Add to ORKGA simulator for connectionist networks which uses gradient methods of nonlinear optimization for network learning is described. The simulator (GRADSIM) was designed for temporal flow model connectionist networks. The complete gradient is computed for networks of general connectivity, including recurrent links. The simulator is written in C, uses simple network and data descriptors for flexibility, and is easily modified for new applications. A version of the simulator which precompiles the network objective function and gradient computations for greatly increased processing speed is also described. Benchmark results for the simulator running on the DEC VAX 8650, SUN 3/260 and CYBER 205 are presented
The simulation of network structures can be an effective method for example in teaching, research, o...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
A simulator for connectionist networks which uses gradient methods of nonlinear optimization for net...
A data-parallel simulator capable of training recurrent time-delay connectionist networks is describ...
The problem of learning using connectionist networks, in which network connection strengths are modi...
: Traditional connectionist networks have homogeneous nodes wherein each node executes the same func...
We introduce the stochastic gradient descent algorithm used in the computational network toolkit (CN...
This paper demonstrates how a multi-layer feed-forward network may be trained, using the method of g...
In this paper we describe the design, development, and performance of a neural network simulator for...
A new approach is presented to neural network simulation and training that is based on the use of ge...
This paper presents the capability of the neural networks as a computational tool for solving constr...
Many connectionist learning algorithms consists of minimizing a cost of the form C(w) = E(J(z; w)) ...
This report describes the implementation of a connectionist simulator on the BBN Butterfly Multiproc...
Methods to speed up learning in back propagation and to optimize the network architecture have been ...
The simulation of network structures can be an effective method for example in teaching, research, o...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...
A simulator for connectionist networks which uses gradient methods of nonlinear optimization for net...
A data-parallel simulator capable of training recurrent time-delay connectionist networks is describ...
The problem of learning using connectionist networks, in which network connection strengths are modi...
: Traditional connectionist networks have homogeneous nodes wherein each node executes the same func...
We introduce the stochastic gradient descent algorithm used in the computational network toolkit (CN...
This paper demonstrates how a multi-layer feed-forward network may be trained, using the method of g...
In this paper we describe the design, development, and performance of a neural network simulator for...
A new approach is presented to neural network simulation and training that is based on the use of ge...
This paper presents the capability of the neural networks as a computational tool for solving constr...
Many connectionist learning algorithms consists of minimizing a cost of the form C(w) = E(J(z; w)) ...
This report describes the implementation of a connectionist simulator on the BBN Butterfly Multiproc...
Methods to speed up learning in back propagation and to optimize the network architecture have been ...
The simulation of network structures can be an effective method for example in teaching, research, o...
Network modeling is a critical component for building self-driving Software-Defined Networks.Traditi...
Any non-associative reinforcement learning algorithm can be viewed as a method for performing functi...