Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Nicola Milano"'
Publikováno v:
Frontiers in Psychology, Vol 15 (2024)
IntroductionMissing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors d
Externí odkaz:
https://doaj.org/article/243211a0ffd24007a709ff67f53f4f42
Publikováno v:
PLoS ONE, Vol 19, Iss 4, p e0302238 (2024)
In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models an
Externí odkaz:
https://doaj.org/article/b68e31d2a38846f7bfc899352cb08d7f
Publikováno v:
Behavioral Sciences, Vol 14, Iss 7, p 527 (2024)
Latent variables analysis is an important part of psychometric research. In this context, factor analysis and other related techniques have been widely applied for the investigation of the internal structure of psychometric tests. However, these meth
Externí odkaz:
https://doaj.org/article/ea7cb0de8630499eb4d680b569e52cfb
Publikováno v:
Frontiers in Psychology, Vol 14 (2023)
IntroductionAutism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are time-consuming and require many specialis
Externí odkaz:
https://doaj.org/article/8a260c14bc7a4790ba49fac5a5ace8e8
Autor:
Nicola Milano, Stefano Nolfi
Publikováno v:
Frontiers in Robotics and AI, Vol 9 (2022)
The propensity of evolutionary algorithms to generate compact solutions have advantages and disadvantages. On one side, compact solutions can be cheaper, lighter, and faster than less compact ones. On the other hand, compact solutions might lack evol
Externí odkaz:
https://doaj.org/article/4f14f6ada6574e6697f5754ad4ac09df
Autor:
Nicola Milano, Stefano Nolfi
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract We demonstrate how the evolutionary training of embodied agents can be extended with a curriculum learning algorithm that automatically selects the environmental conditions in which the evolving agents are evaluated. The environmental condit
Externí odkaz:
https://doaj.org/article/526589e900f34ccc951da87100eb776a
Publikováno v:
Frontiers in Psychology, Vol 12 (2021)
Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a
Externí odkaz:
https://doaj.org/article/5350a715012d4501b47ca3aa5a264d81
Autor:
Nicola Milano, Stefano Nolfi
Publikováno v:
PLoS ONE, Vol 16, Iss 4, p e0250040 (2021)
The efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including an additional neural network dedicated to features extraction trained through self-supervision. In this paper we introd
Externí odkaz:
https://doaj.org/article/c7418941452c4e82b10344cbcae3f066
Publikováno v:
Frontiers in Robotics and AI, Vol 7 (2020)
We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization. Overall, the results collected on a wide variety of qualitatively different benchmark problems indicate that these methods are generally effective an
Externí odkaz:
https://doaj.org/article/c201379bbf22465d8a1e27fe050f6472
Publikováno v:
PLoS ONE, Vol 13, Iss 7, p e0198788 (2018)
In this paper we compare systematically the most promising neuroevolutionary methods and two new original methods on the double-pole balancing problem with respect to: the ability to discover solutions that are robust to variations of the environment
Externí odkaz:
https://doaj.org/article/7a3d168ff3224b6e84e4ee6225c6dda1