The literature suggests that there are two factors that explain why speakers mark contrastive information: either because it is easy for themselves or because it helps their listeners. The present study investigates whether speakers indeed take their listeners’ knowledge into account when prosodically marking contrastive information. A production experiment elicited references to figures (e.g. ...

A Boltzmann Machine is a network of symmetrically connected, neuronlike units that make stochastic decisions about whether to be on or off. Boltzmann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors. The learning algorithm is very slow in networks with many layers of feature detectors, but it can be made much fast...

The restricted Boltzmann machine is a graphical model for binary random variables. Based on a complete bipartite graph separating hidden and observed variables, it is the binary analog to the factor analysis model. We study this graphical model from the perspectives of algebraic statistics and tropical geometry, starting with the observation that its Zariski closure is a Hadamard power of the f...

We present a novel theoretical result that generalises the Discriminative Restricted Boltzmann Machine (DRBM). While originally the DRBM was defined assuming the {0, 1}-Bernoulli distribution in each of its hidden units, this result makes it possible to derive cost functions for variants of the DRBM that utilise other distributions, including some that are often encountered in the literature. T...

Finding suitable features has been an essential problem in computer vision. We focus on Restricted Boltzmann Machines (RBMs), which, despite their versatility, cannot accommodate transformations that may occur in the scene. As result, several approaches have been proposed that consider a set of transformations, which are used to either augment the training set or transform the actual learned fi...

This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE). We first describe the basic CF-NADE model for CF tasks. Then we propose to improve the model by sharing parameters between different ratings. A factored ve...

We present a mixture model whose components are Restricted Boltzmann Machines (RBMs). This possibility has not been considered before because computing the partition function of an RBM is intractable, which appears to make learning a mixture of RBMs intractable as well. Surprisingly, when formulated as a third-order Boltzmann machine, such a mixture model can be learned tractably using contrast...

In this paper, we present the results of a study on dynamic models for predicting sequences of musical pitch in melodies. Such models predict a probability distribution over the possible values of the next pitch in a sequence, which is obtained by combining the prediction of two components (1) a long-term model (LTM) learned offline on a corpus of melodies, as well as (2) a short-term model (ST...

This paper presents a neural approach to sensor modelling and classification as the basis of local data fusion in a wireless sensor network. Data distributions are non-Gaussian. Data clusters are sufficiently complex that the classification problem is markedly non-linear. We prove that a Continuous Restricted Boltzmann Machine can model complex data distributions and can autocalibrate against r...