Aaron Sarna

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 544 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 149 citations into it; #6,619 of 21,575 by trust here, #2,668 by citations
• Artificial Intelligence — 56 citations into it; #33,833 of 35,345 by trust here, #5,600 by citations
• Computational Mechanics — 47 citations into it; #3,898 of 10,001 by trust here, #1,190 by citations
• Representation & Self-Supervised Learning — 36 citations into it; #2,984 of 3,145 by trust here, #755 by citations
• Computer Graphics and Computer-Aided Design — 32 citations into it
• Generative Models — 32 citations into it; #1,499 of 1,956 by trust here, #1,054 by citations
• 3D Vision & Pose — 26 citations into it; #411 of 689 by trust here, #287 by citations
• Optimization & Training — 21 citations into it

Whom Aaron Sarna trusts overall

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

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

Who trusts Aaron Sarna overall

1. Michael Niemeyer
2. Songyou Peng
3. Chieh Hubert Lin
4. Mahmoud Assran
5. Yen-Chi Cheng

Papers from Aaron Sarna's perspective

Ordered by how strongly the researchers Aaron Sarna 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Supervised Contrastive Learning (2020) — 6,562 citations
• Local Deep Implicit Functions for 3D Shape (2019) — 457 citations
• Learning Shape Templates With Structured Implicit Functions (2019) — 432 citations
• Boundless: Generative Adversarial Networks for Image Extension (2019) — 131 citations
• Unsupervised Training for 3D Morphable Model Regression (2018) — 326 citations