11 citations · 41 across the 9 of their papers we have counts for
9 papers
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Hector Zenil, Jesper Tegnér, Felipe S. Abrahão +17
Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contributio…
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages
Michael P. J. Camilleri, Rasneer S. Bains, Christopher K. I. Williams
Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment,…
Structured Generative Models for Scene Understanding
Christopher K. I. Williams
This position paper argues for the use of \emph{structured generative models} (SGMs) for the understanding of static scenes. This requires the reconstruction of a 3D scene from an…
Inference and Learning for Generative Capsule Models
Alfredo Nazabal, Nikolaos Tsagkas, Christopher K. I. Williams
Capsule networks (see e.g. Hinton et al., 2018) aim to encode knowledge of and reason about the relationship between an object and its parts. In this paper we specify a generative…
Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences
Cian Eastwood, Li Nanbo, Christopher K. I. Williams
Given two object images, how can we explain their differences in terms of the underlying object properties? To address this question, we propose Align-Deform-Subtract (ADS) -- an i…
Persistent Animal Identification Leveraging Non-Visual Markers
Michael P. J. Camilleri, Li Zhang, Rasneer S. Bains +2
Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for b…
ptype-cat: Inferring the Type and Values of Categorical Variables
Taha Ceritli, Christopher K. I. Williams
Type inference is the task of identifying the type of values in a data column and has been studied extensively in the literature. Most existing type inference methods support data…
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration
Cian Eastwood, Ian Mason, Christopher K. I. Williams +1
Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain dat…
ptype: Probabilistic Type Inference
Taha Ceritli, Christopher K. I. Williams, James Geddes
Type inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found…