6 papers
Demographically-Informed Heat-Mortality Risk Curves via Risk Graph Neural Networks
Alex O. Davies, Eunice Lo, Rui Zhu
Estimating heat-related mortality risk is a core task in environmental epidemiology, typically addressed with Distributed Lag Non-linear Models (DLNMs); interpretable exposure-resp…
AI co-mathematician: Accelerating mathematicians with agentic AI
Daniel Zheng, Ingrid von Glehn, Yori Zwols +15
We introduce the AI co-mathematician, a workbench for mathematicians to interactively leverage AI agents to pursue open-ended research. The AI co-mathematician is optimized to prov…
Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
Alex O. Davies, Telmo de Menezes e Silva Filho, Nirav Ajmeri
Tabular foundation models are pre-trained on one of three classes of corpus: curated datasets drawn from benchmark repositories, tables harvested at scale from the web, or syntheti…
Language Models Do Not Embed Numbers Continuously
Alex O. Davies, Roussel Nzoyem, Nirav Ajmeri +1
Recent research has extensively studied how large language models manipulate integers in specific arithmetic tasks, and on a more fundamental level, how they represent numeric valu…
A Metric for the Balance of Information in Graph Learning
Alex O. Davies, Nirav S. Ajmeri, Telmo de Menezes e Silva Filho
Graph learning on molecules makes use of information from both the molecular structure and the features attached to that structure. Much work has been conducted on biasing either t…
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph Models
Alex O. Davies, Riku W. Green, Nirav S. Ajmeri +1
The principal benefit of unsupervised representation learning is that a pre-trained model can be fine-tuned where data or labels are scarce. Existing approaches for graph represent…