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stochastic-gradient adaptive algorithms

Acoustic Echo Cancellation by Adaptive Combination of Normalized Sub band Adaptive Filters by Using Stochastic Gradient Algorithm

Acoustic Echo Cancellation by Adaptive Combination of Normalized Sub band Adaptive Filters by Using Stochastic Gradient Algorithm

... systems, adaptive combination of sub- band adaptive filters and its improvement were modeled in MATLAB Simulink and many simulations for different inputs and number of sub-bands were ...the adaptive ...

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Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations

Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations

... of stochastic gradient algorithms, as we have demonstrated in ...discrete-time algorithms and continuous-time SDEs ...of stochastic calculus ...study adaptive algorithms ...

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Quaternion Information Theoretic Learning Adaptive Algorithms for Nonlinear Adaptive

Quaternion Information Theoretic Learning Adaptive Algorithms for Nonlinear Adaptive

... ent. The QKSIG algorithm minimizes Shannon’s entropy of the error between the filter output and desired response and minimizes the divergence between the joint densities of input-desired and input-output pairs. The SIG ...

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Adaptive Minimum BER Linear Multiuser Detection for DS CDMA Signals in Multipath Channels

Adaptive Minimum BER Linear Multiuser Detection for DS CDMA Signals in Multipath Channels

... a gradient search algorithm. In Section IV, the proposed adaptive MBER multiuser detector is ...LMS-style stochastic gradient adaptive algorithm called the ...existing stochastic ...

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A Framework for Analyzing Stochastic Optimization Algorithms Under Dependence

A Framework for Analyzing Stochastic Optimization Algorithms Under Dependence

... variance-reduced stochastic methods, the variance-reduced stochastic Frank-Wolfe (SVRF) method [30] and the proximal variance-reduced stochastic gradient (Prox-SVRG) method [6, ...exact ...

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A Service System with On-Demand Agents, Stochastic Gradient Algorithms and the SARAH Algorithm

A Service System with On-Demand Agents, Stochastic Gradient Algorithms and the SARAH Algorithm

... full gradient descent method and the stochastic gradient ...exact gradient computation at each outer iteration and an inner loop with multiple iterative stochastic gradient ...

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Approximation Algorithms for Stochastic Clustering

Approximation Algorithms for Stochastic Clustering

... Repeated clustering problems. Consider clustering problems where the choice of S can be changed periodically: e.g., S could be the set of k locations in the cloud chosen by a service- provider. This set S can be shuffled ...

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Stochastic gradient Markov chain Monte Carlo

Stochastic gradient Markov chain Monte Carlo

... the algorithms is the computational ...SGMCMC algorithms are between 10 to 100 times faster when d = ...of algorithms can perform well, with significant computational savings, if they are ...SGMCMC ...

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Forecasting GDP growth :  a comprehensive comparison of employing machine learning algorithms and time series regression models

Forecasting GDP growth : a comprehensive comparison of employing machine learning algorithms and time series regression models

... ML algorithms to forecast macroeconomic variables. ML algorithms are able to handle large datasets and to detect underlying, complex relationships between ...ML algorithms widely used in different ...

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On Connecting Stochastic Gradient MCMC and Differential Privacy

On Connecting Stochastic Gradient MCMC and Differential Privacy

... learning algorithms, Bayesian inference has realized significant success recently, due to its capacity to leverage expert knowledge and em- ploy uncertainty ...oped stochastic gradient Markov chain ...

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Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization

Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization

... The gradient-free (zeroth-order) optimization method (Nesterov and Spokoiny, 2017) is a promising choice to address these problems because it only uses the function values in opti- mization ...the ...

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On the long time integration of stochastic gradient systems

On the long time integration of stochastic gradient systems

... for stochastic differential equations focus on properties such as the weak order which estimates the asymptotic (stepsize h → 0) convergence behavior of the error of finite time ...

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Adaptive linear filtering design with minimum symbol error probability criterion

Adaptive linear filtering design with minimum symbol error probability criterion

... converge, compared this with about 40 iterations that was needed with w(0) = w MMSE . The performance of the stochastic gradient adaptive MSER algorithm was then investigated. Fig. 5 shows the ...

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Random Forest and Stochastic Gradient Tree Boosting Based Approach for the Prediction of Airfoil Self-noise

Random Forest and Stochastic Gradient Tree Boosting Based Approach for the Prediction of Airfoil Self-noise

... and stochastic tree boosting method to grow 1000 trees was found to be 4 seconds and 3 seconds respectively, much lower than that of the ANN ...SGTB algorithms mostly depends on the number of leaf nodes ...

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Hyper heuristics: a survey of the state of the art

Hyper heuristics: a survey of the state of the art

... Despite the success of heuristic methods and other search techniques in solving real-world computational search problems, there are still some difficulties in terms of easily applying them to newly encountered problems, ...

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Step Size Optimization of LMS Algorithm Using Aunt Colony Optimization & Its comparison with Particle Swarm optimization Algorithm in System Identification

Step Size Optimization of LMS Algorithm Using Aunt Colony Optimization & Its comparison with Particle Swarm optimization Algorithm in System Identification

... an adaptive algorithm, which uses a gradient-based method of steepest ...the gradient vector from the available ...the gradient vector which eventually leads to the minimum mean square ...

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Adaptive parameter tuning stochastic resonance based on SVD and its application in weak IF digital signal enhancement

Adaptive parameter tuning stochastic resonance based on SVD and its application in weak IF digital signal enhancement

... An adaptive parameter-tuning stochastic resonance method for three types of weak IF digital signals (MASK, MPSK, and MQAM) is proposed, and a frame of signal enhancement preprocessing is pre- sented ...of ...

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Properties of the sign gradient descent algorithms

Properties of the sign gradient descent algorithms

... sign gradient descent algorithms involving the sign of the gradient instead of the gradient itself and first introduced in the RPROP ...sign gradient descent algorithms including ...

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Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning

Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning

... In this section, we provide empirical evaluations of our main algorithm PW SGD. We evaluate its convergence rate and overall running time on both synthetic and real datasets. For PW SGD, we im- plement it with three ...

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Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems

Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems

... Another approach to improve the efficiency for solving problem (1) is the asynchronous parallel implementation, which has been successfully applied to speedup many state- of-the-art algorithms (Recht et al. 2011; ...

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