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Established in 2020, POSTECH Mathematical Institute for Data Science (MINDS) is the community of researchers in the areas of fundamental data science, machine learning, artificial intelligence, scientific computing, and humanitarian data science. MINDS mission is to provide a platform for collaboration among researchers and to provide various opportunities for students in data science. MINDS also aims to use our data science research to serve our local and global communities pursuing humanitarian data science.
MINDS Seminar Series | Kyongmin Yeo (IBM T.J. Watson Research Center) - Towards Foundation Model for Dynamical Systems
|Date||2023-11-08 ~ 2023-11-08||Time||14:00:00 ~ 15:30:00|
|Speaker||Kyongmin Yeo||Affiliation||IBM T.J. Watson Research Center|
|Place||Math Bldg 404 & Online streaming (Zoom)||Streaming link||ID : 688 896 1076 / PW : 54321|
|Topic||Towards Foundation Model for Dynamical Systems|
|Contents||Since the success of large language models, Foundation Model has attracted a great attention. Foundation Model aims to provide a pre-trained model, typically a large deep learning model, that can be easily fine-tuned or re-purposed for a downstream task with a smaller data set. Where there has been much progress in the language and vision domains, Foundation Model for time series data is not relatively well studied. Here, we propose a preliminary Foundation Model targeted to nonlinear dynamical systems. We consider the problem of building a foundation model from a range of dynamical systems and generating a time series data from the foundation model given a short sequence of input data. The foundation model consists of two modules. In the first module, we aim to learn a dictionary of functions. We propose to use a contrastive learning with a hierarchy to build a dictionary of different classes of dynamical systems. Then, we use a denoising diffusion model to generate the time series data from the dictionary. The foundation model can also be used for a forecast of the input sequence.|