Alert button
Picture for Thang D. Bui

Thang D. Bui

Alert button

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning

Feb 28, 2022
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen, Stratis Markou, Adrian Weller, Siddharth Swaroop, Richard E. Turner

Figure 1 for Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Figure 2 for Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Figure 3 for Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Figure 4 for Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Viaarxiv icon

Variational Auto-Regressive Gaussian Processes for Continual Learning

Jun 09, 2020
Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui

Figure 1 for Variational Auto-Regressive Gaussian Processes for Continual Learning
Figure 2 for Variational Auto-Regressive Gaussian Processes for Continual Learning
Figure 3 for Variational Auto-Regressive Gaussian Processes for Continual Learning
Figure 4 for Variational Auto-Regressive Gaussian Processes for Continual Learning
Viaarxiv icon

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

Feb 10, 2020
Theofanis Karaletsos, Thang D. Bui

Figure 1 for Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Figure 2 for Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Figure 3 for Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Figure 4 for Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights
Viaarxiv icon

Improving and Understanding Variational Continual Learning

May 06, 2019
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner

Figure 1 for Improving and Understanding Variational Continual Learning
Figure 2 for Improving and Understanding Variational Continual Learning
Figure 3 for Improving and Understanding Variational Continual Learning
Figure 4 for Improving and Understanding Variational Continual Learning
Viaarxiv icon

Partitioned Variational Inference: A unified framework encompassing federated and continual learning

Nov 27, 2018
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner

Figure 1 for Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Figure 2 for Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Figure 3 for Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Figure 4 for Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Viaarxiv icon

Variational Continual Learning

May 20, 2018
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner

Figure 1 for Variational Continual Learning
Figure 2 for Variational Continual Learning
Figure 3 for Variational Continual Learning
Figure 4 for Variational Continual Learning
Viaarxiv icon

Streaming Sparse Gaussian Process Approximations

Nov 12, 2017
Thang D. Bui, Cuong V. Nguyen, Richard E. Turner

Figure 1 for Streaming Sparse Gaussian Process Approximations
Figure 2 for Streaming Sparse Gaussian Process Approximations
Figure 3 for Streaming Sparse Gaussian Process Approximations
Figure 4 for Streaming Sparse Gaussian Process Approximations
Viaarxiv icon

A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

Oct 05, 2017
Thang D. Bui, Josiah Yan, Richard E. Turner

Figure 1 for A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Figure 2 for A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Figure 3 for A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Figure 4 for A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
Viaarxiv icon

Neural Graph Machines: Learning Neural Networks Using Graphs

Mar 14, 2017
Thang D. Bui, Sujith Ravi, Vivek Ramavajjala

Figure 1 for Neural Graph Machines: Learning Neural Networks Using Graphs
Figure 2 for Neural Graph Machines: Learning Neural Networks Using Graphs
Figure 3 for Neural Graph Machines: Learning Neural Networks Using Graphs
Figure 4 for Neural Graph Machines: Learning Neural Networks Using Graphs
Viaarxiv icon

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Feb 12, 2016
Thang D. Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

Figure 1 for Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Figure 2 for Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Figure 3 for Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Figure 4 for Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Viaarxiv icon