Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
The growing demand for personalized and private on-device applications highlights the importance of source-free unsupervised domain adaptation (SFDA) methods, especially for time-series data, where individual differences produce large domain shifts. As sensor-embedded mobile devices become ubiquitous, optimizing SFDA methods for parameter utilization and data-sample efficiency in time-series contexts becomes crucial. Personalization in time series is necessary to accommodate the unique patterns and behaviors of individual users, enhancing the relevance and accuracy of the predictions. In this…
*=Equal Contributors There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to survey landscape based on activity time series, where time series signal is transformed to frequency domain and compressed into embeddings by a contractive autoencoder, which preserve cyclic…
Accurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from observed data. Existing estimators can be sensitive to model misspecification and censored observations; many analysts have…