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Found 11 papers, 2 papers with code
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework
Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems.
Towards efficient deep autoencoders for multivariate time series anomaly detection
First, pruning reduces the number of weights, while preventing catastrophic drops in accuracy by means of a fast search process that identifies high sparsity levels.
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.
Ada-QPacknet -- adaptive pruning with bit width reduction as an efficient continual learning method without forgetting
Most of them have many problems with learning in dynamic and complex environments.
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection
Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems.
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.
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.
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.
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.
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.
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.
