Sohil Shah

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 227 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 56 citations into it; #11,968 of 21,575 by trust here, #19,310 by citations
• Artificial Intelligence — 35 citations into it; #19,092 of 35,345 by trust here, #30,347 by citations
• Generative Models — 32 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Molecular Biology — 7 citations into it
• Signal Processing — 7 citations into it

Whom Sohil Shah trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Anil K. Jain
7. Michael I. Jordan
8. Kaiming He
9. Jianbo Shi
10. Brian Kulis

Who trusts Sohil Shah overall

1. Jonathan T. Barron
2. Jiangwei Xie
3. Xu Yang
4. Xi Peng
5. Shipeng Yan
6. Cheng Deng
7. Divya Saxena
8. Tengyuan Liang
9. Zhenyu Huang
10. Erico Tjoa

Papers from Sohil Shah's perspective

Ordered by how strongly the researchers Sohil Shah 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Robust continuous clustering (2017) — 187 citations
• Understanding the (un)interpretability of natural image distributions using generative models (2019) — 12 citations
• Stabilizing Adversarial Nets With Prediction Methods (2017) — 92 citations