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]: Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions.
 In particular, we propose the use of $k$-determinantal point processes in  hyperparameter optimization via random search. Compared to conventional uniform random search where hyperparameter settings are sampled independently, a $k$-DPP promotes diversity.   We describe an approach that transforms hyperparameter search spaces for efficient use with a $k$-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from $k$-DPPs defined over spaces with a mixture of discrete and continuous dimensions. Our experiments show significant benefits over uniform random search  in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.
[EX A]: Driven by the need for parallelizable, open-loop hyperparameter optimization methods, we propose the use of $k$-determinantal point processes in  hyperparameter optimization via random search.

[EX Q]: We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference (i.e., thematic fit) and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%. The proposed framework is versatile and holds promise to support learning function-specific representations beyond the SVO structures.
[EX A]: Task-independent neural model for learning associations between interrelated groups of words.

[EX Q]: Human brain function as measured by functional magnetic resonance imaging
 (fMRI), exhibits a rich diversity. In response, understanding the individual variability
 of brain function and its association with behavior has become one of the
 major concerns in modern cognitive neuroscience. Our work is motivated by the
 view that generative models provide a useful tool for understanding this variability.
 To this end, this manuscript presents two novel generative models trained
 on real neuroimaging data which synthesize task-dependent functional brain images.
 Brain images are high dimensional tensors which exhibit structured spatial
 correlations. Thus, both models are 3D conditional Generative Adversarial networks
 (GANs) which apply Convolutional Neural Networks (CNNs) to learn an
 abstraction of brain image representations. Our results show that the generated
 brain images are diverse, yet task dependent. In addition to qualitative evaluation,
 we utilize the generated synthetic brain volumes as additional training data to improve
 downstream fMRI classifiers (also known as decoding, or brain reading).
 Our approach achieves significant improvements for a variety of datasets, classifi-
 cation tasks and evaluation scores. Our classification results provide a quantitative
 evaluation of the quality of the generated images, and also serve as an additional
 contribution of this manuscript.
[EX A]:
Two novel GANs are constructed to generate high-quality 3D fMRI brain images and synthetic brain images greatly help to improve downstream classification tasks.