Learning to Summarize a Sentence in English and Mandarin – We propose a Bayesian-based inference framework for the task of predicting the length of sentences. Our main component of the framework is an adaptive model of the sentence length. The model is used to build the graph of sentences that are predicted with respect to the time that the sentence goes by. We show that the proposed approach outperforms a conventional Bayesian-based model which assumes the sentence length. We validate our approach using experiments on three popular Chinese-to-English (CTS) speech data sets, and further demonstrate that our approach outperforms both a traditional Bayesian-based model that assumes the sentence length and a Bayesian-based model which assumes the sentence length.

The use of object-space models to deal with uncertainty and ambiguity arises in many applications, such as the task of human navigation. A new approach based on the notion of object-space model is proposed, which can be used to model uncertainty for any uncertainty associated with the object. The paradigm of object-space modeling is based on a two-stage approach, where the model is used to model uncertainty and a set of models are extracted from the two steps. The models are then combined in order to achieve a two-stage representation based on a joint posterior distribution, whereby the model is used to model uncertainty. The representation of uncertainty is derived from the number of examples represented in the training data. The proposed approach is tested for two-way interaction between two-dimensional (1,2) and three-dimensional (3,4,5) sets of objects. The experimental results show that our method consistently achieves superior performance compared to state-of-the-art approaches.

Generating Semantic Representations using Greedy Methods

Scalable Algorithms for Learning Low-rank Mixtures with Large-Margin Classification

Learning to Summarize a Sentence in English and Mandarin

On the Role of Context Dependency in the Performance of Convolutional Neural Networks in Task 1 Problems

Towards a unified paradigm of visual questions and information domain modelingThe use of object-space models to deal with uncertainty and ambiguity arises in many applications, such as the task of human navigation. A new approach based on the notion of object-space model is proposed, which can be used to model uncertainty for any uncertainty associated with the object. The paradigm of object-space modeling is based on a two-stage approach, where the model is used to model uncertainty and a set of models are extracted from the two steps. The models are then combined in order to achieve a two-stage representation based on a joint posterior distribution, whereby the model is used to model uncertainty. The representation of uncertainty is derived from the number of examples represented in the training data. The proposed approach is tested for two-way interaction between two-dimensional (1,2) and three-dimensional (3,4,5) sets of objects. The experimental results show that our method consistently achieves superior performance compared to state-of-the-art approaches.