12 citations · 23 across the 13 of their papers we have counts for
13 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…
Task-Agnostic Experts Composition for Continual Learning
Luigi Quarantiello, Andrea Cossu, Vincenzo Lomonaco
Compositionality is one of the fundamental abilities of the human reasoning process, that allows to decompose a complex problem into simpler elements. Such property is crucial also…
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…
Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah +9
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The…