Generalised Approximate Message Passing for Non-I.I.D. Sparse Signals

Generalised approximate message passing (GAMP) is an approximate Bayesian estimation algorithm for signals observed through a linear transform with a possibly non-linear subsequent measurement model. By leveraging prior information about the observed signal, such as sparsity in a known dictionary, GAMP can for example reconstruct signals from under-determined measurements - known as compressed sensing. In the sparse signal setting, most existing signal priors for GAMP assume the input signal to have i.i.d. entries. Here we present sparse signal priors for GAMP to estimate non-i.d.d. signals through a non-uniform weighting of the input prior, for example allowing GAMP to support model-based compressed sensing.Comment: 3 pages, 1 figure, presented at iTWIST 2018, Marseill

Hvad vejer vækst?

Theories with emphasis on issues of proportionality have played a dominant role in the history of art and architecture and have thus contributed to our difficulty in recognising the consequences of growth: Leon Battista Alberti argued it was essential that a large and a small shape be proportioned identically and saw it as an advantage that actual size has no significance for proportions. Alberti’s considerations about what characterises a beautiful – well-proportioned – form can thus be linked to contemporary computer-generated architecture that effortlessly can be scaled up and down at a turn of the zoom button. In the computer, everything is scalable. But that is not the case in reality, where everything changes with size and a cube that on each side is 10 times larger than a smaller one is not 10, but 1,000 times heavier. The article embarks on the discussion of what it means to see the importance of size – and thus scale – in the world we inhabit and which seems to suffer because we ignore many heavy consequences of growth

Surpassing the Theoretical 1-Norm Phase Transition in Compressive Sensing by Tuning the Smoothed L0 Algorithm

<p>This is a poster presented at ICASSP 2013 in Vancouver.</p