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ComMem proposes complementary memory systems inspired by biological memory to improve test-time adaptation of vision-language models, outperforming state-of-the-art on 15 benchmarks.
This paper proposes a framework for neocortical learning that meets criteria for computational, algorithmic, and implementational plausibility, using error-driven predictive learning via temporal derivatives and corticothalamic circuits. It suggests potential improvements over backpropagation.