Alexander Kuhnle

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 265 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #17,640 of 35,345 by trust here, #25,040 by citations
• Reinforcement Learning — 38 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it; #12,116 of 21,575 by trust here, #17,757 by citations
• Natural Language Processing — 24 citations into it
• Model Architectures — 9 citations into it
• Multimodal Learning — 8 citations into it; #646 of 1,211 by trust here, #1,119 by citations
• Optimization & Training — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it; #1,762 of 3,147 by trust here, #2,853 by citations

Whom Alexander Kuhnle trusts overall

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

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

Who trusts Alexander Kuhnle overall

1. Jesse Mu
2. Noah D. Goodman
3. Alec Radford
4. Percy Liang
5. Jacob Andreas
6. Amita Kamath
7. Jong Wook Kim

Papers from Alexander Kuhnle's perspective

Ordered by how strongly the researchers Alexander Kuhnle 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. Going deeper with convolutions

Their papers

• ShapeWorld - A new test methodology for multimodal language understanding (2017) — 76 citations
• Resources for building applications with Dependency Minimal Recursion Semantics (2016) — 31 citations
• A review on Deep Reinforcement Learning for Fluid Mechanics (2019) — 330 citations