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:
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probabilistic inference in deep neural networks, variational Bayes (VB) is theoretically grounded, generally applicable, and computationally efficient. With wide recognition of potential advantages, why is it that variational Bayes has seen very limited practical use for BNNs in real applications? We argue that variational inference in neural networks is fragile: successful implementations require careful initialization and tuning of prior variances, as well as controlling the variance of Monte Carlo gradient estimates. We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances. Combining these two innovations, the resulting method is highly efficient and robust. On the application of heteroscedastic regression we demonstrate good predictive performance over alternative approaches.

Ex Output:
A method for eliminating gradient variance and automatically tuning priors for effective training of bayesian neural networks


Ex Input:
Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems. However, due to biological and technical noise, quantifying the effects of multiple experimental conditions presents an analytical challenge. To overcome this challenge, we developed MELD: Manifold Enhancement of Latent Dimensions. MELD leverages tools from graph signal processing to learn a latent dimension within the data scoring the prototypicality of each datapoint with respect to experimental or control conditions. We call this dimension the Enhanced Experimental Signal (EES). MELD learns the EES by filtering the noisy categorical experimental label in the graph frequency domain to recover a smooth signal with continuous values. This method can be used to identify signature genes that vary between conditions and identify which cell types are most affected by a given perturbation. We demonstrate the advantages of MELD analysis in two biological datasets, including T-cell activation in response to antibody-coated beads and treatment of human pancreatic islet cells with interferon gamma.

Ex Output:

A novel graph signal processing framework for quantifying the effects of experimental perturbations in single cell biomedical data.