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: 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

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. 
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Q: Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems. However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners). In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations: (i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data. The origin of the parameter divergence is also found both empirically and theoretically. (ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data. (iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence.

A: We investigate the internal reasons of our observations, the diminishing effects of the well-known hyperparameter optimization methods on federated learning from decentralized non-IID data.
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Q: Despite impressive performance as evaluated on i.i.d. holdout data, deep neural networks depend heavily on superficial statistics of the training data and are liable to break under distribution shift. For example, subtle changes to the background or texture of an image can break a seemingly powerful classifier. Building on previous work on domain generalization, we hope to produce a classifier that will generalize to previously unseen domains, even when domain identifiers are not available during training. This setting is challenging because the model may extract many distribution-specific (superficial) signals together with distribution-agnostic (semantic) signals. To overcome this challenge, we incorporate the gray-level co-occurrence matrix (GLCM) to extract patterns that our prior knowledge suggests are superficial: they are sensitive to the texture but unable to capture the gestalt of an image. Then we introduce two techniques for improving our networks' out-of-sample performance. The first method is built on the reverse gradient method that pushes our model to learn representations from which the GLCM representation is not predictable. The second method is built on the independence introduced by projecting the model's representation onto the subspace orthogonal to GLCM representation's.
 We test our method on the battery of standard domain generalization data sets and, interestingly, achieve comparable or better performance as compared to other domain generalization methods that explicitly require samples from the target distribution for training.

A:
Building on previous work on domain generalization, we hope to produce a classifier that will generalize to previously unseen domains, even when domain identifiers are not available during training.
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