Invention Application
- Patent Title: Scale-Permuted Machine Learning Architecture
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Application No.: US18784068Application Date: 2024-07-25
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Publication No.: US20240378509A1Publication Date: 2024-11-14
- Inventor: Xianzhi Du , Yin Cui , Tsung-Yi Lin , Quoc V. Le , Pengchong Jin , Mingxing Tan , Golnaz Ghiasi , Xiaodan Song
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Main IPC: G06N20/00
- IPC: G06N20/00 ; G06F11/34 ; G06N3/04

Abstract:
A computer-implemented method of generating scale-permuted models can generate models having improved accuracy and reduced evaluation computational requirements. The method can include defining, by a computing system including one or more computing devices, a search space including a plurality of candidate permutations of a plurality of candidate feature blocks, each of the plurality of candidate feature blocks having a respective scale. The method can include performing, by the computing system, a plurality of search iterations by a search algorithm to select a scale-permuted model from the search space, the scale-permuted model based at least in part on a candidate permutation of the plurality of candidate permutations.
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