Brian Bullins

Optimization & Training · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 60 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 98 citations into it
• Artificial Intelligence — 84 citations into it; #9,683 of 35,345 by trust here, #12,761 by citations
• Computational Mechanics — 38 citations into it; #2,272 of 10,001 by trust here, #3,275 by citations
• Management Science and Operations Research — 20 citations into it; #2,395 of 11,946 by trust here, #2,799 by citations
• Computer Vision and Pattern Recognition — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Statistical Learning Theory — 9 citations into it
• Numerical Analysis — 8 citations into it

Whom Brian Bullins trusts overall

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

1. Yurii E. Nesterov
2. James Martens
3. Elad Hazan
4. Shai Shalev-Shwartz
5. John C. Duchi
6. Tong Zhang
7. Nicolò Cesa-Bianchi
8. Geoffrey E. Hinton
9. Sanjeev Arora
10. Alex Krizhevsky

Who trusts Brian Bullins overall

1. Pang Wei Koh
2. Raghu Bollapragada
3. Albert S. Berahas
4. Mikhail Khodak
5. Percy Liang
6. Sidak Pal Singh
7. Liam Collins
8. Ryan P. Adams
9. Jorge Nocedal
10. Haishan Ye

Papers from Brian Bullins's perspective

Ordered by how strongly the researchers Brian Bullins 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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

• Second Order Stochastic Optimization in Linear Time (2016) — 104 citations
• The Case for Full-Matrix Adaptive Regularization (2018) — 21 citations
• Is Local SGD Better than Minibatch SGD? (2020) — 290 citations
• Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning (2019) — 21 citations
• Second-Order Stochastic Optimization for Machine Learning in Linear Time (2016) — 296 citations