Modern nanoscale processes exhibit stochastic behavior that can no longer be ignored. Statistical error compensation (SEC) has shown significant benefits in achieving energy efficiency and error resiliency by embracing the stochastic nature of the underlying process. Approximate computing (AC), on the other hand, employs deterministic designs that produce imprecise results to achieve energy efficiency. In this paper, we bridge the two design paradigms by utilizing SEC and AC in the design of a machine learning accelerator core. ANT, a form of SEC, was applied to an AC based stereo image matching implementation in a 45 nm process. Simulation results show that ANT combined with AC achieves energy savings of 44% compared to a conventional system, and 32.7% compared to an AC only system, while its performance degradation is less than 4%. This result shows that embracing the stochasticity of the architecture is crucial in achieving high energy efficiency, and that AC and ANT are synergistic.