On a novel training algorithm for sequence-to-sequence predictive recurrent networks
This work addresses the challenge of reducing memory requirements in seq2seq networks, which is particularly relevant for neuroscience applications, though it appears incremental as it builds on existing seq2seq architectures.
The authors tackled the problem of sequence-to-sequence prediction by analyzing dependencies between recurrent network parameters, leading to a memoryless algorithm that improves robustness and accuracy in time series prediction compared to traditional methods.
Neural networks mapping sequences to sequences (seq2seq) lead to significant progress in machine translation and speech recognition. Their traditional architecture includes two recurrent networks (RNs) followed by a linear predictor. In this manuscript we perform analysis of a corresponding algorithm and show that the parameters of the RNs of the well trained predictive network are not independent of each other. Their dependence can be used to significantly improve the network effectiveness. The traditional seq2seq algorithms require short term memory of a size proportional to the predicted sequence length. This requirement is quite difficult to implement in a neuroscience context. We present a novel memoryless algorithm for seq2seq predictive networks and compare it to the traditional one in the context of time series prediction. We show that the new algorithm is more robust and makes predictions with higher accuracy than the traditional one.