Scalable Online Prognostic Coding – The framework of semi-supervised learning provides a new approach for semi-supervised learning by leveraging the rich semantic metadata provided by a user’s input. This is performed by using a deep convolutional neural network (CNN) for classification. In this paper, we take this idea to analyze a dataset of 4M pictures from the web and explore a novel approach for semi-supervised learning in the context of semantic meta-data. Using multiple state-of-the-art models and multi-task learning styles, we show that an end-to-end learning approach without a single pre-trained image classification model significantly outperforms existing semi-supervised learning approaches. The data collected from the dataset is also used to train models for meta-data in the training phase. Moreover, we also present a new benchmark dataset, which is considered as a candidate dataset for future semi-supervised learning approaches. We then compare our semi-supervised learning approach to a fully-supervised CNN algorithm by exploiting the user’s context information to show that our semi-supervised learning approach is not as robust as other such methods.

We present a simple nonlinear regularization method for the nonparametric Bayesian process model. Our algorithm has two important drawbacks. First, the nonlinear regularization is intractable in terms of convergence to state space, which can be a challenge in practice. Since the Bayesian process model assumes state space, this drawback makes our algorithm difficult to implement. Second, while nonlinear regularization can improve convergence to the model, the nonlinear regularization does not seem to improve any prediction accuracy. Nevertheless, our approach is very close to the state space regularization, and has a very good predictive accuracy. We present a new Bayesian Process Model (BMM) model for Bayesian Processes, which is a model without external sparsity. BMMs can be used in a variety of applications, including: graphical models, data inference, regression, and information processing. We show that the BMM model offers significant advantages over traditional methods and can significantly reduce the computational cost of learning the Bayesian process model.

Neural Architectures of Visual Attention

A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning

Scalable Online Prognostic Coding

Stochastic Recurrent Neural Networks for Speech Recognition with Deep Learning

Bayesian Optimization: Estimation, Projections, and the Non-Gaussian BlocWe present a simple nonlinear regularization method for the nonparametric Bayesian process model. Our algorithm has two important drawbacks. First, the nonlinear regularization is intractable in terms of convergence to state space, which can be a challenge in practice. Since the Bayesian process model assumes state space, this drawback makes our algorithm difficult to implement. Second, while nonlinear regularization can improve convergence to the model, the nonlinear regularization does not seem to improve any prediction accuracy. Nevertheless, our approach is very close to the state space regularization, and has a very good predictive accuracy. We present a new Bayesian Process Model (BMM) model for Bayesian Processes, which is a model without external sparsity. BMMs can be used in a variety of applications, including: graphical models, data inference, regression, and information processing. We show that the BMM model offers significant advantages over traditional methods and can significantly reduce the computational cost of learning the Bayesian process model.