On the Unnormalization of the Multivariate Marginal Distribution – The problem of quantification of uncertainty that has been considered in many fields such as prediction, prediction, and machine learning, has recently received much attention. Although some work focused on uncertainty quantification as a convex optimization problem, others focus on quantification of uncertainty as a multivariate regression problem, and have been shown to be NP-hard. In this paper we provide two theoretical results on the problem of quantification of uncertainty that is NP-hard. The first leads to the unification of the quantification of uncertainty problem into two univariate optimization problems: one where the output of the regression algorithm is a continuous point-dependent probability distribution, and the other where the output of the regression algorithm is an undirected graphical model. We demonstrate both the benefits and limitations of the two optimization problems in a unified framework and propose an effective framework for quantifying uncertainty for multivariate regression.
One of major difficulties for learning language from textual data is the fact that it is the learner who is motivated to learn the most relevant features in the data as they are typically most studied in a machine-learned language. In this paper we investigate two approaches for this research. First, by constructing a model from textual features of the data it helps guide the learner in learning features from a representation which can be a neural network model and a machine learning framework. We evaluate our methods in a variety of situations including the task of learning a system of English-to-German and English-to French-to Spanish sentences. In experiments on benchmark datasets, we show that the learned features are capable of representing the language as well as the human brain.
Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation
Stochastic Temporal Models for Natural Language Processing
On the Unnormalization of the Multivariate Marginal Distribution
A Multi-service Anomaly Detection System based on Deep Learning for Big Data Analytics
Learning Lévy Grammars on GPUOne of major difficulties for learning language from textual data is the fact that it is the learner who is motivated to learn the most relevant features in the data as they are typically most studied in a machine-learned language. In this paper we investigate two approaches for this research. First, by constructing a model from textual features of the data it helps guide the learner in learning features from a representation which can be a neural network model and a machine learning framework. We evaluate our methods in a variety of situations including the task of learning a system of English-to-German and English-to French-to Spanish sentences. In experiments on benchmark datasets, we show that the learned features are capable of representing the language as well as the human brain.