Human perception has recently been characterized as statistical inference based on noisy
and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty …

Perception is about making sense, that is, understanding what events in the outside world
caused the sensory observations. Consistent with this intuition, many aspects of human …

We consider the task of estimating, from observed data, a probabilistic model that is
parameterized by a finite number of parameters. In particular, we are considering the …

The success of hand-crafted machine learning systems in many applications raises the
question of making machine learning algorithms more autonomous, ie, to reduce the …

Deep learning has recently led to great successes in tasks such as image recognition (eg
Krizhevsky et al., 2012). However, deep networks are still outmatched by the power and …

A method is presented for learning the reciprocal feedforward and feedback connections
required by the predictive coding model of cortical function. When this method is used …

Although models based on independent component analysis (ICA) have been successful in
explaining various properties of sensory coding in the cortex, it remains unclear how …

Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as
well as geoengineering planning and design. A traditional method depends on the guidance …

Predictive state representations (PSRs) offer an expressive framework for modelling partially
observable systems. By compactly representing systems as functions of observable …

We combine two popular optimization approaches to derive learning algorithms for
generative models: variational optimization and evolutionary algorithms. The combination is …