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20202026
most citedContinual Learning for Recurrent Neural Networks: an Empirical Evaluation

125 citations · 218 across the 27 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.NE2026

Continual Reinforcement Learning with Neuroevolution

Eleni Nisioti, Andrea Cossu, Kathrin Korte +1

Despite many studies about causes and remedies of plasticity loss in Reinforcement Learning (RL) under continual task changes, no RL method has yet consistently achieved a good bal…

cs.NE2026

Continual Evolution Strategies in Control Tasks

Nicola Pitzalis, Eleni Nisioti, Antonio Carta +2

We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive E…

cs.LG2026

Z-PEFT: Zero-shot Backdoor Detection in Parameter-Efficient Fine-Tuning via Canonical Spectral Signatures

Nicola Pitzalis, Donald Shenaj, Giacomo Cignoni +3

Parameter-Efficient Fine-tuned (PEFT) models are frequently downloaded from open repositories by practitioners. This widespread practice creates a significant attack surface, as ma…

cs.LG2026★ 8 cited

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…

cs.LG2026

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…