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Stochastic models for interacting processes feature a dimensionality that grows exponentially with the number of processes. This state space explosion severely impairs the use of standard methods for the numerical analysis of such Markov chains. In this wo ...
In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous ...
We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of lowdimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representa ...
We consider multitype branching processes evolving in a Markovian random environment. To determine whether or not the branching process becomes extinct almost surely is akin to computing the maximal Lyapunov exponent of a sequence of random matrices, which ...
One of the primary steps in building automatic speech recognition (ASR) and text-to-speech systems is the development of a phonemic lexicon that provides a mapping between each word and its pronunciation as a sequence of phonemes. Phoneme lexicons can be d ...
We present novel Markov-type and Nikolskii-type inequalities for multivariate polynomials associated with arbitrary downward closed multi-index sets in any dimension. Moreover, we show how the constant of these inequalities changes, when the polynomial is ...
Automatic non-native accent assessment has potential benefits in language learning and speech technologies. The three fundamental challenges in automatic accent assessment are to characterize, model and assess individual variation in speech of the non-nati ...
We introduce a novel approach that reconstructs 3D urban scenes in the form of levels of detail (LODs). Starting from raw data sets such as surface meshes generated by multi-view stereo systems, our algorithm proceeds in three main steps: classification, a ...
We show that the maximum-likelihood (ML) estimate of models derived from Luce’s choice axiom (e.g., the Plackett–Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inf ...
The traditional synthesis question given a specification asks for the automatic construction of a system that satisfies the specification, whereas often there exists a preference order among the different systems that satisfy the given specification. Under ...
