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Here we present a new method of estimating global variations in outdoor PM2.5 concentrations using satellite images combined with …
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their …
Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to …
Deep learning has mainly thrived by training on large-scale datasets. However, for continual learning in applications such as robotics, …
Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training …
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We …
In this work, we draw connections between planning and latent variable models1. Specifically, planning can be seen as introducing …
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and …
This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The …
Neural networks are prone to adversarial attacks. In general, such attacks deteriorate the quality of the input by either slightly …