Matthias Hüser

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Optimization & Training — 8 citations into it
• Signal Processing — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Medical & Health AI — 7 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Matthias Hüser trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Teuvo Kohonen
4. Yann LeCun
5. Geoffrey E. Hinton
6. Aäron van den Oord
7. Jimmy Ba
8. L. Eon Bottou
9. Oriol Vinyals
10. Alex Krizhevsky

Who trusts Matthias Hüser overall

1. Francesco Locatello

Papers from Matthias Hüser's perspective

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

Their papers

• SOM-VAE: Interpretable Discrete Representation Learning on Time Series (2019) — 155 citations
• Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients (2018) — 792 citations
• Early prediction of circulatory failure in the intensive care unit using machine learning (2020) — 430 citations
• Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series (2018) — 16 citations