Learning to Recognize Chinese Characters by Summarizing the Phonetic Structure – 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.

The goal of this paper is to present the results of an experimental study involving a semi-supervised approach to learning a nonlinear, non-parametric model for a large-scale online video content analysis task. We perform a thorough evaluation of the proposed approach using both human and machine learning. The human and machine learning are the primary techniques used. In our evaluation, we are willing to put our human work at a competitive level, since its ability to handle large-scale problems becomes a key and pivotal issue. In this way, our analysis is conducted on two large-scale problem instances: a large-scale video extraction task from NIMH website, and a video content analysis task from Google Play. In this way, we find that our multi-task approach is very robust, surpassing all previous work on the performance.

Efficient Parallel Training for Deep Neural Networks with Simultaneous Optimization of Latent Embeddings and Tasks

Learning the Spatial Subspace of Neural Networks via Recurrent Neural Networks

Learning to Recognize Chinese Characters by Summarizing the Phonetic Structure

A Survey of Multispectral Image Classification using Gaussian Processes

A Systematic Evaluation of the Impact of MINE on MOOC ComputingThe goal of this paper is to present the results of an experimental study involving a semi-supervised approach to learning a nonlinear, non-parametric model for a large-scale online video content analysis task. We perform a thorough evaluation of the proposed approach using both human and machine learning. The human and machine learning are the primary techniques used. In our evaluation, we are willing to put our human work at a competitive level, since its ability to handle large-scale problems becomes a key and pivotal issue. In this way, our analysis is conducted on two large-scale problem instances: a large-scale video extraction task from NIMH website, and a video content analysis task from Google Play. In this way, we find that our multi-task approach is very robust, surpassing all previous work on the performance.