Weight-Based Quantization
Arash discusses the significance of weight-by-weight quantization, highlighting its potential to reduce quantization widths to as low as three or four bits for efficient architectures. He emphasizes that oscillation has been a major barrier to achieving lower quantization levels. Additionally, he introduces a paper on variational on-the-fly personalization, which aims to adapt machine learning models on edge devices without requiring extensive user data or retraining.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Equivariant Priors for Compressed Sensing with Arash Behboodi - #584
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