6 papers
Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty
Chayan Banerjee, Ethan Goan
Deploying reinforcement learning in safety critical domains, from autonomous vehicles to medical decision support, is constrained by failures arising when systems encounter unfamil…
Bayesian Neural Networks: An Introduction and Survey
Ethan Goan, Clinton Fookes
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of comp…
In Depth We Trust: Reliable Monocular Depth Supervision for Gaussian Splatting
Wenhui Xiao, Ethan Goan, Rodrigo Santa Cruz +4
Using accurate depth priors in 3D Gaussian Splatting helps mitigate artifacts caused by sparse training data and textureless surfaces. However, acquiring accurate depth maps requir…
Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Ethan Goan, Dimitri Perrin, Kerrie Mengersen +1
Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior.…
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems
Ethan Goan, Clinton Fookes
Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addres…
Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters
Akila Pemasiri, Ethan Goan, Glen Lichtwark +3
This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the pr…