Andrew Stevens

Computer Vision and Pattern Recognition · 2015–2016

A researcher in the Science corpus: 3 papers; trusts 296 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #10,137 of 21,575 by trust here, #6,371 by citations
• Artificial Intelligence — 59 citations into it
• Model Architectures — 17 citations into it
• Optimization & Training — 16 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Statistics and Probability — 13 citations into it
• Natural Language Processing — 12 citations into it
• Generative Models — 11 citations into it

Whom Andrew Stevens trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Radford M. Neal
4. Diederik P. Kingma
5. Yann LeCun
6. Yoshua Bengio
7. Karen Simonyan
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. Kaiming He

Who trusts Andrew Stevens overall

1. Chunyuan Li
2. Changyou Chen
3. Yunchen Pu
4. Xiaopeng Li
5. Zhe Gan
6. Kumar Shridhar

Papers from Andrew Stevens's perspective

Ordered by how strongly the researchers Andrew Stevens 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016) — 851 citations
• Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification (2016) — 55 citations
• A Deep Generative Deconvolutional Image Model (2015) — 45 citations