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Dynamics-based sampling methods, such as Hybrid Monte Carlo (HMC) and Langevin dynamics (LD), are commonly used to sample target distributions. Recently , such approaches have been combined with stochastic gradient techniques to increase sampling efficiency when dealing with large datasets. An outstanding problem with this approach is that the stochastic(More)
We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power law properties , unlike Dirichlet(More)
Hierarchical modeling and reasoning are fundamental in machine intelligence, and for this the two-parameter Poisson-Dirichlet Process (PDP) plays an important role. The most popular MCMC sampling algorithm for the hierarchical PDP and hierarchical Dirichlet Process is to conduct an incremental sampling based on the Chinese restaurant metaphor, which(More)
A new framework for topic modeling is developed , based on deep graphical models, where interactions between topics are inferred through deep latent binary hierarchies. The proposed multi-layer model employs a deep sigmoid belief network or restricted Boltzmann machine, the bottom binary layer of which selects topics for use in a Poisson factor analysis(More)
In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions , which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson(More)
Effective training of deep neural networks suffers from two main issues. The first is that the parameter spaces of these models exhibit pathological curvature. Recent methods address this problem by using adaptive pre-conditioning for Stochastic Gradient Descent (SGD). These methods improve convergence by adapting to the local geometry of parameter space. A(More)
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace We note that, technically, the low-dimensional space we compute may not be a subspace of the original high-dimensional space. However, it is convenient to envision it as a subspace when explaining the algorithms. without(More)
Unlike other biometric authentication methods, gait recognition is noninvasive and effective from a distance. However, the performance of gait recognition will suffer in the low-resolution (LR) case. Furthermore, when gait sequences are projected onto a nonoptimal low-dimensional subspace to reduce the data complexity, the performance of gait recognition(More)
A chemical genetics approach identified a cellular target of several proapoptotic farnesyl transferase inhibitors (FTIs). Treatment with these FTIs caused p53-independent apoptosis in Caenorhabditis elegans, which was mimicked by knockdown of endosomal trafficking proteins, including Rab5, Rab7, the HOPS complex, and notably the enzyme Rab geranylgeranyl(More)
  • Sophie I Candille, Catherine D. Van Raamsdonk, Changyou Chen, Sanne Kuijper, Yanru Chen-Tsai, Andreas Russ +2 others
  • 2004
Many members of the animal kingdom display coat or skin color differences along their dorsoventral axis. To determine the mechanisms that control regional differences in pigmentation, we have studied how a classical mouse mutation, droopy ear (de(H)), affects dorsoventral skin characteristics, especially those under control of the Agouti gene. Mice carrying(More)