DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
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FlowTime introduces continuous generative regression using a one-step VAE and normalizing flows for personalized priors to predict watch time while addressing mean-collapse, quantization, and latency issues in prior paradigms.
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DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression
DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
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FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors
FlowTime introduces continuous generative regression using a one-step VAE and normalizing flows for personalized priors to predict watch time while addressing mean-collapse, quantization, and latency issues in prior paradigms.