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Found 11 papers, 2 papers with code
Uncovering inequalities in new knowledge learning by large language models across different languages
In this paper, we explore inequalities in new knowledge learning by LLMs across different languages and four key dimensions: effectiveness, transferability, prioritization, and robustness.
A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining
With the explosive growth of Internet data, users are facing the problem of information overload, which makes it a challenge to efficiently obtain the required resources.
Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing
The development of Internet technology has led to a rapid increase in news information.
Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics
Long-short range time series forecasting is essential for predicting future trends and patterns over extended periods.
Measuring Human Contribution in AI-Assisted Content Generation
This study raises the research question of measuring human contribution in AI-assisted content generation and introduces a framework to address this question that is grounded in information theory.
Attention Mechanism and Context Modeling System for Text Mining Machine Translation
This paper advances a novel architectural schema anchored upon the Transformer paradigm and innovatively amalgamates the K-means categorization algorithm to augment the contextual apprehension capabilities of the schema.
Text-Driven Diverse Facial Texture Generation via Progressive Latent-Space Refinement
SDS boosts GANs with more generative modes, while GANs promote more efficient optimization of SDS.
How COVID-19 has Impacted American Attitudes Toward China: A Study on Twitter
Past research has studied social determinants of attitudes toward foreign countries.
Large-scale Quantitative Evidence of Media Impact on Public Opinion toward China
Do mass media influence people's opinion of other countries?
IMRank: Influence Maximization via Finding Self-Consistent Ranking
Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.
Social and Information Networks Data Structures and Algorithms F.2.2; D.2.8
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization.
Social and Information Networks Data Structures and Algorithms Physics and Society F.2.2; D.2.8
