Social network data is generally incomplete with missing information about nodes and their interactions. Here we propose a spatialtemporal latent point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches, we assume that interactions are not fully observable, and certain interaction events lack information about ...

First, we discuss methods for optimally inferring the synaptic inputs to an electrotonically compact neuron, given intracellular voltage-clamp or current-clamp recordings from the postsynaptic cell. These methods are based on sequential Monte Carlo techniques (“particle filtering”). We demonstrate on model data that these methods can accurately recover the time course of excitatory and inhibito...

It is well-established in the pattern recognition community that the performance of classifiers can be greatly improved by combining the outputs of multiple classifiers. In this paper, we introduce the concept of adaptive performance-based classifier combination, i.e., the weighting of classifiers based on their estimated recognition performance, to generic object recognition. Using an expectat...

In this paper we address the problem of estimating the parameters of a Gaussian mixture model. Although the EM (ExpectationMaximization) algorithm yields the maximum-likelihood solution it requires a careful initialization of the parameters and the optimal number of kernels in the mixture may be unknown beforehand. We propose a criterion based on the entropy of the pdf (probability density func...

This paper presents a Bayesian nonnegative matrix factorization (NMF) approach to extract singing voice from background music accompaniment. Using this approach, the likelihood function based on NMF is represented by a Poisson distribution and the NMF parameters, consisting of basis and weight matrices, are characterized by the exponential priors. A variational Bayesian expectationmaximization ...

We present the Trusted Expectation-Maximization (TEM), a new discriminative training scheme, for speech recognition applications. In particular, the TEM algorithm may be used for Hidden Markov Models (HMMs) based discriminative training. The TEM algorithm has a form similar to the ExpectationMaximization (EM) algorithm, which is an efficient iterative procedure to perform maximum likelihood in ...

We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel penalty functions on the singular values of the low rank matrix. By exploiting a mixture model representation of this penalty, we show that a suitably chosen set of latent variables enables to derive an ExpectationMaximization algorithm to obtain a Maximum A Posteriori estimate of the completed l...

A methodology was developed for estimating the parameters involved in a first-order autoregressive process; these parameters comprise a variance component associated with the random effect, a correlation coefficient, p, and a residual variance. These parameters were estimated using REML with an expectationmaximization algorithm. For two singletrait analyses (milk and fat production being the de...

This paper describes a method for estimating the fundamental frequency (F0) of melody and bass lines in monaural audio signals containing sounds of various instruments. Most previous methods premised mixtures of a few sounds and had great difficulty dealing with audio signals sampled from compact discs. Our method does not rely on the unreliable F0’s component and obtains the most predominant F...

Plan monitoring in a collaborative multi-agent system requires an agent to not only monitor the execution of its own plan, but also to detect possible deviations or failures in the plan execution of its teammates. In domains featuring partial observability and uncertainty in the agents’ sensing and actuation, especially where communication among agents is sparse (as a part of a cost-minimized p...