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462
Rational approximations to rational models: Alternative algorithms for category learning
"... Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible fo ..."
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Cited by 27 (5 self)
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Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of “rational process models” that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson’s (1990, 1991) Rational Model of Categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose two alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure
Automatic classification of MR scans in Alzheimer's disease
 Brain
, 2008
"... These authors contributed equally to this work. To be diagnostically useful, structural MRI must reliably distinguish Alzheimer’s disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detec ..."
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Cited by 25 (0 self)
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These authors contributed equally to this work. To be diagnostically useful, structural MRI must reliably distinguish Alzheimer’s disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states.The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether datasets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without postmortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration.Up to 96 % of pathologically verified AD patients were correctly classified using whole brain
Tied factor analysis for face recognition across large pose changes
 BMVC
, 2006
"... Abstract—Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a onetomany mapping from an idealized “identity ” space to the ..."
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Cited by 24 (4 self)
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Abstract—Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a onetomany mapping from an idealized “identity ” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a posecontingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied ” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches. Index Terms—Computing methodologies, pattern recognition, applications, face and gesture recognition. Ç 1
Beyond Equilibrium: Predicting Human Behaviour in Normal Form
, 2010
"... It is standard in multiagent settings to assume that agents will adopt Nash equilibrium strategies. However, studies in experimental economics demonstrate that Nash equilibrium is a poor description of human players ’ actual behaviour. In this study, we consider a wide range of widelystudied models ..."
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Cited by 23 (2 self)
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It is standard in multiagent settings to assume that agents will adopt Nash equilibrium strategies. However, studies in experimental economics demonstrate that Nash equilibrium is a poor description of human players ’ actual behaviour. In this study, we consider a wide range of widelystudied models from behavioural game theory. For what we believe is the first time, we evaluate each of these models in a metaanalysis, taking as our data set largescale and publiclyavailable experimental data from the literature. We then propose a modified model that we believe is more suitable for practical prediction of human behaviour. ii Table of Contents Abstract................................... ii
Bayesian models of cognition
"... For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational a ..."
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Cited by 23 (1 self)
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For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty
Statistical analysis on stiefel and grassmann manifolds with applications in computer vision
 CVPR
, 2008
"... Many applications in computer vision and pattern recognition involve drawing inferences on certain manifoldvalued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measu ..."
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Cited by 21 (5 self)
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Many applications in computer vision and pattern recognition involve drawing inferences on certain manifoldvalued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatiotemporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and nonparametric density estimators on these manifolds. These methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.
Variational Decoding for Statistical Machine Translation
"... Statistical models in machine translation exhibit spurious ambiguity. That is, the probability of an output string is split among many distinct derivations (e.g., trees or segmentations). In principle, the goodness of a string is measured by the total probability of its many derivations. However, fi ..."
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Cited by 21 (1 self)
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Statistical models in machine translation exhibit spurious ambiguity. That is, the probability of an output string is split among many distinct derivations (e.g., trees or segmentations). In principle, the goodness of a string is measured by the total probability of its many derivations. However, finding the best string (e.g., during decoding) is then computationally intractable. Therefore, most systems use a simple Viterbi approximation that measures the goodness of a string using only its most probable derivation. Instead, we develop a variational approximation, which considers all the derivations but still allows tractable decoding. Our particular variational distributions are parameterized as ngram models. We also analytically show that interpolating these ngram models for different n is similar to minimumrisk decoding for BLEU (Tromble et al., 2008). Experiments show that our approach improves the state of the art. 1
Efficient Highly OverComplete Sparse Coding using a Mixture Model
"... Abstract. Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data into a significantly higherdimensional space with sparse coding can lead to superior classification performan ..."
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Cited by 20 (0 self)
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Abstract. Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data into a significantly higherdimensional space with sparse coding can lead to superior classification performance. However, computationally it is challenging to learn a set of highly overcomplete dictionary bases and to encode the test data with the learned bases. In this paper, we describe a mixture sparse coding model that can produce highdimensional sparse representations very efficiently. Besides the computational advantage, the model effectively encourages data that are similar to each other to enjoy similar sparse representations. What’s more, the proposed model can be regarded as an approximation to the recently proposed local coordinate coding (LCC), which states that sparse coding can approximately learn the nonlinear manifold of the sensory data in a locally linear manner. Therefore, the feature learned by the mixture sparse coding model works pretty well with linear classifiers. We apply the proposed model to PASCAL VOC 2007 and 2009 datasets for the classification task, both achieving stateoftheart performances. Key words: Sparse coding, highly overcomplete dictionary training, mixture model, mixture sparse coding, image classification, PASCAL VOC challenge 1
Active learning of inverse models with intrinsically motivated goal exploration in robots
 ROBOTICS AND AUTONOMOUS SYSTEMS
, 2013
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Did You See Bob?: Human Localization using Mobile Phones
"... Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occ ..."
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Cited by 17 (0 self)
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Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occasions, but such maps may not be always handy. In a small scale poll, 80 % of users responded that the ideal solution would be “to have an escort walk me to the desired person”. This paper identifies the possibility of using mobile phone sensors and opportunistic userintersections to develop an electronic escort service. By periodically learning the walking trails of different individuals, as well as how they encounter each other in spacetime, a route can be computed between any pair of persons. The problem bears resemblance to routing packets in delay tolerant networks, however, its application in the context of human localization raises distinct research challenges. We design and implement Escort, a system that guides a user to the vicinity of a desired person in a public place. We only use an audio beacon, randomly placed in the building, to enable a reference frame. We do not rely on GPS, WiFi, or wardriving to locate a person – the Escort user only needs to follow an arrow displayed on the phone. Evaluation results from experiments in parking lots and university buildings show that, on average, the user is brought to within 8m of the destination. We believe this is an encouraging result, opening new possibilities in mobile, social localization.