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.
AutoPrognosis is an automated predictive modelling pipeline designed for clinical prognosis, leveraging state-of-the-art advances in automated machine learning to optimise ML pipelines, incorporate model explainability tools, and enable deployment of …
TemporAI is a Machine Learning-centric time-series library tailored for medicine, focusing on time-series prediction, time-to-event analysis, and counterfactual inference for individualised treatment effects. How is it unique? TemporAI offers a …
HyperImpute is a comprehensive library for handling missing data in your ML pipelines, simplifying the selection process of a data imputation algorithm and offering a range of novel algorithms compatible …
Synthcity is an open-source synthetic data generation library that outperforms rivals (YData, Gretel, SDV, etc.) in terms of compatible use cases and data modalities, offering solutions for privacy, data scarcity, …
A comprehensive collection of Machine Learning interpretability methods, offering users a reference to select the best-suited method for their needs, with a focus on providing insights into ML model predictions …
Dr Alexander Gimson, member of the CCAIM faculty, talks about the impact of machine learning on transplantation medicine.
This post refers to a newly published paper involving members of the van der Schaar Lab inside CCAIM. Find out more here. In their paper, Changhee Lee, Alexander Light, Evgeny …
Our inaugural online event, on 22 January 2021, featured a stellar line-up of speakers drawn from the frontiers of machine learning, science, clinical research, pharmaceutical R&D and the NHS. The …
The International Conference on Learning Representations (ICLR) 2021 has accepted a paper representing landmark research from the van der Schaar Lab. The paper is entitled Clairvoyance: A Pipeline Toolkit for …
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 machine learning (ML) technology developed at the University of Cambridge promises powerful new prediction and analytical tools to support the clinical care of individuals with the life-limiting condition. The …
The coronavirus pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in Machine Learning are providing an opportunity to …
“Machine learning has the potential to catalyze a complete transformation in healthcare, but researchers in our field are still hamstrung by a lack of access to high-quality data, which is …
The van der Schaar lab, led by CCAIM’s Director, Professor Mihaela van der Schaar, has published a major new thought piece on automated machine learning (AutoML). AutoML will be the …
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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