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Found 6 papers, 1 papers with code
Learning medical triage from clinicians using Deep Q-Learning
We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases.
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty.
A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis
We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy.
A Universal Marginalizer for Amortized Inference in Generative Models
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances.
Resting state brain networks from EEG: Hidden Markov states vs. classical microstates
Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes.
