Boris Knyazev

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 404 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #27,777 of 35,345 by trust here, #24,417 by citations
• Computer Vision and Pattern Recognition — 91 citations into it
• Model Architectures — 25 citations into it
• Graph Learning — 21 citations into it
• Optimization & Training — 20 citations into it
• Materials Chemistry — 11 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Neural Architecture Search & AutoML — 10 citations into it

Whom Boris Knyazev trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Karen Simonyan
8. Jimmy Ba
9. Ross B. Girshick
10. Ilya Sutskever

Who trusts Boris Knyazev overall

1. Siqi Miao
2. Yingmin Wu
3. Christopher Morris
4. Xiang Wang
5. Miaoyuan Liu
6. Yin-wei Wei
7. Nils M. Kriege
8. Dongkwan Kim
9. Péter Mernyei

Papers from Boris Knyazev's perspective

Ordered by how strongly the researchers Boris Knyazev 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. 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation (2020) — 60 citations
• Parameter Prediction for Unseen Deep Architectures (2021) — 109 citations
• Understanding Attention and Generalization in Graph Neural Networks (2019) — 403 citations