AALOK
A single free-space photonic extreme learning machine that learns images, audio, and tabular data in one fixed optical pipeline — light does the feature map, only the readout is trained.
One apparatus. Every modality.
Data becomes a phase pattern on a spatial light modulator, free-space propagation does the high-dimensional mixing, an iris keeps only the informative first diffracted order, and a camera reads out 4096 intensity features. Nothing in the optics is trained.
coherent CW
8 µm · φ = x + W
first order only
4096 features
the only weights
Build a run, get the control code.
Choose a modality and embedding. The mask on the right is computed live from the paper's exact equations — the same fixed phase pattern the SLM would display — and the script updates to drive the SLM, capture the frame, bin to 4096 features, and train the readout.
Four modalities, one fixed reservoir.
Test performance with the Fourier embedding after independent ridge-λ sweeps. To our knowledge this is the first free-space PELM spanning image, audio-derived, and tabular tasks in a single physical pipeline, and the first with spectrogram-based spoken-digit classification.
| Modality | Task | Best λ | Test |
|---|---|---|---|
| MNIST | 10-class handwritten digits | 7.28×10⁻⁴ | 96.56% |
| FSDD | Spoken digits · log-Mel spectrogram | 1.89×10⁻³ | 95.67% |
| Mushroom | Binary tabular · edible vs poisonous | 1.0×10⁻⁵ | 100.00% |
| Abalone | Tabular regression · shell-ring age | 4.89×10⁻³ | 0.0704 NRMSE |