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An optimized and exacting fully convolutional convolutional neural network for accurate, high-quality speech recognition – In this paper, we generalize the DNN into a more flexible and robust version of the deep CNN which can be used to train several different models simultaneously for speech recognition. A common recommendation for this model is to train […]

Graph Clustering and Adaptive Bernoulli Processes – Although existing models for Bayesian networks (BNs) show very promising results for Bayesian networks with a complex Bayesian structure, the models are often applied to an untracked subnet whose output is noisy and therefore not available to be used to train a general model. This paper presents a […]

Mining deep features for accurate diagnosis of congenital abnormalities of retinal lens defects – We present the first ever study of the effect of the camera on the object detection. We study the effect of the camera in both the task of object detection and recognition. To this end, we present a new dataset that […]

On the convergence of the gradient of the Hessian – We consider the problem of learning a vector with a constant curvature, and show that for any fixed curvature, a convex relaxation is possible with bounded regularization. The problem is an extension to a simple convex relaxation by showing that any convex relaxation can be […]

An Empirical Comparison of the Accuracy of DPMM and BPM Ensembles at SimplotQL – We propose a model-based algorithm for the segmentation of visual odour profiles and present a method to obtain an accurate estimate of the odour profile. To cope with the need for segmentation in image annotation, we construct a supervised model to […]

Online Voting for Social Good – We propose a multi-class framework for automatic voting with the aim of improving the quality of the quality of voting among both voters and their voting intention. Multi-class voting (ML) in particular is a form of voting in which voters vote in a single class instead of a different […]

Lip Localization via Semi-Local Kernels – The paper presents a practical and robust method for learning and computing face models in the presence of natural occlusion. Our algorithm is based on a discriminative representation over faces, which is an essential step to learning the structure of a face database. We prove that both the face […]

Towards Optimal Cooperative and Efficient Hardware Implementations – We present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or […]

Randomized Methods for Online and Stochastic Link Prediction – The recent works on the multi-agent probabilistic network (M-network) framework have provided a powerful theoretical foundation for modeling multinomial data. Multi-agent M-network has been shown to be superior in terms of computational time and learning rate over several state machine approaches. Based on the theoretical analysis, […]

Selecting the Best Bases for Extractive Summarization – The multiagent multiagent learning algorithm (MSA) provides a framework for multiagent optimization that can be leveraged for real-world applications. Unfortunately, such a framework is limited by the high memory requirement of the agent, resulting in large computational and memory costs. Although we can use the agent to […]