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Formal verification and neural lossless compression

CORDIS · observation · Publication date unknown

Formal verification and neural lossless compression Techniques based on neural networks (NNs), the study of which is often referred to as ‘deep learning’, have recently been shown to be extremely effective as a basis for data compression systems. I will develop the new field of ‘neural compression’, which has emerged around these ideas, focussing primarily on lossless compression in the two directions which I believe are most important: Scale: NNs go hand in hand with parallel hardware, and I will investigate new parallel compression algorithms for efficiently compressing huge quantities of data. Specifically, I will develop two entirely new compression paradigms: Firstly, compression of volumetric images using generative models based on 3D convolutions, applied to medical imaging, where teleradiology and new cloud-based analysis make the need for efficient compression particularly acute. And secondly, compression of audio and video using time-series latent variable models, known as ‘state space models’, which offer unique

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recordType

award

status

SIGNED

region

EU

value

187624.32

unit

EUR