The Conference for Machine Learning Innovation

Efficient Transformers

Session
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✓ Special discount for freelancers
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Join the ML Revolution!
Until June 2
✓ Save up to 226€
✓ Group discount
✓ Special discount for freelancers
Register Now
Thank you for attending!
Register Now
Thank you for attending!
Register Now
Join the ML Revolution!
Register until conference starts:
✓ 2 in 1 conference special
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until conference starts:
✓ 2 in 1 conference special
✓ 10 % Team Discount
Register Now
Infos
Tuesday, December 7 2021
14:30 - 15:15
Room:
Salon 2

Transformers are the new go-to technology for Natural Language Processing (NLP) and are also starting to gain traction in the computer vision community. However, despite all their successes and widespread adoption, they have one major drawback: Their computation and memory requirements grow quadratically with the input size. Hence training transformer models from scratch is a very resource-intensive task.

In this session we want to take a look at the current state of the research into efficient transformer layers, i.e. reformulations of the vanilla transformers that have computation and/or memory requirements of O(n*log(n)) or even O(n). If your knowledge about transformers or complexity theory is a bit rusty, do not worry: The session will start with a short refresher on both topics so you can make the most of it.

This Session belongs to the Diese Session gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

This Session belongs to the Diese Session gehört zum Programm vom BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Singapore Singapore .

Take me to the full program of Zum vollständigen Programm von Berlin Berlin .

This Session Diese Session belongs to the gehört zum Programm von BerlinBerlin program. Take me to the current program of . Hier geht es zum aktuellen Programm von Munich Munich , Singapore Singapore or oder Berlin Berlin .

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