Emmanuel Müller

Artificial Intelligence · Journal of machine learning research · 2009–2020

A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #12,493 of 35,345 by trust here, #18,586 by citations
• Statistical and Nonlinear Physics — 24 citations into it
• Graph Learning — 23 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it; #8,425 of 21,575 by trust here, #14,822 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Statistics and Probability — 12 citations into it; #4,533 of 12,932 by trust here, #6,080 by citations
• Molecular Biology — 7 citations into it
• Signal Processing — 5 citations into it

Whom Emmanuel Müller trusts overall

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

1. Ian T. Jolliffe
2. Rakesh Agrawal
3. Mark Newman
4. Hans-Peter Kriegel
5. Charu C. Aggarwal
6. Yizong Cheng
7. Arthur P. Dempster
8. Rayadurgam Srikant
9. Jiawei Han
10. Cecilia M. Procopiuc

Who trusts Emmanuel Müller overall

1. Charu C. Aggarwal
2. Tomás Pevný
3. Arthur Zimek
4. Hongzhi Wang
5. Leman Akoglu
6. Bryan Perozzi
7. Ninghao Liu
8. Ricardo José Gabrielli Barreto Campello
9. Ramakrishnan Kannan
10. Jinghui Chen

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. 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Dropout: a simple way to prevent neural networks from overfitting

Their papers

• HiCS: High Contrast Subspaces for Density-Based Outlier Ranking (2012) — 377 citations
• Graph Clustering with Graph Neural Networks (2020) — 410 citations
• Evaluating Clustering in Subspace Projections of High Dimensional Data (2009) — 311 citations