US 11,836,615 B2
Bayesian nonparametric learning of neural networks
Kristjan Herbert Greenewald, Belmont, MA (US); Mikhail Yurochkin, Cambridge, MA (US); Mayank Agarwal, Cambridge, MA (US); Soumya Ghosh, Boston, MA (US); Trong Nghia Hoang, Cambridge, MA (US); and Yasaman Khazaeni, Needham, MA (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Sep. 20, 2019, as Appl. No. 16/576,927.
Prior Publication US 2021/0089878 A1, Mar. 25, 2021
Int. Cl. G06N 3/08 (2023.01); G06N 3/047 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/047 (2023.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method for managing efficient machine learning, the method comprising:
operating a network in which a plurality of client computing devices are communicatively coupled with a centralized computing device, wherein each of the plurality of client computing devices includes a local machine learning model that is pre-trained on locally accessible data, and wherein the locally accessible data has a common structure across all the plurality of client computing devices;
accessing, by the centralized computing device, a plurality of artificial local neurons from each of the local machine learning models;
clustering each of the plurality of artificial local neurons into a plurality of specific groups as part of a set of global neurons is performed using a combination comprising permutation-invariant probabilistic matching of each of the plurality of artificial local neurons using Bayesian nonparametrics; and
forming a global machine learning model layer by averaging the plurality of artificial local neurons previously clustered into one of a plurality of specific groups as part of a set of global neurons.