9 citations · 17 across the 4 of their papers we have counts for
4 papers · 1 filter
Tracking Adaptation Time: Metrics for Temporal Distribution Shift
Lorenzo Iovine, Giacomo Ziffer, Emanuele Della Valle
Evaluating robustness under temporal distribution shift remains an open challenge. Existing metrics quantify the average decline in performance, but fail to capture how models adap…
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…