Emmanuel Müller

Artificial Intelligence · with Binder · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Emmanuel Müller trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Pascal Vincent
6. Yann LeCun
7. Sergey Ioffe
8. Kaiming He
9. Danilo Jimenez Rezende
10. Bernhard Schölkopf

Papers from Emmanuel Müller's perspective

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

Their papers

• Deep Semi-Supervised Anomaly Detection (2019) — 704 citations
• Deep One-Class Classification (2018) — 2,692 citations