An unsupervised model integrates group homomorphism to segment objects and map relative motions like approaching or receding into a one-dimensional additive latent space from unlabeled dynamic images.
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OSCP optimizes conformal prediction score offsets via MILP minimization of an empirical region-size proxy for time-series, with validity guarantees and reduced computation versus prior methods.
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Unsupervised Learning of Inter-Object Relationships via Group Homomorphism
An unsupervised model integrates group homomorphism to segment objects and map relative motions like approaching or receding into a one-dimensional additive latent space from unlabeled dynamic images.
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Efficient Quantification of Time-Series Prediction Error: Optimal Selection Conformal Prediction
OSCP optimizes conformal prediction score offsets via MILP minimization of an empirical region-size proxy for time-series, with validity guarantees and reduced computation versus prior methods.