12 citations · 24 across the 3 of their papers we have counts for
8 papers
Learning medical triage from clinicians using Deep Q-Learning
Albert Buchard, Baptiste Bouvier, Giulia Prando +10
Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately…
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Robert Walecki, Kostis Gourgoulias, Adam Baker +7
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference…
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
Yura Perov, Logan Graham, Kostis Gourgoulias +4
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programmin…
Universal Marginalizer for Amortised Inference and Embedding of Generative Models
Robert Walecki, Albert Buchard, Kostis Gourgoulias +6
Probabilistic graphical models are powerful tools which allow us to formalise our knowledge about the world and reason about its inherent uncertainty. There exist a considerable nu…
Inference Over Programs That Make Predictions
Yura Perov
This abstract extends on the previous work (arXiv:1407.2646, arXiv:1606.00075) on program induction using probabilistic programming. It describes possible further steps to extend t…
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
Salman Razzaki, Adam Baker, Yura Perov +10
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence…