Benjamin Grimmer

Optimization & Training · 2017

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 9 citations into it
• Artificial Intelligence — 6 citations into it; #16,776 of 35,345 by trust here, #32,362 by citations
• Computational Mechanics — 6 citations into it; #4,469 of 10,001 by trust here, #8,947 by citations

Whom Benjamin Grimmer trusts overall

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

1. Yurii E. Nesterov
2. John C. Duchi
3. Elad Hazan
4. Alexander Rakhlin
5. Simon Lacoste-Julien
6. Yoram Singer
7. Satyen Kale
8. Mark W. Schmidt
9. Sébastien Bubeck
10. Ohad Shamir

Who trusts Benjamin Grimmer overall

1. Sebastian U. Stich
2. Ahmed Khaled
3. Peter Richtárik
4. Nan Wu
5. Othmane Sebbouh

Papers from Benjamin Grimmer's perspective

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

Their papers

• Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity (2017) — 57 citations