2 citations · 4 across the 3 of their papers we have counts for
3 papers
stat.ML2022★ 2 cited
Falsehoods that ML researchers believe about OOD detection
Andi Zhang, Damon Wischik
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD. But…
cs.LG2022★ 2 cited
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
Raghul Parthipan, Damon J. Wischik
How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises co…
cs.DC2022
Disentangling Domain and Content
Dan Andrei Iliescu, Aliaksei Mikhailiuk, Damon Wischik +1
Many real-world datasets can be divided into groups according to certain salient features (e.g. grouping images by subject, grouping text by font, etc.). Often, machine learning ta…