%2C%20have%20been%20proposed.%20ELMs%20use%20a%20random%20hidden%20layer%20to%20increase%20the%20feature%20space%20dimensionality%2C%20requiring%20only%20the%20output%20layer%20to%20be%20trained%20through%20linear%20regression%2C%20thus%20reducing%20training%20complexity.%20Here%2C%20we%20experimentally%20demonstrate%20a%20programmable%20photonic%20extreme%20learning%20machine%20(PPELM)%20using%20a%20hexagonal%20waveguide%20mesh%2C%20and%20which%20enables%20to%20program%20directly%20on%20chip%20the%20input%20feature%20vector%20and%20the%20random%20hidden%20layer.%20Our%20system%20also%20permits%20to%20apply%20the%20nonlinearity%20directly%20on-chip%20by%20using%20the%20systems%20integrated%20photodetecting%20elements.%20Using%20the%20PPELM%20we%20solved%20successfully%20three%20different%20complex%20classification%20tasks.%20Additioanlly%2C%20we%20also%20propose%20and%20demonstrate%20two%20techniques%20to%20increase%20the%20accuracy%20of%20the%20models%20and%20reduce%20their%20variability%20using%20an%20evolutionary%20algorithm%20and%20a%20wavelength%20division%20multiplexing%20approach%2C%20obtaining%20excellent%20performance.%20Our%20results%20show%20that%20programmable%20photonic%20processors%20may%20become%20a%20feasible%20way%20to%20train%20competitive%20machine%20learning%20models%20on%20a%20versatile%20and%20compact%20platform.)
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