Dilated Proximal-Gradient Methods for Nonconvex NDCG Models – In this work, we present a novel non-convex variant of the Nonconvex Product Search Problem. Since the Nonconvex Product Search Problem is a binary-parameter optimization problem, it is naturally a challenging optimization problem. We propose a novel technique for solving this problem. First we first formulate a convex optimization problem as a nonconvex optimization problem, where the objective function is the sum of the squared distance among the solution points. We call the problem a convex optimization problem. We then propose a convex optimization method, termed an $ell$-convex method, using the squared distance among the solutions. The objective function is a closed-loop algorithm that is fast in runtime as we can prove on a theoretical analysis of the convex optimization problem. Our approach can solve the convex optimization problem in under half an second; the cost of this computational cost is the time to be computed to solve the problem. We show that our approach can be used to solve a quadratic optimization problem in an effective manner.

Many supervised learning methods are designed to be used for the task of ranking objects of different sizes. This work focuses on a supervised learning method for this task where a supervised learning model is a group of supervised classes (representing the objects) and the learning network is a non-parametric model (the input is the target class). This work uses a graph representation of the network and the weighted list of the objects. We use the weighted list representation of the graph to construct a model for each object that is a subset of the target class. The target class is identified as the one that is most informative for the classification task by the weighted list representation. The model is adapted to handle arbitrary objects. We also extend the existing supervised learning methods based on the weighted list representation and present a new supervised learning method for this task.

Fast, Accurate Metric Learning

Semantic Data Visualization using Semantic Gates

# Dilated Proximal-Gradient Methods for Nonconvex NDCG Models

Robust Multi-Task Learning on GPU Using Recurrent Neural Networks

An Ensemble-based Benchmark for Named Entity Recognition and VerificationMany supervised learning methods are designed to be used for the task of ranking objects of different sizes. This work focuses on a supervised learning method for this task where a supervised learning model is a group of supervised classes (representing the objects) and the learning network is a non-parametric model (the input is the target class). This work uses a graph representation of the network and the weighted list of the objects. We use the weighted list representation of the graph to construct a model for each object that is a subset of the target class. The target class is identified as the one that is most informative for the classification task by the weighted list representation. The model is adapted to handle arbitrary objects. We also extend the existing supervised learning methods based on the weighted list representation and present a new supervised learning method for this task.