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Investigating Generate and Test for Online Representation Search with Softmax Outputs

  • Author / Creator
    Elsayed, Mohamed
  • Modern representation learning methods perform well on offline tasks and primarily revolve around batch updates. However, batch updates preclude those methods from focusing on new experience, which is essential for fast online adaptation. In this thesis, we study an online and incremental representation search algorithm called Generate and Test, which continually replaces the least useful features with newly generated features. In this algorithm, the utility of features is estimated by a heuristic tester based on the magnitude of their corresponding outgoing weights; the least useful features are those with the smallest weight magnitudes. Generate and Test was developed and evaluated only on single-output regression problems. However, it has not been investigated in multi-output regression problems. Moreover, it is not clear that magnitude-based testers are appropriate for other outputs such as softmax. In this thesis, we investigate Generate and Test in these new cases and introduce testbeds for online representation learning in multi-output regression, classification, and reinforcement learning environments with discrete action spaces. We show that magnitude-based feature utility may give wrong estimates of the utility when softmax outputs are used, for example, in classification and discrete control tasks. We propose a new tester to extend the scope of the Generate and Test algorithm to these cases. We empirically show that this new tester can improve representations better than the magnitude-based tester. Thus, ours is the first work to make the Generate and Test algorithm applicable beyond supervised regression tasks.

  • Subjects / Keywords
  • Graduation date
    Fall 2022
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-skxp-h641
  • License
    This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.