This paper presents a new gradient flow dissipation geometry over non-negative and probability measures.

We demonstrate our losses on multilabel classification and imitation learning tasks.

Our derivation of the update for IL (Eq. 3) is based on an expansion of log We examine the consequences of this bias formula for our specific model. Note that the update term in Eq. (S1) is However, we will show in Appendix C that these updates may have high variance. 'reparameterization trick,' in which a change of variables allows the use of stochastic gradient descent It is worth noting that this'reparameterization' will work only for additive Gaussian noise. As already mentioned, WS can be viewed as a special case of IL. Since WS is a special case of IL, the bias properties of its individual samples are identical.

We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence.

The left panel shows the energy profile for a rotation around an O-C-C-C dihedral angle. It can be seen that all models solve this task surprisingly well. In the right panel of Figure 4, we show energy predictions along a minimum energy path of an intramolecular hydrogen transfer reaction. This task probes a model's ability to describe a bond All models accurately reproduce the barrier's shape with the MPNN models closely The acetylacetone dataset contains trajectories of a small reactive molecule sampled at different temperature. Consequently, we also use their internal normalization.