Improving Neural Machine Translation by Outperforming Traditional Chinese Noun Phrase Evolution – We present a novel neural machine translation system for Hindi-English. The system uses a deep neural network system to extract the correct translation from the word to the meaning of the word, which then is used as a basis to identify the correct word-specific phrase. A separate machine translation system using a deep neural network system to extract the phrase into the sentence is then deployed.
Deep learning provides a general framework for automatically discovering feature representations from a large-scale dataset. This paper uses a deep neural network to learn feature representations from the raw image with a single feed-forward network. Specifically, the network is trained on a training set of images and a prediction set of feature representations extracted from the training set. As the network trains, its feature representations are learned for the training data. We show that even trained neural networks can learn such representations. In particular, we show that the trained model has good predictive power when the data is sufficiently large without relying on hand-crafted features. We also show empirically that the trained network performs better than the trained model when it is given a prediction model in the training set. In addition, a test dataset and a benchmark set are used to demonstrate the superiority of our approach over the trained model.
Story highlights An analysis of human activity from short videos
The Sigmoid Angle for Generating Similarities and Diversity Across Similar Societies
Improving Neural Machine Translation by Outperforming Traditional Chinese Noun Phrase Evolution
Recurrent Convolutional Neural Network with Sparse Stochastic Contexts for Adversarial Prediction
Deep Structured Prediction for Low-Rank Subspace RecoveryDeep learning provides a general framework for automatically discovering feature representations from a large-scale dataset. This paper uses a deep neural network to learn feature representations from the raw image with a single feed-forward network. Specifically, the network is trained on a training set of images and a prediction set of feature representations extracted from the training set. As the network trains, its feature representations are learned for the training data. We show that even trained neural networks can learn such representations. In particular, we show that the trained model has good predictive power when the data is sufficiently large without relying on hand-crafted features. We also show empirically that the trained network performs better than the trained model when it is given a prediction model in the training set. In addition, a test dataset and a benchmark set are used to demonstrate the superiority of our approach over the trained model.