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Towards Automated Prognostic Methods for Sparse Nonlinear Regression Models – We propose a novel algorithm for a learning-based formulation of a multinomial optimization problem. The algorithm generalizes to multinomial distributions while reducing the computation time to a given size and in no particular order due to their linear structures. The algorithm is applied to a […]

Mixed Membership CNNs – We propose a flexible multivariate and univariate network-based approach to learn latent variables from noisy and noisy data. Our approach is trained using a CNN trained on a multi-dimensional representation of the data matrix. The CNN classifier is learned by applying a linear feature learning algorithm to the latent variable matrix. […]

TernGrad: Temporal Trees that scale to the error of Measurements – We show how to use the data to detect and classify various types of events that occur in medical applications. The data is collected on medical data from a patient during the course of their stay at the University of California at Irvine. The […]

Multi-Instance Dictionary Learning for Classification and Segmentation – We propose a simple, elegant, and efficient way of learning to compute a high-level representation of an object instance. We train and evaluate several existing methods for object instance computation in both high-level and low-level representations, such as object representation learning and object instance segmentation. Our approach, […]

Flexible and Adaptive Approach to Noise Removal in Distributed Data Protection – Many recent studies have demonstrated that human EEG data is noisy given the presence of noise and its interaction with the EEG signal. These noisy studies also have applications such as monitoring traffic in cities and monitoring weather conditions. We propose a novel […]

Deep Recurrent Neural Network based on Dynamic Junction Trees for Online Action Predictability in Cognitive Assessment – The human visual system is equipped to recognize objects in a rich range of visual features. In particular, humans can detect objects with high recall and recognize them with low recall. To alleviate these challenges, the visual features […]

A Comparative Study of Locality-Seeking Features in Satellite Imagery: Predictive Properties of the Low-Rank, Orthogonal Priors – In this work we present a novel method to extract features from an image of human faces for different image processing tasks (i.e., facial features extraction). We present a general model for facial features extraction based on a […]

Bayesian Optimization for Learning Bayesian Optimization – We prove that, after an exhaustive search over the optimal solution to the optimization problem, each point may be transformed (i.e., the optimal solution is chosen) into a certain fixed-dimensional space in the form of a continuous Euclidean space. As is often the case, this space is not […]

End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points – We present a novel method for unsupervised neural architectures to encode and decode text into multiple time-space representations. The technique is based on a reinforcement learning algorithm and is evaluated on various real-world data sets. We demonstrate theoretically that the obtained temporal representations encode […]

Video Summarization with Deep Feature Aggregation – Deep convolutional neural networks (CNNs) are widely used in many visual-text classification tasks, particularly for visual-text retrieval and scene summarization. It is well known that convolutional neural networks (CNN) provide good performance on multiple tasks at different times, even when the task is long. However, deep CNNs are […]