Recent developments in grid-based and point-based approximation algorithms forPOMDPs have greatly improved the tractability of POMDP planning. These approaches operate on sets of belief points by individually learninga value function for each point. In reality, belief points exist in a highly-structured metric simplex, but current POMDP algorithms donot exploit this property. This paper presents a new metric-tree algorithm which can be used in the context of POMDP planning to sort belief points spatially, and then perform fast value function updates over groups of points. We present results showing that this approach can reduce computationin point-based POMDP algorithms for a wide range of problems.

In this paper, we focus on tasks in the application areas of NLP, such as Machine Translation, Text Summarization, Text Categorization and Question Answering.

The weight change scheme of the STDP synapses can be set to either weight-independent or weight-dependent mode. We present results that characterise the learning window implemented for both modes of operation. When presented with spike trains with different types of synchronisation the neurons develop bimodal weight distributions. We also show that a 2-layered network of silicon spiking neurons with STDP synapses can perform hierarchical synchrony detection.

In the problem of probability forecasting the learner's goal is to output, given a training set and a new object, a suitable probability measure on the possible values of the new object's label. An online algorithm for probability forecasting is said to be well-calibrated if the probabilities it outputs agree with the observed frequencies. We give a natural nonasymptotic formalizationof the notion of well-calibratedness, which we then study under the assumption of randomness (the object/label pairs are independent and identically distributed). It turns out that, although no probability forecasting algorithm is automatically well-calibrated in our sense, there exists a wide class of algorithms for "multiprobability forecasting" (such algorithms are allowed to output a set, ideally very narrow, of probability measures) which satisfy this property; we call the algorithms in this class "Venn probability machines". Our experimental results demonstrate that a 1-Nearest Neighbor Venn probability machine performs reasonably well on a standard benchmark data set, and one of our theoretical results asserts that a simple Venn probability machine asymptotically approaches the true conditional probabilities regardless, and without knowledge, of the true probability measure generating the examples.

The first algorithm is a propagation algorithm which is shown to converge if belief propagation converges to a stable fixed point.

This paper compares the ability of human observers to detect target image curveswith that of an ideal observer. The target curves are sampled froma generative model which specifies (probabilistically) the geometry andlocal intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP estimation. Varyingthe probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers' performance approaches that of the ideal observer and are, in general, closest to the ideal for conditions wherethe target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics.

One of the main applications OfICA is blind source separation (BSS) that aims to separate source signals from their mixtures.

To understand the brain mechanisms involved in reward prediction on different time scales, we developed a Markov decision task that requires prediction of both immediate and future rewards, and analyzed subjects'brain activities using functional MRI. We estimated the time course of reward prediction and reward prediction error on different time scales from subjects' performance data, and used them as the explanatory variables for SPM analysis. We found topographic mapsof different time scales in medial frontal cortex and striatum. The result suggests that different cortico-basal ganglia loops are specialized for reward prediction on different time scales.

We describe a system that localizes a single dipole to reasonable accuracy fromnoisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signalsand head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. Thetraining dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP's output as its initial guess took 15 ms and improved theaccuracy to 0.53 cm, only slightly above the statistical limits on accuracy imposed by the noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually-assisted commercial software.

Classifiers often achieve higher test set accuracy than generative Classifiers (e.g., [6, 4, 14]).