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@INPROCEEDINGS{Raimondo:1034875,
      author       = {Raimondo, Federico and Antonopoulos, Georgios and Eickhoff,
                      Simon and Patil, Kaustubh},
      title        = {{FASTGPR}: {DIVIDE}-{AND}-{CONQUER} {TECHNIQUE} {IN}
                      {NEUROIMAGING} {DATA} {SHORTENS} {TRAINING} {TIME} {AND}
                      {IMPROVES} {ACCURACY}},
      reportid     = {FZJ-2024-07621},
      year         = {2024},
      abstract     = {Gaussian process regression (GPR) has shown great potential
                      for studying healthy aging and disease via brain-age
                      prediction (BAP) using structural MRI[1].A big drawback of
                      GPR is the training complexity which is an $O(N^3)$
                      operation (N=number of data points).The need for expansive
                      datasets and the high dimensionality of MRI data, renders
                      the training of GPR impractical with conventional computing
                      resources.We investigated whether a divide-and-conquer
                      approach can be used together with the GPR model},
      month         = {May},
      date          = {2024-05-27},
      organization  = {International Symposium of Biomedical
                       Imaging 2024, Athens (Greece), 27 May
                       2024 - 30 May 2024},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-07621},
      url          = {https://juser.fz-juelich.de/record/1034875},
}