Statistical Learning of Knowledge from Sequence Data

16 December 2021, online
AI for Times Series seminar by Nick Harley (VUB – Computational Creativity Lab)
VAIA, Flanders AI Research & KU Leuven STADIUS

Nick Harley will present work being conducted in the Computational Creativity Lab at the University of Brussels, which explores the use of statistical models to build cognitive representations of sequence data. Sequence modelling is a central part of cognitive systems. The notion of sequence can be thought of as an abstraction of time series used to account for cognitive flexibility in the perception of time. The goal of their work is to bridge the gap between unsupervised learning and knowledge-based reasoning in cognitive systems.

In this seminar Nick Harley will describe the theoretical background of his team’s approach and two different application contexts. The formal basis is a predictive modelling framework called IDyOM (Pearce, 2005) which finds statistical patterns in multi-dimensional sequence data. Information theoretic measures are used to segment sequences into perceptual or cognitive units which then form the basis of a knowledge representation. One application of this method is in modelling music cognition, which involves the real-time organisation of low-level stimuli, such as notes, into high-level structures, such as chords and phrases. A second application is in reinforcement learning. Partial observability, dynamic environments, hidden rewards and explainability are problems for RL models. Supervised learning incorporating knowledge about sub-tasks can mitigate these problems. Harley will describe how statistical modelling of agent behaviour sequences can be used to learn sub-task knowledge in an unsupervised manner. He will finish with a discussion of the open challenges we are addressing and invite the audience to participate in a discussion.

Nick Harley graduated with an MEng in Electronic and Electrical Engineering from UCL (London) in 2011, and with an MSc in Sound and Music Computing from UPF (Barcelona) in 2014. He undertook a PhD in Computer Science at QMUL (London) and defended his thesis in 2019. His PhD dissertation is in the field of computational musicology and examines formal systems for knowledge representation and reasoning in the domain of music. He is currently a postdoc in the AI Lab at the VUB with research interests focused at the intersection of knowledge representation, statistical modelling and cognitive systems.

Practical

  • 16 December 2021, 14.30-15.30h
  • Location: online streaming
  • Contact: Philippe Dreesen & Katrien De Cock
  • Language: English
  • Target audience: everyone interested in AI and/or time series

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  • Price: free
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AI for Time Series Seminars

Several research groups in the Flanders AI Research Program (FLAIR) conduct world-class research on time series, both in the development of algorithms and tools, as in a wide area of application fields. In a recent poll in the Flanders AI community, ‘time series’ came up as the most wanted topic for future workshops or courses. With this seminar series, we bring together researchers that are interested in, or are conducting research related to, time series. We offer a varied program of national and international speakers.

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