Marzieh Fadaee

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 4 papers; trusts 112 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #6,561 of 35,345 by trust here, #9,760 by citations
• Natural Language Processing — 55 citations into it; #1,437 of 5,688 by trust here, #2,263 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #5,090 of 21,575 by trust here, #7,340 by citations
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 12 citations into it

Whom Marzieh Fadaee trusts overall

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

1. Rico Sennrich
2. Ilya Sutskever
3. Kyunghyun Cho
4. Yoshua Bengio
5. Dzmitry Bahdanau
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Barry Haddow
9. Tomas Mikolov
10. Oriol Vinyals

Who trusts Marzieh Fadaee overall

1. Hongyu Guo
2. Sergey I. Nikolenko
3. Mi-young Kang
4. Hiroshi Inoue
5. Kang Min Yoo
6. Jason Wei
7. Omid Kashefi
8. Viktar Atliha
9. Vukosi Marivate
10. Yong Cheng

Papers from Marzieh Fadaee's perspective

Ordered by how strongly the researchers Marzieh Fadaee 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Data Augmentation for Low-Resource Neural Machine Translation (2017) — 517 citations
• Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model (2024) — 385 citations
• Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2024) — 178 citations
• Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (2018) — 113 citations