Seminar Details
2017-12-19 (14:00) : Data recovery from parsimonious observations: From compressed sensing to deep learning
At Euler building (room A.002)
Organized by Mathematical Engineering
Speaker :
Nikos Deligiannis (VUB)
Abstract :
The problem of recovering high-dimensional data from parsimonious, low-dimensional measurements is linked to a plethora of applications ranging from big data collection in smart environments to the enhancement of multimodal imaging sensors. The problem has been studied from various angles from the signal processing and machine learning communities, including compressed sensing - which enables the acquisition of a sparse (or compressible) signal using far less measurements than the classical Shannon-Nyquist theory - and matrix completion - which is essentially matrix factorisation with low-rank constraints. In the first part of the talk, we will present approaches to regularise such problems using constraints that encode the similarity of the data we wish to recover with observed, correlated (multimodal) data; while in the second part, we will present deep learning formulations that address the problem. Special focus will be put on learning good designs for the data collection mechanisms so as to reduce the associated complexity of the task. Finally, we will present several applications of our frameworks in multimodal image recovery, computer vision, and data recovery from internet-of-things devices measuring the air pollution.