Brendan J. Frey

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|Algorithms that must deal with complicated \global" functions of many variables often exploit the manner in which the given functions factor as a product of \local" functions. Such a factorization can be visualized with a bipartite graph that we call a factor graph. In this tutorial paper, we present a generic message-passing algorithm, the sum-product(More)
Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such "exemplars" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method(More)
This thesis develops two Bayesian learning methods relying on Gaussian processes and a rigorous statistical approach for evaluating such methods. In these experimental designs the sources of uncertainty in the estimated generalisation performances due to both variation in training and test sets are accounted for. The framework allows for estimation of(More)
An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representation in the layer above. In the "wake" phase, neurons are(More)
RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary(More)
We carried out the first analysis of alternative splicing complexity in human tissues using mRNA-Seq data. New splice junctions were detected in approximately 20% of multiexon genes, many of which are tissue specific. By combining mRNA-Seq and EST-cDNA sequence data, we estimate that transcripts from approximately 95% of multiexon genes undergo alternative(More)
See a PPT file with videos at We propose a technique for automatically learning layers of “flexible sprites” – probabilistic 2dimensional appearance maps and masks of moving, occluding objects. The model explains each input image as a layered composition of flexible sprites. A variational(More)
Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face(More)
Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified(More)