Cytoplasmic CstF-77 protein belongs to a Masking Complex with CPEB in Xenopus Oocytes Christel Rouget, Catherine Papin and Elisabeth Mandart From the Centre de Recherches de Biochimie Macromoléculaire, CNRS, 1919 route de Mende, 34293 Montpellier cedex 05, France Running title: Cytoplasmic CstF-77 in masking mRNA Address correspondence to Elisabeth Mandart: CRBM, CNRS, 1919 route de Mende, 3429...

In an effort to better understand the complex courtship behaviour of pigeons, we have built a model learned from motion capture data. We employ a Conditional Restricted Boltzmann Machine (CRBM) with binary latent features and real-valued visible units. The units are conditioned on information from previous time steps to capture dynamics. We validate a trained model by quantifying the characteri...

Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion. They have been widely employed as an unsupervised pre-training method in machine learning. RBMs have been modified to model time series in two main ways: The Temporal RBM stacks a number of RBMs laterally and introduces temporal dependencies bet...

This paper studies the problem of applying machine learning with deep architecture to time series forecasting. While these techniques have shown promise for modeling static data, applying them to sequential data is gaining increasing attention. This paper overviews the particular challenges present in applying Conditional Restricted Boltzmann Machines (CRBM) to univariate time-series forecastin...

In an effort to better understand the complex courtship behaviour of pigeons, we have built a model learned from motion capture data. We employ a Conditional Restricted Boltzmann Machine (CRBM) with binary latent features and real-valued visible units. The units are conditioned on information from previous time steps to capture dynamics. We validate a trained model by quantifying the characteri...

In this paper we develop a class of nonlinear generative models for high-dimensional time series. We first propose a model based on the restricted Boltzmann machine (RBM) that uses an undirected model with binary latent variables and real-valued “visible” variables. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-st...

Through their misuse of authorized privileges, insiders have caused great damage and loss to corporate internal information assets, especially within the Intelligence Community (IC). Intelligence management has faced increasing complexities of delegation and granular protection as more corporate entities have worked together in a dynamic collaborative environment. We have been confronted by the...

In this thesis, we present a method for learning problem-specific hierarchical features specialized for vision applications. Recently, a greedy layerwise learning mechanism has been proposed for tuning parameters of fully connected hierarchical networks. This approach views layers of a network as Restricted Boltzmann Machines (RBM), and trains them separately from the bottom layer upwards. We d...

This paper presents a voice conversion (VC) method that utilizes conditional restricted Boltzmann machines (CRBMs) for each speaker to obtain high-order speaker-independent spaces where voice features are converted more easily than those in an original acoustic feature space. The CRBM is expected to automatically discover common features lurking in time-series data. When we train two CRBMs for ...

Oger (OrGanic Environment for Reservoir computing) is a Python toolbox for building, training and evaluating modular learning architectures on large data sets. It builds on MDP for its modularity, and adds processing of sequential data sets, gradient descent training, several crossvalidation schemes and parallel parameter optimization methods. Additionally, several learning algorithms are imple...