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cs.LG2025
Contrast All the Time: Learning Time Series Representation from Temporal Consistency
Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
Representation learning for time series using contrastive learning has emerged as a critical technique for improving the performance of downstream tasks. To advance this effective…
cs.LG2025
Learning Brenier Potentials with Convex Generative Adversarial Neural Networks
Claudia Drygala, Hanno Gottschalk, Thomas Kruse +2
Brenier proved that under certain conditions on a source and a target probability measure there exists a strictly convex function such that its gradient is a transport map from the…