Probabilistic Models for Constraint-Free Semantic Parsing with Sparse Linear Programming – We propose an efficient method for automatic semantic labeling for multi-label classification. Compared to previous techniques based on model-based approaches, the proposed method aims at representing both the semantic information that is provided by models and the discriminative semantic information that is provided by data. Based on the idea of combining Semantic Hierarchy (a.k.a Semantic Hierarchical Modeling) with a relational model, we implement a sequential-memory, multi-label classification model trained using semantic labels. The proposed model can not be used with any other semantic model, such as a supervised classifier. We further propose a new Semantic Hierarchical Model-based model for semi-supervised classification, incorporating two different models based on semantic labels and a classifier trained on Semantic Hierarchy, and a hierarchical model trained on the Semantic Hierarchical Model.
In this work, we propose to address a fundamental problem in deep learning which is to learn to predict the outcome of a neural network in the form of a posteriori vector embedding. The neural network is trained with a random neural network trained with the divergence function to predict the response of the neural network to a given input. In this work, we propose the posteriori vector embedding for deep learning models which can efficiently learn to predict the outcome of an input vector if it satisfies a generalization error criterion. Experimental evaluation of the proposed posteriori vector embeddings on the MNIST dataset demonstrates the superior performance of the proposed neural networks. A separate study with a different network is also performed on the Penn Treebank datasets to evaluate the performance of the proposed network.
Estimating Energy Requirements for Computation of Complex Interactions
Using Dendroid Support Vector Machines to Detect Rare Instances in Trace Events
Probabilistic Models for Constraint-Free Semantic Parsing with Sparse Linear Programming
An extended IRBMTL from Hadamard divergence to the point of incoherence
Sparse Sparse Coding for Deep Neural Networks via Sparsity DistributionsIn this work, we propose to address a fundamental problem in deep learning which is to learn to predict the outcome of a neural network in the form of a posteriori vector embedding. The neural network is trained with a random neural network trained with the divergence function to predict the response of the neural network to a given input. In this work, we propose the posteriori vector embedding for deep learning models which can efficiently learn to predict the outcome of an input vector if it satisfies a generalization error criterion. Experimental evaluation of the proposed posteriori vector embeddings on the MNIST dataset demonstrates the superior performance of the proposed neural networks. A separate study with a different network is also performed on the Penn Treebank datasets to evaluate the performance of the proposed network.