Recent results on sparse coding and independent component analysis suggest that human vision first represents a visual image by a linear superposition of a relatively small number of localized, elongate, oriented image bases. With this representation, the sketch of an image consists of the locations, orientations, and elongations of the image bases, and the sketch can be visually illustrated by...

Statistical modeling of data sets by neural-network techniques is offered as an alternative to traditional semiempirical approaches to global modeling of nuclear properties. New results are presented to support the position that such novel techniques can rival conventional theory in predictive power, if not in economy of description. Examples include the statistical inference of atomic masses a...

Over the past several years, the successful application of statistical techniques in natural language processing has penetrated further and further into written language technology, proceding with time from the periphery of written language processing into deeper and deeper aspects of language processing. At the periphery of natural language understanding, Hidden Markov Models were first applie...

Formalisms for specifying general statistical models, such as probabilistic-programming languages, typically consist of two components: a specification of a stochastic process (the prior), and a specification of observations that restrict the probability space to a conditional subspace (the posterior). Use cases of such formalisms include the development of algorithms in machine learning and ar...

Robust, high yield IC design requires statistical simulation, and therefore statistical models. Simple “fast” and “slow” sets of model parameters are not sufficient to predict the manufacturing variations of all measures of circuit performance for arbitrary circuit topologies, device geometries, and biases. This paper describes an accurate and efficient approach to statistical modeling and char...

We examine the usefulness of Bayesian statistical methods for the modeling of biochemical reactions. With simulated data, it is shown that these methods can effectively fit mechanistic models of sequences of enzymatic reactions to experimental data. These methods have the advantages of being relatively easy to use and producing probability distributions for the model parameters rather than poin...

As IC technologies scale to finer feature sizes, it becomes increasingly difficult to control the relative process variations. The increasing fluctuations in manufacturing processes have introduced unavoidable and significant uncertainty in circuit performance; hence ensuring manufacturability has been identified as one of the top priorities of today’s IC design problems. In this paper, we revi...

We present context-sensitive dynamic classes – a novel mechanism for integrating contextual information from spoken dialogue into a class n-gram language model. We exploit the dialogue system’s information state to populate dynamic classes, thus percolating contextual constraints to the recognizer’s language model in real time. We describe a technique for training a language model incorporating...

This article introduces a mathematical approach to extracting some statistical parameters from noise-like sounds. This approach could define a new spectral model or extend existing ones to analyze and synthesize such complex sounds. In the future, this method could also permit electro-acoustic composers to make musical transformations. We have performed several synthesis tests with synthetic an...

The demand of intelligent machines that may recognize the spoken speech and respond in a natural voice has been driving speech research. The challenging in speech recognition systems due to the language nature where there are no clear boundaries between words, the phonetic beginning and ending are influenced by neighbouring words, in addition to the variability in different speakers speech: mal...