6 citations · 34 across the 18 of their papers we have counts for
6 papers · 1 filter
Uncertainty Propagation in Node Classification
Zhao Xu, Carolin Lawrence, Ammar Shaker +1
Quantifying predictive uncertainty of neural networks has recently attracted increasing attention. In this work, we focus on measuring uncertainty of graph neural networks (GNNs) f…
State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictions
Cheng Wang, Carolin Lawrence, Mathias Niepert
Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, they are often treated as black-box models and as such it is…
Multi-Source Survival Domain Adaptation
Ammar Shaker, Carolin Lawrence
Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the…
VEGN: Variant Effect Prediction with Graph Neural Networks
Jun Cheng, Carolin Lawrence, Mathias Niepert
Genetic mutations can cause disease by disrupting normal gene function. Identifying the disease-causing mutations from millions of genetic variants within an individual patient is…
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs
Cheng Wang, Carolin Lawrence, Mathias Niepert
Uncertainty quantification is crucial for building reliable and trustable machine learning systems. We propose to estimate uncertainty in recurrent neural networks (RNNs) via stoch…
Explaining Neural Matrix Factorization with Gradient Rollback
Carolin Lawrence, Timo Sztyler, Mathias Niepert
Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the infl…