ShapeEmbed is a self-supervised method using Euclidean distance matrices to encode the shape of objects in 2D images in a way that is robust to shape-preserving geometric transformations

We derive brain-like inference as natural gradient descent on free energy (FOND). The resulting spiking network (iP-VAE) outperforms amortized VAEs in reconstruction-sparsity trade-offs and out-of-distribution generalization.

A continuously autoregressive approach for text to motion generation with gaussian mixture-guided latent sampling.