While advances in Machine Learning have revolutionized certain areas (computer vision, robotics, natural language processing, etc.), the application in wireless communications has been less dramatic. One limiting factor is the (potentially) high computational complexity. Yet another important inhibitor is the lack of realistic datasets. To fully understand the potential of deep learning based methods to solve wireless communication problems, it is critical to leverage representative datasets, i.e., datasets that are captured in a very wide variety of scenarios, conditions and configurations – that fully reflect the places where wireless networks are deployed and used. Collecting over-the-air data (OTA) at scale in all of these settings – the gold standard – is extremely challenging and impractical. In the absence of such, machine learning practitioners are forced to rely on a handful of indoor and outdoor simulation scenarios with limited variability for model training and validation. Adding new scenarios into simulation has been limited by the significant effort and time required to accurately capture the multipath characteristics in real environments. Having the capability to extract multipath parameters from OTA channel samples would allow for faster characterization of different scenarios and a quick turn-around time towards having them introduced in simulation. Taking a step towards this goal, in this paper, we explore the use of deep learning based inverse modeling frameworks for extracting multipath channel information from the channel impulse response. Specifically, we focus on extracting the multipath parameters from captured Multiple-Input-Multiple-Output (MIMO) channel matrices.