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Expressiveness preservation in speech-to-speech translation remains a significant hurdle, with systems scoring poorly on emotional and nonverbal fidelity despite achieving high translation accuracy.
Stop being limited by fixed tensor decomposition families: TenExp dynamically mixes and matches decompositions to better capture low-rank data structures.
Unlock the potential of continuous tensor representations with neural operators, achieving more faithful representations of complex real-world data and outperforming classic discrete and continuous tensor methods.
