On the Semantic Web: Deep Networks Are Better for Visual Speech Recognition – Deep learning is a promising approach for the retrieval and recognition of speech words. The problem has been raised to this point by various research efforts. However, they are usually either prohibitively expensive or impractical in the large-scale applications. In this paper we will propose and analyse two new methods for the retrieval, recognition and learning tasks in deep neural networks.
Non-linear regression (NRL) has recently been widely utilized and well-understood in the context of semantic object recognition (SOL) tasks. In this work, we propose a new model that exploits nonlinearity to train a set of nonlinear units over the underlying semantic structure of a domain and to predict the output of an external dictionary. The dictionary structure allows us to directly learn the relevant structure and to avoid expensive training and annotation costs. To our knowledge, this is the first model that exploits linearity in order to predict semantic structure and to perform accurate predictions on a large corpus of object-based SOL domains under semantic context, where the domains are not hierarchically organized. We demonstrate that our model achieves excellent results on the Oxford-Nordic MOCA task, and also demonstrates that for both real-world and synthetic datasets, it can be used to efficiently learn the semantic structure of an object using a simple linear programming language.
Efficient Sparse Subspace Clustering via Matrix Completion
Sparse Depth Estimation via Sparse Gaussian Mixture Modeling and Constraint Optimization
On the Semantic Web: Deep Networks Are Better for Visual Speech Recognition
T-distributed multi-objective regression with stochastic support vector machines
Augmenting Web Page Visibility Dataset with Disparate Linguistic AttentionNon-linear regression (NRL) has recently been widely utilized and well-understood in the context of semantic object recognition (SOL) tasks. In this work, we propose a new model that exploits nonlinearity to train a set of nonlinear units over the underlying semantic structure of a domain and to predict the output of an external dictionary. The dictionary structure allows us to directly learn the relevant structure and to avoid expensive training and annotation costs. To our knowledge, this is the first model that exploits linearity in order to predict semantic structure and to perform accurate predictions on a large corpus of object-based SOL domains under semantic context, where the domains are not hierarchically organized. We demonstrate that our model achieves excellent results on the Oxford-Nordic MOCA task, and also demonstrates that for both real-world and synthetic datasets, it can be used to efficiently learn the semantic structure of an object using a simple linear programming language.