Deep nets learn backwards: from high-complexity hypotheses down to simplicity, not Solomonoff-style
jd_pressman · x · 2026-10-10
Researcher jdpressman argues that deep networks do not search hypothesis space in the order Solomonoff reasoning would suggest. Instead, they appear to start from very high-complexity "hypotheses" and gradually boil them down to simpler solutions.
He notes the underlying take is reasonable despite an obtuse presentation, and the interesting part is simply that deep nets don't behave like Solomonoff induction.
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