The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

The success of machine learning algorithms generally depends on data representation, and
we hypothesize that this is because different representations can entangle and hide more or …

We explore an original strategy for building deep networks, based on stacking layers of
denoising autoencoders which are trained locally to denoise corrupted versions of their …

Over the past three decades or so, research on machine learning and data mining has led to
a wide variety of algorithms that learn general functions from experience. As machine …

We present an example-based learning approach for locating vertical frontal views of human
faces in complex scenes. The technique models the distribution of human face patterns by …

Single image pose estimation is a fundamental problem in many vision and robotics tasks,
and existing deep learning approaches suffer by not completely modeling and handling: i) …

One of the main challenges faced by the current face recognition techniques lies in the
difficulties of collecting samples. Fewer samples per person mean less laborious effort for …

Practical experience has shown that in order to obtain the best possible performance, prior
knowledge about invariances of a classification problem at hand ought to be incorporated …

Five experiments on the effects of changes of depth orientation on (a) priming the naming of
briefly flashed familiar objects,(b) matching individual sample volumes (geons), and (c) …

The paper addresses the problem of" class-based" image-based recognition and rendering
with varying illumination. The rendering problem is defined as follows: Given a single input …