Search Results for author:
Found 8 papers, 3 papers with code
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments
In designing instructions for annotation tasks to generate training data for these algorithms, researchers often treat the harm concepts that we train algorithms to detect - 'hateful', 'offensive', 'toxic', 'racist', 'sexist', etc.
AttentionFlow: Visualising Influence in Networks of Time Series
The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest.
Variation across Scales: Measurement Fidelity under Twitter Data Sampling
This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades).
Estimating Attention Flow in Online Video Networks
In this paper, we first construct the Vevo network -- a YouTube video network with 60, 740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics.
Unique Sharp Local Minimum in $\ell_1$-minimization Complete Dictionary Learning
First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $\ell_1$ objective function.
Beyond Views: Measuring and Predicting Engagement in Online Videos
The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance.
Social and Information Networks Human-Computer Interaction
Local identifiability of $l_1$-minimization dictionary learning: a sufficient and almost necessary condition
Moreover, our local identifiability results also translate to the finite sample case with high probability provided that the number of signals $N$ scales as $O(K\log K)$.
