Ali Mousavi

Computational Mechanics · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 170 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 41 citations into it; #1,034 of 10,001 by trust here, #1,929 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #6,455 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 31 citations into it
• Reinforcement Learning — 22 citations into it
• Biomedical Engineering — 15 citations into it; #2,014 of 10,734 by trust here, #6,620 by citations
• Signal Processing — 14 citations into it; #991 of 7,572 by trust here, #2,114 by citations
• Management Science and Operations Research — 13 citations into it
• Computer Networks and Communications — 10 citations into it

Whom Ali Mousavi trusts overall

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

1. E.J. Candes
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Alex Krizhevsky
5. Yoshua Bengio
6. David E. Rumelhart
7. Richard Baraniuk
8. Diederik P. Kingma
9. Qiang Liu
10. David L. Donoho

Who trusts Ali Mousavi overall

1. Viraj Shah
2. Mark Borgerding
3. Jen-Hao Rick Chang
4. Jian Zhang
5. Yan Yang
6. Christopher A. Metzler
7. Chinmay Hegde
8. Zhijin Qin
9. Philip Schniter
10. Hamid Palangi

Papers from Ali Mousavi's perspective

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

• Learned D-AMP: Principled Neural Network based Compressive Image Recovery (2017) — 321 citations
• Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning (2020) — 33 citations
• A deep learning approach to structured signal recovery (2015) — 467 citations
• Learning to invert: Signal recovery via Deep Convolutional Networks (2017) — 296 citations