Скачать или смотреть Compressive Sensing - Iman Mossavat | PyData Eindhoven 2021

One can regard the possibility of digital compression as a failure of sensor design. If it is possible to compress measured data, one might argue that too many measurements were taken. - David Brady Compressive sensing main idea is to measure and compress to cope with the scarcity of resources. For example the limited resource can be battery power or limited communication band-width in simple sensors or measurement time in magnetic-resonance imaging. This talk first explains what it means to measure in a compressed mode and then how we can use prior knowledge about the structure of the signals to measure them in a compressed mode. Finally it shows how the prior knowledge used in compressive sensing moved forward over the few years from simple sparsity constraints to more advanced assumptions about the manifold of measurements. Such manifolds can be constructed in a data-driven manner for example by deep-learning. The talk briefly touches upon the statistical physics of inference and uses compressive sensing as a case study where information and computational complexity meet.

Iman Mossavat
I am a machine learning expert with more than 10 years of experience in applications related to challenging inverse problems. Currently, I am looking into lowering the energy needed to power AI.

My interests beyond machine learning are emergent phenomena in complex networked systems such as social media networks, and the brain. I believe that information processing and energy consumption are closely connected via notions such as free-energy, and that the study of complex self-organizing natural phenomena can teach us tremendously in this regard.

In the past years, my focus was on the development of signal processing algorithms for (optical) metrology applications (Tools that measure at nanometer accuracy). (Bayesian) probabilistic modeling, machine learning and numerical optimization are tools I regularly use.

LinkedIn:   / mossavat  

PyData Eindhoven 2021
Website: https://pydata.org/eindhoven2021/
Twitter:   / pydataeindhoven  

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