[2 May, 13:00] Let's Talk ML

Petr Nevyhoštěný - Deep Clustering with Convolutional Autoencoders (slides)

Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. This paper proposes a clustering algorithm that uses convolutional autoencoders to learn embedded features, and then incorporates clustering oriented loss on embedded features to jointly perform feature refinement and cluster assignment.

Václav Ostrožlík - Learning to learn by gradient descent by gradient descent (slides)

We've seen many significant improvements when replacing hand-designed features with learned ones before. However, optimization algorithms are still designed by hand. In this work, authors describe that the optimization algorithm can be seen as learning problem itself allowing to get better performing, specialized optimizer.

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