Výsledky vyhledávání - "Rivera, Juan Pablo"

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Towards Measuring Goal-Directedness in AI Systems

Recent advances in deep learning have brought attention to the possibility of creating advanced, general AI systems that outperform humans across many tasks. However, if these systems pursue unintended goals, there could be catastrophic consequences.

Externí odkaz: http://arxiv.org/abs/2410.04683

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Escalation Risks from Language Models in Military and Diplomatic Decision-Making

Autor: Rivera, Juan-Pablo, Mukobi, Gabriel, Reuel, Anka, Lamparth, Max, Smith, Chandler, Schneider, Jacquelyn

Publikováno v: The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT 24), June 3-6, 2024, Rio de Janeiro, Brazil

Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior

Externí odkaz: http://arxiv.org/abs/2401.03408

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Kniha

La Hermosa Carne : El Cuerpo en la Poesía Puertorriqueña Actual. [elektronicky zdroj]

Autor: Rivera, Juan Pablo

Externí odkaz: Kolekce e-knih KNAV (Registrovani uzivatele: plny text online 5 minut, dalsi pristup na vyzadani. Registered users: full text online 5 minutes, further access on request.)

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Spectral band selection for vegetation properties retrieval using Gaussian processes regression

Autor: Verrelst, Jochem, Rivera, Juan Pablo, Gitelson, Anatoly, Delegido, Jesus, Moreno, José, Camps-Valls, Gustau

Publikováno v: International Journal of Applied Earth Observation and Geoinformation Volume 52, October 2016, Pages 554-567

With current and upcoming imaging spectrometers, automated band analysis techniques are needed to enable efficient identification of most informative bands to facilitate optimized processing of spectral data into estimates of biophysical variables. T

Externí odkaz: http://arxiv.org/abs/2012.08640

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Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval

Autor: Verrelst, ochem, Dethier, Sara, Rivera, Juan Pablo, Muñoz-Marí, Jordi, Camps-Valls, Gustau, Moreno, José

Publikováno v: IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 7, pp. 1012-1016, July 2016

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training datasets. With t

Externí odkaz: http://arxiv.org/abs/2012.04468

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