The Sigmoid Angle for Generating Similarities and Diversity Across Similar Societies – We develop a model-driven approach for a supervised machine translation system based on two-stage learning for both high-level and low-level language models. First, the system learns a mixture of high-level language models and then constructs a high-level language model based on the mixture of such models. Finally, the system learns a semantic model of human language models and the semantic model of human language model. After training, the semantic model is tested on the task of recognizing user-submitted questions for a given language model through the proposed model learning algorithm. The proposed model learning algorithm is very effective for this task because it learns a mixture of both sentences and model parameters simultaneously.
In this paper, we propose a flexible and high-dimensional sparse matrix factorization algorithm for sparse matrix factorization in nonconvex optimization problems. In this work, we propose the use of a flexible matrix factorization algorithm called the sparse matrix factorization algorithm and compare its accuracy against other sparse matrix factorization algorithms. We discuss various applications of the proposed algorithm and demonstrate the use in practice.
Sparse Multiple Instance Learning
The Sigmoid Angle for Generating Similarities and Diversity Across Similar Societies
Proceedings of the 2016 ICML Workshop on Human Interpretability in Artificial Intelligence
Scalable Kernel-Based Classification in Sparse MLIn this paper, we propose a flexible and high-dimensional sparse matrix factorization algorithm for sparse matrix factorization in nonconvex optimization problems. In this work, we propose the use of a flexible matrix factorization algorithm called the sparse matrix factorization algorithm and compare its accuracy against other sparse matrix factorization algorithms. We discuss various applications of the proposed algorithm and demonstrate the use in practice.
Leave a Reply