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.
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Question: 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

Answer: We describe a modular and composable language for describing expressive search spaces over architectures and simple model search algorithms applied to these search spaces. 


Question: We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment.   This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large  temporal contexts into account.   Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the  state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music. We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.

Answer: The Wave-U-Net architecture, recently introduced by Stoller et al for music source separation, is highly effective for speech enhancement, beating the state of the art.


Question: Most approaches to learning action planning models heavily rely on a significantly large volume of training samples or plan observations. In this paper, we adopt a different approach based on deductive learning from domain-specific knowledge, specifically from logic formulae that specify constraints about the possible states of a given domain. The minimal input observability required by our approach is a single example composed of a full initial state and a partial goal state. We will show that exploiting specific domain knowledge enables to constrain the space of possible action models as well as to complete partial observations, both of which turn out helpful to learn good-quality action models.

Answer:
Hybrid approach to model acquisition that compensates a lack of available data with domain specific knowledge provided by experts