6 citations · 34 across the 18 of their papers we have counts for
4 papers · 1 filter
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…