30 citations · 54 across the 6 of their papers we have counts for
6 papers · 1 filter
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen +4
The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional alg…
Continual Deep Learning by Functional Regularisation of Memorable Past
Pingbo Pan, Siddharth Swaroop, Alexander Immer +3
Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this…
Differentially Private Federated Variational Inference
Mrinank Sharma, Michael Hutchinson, Siddharth Swaroop +2
In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, comp…
Practical Deep Learning with Bayesian Principles
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain +4
Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this p…
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…
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop +1
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. However, practitioners are faced with a fragmented literature that offers a…