Allerton 2015 Paper Abstract


Paper ThD3.3

Mousavi, Ali (Rice University), Patel, Ankit (Rice University), Baraniuk, Richard (Rice University)

A Deep Learning Approach to Structured Signal Recovery

Scheduled for presentation during the Regular Session "Machine Learning II" (ThD3), Thursday, October 1, 2015, 16:10−16:30, Butternut

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Sparse Data Analysis, Image and Multimedia Signal Processing, Universal Algorithms and Machine Learning


In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.



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