VIS Lab
VIS Lab
People
Publications
Contact
Light
Dark
Automatic
Phillip Lippe
Latest
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Rotating Features for Object Discovery
BISCUIT: Causal Representation Learning from Binary Interactions
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Differentiable Mathematical Programming for Object-Centric Representation Learning
Scalable Subset Sampling with Neural Conditional Poisson Networks
Complex-Valued Autoencoders for Object Discovery
Weakly supervised causal representation learning
CITRIS - Causal Identifiability from Temporal Intervened Sequences
Efficient Neural Causal Discovery without Acyclicity Constraints
Meta-learning for fast cross-lingual adaptation in dependency parsing
The Hateful Memes Challenge: Competition Report
Categorical Normalizing Flows via Continuous Transformations
Cite
×