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:
We propose a framework to model the distribution of sequential data coming from
a set of entities connected in a graph with a known topology. The method is
based on a mixture of shared hidden Markov models (HMMs), which are trained
in order to exploit the knowledge of the graph structure and in such a way that the
obtained mixtures tend to be sparse. Experiments in different application domains
demonstrate the effectiveness and versatility of the method.
answer:
A method to model the generative distribution of sequences coming from graph connected entities.

question:
For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.
answer:
We present a multi-task benchmark and analysis platform for evaluating generalization in natural language understanding systems.

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.