2 GMM for linear regression with two unknowns with evaluation 2 2.1 The Model and the Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 The Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 Evaluating the Precision of the Estimates . . . . . . . . . . . . . . . . . . . . 6 2.3.1 Simulations vs. Theoretical Derivations . . . . . . . . . . ...

In this paper we consider the combination of hidden Markov models based on Gaussian mixture densities (GMM-HMM) and linear dynamic models (LDM) as the acoustic model for automatic speech recognition systems. In doing so, the individual strengths of both models, i.e. the modelling of long-term temporal dependencies by the GMM-HMM and the direct modelling of statistical dependencies between conse...

• Sparse iterative linear solvers are the most important building block in (implicit) schemes for PDE problems • In FD, FV and FE discretisations • Lots of research on GPUs so far for Krylov subspace methods, ADI approaches and multigrid • But: Limited to simple preconditioners and smoothing operators •Numerically strong smoothers exhibit inherently sequential data dependencies (impossible to p...

Despite successful applications across a broad range of NLP tasks, conditional random fields (“CRFs”), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items. Attempts to extend CRFs to capture long-range dependencies h...

Capturing dependencies in images in an unsupervised manner is important for many image processing applications. We propose a new method for capturing nonlinear dependencies in images of natural scenes. This method is an extension of the linear Independent Component Analysis (ICA) method by building a hierarchical model based on ICA and mixture of Laplacian distribution. The model parameters are...

Recurrent neural networks (RNNs) are capable of learning features and long term dependencies from sequential and time-series data. The RNNs have a stack of non-linear units where at least one connection between units forms a directed cycle. A well-trained RNN can model any dynamical system; however, training RNNs is mostly plagued by issues in learning long-term dependencies. In this paper, we ...

Recently, the issues of how to define functional dependencies (XFDs) and multivalued dependencies (XMVDs) in XML have been investigated. In this paper we consider the problem of checking the satisfaction of a set of XMVDs in an XML document. We present an algorithm using extensible hashing to check whether an XML document satisfies a given set of XMVDs. The performance of the algorithm is shown...

This paper addresses the problem of recognizing and localizing coherent activities of a group of people, called collective activities, in video. Related work has argued the benefits of capturing long-range and higher-order dependencies among video features for robust recognition. To this end, we formulate a new deep model, called Hierarchical Random Field (HiRF). HiRF models only hierarchical d...