Mehdi Fatemi

Artificial Intelligence · 2012–2023

A researcher in the Science corpus: 5 papers; trusts 62 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #20,972 of 35,345 by trust here, #9,870 by citations
• Reinforcement Learning — 74 citations into it
• Natural Language Processing — 27 citations into it; #3,892 of 5,688 by trust here, #3,190 by citations
• Safety, Robustness & Interpretability — 20 citations into it
• Cognitive Neuroscience — 14 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it; #14,376 of 21,575 by trust here, #10,334 by citations
• Management Science and Operations Research — 10 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Mehdi Fatemi trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Volodymyr Mnih
4. Ronald J. Williams
5. Hado van Hasselt
6. Geoffrey E. Hinton
7. Dimitri P. Bertsekas
8. David Silver
9. Alex Graves
10. Yoshua Bengio

Who trusts Mehdi Fatemi overall

1. Zachary Chase Lipton
2. Jianfeng Gao
3. Prithviraj Ammanabrolu
4. Brielen Madureira
5. Pararth Shah
6. Pei-hao Su
7. Yuxi Li
8. Lu Chen
9. Lihong Li
10. Mengyao Cao

Papers from Mehdi Fatemi's perspective

Ordered by how strongly the researchers Mehdi Fatemi 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Policy Networks with Two-Stage Training for Dialogue Systems (2016) — 110 citations
• Hybrid Reward Architecture for Reinforcement Learning (2017) — 292 citations
• Systematic Rectification of Language Models via Dead-end Analysis (2023) — 22 citations
• Dead-ends and Secure Exploration in Reinforcement Learning (2019) — 22 citations
• Cognitive Control (2012) — 329 citations