In this paper, we propose a phase reconstruction framework, named Deep Griffin-Lim Iteration (DeGLI). Phase is fundamental technique for improving the quality of sound obtained through some process in time-frequency domain. It has been shown that recent methods using deep neural networks (DNN) outperformed conventional iterative such as algorithm (GLA). However, computational cost DNN-based not...

In zero-shot learning (ZSL) community, it is generally recognized that transductive performs better than inductive one as the unseen-class samples are also used in its training stage. How to generate pseudo labels for and how use such usually noisy two critical issues learning. this work, we introduce an iterative co-training framework which contains different base ZSL models exchanging module....

we present an improved version of a full nesterov-todd step infeasible interior-point method for linear complementarityproblem over symmetric cone (bull. iranian math. soc.,40(3), 541-564, (2014)). in the earlier version, each iteration consistedof one so-called feasibility step and a few -at most three -centering steps. here, each iteration consists of only a feasibilitystep. thus, the new alg...

Deep Entity Matching (EM) is one of the core research topics in data integration. Typical existing works construct EM models by training deep neural networks (DNNs) based on samples with onehot labels. However, these sharp supervision signals labels harm generalization models, causing them to overfit and perform badly unseen datasets. To solve this problem, we first propose that challenge a wel...

in this paper, we present a comparative study between the modified variational iteration method (mvim) and a hybrid of fourier transform and variational iteration method (ftvim). the study outlines the efficiencyand convergence of the two methods. the analysis is illustrated by investigating four singular partial differential equations with variable coefficients. the solution of singular partia...

The Decision Tree algorithm is a data mining method that often applied as solution to problem for classification. C5.0 has several weaknesses, including: the and other decision tree methods are biased towards modeling whose features have many levels, some problems model can occur such over-fit or under-fit challenges, big changes logic result in small training, experience inconvenience, imbalan...

Each iteration of minimum error rate training involves re-translating a development set. Distributing this work across computational nodes can speed up translation time, but in practice some parts may take much longer to complete than others, leading to computational slack time. To address this problem, we develop three novel algorithms for distributing translation tasks in a parallel computing...

In this paper we deal with the problem of reducing the computational cost of a Genetic Based Machine Learning (GBML) system based on the Pittsburgh Approach. In previous work we studied an incremental learning scheme that divided the training set in several strata and changed the used strata at each iteration. This scheme reduced the computational cost more than expected and even managed to imp...

We design and implement an agent for the popular worker placement and resource management game Euphoria using Neural Fitted Q Iteration (NFQ), a reinforcement learning algorithm that uses an artificial neural network for the action-value function which is updated off-line considering a sequence of training experiences rather than online as in typical Q-learning. We find that the agent is able t...