Matthias Humt

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it
• Probabilistic & Bayesian Methods — 50 citations into it
• Statistics and Probability — 12 citations into it

Whom Matthias Humt trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Yann LeCun
4. David John Cameron MacKay
5. Diederik P. Kingma
6. Yarin Gal
7. Alex Krizhevsky
8. Yoshua Bengio
9. Max Welling
10. Alex Graves

Papers from Matthias Humt's perspective

Ordered by how strongly the researchers Matthias Humt 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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A survey of uncertainty in deep neural networks (2021) — 1,852 citations