In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

 Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which does not require recurrent networks:  Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. 
 On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.
A simple architecture consisting of convolutions and attention achieves results on par with the best documented recurrent models.

This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.
This paper develops a principled method for continual learning in deep models.

Most deep latent factor models choose simple priors for simplicity, tractability
 or not knowing what prior to use. Recent studies show that the choice of
 the prior may have a profound effect on the expressiveness of the model,
 especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders
 (AAEs). We introduce the notion of code generators to transform manually selected
 simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than
 AAEs in both supervised and unsupervised settings. Lastly, we present its
 ability to do cross-domain translation in a  text-to-image synthesis task.

Learning Priors for Adversarial Autoencoders