English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A common technique for compressing a neural network is to compute the$k$-rank $\ell_2$ approximation $A_{k,2}$ of the matrix$A\in\mathbb{R}^{n\times d}$ that corresponds to a fully connected layer (orembedding layer). Here, $d$ is the number of the neurons in the layer, $n$ isthe number in the next one, and $A_{k,2}$ can be stored in $O((n+d)k)$ memoryinstead of $O(nd)$.  This $\ell_2$-approximation minimizes the sum over every entry to the powerof $p=2$ in the matrix $A - A_{k,2}$, among every matrix$A_{k,2}\in\mathbb{R}^{n\times d}$ whose rank is $k$. While it can be computedefficiently via SVD, the $\ell_2$-approximation is known to be very sensitiveto outliers ("far-away" rows). Hence, machine learning uses e.g. LassoRegression, $\ell_1$-regularization, and $\ell_1$-SVM that use the$\ell_1$-norm.  This paper suggests to replace the $k$-rank $\ell_2$ approximation by$\ell_p$, for $p\in [1,2]$. We then provide practical and provableapproximation algorithms to compute it for any $p\geq1$, based on moderntechniques in computational geometry.  Extensive experimental results on the GLUE benchmark for compressing BERT,DistilBERT, XLNet, and RoBERTa confirm this theoretical advantage. For example,our approach achieves $28\%$ compression of RoBERTa's embedding layer with only$0.63\%$ additive drop in the accuracy (without fine-tuning) in average overall tasks in GLUE, compared to $11\%$ drop using the existing$\ell_2$-approximation. Open code is provided for reproducing and extending ourresults.

Compressed Deep Networks: Goodbye SVD, Hello Robust Low-Rank Approximation