We propose the hierarchical Dirichlet process (HDP), a nonparametric Bayesian model for clustering problems involving multiple groups of data. Each group of data is modeled with a mixture, with the number of components being open-ended and inferred automatically by the model. Further, components can be shared across groups, allowing dependencies across groups to be modeled effectively as well as conferring generalization tonew groups. Such grouped clustering problems occur often in practice, e.g. in the problem of topic discovery in document corpora. We report experimental results on three text corpora showing the effective and superior performance of the HDP over previous models.

In this setting, TD learning is often simpler and more data-efficient than other methods.

We describe a three-dimensional geometric hand model suitable for visual trackingapplications. The kinematic constraints implied by the model's joints have a probabilistic structure which is well described by a graphical model. Inference in this model is complicated by the hand's many degrees of freedom, as well as multimodal likelihoods caused by ambiguous image measurements. We use nonparametric belief propagation (NBP)to develop a tracking algorithm which exploits the graph's structure to control complexity, while avoiding costly discretization. While kinematic constraints naturally have a local structure, self-occlusions created by the imaging process lead to complex interpendencies incolor and edge-based likelihood functions. However, we show that local structure may be recovered by introducing binary hidden variables describingthe occlusion state of each pixel. We augment the NBP algorithm to infer these occlusion variables in a distributed fashion, and then analytically marginalize over them to produce hand position estimates whichproperly account for occlusion events. We provide simulations showing that NBP may be used to refine inaccurate model initializations, aswell as track hand motion through extended image sequences.

It has been demonstrated that basic aspects of human visual motion perception arequalitatively consistent with a Bayesian estimation framework, where the prior probability distribution on velocity favors slow speeds. Here, we present a refined probabilistic model that can account for the typical trial-to-trial variabilities observed in psychophysical speed perception experiments. We also show that data from such experiments can be used to constrain both the likelihood and prior functions of the model. Specifically, we measured matching speeds and thresholds in a two-alternative forced choice speed discrimination task. Parametric fits to the data reveal that the likelihood function is well approximated by a LogNormal distribution with a characteristic contrast-dependent variance, andthat the prior distribution on velocity exhibits significantly heavier tails than a Gaussian, and approximately follows a power-law function.

The game of G0 originated in China over 4000 years ago.

We show that anomaly detection can be interpreted as a binary classification problem.Using this interpretation we propose a support vector machine (SVM) for anomaly detection. We then present some theoretical resultswhich include consistency and learning rates. Finally, we experimentally compare our SVM with the standard one-class SVM.

We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margin linear discrimination. We show how to learn low-norm factorizations by solving a semi-definite program, and discuss generalization error bounds for them.

We consider the problem of geometrical surface reconstruction from one or several images using learned shape models. While humans can effortlessly retrieve3D shape information, this inverse problem has turned out to be difficult to perform automatically. We introduce a framework based on level set surface reconstruction and shape models for achieving this goal. Through this merging, we obtain an efficient and robust method for reconstructing surfaces of an object category of interest. The shape model includes surface cues such as point, curve and silhouette features.Based on ideas from Active Shape Models, we show how both the geometry and the appearance of these features can be modelled consistently in a multi-view context. The complete surface is obtained by evolving a level set driven by a PDE, which tries to fit the surface to the inferred 3D features. In addition, an a priori 3D surface model is used to regularize the solution, in particular, where surface features are sparse. Experiments are demonstrated on a database of real face images.

Semantic taxonomies such as WordNet provide a rich source of knowledge fornatural language processing applications, but are expensive to build, maintain, and extend. Motivated by the problem of automatically constructing and extending such taxonomies, in this paper we present a new algorithm for automatically learning hypernym (is-a) relations from text. Our method generalizes earlier work that had relied on using small numbers of handcrafted regular expression patterns to identify hypernym pairs.Using "dependency path" features extracted from parse trees, we introduce a general-purpose formalization and generalization of these patterns. Given a training set of text containing known hypernym pairs, our algorithm automatically extracts useful dependency paths and applies them to new corpora to identify novel pairs. On our evaluation task (determining whethertwo nouns in a news article participate in a hypernym relationship), our automatically extracted database of hypernyms attains both higher precision and higher recall than WordNet.

The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002) demonstrated that many of the filtering properties of the cochlea could be explained in terms of efficient coding of natural sounds. This model, however, did not account for properties such as phase-locking or how sound could be encoded in terms of action potentials. Here, we extend this theoretical approach with algorithm for learning efficient auditory codes using a spiking population code. Here, we propose an algorithm for learning efficient auditory codes using a theoretical model for coding sound in terms of spikes.