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pro vyhledávání: '"Rimal, Ramchandra"'
The autism dataset is studied to identify the differences between autistic and healthy groups. For this, the resting-state Functional Magnetic Resonance Imaging (rs-fMRI) data of the two groups are analyzed, and networks of connections between brain
Externí odkaz:
http://arxiv.org/abs/2209.08601
In the present paper we study a sparse stochastic network enabled with a block structure. The popular Stochastic Block Model (SBM) and the Degree Corrected Block Model (DCBM) address sparsity by placing an upper bound on the maximum probability of co
Externí odkaz:
http://arxiv.org/abs/1910.01931
The paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (2018). We argue that the main appeal of the PABM is the flexibility of the spectral properties of the graph which makes the PABM an attractive choice for
Externí odkaz:
http://arxiv.org/abs/1902.00431
Akademický článek
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Autor:
Rimal, Ramchandra, Pensky, Marianna
The present paper studies density deconvolution in the presence of small Berkson errors, in particular, when the variances of the errors tend to zero as the sample size grows. It is known that when the Berkson errors are present, in some cases, the u
Externí odkaz:
http://arxiv.org/abs/1810.07016
Autor:
Pokhrel, Nawa Raj, Dahal, Keshab Raj, Rimal, Ramchandra, Bhandari, Hum Nath, Khatri, Rajendra K.C., Rimal, Binod, Hahn, William Edward
Publikováno v:
In Machine Learning with Applications 15 September 2022 9
Autor:
Bhandari, Hum Nath, Rimal, Binod, Pokhrel, Nawa Raj, Rimal, Ramchandra, Dahal, Keshab R., Khatri, Rajendra K.C.
Publikováno v:
In Machine Learning with Applications 15 September 2022 9
Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models.
Publikováno v:
Software (2674-113X); Mar2024, Vol. 3 Issue 1, p47-61, 15p
Autor:
Rimal, Ramchandra
Publikováno v:
Statistics, Optimization & Information Computing; Jan2024, Vol. 12 Issue 1, p15-33, 19p
Publikováno v:
International Journal of Data Science and Analytics; 20240101, Issue: Preprints p1-15, 15p