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This work casts partially-labeled multi-task learning as marginalization over a shared affect latent: one variational bottleneck mediates all three task decoders, so a frame annotated for one task shapes the representation the others use, and the masked objective reappears as the reconstruction term of an evidence lowe...
A strength-parity rule is proposed: an added member lowers the ensemble error only when it is both decorrelated from the current members and a near-peer of them in individual accuracy.
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