2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
FAST: Similarity-based Knowledge Transfer for Efficient Policy Learning
Alessandro Capurso, Elia Piccoli, Davide Bacciu
Transfer Learning (TL) offers the potential to accelerate learning by transferring knowledge across tasks. However, it faces critical challenges such as negative transfer, domain a…
Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning
Elia Piccoli, Malio Li, Giacomo Carfì +2
The recent focus and release of pre-trained models have been a key components to several advancements in many fields (e.g. Natural Language Processing and Computer Vision), as a ma…
The Future of Continual Learning in the Era of Foundation Models: Three Key Directions
Jack Bell, Luigi Quarantiello, Eric Nuertey Coleman +5
Continual learning--the ability to acquire, retain, and refine knowledge over time--has always been fundamental to intelligence, both human and artificial. Historically, different…
I Know How: Combining Prior Policies to Solve New Tasks
Malio Li, Elia Piccoli, Vincenzo Lomonaco +1
Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the…
Calibration of Continual Learning Models
Lanpei Li, Elia Piccoli, Andrea Cossu +2
Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowled…