5 papers
MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence
Federico Giannini, Emanuele Della Valle
Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML paradigms face obstacles related to conti…
Don't Look Back in Anger: MAGIC Net for Streaming Continual Learning with Temporal Dependence
Federico Giannini, Sandro D'Andrea, Emanuele Della Valle
Concept drift, temporal dependence, and catastrophic forgetting represent major challenges when learning from data streams. While Streaming Machine Learning and Continual Learning…
cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series
Federico Giannini, Giacomo Ziffer, Emanuele Della Valle
Dealing with an unbounded data stream involves overcoming the assumption that data is identically distributed and independent. A data stream can, in fact, exhibit temporal dependen…
Streaming Continual Learning for Unified Adaptive Intelligence in Dynamic Environments
Federico Giannini, Giacomo Ziffer, Andrea Cossu +1
Developing effective predictive models becomes challenging in dynamic environments that continuously produce data and constantly change. Continual Learning (CL) and Streaming Machi…
A Practical Guide to Streaming Continual Learning
Andrea Cossu, Federico Giannini, Giacomo Ziffer +5
Continual Learning (CL) and Streaming Machine Learning (SML) study the ability of agents to learn from a stream of non-stationary data. Despite sharing some similarities, they addr…