Rahmani, BabakTegin, UgurYildirim, MustafaOguz, IlkerLoterie, DamienKakkava, EiriniBorhani, NavidPsaltis, DemetriMoser, Christophe2021-11-062021-11-062021-11-062021-01-0110.1364/OFC.2021.Th5B.1https://infoscience.epfl.ch/handle/20.500.14299/182748WOS:000698978300233We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement methods. We also show the reverse, meaning that multimode fibers can be used as a computational tool that harnesses spatiotemporal nonlinear effects to perform end to end learning tasks with unprecedented speed and low power consumption.Engineering, Electrical & ElectronicOpticsTelecommunicationsEngineeringOpticsTelecommunicationsholographynetworkstext::conference output::conference proceedings::conference paper