We describe a new approach for creating concise high-level generative models from range images or other approximate representations of real objects. Using data from a variety of a...
An agent must acquire internal representation appropriate for its task, environment, sensors. As a learning algorithm, reinforcement learning is often utilized to acquire the rela...
The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue ...
Most algorithms for extracting illuminant chromaticity from arbitrary images, such as the images found on the web, are based on machine learning techniques. We will show how a phy...
We present a novel connectionist model for acquiring the semantics of a simple language through the behavioral experiences of a real robot. We focus on the “compositionality” ...