S. Meng

Optimization & Training · with Lacoste-Julien · 2019

A researcher in the Science corpus: 1 paper; trusts 58 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 66 citations into it
• Artificial Intelligence — 42 citations into it
• Computational Mechanics — 35 citations into it
• Numerical Analysis — 12 citations into it
• Statistics and Probability — 12 citations into it
• Management Science and Operations Research — 8 citations into it
• Numerical Methods & Scientific Computing — 5 citations into it
• Scaling & Learning Theory — 5 citations into it

Whom S. Meng trusts overall

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

1. Shun-ichi Amari
2. James Martens
3. Jorge Nocedal
4. Mark W. Schmidt
5. Diederik P. Kingma
6. Yoshua Bengio
7. Francis R. Bach
8. Yurii E. Nesterov
9. Yann LeCun
10. Richard H. Byrd

Papers from S. Meng's perspective

Ordered by how strongly the researchers S. Meng 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation (2019) — 40 citations