It is pointed out that the so called momentum method, much used in the neural network literature as an acceleration of the backpropagation method, is a stationary version of the conjugate gradient method. Connections with the continuous optimization method known as heavy ball with friction are also made. In both cases, adaptive (dynamic) choices of the so called learning rate and momentum parameters are obtained using a control Liapunov function analysis of the system
Till today, it has been a great challenge in optimizing the training time in neural networks. This p...
There are a number of algorithms that can be categorized as gradient based. One such algorithm is th...
Abstract — The back propagation algorithm has been successfully applied to wide range of practical p...
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coe...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
Since the presentation of the backpropagation algorithm, a vast variety of improvements of the techn...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
Recently, the popularity of deep artificial neural networks has increased considerably. Generally, t...
Momentum based learning algorithms are one of the most successful learning algorithms in both convex...
In this paper we explore different strategies to guide backpropagation algorithm used for training a...
The momentum parameter is common within numerous optimization and local search algorithms, particula...
Deep neural network optimization is challenging. Large gradients in their chaotic loss landscape lea...
Gradient decent-based optimization methods underpin the parameter training which results in the impr...
The back propagation algorithm has been successfully applied to wide range of practical problems. Si...
Standard backpropagation and many procedures derived from it use the steepestdescent method to minim...
Till today, it has been a great challenge in optimizing the training time in neural networks. This p...
There are a number of algorithms that can be categorized as gradient based. One such algorithm is th...
Abstract — The back propagation algorithm has been successfully applied to wide range of practical p...
In this work, a gradient method with momentum for BP neural networks is considered. The momentum coe...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
Since the presentation of the backpropagation algorithm, a vast variety of improvements of the techn...
A momentum term is usually included in the simulations of connectionist learning algorithms. Althoug...
Recently, the popularity of deep artificial neural networks has increased considerably. Generally, t...
Momentum based learning algorithms are one of the most successful learning algorithms in both convex...
In this paper we explore different strategies to guide backpropagation algorithm used for training a...
The momentum parameter is common within numerous optimization and local search algorithms, particula...
Deep neural network optimization is challenging. Large gradients in their chaotic loss landscape lea...
Gradient decent-based optimization methods underpin the parameter training which results in the impr...
The back propagation algorithm has been successfully applied to wide range of practical problems. Si...
Standard backpropagation and many procedures derived from it use the steepestdescent method to minim...
Till today, it has been a great challenge in optimizing the training time in neural networks. This p...
There are a number of algorithms that can be categorized as gradient based. One such algorithm is th...
Abstract — The back propagation algorithm has been successfully applied to wide range of practical p...