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pro vyhledávání: '"Cotogni P"'
Autor:
Mosconi, Matteo, Sorokin, Andriy, Panariello, Aniello, Porrello, Angelo, Bonato, Jacopo, Cotogni, Marco, Sabetta, Luigi, Calderara, Simone, Cucchiara, Rita
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of Continual Learning is crucial. While numerous studies focus on skelet
Externí odkaz:
http://arxiv.org/abs/2407.01397
In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model's test accuracy without using
Externí odkaz:
http://arxiv.org/abs/2404.12922
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models. This technique primarily aims to eradicate any residual influence of a specific subset of data from the knowle
Externí odkaz:
http://arxiv.org/abs/2312.02052
Publikováno v:
Proceedings of the 2023 ACM International Conference on Multimedia Retrieval
Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact in terms of engagement is of crucial importance to drive the profitable
Externí odkaz:
http://arxiv.org/abs/2305.10103
We propose a new method for exemplar-free class incremental training of ViTs. The main challenge of exemplar-free continual learning is maintaining plasticity of the learner without causing catastrophic forgetting of previously learned tasks. This is
Externí odkaz:
http://arxiv.org/abs/2211.12292
Autor:
Cotogni, Marco, Cusano, Claudio
Nowadays, image-to-image translation methods, are the state of the art for the enhancement of natural images. Even if they usually show high performance in terms of accuracy, they often suffer from several limitations such as the generation of artifa
Externí odkaz:
http://arxiv.org/abs/2207.07092
Autor:
Cotogni, Marco, Cusano, Claudio
Publikováno v:
Neurocomputing 2022
In this paper we present a framework for the design and implementation of offset equivariant networks, that is, neural networks that preserve in their output uniform increments in the input. In a suitable color space this kind of networks achieves eq
Externí odkaz:
http://arxiv.org/abs/2207.00292
Autor:
Cotogni, Marco, Cusano, Claudio
In this paper we present TreEnhance, an automatic method for low-light image enhancement capable of improving the quality of digital images. The method combines tree search theory, and in particular the Monte Carlo Tree Search (MCTS) algorithm, with
Externí odkaz:
http://arxiv.org/abs/2205.12639
Akademický článek
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Autor:
Paolo Cotogni, Federico Bozzetti, François Goldwasser, Paula Jimenez-Fonseca, Sine Roelsgaard Obling, Juan W. Valle
Publikováno v:
Therapeutic Advances in Medical Oncology, Vol 14 (2022)
Malnutrition is an often-overlooked challenge for patients with cancer. It is associated with muscle mass reduction, poor compliance and response to cancer treatments, decreased quality of life, and reduced survival time. The nutritional assessment a
Externí odkaz:
https://doaj.org/article/02bc70a2e6e14161a6e08080f791e80b
