Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.

Opengazer: open-source gaze tracker for ordinary webcams

Opengazer is an open source application that uses an ordinary webcam to estimate head pose, facial gestures, or the direction of your gaze. This information can then be passed to other applications. For example, used in conjunction with Dasher, opengazer allows you to write with your eyes. Opengazer aims to be a low-cost software alternative to commercial hardware-based eye trackers. The first version of Opengazer was developed by Piotr Zieliński, supported by Samsung and the Gatsby Charitable Foundation. Research and development for Opengazer has been continued by Emli-Mari Nel, and was supported until 2012 by the European Commission in the context of the AEGIS project, and also by the Gatsby Charitable Foundation.

Cortical substrates for exploratory decisions in humans.

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

Avoiding pathologies in very deep networks

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

Case study on a deep excavation in soft ground by centrifuge model tests

This paper provides a case study on the deepest excavation carried out so far in the construction of the metro network in Shanghai, which typically features soft ground. The excavation is 38 m deep with retaining walls 65 m deep braced by 9 levels of concrete props. To obtain a quick and rough prediction, two centrifuge model tests were conducted, in which one is for the 'standard' section with green field surrounding and the other with an adjacent piled building. The tests were carried out in a run-stop-excavation-run style, in which excavation was conducted manually. By analyzing the lateral wall displacement, ground deformation, bending moment and earth pressure, the test results are shown to be reasonably convincing and the design and construction were validated. Such industry orientated centrifuge modeling was shown to be useful in understanding the performance of geotechnical processes, especially when engineers lack relevant field experience. © 2010 Taylor & Francis Group, London.