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Maximizing the distance between end members allows for unprecedented clarity in unmixing highly mixed grain-size distribution data, outperforming traditional methods.
Unitary Matrix Product States, optimized with Riemannian geometry, offer a surprisingly efficient and stable approach to generative modeling.
