Theories of access consciousness address how it is that some mental states but not others are available for evaluation, choice behavior, and verbal report. Farah, O'Reilly, and Vecera (1994) argue that quality of representation is critical; Dehaene, Sergent,and Changeux (2003) argue that the ability to communicate representations iscritical. We present a probabilistic information transmission or PIT model that suggests both of these conditions are essential for access consciousness. Havingsuccessfully modeled data from the repetition priming literature in the past, we use the PIT model to account for data from two experiments on subliminal priming, showing that the model produces priming even in the absence ofaccessibility and reportability of internal states. The model provides a mechanistic basis for understanding the dissociation of priming and awareness. Philosophy has made many attempts to identify distinct aspects of consciousness. Perhaps the most famous effort is Block's (1995) delineation of phenomenal and access consciousness. Phenomenalconsciousness has to do with "what it is like" to experience chocolate or a pin prick. Access consciousness refers to internal states whose content is "(1) inferentially promiscuous,i.e., poised to be used as a premise in reasoning, (2) poised for control of action, and (3) poised for rational control of speech."

Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for learning instance-specific models from data that are optimized to predict well for a particular instance. Based on this framework, we present a lazy instance-specific algorithm called ISA that performs selective model averaging over a restricted class of Bayesian networks. On experimental evaluation, this algorithm shows superior performance over model selection. We intend to apply such instance-specific algorithms to improve the performance of patient-specific predictive models induced from medical data.

Given a directed graph in which some of the nodes are labeled, we investigate thequestion of how to exploit the link structure of the graph to infer the labels of the remaining unlabeled nodes. To that extent we propose a regularization framework for functions defined over nodes of a directed graph that forces the classification function to change slowly on densely linked subgraphs. A powerful, yet computationally simple classification algorithm is derived within the proposed framework. The experimental evaluation on real-world Web classification problems demonstrates encouraging resultsthat validate our approach.

We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the dimensionality of a set of face images using principal component analysis, and then train a set of linear classifiers on this reduced representation (linear support vector machines(SVMs), relevance vector machines (RVMs), Fisher linear discriminant (FLD), and prototype (prot) classifiers) using human classification data.Because we combine a linear preprocessor with linear classifiers, the entire system acts as a linear classifier, allowing us to visualise thedecision-image corresponding to the normal vector of the separating hyperplanes(SH) of each classifier. We predict that the female-tomaleness transitionalong the normal vector for classifiers closely mimicking human classification (SVM and RVM [1]) should be faster than the transition along any other direction. A psychophysical discrimination experimentusing the decision images as stimuli is consistent with this prediction.

We use unsupervised probabilistic machine learning ideas to try to explain thekinds of learning observed in real neurons, the goal being to connect abstract principles of self-organisation to known biophysical processes.For example, we would like to explain Spike Timing-Dependent Plasticity (see [5,6] and Figure 3A), in terms of information theory. Starting out, we explore the optimisation of a network sensitivity measurerelated to maximising the mutual information between input spike timings and output spike timings. Our derivations are analogous to those in ICA, except that the sensitivity of output timings to input timings ismaximised, rather than the sensitivity of output'firing rates' to inputs. ICA and related approaches have been successful in explaining the learning of many properties of early visual receptive fields in rate coding models,and we are hoping for similar gains in understanding of spike coding in networks, and how this is supported, in principled probabilistic ways, by cellular biophysical processes. For now, in our initial simulations, weshow that our derived rule can learn synaptic weights which can unmix, or demultiplex, mixed spike trains. That is, it can recover independent pointprocesses embedded in distributed correlated input spike trains, using an adaptive single-layer feedforward spiking network.

In this paper, linear multilayer ICA (LMICA) is proposed for extracting independent components from quite high-dimensional observed signals such as large-size natural scenes. There are two phases in each layer of LMICA. One is the mapping phase, where a one-dimensional mapping is formed by a stochastic gradient algorithm which makes more highlycorrelated (non-independent)signals be nearer incrementally. Another is the local-ICA phase, where each neighbor (namely, highly-correlated) pair of signals in the mapping is separated by the MaxKurt algorithm. Because LMICA separates only the highly-correlated pairs instead of all ones, it can extract independent components quite efficiently from appropriate observedsignals. In addition, it is proved that LMICA always converges. Some numerical experiments verify that LMICA is quite efficient andeffective in large-size natural image processing.

We present a competitive analysis of Bayesian learning algorithms in the online learning setting and show that many simple Bayesian algorithms (such as Gaussian linear regression and Bayesian logistic regression) perform favorablywhen compared, in retrospect, to the single best model in the model class. The analysis does not assume that the Bayesian algorithms' modelingassumptions are "correct," and our bounds hold even if the data is adversarially chosen. For Gaussian linear regression (using logloss),our error bounds are comparable to the best bounds in the online learning literature, and we also provide a lower bound showing that Gaussian linear regression is optimal in a certain worst case sense. We also give bounds for some widely used maximum a posteriori (MAP) estimation algorithms, including regularized logistic regression.

Such a complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N). We also give an error bound for the approximation, and provide experimental results on the UCI datasets.

We tested our architecture in a simulation of a sequential reaching task.

The problem of learning a sparse conic combination of kernel functions or kernel matrices for classification or regression can be achieved via the regularization by a block 1-norm [1]. In this paper, we present an algorithm thatcomputes the entire regularization path for these problems. The path is obtained by using numerical continuation techniques, and involves a running time complexity that is a constant times the complexity ofsolving the problem for one value of the regularization parameter. Working in the setting of kernel linear regression and kernel logistic regression, weshow empirically that the effect of the block 1-norm regularization differsnotably from the (non-block) 1-norm regularization commonly used for variable selection, and that the regularization path is of particular value in the block case.