Hartmut Bauermeister

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Optimization & Training — 7 citations into it

Whom Hartmut Bauermeister trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Sergey Zagoruyko
5. H. Brendan McMahan
6. Kaiming He
7. Jimmy Ba
8. Dong C. Liu
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Hartmut Bauermeister overall

1. Yangsibo Huang
2. Nuria Rodr'iguez-Barroso

Papers from Hartmut Bauermeister's perspective

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