A simplifying observation that maps the recurrent network dynamics, which is configured to operate in relaxation mode as a static optimizer, to feedforward network dynamics is leveraged to facilitate application of a non-recurrent training algorithm such as the standard backpropagation and its variants...
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
The backpropagation algorithm is a form of steepest-descent algorithm in which the error signal, which is the difference between the current output of the neural network and the desired output signal, is used to adjust the weights in the output layer, and is then used to adjust the weights ...
This paper demonstrates how the backpropagation algorithm (BP) and its variants can be accelerated significantly while the quality of the trained nets will... M Joost,W Schiffmann - International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 被引量: 65发表: 1998年 Efficacy of modif...
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as ‘credit assignment’. It has long been assumed that cre
The backtracking mechanism of SLA* consists of back-propagating updated heuristic values to previously vis- ited states while the algorithm retracts its steps. In this paper we separate these hitherto intertwined aspects, and investi- gate the benefits of each independently. We present back- ...
The number of hidden layer neurons used is 10, and the activation function used is Sigmoid. Since weights are adjusted in the steepest descent direction, the backpropagation algorithm does not guarantee the fastest convergence. To avoid that, in this work, we used the scaled conjugate gradient ...
This algorithm consists of 2 phases: forward and backword. While forward phase spreads input value through the whole network until the output layer, backward phase calculates the loss in prediction and use it to accumulatively updates weights. It keeps iterating until a desired condition is met,...
The function of hidden neurons is to connect the input and the network output. Given a training set of input-output data, the most common learning rule for multi-layer perceptron (MLP) neural networks is the back-propagation algorithm which involves two following phases: the first one is a ...
Here we describe a new deep learning algorithm that is fast and accurate, like backprop, but much simpler as it avoids all transport of synaptic weight information. Our aim is to describe this novel algorithm and its potential relevance in as simple a form as possible, meaning that we ...