2 citations · 3 across the 5 of their papers we have counts for
5 papers
Reinforcement Learning-Based Dynamic Management of Structured Parallel Farm Skeletons on Serverless Platforms
Lanpei Li, Massimo Coppola, Malio Li +3
We present a framework for dynamic management of structured parallel processing skeletons on serverless platforms. Our goal is to bring HPC-like performance and resilience to serve…
A Compositional Paradigm for Foundation Models: Towards Smarter Robotic Agents
Luigi Quarantiello, Elia Piccoli, Jack Bell +8
The birth of Foundation Models brought unprecedented results in a wide range of tasks, from language to vision, to robotic control. These models are able to process huge quantities…
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…