Meta-learning goes one level higher than standard machine learning which has evolved only very recently. Meta-learning allows learning across tasks so that new tasks need not be learnt from scratch and can leverage acquired knowledge from previous tasks. This work combines the inference ability of Bayesian methods with the representational power of deep learning in the domain of
meta-learning Currently working to improve the efficiency of the state of the art methods in learning across tasks.
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