SyncedReview
Published in

SyncedReview

AutoDistill: An End-to-End Fully Automated Distillation Framework for Hardware-Efficient Large-Scale NLP Models

As AI-powered language models continue increasing in size, reducing serving cost has become an important research area. Knowledge distillation has emerged as a promising and effective method for model compression, but existing distillation methods can struggle with model-serving in today’s massive…

--

--

--

We produce professional, authoritative, and thought-provoking content relating to artificial intelligence, machine intelligence, emerging technologies and industrial insights.

Recommended from Medium

Experimenting Natural Language Query using HuggingFace Transformers

[ CVPR / Paper Summary ] Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and…

Analyze Behavior of Loan Customers

Latest News Classifier

A simple deep neural network that beats TextBlob and VADER packages at sentiment classification

Universal function approximators a.k.a Deep Neural Networks (part 2)

All About K — means Clustering

If Tom Brings Jerry Home, ML Locks the Cat Door

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Synced

Synced

AI Technology & Industry Review — syncedreview.com | Newsletter: http://bit.ly/2IYL6Y2 | Share My Research http://bit.ly/2TrUPMI | Twitter: @Synced_Global

More from Medium

DAMO Academy Proposes One For All, a Task- and Modality-Agnostic Framework for Multimodal and…

Challenges in using NLP for low-resource languages and how NeuralSpace solves them

What is Deepmind’s retrieval-based transformer (RETRO) & how does it work?

Machine-learned model serving at scale