Optical random projections for large scale machine learning

Light propagation in disordered media can be seen as a linear operation on fields : a multiplication by a random matrix, between a set of input modes (for instance pixels of an SLM) and output modes (for instance pixels of a camera). This operation, akin to a single-layer of a neural network, can be leveraged for a wealth of signal processing and machine learning tasks. I will present some of our works, ranging from classification to time-series prediction, and importantly present our recent approaches to go beyond linear random projections, in order to provide deeper equivalent neural networks and better machine-learning performances across a variety of tasks.