Federico Giannini

Streaming Continual Learning for Unified Adaptive Intelligence in Dynamic Environments

Developing effective predictive models becomes challenging in dynamic environments that continuously produce data and constantly change. Continual Learning (CL) and Streaming Machine Learning...

cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series

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 dependencies (i.e., be a...

A Practical Guide to Streaming Continual Learning

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 address different...

Don't drift away: Advances and Applications of Streaming and Continual Learning

Tenet: Benchmarking Data Stream Classifiers in Presence of Temporal Dependence

cPB: Continuous Piggyback for Streaming Continual Learning with Temporal Dependence

Addressing Temporal Dependence, Concept Drifts, and Forgetting in Data Streams

MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence

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 continuous learning...

Don't Look Back in Anger: MAGIC Net for Streaming Continual Learning with Temporal Dependence

Concept drift, temporal dependence, and catastrophic forgetting represent major challenges when learning from data streams. While Streaming Machine Learning and Continual Learning (CL) address...