Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction [R]

Excerpt

Can training of nonlinear RNNs be efficiently parallelized, ensuring fast convergence even on very long time series from chaotic systems? In our #NeurIPS2026 spotlight “Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems (DS) Reconstruction (DSR)” (preprint: https://arxiv.org/abs/2605.12683 ) we speed up training of nonlinear RNNs on time series from chaotic DS by more than 2 orders of magnitude (>100x) by combining DEER with generalized teacher forcing (GTF). DEER ( htt