98 citations · 158 across the 5 of their papers we have counts for
5 papers
'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…
Fast computation of loudness using a deep neural network
Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore
The present paper introduces a deep neural network (DNN) for predicting the instantaneous loudness of a sound from its time waveform. The DNN was trained using the output of a more…
Improving and Understanding Variational Continual Learning
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui +1
In the continual learning setting, tasks are encountered sequentially. The goal is to learn whilst i) avoiding catastrophic forgetting, ii) efficiently using model capacity, and ii…
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +2
Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their…
Target Fishing: A Single-Label or Multi-Label Problem?
Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner +2
According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant pre…