unsupervised learning

ProSper - A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

ProSper is a python library containing probabilistic algorithms to learn dictionaries. Given a set of data points, the implemented algorithms seek to learn the elementary components that have generated the data. The library widens the scope of …

Truncated Variational Sampling for ‘Black Box’ Optimization of Generative Models

We investigate the optimization of two probabilistic generative models with binary latent variables using a novel variational EM approach. The approach distinguishes itself from previous variational approaches by using latent states as variational …

Discrete Sparse Coding

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by …

Probabilistic Models for Invariant Representations and Transformations

The central task of machine learning research is to extract regularities from data. These regularities are often subject to transformations that arise from the complexity of the process that generates the data. There has been a lot of effort towards …

What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach

We study optimal image encoding based on a generative approach with non-linear feature combinations and explicit position encoding. By far most approaches to unsupervised learning learning of visual features, such as sparse coding or ICA, account for …

Ternary Sparse Coding

We study a novel sparse coding model with discrete and symmetric prior distribution. Instead of using continuous latent variables distributed according to heavy tail distributions, the latent variables of our approach are discrete. In contrast to …

Discrete Symmetric Priors for Sparse Coding

A standard model to explain the receptive fields of simple cells in the primary visual cortex is Sparse Coding (SC) [1]. However, the update equations used to train this model are not derivable in closed form. As a consequence, most state-of-the-art …