公开(公告)号：US20220366260A1

公开(公告)日：2022-11-17

申请号：US17245892

申请日：2021-04-30

Applicant: Google LLC

Inventor： Gayan Sadeep Jayasumana Hirimbura Matara Kankanamge ,  Srikumar Ramalingam ,  Sanjiv Kumar

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: A method includes receiving, by a computing device, training data to train a neural network, wherein the training data comprises a plurality of inputs and a plurality of corresponding labels. The method also includes mapping, by a representation learner of the neural network, the plurality of inputs to a plurality of feature vectors. The method additionally includes training a kernelized classification layer of the neural network to perform nonlinear classification of an input feature vector into one of a plurality of classes, wherein the kernelized classification layer is based on a kernel which enables the nonlinear classification, and wherein the kernel is selected from a space of positive definite kernels based on application of a nonlinear softmax loss function to the plurality of feature vectors and the plurality of corresponding labels. The method further includes outputting a trained neural network comprising the representation learner and the trained kernelized classification layer.