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.

Different from existing works that represent deformation fields by training a general-purpose neural network, we advocate for an approximation based on mesh-free methods. By letting the network learn deformation parameters at a sparse set of positions in space (nodes), we reconstruct the continuous deformation field in a closed-form with guaranteed smoothness.