Title: Hierarchical Bayes Autoencoders. Abstract: Autoencoders are powerful generative models for complex data, such as images. However, standard models like the variational autoencoder (VAE) typically have unimodal Gaussian decoders, which cannot effectively represent the possible semantic variations in the space of images. To address this problem, we present a new probabilistic generative model called the \emph{Hierarchical Bayes Autoencoder (HBAE)}. The HBAE contains a multimodal decoder in the form of an energy-based model (EBM), instead of the commonly adopted unimodal Gaussian distribution. The HBAE can be trained using variational inference, similar to a VAE, to recover latent codes conditioned on inputs. For the decoder, we use an adversarial approximation where a conditional generator is trained to match the EBM distribution. During inference time, the HBAE consists of two sampling steps: first a latent code for the input is sampled, and then this code is passed to the conditional generator to output a stochastic reconstruction. The HBAE is also capable of modeling sets, by inferring a latent code for a set of examples, and sampling set members through the multimodal decoder. In both single image and set cases, the decoder generates plausible variations consistent with the input data, and generates realistic unconditional samples. To the best our knowledge, Set-HBAE is the first model that is able to generate complex image sets.