Add to ORKGA momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer si...
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coe...
Batch gradient descent, \Deltaw(t) = \GammajdE=dw(t), converges to a minimum of quadratic form with ...
Federated learning (FL) provides a communication-efficient approach to solve machine learning proble...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
The article examines in some detail the convergence rate and mean-square-error performance of moment...
Momentum based learning algorithms are one of the most successful learning algorithms in both convex...
Recently, the popularity of deep artificial neural networks has increased considerably. Generally, t...
This paper uses the dynamics of weight space probabilities [3, 4] to address stochastic gradient alg...
Gradient decent-based optimization methods underpin the parameter training which results in the impr...
It is pointed out that the so called momentum method, much used in the neural network literature as ...
There are a number of algorithms that can be categorized as gradient based. One such algorithm is th...
Momentum is known to accelerate the convergence of gradient descent in strongly convex settings with...
With the well-documented popularity of Frank Wolfe (FW) algorithms in machine learning tasks, the pr...
Stochastic optimization algorithms typically use learning rate schedules that behave asymptotically ...
The momentum parameter is common within numerous optimization and local search algorithms, particula...
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coe...
Batch gradient descent, \Deltaw(t) = \GammajdE=dw(t), converges to a minimum of quadratic form with ...
Federated learning (FL) provides a communication-efficient approach to solve machine learning proble...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
The article examines in some detail the convergence rate and mean-square-error performance of moment...
Momentum based learning algorithms are one of the most successful learning algorithms in both convex...
Recently, the popularity of deep artificial neural networks has increased considerably. Generally, t...
This paper uses the dynamics of weight space probabilities [3, 4] to address stochastic gradient alg...
Gradient decent-based optimization methods underpin the parameter training which results in the impr...
It is pointed out that the so called momentum method, much used in the neural network literature as ...
There are a number of algorithms that can be categorized as gradient based. One such algorithm is th...
Momentum is known to accelerate the convergence of gradient descent in strongly convex settings with...
With the well-documented popularity of Frank Wolfe (FW) algorithms in machine learning tasks, the pr...
Stochastic optimization algorithms typically use learning rate schedules that behave asymptotically ...
The momentum parameter is common within numerous optimization and local search algorithms, particula...
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coe...
Batch gradient descent, \Deltaw(t) = \GammajdE=dw(t), converges to a minimum of quadratic form with ...
Federated learning (FL) provides a communication-efficient approach to solve machine learning proble...