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10.1371/journal.pone.0180343 http://scihub22266oqcxt.onion/10.1371/journal.pone.0180343 C5489220!5489220 !28658298     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28658298 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       PLoS+One 2017 ; 12 (6 ): e0180343 Nephropedia Template TP {{tp|p=28658298 |t=2017. Bayesian Lasso and multinomial logistic regression on GPU |pdf=|usr=}}{{28658298 }} gab.com Text Bayesian Lasso and multinomial logistic regression on GPU JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28658298 #FOAvip We describe an efficient Bayesian parallel GPU implementation of two classic statistical models-the Lasso and multinomial logistic regression. We focus on parallelizing the key components: matrix multiplication, matrix inversion, and sampling from the full conditionals. Our GPU implementations of Bayesian Lasso and multinomial logistic regression achieve 100-fold speedups on mid-level and high-end GPUs. Substantial speedups of 25 fold can also be achieved on older and lower end GPUs. Samplers are implemented in OpenCL and can be used on any type of GPU and other types of computational units, thereby being convenient and advantageous in practice compared to related work. Twit Text FOAVip Bayesian Lasso and multinomial logistic regression on GPU ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28658298 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28658298 #FOAvip English Wikipedia <ref>{{Cite pmid|28658298 }}</ref> Bayesian Lasso and multinomial logistic regression on GPU #MMPMID28658298 ?e?novar R ; ?trumbelj E PLoS One 2017[]; 12 (6 ): e0180343 PMID28658298 show ga We describe an efficient Bayesian parallel GPU implementation of two classic statistical models-the Lasso and multinomial logistic regression. We focus on parallelizing the key components: matrix multiplication, matrix inversion, and sampling from the full conditionals. Our GPU implementations of Bayesian Lasso and multinomial logistic regression achieve 100-fold speedups on mid-level and high-end GPUs. Substantial speedups of 25 fold can also be achieved on older and lower end GPUs. Samplers are implemented in OpenCL and can be used on any type of GPU and other types of computational units, thereby being convenient and advantageous in practice compared to related work. |*Bayes Theorem [MESH] |*Computer Graphics [MESH] |*Logistic Models [MESH]Bayesian Lasso and multinomial logistic regression on GPU (epmc).pdf DeepDyve Pubget Overpricing