Mostafa Rohaninejad

Artificial Intelligence · 2017

A researcher in the Science corpus: 3 papers; trusts 95 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it; #7,840 of 35,345 by trust here, #6,956 by citations
• Computer Vision and Pattern Recognition — 45 citations into it; #8,282 of 21,575 by trust here, #8,296 by citations
• Generative Models — 29 citations into it; #817 of 1,956 by trust here, #932 by citations
• Reinforcement Learning — 21 citations into it
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #719 of 3,145 by trust here, #586 by citations
• Optimization & Training — 13 citations into it; #650 of 2,601 by trust here, #414 by citations
• Probabilistic & Bayesian Methods — 13 citations into it

Whom Mostafa Rohaninejad trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Alex Graves
6. Aäron van den Oord
7. Karen Simonyan
8. Alex Krizhevsky
9. Sepp Hochreiter
10. Oriol Vinyals

Who trusts Mostafa Rohaninejad overall

1. Chelsea Finn
2. Sergey Levine
3. Jonathan Ho
4. Tianhe Yu
5. Pieter Abbeel
6. Abhishek Gupta
7. Qianru Sun
8. David W. Romero
9. Luisa M. Zintgraf
10. Trevor Darrell

Papers from Mostafa Rohaninejad's perspective

Ordered by how strongly the researchers Mostafa Rohaninejad 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• PixelSNAIL: An Improved Autoregressive Generative Model (2017) — 310 citations
• Meta-Learning with Temporal Convolutions (2017) — 204 citations
• A Simple Neural Attentive Meta-Learner (2017) — 1,400 citations