Emmanuel LE BORGNE

Disentangling Time Series Representations via Contrastive Independence-of-Support on l-Variational Inference

Learning disentangled representations for time series is a promising path to facilitate reliable generalization to in- and out-of distribution (OOD), offering benefits like feature derivation and improved interpretability and fairness, thereby enhancing downstream tasks. We focus on disentangled rep…