Vanilla transformers on the emg2qwerty dataset improve cross-user typing accuracy up to 109M parameters, and simple logit distillation recovers most of the gain in a 2.2M-parameter student.
Electromyography data for non-invasive naturally-controlled robotic hand prostheses
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Scaling and Distilling Transformer Models for sEMG
Vanilla transformers on the emg2qwerty dataset improve cross-user typing accuracy up to 109M parameters, and simple logit distillation recovers most of the gain in a 2.2M-parameter student.