About
A deep-dive into Google's DeepMind/UC Berkeley breakthrough HOPE, a data-free method that compresses networks by separating a frozen universal core from a plastic slack. We explain why traditional pruning misses value hidden in scale symmetries, how HOPE uses batch-norm statistics and maximum entropy to map a neuron’s true contribution in Hilbert space, and what this could mean for sustainable, continually learning AI—and for how we think about human intelligence.
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Sponsored by Embersilk LLC