@conference{Hemati2023,

title = {Partial Hypernetworks for Continual Learning},

author = {Hamed Hemati and Vincenzo Lomonaco and Davide Bacciu and Damian Borth},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-01},

booktitle = {Proceedings of the International Conference on Lifelong Learning Agents (CoLLAs 2023)},

publisher = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Hemati2023b,

title = {Class-Incremental Learning with Repetition },

author = {Hamed Hemati and Andrea Cossu and Antonio Carta and Julio Hurtado and Lorenzo Pellegrini and Davide Bacciu and Vincenzo Lomonaco and Damian Borth},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-01},

booktitle = {Proceedings of the International Conference on Lifelong Learning Agents (CoLLAs 2023)},

publisher = {Proceedings of Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Lomonaco2023,

title = {AI-Toolkit: a Microservices Architecture for Low-Code Decentralized Machine Intelligence},

author = {Vincenzo Lomonaco and Valerio De Caro and Claudio Gallicchio and Antonio Carta and Christos Sardianos and Iraklis Varlamis and Konstantinos Tserpes and Massimo Coppola and Mina Marpena and Sevasti Politi and Erwin Schoitsch and Davide Bacciu},

year  = {2023},

date = {2023-06-04},

urldate = {2023-06-04},

booktitle = {Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {Artificial Intelligence and Machine Learning toolkits such as Scikit-learn, PyTorch and Tensorflow provide today a solid starting point for the rapid prototyping of R&D solutions. However, they can be hardly ported to heterogeneous decentralised hardware and real-world production environments. A common practice involves outsourcing deployment solutions to scalable cloud infrastructures such as Amazon SageMaker or Microsoft Azure. In this paper, we proposed an open-source microservices-based architecture for decentralised machine intelligence which aims at bringing R&D and deployment functionalities closer following a low-code approach. Such an approach would guarantee flexible integration of cutting-edge functionalities while preserving complete control over the deployed solutions at negligible costs and maintenance efforts.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Matteoni2022,

title = {Continual Learning for Human State Monitoring},

author = {Federico Matteoni and Andrea Cossu and Claudio Gallicchio and Vincenzo Lomonaco and Davide Bacciu},

editor = {Michel Verleysen},

url = {https://arxiv.org/pdf/2207.00010, Arxiv},

year  = {2022},

date = {2022-10-05},

urldate = {2022-10-05},

booktitle = {Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022)},

abstract = {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. We carefully designed the benchmarks to mirror real-world environments in which new subjects are continuously added. We conducted an empirical evaluation to assess the ability of popular CL strategies to mitigate forgetting in our benchmarks. Our results show that, possibly due to the domain-incremental properties of our benchmarks, forgetting can be easily tackled even with a simple finetuning and that existing strategies struggle in accumulating knowledge over a fixed, held-out, test subject.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Sangermano2022,

title = {Sample Condensation in Online Continual Learning},

author = {Matteo Sangermano and Antonio Carta and Andrea Cossu and Vincenzo Lomonaco and Davide Bacciu

},

url = {https://arxiv.org/abs/2206.11849, Arxiv},

year  = {2022},

date = {2022-07-18},

urldate = {2022-07-18},

booktitle = {Proceedings of the 2022 IEEE World Congress on Computational Intelligence},

publisher = {IEEE},

abstract = {Online Continual learning is a challenging learning scenario where the model observes a non-stationary stream of data and learns online. The main challenge is to incrementally learn while avoiding catastrophic forgetting, namely the problem of forgetting previously acquired knowledge while learning from new data. A popular solution in these scenario is to use a small memory to retain old data and rehearse them over time. Unfortunately, due to the limited memory size, the quality of the memory will deteriorate over time. In this paper we propose OLCGM, a novel replay-based continual learning strategy that uses knowledge condensation techniques to continuously compress the memory and achieve a better use of its limited size. The sample condensation step compresses old samples, instead of removing them like other replay strategies. As a result, the experiments show that, whenever the memory budget is limited compared to the complexity of the data, OLCGM improves the final accuracy compared to state-of-the-art replay strategies.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{carta2021ex,

title = {Ex-Model: Continual Learning from a Stream of Trained Models},

author = {Antonio Carta and Andrea Cossu and Vincenzo Lomonaco and Davide Bacciu},

url = {https://arxiv.org/pdf/2112.06511.pdf, Arxiv},

year  = {2022},

date = {2022-06-20},

urldate = {2022-06-20},

booktitle = {Proceedings of the CVPR 2022 Workshop on Continual Learning },

journal = {arXiv preprint arXiv:2112.06511},

pages = {3790-3799},

organization = {IEEE},

abstract = {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 continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Lucchesi2022,

title = {Avalanche RL: a Continual Reinforcement Learning Library},

author = {Nicolò Lucchesi and Antonio Carta and Vincenzo Lomonaco and Davide Bacciu},

url = {https://arxiv.org/abs/2202.13657, Arxiv},

year  = {2022},

date = {2022-05-23},

urldate = {2022-05-23},

booktitle = {Proceedings of the 21st International Conference on Image Analysis and Processing (ICIAP 2021)},

abstract = {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 experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and supports any OpenAI Gym environment. Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Cossu2021,

title = { Continual Learning with Echo State Networks },

author = {Andrea Cossu and Davide Bacciu and Antonio Carta and Claudio Gallicchio and Vincenzo Lomonaco},

editor = {Michel Verleysen},

url = {https://arxiv.org/abs/2105.07674, Arxiv},

doi = {10.14428/esann/2021.ES2021-80},

year  = {2021},

date = {2021-10-06},

urldate = {2021-10-06},

booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning  (ESANN 2021)

},

volume = {275-280},

abstract = { 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 patterns revolves around trained recurrent networks. In this work, instead, we introduce CL in the context of Echo State Networks (ESNs), where the recurrent component is kept fixed. We provide the first evaluation of catastrophic forgetting in ESNs and we highlight the benefits in using CL strategies which are not applicable to trained recurrent models. Our results confirm the ESN as a promising model for CL and open to its use in streaming scenarios. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Bacciu2021d,

title = {TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence},

editor = {Davide Bacciu and Siranush Akarmazyan and Eric Armengaud and Manlio Bacco and George Bravos and Calogero Calandra and Emanuele Carlini and Antonio Carta and Pietro Cassara and Massimo Coppola and Charalampos Davalas and Patrizio Dazzi and Maria Carmela Degennaro and Daniele Di Sarli and Jürgen Dobaj and Claudio Gallicchio and Sylvain Girbal and Alberto Gotta and Riccardo Groppo and Vincenzo Lomonaco and Georg Macher and Daniele Mazzei and Gabriele Mencagli and Dimitrios Michail and Alessio Micheli and Roberta Peroglio and Salvatore Petroni and Rosaria Potenza and Farank Pourdanesh and Christos Sardianos and Konstantinos Tserpes and Fulvio Tagliabò and Jakob Valtl and Iraklis Varlamis and Omar Veledar},

doi = {10.1109/COINS51742.2021.9524099},

year  = {2021},

date = {2021-08-23},

urldate = {2021-08-23},

booktitle = {Proceedings of the 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS) },

abstract = {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 comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Wcci20CL,

title = {Continual Learning with Gated Incremental Memories for Sequential Data Processing},

author = {Andrea Cossu and Antonio Carta and Davide Bacciu},

url = {https://arxiv.org/pdf/2004.04077.pdf, Arxiv},

doi = {10.1109/IJCNN48605.2020.9207550},

year  = {2020},

date = {2020-07-19},

urldate = {2020-07-19},

booktitle = {Proceedings  of the 2020 IEEE World Congress on Computational Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}