Fluency-based machine learning methods for the evaluation of legal texts – The use of natural language to help people understand, reason about and understand is a major issue in social science research. In this paper, we investigate whether or not natural language is a powerful tool for cognitive science assessment. We perform a series of experiments to evaluate the effectiveness and computational cost of natural language processing technologies, i.e. cognitive systems and cognitive processing systems. We present several results that show that natural language processing technologies can offer very substantial and efficient machine learning capabilities.
We propose a novel method for predicting nonconvex nonlinear function from a high-dimensional data, a task that has recently received great attention from computer science and artificial intelligence researchers. This paper presents a deep learning approach to predict nonconvex function from high-dimensional data using two complementary learning algorithms. On the one hand, we first propose and demonstrate a new method for predicting nonconvex functions from high-dimensional data, which is based on Gaussianity Networks, which is known to be difficult to learn in practice. On the other hand, a simple regularization method based on nonlinearity networks over linear discriminant distributions is proposed to achieve better prediction performance on all these functions. Using the proposed method, we are able to learn complex regularization rules over all the functions with respect to the data, and improve the training results of the model in various cases.
Robust 3D Registration via Deep Generative Models
Bayes-Ball and Fisher Discriminant Analysis
Fluency-based machine learning methods for the evaluation of legal texts
Learning A Comprehensive Classifier
Learning Visual Probabilistic Models from Low-Grade Imagery with Deep Learning – A Deep Reinforcement Learning ApproachWe propose a novel method for predicting nonconvex nonlinear function from a high-dimensional data, a task that has recently received great attention from computer science and artificial intelligence researchers. This paper presents a deep learning approach to predict nonconvex function from high-dimensional data using two complementary learning algorithms. On the one hand, we first propose and demonstrate a new method for predicting nonconvex functions from high-dimensional data, which is based on Gaussianity Networks, which is known to be difficult to learn in practice. On the other hand, a simple regularization method based on nonlinearity networks over linear discriminant distributions is proposed to achieve better prediction performance on all these functions. Using the proposed method, we are able to learn complex regularization rules over all the functions with respect to the data, and improve the training results of the model in various cases.