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Found 7 papers, 4 papers with code
Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class Incremental Learning
A common solution to both problems is "replay," where a limited buffer of past instances is utilized to learn cross-task knowledge and mitigate catastrophic interference.
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas.
Fast & Furious: Modelling Malware Detection as Evolving Data Streams
This constant evolution of malware samples causes changes to the data distribution (i. e., concept drifts) that directly affect ML model detection rates, something not considered in the majority of the literature work.
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams
Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare.
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection
In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
River: machine learning for streaming data in Python
It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow.
Machine Learning (In) Security: A Stream of Problems
Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field.
