Inverse Learning

Inverse learning aims to infer underlying parameters or processes from observed data, essentially reversing the typical forward modeling approach. Current research focuses on efficiently solving inverse problems using deep learning models, including invertible neural networks and algorithms tailored to specific classifier types (e.g., logistic regression, softmax), often incorporating techniques like gradient descent and probabilistic inference to improve solution accuracy and speed. This field is crucial for advancing various scientific domains, such as materials science, drug discovery, and signal processing, by enabling the extraction of meaningful insights from complex data and facilitating the design of improved systems.