Activity patterns of neural populations in natural and artificial neural networks constitute representations of data. The nature of these representations and how they are learned are key questions in neuroscience and deep learning. In his talk, Professor Pehlevan will describe his group’s efforts in building a theory of representations as feature maps leading to sample efficient function approximation. Kernel methods are at the heart of these developments. He will present applications of his group's theories to deep learning and neuronal data.
You can learn more on the IACS website: https://bit.ly/2YlAHxc.