Instructions: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: <lb>An associative memory is a framework of content-addressable memory that stores a collection of<lb>message vectors (or a dataset) over a neural network while enabling a neurally feasible mechanism to<lb>recover any message in the dataset from its noisy version. Designing an associative memory requires<lb>addressing two main tasks: 1) learning phase: given a dataset, learn a concise representation of the<lb>dataset in the form of a graphical model (or a neural network), 2) recall phase: given a noisy version of<lb>a message vector from the dataset, output the correct message vector via a neurally feasible algorithm<lb>over the network learnt during the learning phase. This paper studies the problem of designing a class<lb>of neural associative memories which learns a network representation for a large dataset that ensures<lb>correction against a large number of adversarial errors during the recall phase. Specifically, the associa-<lb>tive memories designed in this paper can store dataset containing exp(n) n-length message vectors over<lb>a network with O(n) nodes and can tolerate ( n<lb>polylogn ) adversarial errors. This paper carries out this<lb>memory design by mapping the learning phase and recall phase to the tasks of dictionary learning with<lb>a square dictionary and iterative error correction in an expander code, respectively.
Output:
Associative Memory using Dictionary Learning and Expander Decoding