Valerii Likhosherstov

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 34 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #25,757 of 35,345 by trust here, #4,888 by citations
• Model Architectures — 60 citations into it; #1,684 of 2,278 by trust here, #506 by citations
• Computer Vision and Pattern Recognition — 58 citations into it; #15,848 of 21,575 by trust here, #3,875 by citations
• Natural Language Processing — 42 citations into it
• Efficiency & Compression — 24 citations into it
• Molecular Biology — 20 citations into it
• Computational Biology & Bioinformatics — 15 citations into it
• Representation & Self-Supervised Learning — 15 citations into it

Whom Valerii Likhosherstov trusts overall

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

1. Krzysztof Marcin Choromanski
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Sepp Hochreiter
6. Diederik P. Kingma
7. Ashish Vaswani
8. Alex Krizhevsky
9. Jacob Devlin
10. J. Urgen Schmidhuber

Who trusts Valerii Likhosherstov overall

1. Tri Dao
2. Yi Tay
3. Feyza Duman Keles
4. Beidi Chen
5. Mostafa Dehghani
6. Krzysztof Marcin Choromanski
7. Daniel Y. Fu
8. Tianyang Lin
9. Pruthuvi Maheshakya Wijewardena
10. Irwan Bello

Papers from Valerii Likhosherstov's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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

• Rethinking Attention with Performers (2020) — 2,365 citations
• Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers (2020) — 96 citations
• Sub-Linear Memory: How to Make Performers SLiM (2020) — 25 citations
• Hybrid Random Features (2021) — 27 citations