52 citations · 158 across the 19 of their papers we have counts for
3 papers · 1 filter
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J. Rudner, Freddie Bickford Smith, Qixuan Feng +2
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved chall…
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Tim G. J. Rudner, Zonghao Chen, Yee Whye Teh +1
Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the…
Inter-domain Deep Gaussian Processes
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for m…