Solving for a Weighted Distance with Sparse Perturbation – One of the most important problems in the computational literature is the optimization of the posterior of a given problem. The problem is known as the Optimization of Exponential Value Maps (OPMC). In this paper, we consider this problem in a different way. First, we provide an efficient algorithm for solving this problem. Then we propose a method for the optimization of the Optimization of Exponential Value Maps (OPMC) problem, which, under these algorithms, is efficient. We provide some preliminary evaluations, which indicate that the effectiveness of our method in solving the problem is at least a factor of 3.

We propose an efficient and robust deep learning approach, which is able to learn the phonetic structure of a sequence in a principled way. Our approach consists in learning a novel classifier and an efficient classifier, while also learning a robust classifier that can exploit the phonetic structure of a sequence to better represent the phonetic structure of the sequences.

We present a novel model that learns the structure of Chinese phonetic strings from phonetic strings, the most common representation of Chinese words. This model is based on learning a model of phonetic strings, a grammar, for the purpose of representing phonetic strings. We evaluate the model on Chinese speech recognition tasks, and demonstrate that the model can outperform the current state-of-the-art for such tasks. Finally, we compare the success rates of the model with other approaches to learning Chinese phonetic strings for different languages.

Optimal error bounds for belief functions

A Large Scale Benchmark Dataset for Multimedia Video Annotation and Creation Evaluation

# Solving for a Weighted Distance with Sparse Perturbation

Tight and Conditionally Orthogonal Curvature

Learning to Recognize Chinese Characters by Summarizing the Phonetic StructureWe propose an efficient and robust deep learning approach, which is able to learn the phonetic structure of a sequence in a principled way. Our approach consists in learning a novel classifier and an efficient classifier, while also learning a robust classifier that can exploit the phonetic structure of a sequence to better represent the phonetic structure of the sequences.

We present a novel model that learns the structure of Chinese phonetic strings from phonetic strings, the most common representation of Chinese words. This model is based on learning a model of phonetic strings, a grammar, for the purpose of representing phonetic strings. We evaluate the model on Chinese speech recognition tasks, and demonstrate that the model can outperform the current state-of-the-art for such tasks. Finally, we compare the success rates of the model with other approaches to learning Chinese phonetic strings for different languages.