47 citations · 171 across the 19 of their papers we have counts for
6 papers · 1 filter
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Julian Suk, Pim de Haan, Phillip Lippe +2
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption i…
Differentiable Mathematical Programming for Object-Centric Representation Learning
Adeel Pervez, Phillip Lippe, Efstratios Gavves
We propose topology-aware feature partitioning into disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose…
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has sh…
Complex-Valued Autoencoders for Object Discovery
Sindy Löwe, Phillip Lippe, Maja Rudolph +1
Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings. Currently, most works o…
Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe +1
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under…
CITRIS: Causal Identifiability from Temporal Intervened Sequences
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Understanding the latent causal factors of a dynamical system from visual observations is considered a crucial step towards agents reasoning in complex environments. In this paper,…