Search Results for author: Andrea Cossu

Found 15 papers, 10 papers with code

Avalanche: A PyTorch Library for Deep Continual Learning

1 code implementation2 Feb 2023 Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time.

Continual Learning

Class-Incremental Learning with Repetition

no code implementations26 Jan 2023 Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth

We focus on the family of Class-Incremental with Repetition (CIR) scenarios, where repetition is embedded in the definition of the stream.

class-incremental learning Incremental Learning

Continual Learning for Human State Monitoring

1 code implementation29 Jun 2022 Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu

Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications.

Continual Learning Time Series

Sample Condensation in Online Continual Learning

1 code implementation23 Jun 2022 Mattia Sangermano, Antonio Carta, Andrea Cossu, Davide Bacciu

A popular solution in these scenario is to use a small memory to retain old data and rehearse them over time.

Continual Learning

Continual Pre-Training Mitigates Forgetting in Language and Vision

1 code implementation19 May 2022 Andrea Cossu, Tinne Tuytelaars, Antonio Carta, Lucia Passaro, Vincenzo Lomonaco, Davide Bacciu

We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.

Continual Learning Continual Pretraining

Practical Recommendations for Replay-based Continual Learning Methods

no code implementations19 Mar 2022 Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu, Antonio Carta, Davide Bacciu

Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge.

Continual Learning Data Augmentation

Ex-Model: Continual Learning from a Stream of Trained Models

1 code implementation13 Dec 2021 Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years.

Continual Learning

Is Class-Incremental Enough for Continual Learning?

no code implementations6 Dec 2021 Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, Davide Maltoni, Davide Bacciu, Antonio Carta, Vincenzo Lomonaco

The ability of a model to learn continually can be empirically assessed in different continual learning scenarios.

Continual Learning

Sustainable Artificial Intelligence through Continual Learning

no code implementations17 Nov 2021 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.

Continual Learning

Continual Learning with Echo State Networks

1 code implementation17 May 2021 Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco

Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge.

Continual Learning

Distilled Replay: Overcoming Forgetting through Synthetic Samples

2 code implementations29 Mar 2021 Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training.

Continual Learning

Continual Learning for Recurrent Neural Networks: an Empirical Evaluation

no code implementations12 Mar 2021 Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu

We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications.

Continual Learning

Continual Learning with Gated Incremental Memories for sequential data processing

1 code implementation8 Apr 2020 Andrea Cossu, Antonio Carta, Davide Bacciu

The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions.

Continual Learning reinforcement Learning

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