gradient-descent algorithm problem
Accelerated Mann and CQ algorithms for finding a fixed point of a nonexpansive mapping
... Picard algorithm to the smooth convex minimization problem and point out that the Picard algorithm is the steepest descent method for solving the minimization ...Picard algorithm by ...
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Multi Kernel Learning with Online-Batch Optimization
... OBSCURE algorithm takes the best of the two ...the problem of the upper bound of the norm of the optimal solution using a new online ...online algorithm (Algorithm 1), used to quickly estimate ...
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Properties of the sign gradient descent algorithms
... Gradient descent is one of the powerful local optimization algorithms [12, ...the gradient and is used in many applications as optimal control [2], video coding [33], localization [19] or robotics ...
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A Multiclass Sentiment Classification using Skip-Gram Embedding with Support Vector Machine-Stochastic Gradient Descent (SVM-SGD)
... binary classifiers. Approaches of parallelisation of MC-SVM training are based on OvsO or OvsR which can be done with SGD that are trained over multiple computers [GBW14, BMS16]. The second method is achieved by ...
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On the geometry of Stein variational gradient descent
... challenging problem in high dimensions, where the posterior distribution will only be known up to a constant of ...Hastings algorithm provide a generic approach to sampling from such ...optimisation ...
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Algorithmic Guarantees for Inverse Imaging with Untrained Neural Network Priors
... projected gradient descent (PGD) algorithm for solving the problem of compressive sensing with a deep untrained network ...projected gradient descent scheme to solve the ...
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Rice yield prediction - a comparison between enhanced back propagation learning algorithms
... Back Propagation algorithm failed to produce the desired output due the major problem of being stuck at local minima The outstanding performance of the Conjugate Gradient Descent algorit[r] ...
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Guided Stochastic Gradient Descent Algorithm for inconsistent datasets
... proposed algorithm, guided SGD (GSGD) works as an add-on to all the variations of ...The algorithm names of the guided versions are prefixed with G such as GRMSprop and GAdam for RMSprop and Adam ...
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HANDWRITTEN NUMERAL PATTERN RECOGNITION TECHNIQUES: REVIEW PAPER
... a problem when I use steepest descent to train a multilayer network with sigmoid functions, because the gradient can have a very small magnitude and, therefore, cause small changes in the weights and ...
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Detection of Mobile Keyloggers Using Deep Learning
... This algorithm updates the parameters for a single example of training according to the gradient of the ...above algorithm, which, after evaluating all training examples, updates the ...Stochastic ...
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Optimal Parameter Selection Using Three-term Back Propagation Algorithm for Data Classification
... (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural ...uses Gradient Descent (GD) method to learn the ...learning algorithm have been made by ...
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Blind Adaptive Equalization for Channel Noise Cancellation
... Modulus Algorithm and Newton Method is used for mathematical ...The problem with blind adaptation techniques is their poor convergence property compared to traditional techniques using training ...a ...
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Analysis Of Line To Ground Fault In Transformer By Elman’S Network Using Gradient Descent Back Propagation Algorithm
... categories. Energisation inrush, recovery inrush and sympathetic inrush.The first energisation inrush results from the reapplication of system voltage to a transformer which has been previously deenergised. The second ...
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Parallel Computation of a Maximum-Likelihood Estimator of a Physical Map
... computational problem in ...a problem of high computa- tional complexity that provides the motivation for parallel ...a gradient descent search for determining the optimal spacings between ...
23
Multi variable geometry repair and optimization of passive vibration isolators
... / gradient-descent search of the objective function / feasibility ...genetic algorithm is used to optimize the vibration isolation of a novel passive structure concept, while the gradient ...
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The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks
... BP algorithm has proved satisfactory results when applied to many training tasks, but despite many successful applications, the BP algorithm has several important ...BP algorithm uses the ...
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Mill Load Control System Based on the Improved Ant Colony Neural Network
... solved problem in the modeling ...using gradient descent algorithm such back propagation algorithm (BP) with a longer convergence time and more probability to fall into local ...colony ...
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The effect of adaptive gain and adaptive momentum in improving training time of Gradient Descent back propagation algorithm on classification problems
... propagation algorithm has been successfully applied to wide range of practical ...this algorithm uses a gradient descent method, it has some limitations which are slow learning convergence ...
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The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems
... propagation algorithm has been successfully applied to wide range of practical ...this algorithm uses a gradient descent method, it has some limitations which are slow learning convergence ...
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A Fast Iterative Algorithm Based on the Wirtinger Flow for Phase Retrieval
... retrieval problem in the last three decades, such as Gerchberg-Saxton [1], hybrid input-output algorithm [2] and its improved algorithm ...(WF) algorithm is introduced to guarantee signal ...
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