One significant worry here would be that bounds from (classical) learning theory seem to be pretty vacuous most of the time. But I’m excited about comparing brains and learning algos, also see many empirical papers.
I’m hopeful that SLT’s bounds are less vacuous than classical learning theoretic bounds, partially because non-equilibrium dynamics seem more tractable with such dominating singularities, and partially because all the equations are equality relations right now, not bounds.
One significant worry here would be that bounds from (classical) learning theory seem to be pretty vacuous most of the time. But I’m excited about comparing brains and learning algos, also see many empirical papers.
I’m hopeful that SLT’s bounds are less vacuous than classical learning theoretic bounds, partially because non-equilibrium dynamics seem more tractable with such dominating singularities, and partially because all the equations are equality relations right now, not bounds.