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Showing 1–3 of 3 results
Advanced filters: Author: Patrick Schramowski Clear advanced filters

  • Large language models identify patterns in the relations between words and capture their relations in an embedding space. Schramowski and colleagues show that a direction in this space can be identified that separates ‘right’ and ‘wrong’ actions as judged by human survey participants.

    • Patrick Schramowski
    • Cigdem Turan
    • Kristian Kersting
    Research
    Nature Machine Intelligence
    Volume: 4, P: 258-268

  • Explanatory interactive machine learning methods have been developed to facilitate the learning process between the machine and the user. Friedrich et al. provide a unification of various explanatory interactive machine learning methods into a single typology, and present benchmarks for evaluating such methods.

    • Felix Friedrich
    • Wolfgang Stammer
    • Kristian Kersting
    Research
    Nature Machine Intelligence
    Volume: 5, P: 319-330

  • Deep learning approaches can show excellent performance but still have limited practical use if they learn to predict based on confounding factors in a dataset, for instance text labels in the corner of images. By using an explanatory interactive learning approach, with a human expert in the loop during training, it becomes possible to avoid predictions based on confounding factors.

    • Patrick Schramowski
    • Wolfgang Stammer
    • Kristian Kersting
    Research
    Nature Machine Intelligence
    Volume: 2, P: 476-486