Search Results for author: Jarred Barber

Found 6 papers, 3 papers with code

Challenges and Opportunities in Multi-device Speech Processing

no code implementations27 Jun 2022 Gregory Ciccarelli, Jarred Barber, Arun Nair, Israel Cohen, Tao Zhang

We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices".

Automatic Speech Recognition Keyword Spotting +2

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

2 code implementations13 Jan 2022 Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood

The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks.

Active Learning Continual Learning +1

End-to-end Alexa Device Arbitration

no code implementations8 Dec 2021 Jarred Barber, Yifeng Fan, Tao Zhang

We introduce a variant of the speaker localization problem, which we call device arbitration.

Improving Few-Shot Visual Classification with Unlabelled Examples

2 code implementations28 Sep 2020 Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood

We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance.

Classification Few-Shot Image Classification

Enhancing Few-Shot Image Classification with Unlabelled Examples

2 code implementations17 Jun 2020 Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood

We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance.

Classification Few-Shot Image Classification +2

Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels

no code implementations5 Jun 2020 Jarred Barber

Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their $O(N^3)$ inference complexity.

Gaussian Processes

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