Rethinking Model Size: Train Large, Then Compress with Joseph Gonzalez - #378
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) - Un pódcast de Sam Charrington - Lunes
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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?
