Machine learning offers a principled approach for develo** sophisticated, automatic, and
objective algorithms for analysis of high-dimensional and multimodal biomedical data. This …

Our knowledge of sensory processing has advanced dramatically in the last few decades,
but this understanding remains far from complete, especially for stimuli with the large …

Spatial context in images induces perceptual phenomena associated with salience and
modulates the responses of neurons in primary visual cortex (V1). However, the …

We consider the problem of efficiently encoding a signal by transforming it to a new
representation whose components are statistically independent. A widely studied linear …

The tilt illusion is a paradigmatic example of contextual influences on perception. We
analyze it in terms of a neural population model for the perceptual organization of visual …

Phenomena resembling tinnitus and Zwicker phantom tone are seen to result from an
auditory gain adaptation mechanism that attempts to make full use of a fixed-capacity …

Abstract ℓ1-regularized logistic regression, also known as sparse logistic regression, is
widely used in machine learning, computer vision, data mining, bioinformatics and neural …

Users of signal processing systems are never satis? ed with the system they currently use.
They are constantly asking for higher quality, faster perf-mance, more comfort and lower …

We investigate the use of independent component analysis (ICA) for speech feature
extraction in speech recognition systems. Although initial research suggested that learning …

The local statistical properties of photographic images, when represented in a multi-scale
basis, have been described using Gaussian scale mixtures. Here, we use this local …