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 …

Reconfigurable intelligent surfaces (RISs) hold great potential as one of the key
technological enablers for beyond-5G wireless networks, improving localization and …

We develop a probabilistic framework for deep learning based on the Deep Rendering
Mixture Model (DRMM), a new generative probabilistic model that explicitly capture …

We show that k-means (Lloyd's algorithm) is obtained as a special case when truncated
variational EM approximations are applied to Gaussian mixture models (GMM) with isotropic …

Recent work in signal processing in general and image processing in particular deals with
sparse representation related problems. Two such problems are of paramount importance …

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

Standard probabilistic sparse coding assumes a Laplace prior, a linear mapping from latents
to observables, and Gaussian observable distributions. We here derive a solely entropy …

Latent variable models (LVMs) represent observed variables by parameterized functions of
latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic …

Dynamic topic models (DTM) are of great use toanalyze the evolution of unobserved topics
of a text collectionover time. Recent years have witnessed the explosive growth ofstreaming …

Conventional and electron microscopy visualize structures in the micrometer to nanometer
range, and such visualizations contribute decisively to our understanding of biological …