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公开(公告)号：US12001509B2
公开(公告)日：2024-06-04
申请号：US16821509
申请日：2020-03-17
申请人： Google LLC
发明人： Seungyeon Kim , Jingzhao Zhang , Andreas Veit , Sanjiv Kumar , Sashank Reddi , Praneeth Karimireddy
CPC分类号： G06F17/18 , G06F18/217 , G06N20/00 , G06N3/084
摘要： Generally, the present disclosure is directed to systems and methods that perform adaptive optimization with improved convergence properties. The adaptive optimization techniques described herein are useful in various optimization scenarios, including, for example, training a machine-learned model such as, for example, a neural network. In particular, according to one aspect of the present disclosure, a system implementing the adaptive optimization technique can, over a plurality of iterations, employ an adaptive per coordinate clipping threshold to clip a current first moment of the coordinate to obtain a current update value that enables faster convergence for the machine-learned model when the noise in the stochastic gradients is heavy tailed.
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2.发明公开公开(公告)号：US20240311405A1
公开(公告)日：2024-09-19
申请号：US18337316
申请日：2023-06-19
申请人： GOOGLE LLC
发明人： Seungyeon Kim , Ankit Singh Rawat , Wittawat Jitkrittum , Hari Narasimhan , Sashank Reddi , Neha Gupta , Srinadh Bhojanapalli , Aditya Menon , Manzil Zaheer , Tal Schuster , Sanjiv Kumar , Toby Boyd , Zhifeng Chen , Emanuel Taropa , Vikram Kasivajhula , Trevor Strohman , Martin Baeuml , Leif Schelin , Yanping Huang
IPC分类号： G06F16/332
CPC分类号： G06F16/3329
摘要： Implementations disclose selecting, in response to receiving a request and from among multiple candidate generative models (e.g., multiple candidate large language models (LLMs)) with differing computational efficiencies, a particular generative model to utilize in generating a response to the request. Those implementations reduce latency and/or conserve computational resource(s) through selection, for various requests, of a more computationally efficient generative model for utilization in lieu of a less computationally efficient generative model. Further, those implementations seek to achieve such benefits, through utilization of more computationally efficient generative models, while also still selectively utilizing less computationally efficient generative models for certain requests to mitigate occurrences of a generated response being inaccurate and/or under-specified. This, in turn, can mitigate occurrences of computational and/or network inefficiencies that result from a user issuing a follow-up request to cure the inaccuracies and/or under-specification of a generated response.
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公开(公告)号：US20210295201A1
公开(公告)日：2021-09-23
申请号：US16821509
申请日：2020-03-17
申请人： Google LLC
发明人： Seungyeon Kim , Jingzhao Zhang , Andreas Veit , Sanjiv Kumar , Sashank Reddi , Praneeth Karimireddy
摘要： Generally, the present disclosure is directed to systems and methods that perform adaptive optimization with improved convergence properties. The adaptive optimization techniques described herein are useful in various optimization scenarios, including, for example, training a machine-learned model such as, for example, a neural network. In particular, according to one aspect of the present disclosure, a system implementing the adaptive optimization technique can, over a plurality of iterations, employ an adaptive per coordinate clipping threshold to clip a current first moment of the coordinate to obtain a current update value that enables faster convergence for the machine-learned model when the noise in the stochastic gradients is heavy tailed.
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