Julius von Kügelgen

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 240 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #17,443 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 89 citations into it
• Representation & Self-Supervised Learning — 83 citations into it
• Causal Inference & Discovery — 60 citations into it; #266 of 364 by trust here, #224 by citations
• Probabilistic & Bayesian Methods — 40 citations into it
• Generative Models — 35 citations into it
• Safety Research — 33 citations into it
• Signal Processing — 27 citations into it

Whom Julius von Kügelgen trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Bernhard Schölkopf
5. Kaiming He
6. Aapo Hyvärinen
7. Alex Krizhevsky
8. Michael I. Jordan
9. Yann LeCun
10. Judea Pearl

Who trusts Julius von Kügelgen overall

1. Bernhard Schölkopf
2. Alexander Mey
3. Marco Loog
4. Axel Sauer
5. Amir-Hossein Karimi
6. Francesco Locatello
7. Sébastien Lachapelle
8. Stefan Bauer
9. Andreas Geiger
10. Nan Rosemary Ke

Papers from Julius von Kügelgen's perspective

Ordered by how strongly the researchers Julius von Kügelgen 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Towards causal generative scene models via competition of experts (2020) — 54 citations
• Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects (2020) — 39 citations
• Semi-supervised learning, causality, and the conditional cluster assumption (2019) — 30 citations
• Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020) — 210 citations
• Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks (2019) — 22 citations
• Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features (2018) — 15 citations
• Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style (2021) — 395 citations
• On the Fairness of Causal Algorithmic Recourse (2020) — 105 citations