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Contextually Supervised Source Separation with Application to Energy Disaggregation
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting.
Probabilistic Segmentation via Total Variation Regularization
We present a convex approach to probabilistic segmentation and modeling of time series data.
Machine Learning for AC Optimal Power Flow
We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints.
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