Publications in Scientific Journals:

R. Hasani, M. Lechner, A. Amini, D. Rus, R. Grosu:
"Liquid Time-constant Networks";
ArXiv, . (2020), 25 pages.

English abstract:
We introduce a new class of time-continuous recurrent neural
network models. Instead of declaring a learning system´s dynamics
by implicit nonlinearities, we construct networks of
linear first-order dynamical systems modulated via nonlinear
interlinked gates. The resulting models represent dynamical
systems with varying (i.e., liquid) time-constants coupled to
their hidden state, with outputs being computed by numerical
differential equation solvers. These neural networks exhibit
stable and bounded behavior, yield superior expressivity
within the family of neural ordinary differential equations,
and give rise to improved performance on time-series prediction
tasks. To demonstrate these properties, we first take a
theoretical approach to find bounds over their dynamics, and
compute their expressive power by the trajectory length measure
in a latent trajectory space. We then conduct a series of
time-series prediction experiments to manifest the approximation
capability of Liquid Time-Constant Networks (LTCs)
compared to classical and modern RNNs.1

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.