Matthew Staib

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it
• Optimization & Training — 15 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Matthew Staib trusts overall

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

1. Joel A. Tropp
2. Yurii E. Nesterov
3. Sashank J. Reddi
4. Sebastian Ruder
5. Noam Shazeer
6. Zeyuan Allen-Zhu
7. Diederik P. Kingma
8. Geoffrey E. Hinton
9. Jason D. Lee
10. Alex Krizhevsky

Who trusts Matthew Staib overall

1. Yuege Xie
2. Jingzhao Zhang
3. Xiaoxia Wu

Papers from Matthew Staib's perspective

Ordered by how strongly the researchers Matthew Staib 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Escaping Saddle Points with Adaptive Gradient Methods (2019) — 84 citations