We present a probabilistic generative model for timing deviations in expressive music. The structure of the proposed model is equivalent to a switching state space model. We formulate twowell known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering andmaximum a posteriori (MAP) state estimation tasks. The inferences are carried out using sequential Monte Carlo integration (particlefiltering) techniques. For this purpose, we have derived a novel Viterbi algorithm for Rao-Blackwellized particle filters, wherea subset of the hidden variables is integrated out.

When designing a two-alternative classifier, one ordinarily aims to maximize the classifier's ability to discriminate between members of the two classes. We describe a situation in a real-world business application of machine-learning prediction in which an additional constraint is placed on the nature of the solution: thatthe classifier achieve a specified correct acceptance or correct rejection rate (i.e., that it achieve a fixed accuracy on members of one class or the other). Our domain is predicting churn in the telecommunications industry. Churn refers to customers who switch from one service provider to another. We propose fouralgorithms for training a classifier subject to this domain constraint, and present results showing that each algorithm yields a reliable improvement in performance.

Factor analysis and principal components analysis can be used to model linear relationships between observed variables and linearly map high-dimensional data to a lower-dimensional hidden space. In factor analysis, the observations are modeled as a linear combination ofnormally distributed hidden variables. We describe a nonlinear generalization of factor analysis, called "product analysis", thatmodels the observed variables as a linear combination of products of normally distributed hidden variables. Just as factor analysiscan be viewed as unsupervised linear regression on unobserved, normally distributed hidden variables, product analysis canbe viewed as unsupervised linear regression on products of unobserved, normally distributed hidden variables. The mapping betweenthe data and the hidden space is nonlinear, so we use an approximate variational technique for inference and learning.

We introduce a new type of Self-Organizing Map (SOM) to navigate in the Semantic Space of large text collections. We propose a "hyperbolic SOM"(HSOM) based on a regular tesselation of the hyperbolic plane, which is a non-euclidean space characterized by constant negative gaussian curvature. The exponentially increasing size of a neighborhood around a point in hyperbolic space provides more freedom to map the complex information space arising from language into spatial relations. We describe experiments, showing that the HSOM can successfully be applied to text categorization tasks and yields results comparable to other state-of-the-art methods.

In packet switches, packets queue at switch inputs and contend for outputs. Thecontention arbitration policy directly affects switch performance. Thebest policy depends on the current state of the switch and current traffic patterns. This problem is hard because the state space, possible transitions, and set of actions all grow exponentially with the size of the switch. We present a reinforcement learning formulation of the problem that decomposes the value function into many small independent valuefunctions and enables an efficient action selection.

Motivated by our recent work on rooted tree matching, in this paper we provide a solution to the problem of matching two free (i.e., unrooted) trees by constructing an association graph whose maximal cliques are in one-to-one correspondence with maximal common subtrees. We then solve the problem using simple replicator dynamics from evolutionary game theory. Experiments on hundreds of uniformly random trees are presented. The results are impressive: despite the inherent inability of these simple dynamics to escape from local optima, they always returned a globally optimal solution.

In this paper we will show that a restricted class of constrained minimum divergenceproblems, named generalized inference problems, can be solved by approximating the KL divergence with a Bethe free energy. The algorithm we derive is closely related to both loopy belief propagation anditerative scaling. This unified propagation and scaling algorithm reduces to a convergent alternative to loopy belief propagation when no constraints are present. Experiments show the viability of our algorithm.

We have designed and fabricated a VLSI synapse that can learn a conditional probability or correlation between spike-based inputs and feedback signals. The synapse is low power, compact, provides nonvolatile weight storage, and can perform simultaneous multiplication andadaptation. We can calibrate arrays of synapses to ensure uniform adaptation characteristics. Finally, adaptation in our synapse does not necessarily depend on the signals used for computation. Consequently,our synapse can implement learning rules that correlate past and present synaptic activity. We provide analysis andexperimental chip results demonstrating the operation in learning and calibration mode, and show how to use our synapse to implement various learning rules in silicon.

The usual observable at the level of neuronal populations is the populationaveraged instantaneousfiring rate A(t), with A(t)6.t

We consider a problem of blind source separation from a set of instantaneous linearmixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appropriate representationaccording to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multi scale transforms, such as wavelet or wavelet packets, to decompose signals into sets of local features with various degrees of sparsity. We use this intrinsic property for selecting the best (most sparse) subsets of features for further separation. The performance of the algorithm is verified onnoise-free and noisy data. Experiments with simulated signals, musical sounds and images demonstrate significant improvement of separation qualityover previously reported results. 1 Introduction