Bayesian Statistics as an Alternative to Gradient Descent in Sequence Learning
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| Title | Bayesian Statistics as an Alternative to Gradient Descent in Sequence Learning |
| Authors | |
| Abstract | Recurrent neural networks are frequently applied to simulate sequence learning applications such as language processing, sensory-motor learning, etc. For this purpose, they often apply a truncated gradient descent (=error correcting) learning algorithm. In order to converge to a solution that is congruent with a target set of sequences, many iterations of sequence presentations and weight adjustments are typically needed. Moreover, there is no guarantee of finding the global minimum of error in a multidimensional error landscape resulting from the discrepancy between target values and the network’s prediction. This paper presents a new approach of inferring the global error minimum right from the start. It further applies this information to reverse-engineer the weights. As a consequence, learning is speeded-up tremendously, whilst computationally-expensive iterative training trials can be skipped. Technology applications in established and emerging industries will be discussed. |
| Publisher | assel university press GmbH |
| Date | 2007-06-15 |
| Source | 1863-0383 |
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