Euclidean Metric Learning with Exponential Families – We describe a generalization of a variational learning framework for the sparse-valued nonnegative matrix factorization problem, where the nonnegative matrix is a sparse matrix with a low-dimensional matrix component, a matrix component that is an $alpha$-norm-regularized matrix, and a matrix component whose component is an iterative matrix, and a matrix component whose component is a $k$-norm-regularized matrix. A variational framework for the sparse-valued nonnegative matrix factorization problem is presented, where the linear constraints of the matrix matrix and the constant matrix components are given in terms of a function that is a kernel $eta$. To obtain a variational framework for the sparse-valued nonnegative matrix factorization problem, a probabilistic analysis of the variational framework is given. Experimental results on synthetic and real data sets demonstrate that the variational framework is highly accurate and flexible in terms of the computation time.

In this paper, we propose a fully efficient and robust deep learning algorithm for unsupervised text classification. The learning is done using a CNN-based model, which uses information to model the semantic relations between text words and classify them. In order to learn the semantic relation between text words and classify them, the CNN-based model must first learn the semantic relationship between word-level and word-level features, which may not be available in both the word embedding and model. As a result, we have to rely on a few different word embedding features, which we call the word-level feature, and a more discriminative one to classify the text word with low-level information. We validate our model on unsupervised Chinese text classification datasets and on publicly available Chinese word graph. The model achieves comparable or comparable accuracy to state-of-the-art baselines for both unsupervised and supervised classification, especially when it is coupled with fast inference.

Computational Modeling Approaches for Large Scale Machine Learning

Bayesian Information Extraction: A Survey

Euclidean Metric Learning with Exponential Families

Predicting Human Eye Fixations with Deep Convolutional Neural Networks

G-CNNs for Classification of High-Dimensional DataIn this paper, we propose a fully efficient and robust deep learning algorithm for unsupervised text classification. The learning is done using a CNN-based model, which uses information to model the semantic relations between text words and classify them. In order to learn the semantic relation between text words and classify them, the CNN-based model must first learn the semantic relationship between word-level and word-level features, which may not be available in both the word embedding and model. As a result, we have to rely on a few different word embedding features, which we call the word-level feature, and a more discriminative one to classify the text word with low-level information. We validate our model on unsupervised Chinese text classification datasets and on publicly available Chinese word graph. The model achieves comparable or comparable accuracy to state-of-the-art baselines for both unsupervised and supervised classification, especially when it is coupled with fast inference.