Generative modeling through internal high-dimensional chaotic activity
This approach offers a novel method for generative modeling, potentially impacting fields like AI and neuroscience, though it appears incremental as it builds on existing chaotic system concepts.
The paper tackles generative modeling by using internal chaotic dynamics in high-dimensional systems instead of external noise, showing that simple learning rules can generate datapoints with quality characterized by standard accuracy measures.
Generative modeling aims at producing new datapoints whose statistical properties resemble the ones in a training dataset. In recent years, there has been a burst of machine learning techniques and settings that can achieve this goal with remarkable performances. In most of these settings, one uses the training dataset in conjunction with noise, which is added as a source of statistical variability and is essential for the generative task. Here, we explore the idea of using internal chaotic dynamics in high-dimensional chaotic systems as a way to generate new datapoints from a training dataset. We show that simple learning rules can achieve this goal within a set of vanilla architectures and characterize the quality of the generated datapoints through standard accuracy measures.