Deep learning allows computational models that are composed of multiple processing
layers to learn representations of data with multiple levels of abstraction. These methods …

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 …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

This paper explores the complexity of deep feedforward networks with linear pre-synaptic
couplings and rectified linear activations. This is a contribution to the growing body of work …

Three important properties of a classification machinery are i) the system preserves the core
information of the input data; ii) the training examples convey information about unseen …

Deep learning is a form of machine learning for nonlinear high dimensional pattern
matching and prediction. By taking a Bayesian probabilistic perspective, we provide a …

Context: Developers tend to search and reuse code snippets from a large-scale codebase
when they want to implement some functions that exist in the previous projects, which can …

Deep learning is a computer‐based modeling approach, which is made up of many
processing layers that are used to understand the representation of data with several levels …

We present a new model for multivariate time-series classification, called the hidden-unit
logistic model (HULM), that uses binary stochastic hidden units to model latent structure in …