David Münch

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 269 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it
• Safety, Robustness & Interpretability — 52 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Natural Language Processing — 10 citations into it
• Biophysics — 6 citations into it
• Model Architectures — 5 citations into it

Whom David Münch trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Kaiming He
8. Diederik P. Kingma
9. J. Urgen Schmidhuber
10. Sepp Hochreiter

Papers from David Münch's perspective

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

Their papers

• Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey (2019) — 459 citations