META MODEL EXTENSION FOR A MACHINE LEARNING EQUALIZER

Systems and methods of the present disclosure may be used to improve equalization module architectures for NAND cell read information. For example, embodiments of the present disclosure may provide for de-noising of NAND cell read information using a Multiple Shallow Threshold expert Machine Learning Models (MTM) equalizer. An MTM equalizer may include multiple shallow machine learning models. A meta network may generate parameters for each of the shallow machine learning models such that each shallow machine learning model may be able to solve a classification task (e.g., a binary classification task) corresponding to a weak decision range between two possible read information values for a given NAND cell read operation. Accordingly, during inference, each read sample with a read value within a weak decision range may be passed through a corresponding shallow machine learning model (e.g., a corresponding threshold expert) that is associated with the particular weak decision range.