Rajiv Mathews | DeepAI

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∙ 10/04/2022

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints

All state-of-the-art (SOTA) differentially private machine learning (DP ...

0 Virat Shejwalkar, et al. ∙

∙ 08/05/2022

Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning

Almost none of the 2,000+ languages spoken in Africa have widely availab...

0 Sandy Ritchie, et al. ∙

∙ 07/02/2022

UserLibri: A Dataset for ASR Personalization Using Only Text

Personalization of speech models on mobile devices (on-device personaliz...

0 Theresa Breiner, et al. ∙

∙ 05/26/2022

Mixed Federated Learning: Joint Decentralized and Centralized Learning

Federated learning (FL) enables learning from decentralized privacy-sens...

0 Sean Augenstein, et al. ∙

∙ 05/06/2022

Online Model Compression for Federated Learning with Large Models

This paper addresses the challenges of training large neural network mod...

0 Tien-Ju Yang, et al. ∙

∙ 04/20/2022

Detecting Unintended Memorization in Language-Model-Fused ASR

End-to-end (E2E) models are often being accompanied by language models (...

0 W. Ronny Huang, et al. ∙

∙ 04/18/2022

Extracting Targeted Training Data from ASR Models, and How to Mitigate It

Recent work has designed methods to demonstrate that model updates in AS...

0 Ehsan Amid, et al. ∙

∙ 04/11/2022

Production federated keyword spotting via distillation, filtering, and joint federated-centralized training

We trained a keyword spotting model using federated learning on real use...

0 Andrew Hard, et al. ∙

∙ 11/23/2021

Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift

With privacy as a motivation, Federated Learning (FL) is an increasingly...

0 Sean Augenstein, et al. ∙

∙ 10/31/2021

Revealing and Protecting Labels in Distributed Training

Distributed learning paradigms such as federated learning often involve ...

0 Trung Dang, et al. ∙

∙ 04/15/2021

A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It

End-to-end Automatic Speech Recognition (ASR) models are commonly traine...

0 Trung Dang, et al. ∙

∙ 04/06/2021

Communication-Efficient Agnostic Federated Averaging

In distributed learning settings such as federated learning, the trainin...

5 Jae Ro, et al. ∙

∙ 09/21/2020

Training Production Language Models without Memorizing User Data

This paper presents the first consumer-scale next-word prediction (NWP) ...

0 Swaroop Ramaswamy, et al. ∙

∙ 06/12/2020

Understanding Unintended Memorization in Federated Learning

Recent works have shown that generative sequence models (e.g., language ...

0 Om Thakkar, et al. ∙

∙ 05/21/2020

Training Keyword Spotting Models on Non-IID Data with Federated Learning

We demonstrate that a production-quality keyword-spotting model can be t...

0 Andrew Hard, et al. ∙

∙ 11/15/2019

Generative Models for Effective ML on Private, Decentralized Datasets

To improve real-world applications of machine learning, experienced mode...

29 Sean Augenstein, et al. ∙

∙ 10/22/2019

Federated Evaluation of On-device Personalization

Federated learning is a distributed, on-device computation framework tha...

0 Kangkang Wang, et al. ∙

∙ 10/08/2019

Federated Learning of N-gram Language Models

We propose algorithms to train production-quality n-gram language models...

0 Mingqing Chen, et al. ∙

∙ 06/11/2019

Federated Learning for Emoji Prediction in a Mobile Keyboard

We show that a word-level recurrent neural network can predict emoji fro...

0 Swaroop Ramaswamy, et al. ∙

∙ 03/26/2019

Federated Learning Of Out-Of-Vocabulary Words

We demonstrate that a character-level recurrent neural network is able t...

0 Mingqing Chen, et al. ∙

∙ 11/08/2018

Federated Learning for Mobile Keyboard Prediction

We train a recurrent neural network language model using a distributed, ...

0 Andrew Hard, et al. ∙

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