Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Hybrid Automata: Learning Dynamics with Multiple Modes and
  Stochastic Transitions   Effective control and prediction of dynamical systems often require
appropriate handling of continuous-time and discrete, event-triggered
processes. Stochastic hybrid systems (SHSs), common across engineering domains,
provide a formalism for dynamical systems subject to discrete, possibly
stochastic, state jumps and multi-modal continuous-time flows. Despite the
versatility and importance of SHSs across applications, a general procedure for
the explicit learning of both discrete events and multi-mode continuous
dynamics remains an open problem. This work introduces Neural Hybrid Automata
(NHAs), a recipe for learning SHS dynamics without a priori knowledge on the
number of modes and inter-modal transition dynamics. NHAs provide a systematic
inference method based on normalizing flows, neural differential equations and
self-supervision. We showcase NHAs on several tasks, including mode recovery
and flow learning in systems with stochastic transitions, and end-to-end
learning of hierarchical robot controllers.

34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph