Vardan Papyan

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 6 papers; trusts 355 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #8,211 of 21,575 by trust here, #12,689 by citations
• Artificial Intelligence — 71 citations into it; #19,178 of 35,345 by trust here, #19,439 by citations
• Computational Mechanics — 37 citations into it; #5,542 of 10,001 by trust here, #3,652 by citations
• Optimization & Training — 31 citations into it
• Model Architectures — 19 citations into it
• Statistics and Probability — 18 citations into it
• Signal Processing — 16 citations into it; #4,213 of 7,572 by trust here, #3,784 by citations
• Media Technology — 15 citations into it

Whom Vardan Papyan trusts overall

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

1. Karen Simonyan
2. Sergey Ioffe
3. Kaiming He
4. Yann LeCun
5. Michael Elad
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Yoshua Bengio
9. Jia Deng
10. Levent Sagun

Who trusts Vardan Papyan overall

1. Behrooz Ghorbani
2. Guillaume Alain
3. Shankar Krishnan
4. Xiuyuan Cheng
5. Vishal Monga
6. Ron Levie
7. Alice Lucas
8. Ming Chen
9. Tianlong Chen
10. Qiang Qiu

Papers from Vardan Papyan's perspective

Ordered by how strongly the researchers Vardan Papyan 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Convolutional Neural Networks Analyzed via Convolutional Sparse Coding (2016) — 309 citations
• Prevalence of neural collapse during the terminal phase of deep learning training (2020) — 962 citations
• The Full Spectrum of Deep Net Hessians At Scale: Dynamics with Sample Size (2018) — 36 citations
• Neural Proximal Gradient Descent for Compressive Imaging (2018) — 174 citations
• LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? (2023) — 81 citations
• The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size. (2018) — 120 citations