• Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning 

      Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)
      Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art ...
    • Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning 

      Murad, Abdulmajid; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2020)
      In order to make better use of deep reinforcement learning in the creation of sensing policies for resource-constrained IoT devices, we present and study a novel reward function based on the Fisher information value. This ...
    • IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning 

      Murad, Abdulmajid Abdullah Yahya; Kraemer, Frank Alexander; Bach, Kerstin; Taylor, Gavin (Chapter, 2019)
      We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge ...
    • ML-based profile analysis of CUDA programs' compiler flag impact 

      Bækken, August Landgraff (Master thesis, 2018)
      With the recent successes and interest in machine learning, this project aims to investigate whether machine learning methods can be used to improve compiler optimization selection. Compiler optimization is hard because ...