A function approximation perspective on neural representations
Wednesday, December 2, 2020
05:00 UTC
Cengiz Pehlevan
Harvard University
Recording
Open recording
Hosted by van Vreeswijk TNS70 minutes
Abstract
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, I will describe my 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. I will present applications to deep learning and neuronal data.
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