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Our experiments on eight datasets from the image and time-series domains show that our method leads to higher results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples.
We introduce online probabilistic label trees (OPLTs), an algorithm that trains a label tree classifier in a fully online manner, without any prior knowledge about the number of training instances, their features and labels.
We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs.
To this end, we propose a novel meta-learning framework, called MetaConcept, which learns to abstract concepts via the concept graph.
(2) A generalization error bound invariant of network size was derived by using a data-dependent complexity measure (CMD).
We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task.