Simon Kamronn

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #17,076 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #10,804 of 21,575 by trust here, #10,677 by citations
• Generative Models — 8 citations into it; #1,003 of 1,956 by trust here, #1,200 by citations
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Simon Kamronn trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Sepp Hochreiter
4. Iroon Polytechniou
5. Danilo Jimenez Rezende
6. Yoshua Bengio
7. Sam T. Roweis
8. Nitish Srivastava
9. J. Urgen Schmidhuber
10. Max Welling

Who trusts Simon Kamronn overall

1. Francesco Locatello
2. Bryan Lim
3. Yingzhen Li
4. Vincent Le Guen
5. Syama Sundar Rangapuram
6. Kashif Rasul
7. Raban Iten

Papers from Simon Kamronn's perspective

Ordered by how strongly the researchers Simon Kamronn 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning (2017) — 320 citations