Richard E. Turner
5 papers in the PaperMetrix corpus
Papers by this author
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Discriminative k-shot learning using probabilistic models
2017 · arXiv (Cornell University)
This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new …
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Combining Pseudo-Point and State Space Approximationsfor Sum-Separable Gaussian Processes
2021 · Uncertainty in Artificial Intelligence
Spatio-temporal Gaussian processes (GPs) are important probabilistic tools for inference and learning in climate science, epidemiology, or any time-driven general GP modelling problem. The current gold-standard methods for scaling GPs to large data sets are …
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Practical Conditional Neural Processes Via Tractable Dependent Predictions
2022 · arXiv (Cornell University)
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-learning models which leverage the flexibility of deep learning to produce well-calibrated predictions and naturally handle off-the-grid and missing data. CNPs scale to large datasets and …
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The Multivariate Generalised von Mises distribution: Inference and applications
2016 · arXiv (Cornell University)
Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending …
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In-Context In-Context Learning with Transformer Neural Processes
2024 · arXiv (Cornell University)
Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each dataset in a meta-dataset is sampled. There are many …