Learning Disentangled Representations with Latent Factor Modeling – In some applications, a data-dependent representation of the data may provide insights into the distribution of uncertainty associated with the measurement error. Such an insight can be used in a variety of applications, such as learning to predict a given event, learning from noisy measurements of the underlying structure in a given data, and learning to predict a given distribution of uncertainty at a target time. In this paper, we propose a novel Bayesian inference framework to obtain predictive distributions over the observed data. The proposed framework relies on a Bayesian approach to inference in unstructured data, where only the observed data are available. We provide an efficient method for inference, provide a Bayesian framework to optimize a model, and demonstrate both the utility of the Bayesian framework and the ability to leverage the uncertainty as inputs to the inference task.

Hierarchical classification models are used to identify objects based on structure similarity or similarity metrics. Hierarchical classification models are useful for many natural and natural-looking tasks such as image classification, object recognition and image categorization. Most existing classification methods have a hierarchical representation of object instances but little is known about object types such as shape, shape-based and shape-based pose. In this paper, we propose a new hierarchical classification model, Hierarchical Classification-Hierarchical Classification (ICCD) which has a hierarchical model that represents each instance in its hierarchy according to its shape and pose. The proposed hierarchical classification model achieves classification accuracy with respect to the previous state-of-the-art classification methods with high confidence.

Efficient Ranking of High Dimensional Features using a random partition function, using a generalized kernel

Learning an Integrated Deep Filter based on Hybrid Coherent Cuts

Learning Disentangled Representations with Latent Factor Modeling

Design and Implementation of a Universal System of Intrusion Detection Systems with Application to Air Traffic Management

Sparse Representation based Object Detection with Hierarchy Preserving HomologyHierarchical classification models are used to identify objects based on structure similarity or similarity metrics. Hierarchical classification models are useful for many natural and natural-looking tasks such as image classification, object recognition and image categorization. Most existing classification methods have a hierarchical representation of object instances but little is known about object types such as shape, shape-based and shape-based pose. In this paper, we propose a new hierarchical classification model, Hierarchical Classification-Hierarchical Classification (ICCD) which has a hierarchical model that represents each instance in its hierarchy according to its shape and pose. The proposed hierarchical classification model achieves classification accuracy with respect to the previous state-of-the-art classification methods with high confidence.