109 citations · 260 across the 21 of their papers we have counts for
5 papers · 1 filter
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li +4
The ability for policies to generalize to new environments is key to the broad application of RL agents. A promising approach to prevent an agent's policy from overfitting to a lim…
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
Victor Prokhorov, Ehsan Shareghi, Yingzhen Li +2
Variational Autoencoders (VAEs) are known to suffer from learning uninformative latent representation of the input due to issues such as approximated posterior collapse, or entangl…
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Andrew Y. K. Foong, David R. Burt, Yingzhen Li +1
While Bayesian neural networks (BNNs) hold the promise of being flexible, well-calibrated statistical models, inference often requires approximations whose consequences are poorly…
'In-Between' Uncertainty in Bayesian Neural Networks
Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato +1
We describe a limitation in the expressiveness of the predictive uncertainty estimate given by mean-field variational inference (MFVI), a popular approximate inference method for B…
Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Hiske Overweg, Anna-Lena Popkes, Ari Ercole +4
Clinical decision making is challenging because of pathological complexity, as well as large amounts of heterogeneous data generated as part of routine clinical care. In recent yea…