Markus Kängsepp

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Computer Vision and Pattern Recognition — 5 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Markus Kängsepp trusts overall

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

1. Kaiming He
2. Gao Huang
3. Yann LeCun
4. Jaka Demsar
5. G. Zhang
6. Bianca Zadrozny
7. Yoshua Bengio
8. Geoffrey E. Hinton
9. Sergey Zagoruyko
10. Rob J Hyndman

Papers from Markus Kängsepp's perspective

Ordered by how strongly the researchers Markus Kängsepp 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. Gradient-based learning applied to document recognition
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Densely Connected Convolutional Networks
7. ImageNet: A large-scale hierarchical image database
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration (2019) — 547 citations
• Correlated daily time series and forecasting in the M4 competition (2020) — 8 citations