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Found 16 papers, 5 papers with code
Towards Ethical Content-Based Detection of Online Influence Campaigns
The detection of clandestine efforts to influence users in online communities is a challenging problem with significant active development.
In BLOOM: Creativity and Affinity in Artificial Lyrics and Art
We apply a large multilingual language model (BLOOM-176B) in open-ended generation of Chinese song lyrics, and evaluate the resulting lyrics for coherence and creativity using human reviewers.
Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns.
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
Continual learning (CL) is one of the most promising trends in recent machine learning research.
Contextual One-Class Classification in Data Streams
We conclude that the paradigm of contexts in data streams can be used to improve the performance of streaming one-class classifiers.
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
ReMix: Calibrated Resampling for Class Imbalance in Deep learning
Class imbalance is a problem of significant importance in applied deep learning where trained models are exploited for decision support and automated decisions in critical areas such as health and medicine, transportation, and finance.
On the combined effect of class imbalance and concept complexity in deep learning
Structural concept complexity, class overlap, and data scarcity are some of the most important factors influencing the performance of classifiers under class imbalance conditions.
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data
Detecting relevant changes in dynamic time series data in a timely manner is crucially important for many data analysis tasks in real-world settings.
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems.
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
Anomaly detection is of paramount importance in many real-world domains, characterized by evolving behavior.
A Semi-Supervised Framework for Misinformation Detection
The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest.
Faithful to Whom? Questioning Interpretability Measures in NLP
A common approach to quantifying model interpretability is to calculate faithfulness metrics based on iteratively masking input tokens and measuring how much the predicted label changes as a result.
Nondestructive chicken egg fertility detection using CNN-transfer learning algorithms
In testing set, it achieved an accuracy of 0. 98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0. 96 for identifying non-fertile eggs.
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media
The posts are scraped using seed expressions related to previously known discourse of hatred towards Jews.
