Runa Eschenhagen

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 64 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 103 citations into it; #25,081 of 35,345 by trust here, #11,624 by citations
• Probabilistic & Bayesian Methods — 47 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Optimization & Training — 40 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Reinforcement Learning — 7 citations into it
• Computational Mechanics — 5 citations into it

Whom Runa Eschenhagen trusts overall

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

1. Geoffrey E. Hinton
2. Radford M. Neal
3. Diederik P. Kingma
4. David John Cameron MacKay
5. Alex Graves
6. Alex Krizhevsky
7. Yann LeCun
8. Yoshua Bengio
9. L. Eon Bottou
10. Ilya Sutskever

Who trusts Runa Eschenhagen overall

1. Fei Ye
2. Samuel Kessler
3. Adrian G. Bors

Papers from Runa Eschenhagen's perspective

Ordered by how strongly the researchers Runa Eschenhagen 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Practical Deep Learning with Bayesian Principles (2019) — 280 citations
• Continual Deep Learning by Functional Regularisation of Memorable Past (2020) — 174 citations
• Laplace Redux - Effortless Bayesian Deep Learning (2021) — 467 citations