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.
- Currently PhD student with Jürgen Schmidhuber at IDSIA (The Swiss AI Lab).
- Previously MRes Computational Statistics and Machine Learning student at University College London with David Barber.
- Graduated as the best student of class 2017 from Hasso Plattner Institute
HPI is ranked 1st for computer science in most categories in Germany (CHE ranking 2015)
- Self-employed software developer during high school and my undergraduate studies [Project selection]
My first author paper Modular Networks: Learning to Decompose Neural Computation has been accepted to NIPS 2018 and will be presented later this year.
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)
Recent blog posts
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]
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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