Meta Platforms, Inc.

Facebook, Inc.

Nanshu Wang

Kshitiz Malik

Wenhai Yang

Honglei Liu

David Scott Reiss

Prince Gill

Anuj Kumar

Fan Wang

Meryem Pinar Donmez Ediz

Omer Muzaffar

Vikas Seshagiri Rao Bhardwaj

Shreyan Bakshi

Fadi Botros

In one embodiment, a method includes receiving a first input by a user from a client system associated with the user, wherein the first input is in a voice modality, analyzing the first input to generate one or more candidate hypotheses, determining one or more modalities for presenting output generated by the one or more computing systems to the user at the client system, and sending instructions to the client system for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, wherein each suggested auto-completion comprises the corresponding candidate hypothesis, and wherein the one or more suggested auto-completions are presented in the one or more determined modalities.

Voice-based auto-completions and auto-responses for assistant systems

Exemplary embodiments relate to methods, mediums, and systems for moving language models from a server to the client device. Such embodiments may be deployed in an environment where the server is not able to provide modeling services to the clients, such as an end-to-end encrypted (E2EE) environment. Several different techniques are described to address issues of size and complexity reduction, model architecture optimization, model training, battery power reduction, and latency reduction.

Methods, mediums, and systems for providing a model for an end-user device

Methods, mediums, and systems for training a model

Methods, mediums, and systems for representing a model in a memory of device

Growing community of inventors

Menlo Park, CA, United States of America

Nanshu Wang