Afshin Rahimi

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 119 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it
• Natural Language Processing — 49 citations into it; #2,156 of 5,688 by trust here, #3,574 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Large Language Models — 10 citations into it
• Model Architectures — 8 citations into it
• Signal Processing — 8 citations into it

Whom Afshin Rahimi trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Ilya Sutskever
5. Diederik P. Kingma
6. Richard Socher
7. Yann LeCun
8. Oriol Vinyals
9. Kaiming He
10. Jacob Devlin

Who trusts Afshin Rahimi overall

1. Jonas Pfeiffer
2. Ivan Vulic
3. Sebastian Ruder
4. Zhuang Li
5. Subhabrata Mukherjee
6. Jinlan Fu
7. Zewen Chi
8. Anne Lauscher
9. Phillip Rust
10. Felix Wu

Papers from Afshin Rahimi's perspective

Ordered by how strongly the researchers Afshin Rahimi 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP (2020) — 448 citations
• Does an LSTM forget more than a CNN? An empirical study of catastrophic forgetting in NLP (2019) — 30 citations
• Massively Multilingual Transfer for NER (2019) — 237 citations
• Semi-supervised User Geolocation via Graph Convolutional Networks (2018) — 172 citations