Stefan Bauer

Artificial Intelligence · with Schölkopf · 2016–2024

A researcher in the Science corpus: 27 papers; trusts 821 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 783 citations into it; #6,390 of 35,345 by trust here, #3,338 by citations
Computer Vision and Pattern Recognition — 451 citations into it; #7,173 of 21,575 by trust here, #4,137 by citations
Representation & Self-Supervised Learning — 386 citations into it; #945 of 3,145 by trust here, #563 by citations
Causal Inference & Discovery — 279 citations into it; #208 of 364 by trust here, #77 by citations
Statistics and Probability — 185 citations into it; #9,039 of 12,932 by trust here, #7,165 by citations
Reinforcement Learning — 169 citations into it
Generative Models — 167 citations into it
Probabilistic & Bayesian Methods — 164 citations into it; #1,029 of 1,525 by trust here, #601 by citations

Whom Stefan Bauer trusts overall

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

Who trusts Stefan Bauer overall

Papers from Stefan Bauer's perspective

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

Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
ImageNet classification with deep convolutional neural networks
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Attention is All you Need (Neural Information Processing Systems)
Long Short-Term Memory
Gradient-based learning applied to document recognition
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
GENERATIVE ADVERSARIAL NETS

Their papers

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (2018) — 1,814 citations
On the Fairness of Disentangled Representations (2019) — 250 citations
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning (2020) — 149 citations
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018) — 186 citations
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset (2019) — 170 citations
On the Transfer of Disentangled Representations in Realistic Settings (2020) — 89 citations
Learning Neural Causal Models from Unknown Interventions (2019) — 192 citations
Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning (2021) — 77 citations
Causal Models for Dynamical Systems (2020) — 58 citations
Toward Causal Representation Learning (2021) — 1,515 citations
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures (2021) — 61 citations
Disentangling Factors of Variation Using Few Labels (2019) — 129 citations
Bayesian Structure Learning with Generative Flow Networks (2022) — 202 citations
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs (2018) — 52 citations
Towards Causal Representation Learning (2021) — 361 citations
TriFinger: An Open-Source Robot for Learning Dexterity (2020) — 82 citations
The Arrow of Time in Multivariate Time Series (2016) — 33 citations
Causal machine learning for predicting treatment outcomes (2024) — 334 citations
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data (2020) — 19 citations
Real-time prediction of COVID-19 related mortality using electronic health records (2020) — 55 citations