Linear Models of Computation and Program Learning

Michael Bukatin, Steve Matthews
We consider two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. We argue that the task of program learning should be more tractable for these architectures than for conventional deterministic programs. We look at the recent advances in the "sampling the samplers" paradigm in higher-order probabilistic programming. We also discuss connections between partial inconsistency, non-monotonic inference, and vector semantics.
doi:10.29007/rbdd fatcat:ldzayy3ttnbb3o5hod5i3s3umy