Andrey Zhmoginov

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 935 researchers and is trusted by 1,676 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 230 citations into it; #998 of 21,575 by trust here, #861 by citations
• Artificial Intelligence — 202 citations into it; #1,398 of 35,345 by trust here, #1,223 by citations
• Model Architectures — 92 citations into it; #1,677 of 2,278 by trust here, #1,749 by citations
• Efficiency & Compression — 42 citations into it; #48 of 1,264 by trust here, #28 by citations
• Optimization & Training — 42 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Generative Models — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Andrey Zhmoginov trusts overall

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

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

Who trusts Andrey Zhmoginov overall

1. Jiahui Yu
2. Ji Lin
3. Mingxing Tan
4. Quoc V. Le
5. Han Cai
6. Chuang Gan
7. Song Han
8. Anand Gopalakrishnan
9. Sachin Mehta
10. Xuanyi Dong

Papers from Andrey Zhmoginov's perspective

Ordered by how strongly the researchers Andrey Zhmoginov 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. 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. Going deeper with convolutions

Their papers

• MobileNetV2: Inverted Residuals and Linear Bottlenecks (2018) — 25,197 citations
• Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation (2018) — 1,045 citations
• Convolutional Neural Networks (2017) — 141 citations
• K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning (2018) — 76 citations
• Fine-tuning Image Transformers using Learnable Memory (2022) — 57 citations
• Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution (2019) — 33 citations
• Information-Bottleneck Approach to Salient Region Discovery (2019) — 20 citations
• Transformers learn in-context by gradient descent (2022) — 821 citations
• Large-Scale Generative Data-Free Distillation (2020) — 48 citations