Christina M. Funke

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 53 citations into it
• Artificial Intelligence — 37 citations into it
• Cognitive Neuroscience — 30 citations into it
• Statistics and Probability — 14 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Model Architectures — 7 citations into it
• Media Technology — 6 citations into it
• Generative Models — 5 citations into it

Whom Christina M. Funke trusts overall

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

1. Karen Simonyan
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Josh Tenenbaum
6. Andrew Zisserman
7. Alex Krizhevsky
8. George A. Miller
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Christina M. Funke overall

1. Jianwen Xie
2. Matthew Tesfaldet
3. Robert Geirhos
4. Song-Chun Zhu
5. Marcus A. Brubaker

Papers from Christina M. Funke's perspective

Ordered by how strongly the researchers Christina M. Funke 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Synthesising Dynamic Textures using Convolutional Neural Networks (2017) — 18 citations
• The Notorious Difficulty of Comparing Human and Machine Perception (2020) — 35 citations
• A parametric texture model based on deep convolutional features closely matches texture appearance for humans (2017) — 63 citations