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Towards guarantees for parameter isolation in continual learning
Deep learning has proved to be a successful paradigm for solving many challenges in machine learning.
On the interventional consistency of autoencoders
Autoencoders have played a crucial role in the field of representation learning since its inception, proving to be a flexible learning scheme able to accommodate various notions of optimality of the representation.
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