Judith Bütepage

Artificial Intelligence · 2017

A researcher in the Science corpus: 2 papers; trusts 160 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #25,740 of 35,345 by trust here, #21,316 by citations
• Probabilistic & Bayesian Methods — 78 citations into it
• Computer Vision and Pattern Recognition — 57 citations into it; #17,939 of 21,575 by trust here, #15,955 by citations
• Generative Models — 30 citations into it
• Statistics and Probability — 26 citations into it
• Optimization & Training — 23 citations into it
• Management Science and Operations Research — 12 citations into it
• Statistical and Nonlinear Physics — 11 citations into it

Whom Judith Bütepage trusts overall

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

1. Diederik P. Kingma
2. Radford M. Neal
3. Geoffrey E. Hinton
4. David M. Blei
5. Yoshua Bengio
6. Michael I. Jordan
7. Alex Krizhevsky
8. Yann LeCun
9. Max Welling
10. L. Eon Bottou

Who trusts Judith Bütepage overall

1. Ye Yuan
2. Wei Mao
3. Zhen Zhang
4. Florian Schmidt
5. Emre Aksan
6. Kris M. Kitani
7. Miaomiao Liu
8. Wee Sun Lee
9. Peng Cao
10. Vincent Fortuin

Papers from Judith Bütepage's perspective

Ordered by how strongly the researchers Judith Bütepage 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Deep Representation Learning for Human Motion Prediction and Classification (2017) — 443 citations
• Advances in Variational Inference (2017) — 815 citations