125 citations · 238 across the 24 of their papers we have counts for
25 papers
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…
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…
Continually Learn to Map Visual Concepts to Large Language Models in Resource-constrained Environments
Clea Rebillard, Julio Hurtado, Andrii Krutsylo +2
Learning continually from a stream of non-i.i.d. data is an open challenge in deep learning, even more so when working in resource-constrained environments such as embedded devices…
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…
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
Rudy Semola, Julio Hurtado, Vincenzo Lomonaco +1
Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, suc…
A Comprehensive Empirical Evaluation on Online Continual Learning
Albin Soutif--Cormerais, Antonio Carta, Andrea Cossu +4
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum a…
In-context Interference in Chat-based Large Language Models
Eric Nuertey Coleman, Julio Hurtado, Vincenzo Lomonaco
Large language models (LLMs) have had a huge impact on society due to their impressive capabilities and vast knowledge of the world. Various applications and tools have been create…
Studying Generalization on Memory-Based Methods in Continual Learning
Felipe del Rio, Julio Hurtado, Cristian Buc +2
One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting. To mitigate comple…
Partial Hypernetworks for Continual Learning
Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu +1
Hypernetworks mitigate forgetting in continual learning (CL) by generating task-dependent weights and penalizing weight changes at a meta-model level. Unfortunately, generating all…
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
Antonio Carta, Andrea Cossu, Vincenzo Lomonaco +2
Distributed learning on the edge often comprises self-centered devices (SCD) which learn local tasks independently and are unwilling to contribute to the performance of other SDCs.…
Continual Learning for Predictive Maintenance: Overview and Challenges
Julio Hurtado, Dario Salvati, Rudy Semola +2
Deep learning techniques have become one of the main propellers for solving engineering problems effectively and efficiently. For instance, Predictive Maintenance methods have been…
Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models
Rudy Semola, Vincenzo Lomonaco, Davide Bacciu
Predictive machine learning models nowadays are often updated in a stateless and expensive way. The two main future trends for companies that want to build machine learning-based a…
Continual Learning for Human State Monitoring
Federico Matteoni, Andrea Cossu, Claudio Gallicchio +2
Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications. We propose two new CL benchmarks for Human State Monitoring…
Memory Population in Continual Learning via Outlier Elimination
Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo +3
Catastrophic forgetting, the phenomenon of forgetting previously learned tasks when learning a new one, is a major hurdle in developing continual learning algorithms. A popular met…
Continual Pre-Training Mitigates Forgetting in Language and Vision
Andrea Cossu, Tinne Tuytelaars, Antonio Carta +3
Pre-trained models are nowadays a fundamental component of machine learning research. In continual learning, they are commonly used to initialize the model before training on the s…
Avalanche RL: a Continual Reinforcement Learning Library
Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco +1
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experienc…
Practical Recommendations for Replay-based Continual Learning Methods
Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu +2
Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge. Several approaches have been developed in the li…
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
Valerio De Caro, Saira Bano, Achilles Machumilane +11
This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving per…
Ex-Model: Continual Learning from a Stream of Trained Models
Antonio Carta, Andrea Cossu, Vincenzo Lomonaco +1
Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize contin…
Sustainable Artificial Intelligence through Continual Learning
Andrea Cossu, Marta Ziosi, Vincenzo Lomonaco
The increasing attention on Artificial Intelligence (AI) regulation has led to the definition of a set of ethical principles grouped into the Sustainable AI framework. In this arti…
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
Davide Bacciu, Siranush Akarmazyan, Eric Armengaud +32
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment c…
Continual Learning with Echo State Networks
Andrea Cossu, Davide Bacciu, Antonio Carta +2
Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge. The study of CL for sequential pat…
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Andrea Rosasco, Antonio Carta, Andrea Cossu +2
Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new…