Alexander Mey

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #25,699 of 35,345 by trust here, #28,481 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Causal Inference & Discovery — 18 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Statistical Learning Theory — 8 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Information Theory — 7 citations into it

Whom Alexander Mey trusts overall

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

1. David A. McAllester
2. Thorsten Joachims
3. Yuhai Wu
4. Bernhard Schölkopf
5. Alexandre B. Tsybakov
6. Dominik Janzing
7. Shai Shalev-Shwartz
8. Shai Ben-David
9. Jan Peters
10. Avrim Blum

Who trusts Alexander Mey overall

1. Marco Loog
2. Julius von Kügelgen
3. Bernhard Schölkopf

Papers from Alexander Mey's perspective

Ordered by how strongly the researchers Alexander Mey 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Semi-supervised learning, causality, and the conditional cluster assumption (2019) — 30 citations
• Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features (2018) — 15 citations
• Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results (2019) — 20 citations