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.

[Q]: This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker. Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm. Neither prior domain knowledge about the data nor feature preprocessing is needed. We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC). By parallelizing PHC, Autostacker can provide candidate pipelines with sufficient prediction accuracy within a short amount of time. These pipelines can be used as is or as a starting point for human experts to build on. By focusing on the modelling process, Autostacker breaks the tradition of following fixed order pipelines by exploring not only single model pipeline but also innovative combinations and structures. As we will show in the experiment section, Autostacker achieves significantly better performance both in terms of test accuracy and time cost comparing with human initial trials and recent popular AutoML system.
[A]: Automate machine learning system with efficient search algorithm and innovative structure to provide better model baselines.


[Q]: As deep learning-based classifiers are increasingly adopted in real-world applications, the importance of understanding how a particular label is chosen grows. Single decision trees are an example of a simple, interpretable classifier, but are unsuitable for use with complex, high-dimensional data. On the other hand, the variational autoencoder (VAE) is designed to learn a factored, low-dimensional representation of data, but typically encodes high-likelihood data in an intrinsically non-separable way.   We introduce the differentiable decision tree (DDT) as a modular component of deep networks and a simple, differentiable loss function that allows for end-to-end optimization of a deep network to compress high-dimensional data for classification by a single decision tree.   We also explore the power of labeled data in a  supervised VAE (SVAE) with a Gaussian mixture prior, which leverages label information to produce a high-quality generative model with improved bounds on log-likelihood.   We combine the SVAE with the DDT to get our classifier+VAE (C+VAE), which is competitive in both classification error and log-likelihood, despite optimizing both simultaneously and using a very simple encoder/decoder architecture.
[A]: We combine differentiable decision trees with supervised variational autoencoders to enhance interpretability of classification. 


[Q]: Inspired by the success of self attention mechanism and Transformer architecture
 in sequence transduction and image generation applications, we propose novel self
 attention-based architectures to improve the performance of adversarial latent code-
 based schemes in text generation. Adversarial latent code-based text generation
 has recently gained a lot of attention due to their promising results. In this paper,
 we take a step to fortify the architectures used in these setups, specifically AAE
 and ARAE. We benchmark two latent code-based methods (AAE and ARAE)
 designed based on adversarial setups. In our experiments, the Google sentence
 compression dataset is utilized to compare our method with these methods using
 various objective and subjective measures. The experiments demonstrate the
 proposed (self) attention-based models outperform the state-of-the-art in adversarial
 code-based text generation.
[A]:
We propose a self-attention based GAN architecture for unconditional text generation and improve on previous adversarial code-based results.