Improving Recurrent Neural Networks with Graphs


Improving Recurrent Neural Networks with Graphs – We present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.

K-means is one of the fastest evolving data mining algorithms. It is an algorithm that is able to perform clustering and other computationally intensive experiments while being relatively efficient. This paper presents an experimental evaluation of K-means using synthetic and real data from KDDU. KDDU used a synthetic and real data set for training the algorithm to produce data samples and a real data set for testing the performance of K-means with real data. The simulated data set was used to generate a K-means dataset with a variety of conditions. The dataset size and accuracy was tested using an automated system designed to detect anomalies and analyze the impact of anomalies. This paper presents the experimental results for KDDU and simulated data to illustrate the utility of K-means and the performance of KDDU on synthetic data sets.

Sparse and Robust Subspace Segmentation using Stereo Matching

Inference from Sets with and Without Inputs: Unsupervised Topic Models and Bayesian Queries

Improving Recurrent Neural Networks with Graphs

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  • Neural Architectures of Genomic Functions: From Convolutional Networks to Generative Models

    On the Convergence of K-means ClusteringK-means is one of the fastest evolving data mining algorithms. It is an algorithm that is able to perform clustering and other computationally intensive experiments while being relatively efficient. This paper presents an experimental evaluation of K-means using synthetic and real data from KDDU. KDDU used a synthetic and real data set for training the algorithm to produce data samples and a real data set for testing the performance of K-means with real data. The simulated data set was used to generate a K-means dataset with a variety of conditions. The dataset size and accuracy was tested using an automated system designed to detect anomalies and analyze the impact of anomalies. This paper presents the experimental results for KDDU and simulated data to illustrate the utility of K-means and the performance of KDDU on synthetic data sets.


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