CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
Metro interstate traffic volume data set
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Neural networks are compressed by lumping neurons with approximately matching dynamics in a polynomial ODE encoding, yielding substantial size reduction with preserved accuracy on synthetic and regression tasks.
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Causal Time Series Generation via Diffusion Models
CaTSG is a unified diffusion model for causal time series generation that handles observational, interventional, and counterfactual tasks via backdoor adjustment and abduction-action-prediction.
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Neural Network Compression by Approximate Differential Equivalence
Neural networks are compressed by lumping neurons with approximately matching dynamics in a polynomial ODE encoding, yielding substantial size reduction with preserved accuracy on synthetic and regression tasks.