Lea Duncker

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Cognitive Neuroscience — 11 citations into it
• Electrical and Electronic Engineering — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Lea Duncker trusts overall

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

1. David Sussillo
2. Mikhail A. Lebedev
3. James Martens
4. Gido M. van de Ven
5. Friedemann Zenke
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Valerio Mante
9. Mehrdad Farajtabar
10. Guanxiong Zeng

Who trusts Lea Duncker overall

1. Ta-Chu Kao

Papers from Lea Duncker's perspective

Ordered by how strongly the researchers Lea Duncker 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Organizing recurrent network dynamics by task-computation to enable continual learning (2020) — 98 citations