Abstract: Systems, apparatuses, and methods provide various progressive, bit-level representations of digital data that are useful for a variety of systems and applications within the fields of machine learning, signal and data processing, and data analytics. Systems, apparatus, and methods for such representations incorporate one or more systems for machine learning, predicting, compressing and decompressing data, and are progressive such that the representations embody a sequential organization of information that prioritizes more information over less significant information. Embodiments of the present disclosure include systems for denoising, enhancing, compressing, decompressing, storing, and transmitting digitized media such as text, audio, image, and video. Methods can include partitioning data, modeling partitioned data, predicting partitioned data, transforming partitioned data, analyzing partitioned data, organizing partitioned data, and partially or fully restructuring the original data.

Abstract: Systems, apparatuses, and methods provide various progressive, bit-level representations of digital data that are useful for a variety of systems and applications within the fields of machine learning, signal and data processing, and data analytics. Systems, apparatus, and methods for such representations incorporate one or more systems for machine learning, predicting, compressing and decompressing data, and are progressive such that the representations embody a sequential organization of information that prioritizes more information over less significant information. Embodiments of the present disclosure include systems for denoising, enhancing, compressing, decompressing, storing, and transmitting digitized media such as text, audio, image, and video. Methods can include partitioning data, modeling partitioned data, predicting partitioned data, transforming partitioned data, analyzing partitioned data, organizing partitioned data, and partially or fully restructuring the original data.