instruction:
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.
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:
This paper introduces NEMO, an approach to unsupervised object detection that uses motion---instead of image labels---as a cue to learn object detection. To discriminate between motion of the target object and other changes in the image, it relies on negative examples that show the scene without the object. The required data can be collected very easily by recording two short videos, a positive one showing the object in motion and a negative one showing the scene without the object. Without any additional form of pretraining or supervision and despite of occlusions, distractions, camera motion, and adverse lighting, those videos are sufficient to learn object detectors that can be applied to new videos and even generalize to unseen scenes and camera angles. In a baseline comparison, unsupervised object detection outperforms off-the shelf template matching and tracking approaches that are given an initial bounding box of the object. The learned object representations are also shown to be accurate enough to capture the relevant information from manipulation task demonstrations, which makes them applicable to learning from demonstration in robotics. An example of object detection that was learned from 3 minutes of video can be found here: http://y2u.be/u_jyz9_ETz4
answer:

Learning to detect objects without image labels from 3 minutes of video