The restricted Boltzmann machine is a graphical model for binary random variables. Based on a complete bipartite graph separating hidden and observed variables, it is the binary analog to the factor analysis model. We study this graphical model from the perspectives of algebraic statistics and tropical geometry, starting with the observation that its Zariski closure is a Hadamard power of the f...

There are many classification tasks where we are given a large number of unlabeled examples in addition to only a few labeled training examples. For such scenario, it is important to include unlabeled examples during the training to generalize well to the unseen data, and thus avoid overfitting. Larochelle and Bengio (2008) proposed the semi-supervised training of the discriminative restricted ...

This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary. We are exploring various features to improve the set of sentences selected for the summary, a...

Automatic extraction of distinctive features from a visual information stream is challenging due to the large amount of information contained in most image data. In recent years deep neural networks (DNNs) have gained outstanding popularity for solving visual information processing tasks. This study reports novel contributions, including a new DNN architecture and training method, which increas...

Due to the large increase of malware samples in the last 10 years, the demand of the antimalware industry for an automated classifier has increased. However, this classifier has to satisfy two restrictions in order to be used in real life situations: high detection rate and very low number of false positives. By modifying the perceptron algorithm and combining existing features, we were able to...

Collaborative filtering and Content-based filtering methods are two famous methods used by recommender systems. Restricted Boltzmann Machine(RBM) model rivals the best collaborative filtering methods, but it focuses on modeling the correlation between item ratings. In this paper, we extend RBM model by incorporating content-based features such as user demograohic information, items categorizati...

The problem of imbalanced data, i.e., when the class labels are unequally distributed, is encountered in many real-life application, e.g., credit scoring, medical diagnostics. Various approaches aimed at dealing with the imbalanced data have been proposed. One of the most well known data pre-processing method is the Synthetic Minority Oversampling Technique (SMOTE). However, SMOTE may generate ...

Deep belief nets have been successful in modeling handwritten characters, but it has proved more difficult to apply them to real images. The problem lies in the restricted Boltzmann machine (RBM) which is used as a module for learning deep belief nets one layer at a time. The Gaussian-Binary RBMs that have been used to model real-valued data are not a good way to model the covariance structure ...

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias randomly and do not change them in the whole process. Although the network works well, the random weights in the input layer can make the algorithm less effective and impact on its perfo...

Through the introduction of local receptive fields, we improve the fidelity of restricted Boltzmann machine (RBM) based representations to encodings extracted by visual processing neurons. Our biologically inspired Gaussian receptive field constraints encourage learning of localized features and can seamlessly integrate into RBMs. Moreover, we propose a method for concurrently finding advantage...