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[D] Memory Aware Synapses (MAS): how to compute additional loss?

I am currently reading the paper “Memory Aware Synapses: Learning what (not) to forget” (https://arxiv.org/abs/1711.09601) and am trying to figure out how to compute the additional loss term. The weight importance matrix Ω of the current parameters θ is (as I understand) just the gradients of all individual weights. But how is Ω(θ – θ)) computed, specifically Ω(θ)). I tried looking through the official git repository, but was somehow not able to find the answer. Does anyone have experience with this approach?

submitted by /u/Sp4rk4s
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.