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05/18/2020
Contextual Embeddings: When Are They Worth It?
We study the settings for which deep contextual embeddings (e.g., BERT) ...
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02/29/2020
Understanding the Downstream Instability of Word Embeddings
Many industrial machine learning (ML) systems require frequent retrainin...
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09/03/2019
On the Downstream Performance of Compressed Word Embeddings
Compressing word embeddings is important for deploying NLP models in mem...
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10/31/2018
Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
We investigate how to train kernel approximation methods that generalize...
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01/13/2017
Kernel Approximation Methods for Speech Recognition
We study large-scale kernel methods for acoustic modeling in speech reco...
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03/18/2016
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
We study large-scale kernel methods for acoustic modeling and compare to...
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11/14/2014