Rouhollah Rahmatizadeh

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 38 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #20,305 of 35,345 by trust here, #15,542 by citations
• Control and Systems Engineering — 16 citations into it; #4,204 of 8,257 by trust here, #2,078 by citations
• Reinforcement Learning — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #12,717 of 21,575 by trust here, #10,500 by citations
• Generative Models — 7 citations into it
• Biomedical Engineering — 6 citations into it
• Robotics & Control — 6 citations into it; #298 of 609 by trust here, #345 by citations
• Model Architectures — 5 citations into it

Whom Rouhollah Rahmatizadeh trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Graves
4. Radford M. Neal
5. Yoshua Bengio
6. Ilya Sutskever
7. Jimmy Ba
8. Alex Krizhevsky
9. Sergey Levine
10. Hugo Larochelle

Who trusts Rouhollah Rahmatizadeh overall

1. Corey Lynch
2. Peter R. Florence
3. Pierre Sermanet
4. T. Paine
5. Qingyang Wu
6. Albert Zhan
7. Takayuki Osa
8. Lucas Manuelli
9. Sergio Gomez Colmenarejo
10. Pieter Abbeel

Papers from Rouhollah Rahmatizadeh's perspective

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

Their papers

• Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration (2017) — 295 citations