Academic

Machine learning, and deep probabilistic modelling specifically, seems to be revolutionising the space of data compression. This short post describes 1) the basic components of the data compression pipeline; 2) the objective used to optimise model parameters and its equivalence to training a VAE; and 3) some of the challenges that need to be solved.

Bayesian Optimal Experimental Design (BOED) is an elegant mathematical framework that enables us to design experiments optimally. This introductory post describes the BOED framework and the computational challenges associated with deploying it in applications.