7 papers
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…
Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents
Jessica Woodgate, Paul Marshall, Nirav Ajmeri
Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subj…
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…
Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy
Seth Bullock, Nirav Ajmeri, Mike Batty +15
Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale…