Book chapter about the philosophy behind deep architecture model, motivating them in the context of Artificial Intelligence
Introducing Deep Belief Networks as generative models:
Deep Belief Networks as a simple way of initializing a deep feed-forward neural network:
General study of the framework of initializing a deep feed-forward neural network using a greedy layer-wise procedure:
An application of greedy layer-wise learning of a deep autoassociator for dimensionality reduction:
A way to use the greedy layer-wise learning procedure to learn a useful embeding for k nearest neighbor classification:
Different theoretical results about Restricted Boltzmann Machines (RBMs) and Deep Belief Networks, like the universal approximation property of RBMs:
A novel way of using greedy layer-wise learning for Convolutional Networks:
How to generalize Restricted Boltzmann Machines to types of data other than binary using exponential familly distribution:
An evaluation of deep networks on many datasets related to vision:
Application of deep learning in the context of information retrieval: