Shahram Khadivi

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 85 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #31,525 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 66 citations into it; #5,187 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #19,417 of 21,575 by trust here, #14,478 by citations
• Reinforcement Learning — 19 citations into it
• Management Science and Operations Research — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Model Architectures — 7 citations into it

Whom Shahram Khadivi trusts overall

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

1. Rico Sennrich
2. Yoshua Bengio
3. Dzmitry Bahdanau
4. Diederik P. Kingma
5. Ilya Sutskever
6. Geoffrey E. Hinton
7. Kyunghyun Cho
8. Barry Haddow
9. Matthew G. Snover
10. Artem Sokolov

Who trusts Shahram Khadivi overall

1. Julia Kreutzer
2. Yang Gao

Papers from Shahram Khadivi's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Can Neural Machine Translation be Improved with User Feedback? (2018) — 101 citations
• Generalizing Back-Translation in Neural Machine Translation (2019) — 54 citations
• Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages (2019) — 94 citations
• Learning from Chunk-based Feedback in Neural Machine Translation (2018) — 19 citations