  We introduce Active Tuning, a novel paradigm for optimizing the internal
dynamics of recurrent neural networks (RNNs) on the fly. In contrast to the
conventional sequence-to-sequence mapping scheme, Active Tuning decouples the
RNN's recurrent neural activities from the input stream, using the unfolding
temporal gradient signal to tune the internal dynamics into the data stream. As
a consequence, the model output depends only on its internal hidden dynamics
and the closed-loop feedback of its own predictions; its hidden state is
continuously adapted by means of the temporal gradient resulting from
backpropagating the discrepancy between the signal observations and the model
outputs through time. In this way, Active Tuning infers the signal actively but
indirectly based on the originally learned temporal patterns, fitting the most
plausible hidden state sequence into the observations. We demonstrate the
effectiveness of Active Tuning on several time series prediction benchmarks,
including multiple super-imposed sine waves, a chaotic double pendulum, and
spatiotemporal wave dynamics. Active Tuning consistently improves the
robustness, accuracy, and generalization abilities of all evaluated models.
Moreover, networks trained for signal prediction and denoising can be
successfully applied to a much larger range of noise conditions with the help
of Active Tuning. Thus, given a capable time series predictor, Active Tuning
enhances its online signal filtering, denoising, and reconstruction abilities
without the need for additional training.
