In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks...

A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume each network parameter is fixed. From a Bayesian perspective, however, these network parameters can be random variables that re...

Evidence can be complex in various ways: e.g., it may exhibit structural complexity, containing information about causal, hierarchical or logical structure as well as empirical data, or it may exhibit combinatorial complexity, containing a complex combination of kinds of information. This paper examines evidential complexity from the point of view of Bayesian epistemology, asking: how should co...

Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allows to succinctly describe multi-component continuous-time stochastic processes. A crucial element in applications of such models is inference. Here we introduce a variational approximation scheme, which is a natural ext...

Recently, Brafman and Engel (2009) proposed new concepts of marginal and conditional utility that obey additive analogues of the chain rule and Bayes rule, which they employed to obtain a directed graphical model of utility functions that resembles Bayes nets. In this paper we carry this analogy a step farther by showing that the notion of utility independence, built on conditional utility, sat...

In this work, we present a mathematical model for the emergence of descriptive norms, where the individual decision problem is formalized with the standard Bayesian belief revision machinery. Previous work on the emergence of descriptive norms has relied on heuristic modeling. In this paper we show that with a Bayesian model we can provide a more general picture of the emergence of norms, which...

In this paper we propose an impostor selection method for a Deep Belief Network (DBN) based system which models i-vectors in a multi-session speaker verification task. In the proposed method, instead of choosing a fixed number of most informative impostors, a threshold is defined according to the frequencies of impostors. The selected impostors are then clustered and the centroids are considere...

Facial behaviors represent activities of face or facial feature in spatial or temporal space, such as facial expressions, face pose, gaze, and furrow happenings. An automated system for facial behavior recognition is always desirable. However, it is a challenging task due to the richness and ambiguity in daily facial behaviors. This paper presents an efficient approach to real-world facial beha...

Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions , or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current f...

Many important real-world decision-making problems involve group interactions among individuals with purely informational externalities. Such situations arise for example in jury deliberations, expert commiŠees, medical diagnoses, etc. We model the purely informational interactions of rational agents in a group, where they receive private information and act based on that information while also...