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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 …
We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by …
We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, …
Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we …
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model …