Christian Henning

Artificial Intelligence · with Grewe · 2017–2021

A researcher in the Science corpus: 8 papers; trusts 503 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 274 citations into it
• Computer Vision and Pattern Recognition — 162 citations into it
• Optimization & Training — 68 citations into it
• Meta & Continual Learning — 67 citations into it; #169 of 1,041 by trust here, #226 by citations
• Probabilistic & Bayesian Methods — 61 citations into it
• Generative Models — 54 citations into it
• Model Architectures — 54 citations into it
• Representation & Self-Supervised Learning — 47 citations into it

Whom Christian Henning trusts overall

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

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

Who trusts Christian Henning overall

1. Gyuhak Kim
2. Zixuan Ke
3. Changnan Xiao
4. Yiduo Guo
5. Neale Ratzlaff
6. Bin Liu
7. Johannes von Oswald
8. James Smith
9. Benjamin Ehret
10. Tatsuya Konishi

Papers from Christian Henning's perspective

Ordered by how strongly the researchers Christian Henning 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Posterior Meta-Replay for Continual Learning (2021) — 76 citations
• Uncertainty-based out-of-distribution detection requires suitable function space priors (2021) — 11 citations
• Continual learning in recurrent neural networks (2020) — 52 citations
• Neural networks with late-phase weights (2020) — 40 citations
• Continual Learning in Recurrent Neural Networks with Hypernetworks (2020) — 10 citations
• Continual learning with hypernetworks (2019) — 428 citations
• Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks (2018)
• Estimating the information gap between textual and visual representations (2017) — 33 citations