41 citations · 214 across the 22 of their papers we have counts for
15 papers · 1 filter
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners
Elre T. Oldewage, John Bronskill, Richard E. Turner
This paper examines the robustness of deployed few-shot meta-learning systems when they are fed an imperceptibly perturbed few-shot dataset. We attack amortized meta-learners, whic…
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien, Nina Deliu, Richard E. Turner +3
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A n…
Efficient Gaussian Neural Processes for Regression
Stratis Markou, James Requeima, Wessel Bruinsma +1
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attractive family of meta-learning models which produce well-calibrated predictions, enable fast inference at test t…
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Will Tebbutt, Arno Solin, Richard E. Turner
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. Howev…
Contextual HyperNetworks for Novel Feature Adaptation
Angus Lamb, Evgeny Saveliev, Yingzhen Li +7
While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challeng…
Convolutional conditional neural processes for local climate downscaling
Anna Vaughan, Will Tebbutt, J. Scott Hosking +1
A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently…