Unveiling the benefits of multitasking in disentangled representation formation
Affiliation:
1. Center for Computational Neuroscience, Flatiron Institute, NY, USA;2. Center for Neural Science, New York University, NY, USA
Abstract:
Johnston and Fusi recently investigated the emergence of disentangled representations when a neural network was trained to perform multiple simultaneous tasks. Such experiments explore the benefits of flexible representations and add to a growing field of research investigating the representational geometry of artificial and biological neural networks.
