AILooking back at early low-precision deep learning research
In September 2026, a researcher marked 10 years in deep learning, recalling an early paper on low-precision training—a topic they called “prescient.”
TLDR
One researcher recalled starting deep learning research in 2016 while taking John Canny’s Berkeley course and writing a paper on low-precision training. In a reply, another described working on BinaryConnect and trying to improve its straight-through gradient estimator, linking to the paper “ProxQuant: Quantized Neural Networks via Proximal Operators.” The reply described an effort, not a claimed breakthrough.
Looking back at early low-precision deep learning research
In September 2026, a researcher marked 10 years in deep learning, recalling an early paper on low-precision training—a topic they called “prescient.”
TLDR
One researcher recalled starting deep learning research in 2016 while taking John Canny’s Berkeley course and writing a paper on low-precision training. In a reply, another described working on BinaryConnect and trying to improve its straight-through gradient estimator, linking to the paper “ProxQuant: Quantized Neural Networks via Proximal Operators.” The reply described an effort, not a claimed breakthrough.