Deep Neural Networks on Text: Few-Shot Learning Requires Convolutional Neural Networks – Textual content is becoming increasingly available through the Internet, which is a powerful means of social media. Traditional text detection tasks, such as word identification, feature engineering, and text co-occurrence tasks, are limited to a single set of text features, which usually requires a deep learning model to learn feature from text. In this paper, we present a method for improving text detection performance on a wide range of texts. Specifically we perform segmentation and recognition for the most famous texts (Chi, Yao, and Zhang). Specifically, we perform a segmentation based analysis of a feature set consisting of high intensity texts, and a deep learning model to learn feature from text. Experimental results on two big datasets show that our approach provides improved results compared to other state of the art methods.
This paper presents the first algorithm for clustering of time series for which one-dimensional (i.e., non-Gaussian) vectors are available. The algorithm is based on a nonlinear model that estimates the expected time of the predicted events, and then estimates the nonlinear model using the corresponding Euclidean distance. A dataset with high-resolution 3D images is created, and the classifiers are used to segment and cluster the data of interest, using several techniques including dimensionality reduction, multi-scale regression, and clustering of the data. The datasets are created using standard time series clustering methods using a multi-class classification framework. The algorithm is then applied to an ensemble of data obtained using the 3D time series dataset, consisting of a dataset with a large number of clusters. The method was tested on several datasets with varying number of clusters, and with different data types, including data with small number of clusters. The algorithm was tested on both simulated and real data sets.
On the Runtime and Fusion of Two Generative Adversarial Networks
Tensor-based transfer learning for image recognition
Deep Neural Networks on Text: Few-Shot Learning Requires Convolutional Neural Networks
An ensemble-based model for the classification of partially observable eventsThis paper presents the first algorithm for clustering of time series for which one-dimensional (i.e., non-Gaussian) vectors are available. The algorithm is based on a nonlinear model that estimates the expected time of the predicted events, and then estimates the nonlinear model using the corresponding Euclidean distance. A dataset with high-resolution 3D images is created, and the classifiers are used to segment and cluster the data of interest, using several techniques including dimensionality reduction, multi-scale regression, and clustering of the data. The datasets are created using standard time series clustering methods using a multi-class classification framework. The algorithm is then applied to an ensemble of data obtained using the 3D time series dataset, consisting of a dataset with a large number of clusters. The method was tested on several datasets with varying number of clusters, and with different data types, including data with small number of clusters. The algorithm was tested on both simulated and real data sets.