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11/18/2022
Active Learning with Convolutional Gaussian Neural Processes for Environmental Sensor Placement
Deploying environmental measurement stations can be a costly and time-co...
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07/07/2022
Challenges and Pitfalls of Bayesian Unlearning
Machine unlearning refers to the task of removing a subset of training d...
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03/16/2022
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-lear...
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08/22/2021
Efficient Gaussian Neural Processes for Regression
Conditional Neural Processes (CNP; Garnelo et al., 2018) are an attracti...
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01/10/2021
The Gaussian Neural Process
Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of mode...
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07/02/2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probab...
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03/06/2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning
Modern meta-learning approaches for image classification rely on increas...
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10/29/2019
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a n...
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06/18/2019
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes
The goal of this paper is to design image classification systems that, a...
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02/20/2018
The Gaussian Process Autoregressive Regression Model (GPAR)
Multi-output regression models must exploit dependencies between outputs...
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06/06/2017