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  • An Optimizing Method for Performance and Resource Utilization in Quantum Machine Learning Circuits 

    Salehi, T.; Zomorodi, M.; Pławiak, P.; Abbaszade, M.; Salari, V. (2022-01)
    Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. Itmakes certain kinds of problems be solved easier compared to classical computers. This advantage of ...
  • Contributions to Time Series Classification: Meta-Learning and Explainability 

    Abanda, A.Autoridad BCAM (2021-11-16)
    This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In ...
  • A Finite Element based Deep Learning solver for parametric PDEs 

    Uriarte, C.Autoridad BCAM; Pardo, D.Autoridad BCAM; Omella, A. J. (2021)
    We introduce a dynamic Deep Learning (DL) architecture based on the Finite Element Method (FEM) to solve linear parametric Partial Differential Equations(PDEs). The connections between neurons in the architecture mimic the ...
  • Rotational smoothing 

    Caro, P.; Meroño, C.; Parissis, I. (2022-01-05)
    Rotational smoothing is a phenomenon consisting in a gain of regularity by means of averaging over rotations. This phenomenon is present in operators that regularize only in certain directions, in contrast to operators ...
  • Modeling and optimal control of dengue disease with screening and information 

    Srivastav, A.K.; Kumar, A.; Srivastava, P.K.; Ghosh, M. (2021-11-27)
    This study presents a mathematical model for dengue transmission which quantifies two very important aspects: one, the impact of information-based behavioural response, and the other, the segregation of infected human ...

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