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 Input:
Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
Furthermore, social networks can be extracted from email corpora, tweets, or social media.
When it comes to visualising these large corpora, either the textual content or the network graph are used.

In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Ex Output:
Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.

Ex Input:
Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.

Ex Output:
We propose an algorithm that automatically adjusts parameters of a simulation engine to generate training data for a neural network such that validation accuracy is maximized.

Ex Input:
This paper explores the scenarios under which
an attacker can claim that 'Noise and access to
the softmax layer of the model is all you need'
to steal the weights of a convolutional neural network
whose architecture is already known. We
were able to achieve 96% test accuracy using
the stolen MNIST model and 82% accuracy using
stolen KMNIST model learned using only
i.i.d. Bernoulli noise inputs. We posit that this
theft-susceptibility of the weights is indicative
of the complexity of the dataset and propose a
new metric that captures the same. The goal of
this dissemination is to not just showcase how far
knowing the architecture can take you in terms of
model stealing, but to also draw attention to this
rather idiosyncratic weight learnability aspects of
CNNs spurred by i.i.d. noise input. We also disseminate
some initial results obtained with using
the Ising probability distribution in lieu of the i.i.d.
Bernoulli distribution

Ex Output:
Input only noise , glean the softmax outputs, steal the weights