Výsledky vyhledávání - "Giordani, Ilaria"

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A Bayesian approach for prompt optimization in pre-trained language models

Autor: Sabbatella, Antonio, Ponti, Andrea, Candelieri, Antonio, Giordani, Ilaria, Archetti, Francesco

A prompt is a sequence of symbol or tokens, selected from a vocabulary according to some rule, which is prepended/concatenated to a textual query. A key problem is how to select the sequence of tokens: in this paper we formulate it as a combinatorial

Externí odkaz: http://arxiv.org/abs/2312.00471

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Akademický článek

Increased Pituitary Fluorine-18-Fluorodeoxyglucose Uptake in Patients with Differentiated Thyroid Cancer in Hypothyroidism versus under Recombinant Human Thyroid-Stimulating Hormone Stimulation.

Autor: Shi, Xinyi¹ (AUTHOR) xinyi.shi@chuv.ch, Giordani, Ilaria¹ (AUTHOR) ilaria.giordani@chuv.ch, Nicod Lalonde, Marie² (AUTHOR) marie.nicod-lalonde@chuv.ch, Sykiotis, Gerasimos P.¹ (AUTHOR) gerasimos.sykiotis@chuv.ch

Publikováno v: Cancers. Apr2024, Vol. 16 Issue 7, p1382. 10p.

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Resilience in urban networked infrastructure: the case of Water Distribution Systems

Autor: Candelieri, Antonio, Giordani, Ilaria, Ponti, Andrea, Archetti, Francesco

Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on net-wide measures of connectivity and the role of a single component. Th

Externí odkaz: http://arxiv.org/abs/2006.14622

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Modelling Human Active Search in Optimizing Black-box Functions

Autor: Candelieri, Antonio, Perego, Riccardo, Giordani, Ilaria, Ponti, Andrea, Archetti, Francesco

Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through f

Externí odkaz: http://arxiv.org/abs/2003.04275

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Learning Optimal Control of Water Distribution Networks through Sequential Model-based Optimization

Autor: Candelieri, Antonio, Galuzzi, Bruno, Giordani, Ilaria, Archetti, Francesco

Sequential Model-based Bayesian Optimization has been successful-ly applied to several application domains, characterized by complex search spaces, such as Automated Machine Learning and Neural Architecture Search. This paper focuses on optimal contr

Externí odkaz: http://arxiv.org/abs/2003.04268

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Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization

Autor: Candelieri, Antonio, Giordani, Ilaria, Perego, Riccardo, Archetti, Francesco

Bayesian Optimization has become the reference method for the global optimization of black box, expensive and possibly noisy functions. Bayesian Op-timization learns a probabilistic model about the objective function, usually a Gaussian Process, and

Externí odkaz: http://arxiv.org/abs/2003.04207

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