A Novel Architecture for Building Datasets of Constraint Solvers – Many applications arise when a system is a collection of agents, for example, to solve a complex and complex-valued optimization problem. In this work we propose a novel framework for building a collection of constraint solvers for these systems by learning a hierarchy of constraint solvers and learning a structure that makes use of these solvers. Our framework uses the fact that constraint solvers are generated at the node level rather than the node levels to represent their constraints. This allows us to create problems that are naturally solvable in a distributed architecture. We evaluate our approach on two data sets, namely the data set of the Amazon Alexa (e.g., the purchase of coffee and the product description), and also demonstrate that the framework is effective for these situations.

Learning a linear model of a dynamic environment is a core problem in many machine learning algorithms. This paper presents a framework to construct and use some natural language model structures such as dynamical systems, and show how to adapt to this dynamic environment by using the concepts of memory and spatial modeling. In particular, we show how to transform memory into a sparse representation for the dynamical system and propose an algorithm to learn an effective dynamical system from data.

Fast Non-convex Optimization with Strong Convergence Guarantees

A Data based Approach for Liver and Bone Diseases Prediction

A Novel Architecture for Building Datasets of Constraint Solvers

Learning the Parameters of the LQR Kernel and its Variational Algorithms

On the Relationship Between the Random Forest and Graph MatchingLearning a linear model of a dynamic environment is a core problem in many machine learning algorithms. This paper presents a framework to construct and use some natural language model structures such as dynamical systems, and show how to adapt to this dynamic environment by using the concepts of memory and spatial modeling. In particular, we show how to transform memory into a sparse representation for the dynamical system and propose an algorithm to learn an effective dynamical system from data.