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NoKnow addresses the challenges of understanding machine learning models by focusing on their identifiability. It develops theoretical tools and algorithms to ensure that outcomes from learned representations are identifiable, enhancing trust in machine learning applications.
Machine learning and artificial intelligence techniques are progressing at a tremendous pace and impressive results appear across scientific fields.
However, as machine learning models grow in capacity, they become increasingly ‘black box’, and it becomes harder for humans to reason about the patterns discovered by the machine. A root cause of this difficulty is that most machine learning models can express the same pattern in infinitely many, equally good, ways within their internal representat…
TECHNICAL UNIVERSITY OF DENMARK DTU
DANMARKS TEKNISKE UNIVERSITET
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