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        <title>Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>The recent proliferation of smart devices necessitates methods to learn small-sized models. This paper demonstrates that if there are m features in total but only n = o(\sqrtm) features are required to distinguish examples, with Ømega(łog m) training examples and reasonable settings, it is possible to obtain a good model in a succinct representation using n łog₂ \fracmn + o(m) bits, by using a pipeline of existing compression methods: L1-regularized logistic regression, feature hashing, Elias–Fano indices, and randomized quantization. An experiment shows that a noun phrase chunking task for which an existing library requires 27 megabytes can be compressed to less than 13 kilobytes without notable loss of accuracy.</abstract>
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%0 Conference Proceedings
%T Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization
%A Senuma, Hajime
%A Aizawa, Akiko
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F senuma-aizawa-2016-learning
%X The recent proliferation of smart devices necessitates methods to learn small-sized models. This paper demonstrates that if there are m features in total but only n = o(\sqrtm) features are required to distinguish examples, with Ømega(łog m) training examples and reasonable settings, it is possible to obtain a good model in a succinct representation using n łog₂ \fracmn + o(m) bits, by using a pipeline of existing compression methods: L1-regularized logistic regression, feature hashing, Elias–Fano indices, and randomized quantization. An experiment shows that a noun phrase chunking task for which an existing library requires 27 megabytes can be compressed to less than 13 kilobytes without notable loss of accuracy.
%U https://aclanthology.org/C16-1261
%P 2774-2784

Markdown (Informal)

[Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization](https://aclanthology.org/C16-1261) (Senuma & Aizawa, COLING 2016)

• Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization (Senuma & Aizawa, COLING 2016)

ACL

• Hajime Senuma and Akiko Aizawa. 2016. Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2774–2784, Osaka, Japan. The COLING 2016 Organizing Committee.