194 citations · 194 across the 4 of their papers we have counts for
4 papers
Stratify: Unifying Multi-Step Forecasting Strategies
Riku Green, Grant Stevens, Zahraa Abdallah +1
A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this proces…
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…
Size Matters: Large Graph Generation with HiGGs
Alex O. Davies, Nirav S. Ajmeri, Telmo M. Silva Filho
Large graphs are present in a variety of domains, including social networks, civil infrastructure, and the physical sciences to name a few. Graph generation is similarly widespread…
Classifier Calibration: A survey on how to assess and improve predicted class probabilities
Telmo Silva Filho, Hao Song, Miquel Perello-Nieto +3
This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the…