Maksims Volkovs

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 3 papers; trusts 41 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it; #11,299 of 35,345 by trust here, #24,417 by citations
• Information Systems — 16 citations into it; #1,977 of 8,771 by trust here, #5,501 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #7,650 of 21,575 by trust here, #15,955 by citations
• Natural Language Processing — 14 citations into it
• Model Architectures — 9 citations into it
• Large Language Models — 7 citations into it
• Management Science and Operations Research — 6 citations into it
• Recommender Systems — 5 citations into it

Whom Maksims Volkovs trusts overall

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

1. Geoffrey E. Hinton
2. C. Burges
3. Alex Krizhevsky
4. David M. Blei
5. Prem Gopalan
6. Yoshua Bengio
7. Ilya Sutskever
8. Kaiming He
9. Kalervo Järvelin
10. L. V. D. van der Maaten

Who trusts Maksims Volkovs overall

1. Tao Qin
2. Yasser Ganjisaffar
3. Jianzhu Ma
4. Yang Song
5. Tie-Yan Liu
6. Yuanfu Lu
7. Hang Li
8. Yin-wei Wei
9. R. Caruana
10. Alexander Gerhard Schwing

Papers from Maksims Volkovs's perspective

Ordered by how strongly the researchers Maksims Volkovs 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• BoltzRank: learning to maximize expected ranking gain (2009) — 116 citations
• Improving Transformer Optimization Through Better Initialization (2020) — 186 citations
• DropoutNet: Addressing Cold Start in Recommender Systems (2017) — 372 citations