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Thomas Adler, MSc

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Forschungsthemen

  • Deep Learning and Neural Networks
  • Few-Shot Learning
  • Domain Adaptation
  • LSTM and Recurrent Neural Networks
  • Meta Learning

Ausgewählte Publikationen

Thomas Adler, Johannes Brandstetter, Michael Widrich, Andreas Mayr, David Kreil, Michael Kopp, Günter Klambauer, and Sepp Hochreiter (2020). Cross-Domain Few-Shot Learing by Representation Fusion. [PDF]

Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, and Sepp Hochreiter (2019). Quantum Optical Experiments Modeled by Long Short-Term Memory. NeurIPS 2019 – Machine Learning and the Physical Sciences Workshop. [PDF]

Johannes Lehner, Andreas Mitterecker, Thomas Adler, Markus Hofmarcher, Bernhard Nessler, and Sepp Hochreiter (2019).  Patch Refinement – Localized 3D Object Detection. NeurIPS 2019 – Machine Learning for Autonomous Driving Workshop. [PDF]

Lehre

  • 2020: Exercises in LSTM and Recurrent Neural Nets, Lecturer, JKU Linz
  • 2020: Lecture in LSTM and Recurrent Neural Nets, Co-Lecturer, JKU Linz
  • 2020: Lecture in Machine Learning: Unsupervised Techniques, Co-Lecturer, JKU Linz
  • 2020: Lecture in Programming in Python II, Co-Lecturer, JKU Linz
  • 2019: Exercises in LSTM and Recurrent Neural Nets, Lecturer, JKU Linz
  • 2019: Lecture in LSTM and Recurrent Neural Nets, Co-Lecturer, JKU Linz
  • 2018: Exercises in Theoretical Concepts of Machine Learning, Co-Lecturer, JKU Linz

Vorträge

  • Jänner 2019: From Machine Translation to Self-Driving Cars Using LSTMs. OeAW Hackathon, Vienna 
  • October 2019: Quantum Optical Experiments Modeled by Long Short-Term Memory. Machine Learning Retreat IQOQI Innsbruck and Institute for Machine Learning JKU, Nussdorf am Attersee, Austria