3 accepted papers
We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that…
In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to…
Hyperspectral imaging has a wide range of applications; however, due to the high dimensionality of the data involved, the complexity and cost of hyperspectral imagers can be prohibitive. Exploiting redundancies along the spatial and spectral dimensions of a hyperspectral image of a scene has created…