[IDA ML Seminar] Machine Learning Seminar, 15/11 at 15:15: Pawel Morkisz, "Deep learning-based estimation of time-dependent parameters in Markov models with application to SDEs - machine learning aspects and experiments details"

[Fredrik Lindsten]

Next week we will have a bonus Machine Learning seminar by Paweł Morkisz, Wednesday November 15 at 15:15 in Alan Turing

Deep learning-based estimation of time-dependent parameters in Markov models with application to SDEs - machine learning aspects and experiments details
Paweł Morkisz<https://scholar.google.com/citations?user=E8gToekAAAAJ>, NVIDIA & AGH University of Kraków


Abstract: We present a novel method for estimation of time-dependent unknown parameters based on discrete sampling of Markov processes using deep learning techniques. Neural networks have enabled a variety of applications. Usually, we mean machine learning, in which a computer learns to perform some task by analyzing training examples and directly having access to the predicted values.



In this work, we employ the deep learning framework to solve the problem of approximation of time-dependent parameters from the actual data. The idea is to change this approximation task to an optimization problem using the maximum likelihood approach and then obtain the appropriate loss function, which can be used to train neural networks. We demonstrate the effectiveness of our approach through a series of numerical experiments using the Deep Learning framework -- TensorFlow. We focus on estimating parameters in multivariate regression and stochastic differential equations (SDEs). Moreover, we base this approach on theoretical results in the SDEs case - we prove that under certain conditions, the solution process of the underlying  SDE with the actual parameter function is close to the SDE with the parameter function obtained from our neural network.

Location: Alan Turing, https://www.ida.liu.se/department/location/search.en.shtml?keyword=alan

Welcome!!

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