Sergey Ivanov

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 67 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #13,764 of 35,345 by trust here, #23,731 by citations
• Graph Learning — 23 citations into it
• Reinforcement Learning — 17 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Natural Language Processing — 9 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Computer Networks and Communications — 5 citations into it; #3,074 of 9,886 by trust here, #5,733 by citations

Whom Sergey Ivanov trusts overall

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

1. Tomas Mikolov
2. David Haussler
3. Yoshua Bengio
4. Quoc V. Le
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Michael U Gutmann
8. Ronald J. Williams
9. Diederik P. Kingma
10. Alex Krizhevsky

Who trusts Sergey Ivanov overall

1. Zhang Xinyi
2. Haggai Maron
3. Hongteng Xu
4. Yanbang Wang
5. Simon Shaolei Du
6. Lihu Chen
7. Heli Ben-Hamu
8. Dixin Luo
9. Yen-Yu Chang
10. Kangcheng Hou

Papers from Sergey Ivanov's perspective

Ordered by how strongly the researchers Sergey Ivanov 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Anonymous Walk Embeddings (2018) — 192 citations
• Reinforcement Learning for Combinatorial Optimization: A Survey (2020) — 800 citations