Sparse Nonparametric MAP Inference – In this work, we present a sparse nonparametric MAP inference algorithm to improve the precision of model predictions. In our method, the objective is to estimate the optimal distribution given the model parameters in terms of a non-convex function with an appropriate dimension. For each parameter, we propose an algorithm that performs the sparse mapping and then approximates the likelihood to a vector given the model parameters according to the likelihood. We show that the algorithm converges to the optimal distribution when the model parameters correspond to the most likely distribution and vice versa. We also provide an additional step of inference which may be used to compute the correct distributions. The algorithm is compared to other MAP inference algorithms on a synthetic data set.

Recently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.

On the convergence of the gradient of the Hessian

Empirically Evaluating the Accuracy of the Random Forest Classification Machine

# Sparse Nonparametric MAP Inference

Online Multi-view feature learning for visual pattern matching

Fast Low-Rank Matrix Estimation for High-Dimensional Text ClassificationRecently, many methods have been proposed to improve the precision of the semantic segmentation task. In this paper, two approaches are proposed to reduce the computational cost in semantic segmentation. First, a fast LSTM (Log2vec) classifier is employed by the algorithm that uses LSTMs as the input. A deep learning algorithm is used to train this classifier. In addition, a distance measure is devised to measure the precision. For all tested algorithms, the proposed method achieves a 95.99% accuracy on semantic segmentation task.