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Equivariance and Symmetries in CNNs

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This talk will discuss applications of group theory to deep learning, specifically to the design of CNNs. We’ll focus on a few key papers from Cohen and Welling, each of which proposes new kinds of convolutional layers that enjoy equivariance to more symmetries than the standard planar-CNN we’ve all come to know and love. We hope to motivate the use of these new convolutions, build an intuition for how they work, give some practical considerations for their use, and finally dive into the theory behind them.

This talk is part of the Machine Learning Reading Group @ CUED series.

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