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09/14/2022
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Automatic Speech Recognition models require large amount of speech data ...
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05/06/2022
Online Model Compression for Federated Learning with Large Models
This paper addresses the challenges of training large neural network mod...
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11/19/2021
Periodic Variable Stars Modulated by Time-Varying Parameters
Many astrophysical phenomena are time-varying, in the sense that their b...
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10/11/2021
Partial Variable Training for Efficient On-Device Federated Learning
This paper aims to address the major challenges of Federated Learning (F...
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10/08/2021
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training
Transformer-based architectures have been the subject of research aimed ...
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10/07/2021
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Federated learning can be used to train machine learning models on the e...
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04/23/2021
Joint Mean-Vector and Var-Matrix estimation for Locally Stationary VAR(1) processes
During the last two decades, locally stationary processes have been wide...
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10/29/2020
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework
We propose using federated learning, a decentralized on-device learning ...
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01/24/2020
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network
Training machine learning models on mobile devices has the potential of ...
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12/14/2019