  A grand challenge in machine learning is the development of computational
algorithms that match or outperform humans in perceptual inference tasks that
are complicated by nuisance variation. For instance, visual object recognition
involves the unknown object position, orientation, and scale in object
recognition while speech recognition involves the unknown voice pronunciation,
pitch, and speed. Recently, a new breed of deep learning algorithms have
emerged for high-nuisance inference tasks that routinely yield pattern
recognition systems with near- or super-human capabilities. But a fundamental
question remains: Why do they work? Intuitions abound, but a coherent framework
for understanding, analyzing, and synthesizing deep learning architectures has
remained elusive. We answer this question by developing a new probabilistic
framework for deep learning based on the Deep Rendering Model: a generative
probabilistic model that explicitly captures latent nuisance variation. By
relaxing the generative model to a discriminative one, we can recover two of
the current leading deep learning systems, deep convolutional neural networks
and random decision forests, providing insights into their successes and
shortcomings, as well as a principled route to their improvement.
