Justin Lazarow

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 678 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 91 citations into it; #5,150 of 21,575 by trust here, #9,305 by citations
• Artificial Intelligence — 45 citations into it
• Generative Models — 22 citations into it; #783 of 1,956 by trust here, #1,502 by citations
• Probabilistic & Bayesian Methods — 17 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 11 citations into it
• Statistics and Probability — 7 citations into it
• Signal Processing — 5 citations into it

Whom Justin Lazarow trusts overall

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

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

Who trusts Justin Lazarow overall

1. Jianwen Xie
2. Ruiqi Gao
3. Erik Nijkamp
4. Song-Chun Zhu
5. Ying Nian Wu
6. Kwonjoon Lee
7. Zheng Ding
8. Zhuowen Tu
9. Liang-Chieh Chen
10. Yan Zhang

Papers from Justin Lazarow's perspective

Ordered by how strongly the researchers Justin Lazarow 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. 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. 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

• Introspective Classification with Convolutional Nets (2017) — 62 citations
• Learning Instance Occlusion for Panoptic Segmentation (2019) — 78 citations
• Introspective Neural Networks for Generative Modeling (2017) — 68 citations