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pro vyhledávání: '"Jalali, Hamed"'
Autor:
Jalali, Hamed
This thesis explores the critical role of usability in software development and uses usability heuristics as a cost-effective and efficient method for evaluating various software functions and interfaces. With the proliferation of software developmen
Externí odkaz:
https://digital.library.unt.edu/ark:/67531/metadc2137618/
Autor:
Jalali, Hamed, Kasneci, Gjergji
By distributing the training process, local approximation reduces the cost of the standard Gaussian Process. An ensemble technique combines local predictions from Gaussian experts trained on different partitions of the data. Ensemble methods aggregat
Externí odkaz:
http://arxiv.org/abs/2211.09940
Autor:
Jalali, Hamed, Kasneci, Gjergji
Distributed Gaussian process (DGP) is a popular approach to scale GP to big data which divides the training data into some subsets, performs local inference for each partition, and aggregates the results to acquire global prediction. To combine the l
Externí odkaz:
http://arxiv.org/abs/2202.03287
Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms. Many approaches address the issue of interpreting artificial neural networks, but often provide divergent explanations. Moreo
Externí odkaz:
http://arxiv.org/abs/2111.07379
Local approximations are popular methods to scale Gaussian processes (GPs) to big data. Local approximations reduce time complexity by dividing the original dataset into subsets and training a local expert on each subset. Aggregating the experts' pre
Externí odkaz:
http://arxiv.org/abs/2102.01496
Autor:
Jalali, Hamed, Menezes, Mozart B.C.
Publikováno v:
In European Journal of Operational Research April 2024
Autor:
Jalali, Hamed, Kasneci, Gjergji
Publikováno v:
25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event / Milan, Italy, January 10-15, 2021
Distributed Gaussian processes (DGPs) are prominent local approximation methods to scale Gaussian processes (GPs) to large datasets. Instead of a global estimation, they train local experts by dividing the training set into subsets, thus reducing the
Externí odkaz:
http://arxiv.org/abs/2010.08873
Autor:
Jalali, Hamed1 (AUTHOR), Ansaripoor, Amir2 (AUTHOR), Ramani, Vinay3 (AUTHOR), De Giovanni, Pietro4,5 (AUTHOR) pdegiovanni@luiss.it
Publikováno v:
International Journal of Production Research. May2022, Vol. 60 Issue 10, p3078-3106. 29p. 2 Charts, 15 Graphs.
Publikováno v:
In European Journal of Operational Research 16 February 2023 305(1):128-147
Autor:
Dezhampanah, Hamid1 (AUTHOR) h.dpanah@guilan.ac.ir, Jalali, Hamed Moradmand1 (AUTHOR)
Publikováno v:
Theoretical Foundations of Chemical Engineering. Oct2023, Vol. 57 Issue 5, p889-897. 9p.