Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics

arXiv:2212.07892v2 Announce Type: replace
Abstract: Many, if not most, systems of interest in science are naturally described as nonlinear dynamical systems. Empirically, we commonly access these systems through time series measurements. Often such time series may consist of discrete random variables rather than continuous measurements, or may be composed of measurements from multiple data modalities observed simultaneously. For instance, in neuroscience we may have behavioral labels in addition to spike counts and continuous physiological recordings. While by now there is …

abstract arxiv continuous cs.lg data dynamics generative generative modeling math.ds modeling multimodal multimodal data nlin.cd random science series systems through time series type variables