[PDF] Top 20 A Novel Distributed Variant of Stochastic Gradient Descent and Its Optimization
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A Novel Distributed Variant of Stochastic Gradient Descent and Its Optimization
... The distributed implementation using MPI and Pthreads programming accelerates the convergence process of the objective value greatly with appropriate scaling of computational ... See full document
7
Performance Optimization on Model Synchronization in Parallel Stochastic Gradient Descent Based SVM
... implementing stochastic gradient descent (SGD)-based distributed support vector machines (SVM) algorithm is important in training larger data ...efficient distributed model, the ... See full document
10
Stochastic Gradient Descent Based Support Vector Machines Training Optimization on Big Data and HPC Frameworks
... tool kit which allows designing applications supporting both HPC and dataflow model applications. Twister2 implicitly sup- ports MPI applications to run within its programming model. Aforementioned communication ... See full document
10
SAR Images Co registration Based on Gradient Descent Optimization
... of gradient descent differ in the used data capacity to estimate the cost ...Batch Gradient Descent (BGD), the computations of gradient of the cost function is carried out ...Second; ... See full document
7
Adaptivity of Averaged Stochastic Gradient Descent to Local Strong Convexity for Logistic Regression
... or its gradients is a key methodological problem in many disciplines. Its analysis has been attacked mainly in three scientific communities: stochas- tic approximation (Fabian, 1968; Ruppert, 1988; Polyak ... See full document
33
Calibrated Stochastic Gradient Descent for Convolutional Neural Networks
... for optimization and by far the most important way to train neural ...the gradient descent algorithm optimizes the neural network parameters by calculating the minimum value of a loss ...the ... See full document
8
Certain Systems Arising In Stochastic Gradient Descent
... [AG16] Animashree Anandkumar and Rong Ge. Efficient approaches for es- caping higher order saddle points in non-convex optimization. In Vi- taly Feldman, Alexander Rakhlin, and Ohad Shamir, editors, 29th Annual ... See full document
105
Multi Kernel Learning with Online-Batch Optimization
... a stochastic gradient descent ...the optimization problem directly in the primal formulation, in both ...in optimization theory, the algorithm takes advantage of the abundance of ... See full document
27
Why Does Unsupervised Pre-training Help Deep Learning?
... on stochastic gradient ...online gradient descent procedure defines a trajectory in parameter space, which converges in some sense (the error does not improve anymore, maybe because we are ... See full document
36
Distributed stochastic gradient descent for link prediction in signed social networks
... of the partially asynchronous algorithm, the computa- tional threads use all of the training data. In the fully asynchronous algorithm, it is possible that one training sample is not used while another one has been used ... See full document
11
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
... plain Stochastic Gradient Descent yields particularly effective algorithms when the input patterns are very sparse, taking less than O (d) space and time per iteration to optimize a system with d ... See full document
18
Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator
... classical optimization methods for parameter estimation such as Fisher scoring, the EM algorithm or iterated reweighted least squares (Hastie et ...to its computational and memory efficiency, ... See full document
21
Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting
... This work is concerned with online time-series forecasting in a concept-drifting environment, where a probability distri- bution generating the data may change over time. Concept drift is ubiquitous in real-world ... See full document
8
A parallel and distributed stochastic gradient descent implementation using commodity clusters
... a novel implementation of Parallel SGD on the HPCC sys- tems platform, specifically, a distributed implementation of Parallel ...a distributed and parallel approach in that it distributes data and ... See full document
23
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization
... convex optimization problems, there is a vast literature on coordinate descent methods; see, ...coordinate descent sparked a lot of recent activities on this ...one variant of the SDCA ... See full document
42
Optimal Control of Microgrid Networks Using Gradient Descent and Differential Evolution Methods
... Steepest Descent method, Newton method and Differential Evolution ...of gradient descent methods where as the differential evolution is an Evolutionary ...The gradient descent methods ... See full document
7
A DESCENT PRP CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION
... conjugate gradient methods in practice. However, it is not generally a descent method when Armijo-type line search is used, thus [10] and [4] for satisfying sufficient descent property added the ... See full document
14
How to Make a Mudsparkler
... Note that in principle the opposite of overfitting can occur as well. This is called underfitting, and occurs when a learning algorithm performs poorly on both the train- ing data and on unseen data. Due to the Universal ... See full document
31
A New Descent Nonlinear Conjugate Gradient Method for Unconstrained Optimization
... [1] M. Al-Baali, “Descent Property and Global Convergence of the Fletcher-Reeves Method with Inexact Line Search,” IMA Journal of Numerical Analysis, Vol. 5, No. 1, 1985, pp. 121-124. doi:10.1093/imanum/5.1.121 ... See full document
5
Optimization and design of an aircraft’s morphing wing tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms
... three optimization methods found the global optimum area in almost all the cases and situated their results inside that region, with the GD method having the lowest quality ...the optimization of an airfoil ... See full document
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