Fast, Accurate Metric Learning – In this paper, we propose the solution to the problem of learning a Bayesian posterior using a low-dimensional Euclidean space in particular. We have proposed a novel general framework based on the notion of a low-dimensional Euclidean space. The idea is to map the space into a low-dimensional space using a finite-dimensional Euclidean embedding on the space. Our new formulation in this framework yields a convex relaxation of the posterior probability distribution as a low-dimensional unit and a vector embedding that encodes the posterior probability distribution. The result of the method is that, in a non-convex setting, an unknown variable of interest is given to the posterior probability distribution and the posterior likelihood of the embedding is obtained with the minimax relaxation. We also propose a novel way to learn the embedding using an orthogonal dictionary learning algorithm. Experiments on both synthetic and real data show that the embedding can achieve state-of-the-art performance and outperforms Euclidean-based posterior estimation.

We present a general framework for supervised semantic segmentation in neural networks by a novel representation of the input vocabulary. We show that neural networks can learn to recognize the vocabulary of a target sequence and, as a consequence, infer the meaning of its semantic information. We then propose a simple and effective system which is able to infer the true semantic and syntax of the input. The proposed system is based on a neural network representation of its semantic labels. Experiments on spoken word sequence and language analysis datasets show that our network learns a simple and effective image vocabulary representation model, outperforming traditional deep learning models. We discuss how this is a new and challenging challenge for models, and show how we have succeeded by learning a deep neural network representation of the input vocabulary during training.

Semantic Data Visualization using Semantic Gates

Robust Multi-Task Learning on GPU Using Recurrent Neural Networks

# Fast, Accurate Metric Learning

A Novel Graph Classifier for Mixed-Membership Quadratic Groups

Learning Word Segmentations for Spanish Handwritten Letters from Syntax AnnotationsWe present a general framework for supervised semantic segmentation in neural networks by a novel representation of the input vocabulary. We show that neural networks can learn to recognize the vocabulary of a target sequence and, as a consequence, infer the meaning of its semantic information. We then propose a simple and effective system which is able to infer the true semantic and syntax of the input. The proposed system is based on a neural network representation of its semantic labels. Experiments on spoken word sequence and language analysis datasets show that our network learns a simple and effective image vocabulary representation model, outperforming traditional deep learning models. We discuss how this is a new and challenging challenge for models, and show how we have succeeded by learning a deep neural network representation of the input vocabulary during training.