Xarrada ELLIS 'Modeling Irregular Time Series with Continuous Recurrent Units'
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La xarrada serà impartida per Mona Schirmer el 25 de maig a les 11.30 en la Sala d'Actes de la Politècnica I
Data: Dimecres 25 de maig a les 11:30am
Lloc: Sala d'Actes de la Politècnica I
Ponent: Mona Schirmer
Enllaç videostreaming: https://vertice.cpd.ua.es/269014
Enllaç videostreaming: https://vertice.cpd.ua.es/269014
Title: Modeling Irregular Time Series with Continuous Recurrent Units
Abstract:
Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Their gating mechanism permits weighting previous history encoded in a hidden state with new information from incoming observations. In many applications, such as medical records, observations times are irregular and carry important information. However, LSTMs and GRUs assume constant time intervals between observations. To address this challenge, we propose continuous recurrent units (CRUs) -a neural architecture that can naturally handle irregular time intervals between observations. The gating mechanism of the CRU employs the continuous formulation of a Kalman filter and alternates between (1) continuous latent state propagation according to a linear stochastic differential equation (SDE) and (2) latent state updates whenever a new observation comes in. In an empirical study, we show that the CRU can better interpolate irregular time series than neural ordinary differential equation (neural ODE)-based models. We also show that our model can infer dynamics from images and that the Kalman gain efficiently singles out candidates for valuable state updates from noisy observations.
Short bio:
Mona Schirmer is a Data Science consultant at the World Bank working on the application of Machine Learning for Development Economics. Before that, she completed a Master’s in Statistics at Humboldt University and Technical University in Berlin as well as a French Engineering Diploma at ENSAE. In her Bachelor’s, she studied Economics and Political Science at Humboldt University of Berlin and the University of Munich. Her research interests lie in Probabilistic Machine Learning und Machine Learning for social good.
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