Hi. I’m Louis Kirsch.

I am a PhD with Jürgen Schmidhuber at IDSIA (The Swiss AI Lab) working on fundamental AI / ML questions. Previously I have been at University College London completing my Master of Research. My long-term research goal is to make life-long learning reality, constructing a learning algorithm that can learn continuously from arbitrarily many tasks and changing environments while doing so efficiently using conditional computation.

Short CV

News

My first author paper Modular Networks: Learning to Decompose Neural Computation has been accepted to NIPS 2018 and will be presented later this year.

Recent publications

A complete list can be found on Google scholar.
I also worked on other machine learning projects.

  • Modular Networks: Learning to Decompose Neural Computation
    Published Conference Paper at NIPS 2018 (Kirsch et al. 2018)

  • Contemporary Challenges in Artificial Intelligence [PDF]
    Technical report (Kirsch September 2018)

  • Scaling Neural Networks Through Sparsity [PDF]
    Technical report (Kirsch July-October 2018)

  • Characteristics of Machine Learning Research with Impact [More][PDF]
    Technical report (Kirsch May 2018)

  • Differentiable Convolutional Neural Network Architectures for Time Series Classification [More]
    Bachelor Thesis at Hasso Plattner Institute (Kirsch 2017)

  • Transfer Learning for Speech Recognition on a Budget [More]
    Published Workshop Paper at ACL 2017 (Kunze and Kirsch et al. 2017)

  • Framework for Exploring and Understanding Multivariate Correlations [More]
    Published Demo Track Paper at ECML PKDD 2017 (Kirsch et al. 2017)

Recent blog posts

  • NeurIPS 2018, Updates on the AI road map

    Blog post preview image

    I present an updated roadmap to AGI with four critical challenges: Continual Learning, Meta-Learning, Environments, and Scalability. I motivate the respective areas and discuss how research from NeurIPS 2018 has advanced them and where we need to go next. [Continue reading]

  • A Map of Reinforcement Learning

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    Reinforcement Learning promises to solve the problem of designing intelligent agents in a formal but simple framework. This blog post aims at tackling the massive quantity of approaches and challenges in Reinforcement Learning, providing an overview of the different challenges researchers are working on and the methods they devised to solve these problems. [Continue reading]

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