Farbod Roosta-Khorasani

Optimization & Training · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 95 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 88 citations into it; #419 of 2,601 by trust here, #912 by citations
• Artificial Intelligence — 73 citations into it; #3,614 of 35,345 by trust here, #9,760 by citations
• Computational Mechanics — 53 citations into it; #553 of 10,001 by trust here, #1,536 by citations
• Computational Theory and Mathematics — 22 citations into it; #1,967 of 12,182 by trust here, #5,465 by citations
• Statistical Learning Theory — 18 citations into it
• Statistics and Probability — 16 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Management Science and Operations Research — 13 citations into it

Whom Farbod Roosta-Khorasani trusts overall

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

1. Jorge Nocedal
2. Michael W. Mahoney
3. L. Eon Bottou
4. Mark W. Schmidt
5. Petros Drineas
6. Christopher K. I. Williams
7. Richard H. Byrd
8. Yurii E. Nesterov
9. Shai Shalev-Shwartz
10. Olivier Chapelle

Who trusts Farbod Roosta-Khorasani overall

1. Shusen Wang
2. Frank E. Curtis
3. Raghu Bollapragada
4. Albert S. Berahas
5. Peng Xu
6. Xiang Li
7. Stefania Bellavia
8. S. Meng
9. Michael W. Mahoney
10. Andre Milzarek

Papers from Farbod Roosta-Khorasani's perspective

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

Their papers

• Sub-Sampled Newton Methods II: Local Convergence Rates (2016) — 86 citations
• Out-of-sample extension of graph adjacency spectral embedding (2018)
• GIANT: Globally Improved Approximate Newton Method for Distributed Optimization (2017) — 148 citations
• Sub-Sampled Newton Methods I: Globally Convergent Algorithms (2016) — 94 citations
• Sub-sampled Newton Methods with Non-uniform Sampling (2016) — 124 citations