Michael Moeller

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 41 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Optimization & Training — 24 citations into it
• Model Architectures — 13 citations into it

Whom Michael Moeller trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Sergey Zagoruyko
6. Yoshua Bengio
7. Yann LeCun
8. X. Zhang
9. Robert E. Schapire
10. Christian Szegedy

Papers from Michael Moeller's perspective

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

Their papers

• Inverting Gradients - How easy is it to break privacy in federated learning? (2020) — 1,706 citations
• Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory (2019) — 37 citations