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Found 10 papers, 2 papers with code
Auditing Keyword Queries Over Text Documents
Data security and privacy is an issue of growing importance in the healthcare domain.
Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs
Hence, as a reference implementation, we develop a simple neural model induced with EFT for capturing evolving graph spectra.
Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?
$\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology.
Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions
Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra.
Using Social Media for Word-of-Mouth Marketing
To do WoM marketing, we need to identify a set of people who can use their authoritative position in social network to promote a given product.
Toward Maximizing the Visibility of Content in Social Media Brand Pages: A Temporal Analysis
One of the main goals of content creators is to spread their information to a large audience.
Sentiment Dynamics in Social Media News Channels
We also analyze users' reactions and opinion sentiment on news posts with different sentiments.
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.
Efficient Twitter Sentiment Classification using Subjective Distant Supervision
In this paper, we propose techniques to speed up the computation process for sentiment analysis.
A Bayesian Framework for Figure-Ground Interpretation
Figure/ground assignment, in which the visual image is divided into nearer (figural) and farther (ground) surfaces, is an essential step in visual processing, but its underlying computational mechanisms are poorly understood.
