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Rethinking Model Size: Train Large, Then Compress with Joseph Gonzalez - #378
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
English - May 25, 2020 13:59 - 52 minutes - ★★★★★ - 323 ratingsTechnology News Tech News machinelearning artificialintelligence datascience samcharrington tech technology thetwimlaipocast thisweekinmachinelearning twiml twimlaipodcast Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Previous Episode: The Physics of Data with Alpha Lee - #377
Today we’re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley.
In our conversation, we explore Joseph’s paper “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,” which looks at compute-efficient training strategies for models. We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency?