Task-Agnostic Experts Composition for Continual Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915350187081728 |
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| author | Quarantiello, Luigi Cossu, Andrea Lomonaco, Vincenzo |
| author_facet | Quarantiello, Luigi Cossu, Andrea Lomonaco, Vincenzo |
| contents | Compositionality is one of the fundamental abilities of the human reasoning process, that allows to decompose a complex problem into simpler elements. Such property is crucial also for neural networks, especially when aiming for a more efficient and sustainable AI framework. We propose a compositional approach by ensembling zero-shot a set of expert models, assessing our methodology using a challenging benchmark, designed to test compositionality capabilities. We show that our Expert Composition method is able to achieve a much higher accuracy than baseline algorithms while requiring less computational resources, hence being more efficient. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_15566 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Task-Agnostic Experts Composition for Continual Learning Quarantiello, Luigi Cossu, Andrea Lomonaco, Vincenzo Machine Learning Compositionality is one of the fundamental abilities of the human reasoning process, that allows to decompose a complex problem into simpler elements. Such property is crucial also for neural networks, especially when aiming for a more efficient and sustainable AI framework. We propose a compositional approach by ensembling zero-shot a set of expert models, assessing our methodology using a challenging benchmark, designed to test compositionality capabilities. We show that our Expert Composition method is able to achieve a much higher accuracy than baseline algorithms while requiring less computational resources, hence being more efficient. |
| title | Task-Agnostic Experts Composition for Continual Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.15566 |