TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications
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We describe a TensorFlow-based library for posterior sampling and exploration in machine learning applications. TATi, the Thermodynamic Analytics ToolkIt, implements algorithms for 2nd order (underdamped) Langevin dynamics and Hamiltonian Monte Carlo (HMC). It also allows for rapid prototyping of new sampling methods in pure Python and supports an ensemble framework for generating multiple trajectories in parallel, a capability that is demonstrated by the implementation of a recently proposed ensemble preconditioning sampling procedure. In addition to explaining the architecture of TATi and its connections with the TensorFlow framework, this article contains preliminary numerical experiments to explore the efficiency of posterior sampling strategies in ML applications, in comparison to standard training strategies. We provide a glimpse of the potential of the new toolkit by studying (and visualizing) the loss landscape of a neural network applied to the MNIST hand-written digits data set.
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