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Unsupervised Representation Learning and Subgroup Analysis in a Hybrid Scoring Model for Statistical Machine Learning – We present a novel algorithm for unsupervised clustering in latent space that achieves state-of-the-art performance on a variety of real-world datasets. Our algorithm uses a weighted sum-of-squares (SWS) approach to cluster models, which is a simple and effective way […]

High Dimensional Feature Selection Methods for Sparse Classifiers – This paper studies the use of latent Dirichlet allocation (LDA) in the classification task of image segmentation from a single dataset. The purpose of our work is to leverage the ability of lDA to obtain discriminative features from the source dataset. A lDA can be viewed […]

End-to-End Action Detection with Dynamic Contextual Mapping – We propose a deep-learning method to predict the action using contextual MAPs for action prediction in real-time. The state of the art works use a mixture of the following two strategies. First the action prediction is used to predict whether two events should be considered as a […]

Towards Estimating the Effects of Content on Sponsored Search Quality – In order to address the problem of censorship (in which a website is being used by advertisers to promote the product of its product) the need to be able to easily provide an accurate user feedback to advertisers can be alleviated by making use […]

User-driven indexing of papers in Educational Data Mining – In this paper, we propose a new deep neural-image visual learning approach called Deep-Named Entity Recognition, which is designed for text text and for image text. The proposed method includes a novel deep neural network architecture that is capable of both recognition and identification tasks. This […]

Deep Pose Tracking with Partified Watermark Combinations – Nam-style images of a child are a common feature of children. In this paper, we apply a method to segment images of children in both 2D and 3D scenarios using a deep convolutional network (CNN). We use the deep learning tool ChainView to perform classification on these […]

Deep Learning for Scalable Object Detection and Recognition – We address the computational bottleneck of the recently proposed unsupervised learning algorithm (NSTA) for fine-grained classification of objects in videos. In this paper, we propose a novel unsupervised learning algorithm for fine-grained classification of objects in videos. Specifically, we leverage the non-stationary model of the video, […]

Adaptive Orthogonal Gradient Method for Multi-relatikernels – Deep learning has become increasingly popular and the underlying framework of learning, based on deep neural networks, has become very popular due to its simplicity. In this paper, we study a new framework for learning by learning recurrent neural networks from deep neural networks to handle adversarial examples. […]

Deterministic Kriging based Nonlinear Modeling with Gaussian Processes – We present a novel approach to learn a non-parametric model for the problem of learning a stochastic trajectory over a network. At each time step, a set of nodes in another network is selected from a graph of non-parametric models. Under a Bayesian setting we consider […]

A Minimax Stochastic Loss Benchmark – The recent explosion of computer graphics in the last two decades have made great advancements in artificial neural networks (ANNs). In the recent years ANNs have become extremely popular for computational tasks, and this has led to increased interest in ANNs. ANNs have been extensively used in many applications. […]