6 citations · 11 across the 5 of their papers we have counts for
11 papers
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…
Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks
Julia Kreutzer, Stefan Riezler, Carolin Lawrence
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction log…
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…
Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders
Bhushan Kotnis, Carolin Lawrence, Mathias Niepert
Representation learning for knowledge graphs (KGs) has focused on the problem of answering simple link prediction queries. In this work we address the more ambitious challenge of p…
Attending to Future Tokens For Bidirectional Sequence Generation
Carolin Lawrence, Bhushan Kotnis, Mathias Niepert
Neural sequence generation is typically performed token-by-token and left-to-right. Whenever a token is generated only previously produced tokens are taken into consideration. In c…