公开(公告)号：US20200302339A1

公开(公告)日：2020-09-24

申请号：US16825953

申请日：2020-03-20

Applicant: SRI International

Inventor： Aswin Nadamuni Raghavan ,  Jesse Hostetler ,  Indranil Sur ,  Abrar Abdullah Rahman ,  Sek Meng Chai

IPC: G06N20/00 ,  G06N3/08 ,  G06K9/62 ,  G06F9/48 ,  G06F9/38

Abstract: Techniques are disclosed for training machine learning systems. An input device receives training data comprising pairs of training inputs and training labels. A generative memory assigns training inputs to each archetype task of a plurality of archetype tasks, each archetype task representative of a cluster of related tasks within a task space and assigns a skill to each archetype task. The generative memory generates, from each archetype task, auxiliary data comprising pairs of auxiliary inputs and auxiliary labels. A machine learning system trains a machine learning model to apply a skill assigned to an archetype task to training and auxiliary inputs assigned to the archetype task to obtain output labels corresponding to the training and auxiliary labels associated with the training and auxiliary inputs assigned to the archetype task to enable scalable learning to obtain labels for new tasks for which the machine learning model has not previously been trained.

公开(公告)号：US11494597B2

公开(公告)日：2022-11-08

申请号：US16825953

申请日：2020-03-20

Applicant: SRI International

Inventor： Aswin Nadamuni Raghavan ,  Jesse Hostetler ,  Indranil Sur ,  Abrar Abdullah Rahman ,  Sek Meng Chai

IPC: G06K9/62 ,  G06N20/00 ,  G06N3/08 ,  G06F9/38 ,  G06F9/48 ,  G06F3/06 ,  G06Q40/08 ,  G06N3/04

Abstract: Techniques are disclosed for training machine learning systems. An input device receives training data comprising pairs of training inputs and training labels. A generative memory assigns training inputs to each archetype task of a plurality of archetype tasks, each archetype task representative of a cluster of related tasks within a task space and assigns a skill to each archetype task. The generative memory generates, from each archetype task, auxiliary data comprising pairs of auxiliary inputs and auxiliary labels. A machine learning system trains a machine learning model to apply a skill assigned to an archetype task to training and auxiliary inputs assigned to the archetype task to obtain output labels corresponding to the training and auxiliary labels associated with the training and auxiliary inputs assigned to the archetype task to enable scalable learning to obtain labels for new tasks for which the machine learning model has not previously been trained.