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Found 13 papers, 4 papers with code
Identify Critical Nodes in Complex Network with Large Language Models
Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility.
Large Language Model Agent for Hyper-Parameter Optimization
Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
Open-Domain Event Graph Induction for Mitigating Framing Bias
However, none of the existing methods have explicitly addressed the issue of framing bias that is inherent in news articles.
Creating Multimedia Summaries Using Tweets and Videos
While popular televised events such as presidential debates or TV shows are airing, people provide commentary on them in real-time.
Design Challenges for a Multi-Perspective Search Engine
Naturally, identifying such responses within a document is a natural language understanding task.
MultiOpEd: A Corpus of Multi-Perspective News Editorials
We propose MultiOpEd, an open-domain news editorial corpus that supports various tasks pertaining to the argumentation structure in news editorials, focusing on automatic perspective discovery.
Learnable Embedding Sizes for Recommender Systems
Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost.
Cold-start Sequential Recommendation via Meta Learner
As there is generally no side information in the setting of sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available.
DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session.
Ranked #6 on
Session-Based Recommendations
on Diginetica
Long-tail Session-based Recommendation
Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data.
Learning to mirror speaking styles incrementally
Our method extracts ngrams that capture a persons speaking styles and uses the ngrams to create patterns for transforming sentences to the persons speaking styles.
Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding U.S. Gun Violence
We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U. S. between 2016 and 2018.
Balancing Multi-level Interactions for Session-based Recommendation
For mining the new data without breaking the equilibrium of the model between different interactions, we construct an intra-session graph and an inter-session graph for the current session.
