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Found 2 papers, 2 papers with code
PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series
In this paper, we present PAITS (Pretraining and Augmentation for Irregularly-sampled Time Series), a framework for identifying suitable pretraining strategies for sparse and irregularly sampled time series datasets.
Moment Matching Deep Contrastive Latent Variable Models
In the contrastive analysis (CA) setting, machine learning practitioners are specifically interested in discovering patterns that are enriched in a target dataset as compared to a background dataset generated from sources of variation irrelevant to the task at hand.
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