21 citations · 44 across the 11 of their papers we have counts for
15 papers
From Pixels to Components: Eigenvector Masking for Visual Representation Learning
Alice Bizeul, Thomas Sutter, Alain Ryser +3
Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches o…
Interaction Asymmetry: A General Principle for Learning Composable Abstractions
Jack Brady, Julius von Kügelgen, Sébastien Lachapelle +3
Learning disentangled representations of concepts and re-composing them in unseen ways is crucial for generalizing to out-of-domain situations. However, the underlying properties o…
From Statistical to Causal Learning
Bernhard Schölkopf, Julius von Kügelgen
We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models rel…
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"
Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen +2
Model identifiability is a desirable property in the context of unsupervised representation learning. In absence thereof, different models may be observationally indistinguishable…
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
Zhijing Jin, Julius von Kügelgen, Jingwei Ni +4
The principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other.…
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Osama Makansi, Julius von Kügelgen, Francesco Locatello +4
Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involv…