clarkson
5. Simon Lacoste-Julien
6. Constantin Zalinescu
7. Boris Polyak
8. Philip Wolfe
9. Mark W. Schmidt
10. Alexandre d'Aspremont

Who trusts Robert M. Freund overall

1. Javier F. Peña
2. Yi Xu
3. Ashia C. Wilson
4. Motonobu Kanagawa
5. Aurélien Bellet
6. Yu-Xiang Wang
7. Tianbao Yang
8. Guanghui Lan
9. B. Recht
10. Simon Lacoste-Julien

Papers from Robert M. Freund's perspective

Ordered by how strongly the researchers Robert M. Freund 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. An algorithm for quadratic programming
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
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

• New analysis and results for the Frank–Wolfe method (2013) — 160 citations
• Relatively Smooth Convex Optimization by First-Order Methods, and Applications (2016) — 425 citations
• Review of A mathematical view of interior-point methods in convex optimization, by James Renegar, SIAM, Philadelphia, PA (2004) — 110 citations