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.

[EX Q]: In deep learning, performance is strongly affected by the choice of architecture
and hyperparameters. While there has been extensive work on automatic hyperpa-
rameter optimization for simple spaces, complex spaces such as the space of deep
architectures remain largely unexplored. As a result, the choice of architecture is
done manually by the human expert through a slow trial and error process guided
mainly by intuition. In this paper we describe a framework for automatically
designing and training deep models. We propose an extensible and modular lan-
guage that allows the human expert to compactly represent complex search spaces
over architectures and their hyperparameters. The resulting search spaces are tree-
structured and therefore easy to traverse. Models can be automatically compiled to
computational graphs once values for all hyperparameters have been chosen. We
can leverage the structure of the search space to introduce different model search
algorithms, such as random search, Monte Carlo tree search (MCTS), and sequen-
tial model-based optimization (SMBO). We present experiments comparing the
different algorithms on CIFAR-10 and show that MCTS and SMBO outperform
random search. We also present experiments on MNIST, showing that the same
search space achieves near state-of-the-art performance with a few samples. These
experiments show that our framework can be used effectively for model discov-
ery, as it is possible to describe expressive search spaces and discover competitive
models without much effort from the human expert. Code for our framework and
experiments has been made publicly available
[EX A]: We describe a modular and composable language for describing expressive search spaces over architectures and simple model search algorithms applied to these search spaces.

[EX Q]: Learning effective text representations is a key foundation for numerous machine learning and NLP applications. While the celebrated Word2Vec technique yields semantically rich word representations, it is less clear whether sentence or document representations should be built upon word representations or from scratch. Recent work has demonstrated that a distance measure between documents called \emph{Word Mover's Distance} (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is very expensive to compute, and is harder to apply beyond simple KNN than feature embeddings. In this paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. Our technique extends the theory of \emph{Random Features} to show convergence of the inner product between WMEs to a positive-definite kernel that can be interpreted as a soft version of (inverse) WMD. The proposed embedding is more efficient and flexible than WMD in many situations. As an example, WME with a simple linear classifier reduces the computational cost of WMD-based KNN \emph{from cubic to linear} in document length and \emph{from quadratic to linear} in number of samples, while simultaneously improving accuracy. In experiments on 9 benchmark text classification datasets and 22 textual similarity tasks the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.
[EX A]: A novel approach to building an unsupervised document (sentence) embeddings from pre-trainedword embeddings

[EX Q]: Building on the success of deep learning, two modern approaches to learn a probability model of the observed data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational lower-bound on the log-likelihood function. GANs, however, compute a generative model by minimizing a distance between observed and generated probability distributions without considering an explicit model for the observed data. The lack of having explicit probability models in GANs prohibits computation of sample likelihoods in their frameworks and limits their use in statistical inference problems. In this work, we show that an optimal transport GAN with the entropy regularization can be viewed as a generative model that maximizes a lower-bound on average sample likelihoods, an approach that VAEs are based on. In particular, our proof constructs an explicit probability model for GANs that can be used to compute likelihood statistics within GAN's framework. Our numerical results on several datasets demonstrate consistent trends with the proposed theory.
[EX A]:
A statistical approach to compute sample likelihoods in Generative Adversarial Networks