137 citations · 550 across the 55 of their papers we have counts for
63 papers
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Sparse Function-space Representation of Neural Networks
Aidan Scannell, Riccardo Mereu, Paul Chang +3
Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that mitigates these issues by converting NNs from weig…
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Rui Li, ST John, Arno Solin
Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve hyperparameter learni…
Memory-Based Dual Gaussian Processes for Sequential Learning
Paul E. Chang, Prakhar Verma, S. T. John +2
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accum…
Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping
Manon Kok, Arno Solin, Thomas B. Schön
Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic…
Variational Gaussian Process Diffusion Processes
Prakhar Verma, Vincent Adam, Arno Solin
Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilis…