Yuansong Liao and John Moody Department of Computer Science, Oregon Graduate Institute, P.O.Box 91000, Portland, OR 97291-1000 Abstract The committee approach has been proposed for reducing model uncertainty and improving generalization performance. The advantage ofcommittees depends on (1) the performance of individual members and (2) the correlational structure of errors between members. This paper presents an input grouping technique for designing aheterogeneous committee. With this technique, all input variables are first grouped based on their mutual information. Statistically similarvariables are assigned to the same group.

The project pursued in this paper is to develop from first information-geometric principles a general method for learning the similarity between text documents.

The Facial Action Coding System (FACS) (9) is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding ispresently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facialactions in sequences of images. These methods include unsupervised learning techniques for finding basis images such as principal component analysis, independent component analysis and local feature analysis, and supervised learning techniques such as Fisher's linear discriminants.

In this paper, we propose that information maximization can provide aunified framework for understanding saccadic eye movements. Inthis framework, the mutual information among the cortical representations of the retinal image, the priors constructed from our long term visual experience, and a dynamic short-term internal representation constructed from recent saccades provides a map for guiding eye navigation. By directing the eyes to locations ofmaximum complexity in neuronal ensemble responses at each step, the automatic saccadic eye movement system greedily collects information about the external world, while modifying the neural representations in the process. This framework attempts to connect several psychological phenomena, such as pop-out and inhibition of return, to long term visual experience and short term working memory. It also provides an interesting perspective on contextual computation and formation of neural representation in the visual system. 1 Introduction When we look at a painting or a visual scene, our eyes move around rapidly and constantly to look at different parts of the scene.

Department of Cognitive Science University of California, San Diego La Jolla, CA 92093-0515 Abstract Psychophysical and physiological evidence shows that sound localization ofacoustic signals is strongly influenced by their synchrony with visual signals. This effect, known as ventriloquism, is at work when sound coming from the side of a TV set feels as if it were coming from the mouth of the actors. The ventriloquism effect suggests that there is important information about sound location encoded in the synchrony between the audio and video signals. In spite of this evidence, audiovisual synchrony is rarely used as a source of information in computer vision tasks. In this paper we explore the use of audio visual synchrony to locate sound sources. We developed a system that searches for regions of the visual landscape thatcorrelate highly with the acoustic signals and tags them as likely to contain an acoustic source.

Stochastic meta-descent (SMD) is a new technique for online adaptation oflocal learning rates in arbitrary twice-differentiable systems. Like matrix momentum it uses full second-order information while retaining O(n) computational complexity by exploiting the efficient computation of Hessian-vector products. Here we apply SMD to independent component analysis, and employ the resulting algorithmfor the blind separation of time-varying mixtures. By matching individual learning rates to the rate of change in each source signal's mixture coefficients, our technique is capable of simultaneously trackingsources that move at very different, a priori unknown speeds. 1 Introduction Independent component analysis (ICA) methods are typically run in batch mode in order to keep the stochasticity of the empirical gradient low. Often this is combined with a global learning rate annealing scheme that negotiates the tradeoff between fast convergence and good asymptotic performance.

This paper examines the role of biological constraints in the human auditory localizationprocess. A psychophysical and neural system modeling approach was undertaken in which performance comparisons between competing models and a human subject explore the relevant biologically plausible"realism constraints". The directional acoustical cues, upon which sound localization is based, were derived from the human subject's head-related transfer functions (HRTFs). Sound stimuli were generated by convolving bandpass noise with the HRTFs and were presented toboth the subject and the model. The input stimuli to the model was processed using the Auditory Image Model of cochlear processing.

We have developed a VLSI silicon neuron and a corresponding mathematical modelthat is a two state-variable system. We describe the circuit implementation and compare the behaviors observed in the silicon neuron and the mathematical model. We also perform bifurcation analysis ofthe mathematical model by varying the externally applied current and show that the behaviors exhibited by the silicon neuron under corresponding conditionsare in good agreement to those predicted by the bifurcation analysis.

A system emulating the functionality of a moving eye-hence the name oculomotor system-has been built and successfully tested.

We present a variational Bayesian method for model selection over families of kernels classifiers like Support Vector machines or Gaussian processes.The algorithm needs no user interaction and is able to adapt a large number of kernel parameters to given data without having to sacrifice training cases for validation. This opens the possibility touse sophisticated families of kernels in situations where the small "standard kernel" classes are clearly inappropriate. We relate the method to other work done on Gaussian processes and clarify the relation between Support Vector machines and certain Gaussian process models. 1 Introduction Bayesian techniques have been widely and successfully used in the neural networks and statistics community and are appealing because of their conceptual simplicity, generality and consistency with which they solve learning problems. In this paper we present a new method for applying the Bayesian methodology to Support Vector machines. We will briefly review Gaussian Process and Support Vector classification in this section and clarify their relationship by pointing out the common roots. Although we focus on classification here, it is straightforward to apply the methods to regression problems as well. In section 2 we introduce our algorithm and show relations to existing methods. Finally, we present experimental results in section 3 and close with a discussion in section 4. Let X be a measure space (e.g.