Researchers have analyzed the capacity limits of linear associative memory, finding that the retrieval criterion significantly impacts how many associations can be stored. For top-1 retrieval, where a signal must outperform all others, the memory size scales as $d^2 \asymp n \log n$. When considering listwise retrieval, which allows the correct target to be among a controlled list of strong candidates, the capacity scales quadratically as $d^2 \asymp n$. This work introduces the Tail-Average Margin (TAM) criterion to formalize listwise retrieval and develops an asymptotic theory for its performance. AI
IMPACT Provides theoretical insights into the capacity limits of memory systems, relevant for designing future AI architectures.
RANK_REASON This is a research paper detailing theoretical findings on associative memory capacity.
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →
