Learning Representations without Compositional Assumptions
We introduce LEGATO, a hierarchical graph auto-encoder that learns a smaller, latent graph to dynamically aggregate information from multiple views.
We introduce LEGATO, a hierarchical graph auto-encoder that learns a smaller, latent graph to dynamically aggregate information from multiple views.
SMC builds a new ensemble weighting existing models according to their likelihood to accurately represent a novel case. Based on our results, SMC is more robust and gives more accurate predictions than existing models.
A paper recently accepted for publication by PLOS Digital Health explores machine-learning based prognostic scores and their validity when applied to demographically different patient cohorts: the ‘external validity’. The publication …
Members of the CCAIM research team will publish ten papers at this year’s International Conference on Machine Learning (ICML), the leading international academic conference in machine learning. Taking place completely …
The question of which patient should receive a life-saving donor organ is one of the thorniest in medicine. Donor organs are a scarce resource, each one as unique as the …
Four members of the 10-strong CCAIM faculty have clocked up a total of 18 papers accepted at NeurIPS 2020 – one of the most prestigious international conferences for AI and …
CCAIM Co-Director Andres Floto, pictured above outside Royal Papworth Hospital in Cambridge, is leading a study on home monitoring for people with cystic fibrosis. The research is central to Project …
World-leading AI technology developed at the University of Cambridge by the directors of the Cambridge Centre for AI in Medicine and their colleagues offers a glimpse of the future of …
World-leading expertise in healthcare-focused machine learning combined with the world’s largest, high-quality cancer data collection service could lead to a quantum leap in personalised medicine. (This article, featuring the work …
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