Mikhail S. Burtsev

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 130 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #24,982 of 35,345 by trust here, #8,283 by citations
• Model Architectures — 45 citations into it; #1,153 of 2,278 by trust here, #1,882 by citations
• Natural Language Processing — 37 citations into it; #5,337 of 5,688 by trust here, #2,011 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #17,782 of 21,575 by trust here, #18,266 by citations
• Reinforcement Learning — 21 citations into it
• Large Language Models — 12 citations into it
• Electrical and Electronic Engineering — 10 citations into it; #5,914 of 16,539 by trust here, #14,379 by citations
• Efficiency & Compression — 7 citations into it

Whom Mikhail S. Burtsev trusts overall

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

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

Who trusts Mikhail S. Burtsev overall

1. Amirkeivan Mohtashami
2. Timothée Darcet
3. Martin Jaggi
4. Maxime Oquab

Papers from Mikhail S. Burtsev's perspective

Ordered by how strongly the researchers Mikhail S. Burtsev 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• The Second Conversational Intelligence Challenge (ConvAI2) (2019) — 403 citations
• Memory Transformer (2020) — 99 citations
• Continual and Multi-task Reinforcement Learning With Shared Episodic Memory (2019) — 5 citations
• Recurrent Memory Transformer (2022) — 205 citations