Robust Low-rank Spatial Pyramid Modeling with Missing Labels using Generative Adversarial Network – An approach for generating natural language sentences based on semantic parsing of a text is presented. This is done by using the concept of text-semantic representations as a basis for constructing a set of words. The word representation is defined as a set of semantic classes that are related to each other and different in a language. An automatic semantic parsing of these text-semantic representations using different text types is performed. The resulting semantic parsers’ performance is evaluated over four different corpora: English, German, French and Spanish. The results from the evaluation of the syntactic and natural language parser indicate that the proposed approach performs well even when the syntactic and natural word classes are different.
Anomaly detection provides a means for automatic and interpretable diagnosis of real-world events. We consider the problem of detecting anomalous systems in two aspects: (1) detecting the presence of anomalous devices and (2) detecting the presence of anomalous systems. Our approach proposes to first detect and assess any anomalous system and then apply a predictive model to infer the anomaly. Based on our proposed approach, we identify anomaly systems as well as a system that is the result of the system anomalous. To evaluate the predictive models, we develop a method of combining the predictive models with the hypothesis testing model and create a new anomaly detection model for each problem that is capable of detecting or recognizing anomalous systems with high probability. The method has been applied to a series of real-world datasets for which it showed similar or higher accuracy than the state-of-the-art methods.
A Deep Neural Network based on Energy Minimization
Robust Low-rank Spatial Pyramid Modeling with Missing Labels using Generative Adversarial Network
Multi-dimensional Recurrent Neural Networks for Music Genome Analysis
Towards a Deep Multitask Understanding of Task DynamicsAnomaly detection provides a means for automatic and interpretable diagnosis of real-world events. We consider the problem of detecting anomalous systems in two aspects: (1) detecting the presence of anomalous devices and (2) detecting the presence of anomalous systems. Our approach proposes to first detect and assess any anomalous system and then apply a predictive model to infer the anomaly. Based on our proposed approach, we identify anomaly systems as well as a system that is the result of the system anomalous. To evaluate the predictive models, we develop a method of combining the predictive models with the hypothesis testing model and create a new anomaly detection model for each problem that is capable of detecting or recognizing anomalous systems with high probability. The method has been applied to a series of real-world datasets for which it showed similar or higher accuracy than the state-of-the-art methods.