Sequence modeling is a fundamental task in many fields, including natural language processing, speech recognition, and financial forecasting. To capture complex dependencies in sequences, recurrent neural networks (RNNs) have been widely used. However, RNNs can suffer from vanishing or exploding gradients, limiting their ability to learn long-term dependencies.
Rational Transfer Function
Rational transfer functions (RTFs) provide a solution to mitigate the limitations of RNNs. RTFs introduce a rational function to model the hidden state dynamics of an RNN, which allows for explicit control over the gradient flow.
Leveraging Fast Fourier Transforms (FFTs)
To efficiently compute the RTF, we leverage FFT techniques. FFTs allow for rapid evaluation of the rational function by transforming the time domain to the frequency domain, where operations can be performed more efficiently.
Benefits of RTFs
Experimental Results
Extensive experiments on benchmark datasets demonstrate the effectiveness of RTFs. They consistently outperform RNNs in sequence modeling tasks, achieving state-of-the-art results.
Conclusion
RTFs provide a powerful approach to enhanced sequence modeling. By leveraging FFT techniques, they mitigate the limitations of RNNs and enable effective learning of long-term dependencies. The improved gradient flow and computational efficiency make them a promising choice for various sequence modeling applications.
Kind regards
J.O. Schneppat
