Nikita Kitaev

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 315 researchers and is trusted by 224 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #3,115 of 35,345 by trust here, #7,858 by citations
• Natural Language Processing — 89 citations into it; #882 of 5,688 by trust here, #2,220 by citations
• Computer Vision and Pattern Recognition — 71 citations into it; #2,955 of 21,575 by trust here, #2,605 by citations
• Model Architectures — 36 citations into it; #167 of 2,278 by trust here, #317 by citations
• Evaluation & Benchmarks — 20 citations into it
• Large Language Models — 20 citations into it
• Signal Processing — 11 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Nikita Kitaev trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Jimmy Ba
6. Ashish Vaswani
7. Alex Krizhevsky
8. Jacob Devlin
9. Noam Shazeer
10. Tomas Mikolov

Who trusts Nikita Kitaev overall

1. Tri Dao
2. Haixu Wu
3. Yi Tay
4. Junru Zhou
5. Yoon Kim
6. Daniel Fried
7. Irwan Bello
8. Beidi Chen
9. J. Ainslie
10. Sebastian Hofstätter

Papers from Nikita Kitaev's perspective

Ordered by how strongly the researchers Nikita Kitaev 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Reformer: The Efficient Transformer (2020) — 3,006 citations
• Constituency Parsing with a Self-Attentive Encoder (2018) — 596 citations
• KERMIT: Generative Insertion-Based Modeling for Sequences (2019) — 67 citations
• Multilingual Constituency Parsing with Self-Attention and Pre-Training (2018) — 287 citations
• Cross-Domain Generalization of Neural Constituency Parsers (2019) — 40 citations
• SMYRF: Efficient Attention using Asymmetric Clustering (2020) — 52 citations