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.

To help address these issues, a natural approach is to consider games with special structures. In this paper, we focus on congestion games.

To help address these issues, a natural approach is to consider games with special structures. In this paper, we focus on congestion games.

We report values measured at the deepest DFA layer. Table A.1: Alignment cosine similarity (higher is better, standard deviation in parenthesis) of Table A.2: Alignment cosine similarity (standard deviation in parenthesis) of various graph convolutions architectures as measured on the Cora dataset. We compare DFA to BP, but also to shallow learning-where only the topmost layer is trained. On a simple task like MNIST, a shallow baseline may be as high as 90%. Furthermore, the network is cut down to 3 layers of half the width of NeRF, and no coarse network is used to inform the sampling.

Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient paral-lelization of the training process.