Independent Component Analysis (ICA) is a method for blind signal separation.

This paper presents a unified probabilistic framework for denoising and dereverberation of speech signals. The framework transforms the denoising anddereverberation problems into Bayes-optimal signal estimation. The key idea is to use a strong speech model that is pre-trained on a large data set of clean speech. Computational efficiency is achieved by using variational EM, working in the frequency domain, and employing conjugate priors. The framework covers both single and multiple microphones. Weapply this approach to noisy reverberant speech signals and get results substantially better than standard methods.

To control the walking gaits of a four-legged robot we present a novel neuromorphic VLSI chip that coordinates the relative phasing of the robot's legs similar to how spinal Central Pattern Generators are believed to control vertebrate locomotion [3]. The chip controls the leg movements bydriving motors with time varying voltages which are the outputs of a small network of coupled oscillators. The characteristics of the chip's output voltages depend on a set of input parameters. The relationship betweeninput parameters and output voltages can be computed analytically for an idealized system.

Competitive learning is a technique for training classification and clustering networks. We have designed and fabricated an 11-transistor primitive, that we term an automaximizing bump circuit, that implements competitive learning dynamics. The circuit performs asimilarity computation, affords nonvolatile storage, and implements simultaneous local adaptation and computation. We show that our primitive is suitable for implementing competitive learning in VLSI, and demonstrate its effectiveness in a standard clustering task. 1 Introduction Competitive learning is a family of neural learning algorithms that has proved useful fortraining many classification and clustering networks [1]. In these networks, a neuron's synaptic weight vector typically represents a tight cluster of data points.

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This leads to a metric MDS algorithm where the desired configuration of points is found via the solution of an eigenproblem rather than through the iterative optimization of the stress objective function. The question of kernel choice is also discussed. 1 Introduction Suppose we are given n objects, and for each pair (i,j) we have a measurement of the "dissimilarity" Oij between the two objects. In multidimensional scaling (MDS) the aim is to place n points in a low dimensional space (usually Euclidean) so that the interpoint distances dij have a particular relationship to the original dissimilarities. In classical scaling we would like the interpoint distances to be equal to the dissimilarities. For example, classical scaling can be used to reconstruct a map of the locations of some cities given the distances between them.

We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA micro array data. 1 Introduction In many supervised learning problems feature selection is important for a variety of reasons: generalizationperformance, running time requirements, and constraints and interpretational issuesimposed by the problem itself.

We present the embedded trees algorithm, an iterative technique for estimation of Gaussian processes defined on arbitrary graphs. By exactly solving a series of modified problems on embedded spanning trees,it computes the conditional means with an efficiency comparable to or better than other techniques. Unlike other methods, theembedded trees algorithm also computes exact error covariances. Theerror covariance computation is most efficient for graphs in which removing a small number of edges reveals an embedded tree.In this context, we demonstrate that sparse loopy graphs can provide a significant increase in modeling power relative totrees, with only a minor increase in estimation complexity. 1 Introduction Graphical models are an invaluable tool for defining and manipulating probability distributions. In modeling stochastic processes with graphical models, two basic problems arise: (i) specifying a class of graphs with which to model or approximate the process; and (ii) determining efficient techniques for statistical inference.

We introduce the mixture of Gaussian processes (MGP) model which is useful for applications in which the optimal bandwidth of a map is input dependent. The MGP is derived from the mixture of experts model and can also be used for modeling general conditional probability densities. We discuss how Gaussian processes -in particular in form of Gaussian process classification, the support vector machine and the MGP modelcan beused for quantifying the dependencies in graphical models. 1 Introduction Gaussian processes are typically used for regression where it is assumed that the underlying functionis generated by one infinite-dimensional Gaussian distribution (i.e.

Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with a data set consisting of randomly generated instances from the underlying distribution. In many situations, however, we also have the option of active learning, where we have the possibility of guiding the sampling process by querying for certain types of samples. This paper addresses the problem of estimating the parameters of Bayesian networks in an active learning setting. We provide a theoretical framework for this problem, and an algorithm that chooses which active learning queries to generate based on the model learned so far. We present experimental results showing that our active learning algorithm can significantly reduce the need for training data in many situations.