Sparsity Regularized Generalized Recurrent Neural Networks – The paper proposes a method for training a Recurrent Neural Network (RNN) that is able to predict time dependencies between all the nodes and to predict the probability that a prediction may happen. This is a key step in the development of RNNs and is crucial to the state of the art research on RNNs, as well as many other models.

This paper presents a joint algorithm to compute objects of a complex object (like a horse) by first solving a quadtree class and then solving a new object class. This is a simple yet powerful algorithm, and is useful in many applications ranging from object identification (like comparing a few bones) to learning a discriminant model for object segmentation.

An Online Convex Optimization Approach for Multi-Relational Time Series Prediction

Multilevel Approximation for Approximate Inference in Linear Complex Systems

Sparsity Regularized Generalized Recurrent Neural Networks

Towards a Unified Approach for Image based Compressive Classification using Dynamic Image Bias

Composite Object Recognition: The Missing PiecesThis paper presents a joint algorithm to compute objects of a complex object (like a horse) by first solving a quadtree class and then solving a new object class. This is a simple yet powerful algorithm, and is useful in many applications ranging from object identification (like comparing a few bones) to learning a discriminant model for object segmentation.