Daniel Kaiser

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 1 paper; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it
• Cognitive Neuroscience — 22 citations into it
• Artificial Intelligence — 19 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Neuroscience — 6 citations into it
• Safety, Robustness & Interpretability — 6 citations into it
• Biophysics — 5 citations into it

Whom Daniel Kaiser trusts overall

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

1. Geoffrey E. Hinton
2. Daniel L. K. Yamins
3. Ha Hong
4. Yoshua Bengio
5. Jason Yosinski
6. Alex Krizhevsky
7. Matthias Kümmerer
8. Nikolaus Kriegeskorte
9. Alex Graves
10. Yann LeCun

Papers from Daniel Kaiser's perspective

Ordered by how strongly the researchers Daniel Kaiser trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Neural Networks as Scientific Models. (2019) — 406 citations