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pro vyhledávání: '"Mahlow, Felipe"'
Autor:
Mahlow, Felipe, Zanella, André Felipe, Castañeda, William Alberto Cruz, Sarzi-Ribeiro, Regilene Aparecida
In recent years, Generative Artificial Intelligence (GenAI) has undergone a profound transformation in addressing intricate tasks involving diverse modalities such as textual, auditory, visual, and pictorial generation. Within this spectrum, text-to-
Externí odkaz:
http://arxiv.org/abs/2408.00544
Autor:
Perche-Mahlow, Felipe Rodrigues, Felipe-Zanella, André, Cruz-Castañeda, William Alberto, Amadeus, Marcellus
In recent years, groundbreaking advancements in Generative Artificial Intelligence (GenAI) have triggered a transformative paradigm shift, significantly influencing various domains. In this work, we specifically explore an integrated approach, levera
Externí odkaz:
http://arxiv.org/abs/2402.03501
Autor:
Amadeus, Marcellus, Castañeda, William Alberto Cruz, Zanella, André Felipe, Mahlow, Felipe Rodrigues Perche
Generative AI has become pervasive in society, witnessing significant advancements in various domains. Particularly in the realm of Text-to-Image (TTI) models, Latent Diffusion Models (LDMs), showcase remarkable capabilities in generating visual cont
Externí odkaz:
http://arxiv.org/abs/2401.05520
Publikováno v:
Phys. Rev. A 109, 052411 (2024)
We have applied a machine learning algorithm to predict the emergence of environment-induced spontaneous synchronization between two qubits in an open system setting. In particular, we have considered three different models, encompassing global and l
Externí odkaz:
http://arxiv.org/abs/2308.15330
Publikováno v:
Scientific Reports 13 (2023)
Machine learning has revolutionized many fields of science and technology. Through the $k$-Nearest Neighbors algorithm, we develop a model-independent classifier, where the algorithm can classify phases of a model to which it has never had access. Fo
Externí odkaz:
http://arxiv.org/abs/2109.00625