30 citations · 38 across the 5 of their papers we have counts for
7 papers · 1 filter
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
Vu C. Dinh, Lam Si Tung Ho, Cuong V. Nguyen
We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of…
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…
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…
Variational Continual Learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui +1
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in…
Bayesian Pool-based Active Learning With Abstention Feedbacks
Cuong V. Nguyen, Lam Si Tung Ho, Huan Xu +2
We study pool-based active learning with abstention feedbacks, where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important p…