DPP-selected representatives (plus RFF) yield a scalable MADD whose NN classifier matches full-MADD error rates at a fraction of the compute cost for large n and high d.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
CDLF applies conditional diffusion models to produce probabilistic life-cycle forecasts for new products by conditioning on static descriptors and reference trajectories from similar items.
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A scalable version of MADD for big-data classification
DPP-selected representatives (plus RFF) yield a scalable MADD whose NN classifier matches full-MADD error rates at a fraction of the compute cost for large n and high d.
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Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models
CDLF applies conditional diffusion models to produce probabilistic life-cycle forecasts for new products by conditioning on static descriptors and reference trajectories from similar items.