Anders Boesen Lindbo Larsen

Computer Vision and Pattern Recognition · 2015

A researcher in the Science corpus: 2 papers; trusts 76 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #18,635 of 21,575 by trust here, #14,151 by citations
• Artificial Intelligence — 20 citations into it; #30,355 of 35,345 by trust here, #21,316 by citations
• Generative Models — 13 citations into it; #261 of 1,956 by trust here, #267 by citations

Whom Anders Boesen Lindbo Larsen trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Ilya Sutskever
7. Danilo Jimenez Rezende
8. Individualized Treat
9. Tomas Mikolov
10. Max Welling

Who trusts Anders Boesen Lindbo Larsen overall

1. Fei Ye
2. Ngoc-Trung Tran
3. Patrick Esser
4. Ming-Yu Liu
5. Sergey I. Nikolenko
6. Aditya Grover
7. Jun-Yan Zhu
8. Yoshua Bengio
9. Chunyuan Li
10. Sergey Levine

Papers from Anders Boesen Lindbo Larsen's perspective

Ordered by how strongly the researchers Anders Boesen Lindbo Larsen 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
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

• Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation (2015) — 160 citations
• Autoencoding beyond pixels using a learned similarity metric (2015) — 2,248 citations