In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original sourcesignal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the objectives ofstatistical independence and temporal slowness removes this indeterminacy leading to a new method for nonlinear blind source separation. Theprinciple of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.

We introduce a computationally efficient method to estimate the validity of the BP method as a function of graph topology, the connectivity strength, frustration and network size.

We examine the marriage of recent probabilistic generative models for social networks with classical frameworks from mathematical economics. Weare particularly interested in how the statistical structure of such networks influences global economic quantities such as price variation. Ourfindings are a mixture of formal analysis, simulation, and experiments on an international trade data set from the United Nations.

We propose a gossip-based distributed algorithm for Gaussian mixture learning, Newscast EM. The algorithm operates on network topologies where each node observes a local quantity and can communicate with other nodes in an arbitrary point-to-point fashion. The main difference between Newscast EM and the standard EM algorithm is that the M-step in our case is implemented in a decentralized manner: (random) pairs of nodes repeatedly exchange their local parameter estimates and combine themby (weighted) averaging. We provide theoretical evidence and demonstrate experimentally that, under this protocol, nodes converge exponentially fastto the correct estimates in each M-step of the EM algorithm.

De novo Sequencing of peptides is a challenging task in proteome research. Whilethere exist reliable DNAsequencing methods, the highthroughput denovo sequencing of proteins by mass spectrometry is still an open problem. Current approaches suffer from a lack in precision to detect mass peaks in the spectrograms. In this paper we present a novel method for de novo peptide sequencing based on a hidden Markov model. Experiments effectively demonstrate that this new method significantly outperformsstandard approaches in matching quality.

Many works have shown that strong connections relate learning from examples toregularization techniques for ill-posed inverse problems. Nevertheless bynow there was no formal evidence neither that learning from examples could be seen as an inverse problem nor that theoretical results in learning theory could be independently derived using tools from regularization theory.In this paper we provide a positive answer to both questions. Indeed, considering the square loss, we translate the learning problem in the language of regularization theory and show that consistency resultsand optimal regularization parameter choice can be derived by the discretization of the corresponding inverse problem.

Saliency mechanisms play an important role when visual recognition must be performed in cluttered scenes. We propose a computational definition ofsaliency that deviates from existing models by equating saliency to discrimination. In particular, the salient attributes of a given visual class are defined as the features that enable best discrimination between that class and all other classes of recognition interest. It is shown that this definition leads to saliency algorithms of low complexity, that are scalable to large recognition problems, and is compatible with existing models of early biological vision. Experimental results demonstrating success in the context of challenging recognition problems are also presented.

We propose a novel a framework for deriving approximations for intractable probabilisticmodels. This framework is based on a free energy (negative log marginal likelihood) and can be seen as a generalization of adaptive TAP [1, 2, 3] and expectation propagation (EP) [4, 5]. The free energy is constructed from two approximating distributions which encode different aspects of the intractable model such a single node constraints andcouplings and are by construction consistent on a chosen set of moments. We test the framework on a difficult benchmark problem with binary variables on fully connected graphs and 2D grid graphs. We find good performance using sets of moments which either specify factorized nodesor a spanning tree on the nodes (structured approximation). Surprisingly, the Bethe approximation gives very inferior results even on grids.

Dixon describes beats as follows: "much music has as its rhythmic basis a series of pulses, spaced approximately equally in time, relative to which the timing of all musical events can be described. This phenomenon is called the beat, and the individual pulses are also called beats"[1]. Given a piece of recorded music (an MP3 file, for example), we wish to produce a set of beats that correspond to the beats perceived by human listeners. The set of beats of a song can be characterised by the trajectories through time of thetempo and phase offset. Tempo is typically measured in beats per minute (BPM), and describes the frequency of beats.

There has been substantial progress in the past decade in the development of object classifiers for images, for example of faces, humans and vehicles. Herewe address the problem of contaminations (e.g.