Khashayar Khosravi

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 217 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it; #2,573 of 35,345 by trust here, #7,346 by citations
• Natural Language Processing — 16 citations into it; #854 of 5,688 by trust here, #1,692 by citations
• Management Science and Operations Research — 15 citations into it
• Economics and Econometrics — 11 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 8 citations into it; #463 of 2,601 by trust here, #685 by citations
• Reinforcement Learning — 7 citations into it

Whom Khashayar Khosravi trusts overall

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

1. Tomas Mikolov
2. Yoshua Bengio
3. Sepp Hochreiter
4. Geoffrey E. Hinton
5. J. Urgen Schmidhuber
6. Yarin Gal
7. Ilya Sutskever
8. Diederik P. Kingma
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Khashayar Khosravi overall

1. Ofir Press
2. Stephen Merity
3. Barret Zoph
4. Richard Socher
5. Alexei Baevski
6. Noah A. Smith
7. Shuai Tang
8. Sho Takase
9. Quoc V. Le
10. Nitish Shirish Keskar

Papers from Khashayar Khosravi's perspective

Ordered by how strongly the researchers Khashayar Khosravi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling (2016) — 406 citations
• NBER WORKING PAPER SERIES MATRIX COMPLETION METHODS FOR CAUSAL PANEL DATA MODELS (2018)
• Exploiting the Natural Exploration In Contextual Bandits (2017) — 30 citations