67 citations · 90 across the 16 of their papers we have counts for
16 papers
GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter Merging
Irene Testa, Luigi Quarantiello, Eric Nuertey Coleman +3
The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models. While the rich representations lear…
Parameter-Efficient Continual Fine-Tuning: A Survey
Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu +4
The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance. However, these models inherit a fu…
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…
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…
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…
Class-Incremental Learning with Repetition
Hamed Hemati, Andrea Cossu, Antonio Carta +5
Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike r…
PIVOT: Prompting for Video Continual Learning
Andrés Villa, Juan León Alcázar, Motasem Alfarra +5
Modern machine learning pipelines are limited due to data availability, storage quotas, privacy regulations, and expensive annotation processes. These constraints make it difficult…
How Relevant is Selective Memory Population in Lifelong Language Learning?
Vladimir Araujo, Helena Balabin, Julio Hurtado +2
Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting. State-of-the-art approaches…
A Study on the Predictability of Sample Learning Consistency
Alain Raymond-Saez, Julio Hurtado, Alvaro Soto
Curriculum Learning is a powerful training method that allows for faster and better training in some settings. This method, however, requires having a notion of which examples are…
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…
Entropy-based Stability-Plasticity for Lifelong Learning
Vladimir Araujo, Julio Hurtado, Alvaro Soto +1
The ability to continuously learn remains elusive for deep learning models. Unlike humans, models cannot accumulate knowledge in their weights when learning new tasks, mainly due t…
Sequoia: A Software Framework to Unify Continual Learning Research
Fabrice Normandin, Florian Golemo, Oleksiy Ostapenko +10
The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non-stationary environments. In practice, a…
Optimizing Reusable Knowledge for Continual Learning via Metalearning
Julio Hurtado, Alain Raymond-Saez, Alvaro Soto
When learning tasks over time, artificial neural networks suffer from a problem known as Catastrophic Forgetting (CF). This happens when the weights of a network are overwritten du…