Rodent: Relevance determination in ODE

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00339214" target="_blank" >RIV/68407700:21230/19:00339214 - isvavai.cz</a>

Výsledek na webu

<a href="http://bayesiandeeplearning.org/2019/papers/59.pdf" target="_blank" >http://bayesiandeeplearning.org/2019/papers/59.pdf</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Rodent: Relevance determination in ODE

Popis výsledku v původním jazyce

From a set of observed trajectories of a partially observed system, we aim to learnits underlying (physical) process without having to make too many assumptionsabout the generating model. We start with a very general, over-parameterizedordinary differential equation(ODE) of orderNand learn the minimal complexityof the model, by which we mean both the order of the ODE as well as the minimumnumber of non-zero parameters that are needed to solve the problem. The minimalcomplexity is found by combining theVariational Auto-Encoder(VAE) withAuto-matic Relevance Determination(ARD) to the problem of learning the parametersof an ODE which we callRodent. We show that it is possible to learn not onlyone specific model for a single process, but a manifold of models representingharmonic signals in general.

Název v anglickém jazyce

Rodent: Relevance determination in ODE

Popis výsledku anglicky

From a set of observed trajectories of a partially observed system, we aim to learnits underlying (physical) process without having to make too many assumptionsabout the generating model. We start with a very general, over-parameterizedordinary differential equation(ODE) of orderNand learn the minimal complexityof the model, by which we mean both the order of the ODE as well as the minimumnumber of non-zero parameters that are needed to solve the problem. The minimalcomplexity is found by combining theVariational Auto-Encoder(VAE) withAuto-matic Relevance Determination(ARD) to the problem of learning the parametersof an ODE which we callRodent. We show that it is possible to learn not onlyone specific model for a single process, but a manifold of models representingharmonic signals in general.

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů